1):\n raise IOError(\"isym must be either 0 or 1\")\n if (npsi < 1) or (ntheta < 1):\n raise IOError(\"Invalid npsi=\"+str(npsi)+\" or ntheta=\" + str(ntheta))\n \n # Read normalisation factors\n\n try:\n rcnt = float(token.next())\n xma = float(token.next())\n zma = float(token.next())\n btor = float(token.next())\n curtot = float(token.next())\n eaxe = float(token.next())\n dnorm = float(token.next())\n except:\n raise IOError(\"Couldn't read normalisation factors\")\n \n def read_array(n, name=\"Unknown\"):\n data = np.zeros([n])\n try:\n for i in np.arange(n):\n data[i] = float(token.next())\n except:\n raise IOError(\"Failed reading array '\"+name+\"' of size \", n)\n return data\n\n def read_2d(nx, ny, name=\"Unknown\"):\n data = np.zeros([nx, ny])\n for i in np.arange(nx):\n data[i,:] = read_array(ny, name+\"[\"+str(i)+\"]\")\n return data\n\n # Read 1D arrays\n psiflux = read_array(npsi, \"psiflux\")\n fnorm = read_array(npsi, \"fnorm\")\n ffpnorm = read_array(npsi, \"ffpnorm\")\n ponly = read_array(npsi, \"ponly\")\n pponly = read_array(npsi, \"pponly\")\n qsf = read_array(npsi, \"qsf\")\n d = read_array(npsi, \"d\")\n \n dpdz = read_array(ntheta, \"dpdz\")\n dpdr = read_array(ntheta, \"dpdr\")\n \n # 2D arrays\n \n xnorm = read_2d(ntheta, npsi, \"xnorm\")\n znorm = read_2d(ntheta, npsi, \"znorm\")\n \n # Try to read Br and Bz (may be present)\n try:\n Br = read_2d(ntheta, npsi, \"Br\")\n Bz = read_2d(ntheta, npsi, \"Bz\")\n except:\n Br = Bz = None\n \n ny = ntheta\n\n if isym == 1:\n # Fill in values for up-down symmetric case\n print(\"Grid is up-down symmetric. Reflecting grid about midplane\")\n ny = tsize = 2*(ntheta - 1) + 1\n \n def reflect(data, mapfunc = lambda x:x):\n \"\"\" Reflect a variable about midplane\n Optionally supply a mapping function\"\"\"\n data2 = np.zeros([tsize, npsi])\n # Copy the original data\n for i in np.arange(ntheta):\n data2[i,:] = data[i,:]\n # Now fill in the remainder\n for i in np.arange(ntheta, tsize):\n t0 = tsize - 1 - i\n data2[i,:] = mapfunc(data[t0,:])\n return data2\n \n xnorm = reflect(xnorm)\n znorm = reflect(znorm, lambda x: 2.*zma - x) # Reflect about zma\n if Br != None:\n Br = reflect(Br, lambda x:-x) # Br reverses\n if Bz != None:\n Bz = reflect(Bz) # Bz remains the same\n theta = tsize\n\n # Make sure we have Br, Bz and Bpol\n\n if (Br == None) or (Bz == None):\n # Calculate Bpol from psi then Br and Bz from Bpol\n # Use dpsi = R*Bp dx (for now)\n Bpol = np.zeros([ny, npsi])\n \n def deriv(f):\n n = np.size(f)\n dfdi = np.zeros(n)\n dfdi[1:-1] = (f[2:n] - f[0:-2])/2. # Central difference in the middle\n dfdi[0] = f[1] - f[0]\n dfdi[-1] = f[-1] - f[-2]\n return dfdi\n \n for i in np.arange(ntheta):\n drdi = deriv(xnorm[i, :])\n dzdi = deriv(znorm[i, :])\n dldi = sqrt(drdi**2 + dzdi**2) # Arc length\n dpsidi = deriv(psiflux)\n \n Bpol[i, :] = dpsidi / (dldi * xnorm[i,:])\n else:\n Bpol = np.sqrt(Br**2 + Bz**2)\n \n # Calculate toroidal field\n Btor = fnorm / xnorm\n \n #########################################\n # Create a dictionary of values to return\n # \n # Need to transpose 2D arrays to [psi, theta] \n # to be consistent with elite inputs\n \n var = {\"npsi\":npsi, \"npol\":ny, # Sizes\n \n \"psi\":psiflux,\n \"f(psi)\":fnorm,\n \"p\":ponly,\n \n \"R\": np.transpose(xnorm),\n \"Z\": np.transpose(znorm),\n\n \"Bp\":np.transpose(Bpol),\n \"Bt\":np.transpose(Btor),\n\n \"q\":qsf,\n\n \"ffprime\":ffpnorm,\n \"pprime\":pponly}\n\n if Br != None:\n var['Br'] = np.transpose(Br)\n if Bz != None:\n var['Bz'] = np.transpose(Bz)\n \n return var","def read_pattern_file(file_path: str) -> np.ndarray:\n\n # Check if the example file exists\n if not os.path.isfile(file_path):\n return None\n\n rows = 0\n cols = 0\n with open(file_path) as f:\n for i, l in enumerate(f):\n if l[0] != \"!\":\n rows += 1\n if len(l) > cols:\n cols = len(l) - 1 # Exclude the end of line char from the column count\n\n grid = np.zeros((rows, cols), dtype=np.uint8)\n\n skip_rows = 0\n with open(file_path) as f:\n for j, line in enumerate(f):\n for k, c in enumerate(line):\n if c == \"!\" and k == 0:\n skip_rows += 1\n break\n elif c == \"O\":\n grid[j - skip_rows, k] = 1\n\n return grid","def make_from_file(filehandle):\n lines = filehandle.readlines()\n label = str(lines[0].rstrip('\\n'))\n mass = float(lines[1].rstrip('\\n'))\n position = list(lines[2].rstrip('\\n').split(','))\n velocity = list(lines[3].rstrip('\\n').split(','))\n particle = Particle3D(label=label, mass=mass, position=position, velocity=velocity)\n filehandle.close()\n return particle","def readmesh(filename):\n f = open(filename, 'rb')\n cells = []\n edges = []\n\n # create each cell and edge, etc\n for line in f:\n \n return cells, edges","def _read_datafile(self,path):\n \tlabels, images = [], []\n \twith gzip.GzipFile(path) as f:\n \t for line in f:\n \t vals = line.strip().split()\n \t labels.append(float(vals[0]))\n \t images.append([float(val) for val in vals[1:]])\n \tlabels = np.array(labels, dtype=np.int32)\n \tlabels[labels == 10] = 0 # fix weird 0 labels\n \timages = np.array(images, dtype=np.float32).reshape(-1, 16, 16, 1)\n \timages = (images + 1) / 2\n \treturn images, labels","def load_pfm(filename):\n filename = process(filename)\n with open(filename, \"r\", encoding=\"ISO-8859-1\") as file:\n nc = 3 if file.readline().rstrip() == \"PF\" else 1\n width, height = [int(x) for x in file.readline().rstrip().split()]\n shape = (height, width, nc)\n img = np.fromfile(file, '{0}{1}'.format(\"<\" if float(file.readline().rstrip()) < 0 else \">\",'f') )\n img = np.reshape(img, shape)\n return np.flip(np.flip(img, 2), 0).copy()","def load_raster_xyz(self, filename):\n with rasterio.open(filename, 'r') as src:\n ## Alias 'affine' no longer works for 'transform'\n ##matrix = src.affine\n matrix = src.transform\n self.size = (src.width, src.height)\n # read per scan line\n for row in range(0, src.height):\n window = ((row, row+1), (0, src.width))\n data = src.read(window=window)\n this_row = data[0][0]\n for column in range(0, src.width):\n x, y = matrix * (column, row)\n yield x, y, this_row[column]","def read_image(filename, representation):\n image = scipy.misc.imread(filename)\n if int(representation) == 1:\n image = rgb2gray(image)\n return img_as_float(image)","def make_image(file):\n image = cv2.imread(file, 0)\n image = cv2.resize(image, (IMG_SIZE, IMG_SIZE))\n return np.array([np.array(image), np.array([0, 0])])","def _fileToMatrix(file_name):\r\n # TODO: np.loadtxt() might be an alternative\r\n # try:\r\n if 1 < 3:\r\n lres = []\r\n for line in open(file_name, 'r').readlines():\r\n if len(line) > 0 and line[0] not in ('%', '#'):\r\n lres.append(list(map(float, line.split())))\r\n res = lres\r\n else:\r\n fil = open(file_name, 'r')\r\n fil.readline() # rudimentary, assume one comment line\r\n lineToRow = lambda line: list(map(float, line.split()))\r\n res = list(map(lineToRow, fil.readlines()))\r\n fil.close() # close file could be omitted, reference counting should do during garbage collection, but...\r\n\r\n while res != [] and res[0] == []: # remove further leading empty lines\r\n del res[0]\r\n return res\r\n # except:\r\n print('could not read file ' + file_name)","def read_flow_png(flow_file):\n flow_object = png.Reader(filename=flow_file)\n flow_direct = flow_object.asDirect()\n flow_data = list(flow_direct[2])\n (w, h) = flow_direct[3]['size']\n flow = np.zeros((h, w, 3), dtype=np.float64)\n for i in range(len(flow_data)):\n flow[i, :, 0] = flow_data[i][0::3]\n flow[i, :, 1] = flow_data[i][1::3]\n flow[i, :, 2] = flow_data[i][2::3]\n\n invalid_idx = (flow[:, :, 2] == 0)\n flow[:, :, 0:2] = (flow[:, :, 0:2] - 2 ** 15) / 64.0\n flow[invalid_idx, 0] = 0\n flow[invalid_idx, 1] = 0\n return flow","def load(filename):\n lines = [l.strip('\\r\\n ') for l in open(filename, 'r').readlines()]\n lines = [l for l in lines if l != '']\n dims = [re.split(r'\\s+', l) for l in lines]\n f = np.array([[float(f) for f in d] for d in dims])\n return f","def parseMatrix(file):\n array = genfromtxt(file, delimiter=',')\n\n if not np.isnan(array).any():\n raise ValueError(\"This array does not contain any 'nan' values\")\n\n return array","def load(self, _name):\r\n with open(_name, 'r') as fin:\r\n self.filename = _name\r\n\r\n self.comment_1 = fin.readline() # Save 1st comment\r\n self.comment_2 = fin.readline() # Save 2nd comment\r\n\r\n _str = fin.readline().split() # Number of Atoms and Origin\r\n self.n_atoms = int(_str[0]) # Number of Atoms\r\n self.origin = np.array([float(_str[1]), float(_str[2]), float(_str[3])]) # Position of Origin\r\n\r\n nVoxel = fin.readline().split() # Number of Voxels\r\n self.n_x = int(nVoxel[0])\r\n self.x = np.array([float(nVoxel[1]), float(nVoxel[2]), float(nVoxel[3])])\r\n\r\n nVoxel = fin.readline().split() #\r\n self.n_y = int(nVoxel[0])\r\n self.y = np.array([float(nVoxel[1]), float(nVoxel[2]), float(nVoxel[3])])\r\n\r\n nVoxel = fin.readline().split() #\r\n self.n_z = int(nVoxel[0])\r\n self.z = np.array([float(nVoxel[1]), float(nVoxel[2]), float(nVoxel[3])])\r\n\r\n self.atoms = []\r\n self.atoms_xyz = []\r\n for atom in range(self.n_atoms):\r\n line = fin.readline().split()\r\n self.atoms.append(line[0])\r\n self.atoms_xyz.append(list(map(float, [line[2], line[3], line[4]])))\r\n\r\n self.data = np.zeros((self.n_x, self.n_y, self.n_z))\r\n\r\n i = int(0)\r\n for s in fin:\r\n for v in s.split():\r\n self.data[int(i / (self.n_y * self.n_z)), int((i / self.n_z) % self.n_y),\r\n int(i % self.n_z)] = float(v)\r\n i += 1\r\n\r\n return None","def loadtxt(filename, save=True, stripe=True, blank=False):\n a = []\n f = open(filename, 'r')\n fn = filename.split('.')[0]\n for line in f.readlines():\n line = line.strip()\n elems = [int(i) for i in list(line)]\n a.append(elems)\n b = np.array(a, dtype=np.int)\n np.fill_diagonal(b, 0)\n if blank:\n b = remove_blanks(b)\n if save:\n np.savez('.'.join((fn, 'npz')), a=b)\n else:\n return b","def read_ascii_raster(ascii_raster_file):\n import numpy as np\n\n with open(ascii_raster_file) as f:\n header_data = [float(f.next().split()[1]) for x in xrange(6)] #read the first 6 lines\n\n raster_data = np.genfromtxt(ascii_raster_file, delimiter=' ', skip_header=6)\n raster_data = raster_data.reshape(header_data[1], header_data[0]) #rows, columns\n\n return raster_data, header_data","def load_image_patch(filename):\n im = Image.open(filename) # .convert('L')\n width, height = im.size\n pixels = list(im.getdata())\n features = [pixels[i * width:(i + 1) * width] for i in range(height)]\n features = np.asarray(im, dtype=np.float32).flatten()\n features /= 255.0\n return features","def load_txt(file_path):\n lines = load_lines(file_path)\n\n if 'E' in lines[0]:\n dtype = np.float32\n else:\n dtype = np.int32\n\n data = list(map(str.split, lines))\n array = np.array(data, dtype=dtype)\n return array","def load_picked_points(filename):\n\n f = open(filename, 'r')\n\n def get_num(string):\n pos1 = string.find('\\\"')\n pos2 = string.find('\\\"', pos1 + 1)\n return float(string[pos1 + 1:pos2])\n\n def get_point(str_array):\n if 'x=' in str_array[0] and 'y=' in str_array[1] and 'z=' in str_array[2]:\n return [get_num(str_array[0]), get_num(str_array[1]), get_num(str_array[2])]\n else:\n return []\n\n pickedPoints = []\n for line in f:\n if 'point' in line:\n str = line.split()\n if len(str) < 4:\n continue\n ix = [i for i, s in enumerate(str) if 'x=' in s][0]\n iy = [i for i, s in enumerate(str) if 'y=' in s][0]\n iz = [i for i, s in enumerate(str) if 'z=' in s][0]\n pickedPoints.append(get_point([str[ix], str[iy], str[iz]]))\n f.close()\n return np.array(pickedPoints)","def fromfile(self, path):\n\t\tdata = filetools.read_data(path)\n\t\tprint \"File read: %i lines\" % len(data)\n\t\tself.build_matrix(data)","def load_file(filename):\r\n file =np.genfromtxt(filename, delimiter=',')\r\n return file","def from_file(cls, filename: str) -> \"NDOptimiser\":\n from autode.opt.coordinates.cartesian import CartesianCoordinates\n\n lines = open(filename, \"r\").readlines()\n n_atoms = int(lines[0].split()[0])\n\n title_line = NumericStringDict(lines[1])\n optimiser = cls(\n maxiter=int(title_line[\"maxiter\"]),\n gtol=GradientRMS(title_line[\"gtol\"]),\n etol=PotentialEnergy(title_line[\"etol\"]),\n )\n\n for i in range(0, len(lines), n_atoms + 2):\n raw_coordinates = np.zeros(shape=(n_atoms, 3))\n gradient = np.zeros(shape=(n_atoms, 3))\n\n for j, line in enumerate(lines[i + 2 : i + n_atoms + 2]):\n _, x, y, z, dedx, dedy, dedz = line.split()\n raw_coordinates[j, :] = [float(x), float(y), float(z)]\n gradient[j, :] = [float(dedx), float(dedy), float(dedz)]\n\n coords = CartesianCoordinates(raw_coordinates)\n coords.e = NumericStringDict(lines[i + 1])[\"E\"]\n coords.g = gradient.flatten()\n\n optimiser._history.append(coords)\n\n return optimiser","def from_text_file(cls, filename):\n raise NotImplementedError()","def load_data (filename):\n from StringIO import StringIO # StringIO behaves like a file object\n example_data = \"0.0001E-10 1.0E-9\\n0.5E-10 1.2E-9\\n1.0E-10 1.5E-9\\n1.5E-10 1.6E-9\\n2.0E-10 1.7E-9\\n \\\n 2.5E-10 1.8E-9\\n3.0E-10 1.7E-9\\n3.5E-10 1.6E-9\\n4.0E-10 1.5E-9\\n4.5E-10 1.2E-9\\n4.9999E-10 1.0E-9\"\n if (filename==None):\n FILE = StringIO(example_data)\n print \"Using example data\"\n else:\n FILE = open(filename,'r')\n print \"Loading data from %s\" % filename\n x,y = np.loadtxt(FILE, unpack=True)\n FILE.close()\n return(x,y)","def fill(fname):\n return [[float(line.split()[-3]), float(line.split()[-1])]\\\n for line in open(fname).readlines()\\\n if FITRESRE1.match(line)]","def read_txt(path):\n mz = []\n i = []\n with open(path) as f:\n for line in f:\n line = line.split()\n mz.append(float(line[0]))\n i.append(float(line[1]))\n return mz, i","def read_image(filename, representation):\n\n color_flag = True #if RGB image\n image = imread(filename)\n\n float_image = image.astype(np.float64)\n\n if not np.all(image <= 1):\n float_image /= NORMALIZE #Normalized to range [0,1]\n\n if len(float_image.shape) != 3 : #Checks if RGB or Grayscale\n color_flag = False\n\n if color_flag and representation == 1 : #Checks if need RGB to Gray\n return skimage.color.rgb2gray(float_image)\n\n # Same coloring already\n return float_image","def load_data(file_name):\r\n with open(str(file_name)) as f:\r\n file = f.readlines()\r\n label = file[0].strip().split(\",\")[1:-1]\r\n file = file[1:]\r\n row = len(file)\r\n col = len(file[0].strip().split(\",\"))-1\r\n mat = np.zeros((row, col))\r\n for i in range(len(file)):\r\n row = file[i].strip()\r\n cols = row.split(\",\")[1:]\r\n for j in range(len(cols)):\r\n mat[i][j] = int(cols[j])\r\n np.random.seed(10)\r\n np.random.shuffle(mat)\r\n X = mat[:, 0:-1]\r\n y = mat[:, -1]\r\n\r\n return X, y, label","def read_ascii(file):\n wvlen, band, mag, emag, fmag, unit, beam, odate, ref = [],[],[],[],[],[],[],[],[]\n with open(file, 'r') as f_in:\n for line in f_in:\n try:\n # ensure line contains data:\n a = float(line[0])\n except ValueError:\n a = 'dummy'\n try:\n # ensure mag or flux entry is not '--'\n m = float(line.split(' ')[2])\n except ValueError:\n m = 'dummy'\n \n if isinstance(a, float) and isinstance(m, float):\n wvlen.append(float(line.strip().split(' ')[0])) # in metres\n band.append(line.strip().split(' ')[1])\n mag.append(float(line.strip().split(' ')[2]))\n emag.append(line.strip().split(' ')[3])\n fmag.append(line.strip().split(' ')[4])\n unit.append(line.strip().split(' ')[5])\n beam.append(line.strip().split(' ')[6])\n odate.append(line.strip().split(' ')[7])\n ref.append(line.strip().split(' ')[8])\n \n return wvlen, band, mag, emag, fmag, unit, beam, odate, ref","def get_g(file_name):\n \n r,g = np.loadtxt(file_name, dtype = 'float', unpack = 'true')\n \n return r,g","def initFromFile(self,file):\n self.source = file\n file_reader = open(file,\"r\")\n self.isInit = True\n lineCounter = 0\n firstLine = None\n SecondLine = None\n ThirdLine = None\n for line in file_reader:\n if(lineCounter == 0):\n firstLine = line.split()\n self.rowsNumber = int(firstLine[0])\n self.columnsNumber = int(firstLine[1])\n self.routerRangeRadius = int(firstLine[2])\n if(lineCounter == 1):\n SecondLine = line.split()\n self.backBoneCosts = int(SecondLine[0])\n Path.backBoneCost = self.backBoneCosts\n self.routerCosts = int(SecondLine[1])\n self.budget = int(SecondLine[2])\n if(lineCounter == 2):\n ThirdLine = line.split()\n self.firstCell = Cell(int(ThirdLine[0]),int(ThirdLine[1]))\n if(lineCounter>2):\n self.map.append([])\n LINE = line\n columnCounter = 0\n for char in LINE:\n temp = Cell(len(self.map)-1,columnCounter,Cell.getCellType(char))\n self.map[len(self.map)-1].append(temp)\n if(temp.cellType == \"FLOOR\"):\n self.notComputeRouter.append(temp)\n columnCounter += 1\n lineCounter +=1\n self.isInit = True","def _parse_single(filename, label, image_size=IMAGE_SIZE):\n # Decode and convert image to appropriate type\n image = tf.image.decode_png(tf.read_file(filename), channels=image_size[2])\n image = tf.image.convert_image_dtype(image, tf.float32) # Also scales from [0, 255] to [0, 1)\n # Resize according to module requirements\n image = tf.image.resize_images(image, image_size[:2])\n return image, label","def open_xy(data):\n twotheta, intensity = [], []\n with open(data) as f:\n for line in f:\n row = line.split()\n twotheta.append(row[0])\n intensity.append(row[1])\n xyarray = list(zip(twotheta, intensity))\n xyarray = np.asarray(xyarray)\n xyarray = xyarray.astype(np.float)\n return xyarray","def from_file(file_handle, delimiter=\",\"):\r\n # Reading in the data\r\n line = file_handle.readline()\r\n line = line.rstrip(\"\\n\")\r\n line = line.split(delimiter)\r\n\r\n # Assigning the data\r\n label = line[0]\r\n mass = float(line[1])\r\n position = np.array(line[2:5], float)\r\n velocity = np.array(line[5:], float)\r\n\r\n return Particle3D(label, mass, position, velocity)","def import_data(textfile, uncertainty):\n\tf = open(textfile, 'r')\n\tdata = f.readlines()\n\tx = []\n\ty = []\n\tfor line in data:\n\t\tcoords = line.strip()\n\t\tcoords = coords.split(', ')\n\t\tx.append(coords[0])\n\t\ty.append(coords[1]) \n\t\n\tx = [float(i) for i in x]\n\tx = np.array(x).reshape((10,1))\n\ty = [float(i) for i in y]\n\ty = np.array(y).reshape((10,1))\n\tdata = {'x': x, 'y': y, 'var': uncertainty}\n\n\treturn data","def _image_set(gt_txt, images_dir):\n with open(gt_txt) as f:\n filename = f.readline().rstrip()\n total = 1\n while filename:\n log.debug(filename)\n image = Image(os.path.join(images_dir, filename), filename)\n face_num = int(f.readline().rstrip())\n\n if face_num == 0:\n log.warning('No faces for {}. Ignoring next line {}'.format(image.filename, f.readline().rstrip()))\n\n log.debug(face_num)\n for _ in range(face_num):\n anno = f.readline().rstrip().split()\n log.debug(anno)\n face = Face(anno)\n if face.is_valid():\n image.add_face(face)\n else:\n log.debug('Skipping INVALID %s from %s', face, image)\n filename = f.readline().rstrip()\n total += 1\n yield image","def getFile(self):\r\n self.file_name=QtGui.QFileDialog.getOpenFileName(self, \"Open Image file\", self.path, \"*tif\")\r\n if self.file_name!='':\r\n \r\n self.img= skimage.io.imread(str(self.file_name), plugin='tifffile')\r\n \"\"\"sets self.img equal to the chosen image\"\"\"\r\n \r\n self.temp= interp_img(np.zeros(self.img.shape), self.zinterp)\r\n self.edge= interp_img(np.zeros(self.img.shape), self.zinterp)\r\n self.shrink= np.zeros(self.img.shape)\r\n self.count=0\r\n \r\n self.z_stack=self.img.shape[0]/2\r\n self.y_stack=self.img.shape[1]/2\r\n self.x_stack=self.img.shape[2]/2\r\n \r\n self.dispedge = to_rgb(self.img[self.z_stack])\r\n self.y_dispedge= to_rgb(self.img[:,self.y_stack,:])\r\n self.x_dispedge= to_rgb(self.img[:,:,self.x_stack])\r\n #self.pixmap=QtGui.QPixmap.fromImage(ImageQt.ImageQt(misc.toimage(self.img[self.z_stack]))).scaled(500,500)\r\n \r\n self.resetImages()","def Read_txt_Image(Name):\r\n\ttry:\r\n\t\twith open(Name, 'r') as f: #If the file already exists\r\n\t\t\tx = f.readlines() #Readd all the rows and put them in a list\r\n\texcept: \r\n\t\twith open(Name, 'w') as f: #If the file does not exist\r\n\t\t\tx = [] #Create an empty list\r\n\treturn x #This is the list that contains all the information for the file\r","def import_cif(file,xyz='',n=[0,0,1],rep=[1,1,1],pad=0,dopt='s',lfact=1.0,tail=''):\n crys = Crystal.from_cif(file)\n lat_vec = np.array(crys.lattice_vectors)\n lat_params = crys.lattice_parameters[:3]\n pattern = np.array([[a.atomic_number]+list(lfact*a.coords_cartesian)+[a.occupancy,1.0] for a in crys.atoms])\n\n if xyz:make_xyz(xyz,pattern,lat_vec,lat_params,n=n,pad=pad,rep=rep,fmt='%.4f',dopt=dopt)\n pattern[:,1:4] = rcc.orient_crystal(pattern[:,1:4],n_u=n) #,lat_params #pattern,crys # file\n return pattern","def main(filename, resolution=0.3, verbose=False):\n\n np.random.seed(1986) # for reproducibility\n\n image_data = io.imread(filename)\n image_data = image_data[66:532, 105:671, :]\n image_data = rgb2gray(image_data)\n image_data = rescale(image_data, resolution, mode='constant',\n preserve_range='True')\n dim = np.int(reduce(lambda x, y: x * y, image_data.shape))\n image_data = np.reshape(image_data, (dim))\n image_data = np.array(image_data, dtype='f')\n\n return image_data","def FillBox(file):\n with open(file,'r') as f:\n info = f.readline().split()\n box_list = []\n id = 1\n I_list =[]\n for i in range(0,len(info)):\n box_list.append(box(float(info[i]),id,0,[]))\n id+=1\n for line in f:\n words = line.split()\n I_list.append(items(words[0], float(words[1])))\n return box_list, I_list","def readpil3d(self):\r\n\r\n # Read the data in as an array.\r\n res = np.loadtxt(self.name, delimiter=' ')\r\n\r\n # Split into useful chunks\r\n self.pos = res[:, 0:3] # Grid point locations\r\n self.Pn = res[:, 3:4] # Normal pressure [Pa]\r\n self.flux = res[:, -1] # Flux\r","def readGR3File(inputFilename):\n print 'Reading ' + inputFilename + ' ...'\n infile = open(inputFilename, 'r')\n description = infile.readline().strip() # remove leading/trailing whitespace\n tmpStr = infile.readline()\n nTriangles, nNodes = (int(s) for s in tmpStr.split())\n print ' nTriangles={0:d} nNodes={1:d}'.format(nTriangles, nNodes)\n\n # nodes\n nodeArray = readNodeBlock(infile, nNodes)\n nodenum = np.array(nodeArray[:, 0].flatten(), dtype=int)\n nodexyz = np.zeros((nNodes, 3))\n nodexyz[:, :2] = nodeArray[:, 1:3]\n nodalValues = nodeArray[:, 3]\n\n print ' Nodal values min={0:g} max={1:g}'.format(min(nodalValues), max(nodalValues))\n\n # triangular elements\n triArray = readElemBlock(infile, nTriangles)\n\n trinum = triArray[:, 0].flatten()\n tritype = triArray[0, 1]\n trinodes = triArray[:, -3:] - 1 # three last columns, 0-based indexing\n #triangles = meshElements(trinodes,trinum,tritype)\n\n x = nodexyz[:, 0]\n y = nodexyz[:, 1]\n\n tmpStr = infile.readline()\n boundaries = []\n if len(tmpStr) > 0:\n # boundary information, if not end of file\n nOpenBndSegments = int(tmpStr.split()[0])\n nOpenBndNodesTot = int(infile.readline().split()[0])\n print ' nOpenBndSegments={0:d} nOpenBndNodesTot={1:d}'.format(nOpenBndSegments, nOpenBndNodesTot)\n for iBnd in range(nOpenBndSegments):\n bndHeader = infile.readline().split()\n nBndNodes = int(bndHeader[0])\n tag = bndHeader[-1]\n if tag.isdigit():\n tag = 'open' + tag\n print ' open bnd {0:d} {1:s}: {2:d} nodes'.format(iBnd + 1, tag, nBndNodes)\n tmpList = []\n for iN in range(nBndNodes):\n tmpList.append(int(infile.readline()))\n nodes = np.array(tmpList, dtype=int) - 1\n boundaries.append(meshContainer.meshBoundary('open', tag, nodes))\n nLandBndSegments = int(infile.readline().split()[0])\n nLandBndNodesTot = int(infile.readline().split()[0])\n landBndTags = range(\n nOpenBndSegments + 1,\n nOpenBndSegments + nLandBndSegments + 1)\n print ' nLandBndSegments={0:d} nLandBndNodesTot={1:d}'.format(nLandBndSegments, nLandBndNodesTot)\n for iBnd in range(nLandBndSegments):\n bndHeader = infile.readline().split()\n nBndNodes = int(bndHeader[0])\n try:\n landType = int(bndHeader[1])\n except:\n print \"\"\"Land boundary type missing in gr3 file. Add 0/1 (land/island) after number of nodes in each land boudary, e.g.\n 1002 = Total number of closed boundary nodes\n 501 0 = Number of nodes in closed boundary 1\"\"\"\n raise Exception(\n 'Could not parse land boundary type (0/1 - land/island)\\n')\n landType = 'island' if landType == 1 else 'land'\n tag = landType + bndHeader[-1]\n print ' land bnd {0:d} {1:s}: {2:d} nodes'.format(iBnd + 1, tag, nBndNodes)\n tmpList = []\n for iN in range(nBndNodes):\n tmpList.append(int(infile.readline()))\n #tmpList = fromfile(infile,dtype=int,count=nBndNodes,sep=' ')\n nodes = np.array(tmpList, dtype=int) - 1\n boundaries.append(meshContainer.meshBoundary(landType, tag, nodes))\n\n infile.close()\n\n # for better interpolation, round coordinates to 1e-4\n nDig = 4\n x = np.round(x, nDig)\n y = np.round(y, nDig)\n\n return x, y, nodalValues, trinodes, boundaries, description","def read_file(file_name):\n with open(file_name) as _r:\n _pixlst = []\n for line in _r:\n if line != \"P3\\n\":\n _new = (line[0:len(line)-1]).split()\n for item in _new:\n _pixlst.append(int(item)) \n return _pixlst","def create_from_file(cls, file_path: str) -> \"ImageClassifier\":\n base_options = _BaseOptions(file_name=file_path)\n options = ImageClassifierOptions(base_options=base_options)\n return cls.create_from_options(options)","def from_file(path, scale):\n from imageio import imread\n imgarr = imread(path)\n s = imgarr.shape\n extx, exty = (s[1] * scale) / 2, (s[0] * scale) / 2\n ux, uy = e.arange(-extx, extx, scale), e.arange(-exty, exty, scale)\n return Convolvable(data=e.flip(imgarr, axis=0).astype(config.precision),\n x=ux, y=uy, has_analytic_ft=False)","def flow_read(filename):\n TAG_FLOAT = 202021.25\n f = open(filename,'rb')\n check = np.fromfile(f,dtype=np.float32,count=1)[0]\n assert check == TAG_FLOAT, ' flow_read:: Wrong tag in flow file (should be: {0}, is: {1}). Big-endian machine? '.format(TAG_FLOAT,check)\n width = np.fromfile(f,dtype=np.int32,count=1)[0]\n height = np.fromfile(f,dtype=np.int32,count=1)[0]\n size = width*height\n assert width > 0 and height > 0 and size > 1 and size < 100000000, ' flow_read:: Wrong input size (width = {0}, height = {1}).'.format(width,height)\n tmp = np.fromfile(f,dtype=np.float32,count=-1).reshape((height,width*2))\n u = tmp[:,np.arange(width)*2]\n v = tmp[:,np.arange(width)*2 + 1]\n return u,v","def read_pixelmap(filename):\n\n # Open HDF5 pixelmap file\n try:\n with h5py.File(filename, 'r') as fp:\n x = fp['x'][:]\n y = fp['y'][:]\n except:\n print(\"Error reading the pixelmap file: \", filename)\n exit()\n\n # Correct for pixel size (meters --> pixels)\n # Currently hard coded for CSPAD\n # We can figure this out from the pixel map\n dx = 110e-6\n x /= dx\n y /= dx\n \n\n # Calculate radius\n r = numpy.sqrt(numpy.square(x) + numpy.square(y))\n\n return x, y, r, dx"],"string":"[\n \"def read_image(filename):\\n try:\\n fi = open(filename,\\\"r\\\")\\n lines = fi.readlines()\\n n = int(lines[0]);\\n img = create_zeroed_image(n)\\n for i,line in enumerate(lines[1:]):\\n clean_line = line.strip() # remove whitespace and newlines\\n for j,char in enumerate(clean_line):\\n # your code here\\n \\n img[i][j]=char\\n # end your code here\\n return img\\n except IOError:\\n raise Exception(\\\"Cannot find file \\\" + filename);\\n finally:\\n fi.close()\",\n \"def read_from_file(self,fn):\\n fh = open(fn,\\\"r\\\")\\n labels = []\\n xyz = []\\n sizes = []\\n colors = []\\n for line in fh.readlines():\\n try:\\n if not line.startswith(\\\"#\\\"):\\n label,x,y,z,size,r,g,b = line.split(\\\",\\\")\\n labels.append(label)\\n xyz.append([x,y,z])\\n sizes.append(size)\\n colors.append((float(r),float(g),float(b)))\\n except IOError, ioe:\\n print \\\"IOError:\\\", ioe\\n self._labels = np.array(labels)\\n self._xyz = np.array(xyz).astype(\\\"f\\\")\\n self._sizes = np.array(sizes).astype(\\\"f\\\")\\n self._colors = np.array(colors)\",\n \"def read_from_grid(filename):\\n\\n x=[]\\n y=[]\\n z=[]\\n\\n fid=open(filename,'r')\\n\\n for point in fid:\\n x.append(float(point.split()[0]))\\n y.append(float(point.split()[1]))\\n z.append(float(point.split()[2]))\\n\\n fid.close()\\n\\n return x, y, z\",\n \"def tag_parser(file_path: str):\\n with open(file_path) as f:\\n t = f.read()\\n t = t.split(\\\"Points =\\\\n\\\")[1]\\n t = t.replace(\\\" 0.1 1 1 \\\\\\\"Marker\\\\\\\"\\\", \\\"\\\")\\n t = t.replace(\\\";\\\", \\\"\\\")\\n t = t.replace(\\\" \\\\n\\\", \\\"\\\\n\\\")\\n t = t[1:]\\n t = StringIO(t)\\n\\n return np.genfromtxt(t, delimiter=' ')\",\n \"def inithr(_filename):\\n # Open file provided\\n _file = open(_filename)\\n # Create empty array to hold data\\n _data = np.zeros((1, 3), dtype=float)\\n\\n # Iterate through the file line by line\\n for _line in _file:\\n # Split each line into constituent values\\n _x = _line.split()\\n # Append data array with each value, converted to float, convert parallax angle to distance\\n _data = np.append(_data, np.array([float(_x[1]), float(_x[2]), (1 / float(_x[3]))], ndmin=2), axis=0)\\n\\n # Iterate through data array\\n for _row in _data:\\n np.seterr(divide='ignore')\\n # Convert magnitude to luminosity\\n _row[0] = _row[0] - 5 * (np.log10(_row[2]) - 1)\\n # Convert B-V colour to temperature\\n _row[1] = 4600 * ((1 / (0.92 * _row[1] + 1.7)) + 1 / (0.92 * _row[1] + 0.62))\\n\\n # Delete first empty row\\n _data = np.delete(_data, 0, axis=0)\\n\\n # Return parsed data\\n return _data\",\n \"def file_parser(file_name):\\n h = 480\\n w = 640\\n out = []\\n with open(file_name, 'r') as f:\\n line_num = 1\\n for line in f:\\n if line_num < 17:\\n # Read to where data starts\\n line_num += 1\\n continue\\n elif line_num > 74:\\n break\\n # print(list(map(int, line.strip().split(\\\" \\\"))))\\n vals = line.split()\\n # print(list(\\\"\\\".join(line)))\\n # print(line.split())\\n assert(float(vals[2]) < 640)\\n assert(float(vals[3]) < 480)\\n point = [float(vals[2]) * w, float(vals[3]) * h]\\n # print(point)\\n out.append(point)\\n line_num += 1\\n\\n out.append([0,0])\\n out.append([w-1, 0])\\n out.append([0, h-1])\\n out.append([w-1, h-2])\\n return out\",\n \"def read_from(self, filename):\\n self.x, self.y = np.loadtxt(filename, unpack=True, usecols=(0, 1))\",\n \"def init_from_obj_file(cls, f, scale=1, density=1):\\n lines = [line.strip() for line in f.readlines()]\\n vertices = []\\n indexes = []\\n for line in lines:\\n if line.startswith(\\\"v\\\"): # vertex\\n nums = list(map(float, string_to_list(line[2:])))\\n vertices.append(scale * np.array(nums[:3]))\\n # x.append(nums[0] * scale)\\n # y.append(nums[1] * scale)\\n # z.append(nums[2] * scale)\\n elif line.startswith(\\\"f\\\"): # face\\n nums = list(map(lambda a: int(a) - 1, string_to_list(line[2:])))\\n indexes.append(nums)\\n return cls(vertices, indexes, density)\",\n \"def readogle(filename, **kw):\\n \\n # 2008-12-21 18:53 IJC: Created\\n\\n f = open(filename, 'r')\\n raw = f.readlines()\\n f.close()\\n\\n nstars = len(raw)\\n\\n raw2 = array([line.split() for line in raw])\\n ra = raw2[:,1]\\n dec = raw2[:,2]\\n xref = raw2[:,3]\\n yref = raw2[:,4]\\n vmag = raw2[:,5]\\n imag = raw2[:,7]\\n \\n xref = [map(float, [x]) for x in xref]\\n yref = [map(float, [y]) for y in yref]\\n vmag = [map(float, [v]) for v in vmag]\\n imag = [map(float, [i]) for i in imag]\\n\\n return (ra, dec, xref, yref, vmag, imag)\",\n \"def add_to_matrix_from_file(filename):\\n\\tif not os.path.exists(filename):\\n\\t\\treturn []\\n\\n\\tpng_image = Image.open(filename)\\n\\tpng_image.thumbnail((32, 32), Image.ANTIALIAS)\\n\\n\\tcol = 0\\n\\timage = []\\n\\tfor pixel in list(png_image.getdata()):\\n\\t\\t# TODO: something better than taking the red value\\n\\t\\tif png_image.mode == 'L':\\n\\t\\t\\tpixelr = pixel\\n\\t\\telif png_image.mode == 'RGB':\\n\\t\\t\\tpixelr, pixelg, pixelb = pixel\\n\\t\\telif png_image.mode == 'RGBA':\\n\\t\\t\\tpixelr, pixelg, pixelb, pixela = pixel\\n\\t\\timage.append(int(pixelr))\\n\\t\\tcol += 1\\n\\treturn image\",\n \"def create_from_file(cls, file_name: str) -> \\\"TensorImage\\\":\\n image_data = image_utils.decode_image_from_file(file_name)\\n return cls(image_data, is_from_numpy_array=False)\",\n \"def format_data(filename,dummy=False):\\n data = np.matrix(np.loadtxt(filename))\\n Y = data[:,-1]\\n X = data[:,0:-1]\\n if dummy:\\n X = np.concatenate((np.ones((len(X), 1)), X), 1)\\n return(X,Y)\",\n \"def load_grd(filename):\\n with open(filename, 'r') as f:\\n meta = {}\\n meta['header'] = []\\n meta['header'].append(f.readline().rstrip('\\\\n'))\\n while meta['header'][-1] != '++++':\\n meta['header'].append(f.readline().rstrip('\\\\n'))\\n # These determine the type of grid and the field format.\\n meta['KTYPE'] = int(f.readline().split()[0])\\n if meta['KTYPE'] != 1:\\n raise ValueError(\\\"Not implemented.\\\")\\n meta['NSET'], meta['ICOMP'], meta['NCOMP'], meta['IGRID'] = [int(s) for s in f.readline().split()]\\n # The grid center in units of the x and y grid spacing.\\n meta['IX'], meta['IY'] = [int(s) for s in f.readline().split()]\\n # These are the x and y grid limits: S is lower, and E is upper.\\n meta['XS'], meta['YS'], meta['XE'], meta['YE'] = [float(s) for s in f.readline().split()]\\n # These are the numbers of grid points in x and y.\\n meta['NX'], meta['NY'], meta['KLIMIT'] = [int(s) for s in f.readline().split()]\\n # Implement this to read elliptically truncated grids.\\n if meta['KLIMIT'] != 0:\\n raise ValueError(\\\"Not implemented.\\\")\\n # Load the field data. This returns an array with shape (NX * NY, 2 * NCOMP).\\n conv = dict([(column, string_to_float) for column in range(2 * meta['NCOMP'])])\\n data = np.loadtxt(f, dtype=float, converters=conv)\\n # Determine the grid spacing and center values.\\n meta['DX'] = (meta['XE'] - meta['XS']) / (meta['NX'] - 1)\\n meta['DY'] = (meta['YE'] - meta['YS']) / (meta['NY'] - 1)\\n meta['XCEN'] = meta['DX'] * meta['IX']\\n meta['YCEN'] = meta['DY'] * meta['IY']\\n # Reshape the data.\\n map = np.empty((meta['NX'], meta['NY'], meta['NCOMP']),\\n dtype=np.complex)\\n for component in range(meta['NCOMP']):\\n column = data[:, 2 * component] + 1j * data[:, 2 * component + 1]\\n map[:, :, component] = column.reshape(meta['NX'], meta['NY'], order='F')\\n return meta, map\",\n \"def parse_txt_file(txtfile):\\n array = np.genfromtxt(txtfile)\\n return array\",\n \"def build_from_file(path):\\n with open(path) as obj:\\n raw_file = obj.read()\\n file_lines = [line.split(\\\" \\\") for line in raw_file.split(\\\"\\\\n\\\")]\\n\\n vertices = {}\\n faces = []\\n for number, line in enumerate(file_lines):\\n if line[0] == \\\"v\\\":\\n vertices[number + 1] = tuple(map(float, line[1:]))\\n if line[0] == \\\"f\\\":\\n face = []\\n for index in line[1:]:\\n face.append(vertices[int(index)])\\n face.append(vertices[int(line[1])])\\n faces.append(face)\\n return Object(points=faces)\",\n \"def read_file(file):\\n if opts.input_type == 'fits':\\n data = fileio.read_fits(file)\\n else:\\n data = fileio.read_ascii(file)\\n c_id = data[0,:]\\n g_num = np.array(range(len(c_id)), dtype = 'int')\\n g_id = data[3,:]\\n g_ra = np.array(data[4,:], dtype = 'float')\\n g_dec = np.array(data[5,:], dtype = 'float')\\n g_z = np.array(data[6,:], dtype = 'float')\\n return c_id, g_num, g_id, g_ra, g_dec, g_z\",\n \"def read_2d_analysis_data(f):\\n \\n data = np.transpose(np.loadtxt(f, dtype=np.float64))\\n x = data[0]\\n y = data[1]\\n\\n return x, y\",\n \"def _parse_txt(path, n_channels):\\n f = open(path)\\n lines = f.readlines()\\n f.close()\\n\\n geom = np.zeros((0, 2))\\n\\n for i, line in zip(range(n_channels), lines):\\n line = line.replace('\\\\r', '')\\n line = line.replace('\\\\n', '')\\n row = line.split(' ')\\n geom = np.vstack((geom, row[:2])).astype('float')\\n\\n return geom\",\n \"def get_preprocessed_image(file_name):\\n\\n im = np.array(Image.open(file_name)).astype(np.float32)\\n assert im.ndim == 3, 'Only RGB images are supported.'\\n im = im - _IMAGENET_MEANS\\n im = im[:, :, ::-1] # Convert to BGR\\n img_h, img_w, img_c = im.shape\\n assert img_c == 3, 'Only RGB images are supported.'\\n if img_h > 500 or img_w > 500:\\n raise ValueError('Please resize your images to be not bigger than 500 x 500.')\\n\\n pad_h = 500 - img_h\\n pad_w = 500 - img_w\\n im = np.pad(im, pad_width=((0, pad_h), (0, pad_w), (0, 0)), mode='constant', constant_values=0)\\n return np.expand_dims(im.astype(np.float32), 0), img_h, img_w\",\n \"def loadtext(infile):\\n warr, farr, earr=np.loadtxt(infile, usecols=(0,1,2), unpack=True)\\n return create_spectrum(warr, farr, earr)\",\n \"def read_from(self, filename):\\n\\n lon, lat, field, weight = [], [], [], []\\n\\n if os.path.exists(filename):\\n logger.info(\\\"Reading data from file {0}\\\".format(filename))\\n with open(filename, 'r') as f:\\n line = f.readline()\\n ncols = len(line.split())\\n while ncols >= 3:\\n lon.append(float(line.split()[0]))\\n lat.append(float(line.split()[1]))\\n field.append(float(line.split()[2]))\\n if ncols >= 4:\\n weight.append(float(line.split()[3]))\\n else:\\n weight.append(1.)\\n line = f.readline()\\n ncols = len(line.split())\\n\\n self.x = np.array(lon)\\n self.y = np.array(lat)\\n self.field = np.array(field)\\n self.weight = np.array(weight)\\n return self\\n else:\\n logger.error(\\\"File {0} does not exist\\\".format(filename))\\n raise FileNotFoundError('File does not exist')\",\n \"def read_file ( filename ):\\r\\n\\t# lecture de l'en-tete\\r\\n\\tinfile = open ( filename, \\\"r\\\" ) \\r\\n\\tnb_classes, nb_features = [ int( x ) for x in infile.readline().split() ]\\r\\n\\r\\n\\t# creation de la structure de donnees pour sauver les images :\\r\\n\\t# c'est un tableau de listes (1 par classe)\\r\\n\\tdata = np.empty ( 10, dtype=object ) \\r\\n\\tfiller = np.frompyfunc(lambda x: list(), 1, 1)\\r\\n\\tfiller( data, data )\\r\\n\\r\\n\\t# lecture des images du fichier et tri, classe par classe\\r\\n\\tfor ligne in infile:\\r\\n\\t\\tchamps = ligne.split ()\\r\\n\\t\\tif len ( champs ) == nb_features + 1:\\r\\n\\t\\t\\tclasse = int ( champs.pop ( 0 ) )\\r\\n\\t\\t\\tdata[classe].append ( map ( lambda x: float(x), champs ) ) \\r\\n\\tinfile.close ()\\r\\n\\r\\n\\t# transformation des list en array\\r\\n\\toutput = np.empty ( 10, dtype=object )\\r\\n\\tfiller2 = np.frompyfunc(lambda x: np.asarray (x), 1, 1)\\r\\n\\tfiller2 ( data, output )\\r\\n\\r\\n\\treturn output\",\n \"def loadtext(infile):\\n warrsn, farrsn =np.loadtxt(infile, usecols=(0, 1), unpack=True)\\n return create_spectrum(warrsn, farrsn)\",\n \"def read_data(infile):\\n extension = os.path.splitext(infile)[1]\\n h = read_header(infile)\\n nx = int(h['num_x_pts'])\\n ny = int(h['num_y_pts'])\\n nt = int(h['num_t_pts'])\\n fid = open(infile, 'rb')\\n fid.seek(512) #skip header\\n if extension == '.aps' or extension == '.a3daps':\\n if(h['word_type']==7): #float32\\n data = np.fromfile(fid, dtype = np.float32, count = nx * ny * nt)\\n elif(h['word_type']==4): #uint16\\n data = np.fromfile(fid, dtype = np.uint16, count = nx * ny * nt)\\n data = data * h['data_scale_factor'] #scaling factor\\n data = data.reshape(nx, ny, nt, order='F').copy() #make N-d image\\n elif extension == '.a3d':\\n if(h['word_type']==7): #float32\\n data = np.fromfile(fid, dtype = np.float32, count = nx * ny * nt)\\n elif(h['word_type']==4): #uint16\\n data = np.fromfile(fid, dtype = np.uint16, count = nx * ny * nt)\\n data = data * h['data_scale_factor'] #scaling factor\\n data = data.reshape(nx, nt, ny, order='F').copy() #make N-d image\\n elif extension == '.ahi':\\n data = np.fromfile(fid, dtype = np.float32, count = 2* nx * ny * nt)\\n data = data.reshape(2, ny, nx, nt, order='F').copy()\\n real = data[0,:,:,:].copy()\\n imag = data[1,:,:,:].copy()\\n fid.close()\\n if extension != '.ahi':\\n return data\\n else:\\n return real, imag\",\n \"def read_data(filename):\\n data = np.genfromtxt(filename, delimiter=',', dtype=str)\\n X = data[1:,2:].astype(np.float)\\n y = data[1:,0]\\n y[y==label0]='0'\\n y[y==label1]='1'\\n y[y==label2]='2'\\n y=y.astype(np.float)\\n return X, y\",\n \"def _openFlt(self, fname):\\n image = np.loadtxt(fname)\\n\\n if(image !=None):\\n M,N=(int(image[0]), int(image[1]))\\n image = image[2:image.shape[0]]\\n image = image.reshape((M,N))\\n else:\\n raise IOError, \\\"Image file can not be opened\\\"\\n\\n return image\",\n \"def img2vector(filename):\\n img_vector = zeros((1, 32*32))\\n with open(filename, 'r') as fr:\\n for row in xrange(32):\\n line = fr.readline()\\n for column in xrange(32):\\n img_vector[0, 32*row+column] = int(line[column])\\n\\n return img_vector\",\n \"def open_image(infile):\\n with fits.open(infile) as f:\\n header = f[0].header\\n data = f[0].data\\n if data.ndim == 2:\\n # NAXIS=2: [Y, X]\\n image = data\\n elif data.ndim == 3 and data.shape[0] == 1:\\n # NAXIS=3: [FREQ=1, Y, X]\\n image = data[0, :, :]\\n elif data.ndim == 4 and data.shape[0] == 1 and data.shape[1] == 1:\\n # NAXIS=4: [STOKES=1, FREQ=1, Y, X]\\n image = data[0, 0, :, :]\\n else:\\n raise ValueError(\\\"Slice '{0}' has invalid dimensions: {1}\\\".format(\\n infile, data.shape))\\n return (header, image)\",\n \"def read_data(filename):\\n data = np.genfromtxt(filename, delimiter=',', dtype = str)\\n X = data[1:,2:].astype(np.float)\\n y = data[1:,0]\\n y[y==label0]='0' \\n y[y==label1]='1' \\n y[y==label2]='2'\\n y.astype(np.float) \\n return X, y\",\n \"def f2image(infile, maskit=True):\\n global master_mask\\n\\n tab = Table.read(infile, format=\\\"ascii\\\", names=(\\\"quadrant\\\", \\\"detx\\\", \\\"dety\\\", \\\"area\\\"), comment=\\\"#\\\")\\n pixel_edges = np.arange(-0.5, 63.6)\\n image = np.zeros((128,128))\\n im_sub = np.zeros((64,64))\\n\\n data = tab[tab[\\\"quadrant\\\"] == 0]\\n im_sub[data['detx'], data['dety']] = data['area']\\n im_sub = np.transpose(im_sub)\\n image[64:128,0:64] = np.copy(im_sub)\\n\\n data = tab[tab[\\\"quadrant\\\"] == 1]\\n im_sub[data['detx'], data['dety']] = data['area']\\n im_sub = np.transpose(im_sub)\\n image[64:128,64:128] = np.copy(im_sub)\\n\\n data = tab[tab[\\\"quadrant\\\"] == 2]\\n im_sub[data['detx'], data['dety']] = data['area']\\n im_sub = np.transpose(im_sub)\\n image[0:64,64:128] = np.copy(im_sub)\\n\\n data = tab[tab[\\\"quadrant\\\"] == 3]\\n im_sub[data['detx'], data['dety']] = data['area']\\n im_sub = np.transpose(im_sub)\\n image[0:64,0:64] = np.copy(im_sub)\\n\\n if maskit:\\n image[master_mask] = 0\\n\\n return image\",\n \"def from_file(self, file, num):\\n l = file.readline()\\n\\n name = None\\n left = None\\n right = None\\n center = None\\n order = None\\n shift = None\\n svs = None\\n\\n while l:\\n if l.find('%d=' % num) > -1:\\n if name is None: name = parse_name(l, 'kernel_name%d' % num)\\n if left is None: left = parse_int(l, 'kernel_left%d' % num)\\n if right is None: right = parse_int(l, 'kernel_right%d' % num)\\n if center is None: center = parse_int(l, 'kernel_center%d' % num)\\n if order is None: order = parse_int(l, 'kernel_order%d' % num)\\n if shift is None: shift = parse_int(l, 'kernel_shift%d' % num)\\n if svs is None: svs = parse_string(l, file, 'kernel_svs%d' % num)\\n else:\\n self.window = (left, center, right)\\n return self.init_sensor({ 'name' : name, 'order': order, 'shift' : shift}, svs)\\n\\n l = file.readline()\",\n \"def read_KNN_dataFile(file):\\n A = np.genfromtxt(file)\\n return A\",\n \"def read( self, Filename ):\\n try:\\n self.name = Filename\\n Filedata = open(self.name,'r').readlines()\\n self.ncols = string.atoi( Filedata[0].strip().split()[-1] )\\n self.nrows = string.atoi( Filedata[1].strip().split()[-1] )\\n self.xllcorner = string.atof( Filedata[2].strip().split()[-1] )\\n self.yllcorner = string.atof( Filedata[3].strip().split()[-1] )\\n self.cellsize = string.atof( Filedata[4].strip().split()[-1] )\\n self.nodata = string.atof( Filedata[5].strip().split()[-1] )\\n self.data = numpy.ones( (self.nrows, self.ncols ) ) *1.0\\n row = self.nrows\\n for t in Filedata[6:]:\\n row -= 1\\n col = -1\\n values = map(string.atof, t.strip().split())\\n for x in values:\\n col += 1\\n self.data[(row,col)] = x\\n except:\\n print \\\"Error opening grid ::\\\", Filename\\n raise\",\n \"def load_stack(filename):\\n data = np.genfromtxt(filename, skip_header=1)\\n index_arr = data[:, 2]\\n thickness_arr = data[:, 3] / 1e9\\n stack = Stack(index_arr, thickness_arr)\\n return stack\",\n \"def read_info(file, shape):\\n for line in file:\\n if line.startswith('mid'):\\n shape.set_id(int(line.split()[-1])) \\n\\n if line.startswith('avg_depth'):\\n shape.set_avg_depth(float(line.split()[-1]))\\n if line.startswith('center'):\\n pattern = 'center: \\\\((?P<x>.*),(?P<y>.*),(?P<z>.*)\\\\)'\\n matches = re.match(pattern, line)\\n shape.set_center((float(matches.group('x')),\\n float(matches.group('y')),\\n float(matches.group('z'))))\\n if line.startswith('scale'):\\n shape.set_scale(float(line.split()[-1]))\\n\\n return shape\",\n \"def readCubeFile(self, filename):\\n\\n inputfile = open(filename, \\\"r\\\")\\n header = \\\"\\\".join([inputfile.readline(), inputfile.readline()])\\n\\n temp = inputfile.readline().strip().split()\\n self.numAtoms = int(temp[0])\\n self.origin = list(map(float, temp[1:]))\\n\\n self.numPoints = [0] * 3\\n self.spacing = [0] * 3\\n for i in range(3):\\n line = inputfile.readline().strip().split()\\n self.numPoints[i] = int(line[0])\\n temp = list(map(float, line[1:]))\\n self.spacing[i] = temp[i]\\n assert sum(temp[:i] + temp[i + 1:]) == 0\\n\\n # Read in the lines with atom data\\n for i in range(self.numAtoms):\\n line = inputfile.readline()\\n\\n self.data = np.zeros((self.numPoints[1], self.numPoints[0], self.numPoints[2]), \\\"float\\\")\\n i = j = k = 0\\n while i < self.numPoints[1]:\\n line = next(inputfile)\\n temp = list(map(float, line.strip().split()))\\n for x in range(0, len(temp)):\\n self.data[j, i, x + k] = temp[x]\\n\\n k += len(temp)\\n if k == self.numPoints[2]:\\n j += 1\\n k = 0\\n if j == self.numPoints[1]:\\n i += 1\\n j = 0\\n\\n inputfile.close()\",\n \"def file_reader(image_file, label_file):\\n\\n image = im.imread(image_file)\\n\\n with open(label_file, \\\"r\\\") as file:\\n label = float(file.read())\\n\\n return image, label\",\n \"def loadtext2(infile):\\n warrsn, farrsn =np.loadtxt(infile, usecols=(0, 1), unpack=True)\\n return create_spectrum(warrsn, farrsn)\",\n \"def read_image(filename, representation):\\n img = imread(filename)\\n img = int2float(img)\\n if representation == GS_REP:\\n img = rgb2gray(img)\\n return img\",\n \"def parse_data(filename, data_path, ground_truths_path):\\n with open(filename) as f:\\n content = f.readlines()\\n\\n content = [x.strip() for x in content]\\n\\n data = []\\n for i, item in enumerate(content):\\n if i == 0:\\n continue\\n parametres = item.split(',')\\n\\n image = cv2.imread(os.path.join(data_path, parametres[0]), -1)\\n image_processed = image * np.uint16(65535.0 / max(image.ravel()))\\n image_processed = cv2.resize(image_processed, (960, 960), interpolation = cv2.INTER_AREA)\\n\\n ground_truth = cv2.imread(os.path.join(ground_truths_path, parametres[0][:parametres[0].rfind('.')] + \\\".png\\\"), -1)\\n ground_truth_processed = np.uint16(np.copy(ground_truth))\\n indices = np.where(np.any(ground_truth_processed != [0, 0, 255], axis = -1))\\n ground_truth_processed[indices] = [0, 0, 0]\\n indices = np.where(np.all(ground_truth_processed == [0, 0, 255], axis = -1))\\n ground_truth_processed[indices] = [65535, 65535, 65535]\\n ground_truth_processed = cv2.cvtColor(ground_truth_processed, cv2.COLOR_BGR2GRAY)\\n ground_truth_processed = cv2.resize(ground_truth_processed, (960, 960), interpolation = cv2.INTER_AREA)\\n \\n img = Image(image, image_processed, ground_truth, ground_truth_processed,\\n parametres[0], parametres[1], parametres[2], parametres[3], \\n parametres[4], parametres[5], parametres[6], parametres[7], \\n parametres[8])\\n data.append(img)\\n\\n return data\",\n \"def parse_file(filename):\\n\\n f = open(filename, 'r')\\n BoardSize = int( f.readline())\\n NumVals = int(f.readline())\\n\\n #initialize a blank board\\n board= [ [ 0 for i in range(BoardSize) ] for j in range(BoardSize) ]\\n\\n #populate the board with initial values\\n for i in range(NumVals):\\n line = f.readline()\\n chars = line.split()\\n row = int(chars[0])\\n col = int(chars[1])\\n val = int(chars[2])\\n board[row-1][col-1]=val\\n\\n return board\",\n \"def load_velodyne_points(filename):\\n points = np.fromfile(filename, dtype=np.float32).reshape(-1, 4)\\n points[:, 3] = 1.0 # homogeneous\\n return points\",\n \"def imread_float(infile):\\n return img_as_float(imread(infile))\",\n \"def __init__(self, f):\\n with open(f,'r') as gridfile:\\n self.matrix = ([[char for char in row if char != '\\\\n'] \\n for row in gridfile.readlines()])\\n # beginning represents the upper most \\n self.beginning = Square(0, 0, self)\",\n \"def read_from_ascii(self, filename):\\n self.ascii_filename = filename\\n # read file content into a string\\n f=open(filename,'r')\\n file_str=f.read()\\n f.close()\\n # make dictionary with file content\\n reg_exp_data_groups=re.compile(r'^#>>(\\\\w+):.*\\\\n',re.M)\\n file_dict=self.make_data_dict_from_str(reg_exp_data_groups,file_str)\\n # read arrays ------------------------------\\n self.x=np.loadtxt(StringIO.StringIO(file_dict['x']))\\n self.p=np.loadtxt(StringIO.StringIO(file_dict['p']))\\n self.fmci_XP=np.loadtxt(StringIO.StringIO(file_dict['XP']))\\n # regular expression for extracting parameter=value\\n reg_exp_param_val=re.compile(r'\\\\n*(\\\\w+)=',re.M)\\n # read params_physics -----------------------\\n params_physics_dict=self.make_data_dict_from_str(reg_exp_param_val,file_dict['params_physics'])\\n self.name=self.__get_particle_name(params_physics_dict['particle'])\\n self.time=float(params_physics_dict['time'])\\n # read params_TDC ---------------------------\\n params_TDC_dict=self.make_data_dict_from_str(reg_exp_param_val,file_dict['params_TDC'])\\n self.calc_id=params_TDC_dict['calc_id']\\n self.i_ts=int(params_TDC_dict['i_ts'])\",\n \"def file_reader(filename = 'conv_params'):\\n\\n with open(filename) as f:\\n info = f.readlines()\\n info = [i.strip() for i in info] # each element in info is a string of a line from the file\\n info = [i.split() for i in info] # split each whitespace delimited element into a list of lists\\n info = [[i.split('-') for i in j] for j in info] # note info is 3 layers deep\\n\\n info[2] = info[2][0] # makes default E just a single string of the number\\n info[3] = info[3][0]\\n\\n return info\",\n \"def get_pict_data(fname):\\n with open(fname, 'r') as f:\\n return np.asarray(f.read().split(',')).reshape((11, 1024)).astype(int)\",\n \"def read_and_interp_psd_from_txt(filename):\\n psd = read_psd_from_txt(filename)\\n return interpolate_psd(psd)\",\n \"def myloadtxt(fname, skiprows = 0):\\n fin = file(fname)\\n for i in range(skiprows):\\n fin.readline()\\n ln = fin.readline()\\n lns = []\\n while (ln != \\\"\\\"):\\n thisln = []\\n ln = ln.strip().split()\\n for s in ln:\\n try:\\n f = float(s)\\n except:\\n f = None\\n thisln.append(f)\\n lns.append(thisln)\\n ln = fin.readline()\\n return np.array(lns)\",\n \"def readprimitive(f): \\n \\n ## read in lines from input file and ignore blank lines and comment lines\\n lines = [line.rstrip() for line in f if line.rstrip() if line[0] != '#']\\n\\n # a1,a2,a3\\n A = np.array([[float(lines[0].split()[0]),float(lines[0].split()[1]),float(lines[0].split()[2])],\\n [float(lines[1].split()[0]),float(lines[1].split()[1]),float(lines[1].split()[2])],\\n [float(lines[2].split()[0]),float(lines[2].split()[1]),float(lines[2].split()[2])]]).T\\n \\n # number of basis atoms\\n num_basis = int(lines[3].split()[0]) \\n\\n # basis atom positions in unit cell\\n unitcell_pos = []\\n for i in range(num_basis): \\n unitcell_pos.append([float(lines[4+i].split()[0]),float(lines[4+i].split()[1]),float(lines[4+i].split()[2])]) \\n \\n return (A,unitcell_pos)\",\n \"def Read_CCD_image(Path):\\n fs = open(Path, 'r')\\n \\n #Compte le nombre de lignes, oblige pr le moment de tout lire\\n # la dernière ligne est vide ! attention, j'initialise nb_line à -1 pour compenser\\n nb_line = -1\\n while 1: \\n txt = fs.readline()\\n nb_line = nb_line+1\\n if ((txt =='')|(txt == '\\\\r\\\\n')): \\n break\\n fs.close()\\n \\n \\n # je lis une ligne, compte le nombre d'espace et en deduit le nombre de colonne de la matrice\\n fs = open(Path, 'r')\\n txt = fs.readline()\\n ii = 0\\n index_line = []\\n while 1: # on cherche le premier espace qui limite le premier nombre\\n ii = ii+1 \\n if (txt[ii:ii+1] == '\\\\t'):\\n index_line.append(ii)\\n if (txt[ii:ii+4] == '\\\\r\\\\n'):\\n break\\n nb_col = np.array(index_line).size\\n fs.close()\\n \\n image = np.ones((nb_line,nb_col), dtype = float) # Create the image matrix\\n # Pour les axes, je reprends les chiffres obtenus lors de la calibration du mouvement de la pointe.... cad 31nm/pixel...\\n #axex = np.linspace(0,0.032*nb_line,nb_line) #microns\\n #axey = np.linspace(0,0.032*nb_col,nb_col) #microns\\n axex = np.linspace(0,nb_line,nb_line) #pixels\\n axey = np.linspace(0,nb_col,nb_col) #pixels\\n \\n fs = open(Path, 'r')\\n \\n nb_line = 0 # I need to count the lines to fill the matrix\\n while 1: \\n txt = fs.readline()\\n if ((txt =='')|(txt == '\\\\r\\\\n')): \\n break\\n if txt[0] =='#':\\n pass\\n else:\\n #print(txt)\\n ii=-1\\n index_line=[]\\n while 1: # on cherche le premier espace qui limite le premier nombre\\n ii = ii+1 \\n if (txt[ii:ii+1] == '\\\\t'):\\n index_line.append(ii)\\n if (txt[ii:ii+4] == '\\\\r\\\\n'):\\n break\\n # ici j'ai tous mes index d'espace pour une ligne normalement\\n line = []\\n line.append(txt[:index_line[0]])\\n index_line = np.array(index_line) # premier nombre\\n for ii in range (index_line.size -1):\\n line.append(np.float(txt[index_line[ii]:index_line[ii+1]]))\\n # Il me manque le dernier aussi\\n #line.append(np.float(txt[index_line[-1]:])) \\n image[nb_line,:] = line\\n nb_line = nb_line+1\\n #flipping up-down with [::-1,...] then image appears in Python as in the screen in HiPic \\n return axex,axey,image[::-1,...]\",\n \"def read_data(self,filename):\\n self.x = [] #Input values\\n self.t = [] #Target values\\n\\n with open(filename, \\\"r\\\") as infile:\\n lines = infile.readlines()\\n self.n = len(lines)\\n for line in lines:\\n words = line.split()\\n self.x.append(float(words[0]))\\n self.t.append(float(words[1]))\\n\\n self.x = np.array(self.x)\\n self.t = np.array(self.t)\\n self.create_design_matrix()\",\n \"def read(f):\\n \\n if isinstance(f, basestring):\\n # If the input is a string, treat as file name\\n with open(f) as fh: # Ensure file is closed\\n return read(fh) # Call again with file object\\n \\n # First line contains the date\\n date = f.readline()\\n if not date:\\n raise IOError(\\\"Cannot read from input file \\\"+str(filename))\\n \\n # Second is description\\n desc = f.readline()\\n \\n token = file_numbers(f)\\n \\n # Third contains number of mesh points\\n try:\\n npsi = int(token.next())\\n ntheta = int(token.next())\\n isym = int(token.next())\\n except StopIteration:\\n raise IOError(\\\"Unexpected end of file while reading grid size\\\")\\n except ValueError:\\n raise IOError(\\\"Third line should contain npsi, ntheta and isym\\\")\\n \\n # Check values\\n if (isym < 0) or (isym > 1):\\n raise IOError(\\\"isym must be either 0 or 1\\\")\\n if (npsi < 1) or (ntheta < 1):\\n raise IOError(\\\"Invalid npsi=\\\"+str(npsi)+\\\" or ntheta=\\\" + str(ntheta))\\n \\n # Read normalisation factors\\n\\n try:\\n rcnt = float(token.next())\\n xma = float(token.next())\\n zma = float(token.next())\\n btor = float(token.next())\\n curtot = float(token.next())\\n eaxe = float(token.next())\\n dnorm = float(token.next())\\n except:\\n raise IOError(\\\"Couldn't read normalisation factors\\\")\\n \\n def read_array(n, name=\\\"Unknown\\\"):\\n data = np.zeros([n])\\n try:\\n for i in np.arange(n):\\n data[i] = float(token.next())\\n except:\\n raise IOError(\\\"Failed reading array '\\\"+name+\\\"' of size \\\", n)\\n return data\\n\\n def read_2d(nx, ny, name=\\\"Unknown\\\"):\\n data = np.zeros([nx, ny])\\n for i in np.arange(nx):\\n data[i,:] = read_array(ny, name+\\\"[\\\"+str(i)+\\\"]\\\")\\n return data\\n\\n # Read 1D arrays\\n psiflux = read_array(npsi, \\\"psiflux\\\")\\n fnorm = read_array(npsi, \\\"fnorm\\\")\\n ffpnorm = read_array(npsi, \\\"ffpnorm\\\")\\n ponly = read_array(npsi, \\\"ponly\\\")\\n pponly = read_array(npsi, \\\"pponly\\\")\\n qsf = read_array(npsi, \\\"qsf\\\")\\n d = read_array(npsi, \\\"d\\\")\\n \\n dpdz = read_array(ntheta, \\\"dpdz\\\")\\n dpdr = read_array(ntheta, \\\"dpdr\\\")\\n \\n # 2D arrays\\n \\n xnorm = read_2d(ntheta, npsi, \\\"xnorm\\\")\\n znorm = read_2d(ntheta, npsi, \\\"znorm\\\")\\n \\n # Try to read Br and Bz (may be present)\\n try:\\n Br = read_2d(ntheta, npsi, \\\"Br\\\")\\n Bz = read_2d(ntheta, npsi, \\\"Bz\\\")\\n except:\\n Br = Bz = None\\n \\n ny = ntheta\\n\\n if isym == 1:\\n # Fill in values for up-down symmetric case\\n print(\\\"Grid is up-down symmetric. Reflecting grid about midplane\\\")\\n ny = tsize = 2*(ntheta - 1) + 1\\n \\n def reflect(data, mapfunc = lambda x:x):\\n \\\"\\\"\\\" Reflect a variable about midplane\\n Optionally supply a mapping function\\\"\\\"\\\"\\n data2 = np.zeros([tsize, npsi])\\n # Copy the original data\\n for i in np.arange(ntheta):\\n data2[i,:] = data[i,:]\\n # Now fill in the remainder\\n for i in np.arange(ntheta, tsize):\\n t0 = tsize - 1 - i\\n data2[i,:] = mapfunc(data[t0,:])\\n return data2\\n \\n xnorm = reflect(xnorm)\\n znorm = reflect(znorm, lambda x: 2.*zma - x) # Reflect about zma\\n if Br != None:\\n Br = reflect(Br, lambda x:-x) # Br reverses\\n if Bz != None:\\n Bz = reflect(Bz) # Bz remains the same\\n theta = tsize\\n\\n # Make sure we have Br, Bz and Bpol\\n\\n if (Br == None) or (Bz == None):\\n # Calculate Bpol from psi then Br and Bz from Bpol\\n # Use dpsi = R*Bp dx (for now)\\n Bpol = np.zeros([ny, npsi])\\n \\n def deriv(f):\\n n = np.size(f)\\n dfdi = np.zeros(n)\\n dfdi[1:-1] = (f[2:n] - f[0:-2])/2. # Central difference in the middle\\n dfdi[0] = f[1] - f[0]\\n dfdi[-1] = f[-1] - f[-2]\\n return dfdi\\n \\n for i in np.arange(ntheta):\\n drdi = deriv(xnorm[i, :])\\n dzdi = deriv(znorm[i, :])\\n dldi = sqrt(drdi**2 + dzdi**2) # Arc length\\n dpsidi = deriv(psiflux)\\n \\n Bpol[i, :] = dpsidi / (dldi * xnorm[i,:])\\n else:\\n Bpol = np.sqrt(Br**2 + Bz**2)\\n \\n # Calculate toroidal field\\n Btor = fnorm / xnorm\\n \\n #########################################\\n # Create a dictionary of values to return\\n # \\n # Need to transpose 2D arrays to [psi, theta] \\n # to be consistent with elite inputs\\n \\n var = {\\\"npsi\\\":npsi, \\\"npol\\\":ny, # Sizes\\n \\n \\\"psi\\\":psiflux,\\n \\\"f(psi)\\\":fnorm,\\n \\\"p\\\":ponly,\\n \\n \\\"R\\\": np.transpose(xnorm),\\n \\\"Z\\\": np.transpose(znorm),\\n\\n \\\"Bp\\\":np.transpose(Bpol),\\n \\\"Bt\\\":np.transpose(Btor),\\n\\n \\\"q\\\":qsf,\\n\\n \\\"ffprime\\\":ffpnorm,\\n \\\"pprime\\\":pponly}\\n\\n if Br != None:\\n var['Br'] = np.transpose(Br)\\n if Bz != None:\\n var['Bz'] = np.transpose(Bz)\\n \\n return var\",\n \"def read_pattern_file(file_path: str) -> np.ndarray:\\n\\n # Check if the example file exists\\n if not os.path.isfile(file_path):\\n return None\\n\\n rows = 0\\n cols = 0\\n with open(file_path) as f:\\n for i, l in enumerate(f):\\n if l[0] != \\\"!\\\":\\n rows += 1\\n if len(l) > cols:\\n cols = len(l) - 1 # Exclude the end of line char from the column count\\n\\n grid = np.zeros((rows, cols), dtype=np.uint8)\\n\\n skip_rows = 0\\n with open(file_path) as f:\\n for j, line in enumerate(f):\\n for k, c in enumerate(line):\\n if c == \\\"!\\\" and k == 0:\\n skip_rows += 1\\n break\\n elif c == \\\"O\\\":\\n grid[j - skip_rows, k] = 1\\n\\n return grid\",\n \"def make_from_file(filehandle):\\n lines = filehandle.readlines()\\n label = str(lines[0].rstrip('\\\\n'))\\n mass = float(lines[1].rstrip('\\\\n'))\\n position = list(lines[2].rstrip('\\\\n').split(','))\\n velocity = list(lines[3].rstrip('\\\\n').split(','))\\n particle = Particle3D(label=label, mass=mass, position=position, velocity=velocity)\\n filehandle.close()\\n return particle\",\n \"def readmesh(filename):\\n f = open(filename, 'rb')\\n cells = []\\n edges = []\\n\\n # create each cell and edge, etc\\n for line in f:\\n \\n return cells, edges\",\n \"def _read_datafile(self,path):\\n \\tlabels, images = [], []\\n \\twith gzip.GzipFile(path) as f:\\n \\t for line in f:\\n \\t vals = line.strip().split()\\n \\t labels.append(float(vals[0]))\\n \\t images.append([float(val) for val in vals[1:]])\\n \\tlabels = np.array(labels, dtype=np.int32)\\n \\tlabels[labels == 10] = 0 # fix weird 0 labels\\n \\timages = np.array(images, dtype=np.float32).reshape(-1, 16, 16, 1)\\n \\timages = (images + 1) / 2\\n \\treturn images, labels\",\n \"def load_pfm(filename):\\n filename = process(filename)\\n with open(filename, \\\"r\\\", encoding=\\\"ISO-8859-1\\\") as file:\\n nc = 3 if file.readline().rstrip() == \\\"PF\\\" else 1\\n width, height = [int(x) for x in file.readline().rstrip().split()]\\n shape = (height, width, nc)\\n img = np.fromfile(file, '{0}{1}'.format(\\\"<\\\" if float(file.readline().rstrip()) < 0 else \\\">\\\",'f') )\\n img = np.reshape(img, shape)\\n return np.flip(np.flip(img, 2), 0).copy()\",\n \"def load_raster_xyz(self, filename):\\n with rasterio.open(filename, 'r') as src:\\n ## Alias 'affine' no longer works for 'transform'\\n ##matrix = src.affine\\n matrix = src.transform\\n self.size = (src.width, src.height)\\n # read per scan line\\n for row in range(0, src.height):\\n window = ((row, row+1), (0, src.width))\\n data = src.read(window=window)\\n this_row = data[0][0]\\n for column in range(0, src.width):\\n x, y = matrix * (column, row)\\n yield x, y, this_row[column]\",\n \"def read_image(filename, representation):\\n image = scipy.misc.imread(filename)\\n if int(representation) == 1:\\n image = rgb2gray(image)\\n return img_as_float(image)\",\n \"def make_image(file):\\n image = cv2.imread(file, 0)\\n image = cv2.resize(image, (IMG_SIZE, IMG_SIZE))\\n return np.array([np.array(image), np.array([0, 0])])\",\n \"def _fileToMatrix(file_name):\\r\\n # TODO: np.loadtxt() might be an alternative\\r\\n # try:\\r\\n if 1 < 3:\\r\\n lres = []\\r\\n for line in open(file_name, 'r').readlines():\\r\\n if len(line) > 0 and line[0] not in ('%', '#'):\\r\\n lres.append(list(map(float, line.split())))\\r\\n res = lres\\r\\n else:\\r\\n fil = open(file_name, 'r')\\r\\n fil.readline() # rudimentary, assume one comment line\\r\\n lineToRow = lambda line: list(map(float, line.split()))\\r\\n res = list(map(lineToRow, fil.readlines()))\\r\\n fil.close() # close file could be omitted, reference counting should do during garbage collection, but...\\r\\n\\r\\n while res != [] and res[0] == []: # remove further leading empty lines\\r\\n del res[0]\\r\\n return res\\r\\n # except:\\r\\n print('could not read file ' + file_name)\",\n \"def read_flow_png(flow_file):\\n flow_object = png.Reader(filename=flow_file)\\n flow_direct = flow_object.asDirect()\\n flow_data = list(flow_direct[2])\\n (w, h) = flow_direct[3]['size']\\n flow = np.zeros((h, w, 3), dtype=np.float64)\\n for i in range(len(flow_data)):\\n flow[i, :, 0] = flow_data[i][0::3]\\n flow[i, :, 1] = flow_data[i][1::3]\\n flow[i, :, 2] = flow_data[i][2::3]\\n\\n invalid_idx = (flow[:, :, 2] == 0)\\n flow[:, :, 0:2] = (flow[:, :, 0:2] - 2 ** 15) / 64.0\\n flow[invalid_idx, 0] = 0\\n flow[invalid_idx, 1] = 0\\n return flow\",\n \"def load(filename):\\n lines = [l.strip('\\\\r\\\\n ') for l in open(filename, 'r').readlines()]\\n lines = [l for l in lines if l != '']\\n dims = [re.split(r'\\\\s+', l) for l in lines]\\n f = np.array([[float(f) for f in d] for d in dims])\\n return f\",\n \"def parseMatrix(file):\\n array = genfromtxt(file, delimiter=',')\\n\\n if not np.isnan(array).any():\\n raise ValueError(\\\"This array does not contain any 'nan' values\\\")\\n\\n return array\",\n \"def load(self, _name):\\r\\n with open(_name, 'r') as fin:\\r\\n self.filename = _name\\r\\n\\r\\n self.comment_1 = fin.readline() # Save 1st comment\\r\\n self.comment_2 = fin.readline() # Save 2nd comment\\r\\n\\r\\n _str = fin.readline().split() # Number of Atoms and Origin\\r\\n self.n_atoms = int(_str[0]) # Number of Atoms\\r\\n self.origin = np.array([float(_str[1]), float(_str[2]), float(_str[3])]) # Position of Origin\\r\\n\\r\\n nVoxel = fin.readline().split() # Number of Voxels\\r\\n self.n_x = int(nVoxel[0])\\r\\n self.x = np.array([float(nVoxel[1]), float(nVoxel[2]), float(nVoxel[3])])\\r\\n\\r\\n nVoxel = fin.readline().split() #\\r\\n self.n_y = int(nVoxel[0])\\r\\n self.y = np.array([float(nVoxel[1]), float(nVoxel[2]), float(nVoxel[3])])\\r\\n\\r\\n nVoxel = fin.readline().split() #\\r\\n self.n_z = int(nVoxel[0])\\r\\n self.z = np.array([float(nVoxel[1]), float(nVoxel[2]), float(nVoxel[3])])\\r\\n\\r\\n self.atoms = []\\r\\n self.atoms_xyz = []\\r\\n for atom in range(self.n_atoms):\\r\\n line = fin.readline().split()\\r\\n self.atoms.append(line[0])\\r\\n self.atoms_xyz.append(list(map(float, [line[2], line[3], line[4]])))\\r\\n\\r\\n self.data = np.zeros((self.n_x, self.n_y, self.n_z))\\r\\n\\r\\n i = int(0)\\r\\n for s in fin:\\r\\n for v in s.split():\\r\\n self.data[int(i / (self.n_y * self.n_z)), int((i / self.n_z) % self.n_y),\\r\\n int(i % self.n_z)] = float(v)\\r\\n i += 1\\r\\n\\r\\n return None\",\n \"def loadtxt(filename, save=True, stripe=True, blank=False):\\n a = []\\n f = open(filename, 'r')\\n fn = filename.split('.')[0]\\n for line in f.readlines():\\n line = line.strip()\\n elems = [int(i) for i in list(line)]\\n a.append(elems)\\n b = np.array(a, dtype=np.int)\\n np.fill_diagonal(b, 0)\\n if blank:\\n b = remove_blanks(b)\\n if save:\\n np.savez('.'.join((fn, 'npz')), a=b)\\n else:\\n return b\",\n \"def read_ascii_raster(ascii_raster_file):\\n import numpy as np\\n\\n with open(ascii_raster_file) as f:\\n header_data = [float(f.next().split()[1]) for x in xrange(6)] #read the first 6 lines\\n\\n raster_data = np.genfromtxt(ascii_raster_file, delimiter=' ', skip_header=6)\\n raster_data = raster_data.reshape(header_data[1], header_data[0]) #rows, columns\\n\\n return raster_data, header_data\",\n \"def load_image_patch(filename):\\n im = Image.open(filename) # .convert('L')\\n width, height = im.size\\n pixels = list(im.getdata())\\n features = [pixels[i * width:(i + 1) * width] for i in range(height)]\\n features = np.asarray(im, dtype=np.float32).flatten()\\n features /= 255.0\\n return features\",\n \"def load_txt(file_path):\\n lines = load_lines(file_path)\\n\\n if 'E' in lines[0]:\\n dtype = np.float32\\n else:\\n dtype = np.int32\\n\\n data = list(map(str.split, lines))\\n array = np.array(data, dtype=dtype)\\n return array\",\n \"def load_picked_points(filename):\\n\\n f = open(filename, 'r')\\n\\n def get_num(string):\\n pos1 = string.find('\\\\\\\"')\\n pos2 = string.find('\\\\\\\"', pos1 + 1)\\n return float(string[pos1 + 1:pos2])\\n\\n def get_point(str_array):\\n if 'x=' in str_array[0] and 'y=' in str_array[1] and 'z=' in str_array[2]:\\n return [get_num(str_array[0]), get_num(str_array[1]), get_num(str_array[2])]\\n else:\\n return []\\n\\n pickedPoints = []\\n for line in f:\\n if 'point' in line:\\n str = line.split()\\n if len(str) < 4:\\n continue\\n ix = [i for i, s in enumerate(str) if 'x=' in s][0]\\n iy = [i for i, s in enumerate(str) if 'y=' in s][0]\\n iz = [i for i, s in enumerate(str) if 'z=' in s][0]\\n pickedPoints.append(get_point([str[ix], str[iy], str[iz]]))\\n f.close()\\n return np.array(pickedPoints)\",\n \"def fromfile(self, path):\\n\\t\\tdata = filetools.read_data(path)\\n\\t\\tprint \\\"File read: %i lines\\\" % len(data)\\n\\t\\tself.build_matrix(data)\",\n \"def load_file(filename):\\r\\n file =np.genfromtxt(filename, delimiter=',')\\r\\n return file\",\n \"def from_file(cls, filename: str) -> \\\"NDOptimiser\\\":\\n from autode.opt.coordinates.cartesian import CartesianCoordinates\\n\\n lines = open(filename, \\\"r\\\").readlines()\\n n_atoms = int(lines[0].split()[0])\\n\\n title_line = NumericStringDict(lines[1])\\n optimiser = cls(\\n maxiter=int(title_line[\\\"maxiter\\\"]),\\n gtol=GradientRMS(title_line[\\\"gtol\\\"]),\\n etol=PotentialEnergy(title_line[\\\"etol\\\"]),\\n )\\n\\n for i in range(0, len(lines), n_atoms + 2):\\n raw_coordinates = np.zeros(shape=(n_atoms, 3))\\n gradient = np.zeros(shape=(n_atoms, 3))\\n\\n for j, line in enumerate(lines[i + 2 : i + n_atoms + 2]):\\n _, x, y, z, dedx, dedy, dedz = line.split()\\n raw_coordinates[j, :] = [float(x), float(y), float(z)]\\n gradient[j, :] = [float(dedx), float(dedy), float(dedz)]\\n\\n coords = CartesianCoordinates(raw_coordinates)\\n coords.e = NumericStringDict(lines[i + 1])[\\\"E\\\"]\\n coords.g = gradient.flatten()\\n\\n optimiser._history.append(coords)\\n\\n return optimiser\",\n \"def from_text_file(cls, filename):\\n raise NotImplementedError()\",\n \"def load_data (filename):\\n from StringIO import StringIO # StringIO behaves like a file object\\n example_data = \\\"0.0001E-10 1.0E-9\\\\n0.5E-10 1.2E-9\\\\n1.0E-10 1.5E-9\\\\n1.5E-10 1.6E-9\\\\n2.0E-10 1.7E-9\\\\n \\\\\\n 2.5E-10 1.8E-9\\\\n3.0E-10 1.7E-9\\\\n3.5E-10 1.6E-9\\\\n4.0E-10 1.5E-9\\\\n4.5E-10 1.2E-9\\\\n4.9999E-10 1.0E-9\\\"\\n if (filename==None):\\n FILE = StringIO(example_data)\\n print \\\"Using example data\\\"\\n else:\\n FILE = open(filename,'r')\\n print \\\"Loading data from %s\\\" % filename\\n x,y = np.loadtxt(FILE, unpack=True)\\n FILE.close()\\n return(x,y)\",\n \"def fill(fname):\\n return [[float(line.split()[-3]), float(line.split()[-1])]\\\\\\n for line in open(fname).readlines()\\\\\\n if FITRESRE1.match(line)]\",\n \"def read_txt(path):\\n mz = []\\n i = []\\n with open(path) as f:\\n for line in f:\\n line = line.split()\\n mz.append(float(line[0]))\\n i.append(float(line[1]))\\n return mz, i\",\n \"def read_image(filename, representation):\\n\\n color_flag = True #if RGB image\\n image = imread(filename)\\n\\n float_image = image.astype(np.float64)\\n\\n if not np.all(image <= 1):\\n float_image /= NORMALIZE #Normalized to range [0,1]\\n\\n if len(float_image.shape) != 3 : #Checks if RGB or Grayscale\\n color_flag = False\\n\\n if color_flag and representation == 1 : #Checks if need RGB to Gray\\n return skimage.color.rgb2gray(float_image)\\n\\n # Same coloring already\\n return float_image\",\n \"def load_data(file_name):\\r\\n with open(str(file_name)) as f:\\r\\n file = f.readlines()\\r\\n label = file[0].strip().split(\\\",\\\")[1:-1]\\r\\n file = file[1:]\\r\\n row = len(file)\\r\\n col = len(file[0].strip().split(\\\",\\\"))-1\\r\\n mat = np.zeros((row, col))\\r\\n for i in range(len(file)):\\r\\n row = file[i].strip()\\r\\n cols = row.split(\\\",\\\")[1:]\\r\\n for j in range(len(cols)):\\r\\n mat[i][j] = int(cols[j])\\r\\n np.random.seed(10)\\r\\n np.random.shuffle(mat)\\r\\n X = mat[:, 0:-1]\\r\\n y = mat[:, -1]\\r\\n\\r\\n return X, y, label\",\n \"def read_ascii(file):\\n wvlen, band, mag, emag, fmag, unit, beam, odate, ref = [],[],[],[],[],[],[],[],[]\\n with open(file, 'r') as f_in:\\n for line in f_in:\\n try:\\n # ensure line contains data:\\n a = float(line[0])\\n except ValueError:\\n a = 'dummy'\\n try:\\n # ensure mag or flux entry is not '--'\\n m = float(line.split(' ')[2])\\n except ValueError:\\n m = 'dummy'\\n \\n if isinstance(a, float) and isinstance(m, float):\\n wvlen.append(float(line.strip().split(' ')[0])) # in metres\\n band.append(line.strip().split(' ')[1])\\n mag.append(float(line.strip().split(' ')[2]))\\n emag.append(line.strip().split(' ')[3])\\n fmag.append(line.strip().split(' ')[4])\\n unit.append(line.strip().split(' ')[5])\\n beam.append(line.strip().split(' ')[6])\\n odate.append(line.strip().split(' ')[7])\\n ref.append(line.strip().split(' ')[8])\\n \\n return wvlen, band, mag, emag, fmag, unit, beam, odate, ref\",\n \"def get_g(file_name):\\n \\n r,g = np.loadtxt(file_name, dtype = 'float', unpack = 'true')\\n \\n return r,g\",\n \"def initFromFile(self,file):\\n self.source = file\\n file_reader = open(file,\\\"r\\\")\\n self.isInit = True\\n lineCounter = 0\\n firstLine = None\\n SecondLine = None\\n ThirdLine = None\\n for line in file_reader:\\n if(lineCounter == 0):\\n firstLine = line.split()\\n self.rowsNumber = int(firstLine[0])\\n self.columnsNumber = int(firstLine[1])\\n self.routerRangeRadius = int(firstLine[2])\\n if(lineCounter == 1):\\n SecondLine = line.split()\\n self.backBoneCosts = int(SecondLine[0])\\n Path.backBoneCost = self.backBoneCosts\\n self.routerCosts = int(SecondLine[1])\\n self.budget = int(SecondLine[2])\\n if(lineCounter == 2):\\n ThirdLine = line.split()\\n self.firstCell = Cell(int(ThirdLine[0]),int(ThirdLine[1]))\\n if(lineCounter>2):\\n self.map.append([])\\n LINE = line\\n columnCounter = 0\\n for char in LINE:\\n temp = Cell(len(self.map)-1,columnCounter,Cell.getCellType(char))\\n self.map[len(self.map)-1].append(temp)\\n if(temp.cellType == \\\"FLOOR\\\"):\\n self.notComputeRouter.append(temp)\\n columnCounter += 1\\n lineCounter +=1\\n self.isInit = True\",\n \"def _parse_single(filename, label, image_size=IMAGE_SIZE):\\n # Decode and convert image to appropriate type\\n image = tf.image.decode_png(tf.read_file(filename), channels=image_size[2])\\n image = tf.image.convert_image_dtype(image, tf.float32) # Also scales from [0, 255] to [0, 1)\\n # Resize according to module requirements\\n image = tf.image.resize_images(image, image_size[:2])\\n return image, label\",\n \"def open_xy(data):\\n twotheta, intensity = [], []\\n with open(data) as f:\\n for line in f:\\n row = line.split()\\n twotheta.append(row[0])\\n intensity.append(row[1])\\n xyarray = list(zip(twotheta, intensity))\\n xyarray = np.asarray(xyarray)\\n xyarray = xyarray.astype(np.float)\\n return xyarray\",\n \"def from_file(file_handle, delimiter=\\\",\\\"):\\r\\n # Reading in the data\\r\\n line = file_handle.readline()\\r\\n line = line.rstrip(\\\"\\\\n\\\")\\r\\n line = line.split(delimiter)\\r\\n\\r\\n # Assigning the data\\r\\n label = line[0]\\r\\n mass = float(line[1])\\r\\n position = np.array(line[2:5], float)\\r\\n velocity = np.array(line[5:], float)\\r\\n\\r\\n return Particle3D(label, mass, position, velocity)\",\n \"def import_data(textfile, uncertainty):\\n\\tf = open(textfile, 'r')\\n\\tdata = f.readlines()\\n\\tx = []\\n\\ty = []\\n\\tfor line in data:\\n\\t\\tcoords = line.strip()\\n\\t\\tcoords = coords.split(', ')\\n\\t\\tx.append(coords[0])\\n\\t\\ty.append(coords[1]) \\n\\t\\n\\tx = [float(i) for i in x]\\n\\tx = np.array(x).reshape((10,1))\\n\\ty = [float(i) for i in y]\\n\\ty = np.array(y).reshape((10,1))\\n\\tdata = {'x': x, 'y': y, 'var': uncertainty}\\n\\n\\treturn data\",\n \"def _image_set(gt_txt, images_dir):\\n with open(gt_txt) as f:\\n filename = f.readline().rstrip()\\n total = 1\\n while filename:\\n log.debug(filename)\\n image = Image(os.path.join(images_dir, filename), filename)\\n face_num = int(f.readline().rstrip())\\n\\n if face_num == 0:\\n log.warning('No faces for {}. Ignoring next line {}'.format(image.filename, f.readline().rstrip()))\\n\\n log.debug(face_num)\\n for _ in range(face_num):\\n anno = f.readline().rstrip().split()\\n log.debug(anno)\\n face = Face(anno)\\n if face.is_valid():\\n image.add_face(face)\\n else:\\n log.debug('Skipping INVALID %s from %s', face, image)\\n filename = f.readline().rstrip()\\n total += 1\\n yield image\",\n \"def getFile(self):\\r\\n self.file_name=QtGui.QFileDialog.getOpenFileName(self, \\\"Open Image file\\\", self.path, \\\"*tif\\\")\\r\\n if self.file_name!='':\\r\\n \\r\\n self.img= skimage.io.imread(str(self.file_name), plugin='tifffile')\\r\\n \\\"\\\"\\\"sets self.img equal to the chosen image\\\"\\\"\\\"\\r\\n \\r\\n self.temp= interp_img(np.zeros(self.img.shape), self.zinterp)\\r\\n self.edge= interp_img(np.zeros(self.img.shape), self.zinterp)\\r\\n self.shrink= np.zeros(self.img.shape)\\r\\n self.count=0\\r\\n \\r\\n self.z_stack=self.img.shape[0]/2\\r\\n self.y_stack=self.img.shape[1]/2\\r\\n self.x_stack=self.img.shape[2]/2\\r\\n \\r\\n self.dispedge = to_rgb(self.img[self.z_stack])\\r\\n self.y_dispedge= to_rgb(self.img[:,self.y_stack,:])\\r\\n self.x_dispedge= to_rgb(self.img[:,:,self.x_stack])\\r\\n #self.pixmap=QtGui.QPixmap.fromImage(ImageQt.ImageQt(misc.toimage(self.img[self.z_stack]))).scaled(500,500)\\r\\n \\r\\n self.resetImages()\",\n \"def Read_txt_Image(Name):\\r\\n\\ttry:\\r\\n\\t\\twith open(Name, 'r') as f: #If the file already exists\\r\\n\\t\\t\\tx = f.readlines() #Readd all the rows and put them in a list\\r\\n\\texcept: \\r\\n\\t\\twith open(Name, 'w') as f: #If the file does not exist\\r\\n\\t\\t\\tx = [] #Create an empty list\\r\\n\\treturn x #This is the list that contains all the information for the file\\r\",\n \"def import_cif(file,xyz='',n=[0,0,1],rep=[1,1,1],pad=0,dopt='s',lfact=1.0,tail=''):\\n crys = Crystal.from_cif(file)\\n lat_vec = np.array(crys.lattice_vectors)\\n lat_params = crys.lattice_parameters[:3]\\n pattern = np.array([[a.atomic_number]+list(lfact*a.coords_cartesian)+[a.occupancy,1.0] for a in crys.atoms])\\n\\n if xyz:make_xyz(xyz,pattern,lat_vec,lat_params,n=n,pad=pad,rep=rep,fmt='%.4f',dopt=dopt)\\n pattern[:,1:4] = rcc.orient_crystal(pattern[:,1:4],n_u=n) #,lat_params #pattern,crys # file\\n return pattern\",\n \"def main(filename, resolution=0.3, verbose=False):\\n\\n np.random.seed(1986) # for reproducibility\\n\\n image_data = io.imread(filename)\\n image_data = image_data[66:532, 105:671, :]\\n image_data = rgb2gray(image_data)\\n image_data = rescale(image_data, resolution, mode='constant',\\n preserve_range='True')\\n dim = np.int(reduce(lambda x, y: x * y, image_data.shape))\\n image_data = np.reshape(image_data, (dim))\\n image_data = np.array(image_data, dtype='f')\\n\\n return image_data\",\n \"def FillBox(file):\\n with open(file,'r') as f:\\n info = f.readline().split()\\n box_list = []\\n id = 1\\n I_list =[]\\n for i in range(0,len(info)):\\n box_list.append(box(float(info[i]),id,0,[]))\\n id+=1\\n for line in f:\\n words = line.split()\\n I_list.append(items(words[0], float(words[1])))\\n return box_list, I_list\",\n \"def readpil3d(self):\\r\\n\\r\\n # Read the data in as an array.\\r\\n res = np.loadtxt(self.name, delimiter=' ')\\r\\n\\r\\n # Split into useful chunks\\r\\n self.pos = res[:, 0:3] # Grid point locations\\r\\n self.Pn = res[:, 3:4] # Normal pressure [Pa]\\r\\n self.flux = res[:, -1] # Flux\\r\",\n \"def readGR3File(inputFilename):\\n print 'Reading ' + inputFilename + ' ...'\\n infile = open(inputFilename, 'r')\\n description = infile.readline().strip() # remove leading/trailing whitespace\\n tmpStr = infile.readline()\\n nTriangles, nNodes = (int(s) for s in tmpStr.split())\\n print ' nTriangles={0:d} nNodes={1:d}'.format(nTriangles, nNodes)\\n\\n # nodes\\n nodeArray = readNodeBlock(infile, nNodes)\\n nodenum = np.array(nodeArray[:, 0].flatten(), dtype=int)\\n nodexyz = np.zeros((nNodes, 3))\\n nodexyz[:, :2] = nodeArray[:, 1:3]\\n nodalValues = nodeArray[:, 3]\\n\\n print ' Nodal values min={0:g} max={1:g}'.format(min(nodalValues), max(nodalValues))\\n\\n # triangular elements\\n triArray = readElemBlock(infile, nTriangles)\\n\\n trinum = triArray[:, 0].flatten()\\n tritype = triArray[0, 1]\\n trinodes = triArray[:, -3:] - 1 # three last columns, 0-based indexing\\n #triangles = meshElements(trinodes,trinum,tritype)\\n\\n x = nodexyz[:, 0]\\n y = nodexyz[:, 1]\\n\\n tmpStr = infile.readline()\\n boundaries = []\\n if len(tmpStr) > 0:\\n # boundary information, if not end of file\\n nOpenBndSegments = int(tmpStr.split()[0])\\n nOpenBndNodesTot = int(infile.readline().split()[0])\\n print ' nOpenBndSegments={0:d} nOpenBndNodesTot={1:d}'.format(nOpenBndSegments, nOpenBndNodesTot)\\n for iBnd in range(nOpenBndSegments):\\n bndHeader = infile.readline().split()\\n nBndNodes = int(bndHeader[0])\\n tag = bndHeader[-1]\\n if tag.isdigit():\\n tag = 'open' + tag\\n print ' open bnd {0:d} {1:s}: {2:d} nodes'.format(iBnd + 1, tag, nBndNodes)\\n tmpList = []\\n for iN in range(nBndNodes):\\n tmpList.append(int(infile.readline()))\\n nodes = np.array(tmpList, dtype=int) - 1\\n boundaries.append(meshContainer.meshBoundary('open', tag, nodes))\\n nLandBndSegments = int(infile.readline().split()[0])\\n nLandBndNodesTot = int(infile.readline().split()[0])\\n landBndTags = range(\\n nOpenBndSegments + 1,\\n nOpenBndSegments + nLandBndSegments + 1)\\n print ' nLandBndSegments={0:d} nLandBndNodesTot={1:d}'.format(nLandBndSegments, nLandBndNodesTot)\\n for iBnd in range(nLandBndSegments):\\n bndHeader = infile.readline().split()\\n nBndNodes = int(bndHeader[0])\\n try:\\n landType = int(bndHeader[1])\\n except:\\n print \\\"\\\"\\\"Land boundary type missing in gr3 file. Add 0/1 (land/island) after number of nodes in each land boudary, e.g.\\n 1002 = Total number of closed boundary nodes\\n 501 0 = Number of nodes in closed boundary 1\\\"\\\"\\\"\\n raise Exception(\\n 'Could not parse land boundary type (0/1 - land/island)\\\\n')\\n landType = 'island' if landType == 1 else 'land'\\n tag = landType + bndHeader[-1]\\n print ' land bnd {0:d} {1:s}: {2:d} nodes'.format(iBnd + 1, tag, nBndNodes)\\n tmpList = []\\n for iN in range(nBndNodes):\\n tmpList.append(int(infile.readline()))\\n #tmpList = fromfile(infile,dtype=int,count=nBndNodes,sep=' ')\\n nodes = np.array(tmpList, dtype=int) - 1\\n boundaries.append(meshContainer.meshBoundary(landType, tag, nodes))\\n\\n infile.close()\\n\\n # for better interpolation, round coordinates to 1e-4\\n nDig = 4\\n x = np.round(x, nDig)\\n y = np.round(y, nDig)\\n\\n return x, y, nodalValues, trinodes, boundaries, description\",\n \"def read_file(file_name):\\n with open(file_name) as _r:\\n _pixlst = []\\n for line in _r:\\n if line != \\\"P3\\\\n\\\":\\n _new = (line[0:len(line)-1]).split()\\n for item in _new:\\n _pixlst.append(int(item)) \\n return _pixlst\",\n \"def create_from_file(cls, file_path: str) -> \\\"ImageClassifier\\\":\\n base_options = _BaseOptions(file_name=file_path)\\n options = ImageClassifierOptions(base_options=base_options)\\n return cls.create_from_options(options)\",\n \"def from_file(path, scale):\\n from imageio import imread\\n imgarr = imread(path)\\n s = imgarr.shape\\n extx, exty = (s[1] * scale) / 2, (s[0] * scale) / 2\\n ux, uy = e.arange(-extx, extx, scale), e.arange(-exty, exty, scale)\\n return Convolvable(data=e.flip(imgarr, axis=0).astype(config.precision),\\n x=ux, y=uy, has_analytic_ft=False)\",\n \"def flow_read(filename):\\n TAG_FLOAT = 202021.25\\n f = open(filename,'rb')\\n check = np.fromfile(f,dtype=np.float32,count=1)[0]\\n assert check == TAG_FLOAT, ' flow_read:: Wrong tag in flow file (should be: {0}, is: {1}). Big-endian machine? '.format(TAG_FLOAT,check)\\n width = np.fromfile(f,dtype=np.int32,count=1)[0]\\n height = np.fromfile(f,dtype=np.int32,count=1)[0]\\n size = width*height\\n assert width > 0 and height > 0 and size > 1 and size < 100000000, ' flow_read:: Wrong input size (width = {0}, height = {1}).'.format(width,height)\\n tmp = np.fromfile(f,dtype=np.float32,count=-1).reshape((height,width*2))\\n u = tmp[:,np.arange(width)*2]\\n v = tmp[:,np.arange(width)*2 + 1]\\n return u,v\",\n \"def read_pixelmap(filename):\\n\\n # Open HDF5 pixelmap file\\n try:\\n with h5py.File(filename, 'r') as fp:\\n x = fp['x'][:]\\n y = fp['y'][:]\\n except:\\n print(\\\"Error reading the pixelmap file: \\\", filename)\\n exit()\\n\\n # Correct for pixel size (meters --> pixels)\\n # Currently hard coded for CSPAD\\n # We can figure this out from the pixel map\\n dx = 110e-6\\n x /= dx\\n y /= dx\\n \\n\\n # Calculate radius\\n r = numpy.sqrt(numpy.square(x) + numpy.square(y))\\n\\n return x, y, r, dx\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6697791","0.6425697","0.6267778","0.60752165","0.6063262","0.6061974","0.5948842","0.5867118","0.5839898","0.5778474","0.57653004","0.56806886","0.5678753","0.56717163","0.56614995","0.5646917","0.5627825","0.56230605","0.561317","0.56094795","0.5598929","0.5584855","0.55793023","0.555096","0.5548226","0.5520904","0.55174875","0.5508754","0.5507941","0.55027014","0.5484065","0.547281","0.5469114","0.5464049","0.54579043","0.5440993","0.5435936","0.5434437","0.5429371","0.5427784","0.54247034","0.54014575","0.5398013","0.53842825","0.53822124","0.5375317","0.5375015","0.53719795","0.53710866","0.5342403","0.5333103","0.533207","0.53250206","0.5323032","0.5322411","0.53110224","0.5306975","0.53062236","0.52992445","0.52990323","0.52979016","0.5294942","0.5291765","0.5281398","0.5280882","0.52801126","0.52608144","0.5259221","0.52562314","0.52553976","0.5246886","0.5229311","0.52275926","0.52258754","0.52149963","0.521143","0.5211314","0.52101225","0.5207892","0.5185755","0.5183702","0.5183646","0.5173092","0.51724","0.5161866","0.51614285","0.5160522","0.5159585","0.5158374","0.515755","0.5155839","0.51486665","0.514842","0.51478046","0.51471776","0.5145398","0.5138135","0.5136398","0.5135012","0.5132296"],"string":"[\n \"0.6697791\",\n \"0.6425697\",\n \"0.6267778\",\n \"0.60752165\",\n \"0.6063262\",\n \"0.6061974\",\n \"0.5948842\",\n \"0.5867118\",\n \"0.5839898\",\n \"0.5778474\",\n \"0.57653004\",\n \"0.56806886\",\n \"0.5678753\",\n \"0.56717163\",\n \"0.56614995\",\n \"0.5646917\",\n \"0.5627825\",\n \"0.56230605\",\n \"0.561317\",\n \"0.56094795\",\n \"0.5598929\",\n \"0.5584855\",\n \"0.55793023\",\n \"0.555096\",\n \"0.5548226\",\n \"0.5520904\",\n \"0.55174875\",\n \"0.5508754\",\n \"0.5507941\",\n \"0.55027014\",\n \"0.5484065\",\n \"0.547281\",\n \"0.5469114\",\n \"0.5464049\",\n \"0.54579043\",\n \"0.5440993\",\n \"0.5435936\",\n \"0.5434437\",\n \"0.5429371\",\n \"0.5427784\",\n \"0.54247034\",\n \"0.54014575\",\n \"0.5398013\",\n \"0.53842825\",\n \"0.53822124\",\n \"0.5375317\",\n \"0.5375015\",\n \"0.53719795\",\n \"0.53710866\",\n \"0.5342403\",\n \"0.5333103\",\n \"0.533207\",\n \"0.53250206\",\n \"0.5323032\",\n \"0.5322411\",\n \"0.53110224\",\n \"0.5306975\",\n \"0.53062236\",\n \"0.52992445\",\n \"0.52990323\",\n \"0.52979016\",\n \"0.5294942\",\n \"0.5291765\",\n \"0.5281398\",\n \"0.5280882\",\n \"0.52801126\",\n \"0.52608144\",\n \"0.5259221\",\n \"0.52562314\",\n \"0.52553976\",\n \"0.5246886\",\n \"0.5229311\",\n \"0.52275926\",\n \"0.52258754\",\n \"0.52149963\",\n \"0.521143\",\n \"0.5211314\",\n \"0.52101225\",\n \"0.5207892\",\n \"0.5185755\",\n \"0.5183702\",\n \"0.5183646\",\n \"0.5173092\",\n \"0.51724\",\n \"0.5161866\",\n \"0.51614285\",\n \"0.5160522\",\n \"0.5159585\",\n \"0.5158374\",\n \"0.515755\",\n \"0.5155839\",\n \"0.51486665\",\n \"0.514842\",\n \"0.51478046\",\n \"0.51471776\",\n \"0.5145398\",\n \"0.5138135\",\n \"0.5136398\",\n \"0.5135012\",\n \"0.5132296\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7201921"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":784,"cells":{"query":{"kind":"string","value":"Extract input numbers from given string,"},"document":{"kind":"string","value":"def get_input_from_string(str_input):\n\tfrom string import split\n\tres = []\n\tlist_input = split(str_input)\n\tfor i in xrange(len(list_input)):\n\t\tres.append(float(list_input[i]))\n\treturn res"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def ExtractNumbers(s):\n\n t = s.strip('[]\\n')\n comma_space = r', '\n re_comma_space = re.compile(comma_space)\n z = re_comma_space.split(t)\n #print z\n return z","def extract_nums(string):\r\n num_list = []\r\n mods1 = string.replace('$', '')\r\n mods2 = mods1.replace('K', '')\r\n for s in mods2.split(' '):\r\n try:\r\n num_list.append(int(s))\r\n except Exception:\r\n pass\r\n return np.mean(num_list)","def find_numbers(text):\n result = []\n for word in text.split():\n if word.isdigit():\n result.append(int(word))\n return result","def split_num(s):\n i = 0\n while i < len(s):\n if s[i] < '0' or s[i] > '9':\n break\n i += 1\n if s[i:]:\n return (int(s[:i]), s[i:], )\n return (int(s[:i]), )","def get_numbers(string:str, type_=\"int\") -> list:\n \n num_list = []\n for word in string.split():\n if type_ == \"int\":\n try:\n num_list.append(int(word))\n except:\n pass\n elif type_ == \"float\":\n if isfloat(word):\n num_list.append(float(word))\n return num_list","def _parse_numbers(self, numberstr: str):\n numbers = []\n currentnumber = \"\"\n\n for c in numberstr:\n if c.isdigit() or c == '-' or c == '.':\n currentnumber += c\n elif len(currentnumber) > 0:\n numbers.append(float(currentnumber))\n currentnumber = \"\"\n if len(currentnumber) > 0:\n numbers.append(float(currentnumber))\n\n return np.array(numbers)","def find_number(self, string):\n #string = string.encode('ascii', 'ignore')\n #return int(filter(str.isdigit, string))\n s = (re.findall('\\d+', string))\n return int(''.join(s))","def _parseNumbers(s):\n ss = utils.unclump(s)\n\n m3 = re.match('^\\d+$', ss)\n if m3 is not None:\n return decimal.Decimal(round(float(ss), 2))\n\n m1 = re.match(r'(\\d+)\\s+(\\d)/(\\d)', ss)\n if m1 is not None:\n num = int(m1.group(1)) + (float(m1.group(2)) / float(m1.group(3)))\n return decimal.Decimal(str(round(num, 2)))\n\n m2 = re.match(r'^(\\d)/(\\d)$', ss)\n if m2 is not None:\n num = float(m2.group(1)) / float(m2.group(2))\n return decimal.Decimal(str(round(num, 2)))\n\n return None","def parse_input(string):\n return [int(vote) for vote in string.split()]","def split_string_at_numbers(string):\n split_list = re.compile(r'(\\d+)').split(string)\n filtered_list = []\n skip_next_loops = 0\n for i in range(len(split_list)):\n if skip_next_loops > 0:\n skip_next_loops -= 1\n continue\n part = split_list[i]\n if part.isdigit() or (part == '.' and i < len(split_list) - 1 and split_list[i + 1].isdigit()):\n # Some kind of number\n if part == '.':\n # number of format '.###' (start of string)\n part += split_list[i + 1]\n skip_next_loops = 1\n elif i < len(split_list) - 2 and split_list[i + 1] == '.' and split_list[i + 2].isdigit():\n # number of format '###.###'\n part += split_list[i + 1] + split_list[i + 2]\n skip_next_loops = 2\n elif (i > 0 and len(filtered_list) and len(filtered_list[-1]) and\n filtered_list[-1][-1] == '.'):\n # number of format '.###' (within string)\n filtered_list[-1] = filtered_list[-1][:-1]\n part = '.' + part\n # otherwise just number of format '###'\n factor = 1\n if i < len(split_list) - 1:\n # check next part for unit information\n msg = split_list[i + 1].strip()\n msg = msg.lstrip('_([{')\n msg = re.split('[^a-zA-Zµ]', msg)[0]\n if msg:\n for unit in tools.science.UNIT_SYMBOLS:\n if msg.endswith(unit):\n msg = msg[:-len(unit)]\n break\n if len(msg) == 1:\n factor = 10**tools.science.SI_PREFIXES.get(msg[0], 0)\n filtered_list.append(float(part)*factor)\n else:\n # Actual string\n filtered_list.append(part)\n return filtered_list","def get_digits(string):\n digit_str= ''.join(filter(lambda x: x.isdigit(), string))\n return digit_str","def split_str_digit(s):\n res = []\n for m in re.finditer('(\\d*)(\\D*)', s):\n for g in m.groups():\n if g != '':\n try:\n res.append(int(g))\n except ValueError:\n res.append(g)\n return tuple(res)","def split_num(a_str):\n idx = None\n for i in iter(a_str):\n if i.isdigit():\n idx = a_str.index(i)\n break\n if idx == None:\n return (a_str[:idx], int('1'))\n else:\n return (a_str[:idx], int(a_str[idx:]))","def parse_number(txt):\n return int(txt)","def get_number(x):\n\n return re.findall(r'\\d+', x)[0]","def extract_number(word):\n number_flag = True\n number = ''\n word = word.rstrip('.').lstrip('.')\n for char in word:\n try:\n if char == '.' and number_flag:\n number += char\n else:\n int(char)\n if number_flag:\n number += char\n except:\n if len(number) > 0:\n number_flag = False\n continue\n return number","def extract_int(text):\n m = re.search(r\"\\d+\", text)\n if m is not None:\n return m.group(0)","def extint(input):\n num = None\n try:\n num = int(''.join([s for s in input if s.isdigit()]))\n except ValueError:\n pass\n return num","def get_num_from_string(string):\n\toutput = 0\n\tstring = string.lower()\n\tfor char in string:\n\t\tif char in letter_to_number:\n\t\t\toutput += letter_to_number[char]\n\t\telse:\n\t\t\tpass\n\treturn output","def _number_finder(s, regex, numconv, py3_safe):\n\n # Split. If there are no splits, return now\n s = regex.split(s)\n if len(s) == 1:\n return tuple(s)\n\n # Now convert the numbers to numbers, and leave strings as strings\n s = remove_empty(s)\n for i in py23_range(len(s)):\n try:\n s[i] = numconv(s[i])\n except ValueError:\n pass\n\n # If the list begins with a number, lead with an empty string.\n # This is used to get around the \"unorderable types\" issue.\n # The _py3_safe function inserts \"\" between numbers in the list,\n # and is used to get around \"unorderable types\" in complex cases.\n # It is a separate function that needs to be requested specifically\n # because it is expensive to call.\n if not isinstance(s[0], py23_basestring):\n return _py3_safe([''] + s) if py3_safe else [''] + s\n else:\n return _py3_safe(s) if py3_safe else s","def find_numbers(text):\n # -------------------------------------------------------------------------\n # Notice how expressive the list comprehension syntax is, in that it sounds\n # almost exactly the same as you would describe the problem in English.\n # I.e.\n # Convert each word to an integer, for every word in text split over\n # spaces, if the word is comprised only of digits.\n # \n # int(word) Convert each word to an integer,\n # for word for every word\n # in text.split() in text split over spaces\n # if text.isdigit() if the word is comprised only of digits.\n # -------------------------------------------------------------------------\n return [int(word) for word in text.split() if word.isdigit()]","def extract_only_number(x):\n\tr = re.findall(r'\\d+', x)\n\tif len(r) > 0:\n\t\treturn r[0]\n\treturn 0","def ints_in(x: str) -> list[int]:\n ex = r'(?:(?<!\\d)-)?\\d+'\n return ints(re.findall(ex, x))","def input_parser(input_string: str) -> str: \n if is_int(input_string):\n return input_string\n #he is int, give back plz.\n else:\n try:\n modified_input: str = input_string.strip()\n\n evaluatable_pairs: str = regex_splitter(modified_input)\n\n while not (is_int(evaluatable_pairs)):\n evaluatable_pairs = regex_splitter(evaluatable_pairs)\n\n return (evaluatable_pairs)\n\n except:\n raise Exception(\"Invalid Input\")","def extractDigits(key):\n text = \"\"\n digits = \"\"\n for c in key:\n if c in \"0123456789\":\n digits += c\n else:\n text += c\n return (text, 0 if not digits else int(digits))","def parse_list(list_str):\n return list(map(int, re.findall(r'\\d+', list_str)))","def find_numeric(text):\n text_digits = \\\n re.sub('[^\\d]', '', text)\n if not text_digits:\n return None\n try:\n return int(text_digits)\n except ValueError:\n return None","def extract_numbers(frame):\n numeric_const_pattern = (\n r\"[-+]? (?: (?: \\d* \\. \\d+ ) | (?: \\d+ \\.? ) )(?: [Ee] [+-]? \\d+ ) ?\"\n )\n rx = re.compile(numeric_const_pattern, re.VERBOSE)\n frame = frame.applymap(lambda x: rx.findall(x)[0])\n\n return frame","def parse_input(userstring):\n xsplit = userstring.split()\n stringtovalues = [float(x) for x in xsplit]\n\n return stringtovalues","def _extract_num(self, text):\n try:\n if 'studio' in text.lower():\n return 0.0\n text = text.replace(',', '')\n pattern = r'[-+]?\\d*\\.\\d+|\\d+'\n result = re.findall(pattern, text)[0]\n return float(result)\n except:\n return np.nan","def parseNumList(input):\n\tm = re.match(r'(\\d+)(?:-(\\d+))?(?:-(\\d+))?$', input)\n\t# ^ (or use .split('-'). anyway you like.)\n\tif not m:\n\t\traise ArgumentTypeError(\"'\" + input + \"' is not a range of number. Expected forms like '1-5' or '2' or '10-15-2'.\")\n\tstart = int(m.group(1))\n\tend = int(m.group(2))\n\tif m.group(3):\n\t\tincrement = int(m.group(3))\n\telse:\n\t\tincrement = 1\n\treturn list(range(start, end+1, increment))","def find_int_in_str(s: str) -> int:\n \n i = int(re.search(r'\\d+', s).group())\n\n return i","def parse_sub_num(s, parser):\n s = s.strip()\n if s == '*':\n return s\n nums = s.split(',')\n msg = 'Invalid sub-entry number.'\n res = set()\n for num in nums:\n num = num.strip()\n if num.isdigit():\n try:\n num = int(num)\n assert num > 0\n res.add(num)\n except:\n raise parser.error(msg)\n else:\n try:\n m = re.search('(\\d+)-(\\d+)', num)\n if m is None:\n raise parser.error(msg)\n else:\n a = int(m.group(1))\n b = int(m.group(2))\n assert a > 0\n assert b > 0\n assert a <= b\n r = range(a, b + 1)\n res.update(r)\n except:\n raise parser.error(msg)\n res = list(res)\n res.sort()\n return res","def extract_digits(cls, phone_number):\n extracted_num = \"\"\n for ch in phone_number:\n if ch in cls.INTEGER_STRING:\n extracted_num += ch\n return extracted_num","def parse_proasis(input_string):\n return (\n input_string[:3].strip(),\n int(input_string[5:].strip()),\n input_string[3:5].strip(),\n )","def num(s, filt=float):\n if not s:\n return \"\"\n try:\n return filt(s)\n except ValueError:\n return \"\"","def getNumFromString(self, string):\n \n m = re.search(r'\\d+$', string)\n if m is not None:\n return int(m.group())\n else:\n return 0","def get_number(word):\n return int(re.match(NUMBER, word).group(1))","def parse_pint_string(self, pint_string):\n val = pint_string.split(' ')[0]\n units = pint_string.split(val+' ')[-1]\n return val, units","def extract_numbers_safe(cls, s, decimals=False):\n if decimals:\n tmp = ''.join([i for i in cls.escape(s) if ((i >= '0') and (i <= '9') or i == '.')])\n\n parts = tmp.split('.')\n\n try:\n output = '{a}.{b}'.format(a=parts[0], b=parts[1])\n except IndexError:\n output = parts[0]\n\n else:\n output = ''.join([i for i in cls.escape(s) if (i >= '0') and (i <= '9')])\n\n try:\n if s[0] == '-':\n output = '-{s}'.format(s=output)\n except:\n pass\n\n return output","def integers_only(text) -> str:\n return ''.join(x for x in text if x.isdigit())","def parse_number():\n nonlocal idx\n num = \"\"\n def parse_digits():\n nonlocal idx\n num = \"\"\n while idx < len(source) and is_num_char(source[idx]):\n num += source[idx]\n idx += 1\n return num\n # Parse initial numbers\n oidx = idx\n num += parse_digits()\n if idx < len(source) and source[idx] == '.': # if we find a dot\n # Parse out the second part of the number string\n idx += 1\n num += (\".\" + parse_digits())\n if idx < len(source) and not terminal(source[idx]): # the number didn't terminate... this is an identifier\n idx = oidx\n return parse_symbol()\n idx -= 1 # Backtrack, bc last character is *invalid* and loop assumes we stop on a valid token character\n return num","def clean_numbers(text):\n return regex.sub(\"\\d+\", ' NUM', text)","def _get_numbers(first=False):\n numbers = [int(v) for v in stdin.readline().split()]\n return numbers[0] if first else numbers","def split_number(string):\n\ttry:\n\t\tparts = string.split('-')\n\texcept AttributeError:\n\t\ttry:\n\t\t\tstring * string\n\t\t\treturn ('', string)\n\t\texcept TypeError:\n\t\t\treturn None\n\t\n\t\t\n\tend = parts[-1]\n\tif '.' in end:\n\t\ttry:\n\t\t\tnum = float(end)\n\t\texcept:\n\t\t\tnum = None\n\telse:\n\t\ttry:\n\t\t\tnum = int(end)\n\t\texcept:\n\t\t\tnum = None\n\tif num is not None:\n\t\tparts.pop(-1)\n\treturn ('-'.join(parts), num)","def string_to_digit(string, output):\n string = strip_space(string)\n if not string[0].isdigit() and not string[1].isdigit():\n return None\n\n string_items = []\n for index, item in enumerate(string):\n if item.isdigit():\n string_items.append(item)\n else:\n if item == ',':\n string_items.append('.')\n\n elif item == ' ' and string[index + 1].isdigit():\n pass\n\n elif not item.isdigit() and not string[index + 1].isdigit():\n break\n\n if '.' in string_items and output == int:\n return int(float(''.join(string_items)))\n\n return output(''.join(string_items))","def get_nummeric_only(text):\n\n nummeric_string =\"\"\n \n for character in text:\n if character.isnumeric():\n \n nummeric_string+=character\n \n return nummeric_string","def parse_input_1() -> List[int]:\n with open(\"./data/day_13.txt\", \"r\") as f:\n lines = [int(n) for line in f for n in line.split(\",\") if re.match(\"^\\d+\", n)]\n return lines","def _parseNumber(self, str):\r\n\t\tif (str.count(\".\") == 0):\r\n\t\t\treturn int(str)\r\n\t\tif (str.count(\".\") == 1):\r\n\t\t\treturn float(str)\r\n\t\treturn str","def __get_num_from_str(elements: list, string: str) -> str:\n\n num = list()\n\n element_list = list(elements)\n\n for atom in string.split('-'):\n\n if atom == '*':\n num.append('0')\n else:\n num.append(f'{element_list.index(atom) + 1}')\n\n return ' '.join(num)","def parse_numeric(numeric: str):\r\n if numeric[0] == '-':\r\n polarity = 1\r\n numeric = numeric.lstrip('-')\r\n numeric = numeric.lstrip('0')\r\n else:\r\n polarity = 0\r\n\r\n digits = []\r\n for character in numeric:\r\n try:\r\n digits.append(int(character, 10))\r\n except ValueError:\r\n # (Bases 11 - 16) Hexadecimal replacement characters.\r\n if character in inv_extended_numerals.keys():\r\n digits.append(inv_extended_numerals[character.lower()])\r\n else:\r\n raise ValueError('Invalid numeric string. Can only use digits \\\r\n0 through 9 and hexadecimal a through f.')\r\n\r\n if len(digits) < 1:\r\n raise ValueError('Invalid numeric string. Must supply at least one digit.')\r\n else:\r\n return digits, polarity","def string_to_numbers(str):\n return [ord(ch) - ord(\"a\") for ch in str]","def extract_integers_from_argument(arg, expected=0):\n\n ss = arg.split(\",\")\n\n ns = [ int(s.strip()) for s in ss ]\n\n if ( expected > 0 and len(ns) != expected ):\n raise Exception(\"Expecting {} integers. {} extracted from {}. \".format(expected, len(ns), arg))\n\n return ns","def words_to_numbers(text):\n number_found = 3\n for word, number in number_map.items():\n if word in text or str(number) in text:\n text = text.replace(word, str(number))\n number_found = number\n break\n return [text, number_found]","def parseNumbers(equation):\r\n queue = createQueue()\r\n stack = None\r\n parts = equation.split(' ')\r\n for part in parts:\r\n enqueue(queue, part)\r\n stack = push(stack, part)\r\n return stack, queue","def get_number(word, i_type='S'):\n\n resultdict = {}\n if word is None:\n return resultdict\n\n word = str(word)\n regexStr = None\n if i_type == 'S':\n regexStr = re.search(r'^[0-9\\-]+', word)\n else:\n regexStr = re.search(r'[0-9\\-]+', word)\n\n if regexStr is not None:\n # pdb.set_trace()\n numList = []\n if '-' in word:\n numList = word.split('-')\n else:\n numList.append(word)\n\n for idx, numWord in enumerate(numList):\n if idx > 1:\n resultdict = {}\n break\n \"\"\"\n Let's get number and suffix for number1\n and number2\n \"\"\"\n # to get the number\n regexNum = re.search(r'[0-9]+', numWord)\n key = 'number_' + str(idx + 1)\n if regexNum is not None:\n try:\n resultdict[key] = int(regexNum.group().split(' ')[0])\n except:\n pass\n # resultdict[key] = regexNum.group().split(' ')[0]\n\n # to get suffix\n regexSuff = re.search(r'[a-zA-Z]+', numWord)\n key = key + '_suff'\n if regexSuff:\n # resultdict[key] = regexSuff.group().split(' ')[0]\n \"\"\"\n dont think we should have suffix more than 1\n character\n there are few cases but we are ignoring them...\n \"\"\"\n suff = regexSuff.group().split(' ')[0]\n if i_type == 'S':\n if len(suff) == 1:\n resultdict[key] = suff\n else:\n resultdict = {}\n else:\n if len(suff) < 3:\n resultdict[key] = suff\n\n return resultdict","def filter_and_sort_number_strings_as_numbers():\n# fill it out\n result = []\n for s in STRING_LIST:\n if (s.isnumeric()):\n result.append(s)\n return sorted(result, key = lambda s: int(s))","def intparse(text):\n return int(text, 0)","def find_only_numbers(detected_message_with_numbers):\n detected_message_only_numbers = re.sub(r\"[^\\d \\._]\", \"\", detected_message_with_numbers)\n return \" \".join(split_words(detected_message_only_numbers, only_unique=True))","def parse_input(part=1):\n with open(\"input_23.txt\") as f:\n return [int(x) for x in f.readline().strip()]","def _matching_ints(strings, regex):\n ints = []\n p = re.compile(regex)\n for string in strings:\n m = p.match(string)\n if m:\n integer = int(m.group(1))\n ints.append(integer)\n ints.sort()\n return ints","def letter_to_num(string, dict_):\n patt = re.compile('[' + ''.join(dict_.keys()) + ']')\n num_string = patt.sub(lambda m: dict_[m.group(0)] + ' ', string)\n num = [int(i) for i in num_string.split()]\n return num","def convert_numerals(input_str):\n # credit to: http://code.activestate.com/recipes/81611-roman-numerals/\n copy = input_str[:]\n copy = copy.split(\" \")\n\n nums = ['m', 'd', 'c', 'l', 'x', 'v', 'i']\n ints = [1000, 500, 100, 50, 10, 5, 1]\n places = []\n\n for i in range(len(copy)):\n is_valid = True\n\n if \".\" in copy[i]:\n copy[i] = copy[i].replace(\".\", \"\")\n else:\n # . must be appended to end of string to signify it is a roman\n # numeral\n is_valid = False\n\n if \"xix\" in copy[i] or \"xviii\" in copy[i]:\n is_valid = True\n\n for c in copy[i].lower():\n if c not in nums:\n # return original\n is_valid = False\n\n if is_valid is False:\n continue\n\n for char_index in range(len(copy[i])):\n c = copy[i][char_index].lower()\n value = ints[nums.index(c)]\n # If the next place holds a larger number, this value is negative.\n try:\n nextvalue = ints[nums.index(copy[i][char_index + 1].lower())]\n if nextvalue > value:\n value *= -1\n except IndexError:\n # there is no next place.\n pass\n places.append(value)\n\n out = 0\n\n for n in places:\n out += n\n\n copy[i] = str(out)\n\n return \" \".join(copy)","def parse_reading(data):\n pat =re.compile('([1-9][0-9]*)')\n datum = data.split('\\n')\n #print datum\n for d in datum:\n m = pat.search(d)\n if m is not None:\n return float(m.group(1))\n return float(-1)","def natural_key(string_):\n return [int(s) if s.isdigit() else s for s in re.split(r'(\\d+)', string_)]","def natural_key(string_):\n return [int(s) if s.isdigit() else s for s in re.split(r'(\\d+)', string_)]","def parse_input_2() -> List[int]:\n with open(\"./data/day_13.txt\", \"r\") as f:\n lines = [\n int(n) if re.match(\"^\\d+\", n) else 1 for line in f for n in line.split(\",\")\n ]\n return lines[1:]","def _parse_number(stream):\n rv = \"\"\n while stream.peek() and stream.peek() in string.digits:\n rv += stream.next()\n\n return int(rv)","def parse_selection(selection_str: str) -> List[int]:\n indices = []\n for group in selection_str.split(','):\n if not re.match(r'^(?:-?\\d+)|(?:\\d+(?:-\\d+))$', group):\n print(\"Invalid selection\", group)\n sys.exit()\n spl = group.split('-')\n if len(spl) == 1:\n indices.append(int(spl[0]))\n elif len(spl) == 2:\n begin = int(spl[0]) if spl[0] else 0\n end = int(spl[1])\n indices.extend(range(begin, end + 1))\n return indices","def phoneNumberExtractor(self,data):\n\t\tdata = data.replace(\"\\r\", \" \")\n\t\tdata = data.replace(\"\\r\\n\", \" \")\n\n\t\t#first is identifying 10 digits code\n\t\tdata = data.split()\n\t\tresult = []\n\t\tfor word in data:\n\t\t\tres = None\n\t\t\tres = word if word.isdecimal() and len(word) == 10 and not res else res\n\t\t\tres = word[2:] if word.isdecimal() and len(word) == 12 and not res else res\n\t\t\tres = word[3:] if word[3:].isdecimal() and len(word) == 10 and not res else res\n\t\t\tif (\"(\" and \")\") in word or \"-\" in word:\n\t\t\t\tword = word.replace(\"(\",\"\")\n\t\t\t\tword = word.replace(\")\",\"\")\n\t\t\t\tword = word.replace (\"-\",\"\")\n\t\t\t\tres = word if(len(word) == 10) else None\n\t\t\tif res:\n\t\t\t\tresult.append(res)\n\t\t\t\tdel(res)\n\t\treturn set(result)","def is_num_int_automata(final_word: str) -> list:\n\n if re.match(\"^(0|[1-9]\\d*)$\", final_word):\n token = 'num_int'\n return [True, token]\n else :\n return [False, \"\"]","def extract_numbers_nl(text, short_scale=True, ordinals=False):\n results = _extract_numbers_with_text_nl(tokenize(text),\n short_scale, ordinals)\n return [float(result.value) for result in results]","def _consume_number(infix_string:str,index:int,output:list) -> int:\r\n if not (infix_string[index].isdigit() or infix_string[index]==Roll._minus): # handle integers and dice rolls ('XdY')\r\n raise ValueError(f\"Unexpected value in number token '{infix_string[index]}'\")\r\n digit = \"\"\r\n has_mandatory_segment=False\r\n if infix_string[index]==Roll._minus:\r\n sign=1\r\n while index<len(infix_string) and infix_string[index]==Roll._minus:\r\n sign*=-1\r\n index+=1\r\n if sign<0:\r\n digit+=Roll._minus\r\n while index<len(infix_string) and infix_string[index].isdigit():\r\n has_mandatory_segment=True\r\n digit+=infix_string[index]\r\n index+=1\r\n if index<len(infix_string) and infix_string[index].lower()==Roll._dice_sep:\r\n digit+=infix_string[index].lower()\r\n index+=1\r\n has_mandatory_segment = False\r\n while index<len(infix_string) and infix_string[index].isdigit():\r\n has_mandatory_segment=True\r\n digit+=infix_string[index]\r\n index+=1\r\n if not has_mandatory_segment:\r\n raise ValueError(\"Dice rolls must be supplied with a fixed number of sides (format: 'XdY')\")\r\n output.append(digit)\r\n return index","def split_str(str):\n \n logger = logging.getLogger(__name__)\n \n logger.debug('{0}'.format(str))\n \n match = re.match(r\"([0-9]+.?\\d{0,32}?)(d|m|s)\", str)\n \n if match:\n items = match.groups()\n \n return items[0], items[1]","def parse_int_list(input_str):\n return [int(part) for part in input_str.split(\",\")]","def convert_string_to_version_component_numbers(string: str) \\\n -> Optional[Sequence[int]]:\n\n def _get_valid_version(number: str) -> int:\n \"\"\"Get positive version from the number string.\"\"\"\n version = int(number)\n if version >= 0:\n return version\n raise ValueError(\"Each number version must be positive!\")\n\n try:\n component = list(map(_get_valid_version, string.strip().split('.')))\n return tuple(component[:3] + [0 for _ in range(3 - len(component))])\n except ValueError as e:\n exception(f'Invalid input: {string}\\n' + str(e))\n return None","def get_number(text):\n# if (isinstance(text, str) or isinstance(text, unicode)):\n if True:\n text.replace(\",\",\".\")\n text = re.sub(\"\\xa0\",\"\", text)\n rst = re.findall(\"[0-9]+\\.{0,1}[0-9]*\", text)\n if rst:\n rst = rst[0]\n else:\n rst = \"nan\"\n else:\n rst = text\n try:\n rst = float(rst)\n except:\n rst = float(\"nan\")\n return(rst)","def extract_journal(name):\n match = re.search(\"\\d+\", name)\n if match != None: \n return name[:match.start()], int(name[match.start(): match.end()])\n else: \n return \"\", 0","def _parse_scan_number(self, string):\n \n # match scan number pattern\n match = SCAN_NUMBER_PATTERN.search(string)\n if not match:\n return None\n \n # return as int\n return int(match.group(1))","def process_elem_string(string):\r\n\r\n elem = \"\"\r\n for i, c in enumerate(string):\r\n try:\r\n int(c)\r\n break\r\n except:\r\n elem += c\r\n\r\n return elem","def _replace_numbers(self, data_item):\n\t\tnumber_pattern = re.compile(\"\\d+,\\d+|\\d+\\.\\d+|\\d+|\\d+\\.\\d+%?|\\d+%?\")\n\t\tnums = []\n\t\tinput_seq = []\n\t\t\n\t\tword_tokens = data_item.problem.strip().split()\n\n\t\t# Looking for numbers.\n\t\tfor word_token in word_tokens:\n\t\t\tnumbers_match = re.search(number_pattern, word_token)\n\t\t\tif numbers_match is not None:\n\t\t\t\t# If there are digits in the token, we need to replace them.\n\t\t\t\tif numbers_match.start() > 0:\n\t\t\t\t\tinput_seq.append(word_token[:numbers_match.start()])\n\n\t\t\t\tnum = word_token[numbers_match.start(): numbers_match.end()]\n\n\t\t\t\tnums.append(num) #.replace(\",\", \"\"))\n\t\t\t\tinput_seq.append(DataProcessor.NUM_REPLACER)\n\t\t\t\tif numbers_match.end() < len(word_token):\n\t\t\t\t\tinput_seq.append(word_token[numbers_match.end():])\n\t\t\telse:\n\t\t\t\t# There are no digits in the token, we can safely append it to the input sequence.\n\t\t\t\tinput_seq.append(word_token)\n\n\t\treturn nums, input_seq","def challengeInput(self):\r\n firstLine = input().split(' ')\r\n secondLine = input().split(' ')\r\n numbers = []\r\n for s in firstLine:\r\n numbers.append(float(s) )\r\n for s in secondLine:\r\n numbers.append(float(s) )\r\n return numbers","def str2num(s):\n if s.is_constant() and (s.is_float() or s.is_integer()):\n return True, s.compute_value()\n elif s.functor == \".\":\n values = term2list(s)\n numvalues = []\n for value in values:\n if isinstance(value, int) or isinstance(value, float):\n numvalues.append(value)\n else:\n return None, None\n return True, tuple(numvalues)\n else:\n return None, None","def filter_and_sort_number_strings():\n# fill it out\n result = []\n for s in STRING_LIST:\n if (s.isnumeric()):\n result.append(s)\n return sorted(result)","def parse(self, string):\n parse = re.match(\"^((?:[0-9]{1,3}\\.){3}[0-9]{1,3})\\s\\(((?:\\d)*\\.(?:\\d)*|(?:\\d)*)\\sms\\)$\", string)\n parse_result = parse.groups()\n return parse_result[0], parse_result[1]","def question_11(int_string: str) -> int:\n counter = 0\n for i in int_string:\n if i.isdigit():\n counter += 1\n else:\n continue\n return counter","def _listify_input(self, input_string):\n stripped_string = re.sub(r'\\s+', '', input_string.strip())\n split_list = stripped_string.split(\",\")\n return [(x[0], int(x[1::])) for x in split_list]","def letter_to_num(self, string, dict_):\n #dict_= {'A': '0', 'C': '1', 'D': '2', 'E': '3', 'F': '4', 'G': '5', 'H': '6', 'I': '7', 'K': '8', 'L': '9', 'M': '10', 'N': '11', 'P': '12', 'Q': '13', 'R': '14', 'S': '15', 'T': '16', 'V': '17', 'W': '18', 'Y': '19'}\n patt = re.compile('[' + ''.join(dict_.keys()) + ']')\n num_string = patt.sub(lambda m: dict_[m.group(0)] + ' ', string)\n #print(num_string)\n #print(type(num_string))\n num = [int(i) for i in num_string.split()]\n return num","def clean_eval(exp):\n\n # Split expression using '+' as our split token\n number_string = exp.split(\"+\")\n total = int()\n\n # Cost each number string to int, cleaning up leading zeros, then total\n for num in number_string:\n total += int(num)\n\n return total","def numbers_check(string, logger_=_LOGGER):\n valid_regex_0 = r\"\\d\"\n valid_regex_1 = r\"_\\d+_\\d+_\"\n valid_regex_2 = r\"_\\d+_\"\n if not re.search(valid_regex_0, string):\n logger.log(\n level=\"warning\",\n message='There are no numbers in the string \"' + string + '\"',\n logger=logger_,\n )\n return string\n if re.search(valid_regex_1, string):\n return string\n elif re.search(valid_regex_2, string):\n return string\n else:\n logger.log(\n level=\"warning\",\n message='Numbers not in valid expression. Valid values are \"_(['\n '0-9]+)_([0-9]+)_\" or \"_([0-9]+)_\"',\n logger=logger_,\n )\n return string","def stringToInts(string):\r\n ints=[];\r\n for char in string:\r\n ints.append(charToInt(char));\r\n return ints;","def netflix_read(string):\n val = -1\n ind = -1\n string = string.strip()\n if string.isdigit():\n val = int(string)\n ind = 0\n elif string:\n val = int(string.strip(':'))\n ind = 1\n return (val, ind)","def parse_number(self, word_list):\n\n self.skip(word_list, 'stop')\n\n next_word = self.peek(word_list)\n\n if next_word == 'number':\n return self.match(word_list, 'number')\n\n else:\n raise ParserError('Expected a number. Got a %s' % next_word)","def numDecodings(self, s):\n if not s or s[0] == '0':return 0\n s1,s2 = 1,1\n for m in xrange(1,len(s)):\n if s[m] == '0':s2 = 0\n if s[m-1] == '1' or (s[m-1] == '2' and s[m] <= '6'):\n s2 += s1\n s1 = s2 - s1\n else:\n s1 = s2\n if s2 == 0:return 0\n return s2","def _get_number_from_string(x):\n try:\n return float(x)\n except ValueError:\n raise ValueError('Unknown element')","def numbers_in_line(line):\n ret = []\n pos = 0\n while True:\n loc = find_next_number(line, pos)\n if not loc:\n break\n ret.append(loc)\n pos = loc[1]\n return ret","def sort_numbers(self, value): \n\n\t\tnumbers = re.compile(r'(\\d+)')\n\t\tparts = numbers.split(value)\n\t\tparts[1::2] = map(int, parts[1::2])\n\t\treturn parts","def coords1(s: str) -> list[float]:\n return numarray(re.sub(SPACE, \"\", s).split(\",\"))","def key_to_numeric(x):\n reg = re.compile(r'\\[(\\d+\\_?(\\d+)?)[a-z]?\\]')\n inspect = reg.search(x).groups(0)[0]\n if '_' in inspect:\n left, right = inspect.split('_')\n return int(left), int(right)\n else:\n return int(inspect), 0","def split_numeric(self, text, parse=True):\n\n block = ''\n block_numeric = self.isnum(text[0])\n output = []\n for t in text:\n if self.isnum(t) == block_numeric:\n block += t\n else:\n if block_numeric:\n block = float(block)\n output.append(block)\n block = t\n block_numeric = self.isnum(t)\n if block_numeric:\n block = float(block)\n output.append(block)\n return output"],"string":"[\n \"def ExtractNumbers(s):\\n\\n t = s.strip('[]\\\\n')\\n comma_space = r', '\\n re_comma_space = re.compile(comma_space)\\n z = re_comma_space.split(t)\\n #print z\\n return z\",\n \"def extract_nums(string):\\r\\n num_list = []\\r\\n mods1 = string.replace('$', '')\\r\\n mods2 = mods1.replace('K', '')\\r\\n for s in mods2.split(' '):\\r\\n try:\\r\\n num_list.append(int(s))\\r\\n except Exception:\\r\\n pass\\r\\n return np.mean(num_list)\",\n \"def find_numbers(text):\\n result = []\\n for word in text.split():\\n if word.isdigit():\\n result.append(int(word))\\n return result\",\n \"def split_num(s):\\n i = 0\\n while i < len(s):\\n if s[i] < '0' or s[i] > '9':\\n break\\n i += 1\\n if s[i:]:\\n return (int(s[:i]), s[i:], )\\n return (int(s[:i]), )\",\n \"def get_numbers(string:str, type_=\\\"int\\\") -> list:\\n \\n num_list = []\\n for word in string.split():\\n if type_ == \\\"int\\\":\\n try:\\n num_list.append(int(word))\\n except:\\n pass\\n elif type_ == \\\"float\\\":\\n if isfloat(word):\\n num_list.append(float(word))\\n return num_list\",\n \"def _parse_numbers(self, numberstr: str):\\n numbers = []\\n currentnumber = \\\"\\\"\\n\\n for c in numberstr:\\n if c.isdigit() or c == '-' or c == '.':\\n currentnumber += c\\n elif len(currentnumber) > 0:\\n numbers.append(float(currentnumber))\\n currentnumber = \\\"\\\"\\n if len(currentnumber) > 0:\\n numbers.append(float(currentnumber))\\n\\n return np.array(numbers)\",\n \"def find_number(self, string):\\n #string = string.encode('ascii', 'ignore')\\n #return int(filter(str.isdigit, string))\\n s = (re.findall('\\\\d+', string))\\n return int(''.join(s))\",\n \"def _parseNumbers(s):\\n ss = utils.unclump(s)\\n\\n m3 = re.match('^\\\\d+$', ss)\\n if m3 is not None:\\n return decimal.Decimal(round(float(ss), 2))\\n\\n m1 = re.match(r'(\\\\d+)\\\\s+(\\\\d)/(\\\\d)', ss)\\n if m1 is not None:\\n num = int(m1.group(1)) + (float(m1.group(2)) / float(m1.group(3)))\\n return decimal.Decimal(str(round(num, 2)))\\n\\n m2 = re.match(r'^(\\\\d)/(\\\\d)$', ss)\\n if m2 is not None:\\n num = float(m2.group(1)) / float(m2.group(2))\\n return decimal.Decimal(str(round(num, 2)))\\n\\n return None\",\n \"def parse_input(string):\\n return [int(vote) for vote in string.split()]\",\n \"def split_string_at_numbers(string):\\n split_list = re.compile(r'(\\\\d+)').split(string)\\n filtered_list = []\\n skip_next_loops = 0\\n for i in range(len(split_list)):\\n if skip_next_loops > 0:\\n skip_next_loops -= 1\\n continue\\n part = split_list[i]\\n if part.isdigit() or (part == '.' and i < len(split_list) - 1 and split_list[i + 1].isdigit()):\\n # Some kind of number\\n if part == '.':\\n # number of format '.###' (start of string)\\n part += split_list[i + 1]\\n skip_next_loops = 1\\n elif i < len(split_list) - 2 and split_list[i + 1] == '.' and split_list[i + 2].isdigit():\\n # number of format '###.###'\\n part += split_list[i + 1] + split_list[i + 2]\\n skip_next_loops = 2\\n elif (i > 0 and len(filtered_list) and len(filtered_list[-1]) and\\n filtered_list[-1][-1] == '.'):\\n # number of format '.###' (within string)\\n filtered_list[-1] = filtered_list[-1][:-1]\\n part = '.' + part\\n # otherwise just number of format '###'\\n factor = 1\\n if i < len(split_list) - 1:\\n # check next part for unit information\\n msg = split_list[i + 1].strip()\\n msg = msg.lstrip('_([{')\\n msg = re.split('[^a-zA-Zµ]', msg)[0]\\n if msg:\\n for unit in tools.science.UNIT_SYMBOLS:\\n if msg.endswith(unit):\\n msg = msg[:-len(unit)]\\n break\\n if len(msg) == 1:\\n factor = 10**tools.science.SI_PREFIXES.get(msg[0], 0)\\n filtered_list.append(float(part)*factor)\\n else:\\n # Actual string\\n filtered_list.append(part)\\n return filtered_list\",\n \"def get_digits(string):\\n digit_str= ''.join(filter(lambda x: x.isdigit(), string))\\n return digit_str\",\n \"def split_str_digit(s):\\n res = []\\n for m in re.finditer('(\\\\d*)(\\\\D*)', s):\\n for g in m.groups():\\n if g != '':\\n try:\\n res.append(int(g))\\n except ValueError:\\n res.append(g)\\n return tuple(res)\",\n \"def split_num(a_str):\\n idx = None\\n for i in iter(a_str):\\n if i.isdigit():\\n idx = a_str.index(i)\\n break\\n if idx == None:\\n return (a_str[:idx], int('1'))\\n else:\\n return (a_str[:idx], int(a_str[idx:]))\",\n \"def parse_number(txt):\\n return int(txt)\",\n \"def get_number(x):\\n\\n return re.findall(r'\\\\d+', x)[0]\",\n \"def extract_number(word):\\n number_flag = True\\n number = ''\\n word = word.rstrip('.').lstrip('.')\\n for char in word:\\n try:\\n if char == '.' and number_flag:\\n number += char\\n else:\\n int(char)\\n if number_flag:\\n number += char\\n except:\\n if len(number) > 0:\\n number_flag = False\\n continue\\n return number\",\n \"def extract_int(text):\\n m = re.search(r\\\"\\\\d+\\\", text)\\n if m is not None:\\n return m.group(0)\",\n \"def extint(input):\\n num = None\\n try:\\n num = int(''.join([s for s in input if s.isdigit()]))\\n except ValueError:\\n pass\\n return num\",\n \"def get_num_from_string(string):\\n\\toutput = 0\\n\\tstring = string.lower()\\n\\tfor char in string:\\n\\t\\tif char in letter_to_number:\\n\\t\\t\\toutput += letter_to_number[char]\\n\\t\\telse:\\n\\t\\t\\tpass\\n\\treturn output\",\n \"def _number_finder(s, regex, numconv, py3_safe):\\n\\n # Split. If there are no splits, return now\\n s = regex.split(s)\\n if len(s) == 1:\\n return tuple(s)\\n\\n # Now convert the numbers to numbers, and leave strings as strings\\n s = remove_empty(s)\\n for i in py23_range(len(s)):\\n try:\\n s[i] = numconv(s[i])\\n except ValueError:\\n pass\\n\\n # If the list begins with a number, lead with an empty string.\\n # This is used to get around the \\\"unorderable types\\\" issue.\\n # The _py3_safe function inserts \\\"\\\" between numbers in the list,\\n # and is used to get around \\\"unorderable types\\\" in complex cases.\\n # It is a separate function that needs to be requested specifically\\n # because it is expensive to call.\\n if not isinstance(s[0], py23_basestring):\\n return _py3_safe([''] + s) if py3_safe else [''] + s\\n else:\\n return _py3_safe(s) if py3_safe else s\",\n \"def find_numbers(text):\\n # -------------------------------------------------------------------------\\n # Notice how expressive the list comprehension syntax is, in that it sounds\\n # almost exactly the same as you would describe the problem in English.\\n # I.e.\\n # Convert each word to an integer, for every word in text split over\\n # spaces, if the word is comprised only of digits.\\n # \\n # int(word) Convert each word to an integer,\\n # for word for every word\\n # in text.split() in text split over spaces\\n # if text.isdigit() if the word is comprised only of digits.\\n # -------------------------------------------------------------------------\\n return [int(word) for word in text.split() if word.isdigit()]\",\n \"def extract_only_number(x):\\n\\tr = re.findall(r'\\\\d+', x)\\n\\tif len(r) > 0:\\n\\t\\treturn r[0]\\n\\treturn 0\",\n \"def ints_in(x: str) -> list[int]:\\n ex = r'(?:(?<!\\\\d)-)?\\\\d+'\\n return ints(re.findall(ex, x))\",\n \"def input_parser(input_string: str) -> str: \\n if is_int(input_string):\\n return input_string\\n #he is int, give back plz.\\n else:\\n try:\\n modified_input: str = input_string.strip()\\n\\n evaluatable_pairs: str = regex_splitter(modified_input)\\n\\n while not (is_int(evaluatable_pairs)):\\n evaluatable_pairs = regex_splitter(evaluatable_pairs)\\n\\n return (evaluatable_pairs)\\n\\n except:\\n raise Exception(\\\"Invalid Input\\\")\",\n \"def extractDigits(key):\\n text = \\\"\\\"\\n digits = \\\"\\\"\\n for c in key:\\n if c in \\\"0123456789\\\":\\n digits += c\\n else:\\n text += c\\n return (text, 0 if not digits else int(digits))\",\n \"def parse_list(list_str):\\n return list(map(int, re.findall(r'\\\\d+', list_str)))\",\n \"def find_numeric(text):\\n text_digits = \\\\\\n re.sub('[^\\\\d]', '', text)\\n if not text_digits:\\n return None\\n try:\\n return int(text_digits)\\n except ValueError:\\n return None\",\n \"def extract_numbers(frame):\\n numeric_const_pattern = (\\n r\\\"[-+]? (?: (?: \\\\d* \\\\. \\\\d+ ) | (?: \\\\d+ \\\\.? ) )(?: [Ee] [+-]? \\\\d+ ) ?\\\"\\n )\\n rx = re.compile(numeric_const_pattern, re.VERBOSE)\\n frame = frame.applymap(lambda x: rx.findall(x)[0])\\n\\n return frame\",\n \"def parse_input(userstring):\\n xsplit = userstring.split()\\n stringtovalues = [float(x) for x in xsplit]\\n\\n return stringtovalues\",\n \"def _extract_num(self, text):\\n try:\\n if 'studio' in text.lower():\\n return 0.0\\n text = text.replace(',', '')\\n pattern = r'[-+]?\\\\d*\\\\.\\\\d+|\\\\d+'\\n result = re.findall(pattern, text)[0]\\n return float(result)\\n except:\\n return np.nan\",\n \"def parseNumList(input):\\n\\tm = re.match(r'(\\\\d+)(?:-(\\\\d+))?(?:-(\\\\d+))?$', input)\\n\\t# ^ (or use .split('-'). anyway you like.)\\n\\tif not m:\\n\\t\\traise ArgumentTypeError(\\\"'\\\" + input + \\\"' is not a range of number. Expected forms like '1-5' or '2' or '10-15-2'.\\\")\\n\\tstart = int(m.group(1))\\n\\tend = int(m.group(2))\\n\\tif m.group(3):\\n\\t\\tincrement = int(m.group(3))\\n\\telse:\\n\\t\\tincrement = 1\\n\\treturn list(range(start, end+1, increment))\",\n \"def find_int_in_str(s: str) -> int:\\n \\n i = int(re.search(r'\\\\d+', s).group())\\n\\n return i\",\n \"def parse_sub_num(s, parser):\\n s = s.strip()\\n if s == '*':\\n return s\\n nums = s.split(',')\\n msg = 'Invalid sub-entry number.'\\n res = set()\\n for num in nums:\\n num = num.strip()\\n if num.isdigit():\\n try:\\n num = int(num)\\n assert num > 0\\n res.add(num)\\n except:\\n raise parser.error(msg)\\n else:\\n try:\\n m = re.search('(\\\\d+)-(\\\\d+)', num)\\n if m is None:\\n raise parser.error(msg)\\n else:\\n a = int(m.group(1))\\n b = int(m.group(2))\\n assert a > 0\\n assert b > 0\\n assert a <= b\\n r = range(a, b + 1)\\n res.update(r)\\n except:\\n raise parser.error(msg)\\n res = list(res)\\n res.sort()\\n return res\",\n \"def extract_digits(cls, phone_number):\\n extracted_num = \\\"\\\"\\n for ch in phone_number:\\n if ch in cls.INTEGER_STRING:\\n extracted_num += ch\\n return extracted_num\",\n \"def parse_proasis(input_string):\\n return (\\n input_string[:3].strip(),\\n int(input_string[5:].strip()),\\n input_string[3:5].strip(),\\n )\",\n \"def num(s, filt=float):\\n if not s:\\n return \\\"\\\"\\n try:\\n return filt(s)\\n except ValueError:\\n return \\\"\\\"\",\n \"def getNumFromString(self, string):\\n \\n m = re.search(r'\\\\d+$', string)\\n if m is not None:\\n return int(m.group())\\n else:\\n return 0\",\n \"def get_number(word):\\n return int(re.match(NUMBER, word).group(1))\",\n \"def parse_pint_string(self, pint_string):\\n val = pint_string.split(' ')[0]\\n units = pint_string.split(val+' ')[-1]\\n return val, units\",\n \"def extract_numbers_safe(cls, s, decimals=False):\\n if decimals:\\n tmp = ''.join([i for i in cls.escape(s) if ((i >= '0') and (i <= '9') or i == '.')])\\n\\n parts = tmp.split('.')\\n\\n try:\\n output = '{a}.{b}'.format(a=parts[0], b=parts[1])\\n except IndexError:\\n output = parts[0]\\n\\n else:\\n output = ''.join([i for i in cls.escape(s) if (i >= '0') and (i <= '9')])\\n\\n try:\\n if s[0] == '-':\\n output = '-{s}'.format(s=output)\\n except:\\n pass\\n\\n return output\",\n \"def integers_only(text) -> str:\\n return ''.join(x for x in text if x.isdigit())\",\n \"def parse_number():\\n nonlocal idx\\n num = \\\"\\\"\\n def parse_digits():\\n nonlocal idx\\n num = \\\"\\\"\\n while idx < len(source) and is_num_char(source[idx]):\\n num += source[idx]\\n idx += 1\\n return num\\n # Parse initial numbers\\n oidx = idx\\n num += parse_digits()\\n if idx < len(source) and source[idx] == '.': # if we find a dot\\n # Parse out the second part of the number string\\n idx += 1\\n num += (\\\".\\\" + parse_digits())\\n if idx < len(source) and not terminal(source[idx]): # the number didn't terminate... this is an identifier\\n idx = oidx\\n return parse_symbol()\\n idx -= 1 # Backtrack, bc last character is *invalid* and loop assumes we stop on a valid token character\\n return num\",\n \"def clean_numbers(text):\\n return regex.sub(\\\"\\\\d+\\\", ' NUM', text)\",\n \"def _get_numbers(first=False):\\n numbers = [int(v) for v in stdin.readline().split()]\\n return numbers[0] if first else numbers\",\n \"def split_number(string):\\n\\ttry:\\n\\t\\tparts = string.split('-')\\n\\texcept AttributeError:\\n\\t\\ttry:\\n\\t\\t\\tstring * string\\n\\t\\t\\treturn ('', string)\\n\\t\\texcept TypeError:\\n\\t\\t\\treturn None\\n\\t\\n\\t\\t\\n\\tend = parts[-1]\\n\\tif '.' in end:\\n\\t\\ttry:\\n\\t\\t\\tnum = float(end)\\n\\t\\texcept:\\n\\t\\t\\tnum = None\\n\\telse:\\n\\t\\ttry:\\n\\t\\t\\tnum = int(end)\\n\\t\\texcept:\\n\\t\\t\\tnum = None\\n\\tif num is not None:\\n\\t\\tparts.pop(-1)\\n\\treturn ('-'.join(parts), num)\",\n \"def string_to_digit(string, output):\\n string = strip_space(string)\\n if not string[0].isdigit() and not string[1].isdigit():\\n return None\\n\\n string_items = []\\n for index, item in enumerate(string):\\n if item.isdigit():\\n string_items.append(item)\\n else:\\n if item == ',':\\n string_items.append('.')\\n\\n elif item == ' ' and string[index + 1].isdigit():\\n pass\\n\\n elif not item.isdigit() and not string[index + 1].isdigit():\\n break\\n\\n if '.' in string_items and output == int:\\n return int(float(''.join(string_items)))\\n\\n return output(''.join(string_items))\",\n \"def get_nummeric_only(text):\\n\\n nummeric_string =\\\"\\\"\\n \\n for character in text:\\n if character.isnumeric():\\n \\n nummeric_string+=character\\n \\n return nummeric_string\",\n \"def parse_input_1() -> List[int]:\\n with open(\\\"./data/day_13.txt\\\", \\\"r\\\") as f:\\n lines = [int(n) for line in f for n in line.split(\\\",\\\") if re.match(\\\"^\\\\d+\\\", n)]\\n return lines\",\n \"def _parseNumber(self, str):\\r\\n\\t\\tif (str.count(\\\".\\\") == 0):\\r\\n\\t\\t\\treturn int(str)\\r\\n\\t\\tif (str.count(\\\".\\\") == 1):\\r\\n\\t\\t\\treturn float(str)\\r\\n\\t\\treturn str\",\n \"def __get_num_from_str(elements: list, string: str) -> str:\\n\\n num = list()\\n\\n element_list = list(elements)\\n\\n for atom in string.split('-'):\\n\\n if atom == '*':\\n num.append('0')\\n else:\\n num.append(f'{element_list.index(atom) + 1}')\\n\\n return ' '.join(num)\",\n \"def parse_numeric(numeric: str):\\r\\n if numeric[0] == '-':\\r\\n polarity = 1\\r\\n numeric = numeric.lstrip('-')\\r\\n numeric = numeric.lstrip('0')\\r\\n else:\\r\\n polarity = 0\\r\\n\\r\\n digits = []\\r\\n for character in numeric:\\r\\n try:\\r\\n digits.append(int(character, 10))\\r\\n except ValueError:\\r\\n # (Bases 11 - 16) Hexadecimal replacement characters.\\r\\n if character in inv_extended_numerals.keys():\\r\\n digits.append(inv_extended_numerals[character.lower()])\\r\\n else:\\r\\n raise ValueError('Invalid numeric string. Can only use digits \\\\\\r\\n0 through 9 and hexadecimal a through f.')\\r\\n\\r\\n if len(digits) < 1:\\r\\n raise ValueError('Invalid numeric string. Must supply at least one digit.')\\r\\n else:\\r\\n return digits, polarity\",\n \"def string_to_numbers(str):\\n return [ord(ch) - ord(\\\"a\\\") for ch in str]\",\n \"def extract_integers_from_argument(arg, expected=0):\\n\\n ss = arg.split(\\\",\\\")\\n\\n ns = [ int(s.strip()) for s in ss ]\\n\\n if ( expected > 0 and len(ns) != expected ):\\n raise Exception(\\\"Expecting {} integers. {} extracted from {}. \\\".format(expected, len(ns), arg))\\n\\n return ns\",\n \"def words_to_numbers(text):\\n number_found = 3\\n for word, number in number_map.items():\\n if word in text or str(number) in text:\\n text = text.replace(word, str(number))\\n number_found = number\\n break\\n return [text, number_found]\",\n \"def parseNumbers(equation):\\r\\n queue = createQueue()\\r\\n stack = None\\r\\n parts = equation.split(' ')\\r\\n for part in parts:\\r\\n enqueue(queue, part)\\r\\n stack = push(stack, part)\\r\\n return stack, queue\",\n \"def get_number(word, i_type='S'):\\n\\n resultdict = {}\\n if word is None:\\n return resultdict\\n\\n word = str(word)\\n regexStr = None\\n if i_type == 'S':\\n regexStr = re.search(r'^[0-9\\\\-]+', word)\\n else:\\n regexStr = re.search(r'[0-9\\\\-]+', word)\\n\\n if regexStr is not None:\\n # pdb.set_trace()\\n numList = []\\n if '-' in word:\\n numList = word.split('-')\\n else:\\n numList.append(word)\\n\\n for idx, numWord in enumerate(numList):\\n if idx > 1:\\n resultdict = {}\\n break\\n \\\"\\\"\\\"\\n Let's get number and suffix for number1\\n and number2\\n \\\"\\\"\\\"\\n # to get the number\\n regexNum = re.search(r'[0-9]+', numWord)\\n key = 'number_' + str(idx + 1)\\n if regexNum is not None:\\n try:\\n resultdict[key] = int(regexNum.group().split(' ')[0])\\n except:\\n pass\\n # resultdict[key] = regexNum.group().split(' ')[0]\\n\\n # to get suffix\\n regexSuff = re.search(r'[a-zA-Z]+', numWord)\\n key = key + '_suff'\\n if regexSuff:\\n # resultdict[key] = regexSuff.group().split(' ')[0]\\n \\\"\\\"\\\"\\n dont think we should have suffix more than 1\\n character\\n there are few cases but we are ignoring them...\\n \\\"\\\"\\\"\\n suff = regexSuff.group().split(' ')[0]\\n if i_type == 'S':\\n if len(suff) == 1:\\n resultdict[key] = suff\\n else:\\n resultdict = {}\\n else:\\n if len(suff) < 3:\\n resultdict[key] = suff\\n\\n return resultdict\",\n \"def filter_and_sort_number_strings_as_numbers():\\n# fill it out\\n result = []\\n for s in STRING_LIST:\\n if (s.isnumeric()):\\n result.append(s)\\n return sorted(result, key = lambda s: int(s))\",\n \"def intparse(text):\\n return int(text, 0)\",\n \"def find_only_numbers(detected_message_with_numbers):\\n detected_message_only_numbers = re.sub(r\\\"[^\\\\d \\\\._]\\\", \\\"\\\", detected_message_with_numbers)\\n return \\\" \\\".join(split_words(detected_message_only_numbers, only_unique=True))\",\n \"def parse_input(part=1):\\n with open(\\\"input_23.txt\\\") as f:\\n return [int(x) for x in f.readline().strip()]\",\n \"def _matching_ints(strings, regex):\\n ints = []\\n p = re.compile(regex)\\n for string in strings:\\n m = p.match(string)\\n if m:\\n integer = int(m.group(1))\\n ints.append(integer)\\n ints.sort()\\n return ints\",\n \"def letter_to_num(string, dict_):\\n patt = re.compile('[' + ''.join(dict_.keys()) + ']')\\n num_string = patt.sub(lambda m: dict_[m.group(0)] + ' ', string)\\n num = [int(i) for i in num_string.split()]\\n return num\",\n \"def convert_numerals(input_str):\\n # credit to: http://code.activestate.com/recipes/81611-roman-numerals/\\n copy = input_str[:]\\n copy = copy.split(\\\" \\\")\\n\\n nums = ['m', 'd', 'c', 'l', 'x', 'v', 'i']\\n ints = [1000, 500, 100, 50, 10, 5, 1]\\n places = []\\n\\n for i in range(len(copy)):\\n is_valid = True\\n\\n if \\\".\\\" in copy[i]:\\n copy[i] = copy[i].replace(\\\".\\\", \\\"\\\")\\n else:\\n # . must be appended to end of string to signify it is a roman\\n # numeral\\n is_valid = False\\n\\n if \\\"xix\\\" in copy[i] or \\\"xviii\\\" in copy[i]:\\n is_valid = True\\n\\n for c in copy[i].lower():\\n if c not in nums:\\n # return original\\n is_valid = False\\n\\n if is_valid is False:\\n continue\\n\\n for char_index in range(len(copy[i])):\\n c = copy[i][char_index].lower()\\n value = ints[nums.index(c)]\\n # If the next place holds a larger number, this value is negative.\\n try:\\n nextvalue = ints[nums.index(copy[i][char_index + 1].lower())]\\n if nextvalue > value:\\n value *= -1\\n except IndexError:\\n # there is no next place.\\n pass\\n places.append(value)\\n\\n out = 0\\n\\n for n in places:\\n out += n\\n\\n copy[i] = str(out)\\n\\n return \\\" \\\".join(copy)\",\n \"def parse_reading(data):\\n pat =re.compile('([1-9][0-9]*)')\\n datum = data.split('\\\\n')\\n #print datum\\n for d in datum:\\n m = pat.search(d)\\n if m is not None:\\n return float(m.group(1))\\n return float(-1)\",\n \"def natural_key(string_):\\n return [int(s) if s.isdigit() else s for s in re.split(r'(\\\\d+)', string_)]\",\n \"def natural_key(string_):\\n return [int(s) if s.isdigit() else s for s in re.split(r'(\\\\d+)', string_)]\",\n \"def parse_input_2() -> List[int]:\\n with open(\\\"./data/day_13.txt\\\", \\\"r\\\") as f:\\n lines = [\\n int(n) if re.match(\\\"^\\\\d+\\\", n) else 1 for line in f for n in line.split(\\\",\\\")\\n ]\\n return lines[1:]\",\n \"def _parse_number(stream):\\n rv = \\\"\\\"\\n while stream.peek() and stream.peek() in string.digits:\\n rv += stream.next()\\n\\n return int(rv)\",\n \"def parse_selection(selection_str: str) -> List[int]:\\n indices = []\\n for group in selection_str.split(','):\\n if not re.match(r'^(?:-?\\\\d+)|(?:\\\\d+(?:-\\\\d+))$', group):\\n print(\\\"Invalid selection\\\", group)\\n sys.exit()\\n spl = group.split('-')\\n if len(spl) == 1:\\n indices.append(int(spl[0]))\\n elif len(spl) == 2:\\n begin = int(spl[0]) if spl[0] else 0\\n end = int(spl[1])\\n indices.extend(range(begin, end + 1))\\n return indices\",\n \"def phoneNumberExtractor(self,data):\\n\\t\\tdata = data.replace(\\\"\\\\r\\\", \\\" \\\")\\n\\t\\tdata = data.replace(\\\"\\\\r\\\\n\\\", \\\" \\\")\\n\\n\\t\\t#first is identifying 10 digits code\\n\\t\\tdata = data.split()\\n\\t\\tresult = []\\n\\t\\tfor word in data:\\n\\t\\t\\tres = None\\n\\t\\t\\tres = word if word.isdecimal() and len(word) == 10 and not res else res\\n\\t\\t\\tres = word[2:] if word.isdecimal() and len(word) == 12 and not res else res\\n\\t\\t\\tres = word[3:] if word[3:].isdecimal() and len(word) == 10 and not res else res\\n\\t\\t\\tif (\\\"(\\\" and \\\")\\\") in word or \\\"-\\\" in word:\\n\\t\\t\\t\\tword = word.replace(\\\"(\\\",\\\"\\\")\\n\\t\\t\\t\\tword = word.replace(\\\")\\\",\\\"\\\")\\n\\t\\t\\t\\tword = word.replace (\\\"-\\\",\\\"\\\")\\n\\t\\t\\t\\tres = word if(len(word) == 10) else None\\n\\t\\t\\tif res:\\n\\t\\t\\t\\tresult.append(res)\\n\\t\\t\\t\\tdel(res)\\n\\t\\treturn set(result)\",\n \"def is_num_int_automata(final_word: str) -> list:\\n\\n if re.match(\\\"^(0|[1-9]\\\\d*)$\\\", final_word):\\n token = 'num_int'\\n return [True, token]\\n else :\\n return [False, \\\"\\\"]\",\n \"def extract_numbers_nl(text, short_scale=True, ordinals=False):\\n results = _extract_numbers_with_text_nl(tokenize(text),\\n short_scale, ordinals)\\n return [float(result.value) for result in results]\",\n \"def _consume_number(infix_string:str,index:int,output:list) -> int:\\r\\n if not (infix_string[index].isdigit() or infix_string[index]==Roll._minus): # handle integers and dice rolls ('XdY')\\r\\n raise ValueError(f\\\"Unexpected value in number token '{infix_string[index]}'\\\")\\r\\n digit = \\\"\\\"\\r\\n has_mandatory_segment=False\\r\\n if infix_string[index]==Roll._minus:\\r\\n sign=1\\r\\n while index<len(infix_string) and infix_string[index]==Roll._minus:\\r\\n sign*=-1\\r\\n index+=1\\r\\n if sign<0:\\r\\n digit+=Roll._minus\\r\\n while index<len(infix_string) and infix_string[index].isdigit():\\r\\n has_mandatory_segment=True\\r\\n digit+=infix_string[index]\\r\\n index+=1\\r\\n if index<len(infix_string) and infix_string[index].lower()==Roll._dice_sep:\\r\\n digit+=infix_string[index].lower()\\r\\n index+=1\\r\\n has_mandatory_segment = False\\r\\n while index<len(infix_string) and infix_string[index].isdigit():\\r\\n has_mandatory_segment=True\\r\\n digit+=infix_string[index]\\r\\n index+=1\\r\\n if not has_mandatory_segment:\\r\\n raise ValueError(\\\"Dice rolls must be supplied with a fixed number of sides (format: 'XdY')\\\")\\r\\n output.append(digit)\\r\\n return index\",\n \"def split_str(str):\\n \\n logger = logging.getLogger(__name__)\\n \\n logger.debug('{0}'.format(str))\\n \\n match = re.match(r\\\"([0-9]+.?\\\\d{0,32}?)(d|m|s)\\\", str)\\n \\n if match:\\n items = match.groups()\\n \\n return items[0], items[1]\",\n \"def parse_int_list(input_str):\\n return [int(part) for part in input_str.split(\\\",\\\")]\",\n \"def convert_string_to_version_component_numbers(string: str) \\\\\\n -> Optional[Sequence[int]]:\\n\\n def _get_valid_version(number: str) -> int:\\n \\\"\\\"\\\"Get positive version from the number string.\\\"\\\"\\\"\\n version = int(number)\\n if version >= 0:\\n return version\\n raise ValueError(\\\"Each number version must be positive!\\\")\\n\\n try:\\n component = list(map(_get_valid_version, string.strip().split('.')))\\n return tuple(component[:3] + [0 for _ in range(3 - len(component))])\\n except ValueError as e:\\n exception(f'Invalid input: {string}\\\\n' + str(e))\\n return None\",\n \"def get_number(text):\\n# if (isinstance(text, str) or isinstance(text, unicode)):\\n if True:\\n text.replace(\\\",\\\",\\\".\\\")\\n text = re.sub(\\\"\\\\xa0\\\",\\\"\\\", text)\\n rst = re.findall(\\\"[0-9]+\\\\.{0,1}[0-9]*\\\", text)\\n if rst:\\n rst = rst[0]\\n else:\\n rst = \\\"nan\\\"\\n else:\\n rst = text\\n try:\\n rst = float(rst)\\n except:\\n rst = float(\\\"nan\\\")\\n return(rst)\",\n \"def extract_journal(name):\\n match = re.search(\\\"\\\\d+\\\", name)\\n if match != None: \\n return name[:match.start()], int(name[match.start(): match.end()])\\n else: \\n return \\\"\\\", 0\",\n \"def _parse_scan_number(self, string):\\n \\n # match scan number pattern\\n match = SCAN_NUMBER_PATTERN.search(string)\\n if not match:\\n return None\\n \\n # return as int\\n return int(match.group(1))\",\n \"def process_elem_string(string):\\r\\n\\r\\n elem = \\\"\\\"\\r\\n for i, c in enumerate(string):\\r\\n try:\\r\\n int(c)\\r\\n break\\r\\n except:\\r\\n elem += c\\r\\n\\r\\n return elem\",\n \"def _replace_numbers(self, data_item):\\n\\t\\tnumber_pattern = re.compile(\\\"\\\\d+,\\\\d+|\\\\d+\\\\.\\\\d+|\\\\d+|\\\\d+\\\\.\\\\d+%?|\\\\d+%?\\\")\\n\\t\\tnums = []\\n\\t\\tinput_seq = []\\n\\t\\t\\n\\t\\tword_tokens = data_item.problem.strip().split()\\n\\n\\t\\t# Looking for numbers.\\n\\t\\tfor word_token in word_tokens:\\n\\t\\t\\tnumbers_match = re.search(number_pattern, word_token)\\n\\t\\t\\tif numbers_match is not None:\\n\\t\\t\\t\\t# If there are digits in the token, we need to replace them.\\n\\t\\t\\t\\tif numbers_match.start() > 0:\\n\\t\\t\\t\\t\\tinput_seq.append(word_token[:numbers_match.start()])\\n\\n\\t\\t\\t\\tnum = word_token[numbers_match.start(): numbers_match.end()]\\n\\n\\t\\t\\t\\tnums.append(num) #.replace(\\\",\\\", \\\"\\\"))\\n\\t\\t\\t\\tinput_seq.append(DataProcessor.NUM_REPLACER)\\n\\t\\t\\t\\tif numbers_match.end() < len(word_token):\\n\\t\\t\\t\\t\\tinput_seq.append(word_token[numbers_match.end():])\\n\\t\\t\\telse:\\n\\t\\t\\t\\t# There are no digits in the token, we can safely append it to the input sequence.\\n\\t\\t\\t\\tinput_seq.append(word_token)\\n\\n\\t\\treturn nums, input_seq\",\n \"def challengeInput(self):\\r\\n firstLine = input().split(' ')\\r\\n secondLine = input().split(' ')\\r\\n numbers = []\\r\\n for s in firstLine:\\r\\n numbers.append(float(s) )\\r\\n for s in secondLine:\\r\\n numbers.append(float(s) )\\r\\n return numbers\",\n \"def str2num(s):\\n if s.is_constant() and (s.is_float() or s.is_integer()):\\n return True, s.compute_value()\\n elif s.functor == \\\".\\\":\\n values = term2list(s)\\n numvalues = []\\n for value in values:\\n if isinstance(value, int) or isinstance(value, float):\\n numvalues.append(value)\\n else:\\n return None, None\\n return True, tuple(numvalues)\\n else:\\n return None, None\",\n \"def filter_and_sort_number_strings():\\n# fill it out\\n result = []\\n for s in STRING_LIST:\\n if (s.isnumeric()):\\n result.append(s)\\n return sorted(result)\",\n \"def parse(self, string):\\n parse = re.match(\\\"^((?:[0-9]{1,3}\\\\.){3}[0-9]{1,3})\\\\s\\\\(((?:\\\\d)*\\\\.(?:\\\\d)*|(?:\\\\d)*)\\\\sms\\\\)$\\\", string)\\n parse_result = parse.groups()\\n return parse_result[0], parse_result[1]\",\n \"def question_11(int_string: str) -> int:\\n counter = 0\\n for i in int_string:\\n if i.isdigit():\\n counter += 1\\n else:\\n continue\\n return counter\",\n \"def _listify_input(self, input_string):\\n stripped_string = re.sub(r'\\\\s+', '', input_string.strip())\\n split_list = stripped_string.split(\\\",\\\")\\n return [(x[0], int(x[1::])) for x in split_list]\",\n \"def letter_to_num(self, string, dict_):\\n #dict_= {'A': '0', 'C': '1', 'D': '2', 'E': '3', 'F': '4', 'G': '5', 'H': '6', 'I': '7', 'K': '8', 'L': '9', 'M': '10', 'N': '11', 'P': '12', 'Q': '13', 'R': '14', 'S': '15', 'T': '16', 'V': '17', 'W': '18', 'Y': '19'}\\n patt = re.compile('[' + ''.join(dict_.keys()) + ']')\\n num_string = patt.sub(lambda m: dict_[m.group(0)] + ' ', string)\\n #print(num_string)\\n #print(type(num_string))\\n num = [int(i) for i in num_string.split()]\\n return num\",\n \"def clean_eval(exp):\\n\\n # Split expression using '+' as our split token\\n number_string = exp.split(\\\"+\\\")\\n total = int()\\n\\n # Cost each number string to int, cleaning up leading zeros, then total\\n for num in number_string:\\n total += int(num)\\n\\n return total\",\n \"def numbers_check(string, logger_=_LOGGER):\\n valid_regex_0 = r\\\"\\\\d\\\"\\n valid_regex_1 = r\\\"_\\\\d+_\\\\d+_\\\"\\n valid_regex_2 = r\\\"_\\\\d+_\\\"\\n if not re.search(valid_regex_0, string):\\n logger.log(\\n level=\\\"warning\\\",\\n message='There are no numbers in the string \\\"' + string + '\\\"',\\n logger=logger_,\\n )\\n return string\\n if re.search(valid_regex_1, string):\\n return string\\n elif re.search(valid_regex_2, string):\\n return string\\n else:\\n logger.log(\\n level=\\\"warning\\\",\\n message='Numbers not in valid expression. Valid values are \\\"_(['\\n '0-9]+)_([0-9]+)_\\\" or \\\"_([0-9]+)_\\\"',\\n logger=logger_,\\n )\\n return string\",\n \"def stringToInts(string):\\r\\n ints=[];\\r\\n for char in string:\\r\\n ints.append(charToInt(char));\\r\\n return ints;\",\n \"def netflix_read(string):\\n val = -1\\n ind = -1\\n string = string.strip()\\n if string.isdigit():\\n val = int(string)\\n ind = 0\\n elif string:\\n val = int(string.strip(':'))\\n ind = 1\\n return (val, ind)\",\n \"def parse_number(self, word_list):\\n\\n self.skip(word_list, 'stop')\\n\\n next_word = self.peek(word_list)\\n\\n if next_word == 'number':\\n return self.match(word_list, 'number')\\n\\n else:\\n raise ParserError('Expected a number. Got a %s' % next_word)\",\n \"def numDecodings(self, s):\\n if not s or s[0] == '0':return 0\\n s1,s2 = 1,1\\n for m in xrange(1,len(s)):\\n if s[m] == '0':s2 = 0\\n if s[m-1] == '1' or (s[m-1] == '2' and s[m] <= '6'):\\n s2 += s1\\n s1 = s2 - s1\\n else:\\n s1 = s2\\n if s2 == 0:return 0\\n return s2\",\n \"def _get_number_from_string(x):\\n try:\\n return float(x)\\n except ValueError:\\n raise ValueError('Unknown element')\",\n \"def numbers_in_line(line):\\n ret = []\\n pos = 0\\n while True:\\n loc = find_next_number(line, pos)\\n if not loc:\\n break\\n ret.append(loc)\\n pos = loc[1]\\n return ret\",\n \"def sort_numbers(self, value): \\n\\n\\t\\tnumbers = re.compile(r'(\\\\d+)')\\n\\t\\tparts = numbers.split(value)\\n\\t\\tparts[1::2] = map(int, parts[1::2])\\n\\t\\treturn parts\",\n \"def coords1(s: str) -> list[float]:\\n return numarray(re.sub(SPACE, \\\"\\\", s).split(\\\",\\\"))\",\n \"def key_to_numeric(x):\\n reg = re.compile(r'\\\\[(\\\\d+\\\\_?(\\\\d+)?)[a-z]?\\\\]')\\n inspect = reg.search(x).groups(0)[0]\\n if '_' in inspect:\\n left, right = inspect.split('_')\\n return int(left), int(right)\\n else:\\n return int(inspect), 0\",\n \"def split_numeric(self, text, parse=True):\\n\\n block = ''\\n block_numeric = self.isnum(text[0])\\n output = []\\n for t in text:\\n if self.isnum(t) == block_numeric:\\n block += t\\n else:\\n if block_numeric:\\n block = float(block)\\n output.append(block)\\n block = t\\n block_numeric = self.isnum(t)\\n if block_numeric:\\n block = float(block)\\n output.append(block)\\n return output\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7238783","0.7173874","0.7057713","0.68924135","0.68546575","0.67531013","0.67403173","0.6647867","0.657946","0.6577491","0.65233153","0.65192056","0.6391088","0.638495","0.6383692","0.6383132","0.6349406","0.6332319","0.6319145","0.6285752","0.6280346","0.6221941","0.62218493","0.621139","0.6188021","0.61785156","0.617077","0.61674863","0.61641425","0.6102309","0.61012894","0.5982365","0.5971672","0.59562415","0.5954015","0.5953537","0.5947074","0.592817","0.5905547","0.5884285","0.58802795","0.5879899","0.58785546","0.5875031","0.58504975","0.5846208","0.584069","0.58372575","0.5814535","0.5814364","0.5813299","0.57896495","0.5780152","0.5776927","0.57666546","0.5760409","0.57532895","0.5742961","0.5741771","0.573804","0.5728568","0.57282877","0.5719645","0.57176596","0.57123274","0.57123274","0.56728715","0.5665439","0.5662776","0.5656335","0.5653612","0.56500393","0.5642864","0.56192243","0.56083894","0.5594436","0.55940175","0.55784106","0.55755156","0.5568493","0.5561886","0.5559031","0.55586475","0.5553483","0.5553385","0.55485785","0.5547458","0.55418897","0.5530719","0.55271083","0.55267364","0.5525461","0.5523915","0.5510533","0.55050486","0.5497604","0.5494639","0.54942364","0.5492855","0.54928243"],"string":"[\n \"0.7238783\",\n \"0.7173874\",\n \"0.7057713\",\n \"0.68924135\",\n \"0.68546575\",\n \"0.67531013\",\n \"0.67403173\",\n \"0.6647867\",\n \"0.657946\",\n \"0.6577491\",\n \"0.65233153\",\n \"0.65192056\",\n \"0.6391088\",\n \"0.638495\",\n \"0.6383692\",\n \"0.6383132\",\n \"0.6349406\",\n \"0.6332319\",\n \"0.6319145\",\n \"0.6285752\",\n \"0.6280346\",\n \"0.6221941\",\n \"0.62218493\",\n \"0.621139\",\n \"0.6188021\",\n \"0.61785156\",\n \"0.617077\",\n \"0.61674863\",\n \"0.61641425\",\n \"0.6102309\",\n \"0.61012894\",\n \"0.5982365\",\n \"0.5971672\",\n \"0.59562415\",\n \"0.5954015\",\n \"0.5953537\",\n \"0.5947074\",\n \"0.592817\",\n \"0.5905547\",\n \"0.5884285\",\n \"0.58802795\",\n \"0.5879899\",\n \"0.58785546\",\n \"0.5875031\",\n \"0.58504975\",\n \"0.5846208\",\n \"0.584069\",\n \"0.58372575\",\n \"0.5814535\",\n \"0.5814364\",\n \"0.5813299\",\n \"0.57896495\",\n \"0.5780152\",\n \"0.5776927\",\n \"0.57666546\",\n \"0.5760409\",\n \"0.57532895\",\n \"0.5742961\",\n \"0.5741771\",\n \"0.573804\",\n \"0.5728568\",\n \"0.57282877\",\n \"0.5719645\",\n \"0.57176596\",\n \"0.57123274\",\n \"0.57123274\",\n \"0.56728715\",\n \"0.5665439\",\n \"0.5662776\",\n \"0.5656335\",\n \"0.5653612\",\n \"0.56500393\",\n \"0.5642864\",\n \"0.56192243\",\n \"0.56083894\",\n \"0.5594436\",\n \"0.55940175\",\n \"0.55784106\",\n \"0.55755156\",\n \"0.5568493\",\n \"0.5561886\",\n \"0.5559031\",\n \"0.55586475\",\n \"0.5553483\",\n \"0.5553385\",\n \"0.55485785\",\n \"0.5547458\",\n \"0.55418897\",\n \"0.5530719\",\n \"0.55271083\",\n \"0.55267364\",\n \"0.5525461\",\n \"0.5523915\",\n \"0.5510533\",\n \"0.55050486\",\n \"0.5497604\",\n \"0.5494639\",\n \"0.54942364\",\n \"0.5492855\",\n \"0.54928243\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.67156774"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":785,"cells":{"query":{"kind":"string","value":"Calculate and print the descriptive statistics of an image."},"document":{"kind":"string","value":"def info(image, mask=None, Comment=\"\"):\n\tif(Comment): print \" *** \", Comment\n\te = get_image(image)\n\t[mean, sigma, imin, imax] = Util.infomask(e, mask, True)\n\tnx = e.get_xsize()\n\tny = e.get_ysize()\n\tnz = e.get_zsize()\n\tif (e.is_complex()):\n\t\ts = \"\"\n\t\tif e.is_shuffled():\n\t\t\ts = \" (shuffled)\"\n\t\tif (e.is_fftodd()):\n\t\t\tprint \"Complex odd image%s: nx = %i, ny = %i, nz = %i\" % (s, nx, ny, nz)\n\t\telse:\n\t\t\tprint \"Complex even image%s: nx = %i, ny = %i, nz = %i\" % (s, nx, ny, nz)\n\n\telse:\n\t\tprint \"Real image: nx = %i, ny = %i, nz = %i\" % (nx, ny, nz)\n\n\tprint \"avg = %g, std dev = %g, min = %g, max = %g\" % (mean, sigma, imin, imax)\n\treturn mean, sigma, imin, imax, nx, ny, nz"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def print_images_in_statistics(self):\n self._print_images_statistics(self._images_in_folder, self._pose_class_names)","def print_image_info(image, resize=rsz_default, kernel=kernel_size):\n\tprint \"Image Size: {0}\".format(image.shape)\n\tprint \"Image Max: {0}\".format(image.max())\n\tprint \"Image Min: {0}\".format(image.min())\n\tprint \"Image Mean: {0}\".format(image.mean())\n\tprint \"Image dtype: {0}\\n\".format(image.dtype)\n\timage = to_uint8(image)\n\timage_prep = preprocess(image, resize=resize, kernel=kernel)\n\tcontour = get_contour(image_prep)\n\tM = get_image_moments(contour=contour)\n\tsecond_m = ['m20', 'm11', 'm02', 'm30', 'm21', 'm12', 'm03']\n\tprint \"Zero Order Moment: {0}\".format(M['m00'])\n\tprint \"First Order Moments: {0}, {1}\".format(M['m10'], M['m01'])\n\tprint \"Second Order Moments:\"\n\tsecond_m_str = ''\n\tfor m2 in second_m:\n\t\tsecond_m_str += \"{0},\".format(M[m2])\n\tprint second_m_str[:-1]","def print_image_info(input_image):\n print()\n print(\"Basic Information on image: {}\".format(input_image.filename))\n print(\"Format: {}\".format(input_image.format))\n print(\"Mode: {}\".format(input_image.mode))\n print(\"Size: {}\".format(input_image.size))\n print(\"Width: {}\".format(input_image.width))\n print(\"Height: {}\".format(input_image.height))\n print(\"Palette: {}\".format(input_image.palette))\n print()","def print_stats(cars, notcars):\n print(\"Number of car samples: {0}\".format(len(cars)))\n print(\"Number of non car samples: {0}\".format(len(notcars)))\n img = cv2.imread(cars[0])\n print(\"Image shape: {0}x{1}\".format(img.shape[0], img.shape[1]))\n print(\"Image datatype: {}\".format(img.dtype))","def print_img_data(img,title='Image'):\n print(f'________ {title} _______')\n if len(image.shape) == 2:\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\n\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0])}\\n')\n elif len(image.shape) == 3:\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\n\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0,0])}\\n')","def print_info(self, i):\n\n im_size = self.image_size(i)\n print 'The path of the image is: {}'.format(self.image_path_at(i))\n print 'width: {}, height: {}'.format(im_size[0], im_size[1])\n \n attr_i = self.gtdb['attr'][i, :]\n print 'The attributes are: {}'.format(','.join([self._classes[i] for i in np.where(attr_i==1)[0]]))","def info_np(img):\n import numpy as np\n\n print ('Dimensions: ' + str(np.shape(img)))\n print ('Min value: ' + str(np.min(img)))\n print ('Avg value: ' + str(np.average(img)))\n print ('Med value: ' + str(np.median(img)))\n print ('Max value: ' + str(np.max(img)))\n print ('Std dev: ' + str(np.std(img)))\n print ('Sum: ' + str(np.sum(img)))","def print_images_out_statistics(self):\n self._print_images_statistics(self._images_out_folder, self._pose_class_names)","def image_info(img):\n\tprint(img.format)\n\tprint(img.size)\n\tprint(img.mode)","def display_metrics2(self):\n messagebox.showinfo(\"Original Image Metrics\", self.raw_metrics)","def testSampleImageStats(self):\n \n imgfiles = []\n imgfiles.append(\"v1_i1_g_m400_s20_f.fits\")\n imgfiles.append(\"v1_i1_g_m400_s20_u16.fits\")\n imgfiles.append(\"v1_i2_g_m400_s20_f.fits\")\n imgfiles.append(\"v1_i2_g_m400_s20_u16.fits\")\n imgfiles.append(\"v2_i1_p_m9_f.fits\")\n imgfiles.append(\"v2_i1_p_m9_u16.fits\")\n imgfiles.append(\"v2_i2_p_m9_f.fits\")\n imgfiles.append(\"v2_i2_p_m9_u16.fits\")\n\n afwdataDir = os.getenv(\"AFWDATA_DIR\")\n if not afwdataDir:\n print >> sys.stderr, \"Skipping tests as afwdata is not setup\"\n return\n \n for imgfile in imgfiles:\n \n imgPath = os.path.join(afwdataDir, \"Statistics\", imgfile)\n\n # get the image and header\n dimg = afwImage.DecoratedImageF(imgPath)\n fitsHdr = dimg.getMetadata()\n\n # get the true values of the mean and stdev\n trueMean = fitsHdr.getAsDouble(\"MEANCOMP\")\n trueStdev = fitsHdr.getAsDouble(\"SIGCOMP\")\n\n # measure the mean and stdev with the Statistics class\n img = dimg.getImage()\n statobj = afwMath.makeStatistics(img, afwMath.MEAN | afwMath.STDEV)\n mean = statobj.getValue(afwMath.MEAN)\n stdev = statobj.getValue(afwMath.STDEV)\n\n # print trueMean, mean, trueStdev, stdev\n self.assertAlmostEqual(mean, trueMean, 8)\n self.assertAlmostEqual(stdev, trueStdev, 8)","def summary(self):\r\n self.base.summary()\r\n self.extra_layers.summary()\r\n self.detector.summary()","def print_image_info(self):\r\n\r\n maxt = np.max(self.times)\r\n\r\n print (\" Duration of Image Stack: %9.3f s (%8.3f min) period = %8.3f s\" % (maxt, maxt/60.0, self.period))\r\n\r\n print (' Image shape: ', self.imageData.shape)\r\n\r\n print (' nFrames: %d framerate: %9.3f\\n' % (self.nFrames, self.framerate))","def show_info(filename, abs_=None, center=None):\n fimage = FITSImage(filename)\n print(\"Image data shape: {0}\".format(fimage.shape))\n print(\"Image size: %dx%d\" % (fimage.Nx, fimage.Ny))\n print(\"Data unit: [%s]\" % fimage.bunit)\n pixelsize = fimage.pixelsize\n if pixelsize:\n print(\"Pixel size: %.1f [arcsec]\" % pixelsize)\n print(\"Field of view: (%.2f, %.2f) [deg]\" % fimage.fov)\n data = fimage.image\n if abs_:\n data = np.abs(data)\n if center:\n print(\"Central box size: %d\" % center)\n rows, cols = data.shape\n rc, cc = rows//2, cols//2\n cs1, cs2 = center//2, (center+1)//2\n data = data[(rc-cs1):(rc+cs2), (cc-cs1):(cc+cs2)]\n min_ = np.nanmin(data)\n max_ = np.nanmax(data)\n mean = np.nanmean(data)\n median = np.nanmedian(data)\n std = np.nanstd(data)\n iqr = np.diff(np.nanpercentile(data, q=(25, 75)))\n mad = np.nanmedian(np.abs(data - median))\n rms = np.sqrt(np.nanmean(data**2))\n print(\"min: %13.6e\" % min_)\n print(\"max: %13.6e\" % max_)\n print(\"range: %13.6e (max - min)\" % (max_ - min_))\n print(\"mean: %13.6e\" % mean)\n print(\"median: %13.6e\" % median)\n print(\"std: %13.6e (standard deviation)\" % std)\n print(\"iqr: %13.6e (interquartile range)\" % iqr)\n print(\"mad: %13.6e (median absolute deviation)\" % mad)\n print(\"rms: %13.6e (root-mean-squared)\" % rms)","def image_summary(tag, image):\n tag = _clean_tag(tag)\n image = _prepare_image(image)\n image = _make_image(image)\n return Summary(value=[Summary.Value(tag=tag, image=image)])","def scalarInfo(img, cnt):\n\tm = cntInfo(img, cnt)\n\td = {\"perimeter\":m[\"perimeter\"], \"oreientation\":m[\"orientation\"], \"solidity\":m[\"solidity\"],\"height\":m[\"height\"], \"extent\":m[\"extent\"], \"aspect ratio\":m[\"aspect ratio\"], \"area\":m[\"area\"], \"sum intensity\":m[\"sum intensity\"], \"width\":m[\"width\"], \"equivalent diameter\": m[\"equivalent diameter\"], \"mean intensity\": m[\"mean intensity\"]}\n\treturn d","def image_summary(self, tag, images, step):\n\n for i, img in enumerate(images):\n # Write the image to a string\n try:\n s = StringIO()\n except:\n s = BytesIO()\n scipy.misc.toimage(img).save(s, format=\"png\")\n\n # Create an Image object as a Summary value\n with self.writer.as_default():\n tf.summary.image(name='%s/%d' % (tag, i), data=s.getvalue(), step=step)\n\n # Create and write Summary\n self.writer.flush()","def get_statistics(path):\n images, masks = get_dataset(path)\n buildings = 0\n background = 0\n water = 0\n mean = np.zeros(3)\n std = np.zeros(3)\n\n with tqdm(\n total=len(images), desc=\"Getting statistics..\", leave=False, position=0\n ) as pbar:\n for i, m in zip(images, masks):\n image = Image.open(i)\n stat = ImageStat.Stat(image)\n mean = np.add(np.asarray(stat.mean), mean)\n std = np.add(np.asarray(stat.stddev), std)\n\n mask = Image.open(m)\n\n for c in mask.getcolors():\n if c[1] == 0:\n background += c[0]\n\n if c[1] == 127:\n water += c[0]\n\n if c[1] == 255:\n buildings += c[0]\n pbar.update()\n\n mean = np.divide(mean, len(images))\n std = np.divide(std, len(images))\n\n all_pixels = buildings + background + water\n buildings_perc = (buildings / all_pixels) * 100\n water_perc = (water / all_pixels) * 100\n background_perc = (background / all_pixels) * 100\n\n filename = os.path.join(path, \"myfile.txt\")\n\n with open(filename, \"w\") as file:\n file.write(\"Mean: {}\\n\".format(mean))\n file.write(\"Standard deviation: {}\\n\".format(std))\n\n file.write(\"Building pixels: {}\\n\".format(buildings))\n file.write(\"Water pixels: {}\\n\".format(water))\n file.write(\"Background pixels: {}\\n\".format(background))\n file.write(\"Building percentage: {}\\n\".format(buildings_perc))\n file.write(\"Water percentage: {}\\n\".format(water_perc))\n file.write(\"Background percentage: {}\\n\".format(background_perc))\n\n with open(filename, \"r\") as file_r:\n print(file_r.read())","def dataset_statistics(dataset):\n print (dataset.describe())","def get_image_stats(image, out_dir, cur_file):\n # Output directory\n output_base = osp.join(out_dir, cur_file.split('.')[0])\n os.mkdir(output_base)\n # Print dimensions of the image\n width, height, color = image.shape\n print('The resolution of the image if of {}x{}x{}'.format(width,\n height,\n color))\n print('Total of {} pixels'.format(width * height * color))\n\n # Get histogram\n print('Calculating histogram')\n flat_img = image.mean(axis=2).flatten()\n counts, bins = np.histogram(flat_img, range(257))\n plt.bar(bins[:-1], counts, width=1, edgecolor='none')\n output_file = osp.join(out_dir, output_base, 'histogram.png')\n plt.xlabel('Intensidad')\n plt.ylabel('Número de pixeles')\n print('Saving histogram')\n plt.savefig(output_file, bbox_inches='tight')\n plt.close()\n\n # LAB space\n lab_image = cv2.cvtColor(image[8000:8500, 8000:8500, :], cv2.COLOR_BGR2LAB)\n output_file = osp.join(out_dir, output_base, 'lab.png')\n cv2.imwrite(output_file, lab_image)\n output_file = osp.join(out_dir, output_base, 'original.png')\n cv2.imwrite(output_file, image[8000:8500, 8000:8500, :])","def display_metrics3(self):\n messagebox.showinfo(\"Processed Image Metrics\", self.pro_metrics)","def info(self):\n\n\t\tprint(\"Pixels on a side: {0}\".format(self.data.shape[0]))\n\t\tprint(\"Pixel size: {0}\".format(self.resolution))\n\t\tprint(\"Total angular size: {0}\".format(self.side_angle))\n\t\tprint(\"lmin={0:.1e} ; lmax={1:.1e}\".format(self.lmin,self.lmax))","def dataset_statistics(dataset):\n print(dataset.describe())","def dataset_statistics(dataset):\n print(dataset.describe())","def print_image(img):\r\n # On affiche l'image\r\n plt.figure(figsize=(20, 5))\r\n plt.subplot(1, 2, 1)\r\n plt.imshow(img)\r\n # On affiche l'histogramme\r\n plt.subplot(1, 2, 2)\r\n plt.hist(img.flatten(), bins=range(256))\r\n plt.show()","def _print_img_size(self, img):\n width, height = img.size\n print('{}, {}'.format(width, height))","def info(self):\n\n print(\"pixellisation:\", self.pixel)\n print(\"number of components:\", self.ncomp)\n print(\"number of pixels:\", self.data.shape[:] if self.ncomp == 1 else self.data.shape[1:])\n print(\"nside:\", self.nside)\n print(\"geometry:\", self.geometry)\n print(\"coordinates:\", self.coordinate)","def summary_stats(tile_summary):\n return \"Original Dimensions: %dx%d\\n\" % (tile_summary.orig_w, tile_summary.orig_h) + \\\n \"Original Tile Size: %dx%d\\n\" % (tile_summary.orig_tile_w, tile_summary.orig_tile_h) + \\\n \"Scale Factor: 1/%dx\\n\" % tile_summary.scale_factor + \\\n \"Scaled Dimensions: %dx%d\\n\" % (tile_summary.scaled_w, tile_summary.scaled_h) + \\\n \"Scaled Tile Size: %dx%d\\n\" % (tile_summary.scaled_tile_w, tile_summary.scaled_tile_w) + \\\n \"Total Mask: %3.2f%%, Total Tissue: %3.2f%%\\n\" % (\n tile_summary.mask_percentage(), tile_summary.tissue_percentage) + \\\n \"Tiles: %dx%d = %d\\n\" % (tile_summary.num_col_tiles, tile_summary.num_row_tiles, tile_summary.count) + \\\n \" %5d (%5.2f%%) tiles >=%d%% tissue\\n\" % (\n tile_summary.high, tile_summary.high / tile_summary.count * 100, TISSUE_HIGH_THRESH) + \\\n \" %5d (%5.2f%%) tiles >=%d%% and <%d%% tissue\\n\" % (\n tile_summary.medium, tile_summary.medium / tile_summary.count * 100, TISSUE_LOW_THRESH,\n TISSUE_HIGH_THRESH) + \\\n \" %5d (%5.2f%%) tiles >0%% and <%d%% tissue\\n\" % (\n tile_summary.low, tile_summary.low / tile_summary.count * 100, TISSUE_LOW_THRESH) + \\\n \" %5d (%5.2f%%) tiles =0%% tissue\" % (tile_summary.none, tile_summary.none / tile_summary.count * 100)","def descriptive_stats(array, verbose=True, label='', mean=False, plot=False):\n if mean:\n mean_ = np.mean(array)\n median = np.median(array)\n mini = np.min(array)\n maxi = np.max(array)\n first_qu = np.percentile(array, 25)\n third_qu = np.percentile(array, 75)\n\n if verbose:\n if mean:\n label += 'min={:.1f} / 1st QU={:.1f} / ave={:.1f} / med={:.1f} / '\n label += '3rd QU={:.1f} / max={:.1f}'\n print(label.format(mini, first_qu, mean_, median, third_qu, maxi))\n else:\n label += 'min={:.1f} / 1st QU={:.1f} / med={:.1f} / 3rd QU={:.1f} '\n label += '/ max={:.1f}'\n print(label.format(mini, first_qu, median, third_qu, maxi))\n\n if plot:\n boxplot(array, vert=False, meanline=mean, showfliers=True, sym='.')\n\n if mean:\n return mini, first_qu, mean_, median, third_qu, maxi\n else:\n return mini, first_qu, median, third_qu, maxi","def describe(self, image, mask=None):\n histogram = cv2.calcHist([image], [0, 1, 2], mask, self.bins, [0, 256, 0, 256, 0, 256])\n cv2.normalize(histogram, histogram)\n\n return histogram.flatten()","def estimateInfo(Analysis, ImageData, diffract, print_opt=False): \n total_images = ImageData['totalImages'] \n\n for key in Analysis:\n Analysis[key]['cones'] = [1,2,3,4,5,6]\n Analysis[key]['info'] = np.zeros(len(\n Analysis[key]['cones']))\n\n for amp in ImageData['rawAmp']:\n\n for key in Analysis:\n ind = len(diffract['cpd'])\n fooInfo = info.SingleConeEntropyFunc((amp[ind] ** 2 *\n Analysis[key]['retina']), \n Analysis[key]['cones']) \n Analysis[key]['info'] += fooInfo / total_images\n\n if print_opt == True:\n print ' '\n print 'Information'\n print '------------'\n for key in Analysis:\n print key, ': ', Analysis[key]['info']\n\n return Analysis","def print_results(self, final_table=None):\n\n assert self.info\n\n if not final_table:\n final_table = [\"\\n\\n{:-^80}\\n\".format(\"ANALYSIS OF RESULTS\")]\n\n if not self.info.categories[\"integrated\"]:\n final_table.append(\"NO IMAGES INTEGRATED!\")\n else:\n label_lens = [len(v[\"label\"]) for k, v in self.info.stats.items()]\n max_label = int(5 * round(float(np.max(label_lens)) / 5)) + 5\n for k, v in self.info.stats.items():\n if k in (\"lres\", \"res\", \"beamX\", \"beamY\"):\n continue\n line = (\n \"{: <{l}}: max = {:<6.2f} min = {:<6.2f} \"\n \"avg = {:<6.2f} ({:<6.2f})\"\n \"\".format(\n v[\"label\"], v[\"max\"], v[\"min\"], v[\"mean\"], v[\"std\"], l=max_label\n )\n )\n final_table.append(line)\n\n # TODO: Figure out what to do with summary charts\n # # If more than one integrated image, plot various summary graphs\n # if len(self.info.categories['integrated']) > 1:\n # plot = Plotter(self.params, self.info)\n # if self.params.analysis.summary_graphs:\n # if ( self.params.advanced.processing_backend == 'ha14' and\n # self.params.cctbx_ha14.grid_search.type is not None\n # ):\n # plot.plot_spotfinding_heatmap(write_files=True)\n # plot.plot_res_histogram(write_files=True)\n # med_beamX, med_beamY, pixel_size = plot.plot_beam_xy(write_files=True,\n # return_values=True)\n # else:\n # with warnings.catch_warnings():\n # # To catch any 'mean of empty slice' runtime warnings\n # warnings.simplefilter(\"ignore\", category=RuntimeWarning)\n # beamXY_info = plot.calculate_beam_xy()\n # beamX, beamY = beamXY_info[:2]\n # med_beamX = np.median(beamX)\n # med_beamY = np.median(beamY)\n # pixel_size = beamXY_info[-1]\n\n final_table.append(\n \"{: <{l}}: X = {:<4.2f}, Y = {:<4.2f}\"\n \"\".format(\n \"Median Beam Center\",\n self.info.stats[\"beamX\"][\"mean\"],\n self.info.stats[\"beamY\"][\"mean\"],\n l=max_label,\n )\n )\n\n # Special entry for resolution last\n v = self.info.stats[\"res\"]\n final_table.append(\n \"{: <{l}}: low = {:<6.2f} high = {:<6.2f} \"\n \"avg = {:<6.2f} ({:<6.2f})\"\n \"\".format(\n v[\"label\"], v[\"max\"], v[\"min\"], v[\"mean\"], v[\"std\"], l=max_label\n )\n )\n\n for item in final_table:\n util.main_log(self.info.logfile, item, False)\n self.info.update(final_table=final_table)","def getstats_fromimage(path_data, label, filename):\n path_image = get_path_image(path_data, label, filename)\n image = np.fromfile(path_image, np.float64)\n\n max_ = np.amax(image)\n min_ = np.amin(image)\n mean = np.mean(image)\n std = np.std(image)\n\n return max_, min_, mean, std","def advancedStats():","def compute_statistics(self):","def image_summary(self, tag, images, step):\n\n img_summaries = []\n for i, img in enumerate(images):\n # Write the image to a string\n try:\n s = StringIO()\n except:\n s = BytesIO()\n scipy.misc.toimage(img).save(s, format=\"png\")\n\n # Create an Image object\n img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(),\n height=img.shape[0],\n width=img.shape[1])\n # Create a Summary value\n img_summaries.append(tf.Summary.Value(tag='%s/%d' % (tag, i), image=img_sum))\n\n # Create and write Summary\n summary = tf.Summary(value=img_summaries)\n self.writer.add_summary(summary, step)","def image_summary(self, tag, images, step):\n\n img_summaries = []\n for i, img in enumerate(images):\n # Write the image to a string\n try:\n s = StringIO()\n except:\n s = BytesIO()\n scipy.misc.toimage(img).save(s, format=\"png\")\n\n # Create an Image object\n img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(),\n height=img.shape[0],\n width=img.shape[1])\n # Create a Summary value\n img_summaries.append(tf.Summary.Value(tag='%s/%d' % (tag, i), image=img_sum))\n\n # Create and write Summary\n summary = tf.Summary(value=img_summaries)\n self.writer.add_summary(summary, step)","def print_summary(self, write_files=True):\n\n assert self.info\n\n if not self.info.categories[\"integrated\"]:\n util.main_log(\n self.info.logfile,\n \"NO IMAGES SUCCESSFULLY PROCESSSED!\",\n (not self.gui_mode),\n )\n return\n\n summary = []\n summary.append(\"\\n\\n{:-^80}\\n\".format(\"SUMMARY\"))\n categories = [\n \"total\",\n \"failed_triage\",\n \"have_diffraction\",\n \"failed_spotfinding\",\n \"failed_indexing\",\n \"failed_grid_search\",\n \"failed_integration\",\n \"failed_filter\",\n \"integrated\",\n ]\n for cat in categories:\n lst, fail, fn, _ = self.info.categories[cat]\n path = os.path.join(self.info.int_base, fn)\n if len(lst) > 0 or cat in (\"integrated\", \"diffraction\"):\n summary.append(\"{: <20}: {}\".format(\"{} \".format(fail), len(lst)))\n with open(path, \"w\") as cf:\n for item in lst:\n if isinstance(item, tuple) or isinstance(item, list):\n item = \", \".join([str(i) for i in item])\n cf.write(\"{}\\n\".format(item))\n if cat == \"integrated\" and write_files:\n if not hasattr(self, \"prime_data_path\"):\n self.prime_data_path = path\n\n summary.append(\"\\n\\nIOTA version {0}\".format(iota_version))\n summary.append(\"{}\\n\".format(now))\n\n for item in summary:\n util.main_log(self.info.logfile, \"{}\".format(item), False)\n self.info.update(summary=summary)","def do_info (self, line) :\n\t\tprint\n\t\tprint get_info_string( self.__image )\n\t\tprint","def summarize(self):\n # NOTE: should be moved to abstract superclass\n failcount = len(self.mosaictrees) - len(self)\n msg = \"Parsed %i mosaics from the FluoView project.\\n\\n\" % len(self)\n if failcount > 0:\n msg += (\n \"\\n==== WARNING ====== WARNING ====\\n\\n\"\n \"Parsing failed on %i mosaic(s). Missing files?\\n \"\n \"\\n==== WARNING ====== WARNING ====\\n\\n\\n\" % failcount\n )\n for mos in self:\n msg += \"Mosaic %i: \" % mos.supplement[\"index\"]\n msg += \"%i x %i tiles, \" % (mos.dim[\"X\"], mos.dim[\"Y\"])\n msg += \"%.1f%% overlap.\\n\" % mos.get_overlap()\n return msg","def summarize(self):\n # NOTE: should be moved to abstract superclass\n failcount = len(self.mosaictrees) - len(self)\n msg = \"Parsed %i mosaics from the FluoView project.\\n\\n\" % len(self)\n if failcount > 0:\n msg += (\n \"\\n==== WARNING ====== WARNING ====\\n\\n\"\n \"Parsing failed on %i mosaic(s). Missing files?\\n \"\n \"\\n==== WARNING ====== WARNING ====\\n\\n\\n\" % failcount\n )\n for mos in self:\n msg += \"Mosaic %i: \" % mos.supplement[\"index\"]\n msg += \"%i x %i tiles, \" % (mos.dim[\"X\"], mos.dim[\"Y\"])\n msg += \"%.1f%% overlap.\\n\" % mos.get_overlap()\n return msg","def print_summary(self):\n\t\t\n\t\tif not self.objects:\n\t\t\tsys.stderr.write(\"No objects.\\n\")\n\t\t\treturn\n\t\t\n\t\t# Summary header data\n\t\theader = (\"ok\", \"error\", \"zdata\", \"xdata\", \"odata\", \"ratio\")\n\t\t\n\t\t# Summary header format\n\t\tfield = \" %11s\"\n\t\tfmt = field * len(header)\n\t\twidth = len(field % \"\") * len(header)\n\t\ts_line = \"-\" * width\n\t\td_line = \"=\" * width\n\t\t\n\t\t# Verbose header data\n\t\tvheader = (\"ok?\", \"type\", \"id\", \"zdata\", \"xdata\", \"odata\", \"ratio\")\n\t\t\n\t\t# Verbose header format\n\t\tvfmt = \" %3s %7s\" + field * 5\n\t\t\n\t\t# Summary data\n\t\tc_ratio = None\n\t\to_ok = o_error = 0\n\t\tz_data_size = x_data_size = o_data_size = 0\n\t\t\n\t\tif self.verbose:\n\t\t\tprint vfmt % vheader\n\t\t\tprint s_line\n\t\t\n\t\t# Gather data from objects\n\t\tfor obj in self.objects:\n\t\t\tif obj.v_all:\n\t\t\t\to_ok += 1\n\t\t\t\tif obj.z_data_size: z_data_size += obj.z_data_size\n\t\t\t\tif obj.x_data_size: x_data_size += obj.x_data_size\n\t\t\t\tif obj.o_data_size: o_data_size += obj.o_data_size\n\t\t\telse:\n\t\t\t\to_error += 1\n\t\t\t\n\t\t\tif self.verbose:\n\t\t\t\tv_c_ratio = None\n\t\t\t\t\n\t\t\t\t# Calculate compression if possible\n\t\t\t\tif obj.z_data_size and obj.x_data_size:\n\t\t\t\t\tv_c_ratio = str(100 * obj.z_data_size / obj.x_data_size) + \"%\"\n\t\t\t\t\n\t\t\t\t# Build verbose data\n\t\t\t\tv_data = (\n\t\t\t\t\t\"[Y]\" if obj.v_all else \"[N]\",\n\t\t\t\t\tobj.o_data_type or \"N/A\",\n\t\t\t\t\tobj.id[:10],\n\t\t\t\t\tobj.z_data_size or \"N/A\",\n\t\t\t\t\tobj.x_data_size or \"N/A\",\n\t\t\t\t\tobj.o_data_size or \"N/A\",\n\t\t\t\t\tv_c_ratio or \"N/A\"\n\t\t\t\t)\n\t\t\t\t\n\t\t\t\t# Print verbose data\n\t\t\t\tprint vfmt % v_data\n\t\t\n\t\tif self.verbose:\n\t\t\tprint d_line\n\t\t\n\t\t# Calculate compression ratio\n\t\tif z_data_size and x_data_size:\n\t\t\tc_ratio = str(100 * z_data_size / x_data_size) + \"%\"\n\t\t\n\t\t# Print summary\n\t\tprint fmt % header\n\t\tprint s_line\n\t\tprint fmt % (o_ok, o_error, z_data_size, x_data_size, o_data_size, c_ratio)","def main():\n base_dir = '/home/sjimenez/imagenes_prueba'\n out_dir = '/home/sjimenez/easy_analysis'\n for _, _, files in os.walk(base_dir, topdown=False):\n for f in files:\n print('--------- {} ---------'.format(f))\n act_dir = osp.join(base_dir, f)\n act_im = cv2.imread(act_dir)\n if act_im is not None:\n get_image_stats(act_im, out_dir, f)\n else:\n print('Not able to open the image')","def summary(self):\n name = 'name : ' + self.get_name()\n damage = 'damage : ' + str(self.get_damage())\n ammos = 'ammo : ' + str(self.get_ammos())\n owner = 'owner : ' + str(self.get_owner())\n return '\\n'.join([name, damage, ammos, owner])","def _stats(self):\n return (\"size = \" + str(self.size())\n + \"; height = \" + str(self.height()))","def print_stats():\n if spritegroup_stats[0] > 0:\n generic.print_info(\"Concurrent spritegroups: {}/{} ({})\".format(spritegroup_stats[0], total_action2_ids, str(spritegroup_stats[1])))\n if a2register_stats[0] > 0:\n generic.print_info(\"Concurrent Action2 registers: {}/{} ({})\".format(a2register_stats[0], total_tmp_locations, str(a2register_stats[1])))","def summary(self):\n raise NotImplementedError","def describe():","def describe(self):\n\n print(\"Correlation length: {0}\".format(self.cl))\n print(\"icoordchange: {0}\".format(self.icoordchange))\n print(\"ispec: {0}\".format(self.ispec))\n print(\"ireg: {0}\".format(self.ireg))\n print(\"Domain: x-axis: from {0} to {1} with {2} steps of {3}\".format(self.xori, self.xend,\n self.nx, self.dx))\n print(\"Domain: y-axis: from {0} to {1} with {2} steps of {3}\".format(self.yori, self.yend,\n self.ny, self.dy))\n print(\"Exclusion value: {0}\".format(self.valex))\n print(\"Signal-to-noise ratio: {0}\".format(self.snr))\n print(\"Variance of the background field: {0}\".format(self.varbak))","def statistics(img1_blobs, img2_blobs, matches):\n\tstatistics = {}\n\n\tstatistics['#Interest Points in img1'] = len(img1_blobs)\n\tstatistics['#Interest Points in img2'] = len(img2_blobs)\n\tstatistics['Accepted Matches'] = len(matches)\n\tdissimilarity = [match[2] for match in matches]\n\tstatistics['Mean of accepted matches'] = sum(dissimilarity)/len(dissimilarity)\n\tstatistics['SD of accepted matches'] = np.std(dissimilarity)\n\treturn statistics","def summary(self):\n print('est0: %s (%s) shape: %s' % (str(self.est0.name),\\\n str(self.est0.type_name),str(self.shape0)))\n print('est1: %s (%s) shape: %s' % (str(self.est1.name),\\\n str(self.est1.type_name),str(self.shape1)))","def detail(self):\n return self.uniform(\"detail\",\n self.img_scale * .05,\n self.img_scale * .2)","def analyze(self):\n try:\n self.options[self.multi_image][1]()\n except:\n raise Exception(\"Multi Image Option not defined.\")\n\n self.image = self.data / self.exposure\n\n background = self.min_val = np.min(self.image[:511,:511])\n self.max_val = np.max(self.image[:511,:511])\n # stats.mode returns modal value = value that occours most often\n #background = stats.mode(im[:50,:50].ravel())[0][0]\n\n intensity = self.image.sum() - background*np.size(self.image)\n\n #results.append((self.index, intensity, background))\n self.index =+ 1","def info(self):\n\n print(\"pupil file =\", self.pupil_file)\n print(\"phase file =\", self.phase_file)\n print(\"wavelengths and weights =\")\n for i in range(len(self.filter[0])):\n print(\" %10.5f %6.4f\" % (self.filter[0][i], self.filter[1][i]))\n print(\"pupil diameter (meters) =\", self.D)\n if self.oversample == 2:\n print(\"oversampling factor = 2 (Nyquist sampling)\")\n else:\n r = float(self.oversample) / 2.\n print(\"oversampling factor = %d (%g * Nyquist sampling)\" % \\\n (self.oversample, r))\n if self.type == SINGLE_PREC:\n print(\"computations will use single precision\")\n else:\n print(\"computations will use double precision\")\n print(\"size of output image =\", self.output_size)\n if self.cdelt is not None:\n print(\"output pixel size (arcsec) =\", self.cdelt / ARCSECtoDEGREES)\n if self.output_written:\n print(\"The computed PSF has been written to the output file.\")\n else:\n print(\"The output file has not been written yet.\")","def _printout_images_info(design_path):\r\n _max_pic_number = 8\r\n images = dict()\r\n for foo in os.listdir(design_path):\r\n abs_foo = os.path.join(design_path, foo)\r\n if os.path.isfile(abs_foo):\r\n continue\r\n if foo.endswith(\"Images\"):\r\n images.setdefault(foo, list())\r\n for bar in os.listdir(abs_foo):\r\n if bar.endswith(\".png\"):\r\n images[foo].append(bar)\r\n if images:\r\n for k, v in list(images.items()):\r\n v.sort(key=sort_by_num, reverse=True)\r\n nine_images = dict()\r\n images_number = 0\r\n for i in range(0, 10):\r\n if images_number > _max_pic_number:\r\n break\r\n for k, v in list(images.items()):\r\n nine_images.setdefault(k, list())\r\n try:\r\n nine_images[k].append(v[i])\r\n images_number += 1\r\n if images_number > _max_pic_number:\r\n break\r\n except IndexError:\r\n continue\r\n say_it(\"\")\r\n say_it(\"Images Number: {}\".format(images_number))\r\n ii = 1\r\n for kk, vv in list(nine_images.items()):\r\n for foo in vv:\r\n say_it(\"-PNG{}: {}/{}\".format(ii, kk, foo))\r\n ii += 1","def tf_summary_image(image, boxes, name='image'):\n image = tf.expand_dims(image, 0)\n boxes = tf.expand_dims(boxes, 0)\n image_with_box = tf.image.draw_bounding_boxes(image, boxes)\n tf.summary.image(name, image_with_box)","def printImage(imageObject):\n # TODO\n pass","def print_statistics(session, batch_image, batch_label, cost, accuracy, type=\"VALIDATION\"):\n loss = session.run(\n cost,\n feed_dict={\n x: batch_image,\n y: batch_label,\n keep_prob: 1.0\n }\n )\n accuracy = session.run(\n accuracy,\n feed_dict={\n x: batch_image,\n y: batch_label,\n keep_prob: 1.0\n }\n )\n print(\"{} :: Loss = {} ; Accuracy = {} \".format(type, loss, accuracy))\n return loss, accuracy","def print_file_distribution(num_imgs_total, num_imgs_train, num_imgs_val, num_imgs_test):\n print('Total images: ', num_imgs_total)\n print('Training: ', num_imgs_train)\n print('Validation: ', num_imgs_val)\n print('Testing: ', num_imgs_test)","def info(self):\n ss = \"\\nSummary EffectiveArea2D info\\n\"\n ss += \"----------------\\n\"\n # Summarise data members\n ss += array_stats_str(self.energy, 'energy')\n ss += array_stats_str(self.offset, 'offset')\n ss += array_stats_str(self.eff_area, 'dispersion')\n\n return ss","def main():\n \n # for inserting other images, add tem to /input folder and list them here\n images = (\n 'image-0',\n 'image-1',\n 'image-2'\n )\n\n for image_name in images:\n print(image_name, \"image:\")\n\n image = open_image(image_name)\n display_image(image, \"Original input \" + image_name)\n\n grayscale_v = transform_colors(image)\n display_image(grayscale_v[:,:,0], \"Grayscale \" + image_name)\n save_image(image_name + \"-grayscale\", grayscale_v[:,:,0])\n\n contours_v, contours = get_contours(grayscale_v)\n display_image(contours_v, \"Contours \" + image_name)\n save_image(image_name + \"-contours\", contours_v)\n\n labeled_img, areas = get_measures(image, contours[1:])\n display_image(labeled_img, \"Labeled \" + image_name)\n save_image(image_name + \"-labeled\", labeled_img)\n\n areas_histogram(areas, image_name)","def summary(self):\n\n print(\"input label:\", self.__input_label)\n print(\"target label:\", self.__target_label)\n print(\"denoising label:\", self.denoising_label)\n print(\"contains a successful DE:\", self.is_successful())","def img_series_stats(image_ccd_lst,plots_path,obsdate):\n median_count = []\n mean_count = []\n \n source_hdu = CCDData(image_ccd_lst[0],unit='adu')\n source_image_data = source_hdu.data.astype(float) \n source_image_hdr = source_hdu.header\n target_name = source_image_hdr['FIELD'].strip(' ')\n exptime = source_image_hdr['EXPTIME']\n chip_num = source_image_hdr['CHIP']\n \n for a_file in image_ccd_lst:\n hdu = CCDData(a_file,unit='adu')\n image_data = hdu.data.astype(float) \n image_hdr = hdu.header\n \n median_count.append(np.median(a_file))\n mean_count.append(np.mean(a_file))\n \n min_count_for_median = np.min(median_count)\n min_count_for_mean = np.min(mean_count)\n max_count_for_median = np.max(median_count)\n max_count_for_mean = np.max(mean_count)\n \n plt.figure()\n plt.plot(mean_count, label='mean',color=\"palevioletred\")\n plt.axhline(y=min_count_for_mean,linestyle='-',linewidth=0.5,color='blue',label='min mean {:.2f}'.format(min_count_for_mean),alpha=1)\n plt.axhline(y=max_count_for_mean,linestyle='-',linewidth=0.5,color='blue',label='max mean {:.2f}'.format(max_count_for_mean),alpha=1)\n plt.xlabel('Image number')\n plt.ylabel('Count (ADU)')\n plt.title('Mean pixel value for aligned images')\n plt.legend()\n plt.grid()\n plt.savefig(plots_path/\"{}-{}-{}-aligned_stats_mean.jpg\".format(obsdate,\n target_name,\n exptime,chip_num),\n dpi=900)\n plt.show()\n\n plt.figure()\n plt.plot(median_count, label='median',color=\"darkviolet\")\n plt.axhline(y=min_count_for_median,linestyle='-',linewidth=0.5,color='red',label='min median {:.2f}'.format(min_count_for_median),alpha=1)\n plt.axhline(y=max_count_for_median,linestyle='-',linewidth=0.5,color='red',label='max median {:.2f}'.format(max_count_for_median),alpha=1) \n plt.xlabel('Image number')\n plt.ylabel('Count (ADU)')\n plt.title('Median pixel value for aligned images')\n plt.legend()\n plt.grid()\n plt.savefig(plots_path/\"{}-{}-{}-aligned_stats_median.jpg\".format(obsdate,\n target_name,\n exptime,chip_num),\n dpi=900)\n plt.show()","def identify_image(im):\n score_cures = np.mean(im[1025:1065, 1130:1180, 0])\n score_ingredients = np.mean(im[1025:1065, 675:720, 0])\n if score_cures < 177.5:\n return 'cures'\n if score_ingredients < 177.5:\n return 'ingredients'\n else:\n return 'other'","def statistics(self, **kwargs) -> None:\n print(\n tabulate.tabulate(\n list(self._iter_statistics(**kwargs)),\n headers=[\"path\", \"type\", \"occurences\", \"%\"],\n floatfmt=\".3f\",\n )\n )","def summary(self):\n self.tiles.refreshnames()\n self.glues.refreshnames()\n # self.check_consistent()\n info = {\n \"ntiles\": len(self.tiles),\n \"nrt\": len([x for x in self.tiles if not x.is_fake]),\n \"nft\": len([x for x in self.tiles if x.is_fake]),\n \"nends\": len(self.glues),\n \"ntends\": len(self.tiles.glues_from_tiles()),\n \"tns\": \" \".join(x.name for x in self.tiles if x.name),\n \"ens\": \" \".join(x.name for x in self.glues if x.name)\n # if (\"info\" in self.keys() and \"name\" in self[\"info\"].keys())\n # else \"\",\n }\n tun = sum(1 for x in self.tiles if x.name is None)\n if tun > 0:\n info[\"tns\"] += \" ({} unnamed)\".format(tun)\n eun = sum(1 for x in self.glues if x.name is None)\n if eun > 0:\n info[\"ens\"] += \" ({} unnamed)\".format(eun)\n if info[\"nft\"] > 0:\n info[\"nft\"] = \" (+ {} fake)\".format(info[\"nft\"])\n else:\n info[\"nft\"] = \"\"\n return \"TileSet: {nrt} tiles{nft}, {nends} ends, {ntends} ends in tiles.\\nTiles: {tns}\\nEnds: {ens}\".format(\n **info\n )","def printSummary(self):\n pass","def print_summary(metrics_list, labels_list):\n for metric, name in zip(metrics_list, labels_list):\n print('*' * 108)\n print(name)\n mean_inc_acc = []\n for i in range(metric.shape[0]):\n print('\\t', end='')\n for j in range(metric.shape[1]):\n print('{:5.2f}% '.format(100 * metric[i, j]), end='')\n if np.trace(metric) == 0.0:\n if i > 0:\n avg = 100 * metric[i, :i].mean()\n mean_inc_acc += [avg]\n print('\\tAvg.:{:5.2f}% '.format(avg), end='')\n else:\n avg = 100 * metric[i, :i + 1].mean()\n mean_inc_acc += [avg]\n print('\\tAvg.:{:5.2f}% '.format(avg), end='')\n print()\n print()\n\n # Computing AIA across all incremental states (thus excluding the first non-incremental state)\n print('\\tMean Incremental Acc.: {:5.2f}%'.format(np.mean(mean_inc_acc[1:])))\n print('*' * 108)","def summary(self):\n name = 'name : ' + self.get_name()\n description = 'description : ' + self.get_description()\n agility = 'agility : ' + str(self.get_agility())\n strength = 'strength : ' + str(self.get_strength())\n health_points = 'health_points : ' + str(self.get_health_points())\n summary = '\\n'.join([name, description, agility, strength, health_points])\n if self.take_weapon():\n summary += self.take_weapon().summary()\n return summary","def print_stats(ds):\n print(\"Dataset Name: \" + ds.name)\n print(\"Dataset Mode: \" + ds.mode)\n print(\"Band Count: \" + str(ds.count))\n print(\"Dataset Width: \" + str(ds.width))\n print(\"Dataset Height: \" + str(ds.height))\n print(\"Dataset Bounds: \", ds.bounds)\n print(\"Dataset Transform: \", ds.transform)\n ul = ds.transform * (0, 0)\n print(\"Upper Left Corner: \", ul)\n lr = ds.transform * (ds.width, ds.height)\n print(\"Lower Right Corner: \", lr)\n {i: dtype for i, dtype in zip(ds.indexes, ds.dtypes)}","def print_summary(self):\n self.model.summary()","def summary(self):\n return self.pfm","def generate_image_info(path):\n file_types = ['*.png', '*.jpg', '*.gif']\n for file_type in file_types:\n for img_path in glob.glob(path + file_type):\n img = Image.open(img_path)\n img_name = img_path.split('/')[-1].split('.')[0]\n with open(path + 'resolution.txt', 'a') as file:\n file.write(img_name + ' ' + str(img.size[0]) +\n ' ' + str(img.size[1]) + '\\n')","def show_stats(self):\n print(\"\\nName: \" + self.name)\n print(\"Element Type: \" + self.element)\n print(\"Health: \" + str(self.current_health) + \" / \" + str(self.max_health))\n print(\"Speed: \" + str(self.speed))","def calculate_stats(images, global_normalization=True):\n if global_normalization:\n # flatten first since the images might not be the same size\n flat = np.concatenate(\n [img.ravel() for img in images]\n )\n pmin, pmax, mean, std = np.percentile(flat, 1), np.percentile(flat, 99.6), np.mean(flat), np.std(flat)\n else:\n pmin, pmax, mean, std = None, None, None, None\n\n return {\n 'pmin': pmin,\n 'pmax': pmax,\n 'mean': mean,\n 'std': std\n }","def print_summary_stats(self) -> None:\n print(\"Number of Users: {}\".format(len(self.all_users)))\n print(\"Number of Utterances: {}\".format(len(self.utterances)))\n print(\"Number of Conversations: {}\".format(len(self.conversations)))","def print_summary(stim_table):\n print(\n '{:<20}{:>15}{:>15}\\n'.format('Colname', 'No. conditions', 'Mean N/cond')\n )\n for colname in stim_table.columns:\n conditions, occurrences = np.unique(\n np.nan_to_num(stim_table[colname]), return_counts = True\n )\n print(\n '{:<20}{:>15}{:>15.1f}'.format(\n colname, len(conditions), np.mean(occurrences)\n )\n )","def show_summary(self) -> None:\n all_averages = []\n\n for i in self.album_statistics.values():\n try:\n all_averages.append(i['avg'])\n except (TypeError, ValueError):\n pass\n # print(all_averages)\n try:\n final_average = math.ceil(np.mean(all_averages))\n except ValueError:\n click.echo(\n 'Oops! https://lyrics.ovh couldn\\'t find any lyrics across any'\n ' album. This is caused by inconsistent Artist names from'\n ' Musicbrainz and lyrics.ovh. Try another artist.'\n )\n raise (SystemExit)\n output = BeautifulTable(max_width=200)\n output.set_style(BeautifulTable.STYLE_BOX_ROUNDED)\n output.column_headers = [\n 'Average number of words in tracks across all albums\\n'\n f'for {self.artist}'\n ]\n output.append_row([final_average])\n click.echo(output)\n\n return self","def summary(self, yname=None, xname=None, title=None, alpha=.05):\n # TODO: Make this raise upstream instead of just \"pass\"\n raise NotImplementedError # pragma: no cover\n # TODO: move the GenericLikelihoodModelResults implementation here?","def stats(self):","def visual_image(img, annos, save_path, ratio=None, height=None, width=None, name=None, score_threshold=0.01):\n # annos: list type, in which all the element is dict\n h, w = img.shape[0], img.shape[1]\n if height is not None and width is not None and (height != h or width != w):\n img, annos = resize_image(img, annos, width, height)\n elif ratio not in (None, 1):\n img, annos = resize_image(img, annos, w * ratio, h * ratio)\n\n h, w = img.shape[0], img.shape[1]\n num_objects = len(annos)\n num = 0\n\n def define_color(pair):\n \"\"\"define line color\"\"\"\n left_part = [0, 1, 3, 5, 7, 9, 11, 13, 15]\n right_part = [0, 2, 4, 6, 8, 10, 12, 14, 16]\n if pair[0] in left_part and pair[1] in left_part:\n color = (255, 0, 0)\n elif pair[0] in right_part and pair[1] in right_part:\n color = (0, 0, 255)\n else:\n color = (139, 0, 255)\n return color\n\n def visible(a, w, h):\n return a[0] >= 0 and a[0] < w and a[1] >= 0 and a[1] < h\n\n for i in range(num_objects):\n ann = annos[i]\n bbox = coco_box_to_bbox(ann['bbox'])\n if \"score\" in ann and (ann[\"score\"] >= score_threshold or num == 0):\n num += 1\n txt = (\"p\" + \"{:.2f}\".format(ann[\"score\"]))\n cv2.putText(img, txt, (bbox[0], bbox[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)\n\n ct = (int((bbox[0] + bbox[2]) / 2), int((bbox[1] + bbox[3]) / 2))\n cv2.circle(img, ct, 2, (0, 255, 0), thickness=-1, lineType=cv2.FILLED)\n bbox = np.array(bbox, dtype=np.int32).tolist()\n cv2.rectangle(img, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2)\n\n keypoints = ann[\"keypoints\"]\n keypoints = np.array(keypoints, dtype=np.int32).reshape(_NUM_JOINTS, 3).tolist()\n\n for pair in data_cfg.edges:\n partA = pair[0]\n partB = pair[1]\n color = define_color(pair)\n p_a = tuple(keypoints[partA][:2])\n p_b = tuple(keypoints[partB][:2])\n mask_a = keypoints[partA][2]\n mask_b = keypoints[partB][2]\n if (visible(p_a, w, h) and visible(p_b, w, h) and mask_a * mask_b > 0):\n cv2.line(img, p_a, p_b, color, 2)\n cv2.circle(img, p_a, 3, color, thickness=-1, lineType=cv2.FILLED)\n cv2.circle(img, p_b, 3, color, thickness=-1, lineType=cv2.FILLED)\n\n img_id = annos[0][\"image_id\"] if annos and \"image_id\" in annos[0] else random.randint(0, 9999999)\n image_name = \"cv_image_\" + str(img_id) + \".png\" if name is None else \"cv_image_\" + str(img_id) + name + \".png\"\n cv2.imwrite(\"{}/{}\".format(save_path, image_name), img)","def summary(self, printed=True):\n raise NotImplementedError","def create_summary(image_summaries, scalar_summaries):\n for key, value in image_summaries.items():\n tf.summary.image(key, unscale(value))\n for key, value in scalar_summaries.items():\n tf.summary.scalar(key, value)\n return tf.summary.merge_all()","def summary(self) -> str:\n pass","def imdisplay(filename, representation):\n\n image = read_image(filename, representation)\n plt.imshow(image, cmap=\"gray\")\n plt.show()","def cntInfo(img, cnt):\n\tpts = extremePoints(cnt)\n\troi = crop(img, pts[\"L\"][0], pts[\"T\"][1], pts[\"R\"][0], pts[\"B\"][1])\n\tm = minMaxLoc(roi)\n\tm[\"minLoc\"] = (m[\"minLoc\"][0] + pts[\"L\"][0], m[\"minLoc\"][1] + pts[\"T\"][1])\n\tm[\"maxLoc\"] = (m[\"maxLoc\"][0] + pts[\"L\"][0], m[\"maxLoc\"][1] + pts[\"T\"][1])\n\tcross = abs(pts[\"L\"][0] - pts[\"R\"][0])\n\theight = abs(pts[\"T\"][1] - pts[\"B\"][1])\n\tcent = centroid(cnt)\n\tangle = orientation(cnt)\n\tareaVal = area(cnt)\n\tper = perimeter(cnt)\n\tar = aspectRatio(cnt)\n\text = extent(cnt)\n\tsold = solidity(cnt)\n\teqD = equivalentDiameter(cnt)\n\tme = meanVal(grayscale(roi))\n\tsu = sumPixel(grayscale(roi))\n\td = {\"sum intensity\":su, \"mean intensity\":me, \"area\":areaVal, \"perimeter\":per, \"aspect ratio\":ar, \"extent\":ext,\"solidity\":sold, \"equivalent diameter\":eqD, \"width\": cross, \"height\" : height, \"centroid\" : cent, \"extrema\" : pts, \"min\":m[\"minLoc\"], \"max\":m[\"maxLoc\"], \"orientation\" : angle}\n\treturn d","def summary_images(\n cover,\n encoded,\n transmitted_encoded,\n transmitted_cover,\n step,\n transform_fn=None):\n\n imgs = prep_imgs_to_plot(\n cover, encoded, transmitted_encoded, transmitted_cover, transform_fn)\n\n images_to_plot = imgs['images']\n\n names_to_plot = imgs['names']\n\n descriptions_to_plot = imgs['descriptions']\n\n for i, name in enumerate(names_to_plot):\n tf.summary.image(\n name=name,\n data=images_to_plot[i],\n step=step,\n max_outputs=6,\n description=descriptions_to_plot[i]\n )","def populate_image_stats(self, image):\n ti = image\n image_data = ti.data\n if not ti.data:\n return ti\n ti.size = len(image_data)\n try:\n with connect(Blobby) as c:\n ti.shahash = c.get_data_bhash(image_data)\n except o.Exception, ex:\n raise o.Exception('oException getting shahash: %s' % ex.msg)\n except Exception, ex:\n raise o.Exception('Exception getting shahash: %s' % ex)\n\n try:\n b = StringIO(image_data)\n img = Image.open(b)\n except Exception, ex:\n raise o.Exception('Exception getting PIL img: %s' % ex)\n try:\n ti.xdim, ti.ydim = img.size\n except Exception, ex:\n raise o.Exception('Exception getting dimensions: %s' % ex)\n try:\n ti.vhash = str(average_hash(img))\n except Exception, ex:\n raise o.Exception('Exception getting vhash: %s' % ex)\n\n return ti","def _describe(self) -> Dict[str, Any]:","def display(self, image):\n raise NotImplementedError()","def print_summary(self):\n #outcomes = self.get_outcomes()\n #passes = 'Passes: %i' % sum(1 for outcome in outcomes if outcome == Result.PASS)\n #untested = 'Untested: %i' % sum(1 for outcome in outcomes if outcome == Result.UNTESTED)\n #errors = 'Errors: %i' % sum(1 for outcome in outcomes if outcome == Result.ERROR)\n #fails = 'Fails: %i' % sum(1 for outcome in outcomes if outcome == Result.FAIL)\n print('')\n print ('Passes: %i' % self.get_pass_count())\n print ('Fails: %i' % self.get_fail_count())\n print ('Errors: %i' % self.get_error_count())\n print ('Untested: %i' % self.get_untested_count())\n print ('Skipped: %i' % self.get_skipped_count())","def print_summary_metrics(lst):\n print('*' * 50)\n print(' ' * 16 + 'Summary statistics')\n print('*' * 50)\n print('mean: {} | median: {} | mode: {}'.format(get_mean(lst),\n get_median(lst),\n get_mode(lst)))\n print('range: {} | IQR: {}'.format(get_range(list_nums),\n get_IQR(list_nums)))\n print('\\n')\n print('original list: \\n {}'.format(lst))\n print('sorted list: \\n {}'.format(sorted(lst)))\n print('List without outliers: \\n {}'.format(\n remove_outliers(list_nums)))","def printInfo(matrix):\n\n print(\"Groups:\")\n for group in matrix.matrix.group_labels:\n print(\"\\t{0}\".format(group))\n\n print(\"Samples:\")\n for sample in matrix.matrix.sample_labels:\n print(\"\\t{0}\".format(sample))","def calculates_results_stats(results_dic): \n # Creates empty dictionary for results_stats_dic\n results_stats_dic = dict()\n \n # Sets all counters to initial values of zero so that they can \n # be incremented while processing through the images in results_dic \n results_stats_dic['n_dogs_img'] = 0\n results_stats_dic['n_match'] = 0\n results_stats_dic['n_correct_dogs'] = 0\n results_stats_dic['n_correct_notdogs'] = 0\n results_stats_dic['n_correct_breed'] = 0\n \n # process through the results dictionary\n for key in results_dic:\n \n # Labels Match Exactly\n if results_dic[key][2] == 1:\n results_stats_dic['n_match'] += 1\n\n # TODO: 5a. REPLACE pass with CODE that counts how many pet images of\n # dogs had their breed correctly classified. This happens \n # when the pet image label indicates the image is-a-dog AND \n # the pet image label and the classifier label match. You \n # will need to write a conditional statement that determines\n # when the dog breed is correctly classified and then \n # increments 'n_correct_breed' by 1. Recall 'n_correct_breed' \n # is a key in the results_stats_dic dictionary with it's value \n # representing the number of correctly classified dog breeds.\n # \n # Pet Image Label is a Dog AND Labels match- counts Correct Breed\n if results_dic[key][3] == 1 and results_dic[key][2] == 1:\n results_stats_dic['n_correct_breed'] += 1\n \n # Pet Image Label is a Dog - counts number of dog images\n if results_dic[key][3] == 1:\n results_stats_dic['n_dogs_img'] += 1\n \n # Classifier classifies image as Dog (& pet image is a dog)\n # counts number of correct dog classifications\n if results_dic[key][4] == 1:\n results_stats_dic['n_correct_dogs'] += 1\n\n # TODO: 5b. REPLACE pass with CODE that counts how many pet images \n # that are NOT dogs were correctly classified. This happens \n # when the pet image label indicates the image is-NOT-a-dog \n # AND the classifier label indicates the images is-NOT-a-dog.\n # You will need to write a conditional statement that \n # determines when the classifier label indicates the image \n # is-NOT-a-dog and then increments 'n_correct_notdogs' by 1. \n # Recall the 'else:' above 'pass' already indicates that the \n # pet image label indicates the image is-NOT-a-dog and \n # 'n_correct_notdogs' is a key in the results_stats_dic dictionary \n # with it's value representing the number of correctly \n # classified NOT-a-dog images.\n # \n # Pet Image Label is NOT a Dog\n else:\n # Classifier classifies image as NOT a Dog(& pet image isn't a dog)\n # counts number of correct NOT dog clasifications.\n if results_dic[key][3] == 0 and results_dic[key][4] == 0:\n results_stats_dic['n_correct_notdogs'] += 1\n\n\n # Calculates run statistics (counts & percentages) below that are calculated\n # using the counters from above.\n\n # calculates number of total images\n results_stats_dic['n_images'] = len(results_dic)\n\n # calculates number of not-a-dog images using - images & dog images counts\n results_stats_dic['n_notdogs_img'] = (results_stats_dic['n_images'] - \n results_stats_dic['n_dogs_img']) \n\n # TODO: 5c. REPLACE zero(0.0) with CODE that calculates the % of correctly\n # matched images. Recall that this can be calculated by the\n # number of correctly matched images ('n_match') divided by the \n # number of images('n_images'). This result will need to be \n # multiplied by 100.0 to provide the percentage.\n # \n # Calculates % correct for matches\n results_stats_dic['pct_match'] = (results_stats_dic['n_match'] / results_stats_dic['n_images']) * 100\n\n # TODO: 5d. REPLACE zero(0.0) with CODE that calculates the % of correctly\n # classified dog images. Recall that this can be calculated by \n # the number of correctly classified dog images('n_correct_dogs')\n # divided by the number of dog images('n_dogs_img'). This result \n # will need to be multiplied by 100.0 to provide the percentage.\n # \n # Calculates % correct dogs\n results_stats_dic['pct_correct_dogs'] = (results_stats_dic['n_correct_dogs'] / results_stats_dic['n_dogs_img']) * 100\n\n # TODO: 5e. REPLACE zero(0.0) with CODE that calculates the % of correctly\n # classified breeds of dogs. Recall that this can be calculated \n # by the number of correctly classified breeds of dog('n_correct_breed') \n # divided by the number of dog images('n_dogs_img'). This result \n # will need to be multiplied by 100.0 to provide the percentage.\n # \n # Calculates % correct breed of dog\n results_stats_dic['pct_correct_breed'] = (results_stats_dic['n_correct_breed'] / results_stats_dic['n_dogs_img']) * 100\n\n # Calculates % correct not-a-dog images\n # Uses conditional statement for when no 'not a dog' images were submitted \n if results_stats_dic['n_notdogs_img'] > 0:\n results_stats_dic['pct_correct_notdogs'] = (results_stats_dic['n_correct_notdogs'] /\n results_stats_dic['n_notdogs_img']) * 100.0\n else:\n results_stats_dic['pct_correct_notdogs'] = 0.0\n\n \n # TODO 5f. REPLACE None with the results_stats_dic dictionary that you \n # created with this function \n return results_stats_dic","def print_info(df):\n\n # Data statistics\n # Number of total samples\n print('There are {n_samples} samples in total.'.format(n_samples=len(list(df.index.get_level_values(0).unique()))))\n\n # Count the different types of labels\n unique = df['label'].unique()\n count = []\n\n for label in unique:\n count.append(len(df.index.get_level_values(0)[df['label'] == label].unique()))\n\n count_dict = {unique[i]: count[i] for i in range(len(unique))}\n count_dict_percentage = {\n unique[i]: np.round(count[i] / len(list(df.index.get_level_values(0).unique())), decimals=2)\n for i in range(len(unique))}\n\n print('The types and counts of different labels : \\n {count_dict}'.format(count_dict=count_dict))\n print('The types and counts of different labels as percentage of the total data'\n ' : \\n {count_dict}'.format(count_dict=count_dict_percentage))","def testStatsZebra(self):\n image2 = self.image.Factory(self.image, True)\n #\n # Add 1 to every other row, so the variance is 1/4\n #\n self.assertEqual(image2.getHeight()%2, 0)\n width = image2.getWidth()\n for y in range(1, image2.getHeight(), 2):\n sim = image2.Factory(image2, afwGeom.Box2I(afwGeom.Point2I(0, y), afwGeom.Extent2I(width, 1)),\n afwImage.LOCAL)\n sim += 1\n\n if display:\n ds9.mtv(self.image, frame = 0)\n ds9.mtv(image2, frame = 1)\n\n stats = afwMath.makeStatistics(image2,\n afwMath.NPOINT | afwMath.STDEV | afwMath.MEAN | afwMath.ERRORS)\n mean = stats.getResult(afwMath.MEAN)\n n = stats.getValue(afwMath.NPOINT)\n sd = stats.getValue(afwMath.STDEV)\n\n self.assertEqual(mean[0], image2.get(0, 0) + 0.5)\n self.assertEqual(sd, 1/math.sqrt(4.0)*math.sqrt(n/(n - 1)))\n self.assertAlmostEqual(mean[1], sd/math.sqrt(image2.getWidth()*image2.getHeight()), 10)\n\n meanSquare = afwMath.makeStatistics(image2, afwMath.MEANSQUARE).getValue()\n self.assertEqual(meanSquare, 0.5*(image2.get(0, 0)**2 + image2.get(0, 1)**2))","def describe(image):\n needle = cv2.imread(image, 0)\n orb = cv2.ORB()\n keypoints, description = orb.detectAndCompute(needle, None)\n print(keypoints)\n print(description)\n return keypoints, description","def aligned_comparison_stats(unaligned_image_ccd_lst,aligned_image_ccd_lst,\n plots_path,obsdate):\n aligned_mean_count = []\n aligned_median_count = []\n unaligned_mean_count = []\n unaligned_median_count = []\n \n # getting information from source image for saving purposes\n source_hdu = CCDData(aligned_image_ccd_lst[0],unit='adu')\n source_image_data = source_hdu.data.astype(float) \n source_image_hdr = source_hdu.header\n target_name = source_image_hdr['FIELD'].strip(' ')\n exptime = source_image_hdr['EXPTIME']\n chip_num = source_image_hdr['CHIP']\n \n for a_file in unaligned_image_ccd_lst[1:]:\n hdu = CCDData(a_file,unit='adu')\n image_data = hdu.data.astype(float) \n image_hdr = hdu.header\n \n unaligned_mean_count.append(np.mean(a_file))\n unaligned_median_count.append(np.median(a_file))\n \n min_count_for_unaligned_mean = np.min(unaligned_mean_count)\n max_count_for_unaligned_mean = np.max(unaligned_mean_count)\n min_count_for_unaligned_median = np.min(unaligned_median_count)\n max_count_for_unaligned_median = np.max(unaligned_median_count)\n \n for a_file in aligned_image_ccd_lst:\n hdu = CCDData(a_file,unit='adu')\n image_data = hdu.data.astype(float) \n image_hdr = hdu.header\n \n aligned_mean_count.append(np.mean(a_file))\n aligned_median_count.append(np.median(a_file))\n \n min_count_for_aligned_mean = np.min(aligned_mean_count)\n max_count_for_aligned_mean = np.max(aligned_mean_count)\n min_count_for_aligned_median = np.min(aligned_median_count)\n max_count_for_aligned_median = np.max(aligned_median_count)\n \n # plotting\n fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15,8),sharex=True, sharey=False)\n fig.suptitle('Comparison Statistics for Non-Aligned Images vs Aligned Images for {}'.format(obsdate))\n \n # unaligned mean\n ax1.plot(unaligned_mean_count, label='mean',color=\"deeppink\")\n ax1.axhline(y=min_count_for_unaligned_mean,linestyle='-',linewidth=1,\n color='black',label='min mean {:.2f}'.format(min_count_for_unaligned_mean),alpha=1)\n ax1.axhline(y=max_count_for_unaligned_mean,linestyle='-',linewidth=1,\n color='black',label='max mean {:.2f}'.format(max_count_for_unaligned_mean),alpha=1)\n ax1.set_xlabel('Image number')\n ax1.set_ylabel('Count (ADU)')\n ax1.set_title('Mean pixel value for unaligned images')\n ax1.legend(loc=\"best\")\n ax1.grid(b=True, which='both', axis='both')\n # ax1.sharex(ax3)\n \n # unaligned median\n ax2.plot(unaligned_median_count, label='median',color=\"deeppink\")\n ax2.axhline(y=min_count_for_unaligned_median,linestyle='-',linewidth=1,\n color='black',label='min median {:.2f}'.format(min_count_for_unaligned_median),alpha=1)\n ax2.axhline(y=max_count_for_unaligned_median,linestyle='-',linewidth=1,\n color='black',label='max median {:.2f}'.format(max_count_for_unaligned_median),alpha=1)\n ax2.set_xlabel('Image number')\n ax2.set_ylabel('Count (ADU)')\n ax2.set_title('Median pixel value for unaligned images')\n ax2.legend(loc=\"best\")\n ax2.grid(b=True, which='both', axis='both')\n # ax2.sharex(ax4)\n \n # aligned mean\n ax3.plot(aligned_mean_count, label='mean',color=\"darkviolet\")\n ax3.axhline(y=min_count_for_aligned_mean,linestyle='-',linewidth=1,\n color='black',label='min mean {:.2f}'.format(min_count_for_aligned_mean),alpha=1)\n ax3.axhline(y=max_count_for_aligned_mean,linestyle='-',linewidth=1,\n color='black',label='max mean {:.2f}'.format(max_count_for_aligned_mean),alpha=1)\n ax3.set_xlabel('Image number')\n ax3.set_ylabel('Count (ADU)')\n ax3.set_title('Mean pixel value for aligned images')\n ax3.legend(loc=\"best\")\n ax3.grid(b=True, which='both', axis='both')\n \n \n # aligned median\n ax4.plot(aligned_median_count, label='median',color=\"darkviolet\")\n ax4.axhline(y=min_count_for_aligned_median,linestyle='-',linewidth=1,\n color='black',label='min median {:.2f}'.format(min_count_for_aligned_median),alpha=1)\n ax4.axhline(y=max_count_for_aligned_median,linestyle='-',linewidth=1,\n color='black',label='max median {:.2f}'.format(max_count_for_aligned_median),alpha=1)\n ax4.set_xlabel('Image number')\n ax4.set_ylabel('Count (ADU)')\n ax4.set_title('Median pixel value for unaligned images')\n ax4.legend(loc=\"best\")\n ax4.grid(b=True, which='both', axis='both')\n \n \n for ax in fig.get_axes():\n ax.label_outer()\n \n fig.savefig(plots_path/\"{}-{}-{}-comparison_stats.jpg\".format(obsdate,\n target_name,\n exptime,chip_num),\n dpi=1000)\n fig.show()","def visualize(**images):\n n_images = len(images)\n plt.figure(figsize=(20,8))\n for idx, (name, image) in enumerate(images.items()):\n plt.subplot(1, n_images, idx + 1)\n plt.xticks([]); \n plt.yticks([])\n # get title from the parameter names\n plt.title(name.replace('_',' ').title(), fontsize=20)\n plt.imshow(image)\n plt.savefig('sample_gt_pred_2_max.jpeg')\n plt.show()","def _print_summary(data, metric):\n\n print(u'Cortical thickness {}: {:.2f} \\u00B1 {:.2f} [{:.2f}--{:.2f}]'\n .format(metric, data[:, 0].mean(), data[:, 0].std(ddof=1),\n data[:, 0].min(), data[:, 0].max()))\n print('Other modalities {}: {:.2f} \\u00B1 {:.2f} [{:.2f}--{:.2f}]'\n .format(metric, data[:, 1:].mean(), data[:, 1:].std(ddof=1),\n data[:, 1:].min(), data[:, 1:].max()))\n print('Overall {}: {:.2f} \\u00B1 {:.2f} [{:.2f}--{:.2f}]'\n .format(metric, data.mean(), data.std(ddof=1),\n data.min(), data.max()))"],"string":"[\n \"def print_images_in_statistics(self):\\n self._print_images_statistics(self._images_in_folder, self._pose_class_names)\",\n \"def print_image_info(image, resize=rsz_default, kernel=kernel_size):\\n\\tprint \\\"Image Size: {0}\\\".format(image.shape)\\n\\tprint \\\"Image Max: {0}\\\".format(image.max())\\n\\tprint \\\"Image Min: {0}\\\".format(image.min())\\n\\tprint \\\"Image Mean: {0}\\\".format(image.mean())\\n\\tprint \\\"Image dtype: {0}\\\\n\\\".format(image.dtype)\\n\\timage = to_uint8(image)\\n\\timage_prep = preprocess(image, resize=resize, kernel=kernel)\\n\\tcontour = get_contour(image_prep)\\n\\tM = get_image_moments(contour=contour)\\n\\tsecond_m = ['m20', 'm11', 'm02', 'm30', 'm21', 'm12', 'm03']\\n\\tprint \\\"Zero Order Moment: {0}\\\".format(M['m00'])\\n\\tprint \\\"First Order Moments: {0}, {1}\\\".format(M['m10'], M['m01'])\\n\\tprint \\\"Second Order Moments:\\\"\\n\\tsecond_m_str = ''\\n\\tfor m2 in second_m:\\n\\t\\tsecond_m_str += \\\"{0},\\\".format(M[m2])\\n\\tprint second_m_str[:-1]\",\n \"def print_image_info(input_image):\\n print()\\n print(\\\"Basic Information on image: {}\\\".format(input_image.filename))\\n print(\\\"Format: {}\\\".format(input_image.format))\\n print(\\\"Mode: {}\\\".format(input_image.mode))\\n print(\\\"Size: {}\\\".format(input_image.size))\\n print(\\\"Width: {}\\\".format(input_image.width))\\n print(\\\"Height: {}\\\".format(input_image.height))\\n print(\\\"Palette: {}\\\".format(input_image.palette))\\n print()\",\n \"def print_stats(cars, notcars):\\n print(\\\"Number of car samples: {0}\\\".format(len(cars)))\\n print(\\\"Number of non car samples: {0}\\\".format(len(notcars)))\\n img = cv2.imread(cars[0])\\n print(\\\"Image shape: {0}x{1}\\\".format(img.shape[0], img.shape[1]))\\n print(\\\"Image datatype: {}\\\".format(img.dtype))\",\n \"def print_img_data(img,title='Image'):\\n print(f'________ {title} _______')\\n if len(image.shape) == 2:\\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\\\\n\\\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0])}\\\\n')\\n elif len(image.shape) == 3:\\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\\\\n\\\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0,0])}\\\\n')\",\n \"def print_info(self, i):\\n\\n im_size = self.image_size(i)\\n print 'The path of the image is: {}'.format(self.image_path_at(i))\\n print 'width: {}, height: {}'.format(im_size[0], im_size[1])\\n \\n attr_i = self.gtdb['attr'][i, :]\\n print 'The attributes are: {}'.format(','.join([self._classes[i] for i in np.where(attr_i==1)[0]]))\",\n \"def info_np(img):\\n import numpy as np\\n\\n print ('Dimensions: ' + str(np.shape(img)))\\n print ('Min value: ' + str(np.min(img)))\\n print ('Avg value: ' + str(np.average(img)))\\n print ('Med value: ' + str(np.median(img)))\\n print ('Max value: ' + str(np.max(img)))\\n print ('Std dev: ' + str(np.std(img)))\\n print ('Sum: ' + str(np.sum(img)))\",\n \"def print_images_out_statistics(self):\\n self._print_images_statistics(self._images_out_folder, self._pose_class_names)\",\n \"def image_info(img):\\n\\tprint(img.format)\\n\\tprint(img.size)\\n\\tprint(img.mode)\",\n \"def display_metrics2(self):\\n messagebox.showinfo(\\\"Original Image Metrics\\\", self.raw_metrics)\",\n \"def testSampleImageStats(self):\\n \\n imgfiles = []\\n imgfiles.append(\\\"v1_i1_g_m400_s20_f.fits\\\")\\n imgfiles.append(\\\"v1_i1_g_m400_s20_u16.fits\\\")\\n imgfiles.append(\\\"v1_i2_g_m400_s20_f.fits\\\")\\n imgfiles.append(\\\"v1_i2_g_m400_s20_u16.fits\\\")\\n imgfiles.append(\\\"v2_i1_p_m9_f.fits\\\")\\n imgfiles.append(\\\"v2_i1_p_m9_u16.fits\\\")\\n imgfiles.append(\\\"v2_i2_p_m9_f.fits\\\")\\n imgfiles.append(\\\"v2_i2_p_m9_u16.fits\\\")\\n\\n afwdataDir = os.getenv(\\\"AFWDATA_DIR\\\")\\n if not afwdataDir:\\n print >> sys.stderr, \\\"Skipping tests as afwdata is not setup\\\"\\n return\\n \\n for imgfile in imgfiles:\\n \\n imgPath = os.path.join(afwdataDir, \\\"Statistics\\\", imgfile)\\n\\n # get the image and header\\n dimg = afwImage.DecoratedImageF(imgPath)\\n fitsHdr = dimg.getMetadata()\\n\\n # get the true values of the mean and stdev\\n trueMean = fitsHdr.getAsDouble(\\\"MEANCOMP\\\")\\n trueStdev = fitsHdr.getAsDouble(\\\"SIGCOMP\\\")\\n\\n # measure the mean and stdev with the Statistics class\\n img = dimg.getImage()\\n statobj = afwMath.makeStatistics(img, afwMath.MEAN | afwMath.STDEV)\\n mean = statobj.getValue(afwMath.MEAN)\\n stdev = statobj.getValue(afwMath.STDEV)\\n\\n # print trueMean, mean, trueStdev, stdev\\n self.assertAlmostEqual(mean, trueMean, 8)\\n self.assertAlmostEqual(stdev, trueStdev, 8)\",\n \"def summary(self):\\r\\n self.base.summary()\\r\\n self.extra_layers.summary()\\r\\n self.detector.summary()\",\n \"def print_image_info(self):\\r\\n\\r\\n maxt = np.max(self.times)\\r\\n\\r\\n print (\\\" Duration of Image Stack: %9.3f s (%8.3f min) period = %8.3f s\\\" % (maxt, maxt/60.0, self.period))\\r\\n\\r\\n print (' Image shape: ', self.imageData.shape)\\r\\n\\r\\n print (' nFrames: %d framerate: %9.3f\\\\n' % (self.nFrames, self.framerate))\",\n \"def show_info(filename, abs_=None, center=None):\\n fimage = FITSImage(filename)\\n print(\\\"Image data shape: {0}\\\".format(fimage.shape))\\n print(\\\"Image size: %dx%d\\\" % (fimage.Nx, fimage.Ny))\\n print(\\\"Data unit: [%s]\\\" % fimage.bunit)\\n pixelsize = fimage.pixelsize\\n if pixelsize:\\n print(\\\"Pixel size: %.1f [arcsec]\\\" % pixelsize)\\n print(\\\"Field of view: (%.2f, %.2f) [deg]\\\" % fimage.fov)\\n data = fimage.image\\n if abs_:\\n data = np.abs(data)\\n if center:\\n print(\\\"Central box size: %d\\\" % center)\\n rows, cols = data.shape\\n rc, cc = rows//2, cols//2\\n cs1, cs2 = center//2, (center+1)//2\\n data = data[(rc-cs1):(rc+cs2), (cc-cs1):(cc+cs2)]\\n min_ = np.nanmin(data)\\n max_ = np.nanmax(data)\\n mean = np.nanmean(data)\\n median = np.nanmedian(data)\\n std = np.nanstd(data)\\n iqr = np.diff(np.nanpercentile(data, q=(25, 75)))\\n mad = np.nanmedian(np.abs(data - median))\\n rms = np.sqrt(np.nanmean(data**2))\\n print(\\\"min: %13.6e\\\" % min_)\\n print(\\\"max: %13.6e\\\" % max_)\\n print(\\\"range: %13.6e (max - min)\\\" % (max_ - min_))\\n print(\\\"mean: %13.6e\\\" % mean)\\n print(\\\"median: %13.6e\\\" % median)\\n print(\\\"std: %13.6e (standard deviation)\\\" % std)\\n print(\\\"iqr: %13.6e (interquartile range)\\\" % iqr)\\n print(\\\"mad: %13.6e (median absolute deviation)\\\" % mad)\\n print(\\\"rms: %13.6e (root-mean-squared)\\\" % rms)\",\n \"def image_summary(tag, image):\\n tag = _clean_tag(tag)\\n image = _prepare_image(image)\\n image = _make_image(image)\\n return Summary(value=[Summary.Value(tag=tag, image=image)])\",\n \"def scalarInfo(img, cnt):\\n\\tm = cntInfo(img, cnt)\\n\\td = {\\\"perimeter\\\":m[\\\"perimeter\\\"], \\\"oreientation\\\":m[\\\"orientation\\\"], \\\"solidity\\\":m[\\\"solidity\\\"],\\\"height\\\":m[\\\"height\\\"], \\\"extent\\\":m[\\\"extent\\\"], \\\"aspect ratio\\\":m[\\\"aspect ratio\\\"], \\\"area\\\":m[\\\"area\\\"], \\\"sum intensity\\\":m[\\\"sum intensity\\\"], \\\"width\\\":m[\\\"width\\\"], \\\"equivalent diameter\\\": m[\\\"equivalent diameter\\\"], \\\"mean intensity\\\": m[\\\"mean intensity\\\"]}\\n\\treturn d\",\n \"def image_summary(self, tag, images, step):\\n\\n for i, img in enumerate(images):\\n # Write the image to a string\\n try:\\n s = StringIO()\\n except:\\n s = BytesIO()\\n scipy.misc.toimage(img).save(s, format=\\\"png\\\")\\n\\n # Create an Image object as a Summary value\\n with self.writer.as_default():\\n tf.summary.image(name='%s/%d' % (tag, i), data=s.getvalue(), step=step)\\n\\n # Create and write Summary\\n self.writer.flush()\",\n \"def get_statistics(path):\\n images, masks = get_dataset(path)\\n buildings = 0\\n background = 0\\n water = 0\\n mean = np.zeros(3)\\n std = np.zeros(3)\\n\\n with tqdm(\\n total=len(images), desc=\\\"Getting statistics..\\\", leave=False, position=0\\n ) as pbar:\\n for i, m in zip(images, masks):\\n image = Image.open(i)\\n stat = ImageStat.Stat(image)\\n mean = np.add(np.asarray(stat.mean), mean)\\n std = np.add(np.asarray(stat.stddev), std)\\n\\n mask = Image.open(m)\\n\\n for c in mask.getcolors():\\n if c[1] == 0:\\n background += c[0]\\n\\n if c[1] == 127:\\n water += c[0]\\n\\n if c[1] == 255:\\n buildings += c[0]\\n pbar.update()\\n\\n mean = np.divide(mean, len(images))\\n std = np.divide(std, len(images))\\n\\n all_pixels = buildings + background + water\\n buildings_perc = (buildings / all_pixels) * 100\\n water_perc = (water / all_pixels) * 100\\n background_perc = (background / all_pixels) * 100\\n\\n filename = os.path.join(path, \\\"myfile.txt\\\")\\n\\n with open(filename, \\\"w\\\") as file:\\n file.write(\\\"Mean: {}\\\\n\\\".format(mean))\\n file.write(\\\"Standard deviation: {}\\\\n\\\".format(std))\\n\\n file.write(\\\"Building pixels: {}\\\\n\\\".format(buildings))\\n file.write(\\\"Water pixels: {}\\\\n\\\".format(water))\\n file.write(\\\"Background pixels: {}\\\\n\\\".format(background))\\n file.write(\\\"Building percentage: {}\\\\n\\\".format(buildings_perc))\\n file.write(\\\"Water percentage: {}\\\\n\\\".format(water_perc))\\n file.write(\\\"Background percentage: {}\\\\n\\\".format(background_perc))\\n\\n with open(filename, \\\"r\\\") as file_r:\\n print(file_r.read())\",\n \"def dataset_statistics(dataset):\\n print (dataset.describe())\",\n \"def get_image_stats(image, out_dir, cur_file):\\n # Output directory\\n output_base = osp.join(out_dir, cur_file.split('.')[0])\\n os.mkdir(output_base)\\n # Print dimensions of the image\\n width, height, color = image.shape\\n print('The resolution of the image if of {}x{}x{}'.format(width,\\n height,\\n color))\\n print('Total of {} pixels'.format(width * height * color))\\n\\n # Get histogram\\n print('Calculating histogram')\\n flat_img = image.mean(axis=2).flatten()\\n counts, bins = np.histogram(flat_img, range(257))\\n plt.bar(bins[:-1], counts, width=1, edgecolor='none')\\n output_file = osp.join(out_dir, output_base, 'histogram.png')\\n plt.xlabel('Intensidad')\\n plt.ylabel('Número de pixeles')\\n print('Saving histogram')\\n plt.savefig(output_file, bbox_inches='tight')\\n plt.close()\\n\\n # LAB space\\n lab_image = cv2.cvtColor(image[8000:8500, 8000:8500, :], cv2.COLOR_BGR2LAB)\\n output_file = osp.join(out_dir, output_base, 'lab.png')\\n cv2.imwrite(output_file, lab_image)\\n output_file = osp.join(out_dir, output_base, 'original.png')\\n cv2.imwrite(output_file, image[8000:8500, 8000:8500, :])\",\n \"def display_metrics3(self):\\n messagebox.showinfo(\\\"Processed Image Metrics\\\", self.pro_metrics)\",\n \"def info(self):\\n\\n\\t\\tprint(\\\"Pixels on a side: {0}\\\".format(self.data.shape[0]))\\n\\t\\tprint(\\\"Pixel size: {0}\\\".format(self.resolution))\\n\\t\\tprint(\\\"Total angular size: {0}\\\".format(self.side_angle))\\n\\t\\tprint(\\\"lmin={0:.1e} ; lmax={1:.1e}\\\".format(self.lmin,self.lmax))\",\n \"def dataset_statistics(dataset):\\n print(dataset.describe())\",\n \"def dataset_statistics(dataset):\\n print(dataset.describe())\",\n \"def print_image(img):\\r\\n # On affiche l'image\\r\\n plt.figure(figsize=(20, 5))\\r\\n plt.subplot(1, 2, 1)\\r\\n plt.imshow(img)\\r\\n # On affiche l'histogramme\\r\\n plt.subplot(1, 2, 2)\\r\\n plt.hist(img.flatten(), bins=range(256))\\r\\n plt.show()\",\n \"def _print_img_size(self, img):\\n width, height = img.size\\n print('{}, {}'.format(width, height))\",\n \"def info(self):\\n\\n print(\\\"pixellisation:\\\", self.pixel)\\n print(\\\"number of components:\\\", self.ncomp)\\n print(\\\"number of pixels:\\\", self.data.shape[:] if self.ncomp == 1 else self.data.shape[1:])\\n print(\\\"nside:\\\", self.nside)\\n print(\\\"geometry:\\\", self.geometry)\\n print(\\\"coordinates:\\\", self.coordinate)\",\n \"def summary_stats(tile_summary):\\n return \\\"Original Dimensions: %dx%d\\\\n\\\" % (tile_summary.orig_w, tile_summary.orig_h) + \\\\\\n \\\"Original Tile Size: %dx%d\\\\n\\\" % (tile_summary.orig_tile_w, tile_summary.orig_tile_h) + \\\\\\n \\\"Scale Factor: 1/%dx\\\\n\\\" % tile_summary.scale_factor + \\\\\\n \\\"Scaled Dimensions: %dx%d\\\\n\\\" % (tile_summary.scaled_w, tile_summary.scaled_h) + \\\\\\n \\\"Scaled Tile Size: %dx%d\\\\n\\\" % (tile_summary.scaled_tile_w, tile_summary.scaled_tile_w) + \\\\\\n \\\"Total Mask: %3.2f%%, Total Tissue: %3.2f%%\\\\n\\\" % (\\n tile_summary.mask_percentage(), tile_summary.tissue_percentage) + \\\\\\n \\\"Tiles: %dx%d = %d\\\\n\\\" % (tile_summary.num_col_tiles, tile_summary.num_row_tiles, tile_summary.count) + \\\\\\n \\\" %5d (%5.2f%%) tiles >=%d%% tissue\\\\n\\\" % (\\n tile_summary.high, tile_summary.high / tile_summary.count * 100, TISSUE_HIGH_THRESH) + \\\\\\n \\\" %5d (%5.2f%%) tiles >=%d%% and <%d%% tissue\\\\n\\\" % (\\n tile_summary.medium, tile_summary.medium / tile_summary.count * 100, TISSUE_LOW_THRESH,\\n TISSUE_HIGH_THRESH) + \\\\\\n \\\" %5d (%5.2f%%) tiles >0%% and <%d%% tissue\\\\n\\\" % (\\n tile_summary.low, tile_summary.low / tile_summary.count * 100, TISSUE_LOW_THRESH) + \\\\\\n \\\" %5d (%5.2f%%) tiles =0%% tissue\\\" % (tile_summary.none, tile_summary.none / tile_summary.count * 100)\",\n \"def descriptive_stats(array, verbose=True, label='', mean=False, plot=False):\\n if mean:\\n mean_ = np.mean(array)\\n median = np.median(array)\\n mini = np.min(array)\\n maxi = np.max(array)\\n first_qu = np.percentile(array, 25)\\n third_qu = np.percentile(array, 75)\\n\\n if verbose:\\n if mean:\\n label += 'min={:.1f} / 1st QU={:.1f} / ave={:.1f} / med={:.1f} / '\\n label += '3rd QU={:.1f} / max={:.1f}'\\n print(label.format(mini, first_qu, mean_, median, third_qu, maxi))\\n else:\\n label += 'min={:.1f} / 1st QU={:.1f} / med={:.1f} / 3rd QU={:.1f} '\\n label += '/ max={:.1f}'\\n print(label.format(mini, first_qu, median, third_qu, maxi))\\n\\n if plot:\\n boxplot(array, vert=False, meanline=mean, showfliers=True, sym='.')\\n\\n if mean:\\n return mini, first_qu, mean_, median, third_qu, maxi\\n else:\\n return mini, first_qu, median, third_qu, maxi\",\n \"def describe(self, image, mask=None):\\n histogram = cv2.calcHist([image], [0, 1, 2], mask, self.bins, [0, 256, 0, 256, 0, 256])\\n cv2.normalize(histogram, histogram)\\n\\n return histogram.flatten()\",\n \"def estimateInfo(Analysis, ImageData, diffract, print_opt=False): \\n total_images = ImageData['totalImages'] \\n\\n for key in Analysis:\\n Analysis[key]['cones'] = [1,2,3,4,5,6]\\n Analysis[key]['info'] = np.zeros(len(\\n Analysis[key]['cones']))\\n\\n for amp in ImageData['rawAmp']:\\n\\n for key in Analysis:\\n ind = len(diffract['cpd'])\\n fooInfo = info.SingleConeEntropyFunc((amp[ind] ** 2 *\\n Analysis[key]['retina']), \\n Analysis[key]['cones']) \\n Analysis[key]['info'] += fooInfo / total_images\\n\\n if print_opt == True:\\n print ' '\\n print 'Information'\\n print '------------'\\n for key in Analysis:\\n print key, ': ', Analysis[key]['info']\\n\\n return Analysis\",\n \"def print_results(self, final_table=None):\\n\\n assert self.info\\n\\n if not final_table:\\n final_table = [\\\"\\\\n\\\\n{:-^80}\\\\n\\\".format(\\\"ANALYSIS OF RESULTS\\\")]\\n\\n if not self.info.categories[\\\"integrated\\\"]:\\n final_table.append(\\\"NO IMAGES INTEGRATED!\\\")\\n else:\\n label_lens = [len(v[\\\"label\\\"]) for k, v in self.info.stats.items()]\\n max_label = int(5 * round(float(np.max(label_lens)) / 5)) + 5\\n for k, v in self.info.stats.items():\\n if k in (\\\"lres\\\", \\\"res\\\", \\\"beamX\\\", \\\"beamY\\\"):\\n continue\\n line = (\\n \\\"{: <{l}}: max = {:<6.2f} min = {:<6.2f} \\\"\\n \\\"avg = {:<6.2f} ({:<6.2f})\\\"\\n \\\"\\\".format(\\n v[\\\"label\\\"], v[\\\"max\\\"], v[\\\"min\\\"], v[\\\"mean\\\"], v[\\\"std\\\"], l=max_label\\n )\\n )\\n final_table.append(line)\\n\\n # TODO: Figure out what to do with summary charts\\n # # If more than one integrated image, plot various summary graphs\\n # if len(self.info.categories['integrated']) > 1:\\n # plot = Plotter(self.params, self.info)\\n # if self.params.analysis.summary_graphs:\\n # if ( self.params.advanced.processing_backend == 'ha14' and\\n # self.params.cctbx_ha14.grid_search.type is not None\\n # ):\\n # plot.plot_spotfinding_heatmap(write_files=True)\\n # plot.plot_res_histogram(write_files=True)\\n # med_beamX, med_beamY, pixel_size = plot.plot_beam_xy(write_files=True,\\n # return_values=True)\\n # else:\\n # with warnings.catch_warnings():\\n # # To catch any 'mean of empty slice' runtime warnings\\n # warnings.simplefilter(\\\"ignore\\\", category=RuntimeWarning)\\n # beamXY_info = plot.calculate_beam_xy()\\n # beamX, beamY = beamXY_info[:2]\\n # med_beamX = np.median(beamX)\\n # med_beamY = np.median(beamY)\\n # pixel_size = beamXY_info[-1]\\n\\n final_table.append(\\n \\\"{: <{l}}: X = {:<4.2f}, Y = {:<4.2f}\\\"\\n \\\"\\\".format(\\n \\\"Median Beam Center\\\",\\n self.info.stats[\\\"beamX\\\"][\\\"mean\\\"],\\n self.info.stats[\\\"beamY\\\"][\\\"mean\\\"],\\n l=max_label,\\n )\\n )\\n\\n # Special entry for resolution last\\n v = self.info.stats[\\\"res\\\"]\\n final_table.append(\\n \\\"{: <{l}}: low = {:<6.2f} high = {:<6.2f} \\\"\\n \\\"avg = {:<6.2f} ({:<6.2f})\\\"\\n \\\"\\\".format(\\n v[\\\"label\\\"], v[\\\"max\\\"], v[\\\"min\\\"], v[\\\"mean\\\"], v[\\\"std\\\"], l=max_label\\n )\\n )\\n\\n for item in final_table:\\n util.main_log(self.info.logfile, item, False)\\n self.info.update(final_table=final_table)\",\n \"def getstats_fromimage(path_data, label, filename):\\n path_image = get_path_image(path_data, label, filename)\\n image = np.fromfile(path_image, np.float64)\\n\\n max_ = np.amax(image)\\n min_ = np.amin(image)\\n mean = np.mean(image)\\n std = np.std(image)\\n\\n return max_, min_, mean, std\",\n \"def advancedStats():\",\n \"def compute_statistics(self):\",\n \"def image_summary(self, tag, images, step):\\n\\n img_summaries = []\\n for i, img in enumerate(images):\\n # Write the image to a string\\n try:\\n s = StringIO()\\n except:\\n s = BytesIO()\\n scipy.misc.toimage(img).save(s, format=\\\"png\\\")\\n\\n # Create an Image object\\n img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(),\\n height=img.shape[0],\\n width=img.shape[1])\\n # Create a Summary value\\n img_summaries.append(tf.Summary.Value(tag='%s/%d' % (tag, i), image=img_sum))\\n\\n # Create and write Summary\\n summary = tf.Summary(value=img_summaries)\\n self.writer.add_summary(summary, step)\",\n \"def image_summary(self, tag, images, step):\\n\\n img_summaries = []\\n for i, img in enumerate(images):\\n # Write the image to a string\\n try:\\n s = StringIO()\\n except:\\n s = BytesIO()\\n scipy.misc.toimage(img).save(s, format=\\\"png\\\")\\n\\n # Create an Image object\\n img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(),\\n height=img.shape[0],\\n width=img.shape[1])\\n # Create a Summary value\\n img_summaries.append(tf.Summary.Value(tag='%s/%d' % (tag, i), image=img_sum))\\n\\n # Create and write Summary\\n summary = tf.Summary(value=img_summaries)\\n self.writer.add_summary(summary, step)\",\n \"def print_summary(self, write_files=True):\\n\\n assert self.info\\n\\n if not self.info.categories[\\\"integrated\\\"]:\\n util.main_log(\\n self.info.logfile,\\n \\\"NO IMAGES SUCCESSFULLY PROCESSSED!\\\",\\n (not self.gui_mode),\\n )\\n return\\n\\n summary = []\\n summary.append(\\\"\\\\n\\\\n{:-^80}\\\\n\\\".format(\\\"SUMMARY\\\"))\\n categories = [\\n \\\"total\\\",\\n \\\"failed_triage\\\",\\n \\\"have_diffraction\\\",\\n \\\"failed_spotfinding\\\",\\n \\\"failed_indexing\\\",\\n \\\"failed_grid_search\\\",\\n \\\"failed_integration\\\",\\n \\\"failed_filter\\\",\\n \\\"integrated\\\",\\n ]\\n for cat in categories:\\n lst, fail, fn, _ = self.info.categories[cat]\\n path = os.path.join(self.info.int_base, fn)\\n if len(lst) > 0 or cat in (\\\"integrated\\\", \\\"diffraction\\\"):\\n summary.append(\\\"{: <20}: {}\\\".format(\\\"{} \\\".format(fail), len(lst)))\\n with open(path, \\\"w\\\") as cf:\\n for item in lst:\\n if isinstance(item, tuple) or isinstance(item, list):\\n item = \\\", \\\".join([str(i) for i in item])\\n cf.write(\\\"{}\\\\n\\\".format(item))\\n if cat == \\\"integrated\\\" and write_files:\\n if not hasattr(self, \\\"prime_data_path\\\"):\\n self.prime_data_path = path\\n\\n summary.append(\\\"\\\\n\\\\nIOTA version {0}\\\".format(iota_version))\\n summary.append(\\\"{}\\\\n\\\".format(now))\\n\\n for item in summary:\\n util.main_log(self.info.logfile, \\\"{}\\\".format(item), False)\\n self.info.update(summary=summary)\",\n \"def do_info (self, line) :\\n\\t\\tprint\\n\\t\\tprint get_info_string( self.__image )\\n\\t\\tprint\",\n \"def summarize(self):\\n # NOTE: should be moved to abstract superclass\\n failcount = len(self.mosaictrees) - len(self)\\n msg = \\\"Parsed %i mosaics from the FluoView project.\\\\n\\\\n\\\" % len(self)\\n if failcount > 0:\\n msg += (\\n \\\"\\\\n==== WARNING ====== WARNING ====\\\\n\\\\n\\\"\\n \\\"Parsing failed on %i mosaic(s). Missing files?\\\\n \\\"\\n \\\"\\\\n==== WARNING ====== WARNING ====\\\\n\\\\n\\\\n\\\" % failcount\\n )\\n for mos in self:\\n msg += \\\"Mosaic %i: \\\" % mos.supplement[\\\"index\\\"]\\n msg += \\\"%i x %i tiles, \\\" % (mos.dim[\\\"X\\\"], mos.dim[\\\"Y\\\"])\\n msg += \\\"%.1f%% overlap.\\\\n\\\" % mos.get_overlap()\\n return msg\",\n \"def summarize(self):\\n # NOTE: should be moved to abstract superclass\\n failcount = len(self.mosaictrees) - len(self)\\n msg = \\\"Parsed %i mosaics from the FluoView project.\\\\n\\\\n\\\" % len(self)\\n if failcount > 0:\\n msg += (\\n \\\"\\\\n==== WARNING ====== WARNING ====\\\\n\\\\n\\\"\\n \\\"Parsing failed on %i mosaic(s). Missing files?\\\\n \\\"\\n \\\"\\\\n==== WARNING ====== WARNING ====\\\\n\\\\n\\\\n\\\" % failcount\\n )\\n for mos in self:\\n msg += \\\"Mosaic %i: \\\" % mos.supplement[\\\"index\\\"]\\n msg += \\\"%i x %i tiles, \\\" % (mos.dim[\\\"X\\\"], mos.dim[\\\"Y\\\"])\\n msg += \\\"%.1f%% overlap.\\\\n\\\" % mos.get_overlap()\\n return msg\",\n \"def print_summary(self):\\n\\t\\t\\n\\t\\tif not self.objects:\\n\\t\\t\\tsys.stderr.write(\\\"No objects.\\\\n\\\")\\n\\t\\t\\treturn\\n\\t\\t\\n\\t\\t# Summary header data\\n\\t\\theader = (\\\"ok\\\", \\\"error\\\", \\\"zdata\\\", \\\"xdata\\\", \\\"odata\\\", \\\"ratio\\\")\\n\\t\\t\\n\\t\\t# Summary header format\\n\\t\\tfield = \\\" %11s\\\"\\n\\t\\tfmt = field * len(header)\\n\\t\\twidth = len(field % \\\"\\\") * len(header)\\n\\t\\ts_line = \\\"-\\\" * width\\n\\t\\td_line = \\\"=\\\" * width\\n\\t\\t\\n\\t\\t# Verbose header data\\n\\t\\tvheader = (\\\"ok?\\\", \\\"type\\\", \\\"id\\\", \\\"zdata\\\", \\\"xdata\\\", \\\"odata\\\", \\\"ratio\\\")\\n\\t\\t\\n\\t\\t# Verbose header format\\n\\t\\tvfmt = \\\" %3s %7s\\\" + field * 5\\n\\t\\t\\n\\t\\t# Summary data\\n\\t\\tc_ratio = None\\n\\t\\to_ok = o_error = 0\\n\\t\\tz_data_size = x_data_size = o_data_size = 0\\n\\t\\t\\n\\t\\tif self.verbose:\\n\\t\\t\\tprint vfmt % vheader\\n\\t\\t\\tprint s_line\\n\\t\\t\\n\\t\\t# Gather data from objects\\n\\t\\tfor obj in self.objects:\\n\\t\\t\\tif obj.v_all:\\n\\t\\t\\t\\to_ok += 1\\n\\t\\t\\t\\tif obj.z_data_size: z_data_size += obj.z_data_size\\n\\t\\t\\t\\tif obj.x_data_size: x_data_size += obj.x_data_size\\n\\t\\t\\t\\tif obj.o_data_size: o_data_size += obj.o_data_size\\n\\t\\t\\telse:\\n\\t\\t\\t\\to_error += 1\\n\\t\\t\\t\\n\\t\\t\\tif self.verbose:\\n\\t\\t\\t\\tv_c_ratio = None\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t# Calculate compression if possible\\n\\t\\t\\t\\tif obj.z_data_size and obj.x_data_size:\\n\\t\\t\\t\\t\\tv_c_ratio = str(100 * obj.z_data_size / obj.x_data_size) + \\\"%\\\"\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t# Build verbose data\\n\\t\\t\\t\\tv_data = (\\n\\t\\t\\t\\t\\t\\\"[Y]\\\" if obj.v_all else \\\"[N]\\\",\\n\\t\\t\\t\\t\\tobj.o_data_type or \\\"N/A\\\",\\n\\t\\t\\t\\t\\tobj.id[:10],\\n\\t\\t\\t\\t\\tobj.z_data_size or \\\"N/A\\\",\\n\\t\\t\\t\\t\\tobj.x_data_size or \\\"N/A\\\",\\n\\t\\t\\t\\t\\tobj.o_data_size or \\\"N/A\\\",\\n\\t\\t\\t\\t\\tv_c_ratio or \\\"N/A\\\"\\n\\t\\t\\t\\t)\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t# Print verbose data\\n\\t\\t\\t\\tprint vfmt % v_data\\n\\t\\t\\n\\t\\tif self.verbose:\\n\\t\\t\\tprint d_line\\n\\t\\t\\n\\t\\t# Calculate compression ratio\\n\\t\\tif z_data_size and x_data_size:\\n\\t\\t\\tc_ratio = str(100 * z_data_size / x_data_size) + \\\"%\\\"\\n\\t\\t\\n\\t\\t# Print summary\\n\\t\\tprint fmt % header\\n\\t\\tprint s_line\\n\\t\\tprint fmt % (o_ok, o_error, z_data_size, x_data_size, o_data_size, c_ratio)\",\n \"def main():\\n base_dir = '/home/sjimenez/imagenes_prueba'\\n out_dir = '/home/sjimenez/easy_analysis'\\n for _, _, files in os.walk(base_dir, topdown=False):\\n for f in files:\\n print('--------- {} ---------'.format(f))\\n act_dir = osp.join(base_dir, f)\\n act_im = cv2.imread(act_dir)\\n if act_im is not None:\\n get_image_stats(act_im, out_dir, f)\\n else:\\n print('Not able to open the image')\",\n \"def summary(self):\\n name = 'name : ' + self.get_name()\\n damage = 'damage : ' + str(self.get_damage())\\n ammos = 'ammo : ' + str(self.get_ammos())\\n owner = 'owner : ' + str(self.get_owner())\\n return '\\\\n'.join([name, damage, ammos, owner])\",\n \"def _stats(self):\\n return (\\\"size = \\\" + str(self.size())\\n + \\\"; height = \\\" + str(self.height()))\",\n \"def print_stats():\\n if spritegroup_stats[0] > 0:\\n generic.print_info(\\\"Concurrent spritegroups: {}/{} ({})\\\".format(spritegroup_stats[0], total_action2_ids, str(spritegroup_stats[1])))\\n if a2register_stats[0] > 0:\\n generic.print_info(\\\"Concurrent Action2 registers: {}/{} ({})\\\".format(a2register_stats[0], total_tmp_locations, str(a2register_stats[1])))\",\n \"def summary(self):\\n raise NotImplementedError\",\n \"def describe():\",\n \"def describe(self):\\n\\n print(\\\"Correlation length: {0}\\\".format(self.cl))\\n print(\\\"icoordchange: {0}\\\".format(self.icoordchange))\\n print(\\\"ispec: {0}\\\".format(self.ispec))\\n print(\\\"ireg: {0}\\\".format(self.ireg))\\n print(\\\"Domain: x-axis: from {0} to {1} with {2} steps of {3}\\\".format(self.xori, self.xend,\\n self.nx, self.dx))\\n print(\\\"Domain: y-axis: from {0} to {1} with {2} steps of {3}\\\".format(self.yori, self.yend,\\n self.ny, self.dy))\\n print(\\\"Exclusion value: {0}\\\".format(self.valex))\\n print(\\\"Signal-to-noise ratio: {0}\\\".format(self.snr))\\n print(\\\"Variance of the background field: {0}\\\".format(self.varbak))\",\n \"def statistics(img1_blobs, img2_blobs, matches):\\n\\tstatistics = {}\\n\\n\\tstatistics['#Interest Points in img1'] = len(img1_blobs)\\n\\tstatistics['#Interest Points in img2'] = len(img2_blobs)\\n\\tstatistics['Accepted Matches'] = len(matches)\\n\\tdissimilarity = [match[2] for match in matches]\\n\\tstatistics['Mean of accepted matches'] = sum(dissimilarity)/len(dissimilarity)\\n\\tstatistics['SD of accepted matches'] = np.std(dissimilarity)\\n\\treturn statistics\",\n \"def summary(self):\\n print('est0: %s (%s) shape: %s' % (str(self.est0.name),\\\\\\n str(self.est0.type_name),str(self.shape0)))\\n print('est1: %s (%s) shape: %s' % (str(self.est1.name),\\\\\\n str(self.est1.type_name),str(self.shape1)))\",\n \"def detail(self):\\n return self.uniform(\\\"detail\\\",\\n self.img_scale * .05,\\n self.img_scale * .2)\",\n \"def analyze(self):\\n try:\\n self.options[self.multi_image][1]()\\n except:\\n raise Exception(\\\"Multi Image Option not defined.\\\")\\n\\n self.image = self.data / self.exposure\\n\\n background = self.min_val = np.min(self.image[:511,:511])\\n self.max_val = np.max(self.image[:511,:511])\\n # stats.mode returns modal value = value that occours most often\\n #background = stats.mode(im[:50,:50].ravel())[0][0]\\n\\n intensity = self.image.sum() - background*np.size(self.image)\\n\\n #results.append((self.index, intensity, background))\\n self.index =+ 1\",\n \"def info(self):\\n\\n print(\\\"pupil file =\\\", self.pupil_file)\\n print(\\\"phase file =\\\", self.phase_file)\\n print(\\\"wavelengths and weights =\\\")\\n for i in range(len(self.filter[0])):\\n print(\\\" %10.5f %6.4f\\\" % (self.filter[0][i], self.filter[1][i]))\\n print(\\\"pupil diameter (meters) =\\\", self.D)\\n if self.oversample == 2:\\n print(\\\"oversampling factor = 2 (Nyquist sampling)\\\")\\n else:\\n r = float(self.oversample) / 2.\\n print(\\\"oversampling factor = %d (%g * Nyquist sampling)\\\" % \\\\\\n (self.oversample, r))\\n if self.type == SINGLE_PREC:\\n print(\\\"computations will use single precision\\\")\\n else:\\n print(\\\"computations will use double precision\\\")\\n print(\\\"size of output image =\\\", self.output_size)\\n if self.cdelt is not None:\\n print(\\\"output pixel size (arcsec) =\\\", self.cdelt / ARCSECtoDEGREES)\\n if self.output_written:\\n print(\\\"The computed PSF has been written to the output file.\\\")\\n else:\\n print(\\\"The output file has not been written yet.\\\")\",\n \"def _printout_images_info(design_path):\\r\\n _max_pic_number = 8\\r\\n images = dict()\\r\\n for foo in os.listdir(design_path):\\r\\n abs_foo = os.path.join(design_path, foo)\\r\\n if os.path.isfile(abs_foo):\\r\\n continue\\r\\n if foo.endswith(\\\"Images\\\"):\\r\\n images.setdefault(foo, list())\\r\\n for bar in os.listdir(abs_foo):\\r\\n if bar.endswith(\\\".png\\\"):\\r\\n images[foo].append(bar)\\r\\n if images:\\r\\n for k, v in list(images.items()):\\r\\n v.sort(key=sort_by_num, reverse=True)\\r\\n nine_images = dict()\\r\\n images_number = 0\\r\\n for i in range(0, 10):\\r\\n if images_number > _max_pic_number:\\r\\n break\\r\\n for k, v in list(images.items()):\\r\\n nine_images.setdefault(k, list())\\r\\n try:\\r\\n nine_images[k].append(v[i])\\r\\n images_number += 1\\r\\n if images_number > _max_pic_number:\\r\\n break\\r\\n except IndexError:\\r\\n continue\\r\\n say_it(\\\"\\\")\\r\\n say_it(\\\"Images Number: {}\\\".format(images_number))\\r\\n ii = 1\\r\\n for kk, vv in list(nine_images.items()):\\r\\n for foo in vv:\\r\\n say_it(\\\"-PNG{}: {}/{}\\\".format(ii, kk, foo))\\r\\n ii += 1\",\n \"def tf_summary_image(image, boxes, name='image'):\\n image = tf.expand_dims(image, 0)\\n boxes = tf.expand_dims(boxes, 0)\\n image_with_box = tf.image.draw_bounding_boxes(image, boxes)\\n tf.summary.image(name, image_with_box)\",\n \"def printImage(imageObject):\\n # TODO\\n pass\",\n \"def print_statistics(session, batch_image, batch_label, cost, accuracy, type=\\\"VALIDATION\\\"):\\n loss = session.run(\\n cost,\\n feed_dict={\\n x: batch_image,\\n y: batch_label,\\n keep_prob: 1.0\\n }\\n )\\n accuracy = session.run(\\n accuracy,\\n feed_dict={\\n x: batch_image,\\n y: batch_label,\\n keep_prob: 1.0\\n }\\n )\\n print(\\\"{} :: Loss = {} ; Accuracy = {} \\\".format(type, loss, accuracy))\\n return loss, accuracy\",\n \"def print_file_distribution(num_imgs_total, num_imgs_train, num_imgs_val, num_imgs_test):\\n print('Total images: ', num_imgs_total)\\n print('Training: ', num_imgs_train)\\n print('Validation: ', num_imgs_val)\\n print('Testing: ', num_imgs_test)\",\n \"def info(self):\\n ss = \\\"\\\\nSummary EffectiveArea2D info\\\\n\\\"\\n ss += \\\"----------------\\\\n\\\"\\n # Summarise data members\\n ss += array_stats_str(self.energy, 'energy')\\n ss += array_stats_str(self.offset, 'offset')\\n ss += array_stats_str(self.eff_area, 'dispersion')\\n\\n return ss\",\n \"def main():\\n \\n # for inserting other images, add tem to /input folder and list them here\\n images = (\\n 'image-0',\\n 'image-1',\\n 'image-2'\\n )\\n\\n for image_name in images:\\n print(image_name, \\\"image:\\\")\\n\\n image = open_image(image_name)\\n display_image(image, \\\"Original input \\\" + image_name)\\n\\n grayscale_v = transform_colors(image)\\n display_image(grayscale_v[:,:,0], \\\"Grayscale \\\" + image_name)\\n save_image(image_name + \\\"-grayscale\\\", grayscale_v[:,:,0])\\n\\n contours_v, contours = get_contours(grayscale_v)\\n display_image(contours_v, \\\"Contours \\\" + image_name)\\n save_image(image_name + \\\"-contours\\\", contours_v)\\n\\n labeled_img, areas = get_measures(image, contours[1:])\\n display_image(labeled_img, \\\"Labeled \\\" + image_name)\\n save_image(image_name + \\\"-labeled\\\", labeled_img)\\n\\n areas_histogram(areas, image_name)\",\n \"def summary(self):\\n\\n print(\\\"input label:\\\", self.__input_label)\\n print(\\\"target label:\\\", self.__target_label)\\n print(\\\"denoising label:\\\", self.denoising_label)\\n print(\\\"contains a successful DE:\\\", self.is_successful())\",\n \"def img_series_stats(image_ccd_lst,plots_path,obsdate):\\n median_count = []\\n mean_count = []\\n \\n source_hdu = CCDData(image_ccd_lst[0],unit='adu')\\n source_image_data = source_hdu.data.astype(float) \\n source_image_hdr = source_hdu.header\\n target_name = source_image_hdr['FIELD'].strip(' ')\\n exptime = source_image_hdr['EXPTIME']\\n chip_num = source_image_hdr['CHIP']\\n \\n for a_file in image_ccd_lst:\\n hdu = CCDData(a_file,unit='adu')\\n image_data = hdu.data.astype(float) \\n image_hdr = hdu.header\\n \\n median_count.append(np.median(a_file))\\n mean_count.append(np.mean(a_file))\\n \\n min_count_for_median = np.min(median_count)\\n min_count_for_mean = np.min(mean_count)\\n max_count_for_median = np.max(median_count)\\n max_count_for_mean = np.max(mean_count)\\n \\n plt.figure()\\n plt.plot(mean_count, label='mean',color=\\\"palevioletred\\\")\\n plt.axhline(y=min_count_for_mean,linestyle='-',linewidth=0.5,color='blue',label='min mean {:.2f}'.format(min_count_for_mean),alpha=1)\\n plt.axhline(y=max_count_for_mean,linestyle='-',linewidth=0.5,color='blue',label='max mean {:.2f}'.format(max_count_for_mean),alpha=1)\\n plt.xlabel('Image number')\\n plt.ylabel('Count (ADU)')\\n plt.title('Mean pixel value for aligned images')\\n plt.legend()\\n plt.grid()\\n plt.savefig(plots_path/\\\"{}-{}-{}-aligned_stats_mean.jpg\\\".format(obsdate,\\n target_name,\\n exptime,chip_num),\\n dpi=900)\\n plt.show()\\n\\n plt.figure()\\n plt.plot(median_count, label='median',color=\\\"darkviolet\\\")\\n plt.axhline(y=min_count_for_median,linestyle='-',linewidth=0.5,color='red',label='min median {:.2f}'.format(min_count_for_median),alpha=1)\\n plt.axhline(y=max_count_for_median,linestyle='-',linewidth=0.5,color='red',label='max median {:.2f}'.format(max_count_for_median),alpha=1) \\n plt.xlabel('Image number')\\n plt.ylabel('Count (ADU)')\\n plt.title('Median pixel value for aligned images')\\n plt.legend()\\n plt.grid()\\n plt.savefig(plots_path/\\\"{}-{}-{}-aligned_stats_median.jpg\\\".format(obsdate,\\n target_name,\\n exptime,chip_num),\\n dpi=900)\\n plt.show()\",\n \"def identify_image(im):\\n score_cures = np.mean(im[1025:1065, 1130:1180, 0])\\n score_ingredients = np.mean(im[1025:1065, 675:720, 0])\\n if score_cures < 177.5:\\n return 'cures'\\n if score_ingredients < 177.5:\\n return 'ingredients'\\n else:\\n return 'other'\",\n \"def statistics(self, **kwargs) -> None:\\n print(\\n tabulate.tabulate(\\n list(self._iter_statistics(**kwargs)),\\n headers=[\\\"path\\\", \\\"type\\\", \\\"occurences\\\", \\\"%\\\"],\\n floatfmt=\\\".3f\\\",\\n )\\n )\",\n \"def summary(self):\\n self.tiles.refreshnames()\\n self.glues.refreshnames()\\n # self.check_consistent()\\n info = {\\n \\\"ntiles\\\": len(self.tiles),\\n \\\"nrt\\\": len([x for x in self.tiles if not x.is_fake]),\\n \\\"nft\\\": len([x for x in self.tiles if x.is_fake]),\\n \\\"nends\\\": len(self.glues),\\n \\\"ntends\\\": len(self.tiles.glues_from_tiles()),\\n \\\"tns\\\": \\\" \\\".join(x.name for x in self.tiles if x.name),\\n \\\"ens\\\": \\\" \\\".join(x.name for x in self.glues if x.name)\\n # if (\\\"info\\\" in self.keys() and \\\"name\\\" in self[\\\"info\\\"].keys())\\n # else \\\"\\\",\\n }\\n tun = sum(1 for x in self.tiles if x.name is None)\\n if tun > 0:\\n info[\\\"tns\\\"] += \\\" ({} unnamed)\\\".format(tun)\\n eun = sum(1 for x in self.glues if x.name is None)\\n if eun > 0:\\n info[\\\"ens\\\"] += \\\" ({} unnamed)\\\".format(eun)\\n if info[\\\"nft\\\"] > 0:\\n info[\\\"nft\\\"] = \\\" (+ {} fake)\\\".format(info[\\\"nft\\\"])\\n else:\\n info[\\\"nft\\\"] = \\\"\\\"\\n return \\\"TileSet: {nrt} tiles{nft}, {nends} ends, {ntends} ends in tiles.\\\\nTiles: {tns}\\\\nEnds: {ens}\\\".format(\\n **info\\n )\",\n \"def printSummary(self):\\n pass\",\n \"def print_summary(metrics_list, labels_list):\\n for metric, name in zip(metrics_list, labels_list):\\n print('*' * 108)\\n print(name)\\n mean_inc_acc = []\\n for i in range(metric.shape[0]):\\n print('\\\\t', end='')\\n for j in range(metric.shape[1]):\\n print('{:5.2f}% '.format(100 * metric[i, j]), end='')\\n if np.trace(metric) == 0.0:\\n if i > 0:\\n avg = 100 * metric[i, :i].mean()\\n mean_inc_acc += [avg]\\n print('\\\\tAvg.:{:5.2f}% '.format(avg), end='')\\n else:\\n avg = 100 * metric[i, :i + 1].mean()\\n mean_inc_acc += [avg]\\n print('\\\\tAvg.:{:5.2f}% '.format(avg), end='')\\n print()\\n print()\\n\\n # Computing AIA across all incremental states (thus excluding the first non-incremental state)\\n print('\\\\tMean Incremental Acc.: {:5.2f}%'.format(np.mean(mean_inc_acc[1:])))\\n print('*' * 108)\",\n \"def summary(self):\\n name = 'name : ' + self.get_name()\\n description = 'description : ' + self.get_description()\\n agility = 'agility : ' + str(self.get_agility())\\n strength = 'strength : ' + str(self.get_strength())\\n health_points = 'health_points : ' + str(self.get_health_points())\\n summary = '\\\\n'.join([name, description, agility, strength, health_points])\\n if self.take_weapon():\\n summary += self.take_weapon().summary()\\n return summary\",\n \"def print_stats(ds):\\n print(\\\"Dataset Name: \\\" + ds.name)\\n print(\\\"Dataset Mode: \\\" + ds.mode)\\n print(\\\"Band Count: \\\" + str(ds.count))\\n print(\\\"Dataset Width: \\\" + str(ds.width))\\n print(\\\"Dataset Height: \\\" + str(ds.height))\\n print(\\\"Dataset Bounds: \\\", ds.bounds)\\n print(\\\"Dataset Transform: \\\", ds.transform)\\n ul = ds.transform * (0, 0)\\n print(\\\"Upper Left Corner: \\\", ul)\\n lr = ds.transform * (ds.width, ds.height)\\n print(\\\"Lower Right Corner: \\\", lr)\\n {i: dtype for i, dtype in zip(ds.indexes, ds.dtypes)}\",\n \"def print_summary(self):\\n self.model.summary()\",\n \"def summary(self):\\n return self.pfm\",\n \"def generate_image_info(path):\\n file_types = ['*.png', '*.jpg', '*.gif']\\n for file_type in file_types:\\n for img_path in glob.glob(path + file_type):\\n img = Image.open(img_path)\\n img_name = img_path.split('/')[-1].split('.')[0]\\n with open(path + 'resolution.txt', 'a') as file:\\n file.write(img_name + ' ' + str(img.size[0]) +\\n ' ' + str(img.size[1]) + '\\\\n')\",\n \"def show_stats(self):\\n print(\\\"\\\\nName: \\\" + self.name)\\n print(\\\"Element Type: \\\" + self.element)\\n print(\\\"Health: \\\" + str(self.current_health) + \\\" / \\\" + str(self.max_health))\\n print(\\\"Speed: \\\" + str(self.speed))\",\n \"def calculate_stats(images, global_normalization=True):\\n if global_normalization:\\n # flatten first since the images might not be the same size\\n flat = np.concatenate(\\n [img.ravel() for img in images]\\n )\\n pmin, pmax, mean, std = np.percentile(flat, 1), np.percentile(flat, 99.6), np.mean(flat), np.std(flat)\\n else:\\n pmin, pmax, mean, std = None, None, None, None\\n\\n return {\\n 'pmin': pmin,\\n 'pmax': pmax,\\n 'mean': mean,\\n 'std': std\\n }\",\n \"def print_summary_stats(self) -> None:\\n print(\\\"Number of Users: {}\\\".format(len(self.all_users)))\\n print(\\\"Number of Utterances: {}\\\".format(len(self.utterances)))\\n print(\\\"Number of Conversations: {}\\\".format(len(self.conversations)))\",\n \"def print_summary(stim_table):\\n print(\\n '{:<20}{:>15}{:>15}\\\\n'.format('Colname', 'No. conditions', 'Mean N/cond')\\n )\\n for colname in stim_table.columns:\\n conditions, occurrences = np.unique(\\n np.nan_to_num(stim_table[colname]), return_counts = True\\n )\\n print(\\n '{:<20}{:>15}{:>15.1f}'.format(\\n colname, len(conditions), np.mean(occurrences)\\n )\\n )\",\n \"def show_summary(self) -> None:\\n all_averages = []\\n\\n for i in self.album_statistics.values():\\n try:\\n all_averages.append(i['avg'])\\n except (TypeError, ValueError):\\n pass\\n # print(all_averages)\\n try:\\n final_average = math.ceil(np.mean(all_averages))\\n except ValueError:\\n click.echo(\\n 'Oops! https://lyrics.ovh couldn\\\\'t find any lyrics across any'\\n ' album. This is caused by inconsistent Artist names from'\\n ' Musicbrainz and lyrics.ovh. Try another artist.'\\n )\\n raise (SystemExit)\\n output = BeautifulTable(max_width=200)\\n output.set_style(BeautifulTable.STYLE_BOX_ROUNDED)\\n output.column_headers = [\\n 'Average number of words in tracks across all albums\\\\n'\\n f'for {self.artist}'\\n ]\\n output.append_row([final_average])\\n click.echo(output)\\n\\n return self\",\n \"def summary(self, yname=None, xname=None, title=None, alpha=.05):\\n # TODO: Make this raise upstream instead of just \\\"pass\\\"\\n raise NotImplementedError # pragma: no cover\\n # TODO: move the GenericLikelihoodModelResults implementation here?\",\n \"def stats(self):\",\n \"def visual_image(img, annos, save_path, ratio=None, height=None, width=None, name=None, score_threshold=0.01):\\n # annos: list type, in which all the element is dict\\n h, w = img.shape[0], img.shape[1]\\n if height is not None and width is not None and (height != h or width != w):\\n img, annos = resize_image(img, annos, width, height)\\n elif ratio not in (None, 1):\\n img, annos = resize_image(img, annos, w * ratio, h * ratio)\\n\\n h, w = img.shape[0], img.shape[1]\\n num_objects = len(annos)\\n num = 0\\n\\n def define_color(pair):\\n \\\"\\\"\\\"define line color\\\"\\\"\\\"\\n left_part = [0, 1, 3, 5, 7, 9, 11, 13, 15]\\n right_part = [0, 2, 4, 6, 8, 10, 12, 14, 16]\\n if pair[0] in left_part and pair[1] in left_part:\\n color = (255, 0, 0)\\n elif pair[0] in right_part and pair[1] in right_part:\\n color = (0, 0, 255)\\n else:\\n color = (139, 0, 255)\\n return color\\n\\n def visible(a, w, h):\\n return a[0] >= 0 and a[0] < w and a[1] >= 0 and a[1] < h\\n\\n for i in range(num_objects):\\n ann = annos[i]\\n bbox = coco_box_to_bbox(ann['bbox'])\\n if \\\"score\\\" in ann and (ann[\\\"score\\\"] >= score_threshold or num == 0):\\n num += 1\\n txt = (\\\"p\\\" + \\\"{:.2f}\\\".format(ann[\\\"score\\\"]))\\n cv2.putText(img, txt, (bbox[0], bbox[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)\\n\\n ct = (int((bbox[0] + bbox[2]) / 2), int((bbox[1] + bbox[3]) / 2))\\n cv2.circle(img, ct, 2, (0, 255, 0), thickness=-1, lineType=cv2.FILLED)\\n bbox = np.array(bbox, dtype=np.int32).tolist()\\n cv2.rectangle(img, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2)\\n\\n keypoints = ann[\\\"keypoints\\\"]\\n keypoints = np.array(keypoints, dtype=np.int32).reshape(_NUM_JOINTS, 3).tolist()\\n\\n for pair in data_cfg.edges:\\n partA = pair[0]\\n partB = pair[1]\\n color = define_color(pair)\\n p_a = tuple(keypoints[partA][:2])\\n p_b = tuple(keypoints[partB][:2])\\n mask_a = keypoints[partA][2]\\n mask_b = keypoints[partB][2]\\n if (visible(p_a, w, h) and visible(p_b, w, h) and mask_a * mask_b > 0):\\n cv2.line(img, p_a, p_b, color, 2)\\n cv2.circle(img, p_a, 3, color, thickness=-1, lineType=cv2.FILLED)\\n cv2.circle(img, p_b, 3, color, thickness=-1, lineType=cv2.FILLED)\\n\\n img_id = annos[0][\\\"image_id\\\"] if annos and \\\"image_id\\\" in annos[0] else random.randint(0, 9999999)\\n image_name = \\\"cv_image_\\\" + str(img_id) + \\\".png\\\" if name is None else \\\"cv_image_\\\" + str(img_id) + name + \\\".png\\\"\\n cv2.imwrite(\\\"{}/{}\\\".format(save_path, image_name), img)\",\n \"def summary(self, printed=True):\\n raise NotImplementedError\",\n \"def create_summary(image_summaries, scalar_summaries):\\n for key, value in image_summaries.items():\\n tf.summary.image(key, unscale(value))\\n for key, value in scalar_summaries.items():\\n tf.summary.scalar(key, value)\\n return tf.summary.merge_all()\",\n \"def summary(self) -> str:\\n pass\",\n \"def imdisplay(filename, representation):\\n\\n image = read_image(filename, representation)\\n plt.imshow(image, cmap=\\\"gray\\\")\\n plt.show()\",\n \"def cntInfo(img, cnt):\\n\\tpts = extremePoints(cnt)\\n\\troi = crop(img, pts[\\\"L\\\"][0], pts[\\\"T\\\"][1], pts[\\\"R\\\"][0], pts[\\\"B\\\"][1])\\n\\tm = minMaxLoc(roi)\\n\\tm[\\\"minLoc\\\"] = (m[\\\"minLoc\\\"][0] + pts[\\\"L\\\"][0], m[\\\"minLoc\\\"][1] + pts[\\\"T\\\"][1])\\n\\tm[\\\"maxLoc\\\"] = (m[\\\"maxLoc\\\"][0] + pts[\\\"L\\\"][0], m[\\\"maxLoc\\\"][1] + pts[\\\"T\\\"][1])\\n\\tcross = abs(pts[\\\"L\\\"][0] - pts[\\\"R\\\"][0])\\n\\theight = abs(pts[\\\"T\\\"][1] - pts[\\\"B\\\"][1])\\n\\tcent = centroid(cnt)\\n\\tangle = orientation(cnt)\\n\\tareaVal = area(cnt)\\n\\tper = perimeter(cnt)\\n\\tar = aspectRatio(cnt)\\n\\text = extent(cnt)\\n\\tsold = solidity(cnt)\\n\\teqD = equivalentDiameter(cnt)\\n\\tme = meanVal(grayscale(roi))\\n\\tsu = sumPixel(grayscale(roi))\\n\\td = {\\\"sum intensity\\\":su, \\\"mean intensity\\\":me, \\\"area\\\":areaVal, \\\"perimeter\\\":per, \\\"aspect ratio\\\":ar, \\\"extent\\\":ext,\\\"solidity\\\":sold, \\\"equivalent diameter\\\":eqD, \\\"width\\\": cross, \\\"height\\\" : height, \\\"centroid\\\" : cent, \\\"extrema\\\" : pts, \\\"min\\\":m[\\\"minLoc\\\"], \\\"max\\\":m[\\\"maxLoc\\\"], \\\"orientation\\\" : angle}\\n\\treturn d\",\n \"def summary_images(\\n cover,\\n encoded,\\n transmitted_encoded,\\n transmitted_cover,\\n step,\\n transform_fn=None):\\n\\n imgs = prep_imgs_to_plot(\\n cover, encoded, transmitted_encoded, transmitted_cover, transform_fn)\\n\\n images_to_plot = imgs['images']\\n\\n names_to_plot = imgs['names']\\n\\n descriptions_to_plot = imgs['descriptions']\\n\\n for i, name in enumerate(names_to_plot):\\n tf.summary.image(\\n name=name,\\n data=images_to_plot[i],\\n step=step,\\n max_outputs=6,\\n description=descriptions_to_plot[i]\\n )\",\n \"def populate_image_stats(self, image):\\n ti = image\\n image_data = ti.data\\n if not ti.data:\\n return ti\\n ti.size = len(image_data)\\n try:\\n with connect(Blobby) as c:\\n ti.shahash = c.get_data_bhash(image_data)\\n except o.Exception, ex:\\n raise o.Exception('oException getting shahash: %s' % ex.msg)\\n except Exception, ex:\\n raise o.Exception('Exception getting shahash: %s' % ex)\\n\\n try:\\n b = StringIO(image_data)\\n img = Image.open(b)\\n except Exception, ex:\\n raise o.Exception('Exception getting PIL img: %s' % ex)\\n try:\\n ti.xdim, ti.ydim = img.size\\n except Exception, ex:\\n raise o.Exception('Exception getting dimensions: %s' % ex)\\n try:\\n ti.vhash = str(average_hash(img))\\n except Exception, ex:\\n raise o.Exception('Exception getting vhash: %s' % ex)\\n\\n return ti\",\n \"def _describe(self) -> Dict[str, Any]:\",\n \"def display(self, image):\\n raise NotImplementedError()\",\n \"def print_summary(self):\\n #outcomes = self.get_outcomes()\\n #passes = 'Passes: %i' % sum(1 for outcome in outcomes if outcome == Result.PASS)\\n #untested = 'Untested: %i' % sum(1 for outcome in outcomes if outcome == Result.UNTESTED)\\n #errors = 'Errors: %i' % sum(1 for outcome in outcomes if outcome == Result.ERROR)\\n #fails = 'Fails: %i' % sum(1 for outcome in outcomes if outcome == Result.FAIL)\\n print('')\\n print ('Passes: %i' % self.get_pass_count())\\n print ('Fails: %i' % self.get_fail_count())\\n print ('Errors: %i' % self.get_error_count())\\n print ('Untested: %i' % self.get_untested_count())\\n print ('Skipped: %i' % self.get_skipped_count())\",\n \"def print_summary_metrics(lst):\\n print('*' * 50)\\n print(' ' * 16 + 'Summary statistics')\\n print('*' * 50)\\n print('mean: {} | median: {} | mode: {}'.format(get_mean(lst),\\n get_median(lst),\\n get_mode(lst)))\\n print('range: {} | IQR: {}'.format(get_range(list_nums),\\n get_IQR(list_nums)))\\n print('\\\\n')\\n print('original list: \\\\n {}'.format(lst))\\n print('sorted list: \\\\n {}'.format(sorted(lst)))\\n print('List without outliers: \\\\n {}'.format(\\n remove_outliers(list_nums)))\",\n \"def printInfo(matrix):\\n\\n print(\\\"Groups:\\\")\\n for group in matrix.matrix.group_labels:\\n print(\\\"\\\\t{0}\\\".format(group))\\n\\n print(\\\"Samples:\\\")\\n for sample in matrix.matrix.sample_labels:\\n print(\\\"\\\\t{0}\\\".format(sample))\",\n \"def calculates_results_stats(results_dic): \\n # Creates empty dictionary for results_stats_dic\\n results_stats_dic = dict()\\n \\n # Sets all counters to initial values of zero so that they can \\n # be incremented while processing through the images in results_dic \\n results_stats_dic['n_dogs_img'] = 0\\n results_stats_dic['n_match'] = 0\\n results_stats_dic['n_correct_dogs'] = 0\\n results_stats_dic['n_correct_notdogs'] = 0\\n results_stats_dic['n_correct_breed'] = 0\\n \\n # process through the results dictionary\\n for key in results_dic:\\n \\n # Labels Match Exactly\\n if results_dic[key][2] == 1:\\n results_stats_dic['n_match'] += 1\\n\\n # TODO: 5a. REPLACE pass with CODE that counts how many pet images of\\n # dogs had their breed correctly classified. This happens \\n # when the pet image label indicates the image is-a-dog AND \\n # the pet image label and the classifier label match. You \\n # will need to write a conditional statement that determines\\n # when the dog breed is correctly classified and then \\n # increments 'n_correct_breed' by 1. Recall 'n_correct_breed' \\n # is a key in the results_stats_dic dictionary with it's value \\n # representing the number of correctly classified dog breeds.\\n # \\n # Pet Image Label is a Dog AND Labels match- counts Correct Breed\\n if results_dic[key][3] == 1 and results_dic[key][2] == 1:\\n results_stats_dic['n_correct_breed'] += 1\\n \\n # Pet Image Label is a Dog - counts number of dog images\\n if results_dic[key][3] == 1:\\n results_stats_dic['n_dogs_img'] += 1\\n \\n # Classifier classifies image as Dog (& pet image is a dog)\\n # counts number of correct dog classifications\\n if results_dic[key][4] == 1:\\n results_stats_dic['n_correct_dogs'] += 1\\n\\n # TODO: 5b. REPLACE pass with CODE that counts how many pet images \\n # that are NOT dogs were correctly classified. This happens \\n # when the pet image label indicates the image is-NOT-a-dog \\n # AND the classifier label indicates the images is-NOT-a-dog.\\n # You will need to write a conditional statement that \\n # determines when the classifier label indicates the image \\n # is-NOT-a-dog and then increments 'n_correct_notdogs' by 1. \\n # Recall the 'else:' above 'pass' already indicates that the \\n # pet image label indicates the image is-NOT-a-dog and \\n # 'n_correct_notdogs' is a key in the results_stats_dic dictionary \\n # with it's value representing the number of correctly \\n # classified NOT-a-dog images.\\n # \\n # Pet Image Label is NOT a Dog\\n else:\\n # Classifier classifies image as NOT a Dog(& pet image isn't a dog)\\n # counts number of correct NOT dog clasifications.\\n if results_dic[key][3] == 0 and results_dic[key][4] == 0:\\n results_stats_dic['n_correct_notdogs'] += 1\\n\\n\\n # Calculates run statistics (counts & percentages) below that are calculated\\n # using the counters from above.\\n\\n # calculates number of total images\\n results_stats_dic['n_images'] = len(results_dic)\\n\\n # calculates number of not-a-dog images using - images & dog images counts\\n results_stats_dic['n_notdogs_img'] = (results_stats_dic['n_images'] - \\n results_stats_dic['n_dogs_img']) \\n\\n # TODO: 5c. REPLACE zero(0.0) with CODE that calculates the % of correctly\\n # matched images. Recall that this can be calculated by the\\n # number of correctly matched images ('n_match') divided by the \\n # number of images('n_images'). This result will need to be \\n # multiplied by 100.0 to provide the percentage.\\n # \\n # Calculates % correct for matches\\n results_stats_dic['pct_match'] = (results_stats_dic['n_match'] / results_stats_dic['n_images']) * 100\\n\\n # TODO: 5d. REPLACE zero(0.0) with CODE that calculates the % of correctly\\n # classified dog images. Recall that this can be calculated by \\n # the number of correctly classified dog images('n_correct_dogs')\\n # divided by the number of dog images('n_dogs_img'). This result \\n # will need to be multiplied by 100.0 to provide the percentage.\\n # \\n # Calculates % correct dogs\\n results_stats_dic['pct_correct_dogs'] = (results_stats_dic['n_correct_dogs'] / results_stats_dic['n_dogs_img']) * 100\\n\\n # TODO: 5e. REPLACE zero(0.0) with CODE that calculates the % of correctly\\n # classified breeds of dogs. Recall that this can be calculated \\n # by the number of correctly classified breeds of dog('n_correct_breed') \\n # divided by the number of dog images('n_dogs_img'). This result \\n # will need to be multiplied by 100.0 to provide the percentage.\\n # \\n # Calculates % correct breed of dog\\n results_stats_dic['pct_correct_breed'] = (results_stats_dic['n_correct_breed'] / results_stats_dic['n_dogs_img']) * 100\\n\\n # Calculates % correct not-a-dog images\\n # Uses conditional statement for when no 'not a dog' images were submitted \\n if results_stats_dic['n_notdogs_img'] > 0:\\n results_stats_dic['pct_correct_notdogs'] = (results_stats_dic['n_correct_notdogs'] /\\n results_stats_dic['n_notdogs_img']) * 100.0\\n else:\\n results_stats_dic['pct_correct_notdogs'] = 0.0\\n\\n \\n # TODO 5f. REPLACE None with the results_stats_dic dictionary that you \\n # created with this function \\n return results_stats_dic\",\n \"def print_info(df):\\n\\n # Data statistics\\n # Number of total samples\\n print('There are {n_samples} samples in total.'.format(n_samples=len(list(df.index.get_level_values(0).unique()))))\\n\\n # Count the different types of labels\\n unique = df['label'].unique()\\n count = []\\n\\n for label in unique:\\n count.append(len(df.index.get_level_values(0)[df['label'] == label].unique()))\\n\\n count_dict = {unique[i]: count[i] for i in range(len(unique))}\\n count_dict_percentage = {\\n unique[i]: np.round(count[i] / len(list(df.index.get_level_values(0).unique())), decimals=2)\\n for i in range(len(unique))}\\n\\n print('The types and counts of different labels : \\\\n {count_dict}'.format(count_dict=count_dict))\\n print('The types and counts of different labels as percentage of the total data'\\n ' : \\\\n {count_dict}'.format(count_dict=count_dict_percentage))\",\n \"def testStatsZebra(self):\\n image2 = self.image.Factory(self.image, True)\\n #\\n # Add 1 to every other row, so the variance is 1/4\\n #\\n self.assertEqual(image2.getHeight()%2, 0)\\n width = image2.getWidth()\\n for y in range(1, image2.getHeight(), 2):\\n sim = image2.Factory(image2, afwGeom.Box2I(afwGeom.Point2I(0, y), afwGeom.Extent2I(width, 1)),\\n afwImage.LOCAL)\\n sim += 1\\n\\n if display:\\n ds9.mtv(self.image, frame = 0)\\n ds9.mtv(image2, frame = 1)\\n\\n stats = afwMath.makeStatistics(image2,\\n afwMath.NPOINT | afwMath.STDEV | afwMath.MEAN | afwMath.ERRORS)\\n mean = stats.getResult(afwMath.MEAN)\\n n = stats.getValue(afwMath.NPOINT)\\n sd = stats.getValue(afwMath.STDEV)\\n\\n self.assertEqual(mean[0], image2.get(0, 0) + 0.5)\\n self.assertEqual(sd, 1/math.sqrt(4.0)*math.sqrt(n/(n - 1)))\\n self.assertAlmostEqual(mean[1], sd/math.sqrt(image2.getWidth()*image2.getHeight()), 10)\\n\\n meanSquare = afwMath.makeStatistics(image2, afwMath.MEANSQUARE).getValue()\\n self.assertEqual(meanSquare, 0.5*(image2.get(0, 0)**2 + image2.get(0, 1)**2))\",\n \"def describe(image):\\n needle = cv2.imread(image, 0)\\n orb = cv2.ORB()\\n keypoints, description = orb.detectAndCompute(needle, None)\\n print(keypoints)\\n print(description)\\n return keypoints, description\",\n \"def aligned_comparison_stats(unaligned_image_ccd_lst,aligned_image_ccd_lst,\\n plots_path,obsdate):\\n aligned_mean_count = []\\n aligned_median_count = []\\n unaligned_mean_count = []\\n unaligned_median_count = []\\n \\n # getting information from source image for saving purposes\\n source_hdu = CCDData(aligned_image_ccd_lst[0],unit='adu')\\n source_image_data = source_hdu.data.astype(float) \\n source_image_hdr = source_hdu.header\\n target_name = source_image_hdr['FIELD'].strip(' ')\\n exptime = source_image_hdr['EXPTIME']\\n chip_num = source_image_hdr['CHIP']\\n \\n for a_file in unaligned_image_ccd_lst[1:]:\\n hdu = CCDData(a_file,unit='adu')\\n image_data = hdu.data.astype(float) \\n image_hdr = hdu.header\\n \\n unaligned_mean_count.append(np.mean(a_file))\\n unaligned_median_count.append(np.median(a_file))\\n \\n min_count_for_unaligned_mean = np.min(unaligned_mean_count)\\n max_count_for_unaligned_mean = np.max(unaligned_mean_count)\\n min_count_for_unaligned_median = np.min(unaligned_median_count)\\n max_count_for_unaligned_median = np.max(unaligned_median_count)\\n \\n for a_file in aligned_image_ccd_lst:\\n hdu = CCDData(a_file,unit='adu')\\n image_data = hdu.data.astype(float) \\n image_hdr = hdu.header\\n \\n aligned_mean_count.append(np.mean(a_file))\\n aligned_median_count.append(np.median(a_file))\\n \\n min_count_for_aligned_mean = np.min(aligned_mean_count)\\n max_count_for_aligned_mean = np.max(aligned_mean_count)\\n min_count_for_aligned_median = np.min(aligned_median_count)\\n max_count_for_aligned_median = np.max(aligned_median_count)\\n \\n # plotting\\n fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15,8),sharex=True, sharey=False)\\n fig.suptitle('Comparison Statistics for Non-Aligned Images vs Aligned Images for {}'.format(obsdate))\\n \\n # unaligned mean\\n ax1.plot(unaligned_mean_count, label='mean',color=\\\"deeppink\\\")\\n ax1.axhline(y=min_count_for_unaligned_mean,linestyle='-',linewidth=1,\\n color='black',label='min mean {:.2f}'.format(min_count_for_unaligned_mean),alpha=1)\\n ax1.axhline(y=max_count_for_unaligned_mean,linestyle='-',linewidth=1,\\n color='black',label='max mean {:.2f}'.format(max_count_for_unaligned_mean),alpha=1)\\n ax1.set_xlabel('Image number')\\n ax1.set_ylabel('Count (ADU)')\\n ax1.set_title('Mean pixel value for unaligned images')\\n ax1.legend(loc=\\\"best\\\")\\n ax1.grid(b=True, which='both', axis='both')\\n # ax1.sharex(ax3)\\n \\n # unaligned median\\n ax2.plot(unaligned_median_count, label='median',color=\\\"deeppink\\\")\\n ax2.axhline(y=min_count_for_unaligned_median,linestyle='-',linewidth=1,\\n color='black',label='min median {:.2f}'.format(min_count_for_unaligned_median),alpha=1)\\n ax2.axhline(y=max_count_for_unaligned_median,linestyle='-',linewidth=1,\\n color='black',label='max median {:.2f}'.format(max_count_for_unaligned_median),alpha=1)\\n ax2.set_xlabel('Image number')\\n ax2.set_ylabel('Count (ADU)')\\n ax2.set_title('Median pixel value for unaligned images')\\n ax2.legend(loc=\\\"best\\\")\\n ax2.grid(b=True, which='both', axis='both')\\n # ax2.sharex(ax4)\\n \\n # aligned mean\\n ax3.plot(aligned_mean_count, label='mean',color=\\\"darkviolet\\\")\\n ax3.axhline(y=min_count_for_aligned_mean,linestyle='-',linewidth=1,\\n color='black',label='min mean {:.2f}'.format(min_count_for_aligned_mean),alpha=1)\\n ax3.axhline(y=max_count_for_aligned_mean,linestyle='-',linewidth=1,\\n color='black',label='max mean {:.2f}'.format(max_count_for_aligned_mean),alpha=1)\\n ax3.set_xlabel('Image number')\\n ax3.set_ylabel('Count (ADU)')\\n ax3.set_title('Mean pixel value for aligned images')\\n ax3.legend(loc=\\\"best\\\")\\n ax3.grid(b=True, which='both', axis='both')\\n \\n \\n # aligned median\\n ax4.plot(aligned_median_count, label='median',color=\\\"darkviolet\\\")\\n ax4.axhline(y=min_count_for_aligned_median,linestyle='-',linewidth=1,\\n color='black',label='min median {:.2f}'.format(min_count_for_aligned_median),alpha=1)\\n ax4.axhline(y=max_count_for_aligned_median,linestyle='-',linewidth=1,\\n color='black',label='max median {:.2f}'.format(max_count_for_aligned_median),alpha=1)\\n ax4.set_xlabel('Image number')\\n ax4.set_ylabel('Count (ADU)')\\n ax4.set_title('Median pixel value for unaligned images')\\n ax4.legend(loc=\\\"best\\\")\\n ax4.grid(b=True, which='both', axis='both')\\n \\n \\n for ax in fig.get_axes():\\n ax.label_outer()\\n \\n fig.savefig(plots_path/\\\"{}-{}-{}-comparison_stats.jpg\\\".format(obsdate,\\n target_name,\\n exptime,chip_num),\\n dpi=1000)\\n fig.show()\",\n \"def visualize(**images):\\n n_images = len(images)\\n plt.figure(figsize=(20,8))\\n for idx, (name, image) in enumerate(images.items()):\\n plt.subplot(1, n_images, idx + 1)\\n plt.xticks([]); \\n plt.yticks([])\\n # get title from the parameter names\\n plt.title(name.replace('_',' ').title(), fontsize=20)\\n plt.imshow(image)\\n plt.savefig('sample_gt_pred_2_max.jpeg')\\n plt.show()\",\n \"def _print_summary(data, metric):\\n\\n print(u'Cortical thickness {}: {:.2f} \\\\u00B1 {:.2f} [{:.2f}--{:.2f}]'\\n .format(metric, data[:, 0].mean(), data[:, 0].std(ddof=1),\\n data[:, 0].min(), data[:, 0].max()))\\n print('Other modalities {}: {:.2f} \\\\u00B1 {:.2f} [{:.2f}--{:.2f}]'\\n .format(metric, data[:, 1:].mean(), data[:, 1:].std(ddof=1),\\n data[:, 1:].min(), data[:, 1:].max()))\\n print('Overall {}: {:.2f} \\\\u00B1 {:.2f} [{:.2f}--{:.2f}]'\\n .format(metric, data.mean(), data.std(ddof=1),\\n data.min(), data.max()))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.73849064","0.7212866","0.7087518","0.70753783","0.6967849","0.68458074","0.6760577","0.674482","0.6678487","0.66494477","0.6574271","0.6533716","0.64291257","0.63930213","0.6344323","0.63426876","0.63391554","0.63218725","0.63212085","0.6293195","0.62893045","0.62756395","0.62569714","0.62569714","0.62208235","0.62120205","0.6186777","0.6116503","0.6091363","0.60903686","0.60451055","0.6032356","0.60212535","0.59832823","0.59823805","0.5963851","0.5963851","0.5963006","0.5961557","0.5955537","0.5955537","0.59546375","0.59517586","0.5916972","0.5908537","0.59028816","0.5901189","0.58972985","0.5890638","0.58873147","0.58820784","0.5877452","0.5873755","0.58703023","0.58504575","0.5841564","0.5841547","0.5840304","0.5840249","0.58332604","0.58022314","0.5797618","0.5787392","0.5785679","0.5784243","0.57681316","0.5766618","0.57642233","0.57544005","0.575274","0.5749759","0.5741401","0.5736139","0.57339853","0.5727522","0.5722611","0.5722583","0.5720094","0.5712912","0.5709794","0.5709104","0.5708907","0.570818","0.57000756","0.569805","0.56927973","0.5690715","0.5689314","0.56860584","0.56700975","0.5659011","0.56512415","0.5645127","0.56238735","0.56212085","0.56211215","0.5616154","0.5615","0.5605198","0.56050324"],"string":"[\n \"0.73849064\",\n \"0.7212866\",\n \"0.7087518\",\n \"0.70753783\",\n \"0.6967849\",\n \"0.68458074\",\n \"0.6760577\",\n \"0.674482\",\n \"0.6678487\",\n \"0.66494477\",\n \"0.6574271\",\n \"0.6533716\",\n \"0.64291257\",\n \"0.63930213\",\n \"0.6344323\",\n \"0.63426876\",\n \"0.63391554\",\n \"0.63218725\",\n \"0.63212085\",\n \"0.6293195\",\n \"0.62893045\",\n \"0.62756395\",\n \"0.62569714\",\n \"0.62569714\",\n \"0.62208235\",\n \"0.62120205\",\n \"0.6186777\",\n \"0.6116503\",\n \"0.6091363\",\n \"0.60903686\",\n \"0.60451055\",\n \"0.6032356\",\n \"0.60212535\",\n \"0.59832823\",\n \"0.59823805\",\n \"0.5963851\",\n \"0.5963851\",\n \"0.5963006\",\n \"0.5961557\",\n \"0.5955537\",\n \"0.5955537\",\n \"0.59546375\",\n \"0.59517586\",\n \"0.5916972\",\n \"0.5908537\",\n \"0.59028816\",\n \"0.5901189\",\n \"0.58972985\",\n \"0.5890638\",\n \"0.58873147\",\n \"0.58820784\",\n \"0.5877452\",\n \"0.5873755\",\n \"0.58703023\",\n \"0.58504575\",\n \"0.5841564\",\n \"0.5841547\",\n \"0.5840304\",\n \"0.5840249\",\n \"0.58332604\",\n \"0.58022314\",\n \"0.5797618\",\n \"0.5787392\",\n \"0.5785679\",\n \"0.5784243\",\n \"0.57681316\",\n \"0.5766618\",\n \"0.57642233\",\n \"0.57544005\",\n \"0.575274\",\n \"0.5749759\",\n \"0.5741401\",\n \"0.5736139\",\n \"0.57339853\",\n \"0.5727522\",\n \"0.5722611\",\n \"0.5722583\",\n \"0.5720094\",\n \"0.5712912\",\n \"0.5709794\",\n \"0.5709104\",\n \"0.5708907\",\n \"0.570818\",\n \"0.57000756\",\n \"0.569805\",\n \"0.56927973\",\n \"0.5690715\",\n \"0.5689314\",\n \"0.56860584\",\n \"0.56700975\",\n \"0.5659011\",\n \"0.56512415\",\n \"0.5645127\",\n \"0.56238735\",\n \"0.56212085\",\n \"0.56211215\",\n \"0.5616154\",\n \"0.5615\",\n \"0.5605198\",\n \"0.56050324\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6997572"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":786,"cells":{"query":{"kind":"string","value":"Returns the inverse of the 2d rot and trans matrix"},"document":{"kind":"string","value":"def inverse_transform2(alpha, tx = 0.0, ty = 0.0, mirror = 0):\n\n\tt = Transform({\"type\":\"2D\",\"alpha\":alpha,\"tx\":tx,\"ty\":ty,\"mirror\":mirror,\"scale\":1.0})\n\tt = t.inverse()\n\tt = t.get_params(\"2D\")\n\treturn t[ \"alpha\" ], t[ \"tx\" ], t[ \"ty\" ], t[ \"mirror\" ]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def inverse(self):\n return Rotation(self.matrix.transposed())","def inverse(self):\n rotation_matrix = self.pose_mat[:3, :3]\n translation_vector = self.pose_mat[:3, 3]\n\n rot = np.transpose(rotation_matrix)\n trans = - np.matmul(np.transpose(rotation_matrix), translation_vector)\n return Transformation(rot, trans)","def inv(transform_matrix):\n\n r = transform_matrix[0:3, 0:3]\n t = transform_matrix[0:3, 3]\n t_inv = -1 * r.T.dot(t)\n transform_inv = np.eye(4)\n transform_inv[0:3, 0:3] = r.T\n transform_inv[0:3, 3] = t_inv\n\n return transform_inv","def inverse(self):\r\n \r\n Mi=mat4()\r\n d=self.determinant()\r\n for i in range(4):\r\n for j in range(4):\r\n sign=1-((i+j)%2)*2\r\n m3=self._submat(i,j)\r\n Mi[j,i]=sign*m3.determinant()/d\r\n return Mi","def rot_inv(self):\n if not hasattr(self, '_rot_inv'):\n self._rot_inv=np.linalg.inv(self.rot)\n return self._rot_inv","def inverse_rigid_trans(Tr): \n inv_Tr = np.zeros_like(Tr) # 3x4\n inv_Tr[0:3,0:3] = np.transpose(Tr[0:3,0:3])\n inv_Tr[0:3,3] = np.dot(-np.transpose(Tr[0:3,0:3]), Tr[0:3,3])\n return inv_Tr","def trans_matrix_inv(m:numpy.ndarray):\n was2d = False\n if m.shape[1] == 3:\n was2d = True\n m = numpy.asarray([\n [1.0, 0.0, 0.0, 0.0],\n [0.0, m[0,0], m[0,1], m[0,2]],\n [0.0, m[1,0], m[1,1], m[1,2]],\n [0.0, 0.0, 0.0, 1.0]], numpy.float64)\n trans = m[0:3,3]\n rotate = numpy.zeros(3, numpy.float64)\n r = m[0:3,0:3]\n rc = numpy.linalg.cholesky(numpy.matmul(r.T, r)).T\n scale = numpy.diagonal(rc)\n if numpy.linalg.det(r) < 0.0:\n scale[0] *= -1.0\n rcd = rc * numpy.eye(3, dtype=numpy.float64)\n rc = numpy.linalg.solve(rcd, rc)\n shear = numpy.asarray([rc[0,1], rc[0,2], rc[1,2]], numpy.float64)\n r0 = trans_matrix({'rotate': rotate, 'scale': scale, 'shear': shear})[0:3,0:3]\n r0 = numpy.linalg.solve(numpy.linalg.inv(r), numpy.linalg.inv(r0))\n rotate[1] = numpy.arcsin(_frone(r0[0,2]))\n if numpy.abs((numpy.abs(rotate[1]) - (numpy.pi / 2.0))) < 1.0e-6:\n rotate[0] = 0.0\n rotate[2] = numpy.arctan2(-_frone(r0[1,0]), _frone(-r0[2,0] / r0[0,2]))\n else:\n rc = numpy.cos(rotate[1])\n rotate[0] = numpy.arctan2(_frone(r0[1,2] / rc), _frone(r0[2,2] / rc))\n rotate[2] = numpy.arctan2(_frone(r0[0,1] / rc), _frone(r0[0,0] / rc))\n if was2d:\n trans = trans[1:]\n rotate = rotate[0:1]\n scale = scale[1:]\n shear = shear[2:3]\n return (trans, rotate, scale, shear)","def rotation_inv(R: np.array) -> np.array:\n return R.T","def getInverseMatrix(self) -> CMatrix4:\n ...","def inverse(self):\n data = np.linalg.inv(self._data)\n return self.create(self.rows, self.cols, data)","def inverse_affine_transformation_matrix(A):\n n, n = A.shape\n\n # extract components R, an n-1xn-1 linear transformation matrix, and T, an nx1 translation matrix\n R = A[:n-1, :n-1]\n T = A[:n-1, n-1]\n\n # find R^-1\n R_inv = np.linalg.inv(R)\n\n # Find A^-1/A_inv\n A_inv = np.copy(A).astype(float) # copy A for base of A^-1 matrix and ensure it is of data type float\n A_inv[:n-1, :n-1] = R_inv # set top left nxn sub matrix equal to R^-1\n A_inv[:n-1, n-1] = np.negative(R_inv.dot(T)) # place -R^-1*T in top right corner\n\n return A_inv","def affine_transform_inverse(np_transform):\n rotation = np_transform[:3, :3]\n translation = np_transform[:3, 3]\n rotation_inv = numpy.linalg.inv(rotation)\n translation_inv = -1 * numpy.dot(rotation_inv, translation)\n result = numpy.identity(4)\n result[:3, :3] = rotation_inv\n result[:3, 3] = translation_inv.flatten()\n return result","def inverse_matrice(T):\n a,b,c,d = T[0][0],T[0][1],T[1][0],T[1][1]\n det = a*d-b*c\n aa,bb,cc,dd = d/det,-b/det,-c/det,a/det\n Tinv = [[aa,bb],[cc,dd]]\n return Tinv","def InverseRotation(rotation):\n return RotationMatrix([\n [rotation.rot[0][0], rotation.rot[1][0], rotation.rot[2][0]],\n [rotation.rot[0][1], rotation.rot[1][1], rotation.rot[2][1]],\n [rotation.rot[0][2], rotation.rot[1][2], rotation.rot[2][2]]\n ])","def inverse(self,mat):\n result = np.linalg.inv(mat)\n self.out = result\n return self.out","def ComputeInverseInnerOrientation(self):\n a0 = self.innerOrientationParameters[0]\n b0 = self.innerOrientationParameters[1]\n a1 = self.innerOrientationParameters[2]\n a2 = self.innerOrientationParameters[3]\n b1 = self.innerOrientationParameters[4]\n b2 = self.innerOrientationParameters[5]\n\n mat = np.array([[a1[0], a2[0]], [b1[0], b2[0]]])\n mat = la.inv(mat)\n\n return np.array([a0[0], b0[0], mat[0, 0], mat[0, 1], mat[1, 0], mat[1, 1]]).T","def intrinsic_matrix_inv(self) -> np.ndarray:\n\n # determinant of top left of intrinsic matrix\n tldet = self.kx * self.ky\n\n return np.array([[1 / self.kx, -self.kxy / tldet, (self.py * self.kxy - self.ky * self.px) / tldet],\n [0, 1 / self.ky, -self.py / self.ky]])","def inverse(self):\n self.check_square()\n\n\n N = self.rows\n\n inverse = make_matrix(N, N)\n\n # Solve on a per-column basis using Ax = b formalism\n for j in range(N):\n b = make_matrix(N, 1)\n b[j, 0] = 1\n\n x = self.solve_linear_system(b)\n\n for i in range(N):\n inverse[i, j] = x[i, 0]\n\n return inverse","def inv(self):\n inv = np.linalg.inv(self._mat)\n return MoebTr(inv[0][0], inv[0][1], inv[1][0], inv[1][1])","def inverse(self):\n # TODO\n # detA\n if not self.is_square():\n raise(\n ValueError, \"Non-square Matrix does not have an inverse.\")\n if self.h > 2:\n raise(\n NotImplementedError, \"inversion not implemented for matrices larger than 2x2.\")\n\n mD = self.determinant()\n if self.h == 1:\n if self.g[0][0] = 0:\n raise(NotImplementedError,\n \"The 1x1 Matrix contains 0 can't inverse\")\n else:\n return [[1 / self.g[0][0]]] \n for i in range(self.h): # Calculates the inverse of a 2x2 Matrix.\n my_Matrix = zeroes(2, 2)\n my_Matrix.g[1][1] = self.g[0][0] / mD\n my_Matrix.g[0][0] = self.g[1][1] / mD\n my_Matrix.g[0][1] = - self.g[0][1] / mD\n my_Matrix.g[1][0] = - self.g[1][0] / mD\n return my_Matrix\n\n # trace A\n # 与矩阵TraceA * I identity 单位矩阵","def inverse(self):\n if not self.is_square():\n raise(ValueError, \"Non-square Matrix does not have an inverse.\")\n if self.h > 2:\n raise(NotImplementedError, \"inversion not implemented for matrices larger than 2x2.\")\n # TODO - your code here\n inverse = []\n if self.h == 1:\n temp = []\n temp.append(1/self.g[0][0])\n inverse.append(temp)\n else:\n identity_matrix = identity(self.h)\n det_term = 1/self.determinant()\n trace_term = self.trace()\n # implement intermediate scaling step locally\n # trace_x_I = trace_term * identity_matrix\n trace_x_I = []\n for i in range(len(self.g)):\n temp_row = []\n for j in range(len(self.g[i])):\n temp_row.append(trace_term * identity_matrix[i][j])\n trace_x_I.append(temp_row)\n # implement sub-traction locally\n # sub_term = trace_x_I - self.g\n sub_term = []\n for i in range(len(trace_x_I)):\n temp_row = []\n for j in range(len(trace_x_I[i])):\n temp_row.append(trace_x_I[i][j] - self.g[i][j])\n sub_term.append(temp_row)\n # implement final scaling step locally\n # inverse = det_term * sub_term\n inverse = []\n for i in range(len(sub_term)):\n temp_row = []\n for j in range(len(sub_term[i])):\n temp_row.append(det_term * sub_term[i][j])\n inverse.append(temp_row)\n return Matrix(inverse)\n # TODO - your code here","def GetInverse(self, *args):\n return _itkTranslationTransformPython.itkTranslationTransformD2_GetInverse(self, *args)","def inv(T):\n K, L = T.shape[1:3]\n squ_matrix = np.einsum('ijkl->ikjl', T).reshape((K*L, K*L),order='F')\n t = np.linalg.inv(squ_matrix)\n return np.einsum('ijkl->ikjl', t.reshape((K,L,K,L), order='F'))","def inverse(self):\n invr = np.linalg.inv(self.affine_matrix)\n return SymmOp(invr)","def Invert(*args, **kwargs):\n return _gdi_.GraphicsMatrix_Invert(*args, **kwargs)","def inverse(self):\n if not self.is_square():\n raise(ValueError, \"Non-square Matrix does not have an inverse.\")\n if self.h > 2:\n raise(NotImplementedError, \"inversion not implemented for matrices larger than 2x2.\")\n\n # TODO - your code here\n if self.h == 1:\n inverse = [[1/self.g[0][0]]];\n else:\n a = self.g[0][0];\n b = self.g[0][1];\n c = self.g[1][0];\n d = self.g[1][1];\n if(a*d==b*c):\n raise ValueError('matrix does not have a inverse!');\n else:\n weigh = 1/(a*d-b*c);\n inverse = [[weigh*d,weigh*-1*b],[weigh*-1*c,weigh*a]];\n return Matrix(inverse);","def _get_inv(self):\n m,d = self.B.shape\n Im = np.eye(m)\n Id = np.eye(d)\n BBt = self.B@self.B.T\n I_BBt_inv = np.linalg.pinv(Im + BBt)\n \n return (1/self.alpha)*(Id - self.B.T@( I_BBt_inv@self.B/self.alpha))","def inverse(self):\n # find the determinant of the matrix\n determinant = self.determinant()\n # find the matrix of minors of the matrix\n matrix_of_minors = self.matrix_of_minors()\n # find the cofactor of the matrix of minors\n cofactor_matrix = self.cofactor_matrix(matrix_of_minors)\n # find the transpose of the cofactor matrix\n transpose_cofactor_matrix = self.transpose(cofactor_matrix)\n # find the adjugate (inverse) matrix\n inverse_matrix = self.adjugate_matrix(determinant, transpose_cofactor_matrix)\n\n return inverse_matrix","def _inverse_affine_matrix(self) -> np.ndarray:\n raise NotImplementedError","def invert(self):\n\n if self.rows != self.columns:\n raise ValueError(\"Matrix must be square to invert\")\n\n A, operations = self.to_reduced_row_echelon()\n if not A.is_identity():\n return 0\n\n # If A was reduced to the identity matrix, then the same set of operations will take I to the inverse of A.\n # [A I] -> [I A^(-1)]\n\n I = IdentityMatrix(size = self.rows)\n for operation in operations:\n func = I.__getattribute__(operation[0])\n args = operation[1:]\n func(*args)\n\n return I","def inv(self):\n\n self.x, self.y = self.y, self.x\n self._x_, self._y_ = self._y_, self._x_\n self.xfac, self.yfac = 1 / self.yfac, 1 / self.xfac\n self._xfac_, self._yfac_ = 1 / self._yfac_, 1 / self._xfac_\n self._u = 1 / self._u.conj()","def inverse(self):\n if self.determinant() != 0:\n ops = reduce_to_red_echelon(self.data.copy(), True)[1]\n matrix = identity_matrix(self.n_rows).data\n \n if ops:\n if isinstance(ops[0], str):\n ops = [ops]\n \n for op in ops:\n if op[0] == 'swap':\n matrix = row_swap(matrix, op[1], op[2])\n elif op[0] == 'multiplication':\n matrix = row_multiply(matrix, op[1], op[2])\n elif op[0] == 'subtract':\n matrix = row_subtract(matrix, op[1], op[2], op[3])\n else:\n raise ValueError('Row operation not recognized')\n else:\n raise ValueError('Matrix has a determinant of 0 and is not invertible')\n return Matrix(matrix)","def invert(M):\r\n M = isMatrix(M)\r\n return M.I","def inv(X):\n R, t = Rt(X)\n Ri = R.T\n return np.concatenate((\n np.concatenate((Ri, -Ri.dot(t)[:,np.newaxis]), axis=1),\n np.array([[0, 0, 1]])))","def inverse(self):\n return self.solve(Matrix.I(self.nrows))","def posdef_inv_matrix_inverse(tensor, identity, damping):\n return tf.matrix_inverse(tensor + damping * identity)","def inverse_transform(self, y: Array2D) -> Array2D:","def inverse(self):\n rotation = self.rotation.inverse()\n translation = rotation * (-self.translation)\n\n return Transform(rotation, translation)","def invert(x):\n return linalg.inv(x)","def T_inv(T):\n R, xyz = rigmech.T2Rxyz(T)\n R_inv = R.T\n xyz_inv = -R_inv * xyz\n T_inv = R_inv.row_join(xyz_inv).col_join(sp.Matrix([[0, 0, 0, 1]]))\n return T_inv","def inv_rotation_matrix(self):\n return np.linalg.inv(self._rotation_matrix).tolist()","def inverse_transform(self, matrix):\n\n x = matrix.shape[0]\n y = matrix.shape[1]\n N = x\n\n # Inverse Fourier Transform matrix:\n ift = np.zeros([x, y], complex)\n\n for i in range(0, x):\n for j in range(0, y):\n sum_ift = 0\n for u in range(0, x):\n for v in range(0, y):\n sum_ift = sum_ift + matrix[u, v] * (np.cos(((2 * np.pi) / N) * (u * i + v * j)) + 1j * np.sin(((2 * np.pi) / N) * (u * i + v * j)))\n\n ift[i, j] = sum_ift\n\n\n return ift/(x*x)","def inverse(self):\n def inv(v):\n v[0], v[1] = v[1] , v[0]\n for v in [self.point1 , self.pointN , self.unitv, self.normalv]:\n inv(v)\n\n self.points = numpy.roll(self.points,1,axis=1)\n self.a, self.b = self.b, self.a\n self.angle = numpy.arccos( self.unitv[0] )*numpy.sign(self.unitv[1] )\n return","def reverse_matrix(self):\n return SWAP.matrix @ self.matrix @ SWAP.matrix","def inverseTransformationMatrix(self,index=None):\n if self.method == 'pca':\n if index is not None:\n coordinateIndex = distribution1D.vectori_cxx(len(index))\n for i in range(len(index)):\n coordinateIndex[i] = index[i]\n matrixDim = self._distribution.getInverseTransformationMatrixDimensions(coordinateIndex)\n inverseTransformation = self._distribution.getInverseTransformationMatrix(coordinateIndex)\n else:\n matrixDim = self._distribution.getInverseTransformationMatrixDimensions()\n inverseTransformation = self._distribution.getInverseTransformationMatrix()\n row = matrixDim[0]\n column = matrixDim[1]\n # convert 1D vector to 2D array\n L = np.atleast_1d(inverseTransformation).reshape(row,column)\n else:\n self.raiseAnError(NotImplementedError,' inverse transformationMatrix is not yet implemented for ' + self.method + ' method')\n return L","def matrix_inv(mat):\n\ta = mat[0,0]\n\tb = mat[0,1]\n\tc = mat[0,2]\n\td = mat[1,0]\n\te = mat[1,1]\n\tf = mat[1,2]\n\tg = mat[2,0]\n\th = mat[2,1]\n\ti = mat[2,2]\n\n\tdet = b*f*g + c*d*h + a*e*i - a*f*h - b*d*i - c*e*g\n\n\tinvmat = np.zeros((3,3))\n\tinvmat[0,0] = (e*i - f*h) / det\n\tinvmat[0,1] = (c*h - b*i) / det\n\tinvmat[0,2] = (b*f - c*e) / det\n\tinvmat[1,0] = (f*g - d*i) / det\n\tinvmat[1,1] = (a*i - c*g) / det\n\tinvmat[1,2] = (c*d - a*f) / det\n\tinvmat[2,0] = (d*h - e*g) / det\n\tinvmat[2,1] = (b*g - a*h) / det\n\tinvmat[2,2] = (a*e - b*d) / det\n\treturn invmat","def GetInverse(self, *args):\n return _itkTranslationTransformPython.itkTranslationTransformD3_GetInverse(self, *args)","def inverse( m, context = FloatContext, copy_m=True ):\n n,n_ = shape_mat(m)\n assert (n==n_) #matris should be square\n\n return solve( m, eye(n), context=context, copy_b=False, copy_a=copy_m )","def inverse(self, x, y):","def invert_affine_transform(matrix: Tensor) -> Tensor:\n if not isinstance(matrix, Tensor):\n raise TypeError(f\"Input matrix type is not a Tensor. Got {type(matrix)}\")\n\n if not (len(matrix.shape) == 3 and matrix.shape[-2:] == (2, 3)):\n raise ValueError(f\"Input matrix must be a Bx2x3 tensor. Got {matrix.shape}\")\n\n matrix_tmp: Tensor = convert_affinematrix_to_homography(matrix)\n matrix_inv: Tensor = _torch_inverse_cast(matrix_tmp)\n\n return matrix_inv[..., :2, :3]","def inverse (x_ij):\n\n Hij = xyzrph2matrix (x_ij)\n Rji = Hij[0:3, 0:3]\n tij = Hij[0:3,3]\n Rij = Rji.transpose ()\n tji = -Rij.dot (tij)\n Hji = numpy.zeros ((4,4))\n Hji[0:3,0:3] = Rij\n Hji[0:3,3] = tji\n Hji[3,3] = 1\n return matrix2xyzrph (Hji)","def __invert__(self):\n return self.inverse()","def complex_inverse(c1,cr):","def _inv(self) -> None:\n\n self.inv(inplace=True)","def inverse(self, ys):\n with torch.no_grad():\n xs = torch.matmul(ys, torch.diag(torch.reciprocal(torch.exp(self.scaling_diag))))\n xs = self.layer4.inverse(xs)\n xs = self.layer3.inverse(xs)\n xs = self.layer2.inverse(xs)\n xs = self.layer1.inverse(xs)\n return xs","def transformation_matrix(self):\n t = np.array([[0.0], [0.0], [0.0]])\n Rt = np.hstack([self.rotation_matrix, t])\n return np.vstack([Rt, np.array([0.0, 0.0, 0.0, 1.0])])","def _inv(M):\n ll, mm = M.shape\n M2 = M + 1e-10 * np.eye(ll)\n L = np.linalg.cholesky(M2)\n inv_L = np.linalg.inv(L)\n inv_M = inv_L.T @ inv_L\n return inv_M","def ssc.inverse (x_ij):\n\n Hij = xyzrph2matrix (x_ij)\n Rji = Hij[0:3, 0:3]\n tij = Hij[0:3,3]\n Rij = Rji.transpose ()\n tji = -Rij.dot (tij)\n Hji = zeros ((4,4))\n Hji[0:3,0:3] = Rij\n Hji[0:3,3] = tji\n Hji[3,3] = 1\n return matrix2xyzrph (Hji)","def inverse_transform(self, X):\n ...","def inverse_transform(self, X):\n ...","def inverse_transform(self, X):\n ...","def inverse_transform(self, X):\n ...","def inverse_transform(self, X):\n ...","def inverse(self, y):\n device = y.device\n return t.einsum('ij,k,kj->ik', y, 1. / t.sqrt(self.eig).to(device), self.rot.to(device))","def inverseN(self):\r\n result = Matrix(self.rows, self.columns)\r\n for r in range(self.rows):\r\n for c in range(self.columns):\r\n result.mat[r][c] = self.cofactor(r, c)\r\n result.out()\r\n result = result.transpose()\r\n det = self.determinant()\r\n print(\"1/(\" + str(det) + \")\")\r\n result.out()\r\n return result","def inverse(self):\n return self.invert()","def _inv22_vectorized(M):\n assert (M.ndim == 3)\n assert (M.shape[-2:] == (2, 2))\n M_inv = np.empty_like(M)\n delta_inv = np.reciprocal(M[:, 0, 0]*M[:, 1, 1] - M[:, 0, 1]*M[:, 1, 0])\n M_inv[:, 0, 0] = M[:, 1, 1]*delta_inv\n M_inv[:, 0, 1] = -M[:, 0, 1]*delta_inv\n M_inv[:, 1, 0] = -M[:, 1, 0]*delta_inv\n M_inv[:, 1, 1] = M[:, 0, 0]*delta_inv\n return M_inv","def _vect_matrix_inverse(A):\n identity = np.identity(A.shape[2], dtype=A.dtype)\n return np.array([np.linalg.solve(x, identity) for x in A])","def inv_inplace(a):","def invert(self):\n if self.m != self.n:\n raise exc.LinearAlgebraError(\"cannot invert a non-square matrix\")\n if self.determinant == 0:\n raise exc.LinearAlgebraError(\"cannot invert a singular matrix\")\n # TODO: implement block matrices in their own method\n block_rows = [r1 + r2 for r1, r2 in\n zip(self.data, self.makeIdentity(self.m).data)]\n inverse_block = Matrix.fromRows(block_rows).row_reduce()\n return inverse_block.subset([i for i in range(self.m)],\n [j + self.n for j in range(self.n)])","def _r_inv(self):\n # [output_dim, output_dim]\n return tf.linalg.cholesky_solve(\n self._chol_obs_covariance,\n tf.eye(self.emission.output_dim, dtype=self._chol_obs_covariance.dtype),\n )","def inverse_transform(data, flip, rotate):\n if flip == 'FLIP_LEFT_RIGHT':\n data = np.fliplr(data)\n\n if rotate == 'ROTATE_90':\n data = np.rot90(data, 3)\n\n if rotate == 'ROTATE_180':\n data = np.rot90(data, 2)\n\n if rotate == 'ROTATE_270':\n data = np.rot90(data, 1)\n\n return data","def inverse(self):\n return self._inverse","def inverse(self):\n ss = self._sum_of_squares()\n if ss > 0:\n return self.__class__(array=(self._vector_conjugate() / ss))\n else:\n raise ZeroDivisionError(\"a zero quaternion (0 + 0i + 0j + 0k) cannot be inverted\")","def __invert__(self):\n try:\n B = ~(self.matrix())\n except ZeroDivisionError:\n raise ZeroDivisionError(\"matrix morphism not invertible\")\n try:\n return self.parent().reversed()(B)\n except TypeError:\n raise ZeroDivisionError(\"matrix morphism not invertible\")","def inv(self):\n return self.conjugate()","def inverse(self):\n cdef StdVectorFst result = self.copy()\n result.invert()\n return result","def _r_inv(self):\n raise NotImplementedError","def inverse(im): \t \n x,y = np.shape(im)\n img = np.zeros([x,y])\n\t\n for i in range(x):\n for j in range(y):\n img[i,j] = 255 - im[i,j]\n return img","def tensorinv(a, ind=2):\n return TensorInv(ind)(a)","def __invert__(self):\n a = self.angle\n x, y = Vector.cartesian([1, a])\n return Vector(x, y)","def get_inverse_2x2(u, v):\n if not is_linearly_independent_2x2(u, v):\n return\n uv = get_uv(u, v)\n iden = get_uv([1, 0],[0, 1])\n a = np.zeros((2, 4))\n for i in range(2):\n for j in range(2):\n a[i][j] = uv[i][j]\n a[i][j+2] = iden[i][j]\n\n q = a[0][1] / a[1][1]\n a[0] = a[0] - q * a[1]\n\n q = a[1][0] / a[0][0]\n a[1] = a[1] - q * a[0]\n\n a[0] /= a[0][0]\n\n a[1] /= a[1][1]\n\n for i in range(2):\n for j in range(2):\n uv[i][j] = a[i][j+2]\n return uv","def Inverse(self, freedofs: pyngcore.BitArray = None, inverse: str = '') -> BaseMatrix:","def Inverse(self, freedofs: pyngcore.BitArray = None, inverse: str = '') -> BaseMatrix:","def inverse_cayley_transform(z: torch.Tensor) -> torch.Tensor:\n identity = identity_like(z)\n i_identity = multiply_by_i(identity)\n\n z_minus_id = z - i_identity\n inv_z_plus_id = inverse(z + i_identity)\n return z_minus_id @ inv_z_plus_id","def get_transformation_matrix(theta=45):\n\n theta = theta/360 * 2 * np.pi # in radians\n hx = np.cos(theta)\n sy = np.sin(theta)\n\n S = np.array([[1, hx, 0],\n [0, sy, 0],\n [0, 0, 1]])\n #S_inv = np.linalg.inv(S)\n #old_coords = np.array([[2, 2, 1], [6, 6, 1]]).T\n #new_coords = np.matmul(S, old_coords)\n #recovered_coords = np.matmul(S_inv, new_coords)\n #print('new coords: ', new_coords)\n #print('recovered coords: ', recovered_coords)\n return S","def inv(in_A):\n Q,R = qr(in_A)\n QT = Q.T\n N = shape(in_A)[0]\n \n for n in range(N-1,-1,-1):\n Rnn = R[n,n]\n R[n,:] /= Rnn\n QT[n,:] /= Rnn\n for m in range(n+1,N):\n Rnm = R[n,m]\n R[n,m] = 0\n QT[n,:] -= QT[m,:]*Rnm\n\n return QT","def get_transform_matrix(theta, phi = None, invert_rot = False, invert_focal = False):\n\n if phi is None:\n phi = const.PHI_IDX * 10.0\n\n #extrinsic x intrinsic\n camera_matrix = np.zeros((4, 4), dtype=np.float32)\n\n intrinsic_matrix = np.eye(4, dtype=np.float32)\n extrinsic_matrix = np.eye(4, dtype=np.float32)\n\n sin_phi = np.sin(float(phi) / 180.0 * np.pi)\n cos_phi = np.cos(float(phi) / 180.0 * np.pi)\n sin_theta = np.sin(float(-theta) / 180.0 * np.pi)\n cos_theta = np.cos(float(-theta) / 180.0 * np.pi)\n\n #theta rotation\n rotation_azimuth = np.zeros((3, 3), dtype=np.float32)\n rotation_azimuth[0, 0] = cos_theta\n rotation_azimuth[2, 2] = cos_theta\n rotation_azimuth[0, 2] = -sin_theta\n rotation_azimuth[2, 0] = sin_theta\n rotation_azimuth[1, 1] = 1.0\n\n #phi rotation\n rotation_elevation = np.zeros((3, 3), dtype=np.float32)\n rotation_elevation[0, 0] = cos_phi\n rotation_elevation[0, 1] = sin_phi\n rotation_elevation[1, 0] = -sin_phi\n rotation_elevation[1, 1] = cos_phi\n rotation_elevation[2, 2] = 1.0\n\n #rotate phi, then theta\n rotation_matrix = np.matmul(rotation_azimuth, rotation_elevation)\n if invert_rot:\n rotation_matrix = np.linalg.inv(rotation_matrix)\n\n displacement = np.zeros((3, 1), dtype=np.float32)\n displacement[0, 0] = const.DIST_TO_CAM\n displacement = np.matmul(rotation_matrix, displacement)\n\n #assembling 4x4 from R + T\n extrinsic_matrix[0:3, 0:3] = rotation_matrix\n extrinsic_matrix[0:3, 3:4] = -displacement\n\n if invert_focal:\n intrinsic_matrix[2, 2] = float(const.focal_length)\n intrinsic_matrix[1, 1] = float(const.focal_length)\n else:\n intrinsic_matrix[2, 2] = 1.0 / float(const.focal_length)\n intrinsic_matrix[1, 1] = 1.0 / float(const.focal_length)\n\n camera_matrix = np.matmul(extrinsic_matrix, intrinsic_matrix)\n return camera_matrix","def inv(self):\n return MoebGen(self._d / self._det, - self._b / self._det, - self._c / self._det, self._a / self._det)","def inverse(self):\n myMatrix = np.array(self.Matrix)\n if np.linalg.det(myMatrix) == 0:\n print(\"This matrix has a determinant of 0, meaning it has no inverse\")\n else:\n self.Inverse = np.linalg.inv(myMatrix)\n print(\"This is the inverse to your matrix: \", self.Inverse)","def transAffine2D( iScale=(1, 1), iTrans=(0, 0), iRot=0, iShear=(0, 0) ): \n iRot = iRot * np.pi / 180\n oMatScale = np.matrix( ((iScale[0],0,0),(0,iScale[1],0),(0,0,1)) )\n oMatTrans = np.matrix( ((1,0,iTrans[0]),(0,1,iTrans[1]),(0,0,1)) )\n oMatRot = np.matrix( ((np.cos(iRot),-np.sin(iRot),0),\\\n (np.sin(iRot),np.cos(iRot),0),(0,0,1)) )\n oMatShear = np.matrix( ((1,iShear[0],0),(iShear[1],1,0),(0,0,1)) )\n # ustvari izhodno matriko\n oMat2D = oMatTrans * oMatShear * oMatRot * oMatScale\n return oMat2D","def inverse_basis(T, dimensions, t):\n B = basis(T, dimensions, t)\n return inv(B.T.dot(B)).dot(B.T)","def inv_m(self):\n self.m = -self.m","def __invert__(self):\n \n return Vector(-self.y, self.x)","def inverse(self):\n group = self.group\n r = tuple([(i, -j) for i, j in self.array_form[::-1]])\n return group.dtype(r)","def inverse_transform(self, Xt):\n # Inverse transform\n columns = []\n start = 0\n\n for j in range(self.n_dims):\n dim = self.dimensions[j]\n offset = dim.transformed_size\n\n if offset == 1:\n columns.append(dim.inverse_transform(Xt[:, start]))\n else:\n columns.append(dim.inverse_transform(Xt[:, start : start + offset]))\n\n start += offset\n\n # Transpose\n rows = []\n\n for i in range(len(Xt)):\n r = []\n for j in range(self.n_dims):\n r.append(columns[j][i])\n\n rows.append(r)\n\n return rows","def inverse(self):\n n = self.norm()\n c = self.conj()\n d = 1.0 / (n * n)\n c.scale(d)\n return c","def invert(self, img):\n return self.inverse()(img)","def inv(M):\n\t#clone the matrix and append the identity matrix\n\t# [int(i==j) for j in range_M] is nothing but the i(th row of the identity matrix\n\tm2 = [row[:]+[int(i==j) for j in range(len(M) )] for i,row in enumerate(M) ]\n\t# extract the appended matrix (kind of m2[m:,...]\n\treturn [row[len(M[0]):] for row in m2] if gauss_jordan(m2) else None","def get_world_inv_matrix(m_obj, i):\n if not m_obj.hasFn(oMa.MFn.kTransform):\n return\n\n fn_obj = oMa.MFnDependencyNode(m_obj)\n plug = fn_obj.findPlug('worldInverseMatrix', False).elementByLogicalIndex(i)\n matrix_obj = plug.asMObject()\n matrix_data = oMa.MFnMatrixData(matrix_obj)\n matrix = matrix_data.matrix()\n\n return matrix"],"string":"[\n \"def inverse(self):\\n return Rotation(self.matrix.transposed())\",\n \"def inverse(self):\\n rotation_matrix = self.pose_mat[:3, :3]\\n translation_vector = self.pose_mat[:3, 3]\\n\\n rot = np.transpose(rotation_matrix)\\n trans = - np.matmul(np.transpose(rotation_matrix), translation_vector)\\n return Transformation(rot, trans)\",\n \"def inv(transform_matrix):\\n\\n r = transform_matrix[0:3, 0:3]\\n t = transform_matrix[0:3, 3]\\n t_inv = -1 * r.T.dot(t)\\n transform_inv = np.eye(4)\\n transform_inv[0:3, 0:3] = r.T\\n transform_inv[0:3, 3] = t_inv\\n\\n return transform_inv\",\n \"def inverse(self):\\r\\n \\r\\n Mi=mat4()\\r\\n d=self.determinant()\\r\\n for i in range(4):\\r\\n for j in range(4):\\r\\n sign=1-((i+j)%2)*2\\r\\n m3=self._submat(i,j)\\r\\n Mi[j,i]=sign*m3.determinant()/d\\r\\n return Mi\",\n \"def rot_inv(self):\\n if not hasattr(self, '_rot_inv'):\\n self._rot_inv=np.linalg.inv(self.rot)\\n return self._rot_inv\",\n \"def inverse_rigid_trans(Tr): \\n inv_Tr = np.zeros_like(Tr) # 3x4\\n inv_Tr[0:3,0:3] = np.transpose(Tr[0:3,0:3])\\n inv_Tr[0:3,3] = np.dot(-np.transpose(Tr[0:3,0:3]), Tr[0:3,3])\\n return inv_Tr\",\n \"def trans_matrix_inv(m:numpy.ndarray):\\n was2d = False\\n if m.shape[1] == 3:\\n was2d = True\\n m = numpy.asarray([\\n [1.0, 0.0, 0.0, 0.0],\\n [0.0, m[0,0], m[0,1], m[0,2]],\\n [0.0, m[1,0], m[1,1], m[1,2]],\\n [0.0, 0.0, 0.0, 1.0]], numpy.float64)\\n trans = m[0:3,3]\\n rotate = numpy.zeros(3, numpy.float64)\\n r = m[0:3,0:3]\\n rc = numpy.linalg.cholesky(numpy.matmul(r.T, r)).T\\n scale = numpy.diagonal(rc)\\n if numpy.linalg.det(r) < 0.0:\\n scale[0] *= -1.0\\n rcd = rc * numpy.eye(3, dtype=numpy.float64)\\n rc = numpy.linalg.solve(rcd, rc)\\n shear = numpy.asarray([rc[0,1], rc[0,2], rc[1,2]], numpy.float64)\\n r0 = trans_matrix({'rotate': rotate, 'scale': scale, 'shear': shear})[0:3,0:3]\\n r0 = numpy.linalg.solve(numpy.linalg.inv(r), numpy.linalg.inv(r0))\\n rotate[1] = numpy.arcsin(_frone(r0[0,2]))\\n if numpy.abs((numpy.abs(rotate[1]) - (numpy.pi / 2.0))) < 1.0e-6:\\n rotate[0] = 0.0\\n rotate[2] = numpy.arctan2(-_frone(r0[1,0]), _frone(-r0[2,0] / r0[0,2]))\\n else:\\n rc = numpy.cos(rotate[1])\\n rotate[0] = numpy.arctan2(_frone(r0[1,2] / rc), _frone(r0[2,2] / rc))\\n rotate[2] = numpy.arctan2(_frone(r0[0,1] / rc), _frone(r0[0,0] / rc))\\n if was2d:\\n trans = trans[1:]\\n rotate = rotate[0:1]\\n scale = scale[1:]\\n shear = shear[2:3]\\n return (trans, rotate, scale, shear)\",\n \"def rotation_inv(R: np.array) -> np.array:\\n return R.T\",\n \"def getInverseMatrix(self) -> CMatrix4:\\n ...\",\n \"def inverse(self):\\n data = np.linalg.inv(self._data)\\n return self.create(self.rows, self.cols, data)\",\n \"def inverse_affine_transformation_matrix(A):\\n n, n = A.shape\\n\\n # extract components R, an n-1xn-1 linear transformation matrix, and T, an nx1 translation matrix\\n R = A[:n-1, :n-1]\\n T = A[:n-1, n-1]\\n\\n # find R^-1\\n R_inv = np.linalg.inv(R)\\n\\n # Find A^-1/A_inv\\n A_inv = np.copy(A).astype(float) # copy A for base of A^-1 matrix and ensure it is of data type float\\n A_inv[:n-1, :n-1] = R_inv # set top left nxn sub matrix equal to R^-1\\n A_inv[:n-1, n-1] = np.negative(R_inv.dot(T)) # place -R^-1*T in top right corner\\n\\n return A_inv\",\n \"def affine_transform_inverse(np_transform):\\n rotation = np_transform[:3, :3]\\n translation = np_transform[:3, 3]\\n rotation_inv = numpy.linalg.inv(rotation)\\n translation_inv = -1 * numpy.dot(rotation_inv, translation)\\n result = numpy.identity(4)\\n result[:3, :3] = rotation_inv\\n result[:3, 3] = translation_inv.flatten()\\n return result\",\n \"def inverse_matrice(T):\\n a,b,c,d = T[0][0],T[0][1],T[1][0],T[1][1]\\n det = a*d-b*c\\n aa,bb,cc,dd = d/det,-b/det,-c/det,a/det\\n Tinv = [[aa,bb],[cc,dd]]\\n return Tinv\",\n \"def InverseRotation(rotation):\\n return RotationMatrix([\\n [rotation.rot[0][0], rotation.rot[1][0], rotation.rot[2][0]],\\n [rotation.rot[0][1], rotation.rot[1][1], rotation.rot[2][1]],\\n [rotation.rot[0][2], rotation.rot[1][2], rotation.rot[2][2]]\\n ])\",\n \"def inverse(self,mat):\\n result = np.linalg.inv(mat)\\n self.out = result\\n return self.out\",\n \"def ComputeInverseInnerOrientation(self):\\n a0 = self.innerOrientationParameters[0]\\n b0 = self.innerOrientationParameters[1]\\n a1 = self.innerOrientationParameters[2]\\n a2 = self.innerOrientationParameters[3]\\n b1 = self.innerOrientationParameters[4]\\n b2 = self.innerOrientationParameters[5]\\n\\n mat = np.array([[a1[0], a2[0]], [b1[0], b2[0]]])\\n mat = la.inv(mat)\\n\\n return np.array([a0[0], b0[0], mat[0, 0], mat[0, 1], mat[1, 0], mat[1, 1]]).T\",\n \"def intrinsic_matrix_inv(self) -> np.ndarray:\\n\\n # determinant of top left of intrinsic matrix\\n tldet = self.kx * self.ky\\n\\n return np.array([[1 / self.kx, -self.kxy / tldet, (self.py * self.kxy - self.ky * self.px) / tldet],\\n [0, 1 / self.ky, -self.py / self.ky]])\",\n \"def inverse(self):\\n self.check_square()\\n\\n\\n N = self.rows\\n\\n inverse = make_matrix(N, N)\\n\\n # Solve on a per-column basis using Ax = b formalism\\n for j in range(N):\\n b = make_matrix(N, 1)\\n b[j, 0] = 1\\n\\n x = self.solve_linear_system(b)\\n\\n for i in range(N):\\n inverse[i, j] = x[i, 0]\\n\\n return inverse\",\n \"def inv(self):\\n inv = np.linalg.inv(self._mat)\\n return MoebTr(inv[0][0], inv[0][1], inv[1][0], inv[1][1])\",\n \"def inverse(self):\\n # TODO\\n # detA\\n if not self.is_square():\\n raise(\\n ValueError, \\\"Non-square Matrix does not have an inverse.\\\")\\n if self.h > 2:\\n raise(\\n NotImplementedError, \\\"inversion not implemented for matrices larger than 2x2.\\\")\\n\\n mD = self.determinant()\\n if self.h == 1:\\n if self.g[0][0] = 0:\\n raise(NotImplementedError,\\n \\\"The 1x1 Matrix contains 0 can't inverse\\\")\\n else:\\n return [[1 / self.g[0][0]]] \\n for i in range(self.h): # Calculates the inverse of a 2x2 Matrix.\\n my_Matrix = zeroes(2, 2)\\n my_Matrix.g[1][1] = self.g[0][0] / mD\\n my_Matrix.g[0][0] = self.g[1][1] / mD\\n my_Matrix.g[0][1] = - self.g[0][1] / mD\\n my_Matrix.g[1][0] = - self.g[1][0] / mD\\n return my_Matrix\\n\\n # trace A\\n # 与矩阵TraceA * I identity 单位矩阵\",\n \"def inverse(self):\\n if not self.is_square():\\n raise(ValueError, \\\"Non-square Matrix does not have an inverse.\\\")\\n if self.h > 2:\\n raise(NotImplementedError, \\\"inversion not implemented for matrices larger than 2x2.\\\")\\n # TODO - your code here\\n inverse = []\\n if self.h == 1:\\n temp = []\\n temp.append(1/self.g[0][0])\\n inverse.append(temp)\\n else:\\n identity_matrix = identity(self.h)\\n det_term = 1/self.determinant()\\n trace_term = self.trace()\\n # implement intermediate scaling step locally\\n # trace_x_I = trace_term * identity_matrix\\n trace_x_I = []\\n for i in range(len(self.g)):\\n temp_row = []\\n for j in range(len(self.g[i])):\\n temp_row.append(trace_term * identity_matrix[i][j])\\n trace_x_I.append(temp_row)\\n # implement sub-traction locally\\n # sub_term = trace_x_I - self.g\\n sub_term = []\\n for i in range(len(trace_x_I)):\\n temp_row = []\\n for j in range(len(trace_x_I[i])):\\n temp_row.append(trace_x_I[i][j] - self.g[i][j])\\n sub_term.append(temp_row)\\n # implement final scaling step locally\\n # inverse = det_term * sub_term\\n inverse = []\\n for i in range(len(sub_term)):\\n temp_row = []\\n for j in range(len(sub_term[i])):\\n temp_row.append(det_term * sub_term[i][j])\\n inverse.append(temp_row)\\n return Matrix(inverse)\\n # TODO - your code here\",\n \"def GetInverse(self, *args):\\n return _itkTranslationTransformPython.itkTranslationTransformD2_GetInverse(self, *args)\",\n \"def inv(T):\\n K, L = T.shape[1:3]\\n squ_matrix = np.einsum('ijkl->ikjl', T).reshape((K*L, K*L),order='F')\\n t = np.linalg.inv(squ_matrix)\\n return np.einsum('ijkl->ikjl', t.reshape((K,L,K,L), order='F'))\",\n \"def inverse(self):\\n invr = np.linalg.inv(self.affine_matrix)\\n return SymmOp(invr)\",\n \"def Invert(*args, **kwargs):\\n return _gdi_.GraphicsMatrix_Invert(*args, **kwargs)\",\n \"def inverse(self):\\n if not self.is_square():\\n raise(ValueError, \\\"Non-square Matrix does not have an inverse.\\\")\\n if self.h > 2:\\n raise(NotImplementedError, \\\"inversion not implemented for matrices larger than 2x2.\\\")\\n\\n # TODO - your code here\\n if self.h == 1:\\n inverse = [[1/self.g[0][0]]];\\n else:\\n a = self.g[0][0];\\n b = self.g[0][1];\\n c = self.g[1][0];\\n d = self.g[1][1];\\n if(a*d==b*c):\\n raise ValueError('matrix does not have a inverse!');\\n else:\\n weigh = 1/(a*d-b*c);\\n inverse = [[weigh*d,weigh*-1*b],[weigh*-1*c,weigh*a]];\\n return Matrix(inverse);\",\n \"def _get_inv(self):\\n m,d = self.B.shape\\n Im = np.eye(m)\\n Id = np.eye(d)\\n BBt = self.B@self.B.T\\n I_BBt_inv = np.linalg.pinv(Im + BBt)\\n \\n return (1/self.alpha)*(Id - self.B.T@( I_BBt_inv@self.B/self.alpha))\",\n \"def inverse(self):\\n # find the determinant of the matrix\\n determinant = self.determinant()\\n # find the matrix of minors of the matrix\\n matrix_of_minors = self.matrix_of_minors()\\n # find the cofactor of the matrix of minors\\n cofactor_matrix = self.cofactor_matrix(matrix_of_minors)\\n # find the transpose of the cofactor matrix\\n transpose_cofactor_matrix = self.transpose(cofactor_matrix)\\n # find the adjugate (inverse) matrix\\n inverse_matrix = self.adjugate_matrix(determinant, transpose_cofactor_matrix)\\n\\n return inverse_matrix\",\n \"def _inverse_affine_matrix(self) -> np.ndarray:\\n raise NotImplementedError\",\n \"def invert(self):\\n\\n if self.rows != self.columns:\\n raise ValueError(\\\"Matrix must be square to invert\\\")\\n\\n A, operations = self.to_reduced_row_echelon()\\n if not A.is_identity():\\n return 0\\n\\n # If A was reduced to the identity matrix, then the same set of operations will take I to the inverse of A.\\n # [A I] -> [I A^(-1)]\\n\\n I = IdentityMatrix(size = self.rows)\\n for operation in operations:\\n func = I.__getattribute__(operation[0])\\n args = operation[1:]\\n func(*args)\\n\\n return I\",\n \"def inv(self):\\n\\n self.x, self.y = self.y, self.x\\n self._x_, self._y_ = self._y_, self._x_\\n self.xfac, self.yfac = 1 / self.yfac, 1 / self.xfac\\n self._xfac_, self._yfac_ = 1 / self._yfac_, 1 / self._xfac_\\n self._u = 1 / self._u.conj()\",\n \"def inverse(self):\\n if self.determinant() != 0:\\n ops = reduce_to_red_echelon(self.data.copy(), True)[1]\\n matrix = identity_matrix(self.n_rows).data\\n \\n if ops:\\n if isinstance(ops[0], str):\\n ops = [ops]\\n \\n for op in ops:\\n if op[0] == 'swap':\\n matrix = row_swap(matrix, op[1], op[2])\\n elif op[0] == 'multiplication':\\n matrix = row_multiply(matrix, op[1], op[2])\\n elif op[0] == 'subtract':\\n matrix = row_subtract(matrix, op[1], op[2], op[3])\\n else:\\n raise ValueError('Row operation not recognized')\\n else:\\n raise ValueError('Matrix has a determinant of 0 and is not invertible')\\n return Matrix(matrix)\",\n \"def invert(M):\\r\\n M = isMatrix(M)\\r\\n return M.I\",\n \"def inv(X):\\n R, t = Rt(X)\\n Ri = R.T\\n return np.concatenate((\\n np.concatenate((Ri, -Ri.dot(t)[:,np.newaxis]), axis=1),\\n np.array([[0, 0, 1]])))\",\n \"def inverse(self):\\n return self.solve(Matrix.I(self.nrows))\",\n \"def posdef_inv_matrix_inverse(tensor, identity, damping):\\n return tf.matrix_inverse(tensor + damping * identity)\",\n \"def inverse_transform(self, y: Array2D) -> Array2D:\",\n \"def inverse(self):\\n rotation = self.rotation.inverse()\\n translation = rotation * (-self.translation)\\n\\n return Transform(rotation, translation)\",\n \"def invert(x):\\n return linalg.inv(x)\",\n \"def T_inv(T):\\n R, xyz = rigmech.T2Rxyz(T)\\n R_inv = R.T\\n xyz_inv = -R_inv * xyz\\n T_inv = R_inv.row_join(xyz_inv).col_join(sp.Matrix([[0, 0, 0, 1]]))\\n return T_inv\",\n \"def inv_rotation_matrix(self):\\n return np.linalg.inv(self._rotation_matrix).tolist()\",\n \"def inverse_transform(self, matrix):\\n\\n x = matrix.shape[0]\\n y = matrix.shape[1]\\n N = x\\n\\n # Inverse Fourier Transform matrix:\\n ift = np.zeros([x, y], complex)\\n\\n for i in range(0, x):\\n for j in range(0, y):\\n sum_ift = 0\\n for u in range(0, x):\\n for v in range(0, y):\\n sum_ift = sum_ift + matrix[u, v] * (np.cos(((2 * np.pi) / N) * (u * i + v * j)) + 1j * np.sin(((2 * np.pi) / N) * (u * i + v * j)))\\n\\n ift[i, j] = sum_ift\\n\\n\\n return ift/(x*x)\",\n \"def inverse(self):\\n def inv(v):\\n v[0], v[1] = v[1] , v[0]\\n for v in [self.point1 , self.pointN , self.unitv, self.normalv]:\\n inv(v)\\n\\n self.points = numpy.roll(self.points,1,axis=1)\\n self.a, self.b = self.b, self.a\\n self.angle = numpy.arccos( self.unitv[0] )*numpy.sign(self.unitv[1] )\\n return\",\n \"def reverse_matrix(self):\\n return SWAP.matrix @ self.matrix @ SWAP.matrix\",\n \"def inverseTransformationMatrix(self,index=None):\\n if self.method == 'pca':\\n if index is not None:\\n coordinateIndex = distribution1D.vectori_cxx(len(index))\\n for i in range(len(index)):\\n coordinateIndex[i] = index[i]\\n matrixDim = self._distribution.getInverseTransformationMatrixDimensions(coordinateIndex)\\n inverseTransformation = self._distribution.getInverseTransformationMatrix(coordinateIndex)\\n else:\\n matrixDim = self._distribution.getInverseTransformationMatrixDimensions()\\n inverseTransformation = self._distribution.getInverseTransformationMatrix()\\n row = matrixDim[0]\\n column = matrixDim[1]\\n # convert 1D vector to 2D array\\n L = np.atleast_1d(inverseTransformation).reshape(row,column)\\n else:\\n self.raiseAnError(NotImplementedError,' inverse transformationMatrix is not yet implemented for ' + self.method + ' method')\\n return L\",\n \"def matrix_inv(mat):\\n\\ta = mat[0,0]\\n\\tb = mat[0,1]\\n\\tc = mat[0,2]\\n\\td = mat[1,0]\\n\\te = mat[1,1]\\n\\tf = mat[1,2]\\n\\tg = mat[2,0]\\n\\th = mat[2,1]\\n\\ti = mat[2,2]\\n\\n\\tdet = b*f*g + c*d*h + a*e*i - a*f*h - b*d*i - c*e*g\\n\\n\\tinvmat = np.zeros((3,3))\\n\\tinvmat[0,0] = (e*i - f*h) / det\\n\\tinvmat[0,1] = (c*h - b*i) / det\\n\\tinvmat[0,2] = (b*f - c*e) / det\\n\\tinvmat[1,0] = (f*g - d*i) / det\\n\\tinvmat[1,1] = (a*i - c*g) / det\\n\\tinvmat[1,2] = (c*d - a*f) / det\\n\\tinvmat[2,0] = (d*h - e*g) / det\\n\\tinvmat[2,1] = (b*g - a*h) / det\\n\\tinvmat[2,2] = (a*e - b*d) / det\\n\\treturn invmat\",\n \"def GetInverse(self, *args):\\n return _itkTranslationTransformPython.itkTranslationTransformD3_GetInverse(self, *args)\",\n \"def inverse( m, context = FloatContext, copy_m=True ):\\n n,n_ = shape_mat(m)\\n assert (n==n_) #matris should be square\\n\\n return solve( m, eye(n), context=context, copy_b=False, copy_a=copy_m )\",\n \"def inverse(self, x, y):\",\n \"def invert_affine_transform(matrix: Tensor) -> Tensor:\\n if not isinstance(matrix, Tensor):\\n raise TypeError(f\\\"Input matrix type is not a Tensor. Got {type(matrix)}\\\")\\n\\n if not (len(matrix.shape) == 3 and matrix.shape[-2:] == (2, 3)):\\n raise ValueError(f\\\"Input matrix must be a Bx2x3 tensor. Got {matrix.shape}\\\")\\n\\n matrix_tmp: Tensor = convert_affinematrix_to_homography(matrix)\\n matrix_inv: Tensor = _torch_inverse_cast(matrix_tmp)\\n\\n return matrix_inv[..., :2, :3]\",\n \"def inverse (x_ij):\\n\\n Hij = xyzrph2matrix (x_ij)\\n Rji = Hij[0:3, 0:3]\\n tij = Hij[0:3,3]\\n Rij = Rji.transpose ()\\n tji = -Rij.dot (tij)\\n Hji = numpy.zeros ((4,4))\\n Hji[0:3,0:3] = Rij\\n Hji[0:3,3] = tji\\n Hji[3,3] = 1\\n return matrix2xyzrph (Hji)\",\n \"def __invert__(self):\\n return self.inverse()\",\n \"def complex_inverse(c1,cr):\",\n \"def _inv(self) -> None:\\n\\n self.inv(inplace=True)\",\n \"def inverse(self, ys):\\n with torch.no_grad():\\n xs = torch.matmul(ys, torch.diag(torch.reciprocal(torch.exp(self.scaling_diag))))\\n xs = self.layer4.inverse(xs)\\n xs = self.layer3.inverse(xs)\\n xs = self.layer2.inverse(xs)\\n xs = self.layer1.inverse(xs)\\n return xs\",\n \"def transformation_matrix(self):\\n t = np.array([[0.0], [0.0], [0.0]])\\n Rt = np.hstack([self.rotation_matrix, t])\\n return np.vstack([Rt, np.array([0.0, 0.0, 0.0, 1.0])])\",\n \"def _inv(M):\\n ll, mm = M.shape\\n M2 = M + 1e-10 * np.eye(ll)\\n L = np.linalg.cholesky(M2)\\n inv_L = np.linalg.inv(L)\\n inv_M = inv_L.T @ inv_L\\n return inv_M\",\n \"def ssc.inverse (x_ij):\\n\\n Hij = xyzrph2matrix (x_ij)\\n Rji = Hij[0:3, 0:3]\\n tij = Hij[0:3,3]\\n Rij = Rji.transpose ()\\n tji = -Rij.dot (tij)\\n Hji = zeros ((4,4))\\n Hji[0:3,0:3] = Rij\\n Hji[0:3,3] = tji\\n Hji[3,3] = 1\\n return matrix2xyzrph (Hji)\",\n \"def inverse_transform(self, X):\\n ...\",\n \"def inverse_transform(self, X):\\n ...\",\n \"def inverse_transform(self, X):\\n ...\",\n \"def inverse_transform(self, X):\\n ...\",\n \"def inverse_transform(self, X):\\n ...\",\n \"def inverse(self, y):\\n device = y.device\\n return t.einsum('ij,k,kj->ik', y, 1. / t.sqrt(self.eig).to(device), self.rot.to(device))\",\n \"def inverseN(self):\\r\\n result = Matrix(self.rows, self.columns)\\r\\n for r in range(self.rows):\\r\\n for c in range(self.columns):\\r\\n result.mat[r][c] = self.cofactor(r, c)\\r\\n result.out()\\r\\n result = result.transpose()\\r\\n det = self.determinant()\\r\\n print(\\\"1/(\\\" + str(det) + \\\")\\\")\\r\\n result.out()\\r\\n return result\",\n \"def inverse(self):\\n return self.invert()\",\n \"def _inv22_vectorized(M):\\n assert (M.ndim == 3)\\n assert (M.shape[-2:] == (2, 2))\\n M_inv = np.empty_like(M)\\n delta_inv = np.reciprocal(M[:, 0, 0]*M[:, 1, 1] - M[:, 0, 1]*M[:, 1, 0])\\n M_inv[:, 0, 0] = M[:, 1, 1]*delta_inv\\n M_inv[:, 0, 1] = -M[:, 0, 1]*delta_inv\\n M_inv[:, 1, 0] = -M[:, 1, 0]*delta_inv\\n M_inv[:, 1, 1] = M[:, 0, 0]*delta_inv\\n return M_inv\",\n \"def _vect_matrix_inverse(A):\\n identity = np.identity(A.shape[2], dtype=A.dtype)\\n return np.array([np.linalg.solve(x, identity) for x in A])\",\n \"def inv_inplace(a):\",\n \"def invert(self):\\n if self.m != self.n:\\n raise exc.LinearAlgebraError(\\\"cannot invert a non-square matrix\\\")\\n if self.determinant == 0:\\n raise exc.LinearAlgebraError(\\\"cannot invert a singular matrix\\\")\\n # TODO: implement block matrices in their own method\\n block_rows = [r1 + r2 for r1, r2 in\\n zip(self.data, self.makeIdentity(self.m).data)]\\n inverse_block = Matrix.fromRows(block_rows).row_reduce()\\n return inverse_block.subset([i for i in range(self.m)],\\n [j + self.n for j in range(self.n)])\",\n \"def _r_inv(self):\\n # [output_dim, output_dim]\\n return tf.linalg.cholesky_solve(\\n self._chol_obs_covariance,\\n tf.eye(self.emission.output_dim, dtype=self._chol_obs_covariance.dtype),\\n )\",\n \"def inverse_transform(data, flip, rotate):\\n if flip == 'FLIP_LEFT_RIGHT':\\n data = np.fliplr(data)\\n\\n if rotate == 'ROTATE_90':\\n data = np.rot90(data, 3)\\n\\n if rotate == 'ROTATE_180':\\n data = np.rot90(data, 2)\\n\\n if rotate == 'ROTATE_270':\\n data = np.rot90(data, 1)\\n\\n return data\",\n \"def inverse(self):\\n return self._inverse\",\n \"def inverse(self):\\n ss = self._sum_of_squares()\\n if ss > 0:\\n return self.__class__(array=(self._vector_conjugate() / ss))\\n else:\\n raise ZeroDivisionError(\\\"a zero quaternion (0 + 0i + 0j + 0k) cannot be inverted\\\")\",\n \"def __invert__(self):\\n try:\\n B = ~(self.matrix())\\n except ZeroDivisionError:\\n raise ZeroDivisionError(\\\"matrix morphism not invertible\\\")\\n try:\\n return self.parent().reversed()(B)\\n except TypeError:\\n raise ZeroDivisionError(\\\"matrix morphism not invertible\\\")\",\n \"def inv(self):\\n return self.conjugate()\",\n \"def inverse(self):\\n cdef StdVectorFst result = self.copy()\\n result.invert()\\n return result\",\n \"def _r_inv(self):\\n raise NotImplementedError\",\n \"def inverse(im): \\t \\n x,y = np.shape(im)\\n img = np.zeros([x,y])\\n\\t\\n for i in range(x):\\n for j in range(y):\\n img[i,j] = 255 - im[i,j]\\n return img\",\n \"def tensorinv(a, ind=2):\\n return TensorInv(ind)(a)\",\n \"def __invert__(self):\\n a = self.angle\\n x, y = Vector.cartesian([1, a])\\n return Vector(x, y)\",\n \"def get_inverse_2x2(u, v):\\n if not is_linearly_independent_2x2(u, v):\\n return\\n uv = get_uv(u, v)\\n iden = get_uv([1, 0],[0, 1])\\n a = np.zeros((2, 4))\\n for i in range(2):\\n for j in range(2):\\n a[i][j] = uv[i][j]\\n a[i][j+2] = iden[i][j]\\n\\n q = a[0][1] / a[1][1]\\n a[0] = a[0] - q * a[1]\\n\\n q = a[1][0] / a[0][0]\\n a[1] = a[1] - q * a[0]\\n\\n a[0] /= a[0][0]\\n\\n a[1] /= a[1][1]\\n\\n for i in range(2):\\n for j in range(2):\\n uv[i][j] = a[i][j+2]\\n return uv\",\n \"def Inverse(self, freedofs: pyngcore.BitArray = None, inverse: str = '') -> BaseMatrix:\",\n \"def Inverse(self, freedofs: pyngcore.BitArray = None, inverse: str = '') -> BaseMatrix:\",\n \"def inverse_cayley_transform(z: torch.Tensor) -> torch.Tensor:\\n identity = identity_like(z)\\n i_identity = multiply_by_i(identity)\\n\\n z_minus_id = z - i_identity\\n inv_z_plus_id = inverse(z + i_identity)\\n return z_minus_id @ inv_z_plus_id\",\n \"def get_transformation_matrix(theta=45):\\n\\n theta = theta/360 * 2 * np.pi # in radians\\n hx = np.cos(theta)\\n sy = np.sin(theta)\\n\\n S = np.array([[1, hx, 0],\\n [0, sy, 0],\\n [0, 0, 1]])\\n #S_inv = np.linalg.inv(S)\\n #old_coords = np.array([[2, 2, 1], [6, 6, 1]]).T\\n #new_coords = np.matmul(S, old_coords)\\n #recovered_coords = np.matmul(S_inv, new_coords)\\n #print('new coords: ', new_coords)\\n #print('recovered coords: ', recovered_coords)\\n return S\",\n \"def inv(in_A):\\n Q,R = qr(in_A)\\n QT = Q.T\\n N = shape(in_A)[0]\\n \\n for n in range(N-1,-1,-1):\\n Rnn = R[n,n]\\n R[n,:] /= Rnn\\n QT[n,:] /= Rnn\\n for m in range(n+1,N):\\n Rnm = R[n,m]\\n R[n,m] = 0\\n QT[n,:] -= QT[m,:]*Rnm\\n\\n return QT\",\n \"def get_transform_matrix(theta, phi = None, invert_rot = False, invert_focal = False):\\n\\n if phi is None:\\n phi = const.PHI_IDX * 10.0\\n\\n #extrinsic x intrinsic\\n camera_matrix = np.zeros((4, 4), dtype=np.float32)\\n\\n intrinsic_matrix = np.eye(4, dtype=np.float32)\\n extrinsic_matrix = np.eye(4, dtype=np.float32)\\n\\n sin_phi = np.sin(float(phi) / 180.0 * np.pi)\\n cos_phi = np.cos(float(phi) / 180.0 * np.pi)\\n sin_theta = np.sin(float(-theta) / 180.0 * np.pi)\\n cos_theta = np.cos(float(-theta) / 180.0 * np.pi)\\n\\n #theta rotation\\n rotation_azimuth = np.zeros((3, 3), dtype=np.float32)\\n rotation_azimuth[0, 0] = cos_theta\\n rotation_azimuth[2, 2] = cos_theta\\n rotation_azimuth[0, 2] = -sin_theta\\n rotation_azimuth[2, 0] = sin_theta\\n rotation_azimuth[1, 1] = 1.0\\n\\n #phi rotation\\n rotation_elevation = np.zeros((3, 3), dtype=np.float32)\\n rotation_elevation[0, 0] = cos_phi\\n rotation_elevation[0, 1] = sin_phi\\n rotation_elevation[1, 0] = -sin_phi\\n rotation_elevation[1, 1] = cos_phi\\n rotation_elevation[2, 2] = 1.0\\n\\n #rotate phi, then theta\\n rotation_matrix = np.matmul(rotation_azimuth, rotation_elevation)\\n if invert_rot:\\n rotation_matrix = np.linalg.inv(rotation_matrix)\\n\\n displacement = np.zeros((3, 1), dtype=np.float32)\\n displacement[0, 0] = const.DIST_TO_CAM\\n displacement = np.matmul(rotation_matrix, displacement)\\n\\n #assembling 4x4 from R + T\\n extrinsic_matrix[0:3, 0:3] = rotation_matrix\\n extrinsic_matrix[0:3, 3:4] = -displacement\\n\\n if invert_focal:\\n intrinsic_matrix[2, 2] = float(const.focal_length)\\n intrinsic_matrix[1, 1] = float(const.focal_length)\\n else:\\n intrinsic_matrix[2, 2] = 1.0 / float(const.focal_length)\\n intrinsic_matrix[1, 1] = 1.0 / float(const.focal_length)\\n\\n camera_matrix = np.matmul(extrinsic_matrix, intrinsic_matrix)\\n return camera_matrix\",\n \"def inv(self):\\n return MoebGen(self._d / self._det, - self._b / self._det, - self._c / self._det, self._a / self._det)\",\n \"def inverse(self):\\n myMatrix = np.array(self.Matrix)\\n if np.linalg.det(myMatrix) == 0:\\n print(\\\"This matrix has a determinant of 0, meaning it has no inverse\\\")\\n else:\\n self.Inverse = np.linalg.inv(myMatrix)\\n print(\\\"This is the inverse to your matrix: \\\", self.Inverse)\",\n \"def transAffine2D( iScale=(1, 1), iTrans=(0, 0), iRot=0, iShear=(0, 0) ): \\n iRot = iRot * np.pi / 180\\n oMatScale = np.matrix( ((iScale[0],0,0),(0,iScale[1],0),(0,0,1)) )\\n oMatTrans = np.matrix( ((1,0,iTrans[0]),(0,1,iTrans[1]),(0,0,1)) )\\n oMatRot = np.matrix( ((np.cos(iRot),-np.sin(iRot),0),\\\\\\n (np.sin(iRot),np.cos(iRot),0),(0,0,1)) )\\n oMatShear = np.matrix( ((1,iShear[0],0),(iShear[1],1,0),(0,0,1)) )\\n # ustvari izhodno matriko\\n oMat2D = oMatTrans * oMatShear * oMatRot * oMatScale\\n return oMat2D\",\n \"def inverse_basis(T, dimensions, t):\\n B = basis(T, dimensions, t)\\n return inv(B.T.dot(B)).dot(B.T)\",\n \"def inv_m(self):\\n self.m = -self.m\",\n \"def __invert__(self):\\n \\n return Vector(-self.y, self.x)\",\n \"def inverse(self):\\n group = self.group\\n r = tuple([(i, -j) for i, j in self.array_form[::-1]])\\n return group.dtype(r)\",\n \"def inverse_transform(self, Xt):\\n # Inverse transform\\n columns = []\\n start = 0\\n\\n for j in range(self.n_dims):\\n dim = self.dimensions[j]\\n offset = dim.transformed_size\\n\\n if offset == 1:\\n columns.append(dim.inverse_transform(Xt[:, start]))\\n else:\\n columns.append(dim.inverse_transform(Xt[:, start : start + offset]))\\n\\n start += offset\\n\\n # Transpose\\n rows = []\\n\\n for i in range(len(Xt)):\\n r = []\\n for j in range(self.n_dims):\\n r.append(columns[j][i])\\n\\n rows.append(r)\\n\\n return rows\",\n \"def inverse(self):\\n n = self.norm()\\n c = self.conj()\\n d = 1.0 / (n * n)\\n c.scale(d)\\n return c\",\n \"def invert(self, img):\\n return self.inverse()(img)\",\n \"def inv(M):\\n\\t#clone the matrix and append the identity matrix\\n\\t# [int(i==j) for j in range_M] is nothing but the i(th row of the identity matrix\\n\\tm2 = [row[:]+[int(i==j) for j in range(len(M) )] for i,row in enumerate(M) ]\\n\\t# extract the appended matrix (kind of m2[m:,...]\\n\\treturn [row[len(M[0]):] for row in m2] if gauss_jordan(m2) else None\",\n \"def get_world_inv_matrix(m_obj, i):\\n if not m_obj.hasFn(oMa.MFn.kTransform):\\n return\\n\\n fn_obj = oMa.MFnDependencyNode(m_obj)\\n plug = fn_obj.findPlug('worldInverseMatrix', False).elementByLogicalIndex(i)\\n matrix_obj = plug.asMObject()\\n matrix_data = oMa.MFnMatrixData(matrix_obj)\\n matrix = matrix_data.matrix()\\n\\n return matrix\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.76569027","0.75310546","0.7445371","0.7406346","0.7397322","0.73486006","0.72921807","0.7276845","0.7246478","0.71603876","0.71544224","0.7151582","0.708686","0.7067885","0.7064713","0.7061624","0.7057902","0.70148677","0.70069176","0.69840884","0.69755006","0.69609094","0.6950411","0.6932875","0.6923857","0.69233245","0.69113487","0.6882439","0.6856382","0.6845871","0.6814588","0.6810987","0.67921245","0.67769223","0.67494005","0.6708963","0.669069","0.6635994","0.66139567","0.66060495","0.66048783","0.6604787","0.6590005","0.6586237","0.65720165","0.6560596","0.6530914","0.6530028","0.64909476","0.64656794","0.64309263","0.64240235","0.6368767","0.6367298","0.6363927","0.6349667","0.6346638","0.63394356","0.63391775","0.63391775","0.63391775","0.63391775","0.63391775","0.63323694","0.6328439","0.63247335","0.63154","0.6304914","0.62913346","0.62878436","0.6282993","0.6275106","0.62614816","0.62519175","0.624967","0.62470603","0.62448496","0.62370104","0.62343186","0.62332565","0.62326676","0.62304145","0.62271625","0.62271625","0.622372","0.6207163","0.62006664","0.6193098","0.61809254","0.61493605","0.61459637","0.6138623","0.6126078","0.6125377","0.61244714","0.61181116","0.6116154","0.61076874","0.6100571","0.6099422"],"string":"[\n \"0.76569027\",\n \"0.75310546\",\n \"0.7445371\",\n \"0.7406346\",\n \"0.7397322\",\n \"0.73486006\",\n \"0.72921807\",\n \"0.7276845\",\n \"0.7246478\",\n \"0.71603876\",\n \"0.71544224\",\n \"0.7151582\",\n \"0.708686\",\n \"0.7067885\",\n \"0.7064713\",\n \"0.7061624\",\n \"0.7057902\",\n \"0.70148677\",\n \"0.70069176\",\n \"0.69840884\",\n \"0.69755006\",\n \"0.69609094\",\n \"0.6950411\",\n \"0.6932875\",\n \"0.6923857\",\n \"0.69233245\",\n \"0.69113487\",\n \"0.6882439\",\n \"0.6856382\",\n \"0.6845871\",\n \"0.6814588\",\n \"0.6810987\",\n \"0.67921245\",\n \"0.67769223\",\n \"0.67494005\",\n \"0.6708963\",\n \"0.669069\",\n \"0.6635994\",\n \"0.66139567\",\n \"0.66060495\",\n \"0.66048783\",\n \"0.6604787\",\n \"0.6590005\",\n \"0.6586237\",\n \"0.65720165\",\n \"0.6560596\",\n \"0.6530914\",\n \"0.6530028\",\n \"0.64909476\",\n \"0.64656794\",\n \"0.64309263\",\n \"0.64240235\",\n \"0.6368767\",\n \"0.6367298\",\n \"0.6363927\",\n \"0.6349667\",\n \"0.6346638\",\n \"0.63394356\",\n \"0.63391775\",\n \"0.63391775\",\n \"0.63391775\",\n \"0.63391775\",\n \"0.63391775\",\n \"0.63323694\",\n \"0.6328439\",\n \"0.63247335\",\n \"0.63154\",\n \"0.6304914\",\n \"0.62913346\",\n \"0.62878436\",\n \"0.6282993\",\n \"0.6275106\",\n \"0.62614816\",\n \"0.62519175\",\n \"0.624967\",\n \"0.62470603\",\n \"0.62448496\",\n \"0.62370104\",\n \"0.62343186\",\n \"0.62332565\",\n \"0.62326676\",\n \"0.62304145\",\n \"0.62271625\",\n \"0.62271625\",\n \"0.622372\",\n \"0.6207163\",\n \"0.62006664\",\n \"0.6193098\",\n \"0.61809254\",\n \"0.61493605\",\n \"0.61459637\",\n \"0.6138623\",\n \"0.6126078\",\n \"0.6125377\",\n \"0.61244714\",\n \"0.61181116\",\n \"0.6116154\",\n \"0.61076874\",\n \"0.6100571\",\n \"0.6099422\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73712176"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":787,"cells":{"query":{"kind":"string","value":"Returns the inverse of the 3d rot and trans matrix"},"document":{"kind":"string","value":"def inverse_transform3(phi, theta=0.0, psi=0.0, tx=0.0, ty=0.0, tz=0.0, mirror = 0, scale=1.0):\n\n\td = Transform({'type': 'spider', 'phi': phi, 'theta': theta, 'psi': psi, 'tx': tx, 'ty': ty, 'tz': tz, \"mirror\":mirror,\"scale\":scale})\n\td = d.inverse()\n\td = d.get_params(\"spider\")\n\treturn d[\"phi\"],d[\"theta\"],d[\"psi\"],d[\"tx\"],d[\"ty\"],d[\"tz\"],d[\"mirror\"],d[\"scale\"]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def inverse_rigid_trans(Tr): \n inv_Tr = np.zeros_like(Tr) # 3x4\n inv_Tr[0:3,0:3] = np.transpose(Tr[0:3,0:3])\n inv_Tr[0:3,3] = np.dot(-np.transpose(Tr[0:3,0:3]), Tr[0:3,3])\n return inv_Tr","def inverse(self):\n rotation_matrix = self.pose_mat[:3, :3]\n translation_vector = self.pose_mat[:3, 3]\n\n rot = np.transpose(rotation_matrix)\n trans = - np.matmul(np.transpose(rotation_matrix), translation_vector)\n return Transformation(rot, trans)","def inverse(self):\n return Rotation(self.matrix.transposed())","def inverse(self):\r\n \r\n Mi=mat4()\r\n d=self.determinant()\r\n for i in range(4):\r\n for j in range(4):\r\n sign=1-((i+j)%2)*2\r\n m3=self._submat(i,j)\r\n Mi[j,i]=sign*m3.determinant()/d\r\n return Mi","def inv(transform_matrix):\n\n r = transform_matrix[0:3, 0:3]\n t = transform_matrix[0:3, 3]\n t_inv = -1 * r.T.dot(t)\n transform_inv = np.eye(4)\n transform_inv[0:3, 0:3] = r.T\n transform_inv[0:3, 3] = t_inv\n\n return transform_inv","def GetInverse(self, *args):\n return _itkTranslationTransformPython.itkTranslationTransformD3_GetInverse(self, *args)","def rot_inv(self):\n if not hasattr(self, '_rot_inv'):\n self._rot_inv=np.linalg.inv(self.rot)\n return self._rot_inv","def getInverseMatrix(self) -> CMatrix4:\n ...","def inverse_matrice(T):\n a,b,c,d = T[0][0],T[0][1],T[1][0],T[1][1]\n det = a*d-b*c\n aa,bb,cc,dd = d/det,-b/det,-c/det,a/det\n Tinv = [[aa,bb],[cc,dd]]\n return Tinv","def inv(T):\n K, L = T.shape[1:3]\n squ_matrix = np.einsum('ijkl->ikjl', T).reshape((K*L, K*L),order='F')\n t = np.linalg.inv(squ_matrix)\n return np.einsum('ijkl->ikjl', t.reshape((K,L,K,L), order='F'))","def affine_transform_inverse(np_transform):\n rotation = np_transform[:3, :3]\n translation = np_transform[:3, 3]\n rotation_inv = numpy.linalg.inv(rotation)\n translation_inv = -1 * numpy.dot(rotation_inv, translation)\n result = numpy.identity(4)\n result[:3, :3] = rotation_inv\n result[:3, 3] = translation_inv.flatten()\n return result","def rotation_inv(R: np.array) -> np.array:\n return R.T","def trans_matrix_inv(m:numpy.ndarray):\n was2d = False\n if m.shape[1] == 3:\n was2d = True\n m = numpy.asarray([\n [1.0, 0.0, 0.0, 0.0],\n [0.0, m[0,0], m[0,1], m[0,2]],\n [0.0, m[1,0], m[1,1], m[1,2]],\n [0.0, 0.0, 0.0, 1.0]], numpy.float64)\n trans = m[0:3,3]\n rotate = numpy.zeros(3, numpy.float64)\n r = m[0:3,0:3]\n rc = numpy.linalg.cholesky(numpy.matmul(r.T, r)).T\n scale = numpy.diagonal(rc)\n if numpy.linalg.det(r) < 0.0:\n scale[0] *= -1.0\n rcd = rc * numpy.eye(3, dtype=numpy.float64)\n rc = numpy.linalg.solve(rcd, rc)\n shear = numpy.asarray([rc[0,1], rc[0,2], rc[1,2]], numpy.float64)\n r0 = trans_matrix({'rotate': rotate, 'scale': scale, 'shear': shear})[0:3,0:3]\n r0 = numpy.linalg.solve(numpy.linalg.inv(r), numpy.linalg.inv(r0))\n rotate[1] = numpy.arcsin(_frone(r0[0,2]))\n if numpy.abs((numpy.abs(rotate[1]) - (numpy.pi / 2.0))) < 1.0e-6:\n rotate[0] = 0.0\n rotate[2] = numpy.arctan2(-_frone(r0[1,0]), _frone(-r0[2,0] / r0[0,2]))\n else:\n rc = numpy.cos(rotate[1])\n rotate[0] = numpy.arctan2(_frone(r0[1,2] / rc), _frone(r0[2,2] / rc))\n rotate[2] = numpy.arctan2(_frone(r0[0,1] / rc), _frone(r0[0,0] / rc))\n if was2d:\n trans = trans[1:]\n rotate = rotate[0:1]\n scale = scale[1:]\n shear = shear[2:3]\n return (trans, rotate, scale, shear)","def inverse(self):\n data = np.linalg.inv(self._data)\n return self.create(self.rows, self.cols, data)","def inv(self):\n inv = np.linalg.inv(self._mat)\n return MoebTr(inv[0][0], inv[0][1], inv[1][0], inv[1][1])","def inverse_affine_transformation_matrix(A):\n n, n = A.shape\n\n # extract components R, an n-1xn-1 linear transformation matrix, and T, an nx1 translation matrix\n R = A[:n-1, :n-1]\n T = A[:n-1, n-1]\n\n # find R^-1\n R_inv = np.linalg.inv(R)\n\n # Find A^-1/A_inv\n A_inv = np.copy(A).astype(float) # copy A for base of A^-1 matrix and ensure it is of data type float\n A_inv[:n-1, :n-1] = R_inv # set top left nxn sub matrix equal to R^-1\n A_inv[:n-1, n-1] = np.negative(R_inv.dot(T)) # place -R^-1*T in top right corner\n\n return A_inv","def inverse(self,mat):\n result = np.linalg.inv(mat)\n self.out = result\n return self.out","def intrinsic_matrix_inv(self) -> np.ndarray:\n\n # determinant of top left of intrinsic matrix\n tldet = self.kx * self.ky\n\n return np.array([[1 / self.kx, -self.kxy / tldet, (self.py * self.kxy - self.ky * self.px) / tldet],\n [0, 1 / self.ky, -self.py / self.ky]])","def T_inv(T):\n R, xyz = rigmech.T2Rxyz(T)\n R_inv = R.T\n xyz_inv = -R_inv * xyz\n T_inv = R_inv.row_join(xyz_inv).col_join(sp.Matrix([[0, 0, 0, 1]]))\n return T_inv","def inverse_transform2(alpha, tx = 0.0, ty = 0.0, mirror = 0):\n\n\tt = Transform({\"type\":\"2D\",\"alpha\":alpha,\"tx\":tx,\"ty\":ty,\"mirror\":mirror,\"scale\":1.0})\n\tt = t.inverse()\n\tt = t.get_params(\"2D\")\n\treturn t[ \"alpha\" ], t[ \"tx\" ], t[ \"ty\" ], t[ \"mirror\" ]","def _get_inv(self):\n m,d = self.B.shape\n Im = np.eye(m)\n Id = np.eye(d)\n BBt = self.B@self.B.T\n I_BBt_inv = np.linalg.pinv(Im + BBt)\n \n return (1/self.alpha)*(Id - self.B.T@( I_BBt_inv@self.B/self.alpha))","def inverse (x_ij):\n\n Hij = xyzrph2matrix (x_ij)\n Rji = Hij[0:3, 0:3]\n tij = Hij[0:3,3]\n Rij = Rji.transpose ()\n tji = -Rij.dot (tij)\n Hji = numpy.zeros ((4,4))\n Hji[0:3,0:3] = Rij\n Hji[0:3,3] = tji\n Hji[3,3] = 1\n return matrix2xyzrph (Hji)","def inverse(self):\n self.check_square()\n\n\n N = self.rows\n\n inverse = make_matrix(N, N)\n\n # Solve on a per-column basis using Ax = b formalism\n for j in range(N):\n b = make_matrix(N, 1)\n b[j, 0] = 1\n\n x = self.solve_linear_system(b)\n\n for i in range(N):\n inverse[i, j] = x[i, 0]\n\n return inverse","def inverse(self):\n # find the determinant of the matrix\n determinant = self.determinant()\n # find the matrix of minors of the matrix\n matrix_of_minors = self.matrix_of_minors()\n # find the cofactor of the matrix of minors\n cofactor_matrix = self.cofactor_matrix(matrix_of_minors)\n # find the transpose of the cofactor matrix\n transpose_cofactor_matrix = self.transpose(cofactor_matrix)\n # find the adjugate (inverse) matrix\n inverse_matrix = self.adjugate_matrix(determinant, transpose_cofactor_matrix)\n\n return inverse_matrix","def inverse(self):\n return self.solve(Matrix.I(self.nrows))","def ComputeInverseInnerOrientation(self):\n a0 = self.innerOrientationParameters[0]\n b0 = self.innerOrientationParameters[1]\n a1 = self.innerOrientationParameters[2]\n a2 = self.innerOrientationParameters[3]\n b1 = self.innerOrientationParameters[4]\n b2 = self.innerOrientationParameters[5]\n\n mat = np.array([[a1[0], a2[0]], [b1[0], b2[0]]])\n mat = la.inv(mat)\n\n return np.array([a0[0], b0[0], mat[0, 0], mat[0, 1], mat[1, 0], mat[1, 1]]).T","def inverse(self):\n if self.determinant() != 0:\n ops = reduce_to_red_echelon(self.data.copy(), True)[1]\n matrix = identity_matrix(self.n_rows).data\n \n if ops:\n if isinstance(ops[0], str):\n ops = [ops]\n \n for op in ops:\n if op[0] == 'swap':\n matrix = row_swap(matrix, op[1], op[2])\n elif op[0] == 'multiplication':\n matrix = row_multiply(matrix, op[1], op[2])\n elif op[0] == 'subtract':\n matrix = row_subtract(matrix, op[1], op[2], op[3])\n else:\n raise ValueError('Row operation not recognized')\n else:\n raise ValueError('Matrix has a determinant of 0 and is not invertible')\n return Matrix(matrix)","def inverse(self):\n rotation = self.rotation.inverse()\n translation = rotation * (-self.translation)\n\n return Transform(rotation, translation)","def matrix_inv(mat):\n\ta = mat[0,0]\n\tb = mat[0,1]\n\tc = mat[0,2]\n\td = mat[1,0]\n\te = mat[1,1]\n\tf = mat[1,2]\n\tg = mat[2,0]\n\th = mat[2,1]\n\ti = mat[2,2]\n\n\tdet = b*f*g + c*d*h + a*e*i - a*f*h - b*d*i - c*e*g\n\n\tinvmat = np.zeros((3,3))\n\tinvmat[0,0] = (e*i - f*h) / det\n\tinvmat[0,1] = (c*h - b*i) / det\n\tinvmat[0,2] = (b*f - c*e) / det\n\tinvmat[1,0] = (f*g - d*i) / det\n\tinvmat[1,1] = (a*i - c*g) / det\n\tinvmat[1,2] = (c*d - a*f) / det\n\tinvmat[2,0] = (d*h - e*g) / det\n\tinvmat[2,1] = (b*g - a*h) / det\n\tinvmat[2,2] = (a*e - b*d) / det\n\treturn invmat","def Invert(*args, **kwargs):\n return _gdi_.GraphicsMatrix_Invert(*args, **kwargs)","def ssc.inverse (x_ij):\n\n Hij = xyzrph2matrix (x_ij)\n Rji = Hij[0:3, 0:3]\n tij = Hij[0:3,3]\n Rij = Rji.transpose ()\n tji = -Rij.dot (tij)\n Hji = zeros ((4,4))\n Hji[0:3,0:3] = Rij\n Hji[0:3,3] = tji\n Hji[3,3] = 1\n return matrix2xyzrph (Hji)","def invert(M):\r\n M = isMatrix(M)\r\n return M.I","def GetInverse(self, *args):\n return _itkTranslationTransformPython.itkTranslationTransformD2_GetInverse(self, *args)","def inv(X):\n R, t = Rt(X)\n Ri = R.T\n return np.concatenate((\n np.concatenate((Ri, -Ri.dot(t)[:,np.newaxis]), axis=1),\n np.array([[0, 0, 1]])))","def invert(x):\n return linalg.inv(x)","def InverseRotation(rotation):\n return RotationMatrix([\n [rotation.rot[0][0], rotation.rot[1][0], rotation.rot[2][0]],\n [rotation.rot[0][1], rotation.rot[1][1], rotation.rot[2][1]],\n [rotation.rot[0][2], rotation.rot[1][2], rotation.rot[2][2]]\n ])","def inv(self):\n\n self.x, self.y = self.y, self.x\n self._x_, self._y_ = self._y_, self._x_\n self.xfac, self.yfac = 1 / self.yfac, 1 / self.xfac\n self._xfac_, self._yfac_ = 1 / self._yfac_, 1 / self._xfac_\n self._u = 1 / self._u.conj()","def invert_affine_transform(matrix: Tensor) -> Tensor:\n if not isinstance(matrix, Tensor):\n raise TypeError(f\"Input matrix type is not a Tensor. Got {type(matrix)}\")\n\n if not (len(matrix.shape) == 3 and matrix.shape[-2:] == (2, 3)):\n raise ValueError(f\"Input matrix must be a Bx2x3 tensor. Got {matrix.shape}\")\n\n matrix_tmp: Tensor = convert_affinematrix_to_homography(matrix)\n matrix_inv: Tensor = _torch_inverse_cast(matrix_tmp)\n\n return matrix_inv[..., :2, :3]","def inverse(self):\n if not self.is_square():\n raise(ValueError, \"Non-square Matrix does not have an inverse.\")\n if self.h > 2:\n raise(NotImplementedError, \"inversion not implemented for matrices larger than 2x2.\")\n\n # TODO - your code here\n if self.h == 1:\n inverse = [[1/self.g[0][0]]];\n else:\n a = self.g[0][0];\n b = self.g[0][1];\n c = self.g[1][0];\n d = self.g[1][1];\n if(a*d==b*c):\n raise ValueError('matrix does not have a inverse!');\n else:\n weigh = 1/(a*d-b*c);\n inverse = [[weigh*d,weigh*-1*b],[weigh*-1*c,weigh*a]];\n return Matrix(inverse);","def inverse(self):\n invr = np.linalg.inv(self.affine_matrix)\n return SymmOp(invr)","def inverseTransformationMatrix(self,index=None):\n if self.method == 'pca':\n if index is not None:\n coordinateIndex = distribution1D.vectori_cxx(len(index))\n for i in range(len(index)):\n coordinateIndex[i] = index[i]\n matrixDim = self._distribution.getInverseTransformationMatrixDimensions(coordinateIndex)\n inverseTransformation = self._distribution.getInverseTransformationMatrix(coordinateIndex)\n else:\n matrixDim = self._distribution.getInverseTransformationMatrixDimensions()\n inverseTransformation = self._distribution.getInverseTransformationMatrix()\n row = matrixDim[0]\n column = matrixDim[1]\n # convert 1D vector to 2D array\n L = np.atleast_1d(inverseTransformation).reshape(row,column)\n else:\n self.raiseAnError(NotImplementedError,' inverse transformationMatrix is not yet implemented for ' + self.method + ' method')\n return L","def matrix_translate_3d(tx: float, ty: float, tz: float) -> np.matrix:\n return np.matrix([[1, 0, 0, tx], [0, 1, 0, ty], [0, 0, 1, tz], [0, 0, 0, 1]])","def getDejaVuMatrix(self):\n mtx = self.getRotMatrix((4, 4), transpose=None) # from Quaternion\n mtx[3] = self.getTranslation()\n mtx[:3, 3] = mtx[3, :3]\n mtx[3, :3] = [0, 0, 0]\n return mtx","def invert(self):\n\n if self.rows != self.columns:\n raise ValueError(\"Matrix must be square to invert\")\n\n A, operations = self.to_reduced_row_echelon()\n if not A.is_identity():\n return 0\n\n # If A was reduced to the identity matrix, then the same set of operations will take I to the inverse of A.\n # [A I] -> [I A^(-1)]\n\n I = IdentityMatrix(size = self.rows)\n for operation in operations:\n func = I.__getattribute__(operation[0])\n args = operation[1:]\n func(*args)\n\n return I","def inverseN(self):\r\n result = Matrix(self.rows, self.columns)\r\n for r in range(self.rows):\r\n for c in range(self.columns):\r\n result.mat[r][c] = self.cofactor(r, c)\r\n result.out()\r\n result = result.transpose()\r\n det = self.determinant()\r\n print(\"1/(\" + str(det) + \")\")\r\n result.out()\r\n return result","def inverse(self):\n if not self.is_square():\n raise(ValueError, \"Non-square Matrix does not have an inverse.\")\n if self.h > 2:\n raise(NotImplementedError, \"inversion not implemented for matrices larger than 2x2.\")\n # TODO - your code here\n inverse = []\n if self.h == 1:\n temp = []\n temp.append(1/self.g[0][0])\n inverse.append(temp)\n else:\n identity_matrix = identity(self.h)\n det_term = 1/self.determinant()\n trace_term = self.trace()\n # implement intermediate scaling step locally\n # trace_x_I = trace_term * identity_matrix\n trace_x_I = []\n for i in range(len(self.g)):\n temp_row = []\n for j in range(len(self.g[i])):\n temp_row.append(trace_term * identity_matrix[i][j])\n trace_x_I.append(temp_row)\n # implement sub-traction locally\n # sub_term = trace_x_I - self.g\n sub_term = []\n for i in range(len(trace_x_I)):\n temp_row = []\n for j in range(len(trace_x_I[i])):\n temp_row.append(trace_x_I[i][j] - self.g[i][j])\n sub_term.append(temp_row)\n # implement final scaling step locally\n # inverse = det_term * sub_term\n inverse = []\n for i in range(len(sub_term)):\n temp_row = []\n for j in range(len(sub_term[i])):\n temp_row.append(det_term * sub_term[i][j])\n inverse.append(temp_row)\n return Matrix(inverse)\n # TODO - your code here","def inverse(self):\n # TODO\n # detA\n if not self.is_square():\n raise(\n ValueError, \"Non-square Matrix does not have an inverse.\")\n if self.h > 2:\n raise(\n NotImplementedError, \"inversion not implemented for matrices larger than 2x2.\")\n\n mD = self.determinant()\n if self.h == 1:\n if self.g[0][0] = 0:\n raise(NotImplementedError,\n \"The 1x1 Matrix contains 0 can't inverse\")\n else:\n return [[1 / self.g[0][0]]] \n for i in range(self.h): # Calculates the inverse of a 2x2 Matrix.\n my_Matrix = zeroes(2, 2)\n my_Matrix.g[1][1] = self.g[0][0] / mD\n my_Matrix.g[0][0] = self.g[1][1] / mD\n my_Matrix.g[0][1] = - self.g[0][1] / mD\n my_Matrix.g[1][0] = - self.g[1][0] / mD\n return my_Matrix\n\n # trace A\n # 与矩阵TraceA * I identity 单位矩阵","def posdef_inv_matrix_inverse(tensor, identity, damping):\n return tf.matrix_inverse(tensor + damping * identity)","def inverse(self):\n ss = self._sum_of_squares()\n if ss > 0:\n return self.__class__(array=(self._vector_conjugate() / ss))\n else:\n raise ZeroDivisionError(\"a zero quaternion (0 + 0i + 0j + 0k) cannot be inverted\")","def inv_rotation_matrix(self):\n return np.linalg.inv(self._rotation_matrix).tolist()","def inverse_cayley_transform(z: torch.Tensor) -> torch.Tensor:\n identity = identity_like(z)\n i_identity = multiply_by_i(identity)\n\n z_minus_id = z - i_identity\n inv_z_plus_id = inverse(z + i_identity)\n return z_minus_id @ inv_z_plus_id","def complex_inverse(c1,cr):","def _inverse_affine_matrix(self) -> np.ndarray:\n raise NotImplementedError","def inverse( m, context = FloatContext, copy_m=True ):\n n,n_ = shape_mat(m)\n assert (n==n_) #matris should be square\n\n return solve( m, eye(n), context=context, copy_b=False, copy_a=copy_m )","def inverse(self):\n def inv(v):\n v[0], v[1] = v[1] , v[0]\n for v in [self.point1 , self.pointN , self.unitv, self.normalv]:\n inv(v)\n\n self.points = numpy.roll(self.points,1,axis=1)\n self.a, self.b = self.b, self.a\n self.angle = numpy.arccos( self.unitv[0] )*numpy.sign(self.unitv[1] )\n return","def tensorinv(a, ind=2):\n return TensorInv(ind)(a)","def _r_inv(self):\n # [output_dim, output_dim]\n return tf.linalg.cholesky_solve(\n self._chol_obs_covariance,\n tf.eye(self.emission.output_dim, dtype=self._chol_obs_covariance.dtype),\n )","def inverse_transform(data, flip, rotate):\n if flip == 'FLIP_LEFT_RIGHT':\n data = np.fliplr(data)\n\n if rotate == 'ROTATE_90':\n data = np.rot90(data, 3)\n\n if rotate == 'ROTATE_180':\n data = np.rot90(data, 2)\n\n if rotate == 'ROTATE_270':\n data = np.rot90(data, 1)\n\n return data","def get_transform_matrix(theta, phi = None, invert_rot = False, invert_focal = False):\n\n if phi is None:\n phi = const.PHI_IDX * 10.0\n\n #extrinsic x intrinsic\n camera_matrix = np.zeros((4, 4), dtype=np.float32)\n\n intrinsic_matrix = np.eye(4, dtype=np.float32)\n extrinsic_matrix = np.eye(4, dtype=np.float32)\n\n sin_phi = np.sin(float(phi) / 180.0 * np.pi)\n cos_phi = np.cos(float(phi) / 180.0 * np.pi)\n sin_theta = np.sin(float(-theta) / 180.0 * np.pi)\n cos_theta = np.cos(float(-theta) / 180.0 * np.pi)\n\n #theta rotation\n rotation_azimuth = np.zeros((3, 3), dtype=np.float32)\n rotation_azimuth[0, 0] = cos_theta\n rotation_azimuth[2, 2] = cos_theta\n rotation_azimuth[0, 2] = -sin_theta\n rotation_azimuth[2, 0] = sin_theta\n rotation_azimuth[1, 1] = 1.0\n\n #phi rotation\n rotation_elevation = np.zeros((3, 3), dtype=np.float32)\n rotation_elevation[0, 0] = cos_phi\n rotation_elevation[0, 1] = sin_phi\n rotation_elevation[1, 0] = -sin_phi\n rotation_elevation[1, 1] = cos_phi\n rotation_elevation[2, 2] = 1.0\n\n #rotate phi, then theta\n rotation_matrix = np.matmul(rotation_azimuth, rotation_elevation)\n if invert_rot:\n rotation_matrix = np.linalg.inv(rotation_matrix)\n\n displacement = np.zeros((3, 1), dtype=np.float32)\n displacement[0, 0] = const.DIST_TO_CAM\n displacement = np.matmul(rotation_matrix, displacement)\n\n #assembling 4x4 from R + T\n extrinsic_matrix[0:3, 0:3] = rotation_matrix\n extrinsic_matrix[0:3, 3:4] = -displacement\n\n if invert_focal:\n intrinsic_matrix[2, 2] = float(const.focal_length)\n intrinsic_matrix[1, 1] = float(const.focal_length)\n else:\n intrinsic_matrix[2, 2] = 1.0 / float(const.focal_length)\n intrinsic_matrix[1, 1] = 1.0 / float(const.focal_length)\n\n camera_matrix = np.matmul(extrinsic_matrix, intrinsic_matrix)\n return camera_matrix","def transMatrix( source=None ):\n if source is None:\n return None,None\n else:\n (x,y,z) = source[:3]\n if x == y == z == 0.0:\n return None, None \n return tmatrixaccel.transMatrix( x,y,z ),tmatrixaccel.transMatrix( -x, -y, -z )","def get_world_inv_matrix(m_obj, i):\n if not m_obj.hasFn(oMa.MFn.kTransform):\n return\n\n fn_obj = oMa.MFnDependencyNode(m_obj)\n plug = fn_obj.findPlug('worldInverseMatrix', False).elementByLogicalIndex(i)\n matrix_obj = plug.asMObject()\n matrix_data = oMa.MFnMatrixData(matrix_obj)\n matrix = matrix_data.matrix()\n\n return matrix","def inverse(self):\n cdef StdVectorFst result = self.copy()\n result.invert()\n return result","def __invert__(self):\n return self.inverse()","def inverse_transform(self, matrix):\n\n x = matrix.shape[0]\n y = matrix.shape[1]\n N = x\n\n # Inverse Fourier Transform matrix:\n ift = np.zeros([x, y], complex)\n\n for i in range(0, x):\n for j in range(0, y):\n sum_ift = 0\n for u in range(0, x):\n for v in range(0, y):\n sum_ift = sum_ift + matrix[u, v] * (np.cos(((2 * np.pi) / N) * (u * i + v * j)) + 1j * np.sin(((2 * np.pi) / N) * (u * i + v * j)))\n\n ift[i, j] = sum_ift\n\n\n return ift/(x*x)","def inverse(self):\n q_vector = np.zeros(4)\n q_vector[:3] = self.imaginary*-1\n q_vector[3] = self.w\n return Quat(q_vector,\"xyzw\")","def inv(in_A):\n Q,R = qr(in_A)\n QT = Q.T\n N = shape(in_A)[0]\n \n for n in range(N-1,-1,-1):\n Rnn = R[n,n]\n R[n,:] /= Rnn\n QT[n,:] /= Rnn\n for m in range(n+1,N):\n Rnm = R[n,m]\n R[n,m] = 0\n QT[n,:] -= QT[m,:]*Rnm\n\n return QT","def _inv(self) -> None:\n\n self.inv(inplace=True)","def _inv22_vectorized(M):\n assert (M.ndim == 3)\n assert (M.shape[-2:] == (2, 2))\n M_inv = np.empty_like(M)\n delta_inv = np.reciprocal(M[:, 0, 0]*M[:, 1, 1] - M[:, 0, 1]*M[:, 1, 0])\n M_inv[:, 0, 0] = M[:, 1, 1]*delta_inv\n M_inv[:, 0, 1] = -M[:, 0, 1]*delta_inv\n M_inv[:, 1, 0] = -M[:, 1, 0]*delta_inv\n M_inv[:, 1, 1] = M[:, 0, 0]*delta_inv\n return M_inv","def inverse(self):\n return self.invert()","def inv(self):\n return MoebGen(self._d / self._det, - self._b / self._det, - self._c / self._det, self._a / self._det)","def reverse_matrix(self):\n return SWAP.matrix @ self.matrix @ SWAP.matrix","def inverse_basis(T, dimensions, t):\n B = basis(T, dimensions, t)\n return inv(B.T.dot(B)).dot(B.T)","def rotation_matrix(angle, axis):\n about_z = rotation_about_z(angle)\n z_to_axis = z_to_vector(axis)\n axis_to_z = np.linalg.inv(z_to_axis)\n return reduce(np.dot, [z_to_axis, about_z, axis_to_z])","def transformation_matrix(self):\n t = np.array([[0.0], [0.0], [0.0]])\n Rt = np.hstack([self.rotation_matrix, t])\n return np.vstack([Rt, np.array([0.0, 0.0, 0.0, 1.0])])","def inv_inplace(a):","def _inv(M):\n ll, mm = M.shape\n M2 = M + 1e-10 * np.eye(ll)\n L = np.linalg.cholesky(M2)\n inv_L = np.linalg.inv(L)\n inv_M = inv_L.T @ inv_L\n return inv_M","def inverse(self):\n return self._inverse","def inverse(self):\n group = self.group\n r = tuple([(i, -j) for i, j in self.array_form[::-1]])\n return group.dtype(r)","def inverse_transform(self, X):\n ...","def inverse_transform(self, X):\n ...","def inverse_transform(self, X):\n ...","def inverse_transform(self, X):\n ...","def inverse_transform(self, X):\n ...","def inverse(self, ys):\n with torch.no_grad():\n xs = torch.matmul(ys, torch.diag(torch.reciprocal(torch.exp(self.scaling_diag))))\n xs = self.layer4.inverse(xs)\n xs = self.layer3.inverse(xs)\n xs = self.layer2.inverse(xs)\n xs = self.layer1.inverse(xs)\n return xs","def inverse(self, u: Tensor, covariates: Tensor) -> Tensor:\n return self.real_nvp.inverse(u, covariates)","def rotated_e():\n x = np.zeros((5, 5))\n x[:, 0] = 1.\n y = np.zeros((5, 5))\n y[:, 2] = 1.\n z = np.zeros((5, 5))\n z[:, 4] = 1.\n a = np.zeros((5, 5))\n a[0, :] = 1.\n b = np.zeros((5, 5))\n b[2, :] = 1.\n c = np.zeros((5, 5))\n c[4, :] = 1.\n\n img = np.zeros((4, 5, 5))\n img[0] = x + y + z + a\n img[1] = x + y + z + c\n img[2] = a + b + c + x\n img[3] = a + b + c + z\n img[img > 0] = 1.\n\n return img.astype('float32')","def inv(self):\n\t\tdeterminant = self.det()\n\t\tif determinant:\n\t\t\treturn self.adj() / determinant\n\t\telse:\n\t\t\traise ValueError(\"Not Invertible\")","def invert(self):\n if self.m != self.n:\n raise exc.LinearAlgebraError(\"cannot invert a non-square matrix\")\n if self.determinant == 0:\n raise exc.LinearAlgebraError(\"cannot invert a singular matrix\")\n # TODO: implement block matrices in their own method\n block_rows = [r1 + r2 for r1, r2 in\n zip(self.data, self.makeIdentity(self.m).data)]\n inverse_block = Matrix.fromRows(block_rows).row_reduce()\n return inverse_block.subset([i for i in range(self.m)],\n [j + self.n for j in range(self.n)])","def inv(self):\n return self.conjugate()","def inverse(self):\n n = self.norm()\n c = self.conj()\n d = 1.0 / (n * n)\n c.scale(d)\n return c","def inverse(self):\n real = self.real / self.magnitude()\n pure = -self.pure / self.magnitude()\n return Quaternion((real, pure))","def quaternion_inv(quaternion):\r\n q = numpy.array(quaternion, dtype=numpy.float64, copy=True)\r\n numpy.negative(q[1:], q[1:])\r\n return q / numpy.dot(q, q)","def __invert__(self):\n try:\n B = ~(self.matrix())\n except ZeroDivisionError:\n raise ZeroDivisionError(\"matrix morphism not invertible\")\n try:\n return self.parent().reversed()(B)\n except TypeError:\n raise ZeroDivisionError(\"matrix morphism not invertible\")","def inverseIntermediateJac(self,x):\n \n Ri = self._rotation.T\n si = (1./self._scaled).reshape((1,1,self._dim))\n \n Jac = self.intermediateJacPol2Rot(x)\n \n #Ri.J\n Jac = np.einsum(\"jk,ikl->ijl\",Ri,Jac)\n #(Ri.J).diag(si)\n Jac *= si\n \n return Jac","def inverse_3by3_double(M):\n if len(M.shape) > 1:\n M = M.flatten()\n\n M = np.array(M, 'float')\n\n determinant = 0.\n adj_M = np.zeros((9,), 'float')\n\n # First row of adjugate matrix\n adj_M[0] = (M[4] * M[8] - M[7] * M[5]) # Det #0\n adj_M[1] = -(M[1] * M[8] - M[7] * M[2])\n adj_M[2] = (M[1] * M[5] - M[4] * M[2])\n\n # Second row of adjugate matrix\n adj_M[3] = -(M[3] * M[8] - M[6] * M[5]) # Det #1\n adj_M[4] = (M[0] * M[8] - M[6] * M[2])\n adj_M[5] = -(M[0] * M[5] - M[3] * M[2])\n\n # Third row of adjugate matrix\n adj_M[6] = (M[3] * M[7] - M[6] * M[4]) # Det #2\n adj_M[7] = -(M[0] * M[7] - M[6] * M[1])\n adj_M[8] = (M[0] * M[4] - M[3] * M[1])\n\n determinant += M[0] * adj_M[0]\n determinant += M[1] * adj_M[3] # Using addition since minus is integrated in adjugate matrix.\n determinant += M[2] * adj_M[6]\n\n return (adj_M / determinant)","def BackTransform(self, *args):\n return _itkTranslationTransformPython.itkTranslationTransformD3_BackTransform(self, *args)","def transformation_matrix(self, s1, s2, s3, t1, t2, t3):\n\n s1 = np.array(s1)\n s2 = np.array(s2)\n s3 = np.array(s3)\n t1 = np.array(t1)\n t2 = np.array(t2)\n t3 = np.array(t3)\n\n Q = np.array(\n [\n [t2[0] - t1[0], t2[1] - t1[1], t2[2] - t1[2]],\n [t3[0] - t1[0], t3[1] - t1[1], t3[2] - t1[2]],\n ]\n )\n\n P = np.array([[s2[0] - s1[0], s2[1] - s1[1]], [s3[0] - s1[0], s3[1] - s1[1]]])\n\n try:\n # Invert the P matrix\n Pinv = inv(P)\n\n # Build the dot product\n T = np.dot(Pinv, Q)\n\n # Offset\n V0 = np.subtract(t2, np.transpose(s2[0:2]).dot(T))\n except Exception as e:\n self.log.error(\"An error occured during the transformation.\", exc_info=True)\n return -1, -1\n\n return T, V0","def rotation_matrix(self):\n return self.affine_matrix[0:3][:, 0:3]","def invgeochart(w):\n # u = torch.asin(w[...,2])\n u = torch.acos(w[...,2])\n # v = torch.acos(w[...,0]/torch.cos(u))\n v = torch.atan(w[...,1]/w[...,0])\n return torch.stack((u,v+np.pi))","def getTransposeMatrix(self) -> CMatrix4:\n ..."],"string":"[\n \"def inverse_rigid_trans(Tr): \\n inv_Tr = np.zeros_like(Tr) # 3x4\\n inv_Tr[0:3,0:3] = np.transpose(Tr[0:3,0:3])\\n inv_Tr[0:3,3] = np.dot(-np.transpose(Tr[0:3,0:3]), Tr[0:3,3])\\n return inv_Tr\",\n \"def inverse(self):\\n rotation_matrix = self.pose_mat[:3, :3]\\n translation_vector = self.pose_mat[:3, 3]\\n\\n rot = np.transpose(rotation_matrix)\\n trans = - np.matmul(np.transpose(rotation_matrix), translation_vector)\\n return Transformation(rot, trans)\",\n \"def inverse(self):\\n return Rotation(self.matrix.transposed())\",\n \"def inverse(self):\\r\\n \\r\\n Mi=mat4()\\r\\n d=self.determinant()\\r\\n for i in range(4):\\r\\n for j in range(4):\\r\\n sign=1-((i+j)%2)*2\\r\\n m3=self._submat(i,j)\\r\\n Mi[j,i]=sign*m3.determinant()/d\\r\\n return Mi\",\n \"def inv(transform_matrix):\\n\\n r = transform_matrix[0:3, 0:3]\\n t = transform_matrix[0:3, 3]\\n t_inv = -1 * r.T.dot(t)\\n transform_inv = np.eye(4)\\n transform_inv[0:3, 0:3] = r.T\\n transform_inv[0:3, 3] = t_inv\\n\\n return transform_inv\",\n \"def GetInverse(self, *args):\\n return _itkTranslationTransformPython.itkTranslationTransformD3_GetInverse(self, *args)\",\n \"def rot_inv(self):\\n if not hasattr(self, '_rot_inv'):\\n self._rot_inv=np.linalg.inv(self.rot)\\n return self._rot_inv\",\n \"def getInverseMatrix(self) -> CMatrix4:\\n ...\",\n \"def inverse_matrice(T):\\n a,b,c,d = T[0][0],T[0][1],T[1][0],T[1][1]\\n det = a*d-b*c\\n aa,bb,cc,dd = d/det,-b/det,-c/det,a/det\\n Tinv = [[aa,bb],[cc,dd]]\\n return Tinv\",\n \"def inv(T):\\n K, L = T.shape[1:3]\\n squ_matrix = np.einsum('ijkl->ikjl', T).reshape((K*L, K*L),order='F')\\n t = np.linalg.inv(squ_matrix)\\n return np.einsum('ijkl->ikjl', t.reshape((K,L,K,L), order='F'))\",\n \"def affine_transform_inverse(np_transform):\\n rotation = np_transform[:3, :3]\\n translation = np_transform[:3, 3]\\n rotation_inv = numpy.linalg.inv(rotation)\\n translation_inv = -1 * numpy.dot(rotation_inv, translation)\\n result = numpy.identity(4)\\n result[:3, :3] = rotation_inv\\n result[:3, 3] = translation_inv.flatten()\\n return result\",\n \"def rotation_inv(R: np.array) -> np.array:\\n return R.T\",\n \"def trans_matrix_inv(m:numpy.ndarray):\\n was2d = False\\n if m.shape[1] == 3:\\n was2d = True\\n m = numpy.asarray([\\n [1.0, 0.0, 0.0, 0.0],\\n [0.0, m[0,0], m[0,1], m[0,2]],\\n [0.0, m[1,0], m[1,1], m[1,2]],\\n [0.0, 0.0, 0.0, 1.0]], numpy.float64)\\n trans = m[0:3,3]\\n rotate = numpy.zeros(3, numpy.float64)\\n r = m[0:3,0:3]\\n rc = numpy.linalg.cholesky(numpy.matmul(r.T, r)).T\\n scale = numpy.diagonal(rc)\\n if numpy.linalg.det(r) < 0.0:\\n scale[0] *= -1.0\\n rcd = rc * numpy.eye(3, dtype=numpy.float64)\\n rc = numpy.linalg.solve(rcd, rc)\\n shear = numpy.asarray([rc[0,1], rc[0,2], rc[1,2]], numpy.float64)\\n r0 = trans_matrix({'rotate': rotate, 'scale': scale, 'shear': shear})[0:3,0:3]\\n r0 = numpy.linalg.solve(numpy.linalg.inv(r), numpy.linalg.inv(r0))\\n rotate[1] = numpy.arcsin(_frone(r0[0,2]))\\n if numpy.abs((numpy.abs(rotate[1]) - (numpy.pi / 2.0))) < 1.0e-6:\\n rotate[0] = 0.0\\n rotate[2] = numpy.arctan2(-_frone(r0[1,0]), _frone(-r0[2,0] / r0[0,2]))\\n else:\\n rc = numpy.cos(rotate[1])\\n rotate[0] = numpy.arctan2(_frone(r0[1,2] / rc), _frone(r0[2,2] / rc))\\n rotate[2] = numpy.arctan2(_frone(r0[0,1] / rc), _frone(r0[0,0] / rc))\\n if was2d:\\n trans = trans[1:]\\n rotate = rotate[0:1]\\n scale = scale[1:]\\n shear = shear[2:3]\\n return (trans, rotate, scale, shear)\",\n \"def inverse(self):\\n data = np.linalg.inv(self._data)\\n return self.create(self.rows, self.cols, data)\",\n \"def inv(self):\\n inv = np.linalg.inv(self._mat)\\n return MoebTr(inv[0][0], inv[0][1], inv[1][0], inv[1][1])\",\n \"def inverse_affine_transformation_matrix(A):\\n n, n = A.shape\\n\\n # extract components R, an n-1xn-1 linear transformation matrix, and T, an nx1 translation matrix\\n R = A[:n-1, :n-1]\\n T = A[:n-1, n-1]\\n\\n # find R^-1\\n R_inv = np.linalg.inv(R)\\n\\n # Find A^-1/A_inv\\n A_inv = np.copy(A).astype(float) # copy A for base of A^-1 matrix and ensure it is of data type float\\n A_inv[:n-1, :n-1] = R_inv # set top left nxn sub matrix equal to R^-1\\n A_inv[:n-1, n-1] = np.negative(R_inv.dot(T)) # place -R^-1*T in top right corner\\n\\n return A_inv\",\n \"def inverse(self,mat):\\n result = np.linalg.inv(mat)\\n self.out = result\\n return self.out\",\n \"def intrinsic_matrix_inv(self) -> np.ndarray:\\n\\n # determinant of top left of intrinsic matrix\\n tldet = self.kx * self.ky\\n\\n return np.array([[1 / self.kx, -self.kxy / tldet, (self.py * self.kxy - self.ky * self.px) / tldet],\\n [0, 1 / self.ky, -self.py / self.ky]])\",\n \"def T_inv(T):\\n R, xyz = rigmech.T2Rxyz(T)\\n R_inv = R.T\\n xyz_inv = -R_inv * xyz\\n T_inv = R_inv.row_join(xyz_inv).col_join(sp.Matrix([[0, 0, 0, 1]]))\\n return T_inv\",\n \"def inverse_transform2(alpha, tx = 0.0, ty = 0.0, mirror = 0):\\n\\n\\tt = Transform({\\\"type\\\":\\\"2D\\\",\\\"alpha\\\":alpha,\\\"tx\\\":tx,\\\"ty\\\":ty,\\\"mirror\\\":mirror,\\\"scale\\\":1.0})\\n\\tt = t.inverse()\\n\\tt = t.get_params(\\\"2D\\\")\\n\\treturn t[ \\\"alpha\\\" ], t[ \\\"tx\\\" ], t[ \\\"ty\\\" ], t[ \\\"mirror\\\" ]\",\n \"def _get_inv(self):\\n m,d = self.B.shape\\n Im = np.eye(m)\\n Id = np.eye(d)\\n BBt = self.B@self.B.T\\n I_BBt_inv = np.linalg.pinv(Im + BBt)\\n \\n return (1/self.alpha)*(Id - self.B.T@( I_BBt_inv@self.B/self.alpha))\",\n \"def inverse (x_ij):\\n\\n Hij = xyzrph2matrix (x_ij)\\n Rji = Hij[0:3, 0:3]\\n tij = Hij[0:3,3]\\n Rij = Rji.transpose ()\\n tji = -Rij.dot (tij)\\n Hji = numpy.zeros ((4,4))\\n Hji[0:3,0:3] = Rij\\n Hji[0:3,3] = tji\\n Hji[3,3] = 1\\n return matrix2xyzrph (Hji)\",\n \"def inverse(self):\\n self.check_square()\\n\\n\\n N = self.rows\\n\\n inverse = make_matrix(N, N)\\n\\n # Solve on a per-column basis using Ax = b formalism\\n for j in range(N):\\n b = make_matrix(N, 1)\\n b[j, 0] = 1\\n\\n x = self.solve_linear_system(b)\\n\\n for i in range(N):\\n inverse[i, j] = x[i, 0]\\n\\n return inverse\",\n \"def inverse(self):\\n # find the determinant of the matrix\\n determinant = self.determinant()\\n # find the matrix of minors of the matrix\\n matrix_of_minors = self.matrix_of_minors()\\n # find the cofactor of the matrix of minors\\n cofactor_matrix = self.cofactor_matrix(matrix_of_minors)\\n # find the transpose of the cofactor matrix\\n transpose_cofactor_matrix = self.transpose(cofactor_matrix)\\n # find the adjugate (inverse) matrix\\n inverse_matrix = self.adjugate_matrix(determinant, transpose_cofactor_matrix)\\n\\n return inverse_matrix\",\n \"def inverse(self):\\n return self.solve(Matrix.I(self.nrows))\",\n \"def ComputeInverseInnerOrientation(self):\\n a0 = self.innerOrientationParameters[0]\\n b0 = self.innerOrientationParameters[1]\\n a1 = self.innerOrientationParameters[2]\\n a2 = self.innerOrientationParameters[3]\\n b1 = self.innerOrientationParameters[4]\\n b2 = self.innerOrientationParameters[5]\\n\\n mat = np.array([[a1[0], a2[0]], [b1[0], b2[0]]])\\n mat = la.inv(mat)\\n\\n return np.array([a0[0], b0[0], mat[0, 0], mat[0, 1], mat[1, 0], mat[1, 1]]).T\",\n \"def inverse(self):\\n if self.determinant() != 0:\\n ops = reduce_to_red_echelon(self.data.copy(), True)[1]\\n matrix = identity_matrix(self.n_rows).data\\n \\n if ops:\\n if isinstance(ops[0], str):\\n ops = [ops]\\n \\n for op in ops:\\n if op[0] == 'swap':\\n matrix = row_swap(matrix, op[1], op[2])\\n elif op[0] == 'multiplication':\\n matrix = row_multiply(matrix, op[1], op[2])\\n elif op[0] == 'subtract':\\n matrix = row_subtract(matrix, op[1], op[2], op[3])\\n else:\\n raise ValueError('Row operation not recognized')\\n else:\\n raise ValueError('Matrix has a determinant of 0 and is not invertible')\\n return Matrix(matrix)\",\n \"def inverse(self):\\n rotation = self.rotation.inverse()\\n translation = rotation * (-self.translation)\\n\\n return Transform(rotation, translation)\",\n \"def matrix_inv(mat):\\n\\ta = mat[0,0]\\n\\tb = mat[0,1]\\n\\tc = mat[0,2]\\n\\td = mat[1,0]\\n\\te = mat[1,1]\\n\\tf = mat[1,2]\\n\\tg = mat[2,0]\\n\\th = mat[2,1]\\n\\ti = mat[2,2]\\n\\n\\tdet = b*f*g + c*d*h + a*e*i - a*f*h - b*d*i - c*e*g\\n\\n\\tinvmat = np.zeros((3,3))\\n\\tinvmat[0,0] = (e*i - f*h) / det\\n\\tinvmat[0,1] = (c*h - b*i) / det\\n\\tinvmat[0,2] = (b*f - c*e) / det\\n\\tinvmat[1,0] = (f*g - d*i) / det\\n\\tinvmat[1,1] = (a*i - c*g) / det\\n\\tinvmat[1,2] = (c*d - a*f) / det\\n\\tinvmat[2,0] = (d*h - e*g) / det\\n\\tinvmat[2,1] = (b*g - a*h) / det\\n\\tinvmat[2,2] = (a*e - b*d) / det\\n\\treturn invmat\",\n \"def Invert(*args, **kwargs):\\n return _gdi_.GraphicsMatrix_Invert(*args, **kwargs)\",\n \"def ssc.inverse (x_ij):\\n\\n Hij = xyzrph2matrix (x_ij)\\n Rji = Hij[0:3, 0:3]\\n tij = Hij[0:3,3]\\n Rij = Rji.transpose ()\\n tji = -Rij.dot (tij)\\n Hji = zeros ((4,4))\\n Hji[0:3,0:3] = Rij\\n Hji[0:3,3] = tji\\n Hji[3,3] = 1\\n return matrix2xyzrph (Hji)\",\n \"def invert(M):\\r\\n M = isMatrix(M)\\r\\n return M.I\",\n \"def GetInverse(self, *args):\\n return _itkTranslationTransformPython.itkTranslationTransformD2_GetInverse(self, *args)\",\n \"def inv(X):\\n R, t = Rt(X)\\n Ri = R.T\\n return np.concatenate((\\n np.concatenate((Ri, -Ri.dot(t)[:,np.newaxis]), axis=1),\\n np.array([[0, 0, 1]])))\",\n \"def invert(x):\\n return linalg.inv(x)\",\n \"def InverseRotation(rotation):\\n return RotationMatrix([\\n [rotation.rot[0][0], rotation.rot[1][0], rotation.rot[2][0]],\\n [rotation.rot[0][1], rotation.rot[1][1], rotation.rot[2][1]],\\n [rotation.rot[0][2], rotation.rot[1][2], rotation.rot[2][2]]\\n ])\",\n \"def inv(self):\\n\\n self.x, self.y = self.y, self.x\\n self._x_, self._y_ = self._y_, self._x_\\n self.xfac, self.yfac = 1 / self.yfac, 1 / self.xfac\\n self._xfac_, self._yfac_ = 1 / self._yfac_, 1 / self._xfac_\\n self._u = 1 / self._u.conj()\",\n \"def invert_affine_transform(matrix: Tensor) -> Tensor:\\n if not isinstance(matrix, Tensor):\\n raise TypeError(f\\\"Input matrix type is not a Tensor. Got {type(matrix)}\\\")\\n\\n if not (len(matrix.shape) == 3 and matrix.shape[-2:] == (2, 3)):\\n raise ValueError(f\\\"Input matrix must be a Bx2x3 tensor. Got {matrix.shape}\\\")\\n\\n matrix_tmp: Tensor = convert_affinematrix_to_homography(matrix)\\n matrix_inv: Tensor = _torch_inverse_cast(matrix_tmp)\\n\\n return matrix_inv[..., :2, :3]\",\n \"def inverse(self):\\n if not self.is_square():\\n raise(ValueError, \\\"Non-square Matrix does not have an inverse.\\\")\\n if self.h > 2:\\n raise(NotImplementedError, \\\"inversion not implemented for matrices larger than 2x2.\\\")\\n\\n # TODO - your code here\\n if self.h == 1:\\n inverse = [[1/self.g[0][0]]];\\n else:\\n a = self.g[0][0];\\n b = self.g[0][1];\\n c = self.g[1][0];\\n d = self.g[1][1];\\n if(a*d==b*c):\\n raise ValueError('matrix does not have a inverse!');\\n else:\\n weigh = 1/(a*d-b*c);\\n inverse = [[weigh*d,weigh*-1*b],[weigh*-1*c,weigh*a]];\\n return Matrix(inverse);\",\n \"def inverse(self):\\n invr = np.linalg.inv(self.affine_matrix)\\n return SymmOp(invr)\",\n \"def inverseTransformationMatrix(self,index=None):\\n if self.method == 'pca':\\n if index is not None:\\n coordinateIndex = distribution1D.vectori_cxx(len(index))\\n for i in range(len(index)):\\n coordinateIndex[i] = index[i]\\n matrixDim = self._distribution.getInverseTransformationMatrixDimensions(coordinateIndex)\\n inverseTransformation = self._distribution.getInverseTransformationMatrix(coordinateIndex)\\n else:\\n matrixDim = self._distribution.getInverseTransformationMatrixDimensions()\\n inverseTransformation = self._distribution.getInverseTransformationMatrix()\\n row = matrixDim[0]\\n column = matrixDim[1]\\n # convert 1D vector to 2D array\\n L = np.atleast_1d(inverseTransformation).reshape(row,column)\\n else:\\n self.raiseAnError(NotImplementedError,' inverse transformationMatrix is not yet implemented for ' + self.method + ' method')\\n return L\",\n \"def matrix_translate_3d(tx: float, ty: float, tz: float) -> np.matrix:\\n return np.matrix([[1, 0, 0, tx], [0, 1, 0, ty], [0, 0, 1, tz], [0, 0, 0, 1]])\",\n \"def getDejaVuMatrix(self):\\n mtx = self.getRotMatrix((4, 4), transpose=None) # from Quaternion\\n mtx[3] = self.getTranslation()\\n mtx[:3, 3] = mtx[3, :3]\\n mtx[3, :3] = [0, 0, 0]\\n return mtx\",\n \"def invert(self):\\n\\n if self.rows != self.columns:\\n raise ValueError(\\\"Matrix must be square to invert\\\")\\n\\n A, operations = self.to_reduced_row_echelon()\\n if not A.is_identity():\\n return 0\\n\\n # If A was reduced to the identity matrix, then the same set of operations will take I to the inverse of A.\\n # [A I] -> [I A^(-1)]\\n\\n I = IdentityMatrix(size = self.rows)\\n for operation in operations:\\n func = I.__getattribute__(operation[0])\\n args = operation[1:]\\n func(*args)\\n\\n return I\",\n \"def inverseN(self):\\r\\n result = Matrix(self.rows, self.columns)\\r\\n for r in range(self.rows):\\r\\n for c in range(self.columns):\\r\\n result.mat[r][c] = self.cofactor(r, c)\\r\\n result.out()\\r\\n result = result.transpose()\\r\\n det = self.determinant()\\r\\n print(\\\"1/(\\\" + str(det) + \\\")\\\")\\r\\n result.out()\\r\\n return result\",\n \"def inverse(self):\\n if not self.is_square():\\n raise(ValueError, \\\"Non-square Matrix does not have an inverse.\\\")\\n if self.h > 2:\\n raise(NotImplementedError, \\\"inversion not implemented for matrices larger than 2x2.\\\")\\n # TODO - your code here\\n inverse = []\\n if self.h == 1:\\n temp = []\\n temp.append(1/self.g[0][0])\\n inverse.append(temp)\\n else:\\n identity_matrix = identity(self.h)\\n det_term = 1/self.determinant()\\n trace_term = self.trace()\\n # implement intermediate scaling step locally\\n # trace_x_I = trace_term * identity_matrix\\n trace_x_I = []\\n for i in range(len(self.g)):\\n temp_row = []\\n for j in range(len(self.g[i])):\\n temp_row.append(trace_term * identity_matrix[i][j])\\n trace_x_I.append(temp_row)\\n # implement sub-traction locally\\n # sub_term = trace_x_I - self.g\\n sub_term = []\\n for i in range(len(trace_x_I)):\\n temp_row = []\\n for j in range(len(trace_x_I[i])):\\n temp_row.append(trace_x_I[i][j] - self.g[i][j])\\n sub_term.append(temp_row)\\n # implement final scaling step locally\\n # inverse = det_term * sub_term\\n inverse = []\\n for i in range(len(sub_term)):\\n temp_row = []\\n for j in range(len(sub_term[i])):\\n temp_row.append(det_term * sub_term[i][j])\\n inverse.append(temp_row)\\n return Matrix(inverse)\\n # TODO - your code here\",\n \"def inverse(self):\\n # TODO\\n # detA\\n if not self.is_square():\\n raise(\\n ValueError, \\\"Non-square Matrix does not have an inverse.\\\")\\n if self.h > 2:\\n raise(\\n NotImplementedError, \\\"inversion not implemented for matrices larger than 2x2.\\\")\\n\\n mD = self.determinant()\\n if self.h == 1:\\n if self.g[0][0] = 0:\\n raise(NotImplementedError,\\n \\\"The 1x1 Matrix contains 0 can't inverse\\\")\\n else:\\n return [[1 / self.g[0][0]]] \\n for i in range(self.h): # Calculates the inverse of a 2x2 Matrix.\\n my_Matrix = zeroes(2, 2)\\n my_Matrix.g[1][1] = self.g[0][0] / mD\\n my_Matrix.g[0][0] = self.g[1][1] / mD\\n my_Matrix.g[0][1] = - self.g[0][1] / mD\\n my_Matrix.g[1][0] = - self.g[1][0] / mD\\n return my_Matrix\\n\\n # trace A\\n # 与矩阵TraceA * I identity 单位矩阵\",\n \"def posdef_inv_matrix_inverse(tensor, identity, damping):\\n return tf.matrix_inverse(tensor + damping * identity)\",\n \"def inverse(self):\\n ss = self._sum_of_squares()\\n if ss > 0:\\n return self.__class__(array=(self._vector_conjugate() / ss))\\n else:\\n raise ZeroDivisionError(\\\"a zero quaternion (0 + 0i + 0j + 0k) cannot be inverted\\\")\",\n \"def inv_rotation_matrix(self):\\n return np.linalg.inv(self._rotation_matrix).tolist()\",\n \"def inverse_cayley_transform(z: torch.Tensor) -> torch.Tensor:\\n identity = identity_like(z)\\n i_identity = multiply_by_i(identity)\\n\\n z_minus_id = z - i_identity\\n inv_z_plus_id = inverse(z + i_identity)\\n return z_minus_id @ inv_z_plus_id\",\n \"def complex_inverse(c1,cr):\",\n \"def _inverse_affine_matrix(self) -> np.ndarray:\\n raise NotImplementedError\",\n \"def inverse( m, context = FloatContext, copy_m=True ):\\n n,n_ = shape_mat(m)\\n assert (n==n_) #matris should be square\\n\\n return solve( m, eye(n), context=context, copy_b=False, copy_a=copy_m )\",\n \"def inverse(self):\\n def inv(v):\\n v[0], v[1] = v[1] , v[0]\\n for v in [self.point1 , self.pointN , self.unitv, self.normalv]:\\n inv(v)\\n\\n self.points = numpy.roll(self.points,1,axis=1)\\n self.a, self.b = self.b, self.a\\n self.angle = numpy.arccos( self.unitv[0] )*numpy.sign(self.unitv[1] )\\n return\",\n \"def tensorinv(a, ind=2):\\n return TensorInv(ind)(a)\",\n \"def _r_inv(self):\\n # [output_dim, output_dim]\\n return tf.linalg.cholesky_solve(\\n self._chol_obs_covariance,\\n tf.eye(self.emission.output_dim, dtype=self._chol_obs_covariance.dtype),\\n )\",\n \"def inverse_transform(data, flip, rotate):\\n if flip == 'FLIP_LEFT_RIGHT':\\n data = np.fliplr(data)\\n\\n if rotate == 'ROTATE_90':\\n data = np.rot90(data, 3)\\n\\n if rotate == 'ROTATE_180':\\n data = np.rot90(data, 2)\\n\\n if rotate == 'ROTATE_270':\\n data = np.rot90(data, 1)\\n\\n return data\",\n \"def get_transform_matrix(theta, phi = None, invert_rot = False, invert_focal = False):\\n\\n if phi is None:\\n phi = const.PHI_IDX * 10.0\\n\\n #extrinsic x intrinsic\\n camera_matrix = np.zeros((4, 4), dtype=np.float32)\\n\\n intrinsic_matrix = np.eye(4, dtype=np.float32)\\n extrinsic_matrix = np.eye(4, dtype=np.float32)\\n\\n sin_phi = np.sin(float(phi) / 180.0 * np.pi)\\n cos_phi = np.cos(float(phi) / 180.0 * np.pi)\\n sin_theta = np.sin(float(-theta) / 180.0 * np.pi)\\n cos_theta = np.cos(float(-theta) / 180.0 * np.pi)\\n\\n #theta rotation\\n rotation_azimuth = np.zeros((3, 3), dtype=np.float32)\\n rotation_azimuth[0, 0] = cos_theta\\n rotation_azimuth[2, 2] = cos_theta\\n rotation_azimuth[0, 2] = -sin_theta\\n rotation_azimuth[2, 0] = sin_theta\\n rotation_azimuth[1, 1] = 1.0\\n\\n #phi rotation\\n rotation_elevation = np.zeros((3, 3), dtype=np.float32)\\n rotation_elevation[0, 0] = cos_phi\\n rotation_elevation[0, 1] = sin_phi\\n rotation_elevation[1, 0] = -sin_phi\\n rotation_elevation[1, 1] = cos_phi\\n rotation_elevation[2, 2] = 1.0\\n\\n #rotate phi, then theta\\n rotation_matrix = np.matmul(rotation_azimuth, rotation_elevation)\\n if invert_rot:\\n rotation_matrix = np.linalg.inv(rotation_matrix)\\n\\n displacement = np.zeros((3, 1), dtype=np.float32)\\n displacement[0, 0] = const.DIST_TO_CAM\\n displacement = np.matmul(rotation_matrix, displacement)\\n\\n #assembling 4x4 from R + T\\n extrinsic_matrix[0:3, 0:3] = rotation_matrix\\n extrinsic_matrix[0:3, 3:4] = -displacement\\n\\n if invert_focal:\\n intrinsic_matrix[2, 2] = float(const.focal_length)\\n intrinsic_matrix[1, 1] = float(const.focal_length)\\n else:\\n intrinsic_matrix[2, 2] = 1.0 / float(const.focal_length)\\n intrinsic_matrix[1, 1] = 1.0 / float(const.focal_length)\\n\\n camera_matrix = np.matmul(extrinsic_matrix, intrinsic_matrix)\\n return camera_matrix\",\n \"def transMatrix( source=None ):\\n if source is None:\\n return None,None\\n else:\\n (x,y,z) = source[:3]\\n if x == y == z == 0.0:\\n return None, None \\n return tmatrixaccel.transMatrix( x,y,z ),tmatrixaccel.transMatrix( -x, -y, -z )\",\n \"def get_world_inv_matrix(m_obj, i):\\n if not m_obj.hasFn(oMa.MFn.kTransform):\\n return\\n\\n fn_obj = oMa.MFnDependencyNode(m_obj)\\n plug = fn_obj.findPlug('worldInverseMatrix', False).elementByLogicalIndex(i)\\n matrix_obj = plug.asMObject()\\n matrix_data = oMa.MFnMatrixData(matrix_obj)\\n matrix = matrix_data.matrix()\\n\\n return matrix\",\n \"def inverse(self):\\n cdef StdVectorFst result = self.copy()\\n result.invert()\\n return result\",\n \"def __invert__(self):\\n return self.inverse()\",\n \"def inverse_transform(self, matrix):\\n\\n x = matrix.shape[0]\\n y = matrix.shape[1]\\n N = x\\n\\n # Inverse Fourier Transform matrix:\\n ift = np.zeros([x, y], complex)\\n\\n for i in range(0, x):\\n for j in range(0, y):\\n sum_ift = 0\\n for u in range(0, x):\\n for v in range(0, y):\\n sum_ift = sum_ift + matrix[u, v] * (np.cos(((2 * np.pi) / N) * (u * i + v * j)) + 1j * np.sin(((2 * np.pi) / N) * (u * i + v * j)))\\n\\n ift[i, j] = sum_ift\\n\\n\\n return ift/(x*x)\",\n \"def inverse(self):\\n q_vector = np.zeros(4)\\n q_vector[:3] = self.imaginary*-1\\n q_vector[3] = self.w\\n return Quat(q_vector,\\\"xyzw\\\")\",\n \"def inv(in_A):\\n Q,R = qr(in_A)\\n QT = Q.T\\n N = shape(in_A)[0]\\n \\n for n in range(N-1,-1,-1):\\n Rnn = R[n,n]\\n R[n,:] /= Rnn\\n QT[n,:] /= Rnn\\n for m in range(n+1,N):\\n Rnm = R[n,m]\\n R[n,m] = 0\\n QT[n,:] -= QT[m,:]*Rnm\\n\\n return QT\",\n \"def _inv(self) -> None:\\n\\n self.inv(inplace=True)\",\n \"def _inv22_vectorized(M):\\n assert (M.ndim == 3)\\n assert (M.shape[-2:] == (2, 2))\\n M_inv = np.empty_like(M)\\n delta_inv = np.reciprocal(M[:, 0, 0]*M[:, 1, 1] - M[:, 0, 1]*M[:, 1, 0])\\n M_inv[:, 0, 0] = M[:, 1, 1]*delta_inv\\n M_inv[:, 0, 1] = -M[:, 0, 1]*delta_inv\\n M_inv[:, 1, 0] = -M[:, 1, 0]*delta_inv\\n M_inv[:, 1, 1] = M[:, 0, 0]*delta_inv\\n return M_inv\",\n \"def inverse(self):\\n return self.invert()\",\n \"def inv(self):\\n return MoebGen(self._d / self._det, - self._b / self._det, - self._c / self._det, self._a / self._det)\",\n \"def reverse_matrix(self):\\n return SWAP.matrix @ self.matrix @ SWAP.matrix\",\n \"def inverse_basis(T, dimensions, t):\\n B = basis(T, dimensions, t)\\n return inv(B.T.dot(B)).dot(B.T)\",\n \"def rotation_matrix(angle, axis):\\n about_z = rotation_about_z(angle)\\n z_to_axis = z_to_vector(axis)\\n axis_to_z = np.linalg.inv(z_to_axis)\\n return reduce(np.dot, [z_to_axis, about_z, axis_to_z])\",\n \"def transformation_matrix(self):\\n t = np.array([[0.0], [0.0], [0.0]])\\n Rt = np.hstack([self.rotation_matrix, t])\\n return np.vstack([Rt, np.array([0.0, 0.0, 0.0, 1.0])])\",\n \"def inv_inplace(a):\",\n \"def _inv(M):\\n ll, mm = M.shape\\n M2 = M + 1e-10 * np.eye(ll)\\n L = np.linalg.cholesky(M2)\\n inv_L = np.linalg.inv(L)\\n inv_M = inv_L.T @ inv_L\\n return inv_M\",\n \"def inverse(self):\\n return self._inverse\",\n \"def inverse(self):\\n group = self.group\\n r = tuple([(i, -j) for i, j in self.array_form[::-1]])\\n return group.dtype(r)\",\n \"def inverse_transform(self, X):\\n ...\",\n \"def inverse_transform(self, X):\\n ...\",\n \"def inverse_transform(self, X):\\n ...\",\n \"def inverse_transform(self, X):\\n ...\",\n \"def inverse_transform(self, X):\\n ...\",\n \"def inverse(self, ys):\\n with torch.no_grad():\\n xs = torch.matmul(ys, torch.diag(torch.reciprocal(torch.exp(self.scaling_diag))))\\n xs = self.layer4.inverse(xs)\\n xs = self.layer3.inverse(xs)\\n xs = self.layer2.inverse(xs)\\n xs = self.layer1.inverse(xs)\\n return xs\",\n \"def inverse(self, u: Tensor, covariates: Tensor) -> Tensor:\\n return self.real_nvp.inverse(u, covariates)\",\n \"def rotated_e():\\n x = np.zeros((5, 5))\\n x[:, 0] = 1.\\n y = np.zeros((5, 5))\\n y[:, 2] = 1.\\n z = np.zeros((5, 5))\\n z[:, 4] = 1.\\n a = np.zeros((5, 5))\\n a[0, :] = 1.\\n b = np.zeros((5, 5))\\n b[2, :] = 1.\\n c = np.zeros((5, 5))\\n c[4, :] = 1.\\n\\n img = np.zeros((4, 5, 5))\\n img[0] = x + y + z + a\\n img[1] = x + y + z + c\\n img[2] = a + b + c + x\\n img[3] = a + b + c + z\\n img[img > 0] = 1.\\n\\n return img.astype('float32')\",\n \"def inv(self):\\n\\t\\tdeterminant = self.det()\\n\\t\\tif determinant:\\n\\t\\t\\treturn self.adj() / determinant\\n\\t\\telse:\\n\\t\\t\\traise ValueError(\\\"Not Invertible\\\")\",\n \"def invert(self):\\n if self.m != self.n:\\n raise exc.LinearAlgebraError(\\\"cannot invert a non-square matrix\\\")\\n if self.determinant == 0:\\n raise exc.LinearAlgebraError(\\\"cannot invert a singular matrix\\\")\\n # TODO: implement block matrices in their own method\\n block_rows = [r1 + r2 for r1, r2 in\\n zip(self.data, self.makeIdentity(self.m).data)]\\n inverse_block = Matrix.fromRows(block_rows).row_reduce()\\n return inverse_block.subset([i for i in range(self.m)],\\n [j + self.n for j in range(self.n)])\",\n \"def inv(self):\\n return self.conjugate()\",\n \"def inverse(self):\\n n = self.norm()\\n c = self.conj()\\n d = 1.0 / (n * n)\\n c.scale(d)\\n return c\",\n \"def inverse(self):\\n real = self.real / self.magnitude()\\n pure = -self.pure / self.magnitude()\\n return Quaternion((real, pure))\",\n \"def quaternion_inv(quaternion):\\r\\n q = numpy.array(quaternion, dtype=numpy.float64, copy=True)\\r\\n numpy.negative(q[1:], q[1:])\\r\\n return q / numpy.dot(q, q)\",\n \"def __invert__(self):\\n try:\\n B = ~(self.matrix())\\n except ZeroDivisionError:\\n raise ZeroDivisionError(\\\"matrix morphism not invertible\\\")\\n try:\\n return self.parent().reversed()(B)\\n except TypeError:\\n raise ZeroDivisionError(\\\"matrix morphism not invertible\\\")\",\n \"def inverseIntermediateJac(self,x):\\n \\n Ri = self._rotation.T\\n si = (1./self._scaled).reshape((1,1,self._dim))\\n \\n Jac = self.intermediateJacPol2Rot(x)\\n \\n #Ri.J\\n Jac = np.einsum(\\\"jk,ikl->ijl\\\",Ri,Jac)\\n #(Ri.J).diag(si)\\n Jac *= si\\n \\n return Jac\",\n \"def inverse_3by3_double(M):\\n if len(M.shape) > 1:\\n M = M.flatten()\\n\\n M = np.array(M, 'float')\\n\\n determinant = 0.\\n adj_M = np.zeros((9,), 'float')\\n\\n # First row of adjugate matrix\\n adj_M[0] = (M[4] * M[8] - M[7] * M[5]) # Det #0\\n adj_M[1] = -(M[1] * M[8] - M[7] * M[2])\\n adj_M[2] = (M[1] * M[5] - M[4] * M[2])\\n\\n # Second row of adjugate matrix\\n adj_M[3] = -(M[3] * M[8] - M[6] * M[5]) # Det #1\\n adj_M[4] = (M[0] * M[8] - M[6] * M[2])\\n adj_M[5] = -(M[0] * M[5] - M[3] * M[2])\\n\\n # Third row of adjugate matrix\\n adj_M[6] = (M[3] * M[7] - M[6] * M[4]) # Det #2\\n adj_M[7] = -(M[0] * M[7] - M[6] * M[1])\\n adj_M[8] = (M[0] * M[4] - M[3] * M[1])\\n\\n determinant += M[0] * adj_M[0]\\n determinant += M[1] * adj_M[3] # Using addition since minus is integrated in adjugate matrix.\\n determinant += M[2] * adj_M[6]\\n\\n return (adj_M / determinant)\",\n \"def BackTransform(self, *args):\\n return _itkTranslationTransformPython.itkTranslationTransformD3_BackTransform(self, *args)\",\n \"def transformation_matrix(self, s1, s2, s3, t1, t2, t3):\\n\\n s1 = np.array(s1)\\n s2 = np.array(s2)\\n s3 = np.array(s3)\\n t1 = np.array(t1)\\n t2 = np.array(t2)\\n t3 = np.array(t3)\\n\\n Q = np.array(\\n [\\n [t2[0] - t1[0], t2[1] - t1[1], t2[2] - t1[2]],\\n [t3[0] - t1[0], t3[1] - t1[1], t3[2] - t1[2]],\\n ]\\n )\\n\\n P = np.array([[s2[0] - s1[0], s2[1] - s1[1]], [s3[0] - s1[0], s3[1] - s1[1]]])\\n\\n try:\\n # Invert the P matrix\\n Pinv = inv(P)\\n\\n # Build the dot product\\n T = np.dot(Pinv, Q)\\n\\n # Offset\\n V0 = np.subtract(t2, np.transpose(s2[0:2]).dot(T))\\n except Exception as e:\\n self.log.error(\\\"An error occured during the transformation.\\\", exc_info=True)\\n return -1, -1\\n\\n return T, V0\",\n \"def rotation_matrix(self):\\n return self.affine_matrix[0:3][:, 0:3]\",\n \"def invgeochart(w):\\n # u = torch.asin(w[...,2])\\n u = torch.acos(w[...,2])\\n # v = torch.acos(w[...,0]/torch.cos(u))\\n v = torch.atan(w[...,1]/w[...,0])\\n return torch.stack((u,v+np.pi))\",\n \"def getTransposeMatrix(self) -> CMatrix4:\\n ...\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.75761485","0.7515526","0.7423403","0.7418693","0.73657846","0.7288308","0.7254451","0.7109498","0.7077596","0.7040509","0.700391","0.6975826","0.6933009","0.6900903","0.68664896","0.6786958","0.6786305","0.6780128","0.6755011","0.67143685","0.6650445","0.66385657","0.6633354","0.6624766","0.66143036","0.6612461","0.6605213","0.6582941","0.6561472","0.6557437","0.6527339","0.6505916","0.649954","0.64896667","0.6478087","0.64711523","0.6443079","0.64137226","0.6410265","0.64046216","0.6379611","0.63641703","0.6352396","0.635068","0.6349814","0.634266","0.6339411","0.63392603","0.63342464","0.63139284","0.63096863","0.63089234","0.6298493","0.62805563","0.6243935","0.6226705","0.6206828","0.6178754","0.6172308","0.61550295","0.615501","0.61522955","0.61468667","0.61416644","0.61406046","0.61365795","0.6124081","0.6123134","0.61064184","0.6080443","0.60758823","0.60733616","0.605772","0.60568655","0.6040437","0.60271645","0.602043","0.60196054","0.60136163","0.60136163","0.60136163","0.60136163","0.60136163","0.60130656","0.6000585","0.5996734","0.5985868","0.5982421","0.59794414","0.5977023","0.5974251","0.59635097","0.5961458","0.59595776","0.5959085","0.59561515","0.5950094","0.5942188","0.59249103","0.5910203"],"string":"[\n \"0.75761485\",\n \"0.7515526\",\n \"0.7423403\",\n \"0.7418693\",\n \"0.73657846\",\n \"0.7288308\",\n \"0.7254451\",\n \"0.7109498\",\n \"0.7077596\",\n \"0.7040509\",\n \"0.700391\",\n \"0.6975826\",\n \"0.6933009\",\n \"0.6900903\",\n \"0.68664896\",\n \"0.6786958\",\n \"0.6786305\",\n \"0.6780128\",\n \"0.6755011\",\n \"0.67143685\",\n \"0.6650445\",\n \"0.66385657\",\n \"0.6633354\",\n \"0.6624766\",\n \"0.66143036\",\n \"0.6612461\",\n \"0.6605213\",\n \"0.6582941\",\n \"0.6561472\",\n \"0.6557437\",\n \"0.6527339\",\n \"0.6505916\",\n \"0.649954\",\n \"0.64896667\",\n \"0.6478087\",\n \"0.64711523\",\n \"0.6443079\",\n \"0.64137226\",\n \"0.6410265\",\n \"0.64046216\",\n \"0.6379611\",\n \"0.63641703\",\n \"0.6352396\",\n \"0.635068\",\n \"0.6349814\",\n \"0.634266\",\n \"0.6339411\",\n \"0.63392603\",\n \"0.63342464\",\n \"0.63139284\",\n \"0.63096863\",\n \"0.63089234\",\n \"0.6298493\",\n \"0.62805563\",\n \"0.6243935\",\n \"0.6226705\",\n \"0.6206828\",\n \"0.6178754\",\n \"0.6172308\",\n \"0.61550295\",\n \"0.615501\",\n \"0.61522955\",\n \"0.61468667\",\n \"0.61416644\",\n \"0.61406046\",\n \"0.61365795\",\n \"0.6124081\",\n \"0.6123134\",\n \"0.61064184\",\n \"0.6080443\",\n \"0.60758823\",\n \"0.60733616\",\n \"0.605772\",\n \"0.60568655\",\n \"0.6040437\",\n \"0.60271645\",\n \"0.602043\",\n \"0.60196054\",\n \"0.60136163\",\n \"0.60136163\",\n \"0.60136163\",\n \"0.60136163\",\n \"0.60136163\",\n \"0.60130656\",\n \"0.6000585\",\n \"0.5996734\",\n \"0.5985868\",\n \"0.5982421\",\n \"0.59794414\",\n \"0.5977023\",\n \"0.5974251\",\n \"0.59635097\",\n \"0.5961458\",\n \"0.59595776\",\n \"0.5959085\",\n \"0.59561515\",\n \"0.5950094\",\n \"0.5942188\",\n \"0.59249103\",\n \"0.5910203\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6268844"},"document_rank":{"kind":"string","value":"54"}}},{"rowIdx":788,"cells":{"query":{"kind":"string","value":"Create a list of available symmetries"},"document":{"kind":"string","value":"def list_syms():\n\tSymStringVec=[];\n\tSymStringVec.append(\"CSYM\");\n\tSymStringVec.append(\"DSYM\");\n\tSymStringVec.append(\"TET_SYM\");\n\tSymStringVec.append(\"OCT_SYM\");\n\tSymStringVec.append(\"ICOS_SYM\");\n\tSymStringVec.append(\"ISYM\");\n\treturn SymStringVec"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_symmetries(self):\n temp = self._properties.get('symmetries', [])\n return temp","def __set_symbol_dict(self):\r\n return {0: list(alph) if self.is_case_snstv else list(alph)[:26],\r\n 1: list(dgt),\r\n 2: list(spcl) if self.is_spcl else []}","def z2_symmetries(self) -> \"Z2Symmetries\":\n return self._z2_symmetries","def get_symbols(self):\n\n raise NotImplementedError('''\n Must implement get_symbols. Call help() for details.\n ''')","def get_symbols(**kwargs):\n return Symbols(**kwargs).fetch()","def itersymbols(self):\n for syms in self._symbols.itervalues():\n for sym in syms:\n yield sym","def get_symbol(self):\n return []","def get_symbols_list(self):\n return self.symbols_list","def namelist():\n\n\n session = Session(engine)\n\n results = session.query(lockdown.country).order_by(lockdown.country).all()\n\n #session.close()\n all_symbols = list(np.ravel(results))\n sym = all_symbols[1]\n\n return jsonify(all_symbols)","def list_symbol_tables(mst):\n stlist = []\n def append_st(st):\n #print(st)\n stlist.append(st)\n for s in st.get_symbols():\n for ns in s.get_namespaces():\n append_st(ns)\n if not isinstance(mst, symtable.SymbolTable):\n # Assume it is text of a program to compile\n mst = symtable.symtable(mst, '<string>', 'exec')\n append_st(mst)\n return stlist","def _create_symbol_mapping():\n normal_items = [\"+\", \"-\"]\n unicode_items = [chr(0x2070 + i) for i in range(10, 12)]\n\n # Create a dict mapping the two.\n return DefaultDictionary(zip(normal_items, unicode_items))","def symbols(self):\n pass","def __init__(self, symbols):\r\n self.symbols = set(symbols)","def get_symbols(self, type_name):\n return self._symtab[type_name].get_symbols()","def choices(symbols, k):\n return [R.choice(symbols) for _ in range(k)]","def symbols(self) -> List[SingleMapping]:\n return self._symbols","def getSymbols(self):\n return self.alpha.getSymbols()","def lookup_option_symbols(self, underlying: str) -> List[Symbol]:\n url = \"/v1/markets/options/lookup\"\n params = {\"underlying\": underlying}\n\n data = self.get(url, params)\n res = MarketsAPIResponse(**data)\n return res.symbols","def __init__(self):\r\n self.s_table = SymbolTable.preSymbols","def print_symbols():\n\n global program\n if program is None:\n print \"no program is loaded\"\n return\n for(s, a) in program.symbols():\n print \"0x{:x} : {}\".format(a, s)","def getSymbolMap():\n name = os.path.join(os.path.dirname(__file__), 'nasdaq_nasdaqcom.csv')\n symbols = TickerSymbols(name)\n return symbols.getNameToTicker()","def getSymmetries(self, board, pi):\n return [(board, pi), (board[:, ::-1], pi[::-1])]","def get_symbols(self):\n symbols = os.environ.get('SYMBOLS', 'btc,eth')\n if not symbols:\n return 'btc,eth'\n return symbols","def popSym(self):\r\n res=self.dbConn.execute(\"SELECT * FROM Symposia ORDER BY StartDate\").fetchall()\r\n self.symList = [ln[\"Name\"] for ln in res]\r\n self.symID_Title = {ln[\"SymposiumID\"]:ln[\"Name\"] for ln in res}\r\n for ln in res:\r\n self.symID_Title[ln[\"Name\"]] = ln[\"SymposiumID\"]","def all_currency_codes():\n return [(a, CURRENCIES[a].name) for a in CURRENCIES]","def getSymbols(self, name: unicode, namespace: ghidra.program.model.symbol.Namespace) -> List[ghidra.program.model.symbol.Symbol]:\n ...","def currency_code_mappings():\n return [(a, CURRENCIES[a].name) for a in settings.CURRENCIES]","def ionic_symbols(self) -> list[str]:\n return self.to_list().symbols","async def get_symbols(self):\n uri = \"/v3/spot/symbols\"\n success, error = await self.request(\"GET\", uri)\n return success, error","def getSymbolTable(self) -> ghidra.app.util.bin.format.pe.debug.DebugCodeViewSymbolTable:\n ...","def get_existing_symbols():\n instruments = pyRofex.get_all_instruments()[\"instruments\"]\n existing_symbols = []\n for instrument in instruments:\n existing_symbols.append(instrument[\"instrumentId\"][\"symbol\"])\n return existing_symbols","def symbol_table(self) -> str:\n return self._symbol_table","def find_symbols(self, **kw):\n return list(self.ifind_symbols(**kw))","def add_symbols(self, lst):\n for ii in lst:\n self.__symbols += [Symbol(ii, self)]","def get_iex_symbols(**kwargs):\n return IEXSymbols(**kwargs).fetch()","def symbolsAssign(clusters):\n \n alphabet = ['A','a','B','b','C','c','D','d','E','e',\n 'F','f','G','g','H','h','I','i','J','j',\n 'K','k','L','l','M','m','N','n','O','o',\n 'P','p','Q','q','R','r','S','s','T','t',\n 'U','u','V','v','W','w','X','x','Y','y','Z','z']\n \n clusters = pd.Series(clusters)\n N = len(clusters.unique())\n\n cluster_sort = [0] * N \n counter = collections.Counter(clusters)\n for ind, el in enumerate(counter.most_common()):\n cluster_sort[ind] = el[0]\n\n if N >= len(alphabet):\n alphabet = [chr(i+33) for i in range(0, N)]\n else:\n alphabet = alphabet[:N]\n hashm = dict(zip(cluster_sort + alphabet, alphabet + cluster_sort))\n strings = [hashm[i] for i in clusters]\n return strings, hashm","def get_all_cipher():\n return OpenSSL.cipher_algo.keys()","async def init_trading_pair_symbols(cls, shared_session: Optional[aiohttp.ClientSession] = None):\n client_session = shared_session or aiohttp.ClientSession()\n async with client_session as client:\n async with client.get(BeaxyConstants.PublicApi.SYMBOLS_URL, timeout=5) as response:\n if response.status == 200:\n symbols: List[Dict[str, Any]] = await response.json()\n cls._trading_pair_symbol_map = {\n symbol_data[\"name\"]: (f\"{symbol_data['baseCurrency']}-{symbol_data['termCurrency']}\")\n for symbol_data in symbols\n }\n else:\n IOError(f\"There was an error requesting the list of symbols from the exchange ({response})\")","def find_symbols(lst):\n ret = []\n for ii in lst:\n ret += [find_symbol(ii)]\n return ret","def _binaries_to_symbolize(self):\n raise NotImplementedError()","def __generate_symboltable(self, code):\n\n code_without_lables = []\n address = 0\n for line in code:\n label_code = line.split(':')\n label = label_code[0]\n if len(label) != len(line):\n self.__symboltable[label] = address\n address += REG_SIZE\n instruction = label_code.pop().strip()\n code_without_lables = code_without_lables + [instruction]\n else:\n instruction = label_code.pop().strip()\n code_without_lables = code_without_lables + [instruction]\n\n tokens = instruction.split(' ')\n asm_directive = tokens[0]\n if tokens[0] in AssemblerDirectives.to_string():\n if asm_directive == AssemblerDirectives.ORG.name:\n address = int(tokens[1])\n else:\n address += REG_SIZE\n\n return code_without_lables","def init_dict() -> None:\n for elem in letters:\n ascii_dict[elem] = []\n for elem in numbers:\n ascii_dict[elem] = []\n for elem in symbols:\n ascii_dict[elem] = []","def list_ciphers():\n global AVAILABLE_CIPHERS\n print(\"[!] Available ciphers: \")\n for i in range(len(AVAILABLE_CIPHERS)):\n print(\" ----> %s.%s\"%(i+1, AVAILABLE_CIPHERS[i]))\n exit()","def list():\n\n return cache.codeTableList()","def get_currencies_names():\n names = [x for x in cur_dict]\n return names","def _get_paths(symbol: Union[str, int]) -> str:\n if isinstance(symbol, str):\n return {\n 'circle':\n '\"M\"+b1+\",0A\"+b1+\",\"+b1+\" 0 1,1 0,-\"+b1+\"A\"+b1+\",\"+b1+\" 0 0,1 \"+b1+\",0Z\"',\n 'square':\n '\"M\"+b1+\",\"+b1+\"H-\"+b1+\"V-\"+b1+\"H\"+b1+\"Z\"',\n 'diamond':\n '\"M\"+b1+\",0L0,\"+b1+\"L-\"+b1+\",0L0,-\"+b1+\"Z\"',\n 'hexagram':\n '\"M-\"+b3+\",0l-\"+b2+\",-\"+b1+\"h\"+b3+\"l\"+b2+\",-\"+b1+\"l\"+b2+\",\"+b1+\"h\"+b3+\"l-\"+b2+\",\"+b1+\"l\"+'\n 'b2+\",\"+b1+\"h-\"+b3+\"l-\"+b2+\",\"+b1+\"l-\"+b2+\",-\"+b1+\"h-\"+b3+\"Z\"'\n }[symbol]\n return {\n 37: '\"M-\"+d1+\",\"+d3+\"L0,0M\"+d1+\",\"+d3+\"L0,0M0,-\"+d2+\"L0,0\"',\n 38: '\"M-\"+d1+\",-\"+d3+\"L0,0M\"+d1+\",-\"+d3+\"L0,0M0,\"+d2+\"L0,0\"',\n 39: '\"M\"+d3+\",\"+d1+\"L0,0M\"+d3+\",-\"+d1+\"L0,0M-\"+d2+\",0L0,0\"',\n 40: '\"M-\"+d3+\",\"+d1+\"L0,0M-\"+d3+\",-\"+d1+\"L0,0M\"+d2+\",0L0,0\"',\n 34: '\"M\"+d1+\",\"+d1+\"L-\"+d1+\",-\"+d1+\"M\"+d1+\",-\"+d1+\"L-\"+d1+\",\"+d1',\n 33: '\"M0,\"+d1+\"V-\"+d1+\"M\"+d1+\",0H-\"+d1',\n 35: '\"M0,\"+d1+\"V-\"+d1+\"M\"+d1+\",0H-\"+d1+\"M\"+d2+\",\"+d2+\"L-\"+d2+\",-\"+d2+\"M\"+d2+\",-\"+d2+\"L-\"+d2+\",\"+d2',\n 36: '\"M\"+d1+\",\"+d2+\"V-\"+d2+\"m-\"+d2+\",0V\"+d2+\"M\"+d2+\",\"+d1+\"H-\"+d2+\"m0,-\"+d2+\"H\"+d2'\n }[symbol]","def build_gdb_symbol_table():\n\n tab = Symtab()\n n = gdb.parse_and_eval (\"symtab->nodes\")\n while (long(n)):\n if symtab_node_is_function (n):\n current_symbol = GdbFunction(tab, n)\n tab.all_functions.append (current_symbol)\n elif symtab_node_is_variable (n):\n current_symbol = GdbVariable(tab, n)\n tab.all_variables.append (current_symbol)\n else:\n raise gdb.GdbError (\"Encountered an unknown symbol table node\");\n\n tab.order_to_sym[current_symbol.order] = current_symbol\n tab.all_symbols.append (current_symbol)\n\n n = n[\"next\"]\n pass\n\n tab.fixup()\n return tab","def get_generating_symbols(self) -> AbstractSet[CFGObject]:\n if self._generating_symbols is None:\n self._generating_symbols = self._get_generating_or_nullable(False)\n return self._generating_symbols","def query_all_symbols(self):\n return self._call_txtrader_api('query_symbols', {'data': True})","def _get_symbols(exchange_code: str, token: str) -> List[mtypes.Symbol]:\n _LOG.info(\"Getting symbols list for exchange: '%s'\", exchange_code)\n response = get_client().service.SymbolList(\n Token=token, Exchange=exchange_code\n )\n\n if response.SYMBOLS is None:\n _LOG.error(\"No symbols found for exchange: '%s'\", exchange_code)\n return []\n\n symbols = [\n mtypes.Symbol.from_dict(d=obj)\n for obj in zeep.helpers.serialize_object(response.SYMBOLS[\"SYMBOL\"])\n ]\n\n _LOG.info(\"Got %s symbols for exchange '%s'\", len(symbols), exchange_code)\n return symbols","def ifind_symbols(self, name=\"any\", **kw):\n for sym in self.itersymbols():\n if (name==\"any\" or name==sym.sym.name) and \\\n sym.sym.k==kw:\n yield sym.sym","def get_symbols(request):\n symbols = list(Stock.objects.values('symbol').distinct())\n return JsonResponse(symbols, safe=False)","def create_state_symbols(nVars,nParams):\n\n nSensitivityEqs = nVars * nParams\n\n #state variables\n x_sp = np.array(sp.symbols('x0:' + str(nVars)))\n\n #sensitivity variables\n sensitivity_sp = np.array(list(sp.symbols('s0:' + str(nSensitivityEqs))))\n\n return [x_sp,sensitivity_sp]","def generate_symbol_definitions(mode, symbols, prefix, definition):\n direct = []\n tabled = []\n for ii in symbols:\n direct += [ii.generate_rename_direct(prefix)]\n tabled += [ii.generate_rename_tabled(prefix)]\n if \"vanilla\" == mode:\n tabled = direct\n return template_symbol_definitions % (definition, \"\\n\".join(direct), \"\\n\".join(tabled))","def get_codecs_list():\n for codec in CODECS_IN_FILE.iterkeys():\n print codec","def encodings():\n from . import factory\n return factory.MAPPINGS.keys()","def slot_key_db() -> Dict[str, List]:\n\n return {'q50': 'second_person_plural',\n 'q28': 'cot_caught',\n 'q80': 'rain_sun',\n 'q66': 'crawfish',\n 'q110': 'halloween',\n 'q64': 'sandwich',\n 'q90': 'side_road',\n 'q105': 'beverage',\n 'q73': 'shoes',\n 'q79': 'highway',\n 'q58': 'yard_sale',\n 'q107': 'rubbernecking',\n 'q94': 'frosting',\n 'q14': 'lawyer',\n 'q76': 'kitty_corner',\n 'q65': 'firefly',\n 'q60': 'verge',\n 'q118': 'brew_thru',\n 'q103': 'water_fountain'}","def get_short_currencies_names():\n short_names = [[x, cur_dict[x][1]] for x in cur_dict]\n return short_names\n # return [['Bitcoin','BTC'], ['Litecoin', 'LTC']]","def test_find_z2_symmetries_op_without_sym(self):\n qubit_op = SparsePauliOp.from_list(\n [\n (\"I\", -1.0424710218959303),\n (\"Z\", -0.7879673588770277),\n (\"X\", -0.18128880821149604),\n ]\n )\n z2_symmetries = Z2Symmetries.find_z2_symmetries(qubit_op)\n self.assertTrue(z2_symmetries.is_empty())","def codelists():\n return CodelistSet()","def test_get_pci_switch_list(self):\n pass","def _get_v2_symbols(self, assets):\n\n v2_symbols = []\n for asset in assets:\n v2_symbols.append(self._get_v2_symbol(asset))\n\n return v2_symbols","def perms_sym_init(g, sym=None):\n if g is None or len(g) == 0:\n if sym is None:\n sym = SymmetricGroup(0)\n return sym, [sym([])]\n\n if sym is None:\n domain = set().union(*[perm_sym_domain(gg) for gg in g])\n if all(isinstance(s, (Integer, int, long)) and s > 0 for s in domain):\n domain = max(domain)\n else:\n domain = sorted(domain)\n sym = SymmetricGroup(domain)\n\n try:\n return sym, [sym(u) for u in g]\n except (ValueError, TypeError):\n return sym, None","def filter_compatible(symmetries, *, structure):\n raise ValueError(\n \"Unrecognized type '{}' for 'symmetries'\".format(type(symmetries))\n )","def singleglyph(x):\n return [glyph(x)]","def _gen_freeze_scheme():\n freeze_scheme = {}\n\n for key in SCHEME_KEYS:\n paths = []\n for scheme_name, install_scheme in INSTALL_SCHEMES.iteritems():\n val = install_scheme[key]\n if scheme_name == 'unix_home':\n val = val.replace('$base', '$home', 1)\n else:\n val = val.replace('$base', '$prefix', 1)\n val = val.replace('$platbase', '$exec_prefix', 1)\n paths.append(val)\n freeze_scheme[key] = paths\n\n return freeze_scheme","def symbols(self, symbols: List[SingleMapping]):\n\n self._symbols = symbols","def set_symbols_from(self,sigma=_Set())-> None:\n \n m=len(self.chars)\n \n _s=[]\n symbols=set()\n for e in sigma.symbols:\n \"ignoring the empty word\"\n if not e == '':\n symbols.add(e)\n \n for s in symbols:\n _s=split(s)\n n=len(s)\n for i in range(0,m-n+1):\n if _s==self.chars[i:i+n]:\n self.symbols[i]=s\n i=+1\n\n \"\"\"Eliminating None values from the list object self.symbols\"\"\"\n temp=[]\n for elt in self.symbols:\n if elt==None:\n pass\n else:\n temp.append(elt)\n self.symbols=temp","def query_symbols(self):\n return self._call_txtrader_api('query_symbols', {'data': False})","def get_palette_names(scheme: ColorScheme | ColorSchemeShort) -> list[str]:\n mod = get_palette_module(scheme)\n names = mod.__all__\n return names.copy()","def _available_algorithms(**_: str) -> Set[str]:\n avail = set()\n pass2 = set()\n for algo in hashlib.algorithms_available:\n lalgo = algo.lower()\n if \"with\" in lalgo:\n continue # skip apparently redundant ones\n if lalgo != algo:\n pass2.add(algo)\n else:\n avail.add(lalgo)\n for algo in pass2:\n if algo.lower() not in avail:\n avail.add(algo)\n return avail","def free_symbols(self) -> Iterable[sympy.Symbol]:\n return get_free_symbols(self.params)","def free_symbols(self) -> Iterable[sympy.Symbol]:\n return get_free_symbols(self.params)","def symbols_details(self):\n pass","def _symbols_of_input(label: str) -> List[str]:\n if label == common.EPSILON:\n return [label]\n\n # We add a state transition arc for each digit of a multi-digit number.\n if \"[\" not in label:\n return list(label)\n\n # We add a state transition arc for each inflectional or derivational\n # morpheme, inflectional group boundary, and proper noun analysis tag.\n return _SYMBOLS_REGEX.findall(label)","def free_symbols(self) -> set[Basic]:\n empty: set[Basic] = set()\n return empty.union(*(a.free_symbols for a in self.args))","def get_related_forex_currencies(self, currency = None):\n new_symbol_names_list = []\n for symbol_name in self.symbol_names_list:\n if (symbol_name[:3] == currency ) | (symbol_name[3:] == currency ):\n new_symbol_names_list.append(symbol_name)\n symbol_names_list = new_symbol_names_list\n return symbol_names_list","def gene_symbol_wrangler(inpAcc):\n \n print(\"processing gene symbols\")\n \n resD = {}\n \n for convI in inpAcc:\n keyI = convI[\"InputValue\"]\n valueI = convI[\"Gene Symbol\"]\n resD[keyI] = valueI\n\n return resD","def _getAvailableMethods(self):\n bsc = getToolByName(self, 'bika_setup_catalog')\n items = [(c.UID, c.Title) \\\n for c in bsc(portal_type='Method',\n inactive_state = 'active')]\n items.sort(lambda x,y:cmp(x[1], y[1]))\n items.insert(0, ('', t(_('None'))))\n return DisplayList(items)","def symbolize_pairs(list_of_pair_string: str) -> list:\n symbolized_pairs = []\n for pair in list_of_pair_string:\n symbolized_pairs.append(pair[0] + '-' + pair[1])\n\n return symbolized_pairs","def symbol_state_map(self):\n map = {}\n for state in self:\n map[state.symbol] = state\n map.update(self.symbol_synonyms)\n if not self.case_sensitive:\n for state in self:\n if state.symbol.islower():\n map[state.symbol.upper()] = state\n else:\n map[state.symbol.lower()] = state\n for symbol, state in self.symbol_synonyms.items():\n if symbol.islower():\n map[symbol.upper()] = state\n else:\n map[symbol.lower()] = state\n return map","def _get_symbols(self):\n\n symbols = self.get_symbols()\n\n if isinstance(symbols, dict):\n keys = ['symbol', 'from_symbol', 'to_symbol']\n correct_keys = np.isin(keys, list(symbols.keys())).all()\n\n if not correct_keys:\n raise ImplementationError('''\n Dict should be in the form:\n {'symbol':[], 'from_symbol':[], 'to_symbol':[]}\n ''')\n else:\n symbols = pd.DataFrame(symbols, index = [symbols['symbol']])\n\n symbols.index = symbols.symbol\n\n return symbols","def symbols_zero_spin(self) -> List[str]:\n return self._symbols_zero_spin","def IterUniqueSymbols(self):\n return SymbolGroup._IterUnique(self)","def set_symbols(self, symboltable: dict):\n\n for index in range(1, self.symbol_layout.rowCount()):\n self.symbol_layout.removeRow(index)\n\n font = QFont('Fira Code', 8, QFont.Medium)\n for entry in symboltable:\n symbol = QLineEdit()\n symbol.setReadOnly(True)\n symbol.setText(entry)\n symbol.setFont(font)\n address = QLineEdit()\n address.setReadOnly(True)\n address.setFont(font)\n address.setText(str(symboltable[entry]))\n self.symbol_layout.addRow(address, symbol)","def list():\n return [Drive.ENCODER_L,\n Drive.ENCODER_R]","def sym_elements(self):\n def trans(name, *sym):\n t = Element.TRANSFORMS\n n = name.split('_')\n\n for x in sym:\n n[-1] = t[x][n[-1]]\n\n return '_'.join(n)\n\n def primary():\n e = self.copy()\n e.name = '{}_p'.format(self.name)\n return e\n\n def x_sym():\n e = self.copy()\n e.name = '{}_x'.format(self.name)\n e.inode = trans(self.inode, 'x')\n e.jnode = trans(self.jnode, 'x')\n return e\n\n def y_sym():\n e = self.copy()\n e.name = '{}_y'.format(self.name)\n e.inode = trans(self.inode, 'y')\n e.jnode = trans(self.jnode, 'y')\n return e\n\n def xy_sym():\n e = self.copy()\n e.name = '{}_xy'.format(self.name)\n e.inode = trans(self.inode, 'x', 'y')\n e.jnode = trans(self.jnode, 'x', 'y')\n return e\n\n if self.symmetry is None:\n return primary(),\n\n elif self.symmetry == 'x':\n return primary(), x_sym()\n\n elif self.symmetry == 'y':\n return primary(), y_sym()\n\n elif self.symmetry == 'xy':\n return primary(), x_sym(), y_sym(), xy_sym()","def create_possible_symbols_to_cells_mapping(self):\r\n possibles_to_cells = defaultdict(set)\r\n for cell in self.iterate_empty_cells():\r\n possibles_to_cells[frozenset(cell.get_possible_symbols())].add(cell)\r\n return possibles_to_cells","def r_symbols(size, symbols, length, used=None):\n if length == 1 and not used:\n return R.sample(symbols, size)\n rset, used = set(), set(used or [])\n while len(rset) < size:\n s = r_string(symbols, R.randint(1, length))\n if s not in used:\n rset.add(s)\n return list(rset)","def get_all_symbolic_models(self):\n return get_symbolic_constraint_models(self.constraints)","def _init_symbol_tracker(self):\n # Initialize with an empty set\n atoms_indx = {symb: set([]) for symb in self.symbols}\n\n # Populate the sets\n for atom in self.atoms:\n symb = atom.symbol\n atoms_indx[symb].add(atom.index)\n return atoms_indx","def sym(self) -> np.ndarray:\n if self._sym is None:\n self._sym = symmetrize_discrete_vector_field(self.F, mode=\"sym\")\n return self._sym","def __init__(self, sym: ghidra.program.model.pcode.HighSymbol):\n ...","def get_symbol(symbols):\n # Figure out if list of symbols or single symbol.\n if not hasattr(symbols, '__getitem__'):\n symbols = [symbols]\n elif len(symbols) == 3 and symbols[0] in ('p', 'P'):\n # Most likely a polygon specification (at least not a valid other\n # symbol).\n symbols = [symbols]\n\n symbols = [symbol_dict[symbol] if symbol in symbol_dict else symbol for\n symbol in symbols]\n\n paths = []\n for symbol in symbols:\n if isinstance(symbol, matplotlib.path.Path):\n return symbol\n elif hasattr(symbol, '__getitem__') and len(symbol) == 3:\n kind, n, angle = symbol\n\n if kind in ['p', 'P']:\n if kind == 'p':\n radius = 1. / cos(pi / n)\n else:\n # make the polygon such that it has area equal\n # to a unit circle\n radius = sqrt(2 * pi / (n * sin(2 * pi / n)))\n\n angle = pi * angle / 180\n patch = matplotlib.patches.RegularPolygon((0, 0), n,\n radius=radius,\n orientation=angle)\n else:\n raise ValueError(\"Unknown symbol definition \" + str(symbol))\n elif symbol == 'o':\n patch = matplotlib.patches.Circle((0, 0), 1)\n\n paths.append(patch.get_path().transformed(patch.get_transform()))\n\n return paths","def findBrains():\r\n keys = livingDead.Frankenstein.genKeys()\r\n return keys","def set_symbols(self, symbols=None):\n if symbols and isinstance(symbols, str):\n if symbols.find(',') != -1:\n symbols_list = symbols.split(',')\n for item in symbols_list:\n self.symbols.append(item.strip())\n else:\n self.symbols.append(symbols)\n else:\n self.symbols = []","def _symbols_of_output(label: str) -> List[str]:\n if label == common.EPSILON:\n return [label]\n\n # We add a new state transition arc for each character of the output token.\n return list(label)","def __init__(self, DEBUG=False):\n self.DEBUG = DEBUG\n\n self.classTable = {}\n self.subroutineTable = {}\n\n self.counts = {}\n self.counts[\"STATIC\"] = 0\n self.counts[\"FIELD\"] = 0\n self.counts[\"ARG\"] = 0\n self.counts[\"VAR\"] = 0\n\n if self.DEBUG:\n print(\"DEBUG(SymbolTable): INITIALIZED SYMBOL TABLES\")","def sym_nodes(self):\n def primary():\n n = self.copy()\n n.name = '{}_p'.format(self.name)\n return n\n\n def x_sym():\n n = self.copy()\n n.name = '{}_x'.format(self.name)\n n[1] *= -1\n return n\n\n def y_sym():\n n = self.copy()\n n.name = '{}_y'.format(self.name)\n n[0] *= -1\n return n\n\n def xy_sym():\n n = self.copy()\n n.name = '{}_xy'.format(self.name)\n n[:2] *= -1\n return n\n\n if self.symmetry is None:\n return primary(),\n\n elif self.symmetry == 'x':\n return primary(), x_sym()\n\n elif self.symmetry == 'y':\n return primary(), y_sym()\n\n elif self.symmetry == 'xy':\n return primary(), x_sym(), y_sym(), xy_sym()","def choices(self) -> list:\n return [self.mapper(i) for i in self.keys()]"],"string":"[\n \"def get_symmetries(self):\\n temp = self._properties.get('symmetries', [])\\n return temp\",\n \"def __set_symbol_dict(self):\\r\\n return {0: list(alph) if self.is_case_snstv else list(alph)[:26],\\r\\n 1: list(dgt),\\r\\n 2: list(spcl) if self.is_spcl else []}\",\n \"def z2_symmetries(self) -> \\\"Z2Symmetries\\\":\\n return self._z2_symmetries\",\n \"def get_symbols(self):\\n\\n raise NotImplementedError('''\\n Must implement get_symbols. Call help() for details.\\n ''')\",\n \"def get_symbols(**kwargs):\\n return Symbols(**kwargs).fetch()\",\n \"def itersymbols(self):\\n for syms in self._symbols.itervalues():\\n for sym in syms:\\n yield sym\",\n \"def get_symbol(self):\\n return []\",\n \"def get_symbols_list(self):\\n return self.symbols_list\",\n \"def namelist():\\n\\n\\n session = Session(engine)\\n\\n results = session.query(lockdown.country).order_by(lockdown.country).all()\\n\\n #session.close()\\n all_symbols = list(np.ravel(results))\\n sym = all_symbols[1]\\n\\n return jsonify(all_symbols)\",\n \"def list_symbol_tables(mst):\\n stlist = []\\n def append_st(st):\\n #print(st)\\n stlist.append(st)\\n for s in st.get_symbols():\\n for ns in s.get_namespaces():\\n append_st(ns)\\n if not isinstance(mst, symtable.SymbolTable):\\n # Assume it is text of a program to compile\\n mst = symtable.symtable(mst, '<string>', 'exec')\\n append_st(mst)\\n return stlist\",\n \"def _create_symbol_mapping():\\n normal_items = [\\\"+\\\", \\\"-\\\"]\\n unicode_items = [chr(0x2070 + i) for i in range(10, 12)]\\n\\n # Create a dict mapping the two.\\n return DefaultDictionary(zip(normal_items, unicode_items))\",\n \"def symbols(self):\\n pass\",\n \"def __init__(self, symbols):\\r\\n self.symbols = set(symbols)\",\n \"def get_symbols(self, type_name):\\n return self._symtab[type_name].get_symbols()\",\n \"def choices(symbols, k):\\n return [R.choice(symbols) for _ in range(k)]\",\n \"def symbols(self) -> List[SingleMapping]:\\n return self._symbols\",\n \"def getSymbols(self):\\n return self.alpha.getSymbols()\",\n \"def lookup_option_symbols(self, underlying: str) -> List[Symbol]:\\n url = \\\"/v1/markets/options/lookup\\\"\\n params = {\\\"underlying\\\": underlying}\\n\\n data = self.get(url, params)\\n res = MarketsAPIResponse(**data)\\n return res.symbols\",\n \"def __init__(self):\\r\\n self.s_table = SymbolTable.preSymbols\",\n \"def print_symbols():\\n\\n global program\\n if program is None:\\n print \\\"no program is loaded\\\"\\n return\\n for(s, a) in program.symbols():\\n print \\\"0x{:x} : {}\\\".format(a, s)\",\n \"def getSymbolMap():\\n name = os.path.join(os.path.dirname(__file__), 'nasdaq_nasdaqcom.csv')\\n symbols = TickerSymbols(name)\\n return symbols.getNameToTicker()\",\n \"def getSymmetries(self, board, pi):\\n return [(board, pi), (board[:, ::-1], pi[::-1])]\",\n \"def get_symbols(self):\\n symbols = os.environ.get('SYMBOLS', 'btc,eth')\\n if not symbols:\\n return 'btc,eth'\\n return symbols\",\n \"def popSym(self):\\r\\n res=self.dbConn.execute(\\\"SELECT * FROM Symposia ORDER BY StartDate\\\").fetchall()\\r\\n self.symList = [ln[\\\"Name\\\"] for ln in res]\\r\\n self.symID_Title = {ln[\\\"SymposiumID\\\"]:ln[\\\"Name\\\"] for ln in res}\\r\\n for ln in res:\\r\\n self.symID_Title[ln[\\\"Name\\\"]] = ln[\\\"SymposiumID\\\"]\",\n \"def all_currency_codes():\\n return [(a, CURRENCIES[a].name) for a in CURRENCIES]\",\n \"def getSymbols(self, name: unicode, namespace: ghidra.program.model.symbol.Namespace) -> List[ghidra.program.model.symbol.Symbol]:\\n ...\",\n \"def currency_code_mappings():\\n return [(a, CURRENCIES[a].name) for a in settings.CURRENCIES]\",\n \"def ionic_symbols(self) -> list[str]:\\n return self.to_list().symbols\",\n \"async def get_symbols(self):\\n uri = \\\"/v3/spot/symbols\\\"\\n success, error = await self.request(\\\"GET\\\", uri)\\n return success, error\",\n \"def getSymbolTable(self) -> ghidra.app.util.bin.format.pe.debug.DebugCodeViewSymbolTable:\\n ...\",\n \"def get_existing_symbols():\\n instruments = pyRofex.get_all_instruments()[\\\"instruments\\\"]\\n existing_symbols = []\\n for instrument in instruments:\\n existing_symbols.append(instrument[\\\"instrumentId\\\"][\\\"symbol\\\"])\\n return existing_symbols\",\n \"def symbol_table(self) -> str:\\n return self._symbol_table\",\n \"def find_symbols(self, **kw):\\n return list(self.ifind_symbols(**kw))\",\n \"def add_symbols(self, lst):\\n for ii in lst:\\n self.__symbols += [Symbol(ii, self)]\",\n \"def get_iex_symbols(**kwargs):\\n return IEXSymbols(**kwargs).fetch()\",\n \"def symbolsAssign(clusters):\\n \\n alphabet = ['A','a','B','b','C','c','D','d','E','e',\\n 'F','f','G','g','H','h','I','i','J','j',\\n 'K','k','L','l','M','m','N','n','O','o',\\n 'P','p','Q','q','R','r','S','s','T','t',\\n 'U','u','V','v','W','w','X','x','Y','y','Z','z']\\n \\n clusters = pd.Series(clusters)\\n N = len(clusters.unique())\\n\\n cluster_sort = [0] * N \\n counter = collections.Counter(clusters)\\n for ind, el in enumerate(counter.most_common()):\\n cluster_sort[ind] = el[0]\\n\\n if N >= len(alphabet):\\n alphabet = [chr(i+33) for i in range(0, N)]\\n else:\\n alphabet = alphabet[:N]\\n hashm = dict(zip(cluster_sort + alphabet, alphabet + cluster_sort))\\n strings = [hashm[i] for i in clusters]\\n return strings, hashm\",\n \"def get_all_cipher():\\n return OpenSSL.cipher_algo.keys()\",\n \"async def init_trading_pair_symbols(cls, shared_session: Optional[aiohttp.ClientSession] = None):\\n client_session = shared_session or aiohttp.ClientSession()\\n async with client_session as client:\\n async with client.get(BeaxyConstants.PublicApi.SYMBOLS_URL, timeout=5) as response:\\n if response.status == 200:\\n symbols: List[Dict[str, Any]] = await response.json()\\n cls._trading_pair_symbol_map = {\\n symbol_data[\\\"name\\\"]: (f\\\"{symbol_data['baseCurrency']}-{symbol_data['termCurrency']}\\\")\\n for symbol_data in symbols\\n }\\n else:\\n IOError(f\\\"There was an error requesting the list of symbols from the exchange ({response})\\\")\",\n \"def find_symbols(lst):\\n ret = []\\n for ii in lst:\\n ret += [find_symbol(ii)]\\n return ret\",\n \"def _binaries_to_symbolize(self):\\n raise NotImplementedError()\",\n \"def __generate_symboltable(self, code):\\n\\n code_without_lables = []\\n address = 0\\n for line in code:\\n label_code = line.split(':')\\n label = label_code[0]\\n if len(label) != len(line):\\n self.__symboltable[label] = address\\n address += REG_SIZE\\n instruction = label_code.pop().strip()\\n code_without_lables = code_without_lables + [instruction]\\n else:\\n instruction = label_code.pop().strip()\\n code_without_lables = code_without_lables + [instruction]\\n\\n tokens = instruction.split(' ')\\n asm_directive = tokens[0]\\n if tokens[0] in AssemblerDirectives.to_string():\\n if asm_directive == AssemblerDirectives.ORG.name:\\n address = int(tokens[1])\\n else:\\n address += REG_SIZE\\n\\n return code_without_lables\",\n \"def init_dict() -> None:\\n for elem in letters:\\n ascii_dict[elem] = []\\n for elem in numbers:\\n ascii_dict[elem] = []\\n for elem in symbols:\\n ascii_dict[elem] = []\",\n \"def list_ciphers():\\n global AVAILABLE_CIPHERS\\n print(\\\"[!] Available ciphers: \\\")\\n for i in range(len(AVAILABLE_CIPHERS)):\\n print(\\\" ----> %s.%s\\\"%(i+1, AVAILABLE_CIPHERS[i]))\\n exit()\",\n \"def list():\\n\\n return cache.codeTableList()\",\n \"def get_currencies_names():\\n names = [x for x in cur_dict]\\n return names\",\n \"def _get_paths(symbol: Union[str, int]) -> str:\\n if isinstance(symbol, str):\\n return {\\n 'circle':\\n '\\\"M\\\"+b1+\\\",0A\\\"+b1+\\\",\\\"+b1+\\\" 0 1,1 0,-\\\"+b1+\\\"A\\\"+b1+\\\",\\\"+b1+\\\" 0 0,1 \\\"+b1+\\\",0Z\\\"',\\n 'square':\\n '\\\"M\\\"+b1+\\\",\\\"+b1+\\\"H-\\\"+b1+\\\"V-\\\"+b1+\\\"H\\\"+b1+\\\"Z\\\"',\\n 'diamond':\\n '\\\"M\\\"+b1+\\\",0L0,\\\"+b1+\\\"L-\\\"+b1+\\\",0L0,-\\\"+b1+\\\"Z\\\"',\\n 'hexagram':\\n '\\\"M-\\\"+b3+\\\",0l-\\\"+b2+\\\",-\\\"+b1+\\\"h\\\"+b3+\\\"l\\\"+b2+\\\",-\\\"+b1+\\\"l\\\"+b2+\\\",\\\"+b1+\\\"h\\\"+b3+\\\"l-\\\"+b2+\\\",\\\"+b1+\\\"l\\\"+'\\n 'b2+\\\",\\\"+b1+\\\"h-\\\"+b3+\\\"l-\\\"+b2+\\\",\\\"+b1+\\\"l-\\\"+b2+\\\",-\\\"+b1+\\\"h-\\\"+b3+\\\"Z\\\"'\\n }[symbol]\\n return {\\n 37: '\\\"M-\\\"+d1+\\\",\\\"+d3+\\\"L0,0M\\\"+d1+\\\",\\\"+d3+\\\"L0,0M0,-\\\"+d2+\\\"L0,0\\\"',\\n 38: '\\\"M-\\\"+d1+\\\",-\\\"+d3+\\\"L0,0M\\\"+d1+\\\",-\\\"+d3+\\\"L0,0M0,\\\"+d2+\\\"L0,0\\\"',\\n 39: '\\\"M\\\"+d3+\\\",\\\"+d1+\\\"L0,0M\\\"+d3+\\\",-\\\"+d1+\\\"L0,0M-\\\"+d2+\\\",0L0,0\\\"',\\n 40: '\\\"M-\\\"+d3+\\\",\\\"+d1+\\\"L0,0M-\\\"+d3+\\\",-\\\"+d1+\\\"L0,0M\\\"+d2+\\\",0L0,0\\\"',\\n 34: '\\\"M\\\"+d1+\\\",\\\"+d1+\\\"L-\\\"+d1+\\\",-\\\"+d1+\\\"M\\\"+d1+\\\",-\\\"+d1+\\\"L-\\\"+d1+\\\",\\\"+d1',\\n 33: '\\\"M0,\\\"+d1+\\\"V-\\\"+d1+\\\"M\\\"+d1+\\\",0H-\\\"+d1',\\n 35: '\\\"M0,\\\"+d1+\\\"V-\\\"+d1+\\\"M\\\"+d1+\\\",0H-\\\"+d1+\\\"M\\\"+d2+\\\",\\\"+d2+\\\"L-\\\"+d2+\\\",-\\\"+d2+\\\"M\\\"+d2+\\\",-\\\"+d2+\\\"L-\\\"+d2+\\\",\\\"+d2',\\n 36: '\\\"M\\\"+d1+\\\",\\\"+d2+\\\"V-\\\"+d2+\\\"m-\\\"+d2+\\\",0V\\\"+d2+\\\"M\\\"+d2+\\\",\\\"+d1+\\\"H-\\\"+d2+\\\"m0,-\\\"+d2+\\\"H\\\"+d2'\\n }[symbol]\",\n \"def build_gdb_symbol_table():\\n\\n tab = Symtab()\\n n = gdb.parse_and_eval (\\\"symtab->nodes\\\")\\n while (long(n)):\\n if symtab_node_is_function (n):\\n current_symbol = GdbFunction(tab, n)\\n tab.all_functions.append (current_symbol)\\n elif symtab_node_is_variable (n):\\n current_symbol = GdbVariable(tab, n)\\n tab.all_variables.append (current_symbol)\\n else:\\n raise gdb.GdbError (\\\"Encountered an unknown symbol table node\\\");\\n\\n tab.order_to_sym[current_symbol.order] = current_symbol\\n tab.all_symbols.append (current_symbol)\\n\\n n = n[\\\"next\\\"]\\n pass\\n\\n tab.fixup()\\n return tab\",\n \"def get_generating_symbols(self) -> AbstractSet[CFGObject]:\\n if self._generating_symbols is None:\\n self._generating_symbols = self._get_generating_or_nullable(False)\\n return self._generating_symbols\",\n \"def query_all_symbols(self):\\n return self._call_txtrader_api('query_symbols', {'data': True})\",\n \"def _get_symbols(exchange_code: str, token: str) -> List[mtypes.Symbol]:\\n _LOG.info(\\\"Getting symbols list for exchange: '%s'\\\", exchange_code)\\n response = get_client().service.SymbolList(\\n Token=token, Exchange=exchange_code\\n )\\n\\n if response.SYMBOLS is None:\\n _LOG.error(\\\"No symbols found for exchange: '%s'\\\", exchange_code)\\n return []\\n\\n symbols = [\\n mtypes.Symbol.from_dict(d=obj)\\n for obj in zeep.helpers.serialize_object(response.SYMBOLS[\\\"SYMBOL\\\"])\\n ]\\n\\n _LOG.info(\\\"Got %s symbols for exchange '%s'\\\", len(symbols), exchange_code)\\n return symbols\",\n \"def ifind_symbols(self, name=\\\"any\\\", **kw):\\n for sym in self.itersymbols():\\n if (name==\\\"any\\\" or name==sym.sym.name) and \\\\\\n sym.sym.k==kw:\\n yield sym.sym\",\n \"def get_symbols(request):\\n symbols = list(Stock.objects.values('symbol').distinct())\\n return JsonResponse(symbols, safe=False)\",\n \"def create_state_symbols(nVars,nParams):\\n\\n nSensitivityEqs = nVars * nParams\\n\\n #state variables\\n x_sp = np.array(sp.symbols('x0:' + str(nVars)))\\n\\n #sensitivity variables\\n sensitivity_sp = np.array(list(sp.symbols('s0:' + str(nSensitivityEqs))))\\n\\n return [x_sp,sensitivity_sp]\",\n \"def generate_symbol_definitions(mode, symbols, prefix, definition):\\n direct = []\\n tabled = []\\n for ii in symbols:\\n direct += [ii.generate_rename_direct(prefix)]\\n tabled += [ii.generate_rename_tabled(prefix)]\\n if \\\"vanilla\\\" == mode:\\n tabled = direct\\n return template_symbol_definitions % (definition, \\\"\\\\n\\\".join(direct), \\\"\\\\n\\\".join(tabled))\",\n \"def get_codecs_list():\\n for codec in CODECS_IN_FILE.iterkeys():\\n print codec\",\n \"def encodings():\\n from . import factory\\n return factory.MAPPINGS.keys()\",\n \"def slot_key_db() -> Dict[str, List]:\\n\\n return {'q50': 'second_person_plural',\\n 'q28': 'cot_caught',\\n 'q80': 'rain_sun',\\n 'q66': 'crawfish',\\n 'q110': 'halloween',\\n 'q64': 'sandwich',\\n 'q90': 'side_road',\\n 'q105': 'beverage',\\n 'q73': 'shoes',\\n 'q79': 'highway',\\n 'q58': 'yard_sale',\\n 'q107': 'rubbernecking',\\n 'q94': 'frosting',\\n 'q14': 'lawyer',\\n 'q76': 'kitty_corner',\\n 'q65': 'firefly',\\n 'q60': 'verge',\\n 'q118': 'brew_thru',\\n 'q103': 'water_fountain'}\",\n \"def get_short_currencies_names():\\n short_names = [[x, cur_dict[x][1]] for x in cur_dict]\\n return short_names\\n # return [['Bitcoin','BTC'], ['Litecoin', 'LTC']]\",\n \"def test_find_z2_symmetries_op_without_sym(self):\\n qubit_op = SparsePauliOp.from_list(\\n [\\n (\\\"I\\\", -1.0424710218959303),\\n (\\\"Z\\\", -0.7879673588770277),\\n (\\\"X\\\", -0.18128880821149604),\\n ]\\n )\\n z2_symmetries = Z2Symmetries.find_z2_symmetries(qubit_op)\\n self.assertTrue(z2_symmetries.is_empty())\",\n \"def codelists():\\n return CodelistSet()\",\n \"def test_get_pci_switch_list(self):\\n pass\",\n \"def _get_v2_symbols(self, assets):\\n\\n v2_symbols = []\\n for asset in assets:\\n v2_symbols.append(self._get_v2_symbol(asset))\\n\\n return v2_symbols\",\n \"def perms_sym_init(g, sym=None):\\n if g is None or len(g) == 0:\\n if sym is None:\\n sym = SymmetricGroup(0)\\n return sym, [sym([])]\\n\\n if sym is None:\\n domain = set().union(*[perm_sym_domain(gg) for gg in g])\\n if all(isinstance(s, (Integer, int, long)) and s > 0 for s in domain):\\n domain = max(domain)\\n else:\\n domain = sorted(domain)\\n sym = SymmetricGroup(domain)\\n\\n try:\\n return sym, [sym(u) for u in g]\\n except (ValueError, TypeError):\\n return sym, None\",\n \"def filter_compatible(symmetries, *, structure):\\n raise ValueError(\\n \\\"Unrecognized type '{}' for 'symmetries'\\\".format(type(symmetries))\\n )\",\n \"def singleglyph(x):\\n return [glyph(x)]\",\n \"def _gen_freeze_scheme():\\n freeze_scheme = {}\\n\\n for key in SCHEME_KEYS:\\n paths = []\\n for scheme_name, install_scheme in INSTALL_SCHEMES.iteritems():\\n val = install_scheme[key]\\n if scheme_name == 'unix_home':\\n val = val.replace('$base', '$home', 1)\\n else:\\n val = val.replace('$base', '$prefix', 1)\\n val = val.replace('$platbase', '$exec_prefix', 1)\\n paths.append(val)\\n freeze_scheme[key] = paths\\n\\n return freeze_scheme\",\n \"def symbols(self, symbols: List[SingleMapping]):\\n\\n self._symbols = symbols\",\n \"def set_symbols_from(self,sigma=_Set())-> None:\\n \\n m=len(self.chars)\\n \\n _s=[]\\n symbols=set()\\n for e in sigma.symbols:\\n \\\"ignoring the empty word\\\"\\n if not e == '':\\n symbols.add(e)\\n \\n for s in symbols:\\n _s=split(s)\\n n=len(s)\\n for i in range(0,m-n+1):\\n if _s==self.chars[i:i+n]:\\n self.symbols[i]=s\\n i=+1\\n\\n \\\"\\\"\\\"Eliminating None values from the list object self.symbols\\\"\\\"\\\"\\n temp=[]\\n for elt in self.symbols:\\n if elt==None:\\n pass\\n else:\\n temp.append(elt)\\n self.symbols=temp\",\n \"def query_symbols(self):\\n return self._call_txtrader_api('query_symbols', {'data': False})\",\n \"def get_palette_names(scheme: ColorScheme | ColorSchemeShort) -> list[str]:\\n mod = get_palette_module(scheme)\\n names = mod.__all__\\n return names.copy()\",\n \"def _available_algorithms(**_: str) -> Set[str]:\\n avail = set()\\n pass2 = set()\\n for algo in hashlib.algorithms_available:\\n lalgo = algo.lower()\\n if \\\"with\\\" in lalgo:\\n continue # skip apparently redundant ones\\n if lalgo != algo:\\n pass2.add(algo)\\n else:\\n avail.add(lalgo)\\n for algo in pass2:\\n if algo.lower() not in avail:\\n avail.add(algo)\\n return avail\",\n \"def free_symbols(self) -> Iterable[sympy.Symbol]:\\n return get_free_symbols(self.params)\",\n \"def free_symbols(self) -> Iterable[sympy.Symbol]:\\n return get_free_symbols(self.params)\",\n \"def symbols_details(self):\\n pass\",\n \"def _symbols_of_input(label: str) -> List[str]:\\n if label == common.EPSILON:\\n return [label]\\n\\n # We add a state transition arc for each digit of a multi-digit number.\\n if \\\"[\\\" not in label:\\n return list(label)\\n\\n # We add a state transition arc for each inflectional or derivational\\n # morpheme, inflectional group boundary, and proper noun analysis tag.\\n return _SYMBOLS_REGEX.findall(label)\",\n \"def free_symbols(self) -> set[Basic]:\\n empty: set[Basic] = set()\\n return empty.union(*(a.free_symbols for a in self.args))\",\n \"def get_related_forex_currencies(self, currency = None):\\n new_symbol_names_list = []\\n for symbol_name in self.symbol_names_list:\\n if (symbol_name[:3] == currency ) | (symbol_name[3:] == currency ):\\n new_symbol_names_list.append(symbol_name)\\n symbol_names_list = new_symbol_names_list\\n return symbol_names_list\",\n \"def gene_symbol_wrangler(inpAcc):\\n \\n print(\\\"processing gene symbols\\\")\\n \\n resD = {}\\n \\n for convI in inpAcc:\\n keyI = convI[\\\"InputValue\\\"]\\n valueI = convI[\\\"Gene Symbol\\\"]\\n resD[keyI] = valueI\\n\\n return resD\",\n \"def _getAvailableMethods(self):\\n bsc = getToolByName(self, 'bika_setup_catalog')\\n items = [(c.UID, c.Title) \\\\\\n for c in bsc(portal_type='Method',\\n inactive_state = 'active')]\\n items.sort(lambda x,y:cmp(x[1], y[1]))\\n items.insert(0, ('', t(_('None'))))\\n return DisplayList(items)\",\n \"def symbolize_pairs(list_of_pair_string: str) -> list:\\n symbolized_pairs = []\\n for pair in list_of_pair_string:\\n symbolized_pairs.append(pair[0] + '-' + pair[1])\\n\\n return symbolized_pairs\",\n \"def symbol_state_map(self):\\n map = {}\\n for state in self:\\n map[state.symbol] = state\\n map.update(self.symbol_synonyms)\\n if not self.case_sensitive:\\n for state in self:\\n if state.symbol.islower():\\n map[state.symbol.upper()] = state\\n else:\\n map[state.symbol.lower()] = state\\n for symbol, state in self.symbol_synonyms.items():\\n if symbol.islower():\\n map[symbol.upper()] = state\\n else:\\n map[symbol.lower()] = state\\n return map\",\n \"def _get_symbols(self):\\n\\n symbols = self.get_symbols()\\n\\n if isinstance(symbols, dict):\\n keys = ['symbol', 'from_symbol', 'to_symbol']\\n correct_keys = np.isin(keys, list(symbols.keys())).all()\\n\\n if not correct_keys:\\n raise ImplementationError('''\\n Dict should be in the form:\\n {'symbol':[], 'from_symbol':[], 'to_symbol':[]}\\n ''')\\n else:\\n symbols = pd.DataFrame(symbols, index = [symbols['symbol']])\\n\\n symbols.index = symbols.symbol\\n\\n return symbols\",\n \"def symbols_zero_spin(self) -> List[str]:\\n return self._symbols_zero_spin\",\n \"def IterUniqueSymbols(self):\\n return SymbolGroup._IterUnique(self)\",\n \"def set_symbols(self, symboltable: dict):\\n\\n for index in range(1, self.symbol_layout.rowCount()):\\n self.symbol_layout.removeRow(index)\\n\\n font = QFont('Fira Code', 8, QFont.Medium)\\n for entry in symboltable:\\n symbol = QLineEdit()\\n symbol.setReadOnly(True)\\n symbol.setText(entry)\\n symbol.setFont(font)\\n address = QLineEdit()\\n address.setReadOnly(True)\\n address.setFont(font)\\n address.setText(str(symboltable[entry]))\\n self.symbol_layout.addRow(address, symbol)\",\n \"def list():\\n return [Drive.ENCODER_L,\\n Drive.ENCODER_R]\",\n \"def sym_elements(self):\\n def trans(name, *sym):\\n t = Element.TRANSFORMS\\n n = name.split('_')\\n\\n for x in sym:\\n n[-1] = t[x][n[-1]]\\n\\n return '_'.join(n)\\n\\n def primary():\\n e = self.copy()\\n e.name = '{}_p'.format(self.name)\\n return e\\n\\n def x_sym():\\n e = self.copy()\\n e.name = '{}_x'.format(self.name)\\n e.inode = trans(self.inode, 'x')\\n e.jnode = trans(self.jnode, 'x')\\n return e\\n\\n def y_sym():\\n e = self.copy()\\n e.name = '{}_y'.format(self.name)\\n e.inode = trans(self.inode, 'y')\\n e.jnode = trans(self.jnode, 'y')\\n return e\\n\\n def xy_sym():\\n e = self.copy()\\n e.name = '{}_xy'.format(self.name)\\n e.inode = trans(self.inode, 'x', 'y')\\n e.jnode = trans(self.jnode, 'x', 'y')\\n return e\\n\\n if self.symmetry is None:\\n return primary(),\\n\\n elif self.symmetry == 'x':\\n return primary(), x_sym()\\n\\n elif self.symmetry == 'y':\\n return primary(), y_sym()\\n\\n elif self.symmetry == 'xy':\\n return primary(), x_sym(), y_sym(), xy_sym()\",\n \"def create_possible_symbols_to_cells_mapping(self):\\r\\n possibles_to_cells = defaultdict(set)\\r\\n for cell in self.iterate_empty_cells():\\r\\n possibles_to_cells[frozenset(cell.get_possible_symbols())].add(cell)\\r\\n return possibles_to_cells\",\n \"def r_symbols(size, symbols, length, used=None):\\n if length == 1 and not used:\\n return R.sample(symbols, size)\\n rset, used = set(), set(used or [])\\n while len(rset) < size:\\n s = r_string(symbols, R.randint(1, length))\\n if s not in used:\\n rset.add(s)\\n return list(rset)\",\n \"def get_all_symbolic_models(self):\\n return get_symbolic_constraint_models(self.constraints)\",\n \"def _init_symbol_tracker(self):\\n # Initialize with an empty set\\n atoms_indx = {symb: set([]) for symb in self.symbols}\\n\\n # Populate the sets\\n for atom in self.atoms:\\n symb = atom.symbol\\n atoms_indx[symb].add(atom.index)\\n return atoms_indx\",\n \"def sym(self) -> np.ndarray:\\n if self._sym is None:\\n self._sym = symmetrize_discrete_vector_field(self.F, mode=\\\"sym\\\")\\n return self._sym\",\n \"def __init__(self, sym: ghidra.program.model.pcode.HighSymbol):\\n ...\",\n \"def get_symbol(symbols):\\n # Figure out if list of symbols or single symbol.\\n if not hasattr(symbols, '__getitem__'):\\n symbols = [symbols]\\n elif len(symbols) == 3 and symbols[0] in ('p', 'P'):\\n # Most likely a polygon specification (at least not a valid other\\n # symbol).\\n symbols = [symbols]\\n\\n symbols = [symbol_dict[symbol] if symbol in symbol_dict else symbol for\\n symbol in symbols]\\n\\n paths = []\\n for symbol in symbols:\\n if isinstance(symbol, matplotlib.path.Path):\\n return symbol\\n elif hasattr(symbol, '__getitem__') and len(symbol) == 3:\\n kind, n, angle = symbol\\n\\n if kind in ['p', 'P']:\\n if kind == 'p':\\n radius = 1. / cos(pi / n)\\n else:\\n # make the polygon such that it has area equal\\n # to a unit circle\\n radius = sqrt(2 * pi / (n * sin(2 * pi / n)))\\n\\n angle = pi * angle / 180\\n patch = matplotlib.patches.RegularPolygon((0, 0), n,\\n radius=radius,\\n orientation=angle)\\n else:\\n raise ValueError(\\\"Unknown symbol definition \\\" + str(symbol))\\n elif symbol == 'o':\\n patch = matplotlib.patches.Circle((0, 0), 1)\\n\\n paths.append(patch.get_path().transformed(patch.get_transform()))\\n\\n return paths\",\n \"def findBrains():\\r\\n keys = livingDead.Frankenstein.genKeys()\\r\\n return keys\",\n \"def set_symbols(self, symbols=None):\\n if symbols and isinstance(symbols, str):\\n if symbols.find(',') != -1:\\n symbols_list = symbols.split(',')\\n for item in symbols_list:\\n self.symbols.append(item.strip())\\n else:\\n self.symbols.append(symbols)\\n else:\\n self.symbols = []\",\n \"def _symbols_of_output(label: str) -> List[str]:\\n if label == common.EPSILON:\\n return [label]\\n\\n # We add a new state transition arc for each character of the output token.\\n return list(label)\",\n \"def __init__(self, DEBUG=False):\\n self.DEBUG = DEBUG\\n\\n self.classTable = {}\\n self.subroutineTable = {}\\n\\n self.counts = {}\\n self.counts[\\\"STATIC\\\"] = 0\\n self.counts[\\\"FIELD\\\"] = 0\\n self.counts[\\\"ARG\\\"] = 0\\n self.counts[\\\"VAR\\\"] = 0\\n\\n if self.DEBUG:\\n print(\\\"DEBUG(SymbolTable): INITIALIZED SYMBOL TABLES\\\")\",\n \"def sym_nodes(self):\\n def primary():\\n n = self.copy()\\n n.name = '{}_p'.format(self.name)\\n return n\\n\\n def x_sym():\\n n = self.copy()\\n n.name = '{}_x'.format(self.name)\\n n[1] *= -1\\n return n\\n\\n def y_sym():\\n n = self.copy()\\n n.name = '{}_y'.format(self.name)\\n n[0] *= -1\\n return n\\n\\n def xy_sym():\\n n = self.copy()\\n n.name = '{}_xy'.format(self.name)\\n n[:2] *= -1\\n return n\\n\\n if self.symmetry is None:\\n return primary(),\\n\\n elif self.symmetry == 'x':\\n return primary(), x_sym()\\n\\n elif self.symmetry == 'y':\\n return primary(), y_sym()\\n\\n elif self.symmetry == 'xy':\\n return primary(), x_sym(), y_sym(), xy_sym()\",\n \"def choices(self) -> list:\\n return [self.mapper(i) for i in self.keys()]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7745224","0.669557","0.6515156","0.63807356","0.63562876","0.6307514","0.6297486","0.6230556","0.61550385","0.6110371","0.60140777","0.600392","0.58965176","0.5893767","0.5836222","0.58326477","0.5825521","0.5808387","0.57900697","0.5775794","0.57617795","0.5722399","0.5715164","0.5712698","0.56934786","0.56902784","0.5653735","0.56444454","0.56345713","0.5575523","0.5574133","0.5573711","0.55713034","0.55576456","0.5502158","0.54827964","0.54524845","0.5438015","0.54110336","0.54000306","0.53952265","0.5394388","0.53913903","0.53579646","0.53526795","0.53345627","0.5327537","0.531732","0.5302704","0.5281369","0.5279933","0.5279217","0.5278623","0.5272957","0.526886","0.5265955","0.5263171","0.52604216","0.5256973","0.5242773","0.52398944","0.5230909","0.52263564","0.5219636","0.5219259","0.52074474","0.52036786","0.51703924","0.5151208","0.5147567","0.513742","0.51361126","0.51361126","0.5132553","0.5132269","0.5123207","0.510926","0.51091474","0.51076","0.5105021","0.51013315","0.5091543","0.5086675","0.5086598","0.5083679","0.50824267","0.5076769","0.50710225","0.5069725","0.50670815","0.5054676","0.505128","0.50496763","0.5011902","0.5007575","0.49962187","0.4989683","0.4988482","0.49785995","0.4971149"],"string":"[\n \"0.7745224\",\n \"0.669557\",\n \"0.6515156\",\n \"0.63807356\",\n \"0.63562876\",\n \"0.6307514\",\n \"0.6297486\",\n \"0.6230556\",\n \"0.61550385\",\n \"0.6110371\",\n \"0.60140777\",\n \"0.600392\",\n \"0.58965176\",\n \"0.5893767\",\n \"0.5836222\",\n \"0.58326477\",\n \"0.5825521\",\n \"0.5808387\",\n \"0.57900697\",\n \"0.5775794\",\n \"0.57617795\",\n \"0.5722399\",\n \"0.5715164\",\n \"0.5712698\",\n \"0.56934786\",\n \"0.56902784\",\n \"0.5653735\",\n \"0.56444454\",\n \"0.56345713\",\n \"0.5575523\",\n \"0.5574133\",\n \"0.5573711\",\n \"0.55713034\",\n \"0.55576456\",\n \"0.5502158\",\n \"0.54827964\",\n \"0.54524845\",\n \"0.5438015\",\n \"0.54110336\",\n \"0.54000306\",\n \"0.53952265\",\n \"0.5394388\",\n \"0.53913903\",\n \"0.53579646\",\n \"0.53526795\",\n \"0.53345627\",\n \"0.5327537\",\n \"0.531732\",\n \"0.5302704\",\n \"0.5281369\",\n \"0.5279933\",\n \"0.5279217\",\n \"0.5278623\",\n \"0.5272957\",\n \"0.526886\",\n \"0.5265955\",\n \"0.5263171\",\n \"0.52604216\",\n \"0.5256973\",\n \"0.5242773\",\n \"0.52398944\",\n \"0.5230909\",\n \"0.52263564\",\n \"0.5219636\",\n \"0.5219259\",\n \"0.52074474\",\n \"0.52036786\",\n \"0.51703924\",\n \"0.5151208\",\n \"0.5147567\",\n \"0.513742\",\n \"0.51361126\",\n \"0.51361126\",\n \"0.5132553\",\n \"0.5132269\",\n \"0.5123207\",\n \"0.510926\",\n \"0.51091474\",\n \"0.51076\",\n \"0.5105021\",\n \"0.51013315\",\n \"0.5091543\",\n \"0.5086675\",\n \"0.5086598\",\n \"0.5083679\",\n \"0.50824267\",\n \"0.5076769\",\n \"0.50710225\",\n \"0.5069725\",\n \"0.50670815\",\n \"0.5054676\",\n \"0.505128\",\n \"0.50496763\",\n \"0.5011902\",\n \"0.5007575\",\n \"0.49962187\",\n \"0.4989683\",\n \"0.4988482\",\n \"0.49785995\",\n \"0.4971149\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69593567"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":789,"cells":{"query":{"kind":"string","value":"Create a centered circle (or sphere) having radius r."},"document":{"kind":"string","value":"def model_circle(r, nx, ny, nz=1):\n\te = EMData()\n\te.set_size(nx, ny, nz)\n\te.process_inplace(\"testimage.circlesphere\", {\"radius\":r, \"fill\":1})\n\treturn e"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def make_circle(x, y, r):\n\tnew_circle = Circle()\n\tnew_circle.x = x\n\tnew_circle.y = y\n\tnew_circle.r = r\n\treturn new_circle","def create_circle(self, x, y, r, **kwargs):\n return self.create_oval(*self.circ_to_oval(x, y, r), **kwargs)","def _generate_circle(self, center, radius):\n assert len(center) in [2, 3], 'Center of circle must have 2 or 3 elements'\n assert radius > 0, 'Radius must be greater than zero'\n return Point(*center).buffer(radius)","def circle(radius, center, dim):\n kern = np.zeros(shape=(radius*2,radius*2))\n kern[draw.circle(r=radius, c=radius, radius=radius)] = 1\n return kern","def circle(self, center, radius, color=(255, 255, 255), width=0):\n center = self._transform(center)\n pygame.draw.circle(self.screen, color, center, radius, width)","def generate_circle(R,center,N=100,t0=0.0,t1=2.0*np.pi):\r\n theta = np.linspace(t0,t0+t1,N)\r\n y = R*np.sin(theta) + center[1]\r\n x = R*np.cos(theta) + center[0]\r\n return x,y","def get_circle_coords(center, r):\n circle = [[r, 180* phi/3.14159265] for phi in range(0, 180, 5)]\n circle = [pol2cart(p[0], p[1]) + (center[0], center[1]) for p in circle]\n return circle","def circle(draw, centrex, centrey, radius, color=\"#AAAAAAFF\") -> None:\n # convert cartesian centre to pixel centre\n cx, cy = pixelcoord(centrex, centrey)\n # top left and bottom right coordinates\n rect = [(cx-radius, cy-radius), (cx+radius, cy+radius)]\n # draw\n draw.arc(rect, 0, 360, color)","def _circle(i, r=.05):\n\treturn Circle((i, 0), r, fill=True, color='black')","def circle(center, radius, *args, **kwargs):\n return patch.Circle(center, radius, *args, **kwargs)","def circle(t, r):\n circumference = math.pi * 2 * r\n n = 60\n length = circumference / n\n polygon(t, length, n)","def DrawSolidCircle(self, center, radius, axis, color):\r\n radius *= self.zoom\r\n if radius < 1:\r\n radius = 1\r\n else: radius = int(radius)\r\n\r\n pygame.draw.circle(self.surface, (color/2).bytes+[127],\r\n center, radius, 0)\r\n pygame.draw.circle(self.surface, color.bytes, center, radius, 1)\r\n pygame.draw.aaline(self.surface, (255, 0, 0), center,\r\n (center[0] - radius*axis[0], center[1] +\r\n radius*axis[1]))","def circle(self):\n return circle(self.N, self.o, self.r)","def __init__(self,r):\n self.radius = r\n self.uc_centered_a = r\n self.uc_centered_b = r*np.sqrt(3.0)","def DrawCircle(self, center, radius, color, drawwidth=1):\r\n radius *= self.zoom\r\n if radius < 1:\r\n radius = 1\r\n else: radius = int(radius)\r\n\r\n pygame.draw.circle(self.surface, color.bytes, center, radius, drawwidth)","def circle(r=0):\n\tteta = 2*pi*random()\n\tx = (r+1)*cos(teta) + L//2\n\ty = (r+1)*sin(teta) + L//2\n\t\n\ti = int(x) + 1\n\tj = int(y) + 1\n\tprint(r)\n\treturn i,j","def drawCircle(self, r):\n assert (type(r) in [int, float]), \"parameter r:%s is not a valid number\" % `r` \n x = self._turtle.xcor()\n y = self._turtle.ycor()\n \n # Move the pen into position\n fstate = self._turtle.pendown()\n if fstate:\n self._turtle.penup()\n self._turtle.setposition(x, y-r)\n if fstate:\n self._turtle.pendown()\n \n # Draw the circle and fill if necessary\n self._turtle.circle(r)\n self.flush()\n self._turtle.forward(0)\n \n # Return the pen to the position\n if fstate:\n self._turtle.penup()\n self._turtle.setposition(x, y)\n if fstate:\n self._turtle.pendown()","def Circle(radius=0.5, resolution=100):\n points = np.zeros((resolution, 3))\n theta = np.linspace(0.0, 2.0 * np.pi, resolution, endpoint=False)\n points[:, 0] = radius * np.cos(theta)\n points[:, 1] = radius * np.sin(theta)\n cells = np.array([np.append(np.array([resolution]), np.arange(resolution))])\n return wrap(pyvista.PolyData(points, cells))","def circle(self, x, y, r, solid = False):\n px = 0\n py = r\n d = 1 - 2 * r\n err = 0\n while py >= 0:\n if solid:\n for i in range(x - px, x + px + 1):\n self.pixel(i, y + py, 1)\n self.pixel(i, y - py, 1)\n else:\n self.pixel(x + px, y + py, 1)\n self.pixel(x + px, y - py, 1)\n self.pixel(x - px, y + py, 1)\n self.pixel(x - px, y - py, 1)\n err = 2 * (d + py) - 1\n if d < 0 and err <= 0:\n px += 1\n d += 2 *px + 1\n else:\n err = 2 * (d - px) - 1\n if d > 0 and err > 0:\n py -= 1\n d += 1 - 2 * py\n else:\n px += 1\n d += 2 * (px - py)\n py -= 1","def circle(self, x, y, r, cls=None, style=None):\n x, y, r = self._meta.units(x, y, r)\n cls_str = 'class=\"%s\" ' % cls if cls else ''\n style_str = 'style=\"%s\" ' % self._meta.make_style(style) if style else ''\n self.elements.append(\"\"\"\n <circle cx=\"%s\" cy=\"%s\" r=\"%s\" %s%s/>\n \"\"\".strip() % (\n x, y, r, cls_str, style_str\n ))\n return self","def circle(self, x, y, r, cls=None, style=None):\n x, y, r = self._meta.units(x, y, r)\n cls_str = 'class=\"%s\" ' % cls if cls else ''\n style_str = 'style=\"%s\" ' % self._meta.make_style(style) if style else ''\n self.elements.append(\"\"\"\n <circle cx=\"%s\" cy=\"%s\" r=\"%s\" %s%s/>\n \"\"\".strip() % (\n x, y, r, cls_str, style_str\n ))\n return self","def circleCirc(radius):\n radius = float(radius)\n return 2*math.pi*radius","def circle_area(r):\n if r < 0:\n raise ValueError(\"Radius cannot be negative\")\n\n return pi*(r**2)","def circle(cls, radius, position, open_circle=False):\n\n nb_points = 2*np.pi*radius/1\n points1 = radius*np.transpose(np.concatenate(([np.cos(2*np.pi*np.arange(0,nb_points+1)/nb_points)],[np.sin(2*np.pi*np.arange(0,nb_points+1)/nb_points)]),axis=0))\n \n for y in range(points1.shape[0]):\n points1[y,:]=points1[y,:]+position\n \n circle_obj = cls()\n circle_obj.coord = [points1]\n circle_obj.open = open_circle\n return circle_obj","def circle(self, center_x, center_y, radius, color):\n x = radius - 1\n y = 0\n d_x = 1\n d_y = 1\n err = d_x - (radius << 1)\n while x >= y:\n self.pixel(center_x + x, center_y + y, color)\n self.pixel(center_x + y, center_y + x, color)\n self.pixel(center_x - y, center_y + x, color)\n self.pixel(center_x - x, center_y + y, color)\n self.pixel(center_x - x, center_y - y, color)\n self.pixel(center_x - y, center_y - x, color)\n self.pixel(center_x + y, center_y - x, color)\n self.pixel(center_x + x, center_y - y, color)\n if err <= 0:\n y += 1\n err += d_y\n d_y += 2\n if err > 0:\n x -= 1\n d_x += 2\n err += d_x - (radius << 1)","def __drawCircle(self, center, radius, color, drawwidth=1):\n radius *= self.viewZoom\n if radius < 1: radius = 1\n else: radius = int(radius)\n\n pygame.draw.circle(self.screen, color, center, radius, drawwidth)","def filled_sphere(shape, radius, center=None):\n\tr2 = radius*radius\n\tif center is None:\n\t\t### set to center of array\n\t\tcenter = (shape[0]-1)/2.0,(shape[1]-1)/2.0,(shape[2]-1)/2.0\n\tdef func(i0, i1, i2):\n\t\tii0 = i0 - center[0]\n\t\tii1 = i1 - center[1]\n\t\tii2 = i2 - center[2]\n\t\trr2 = ii0**2 + ii1**2 + ii2**2\n\t\tc = numpy.where(rr2<r2, 0.0, 1.0)\n\t\treturn c\n\treturn numpy.fromfunction(func, shape)","def circle(radius = 10, angle_resolution = 2.5, layer = 0):\n D = Device(name = 'circle')\n t = np.linspace(0, 360, int(np.ceil(360/angle_resolution) + 1)) * pi/180\n xpts = (radius*cos(t)).tolist()\n ypts = (radius*sin(t)).tolist()\n D.add_polygon(points = (xpts, ypts), layer = layer)\n return D","def draw_circle(self, color, center, radius, width):\n _c = self.T.itrans(center)\n pg.draw.circle(self.screen, color, _c(), radius, width)","def circle(self, p, radius, **kwargs):\n cx, cy = self._sky2img(p)\n self._draw.ellipse([cx-radius, cy-radius, cx+radius, cy+radius], **kwargs)","def circle(n=5000, r=1, noise=0.05):\n phis = 2 * np.pi * np.random.rand(n)\n x = [[r * np.sin(phi), r * np.cos(phi)] for phi in phis]\n x = np.array(x)\n x = x + noise * np.random.randn(n, 2)\n return x","def circular_aperture(self, r, scale=True):\n if scale:\n radius = r * self.a\n else:\n radius = r\n return CircularAperture(self.coords, float(np.abs(radius)))","def circle_point(radius, phi):\n if radius <= 0:\n raise AssertionError('Radius mast be grater than 0')\n x = radius * cos(radians(phi))\n y = radius * sin(radians(phi))\n z = 0\n\n return x, y, z","def __init__(self, center=None, radius=1):\n if center is None:\n center = Point()\n self.center = center\n self.radius = radius","def add_circle(self, r_center, c_center, radius, color=BLUE, image=np.full((640, 480, 3), BLACK)):\n circle = np.fromfunction(lambda r, c, _: (r - r_center) ** 2 + (c - c_center) ** 2 <= radius ** 2, image.shape)\n return np.where(circle, color, image)","def draw_circle(self, color, position, radius, width=0, anchor='topleft'):\n offset = self._calculate_offset(anchor)\n pygame.draw.circle(self._surf, color, (position + offset).floor(),\n radius, width)\n self._version += 1\n spyral.util.scale_surface.clear(self._surf)\n return self","def randomPointOnSphere(r):\n x = np.random.normal()\n y = np.random.normal()\n z = np.random.normal()\n point = np.array([x, y, z])\n point *= r/(x**2 + y**2 + z**2)**.5\n return point","def AddCircle(self,centerPnt,radius):\n\t\tcircle=self.Space.AddCircle(centerPnt,radius)\n\t\treturn circle","def circle(self, center, rad):\n self.gc.show_circles(center[0], center[1], rad, facecolor='none', edgecolor=self.color, linewidth=0.5)","def filled_circle(shape, radius, center=None):\n\tr2 = radius*radius\n\tif center is None:\n\t\t### set to center of array\n\t\tcenter = (shape[0]-1)/2.0,(shape[1]-1)/2.0\n\tdef func(i0, i1):\n\t\tii0 = i0 - center[0]\n\t\tii1 = i1 - center[1]\n\t\trr2 = ii0**2 + ii1**2\n\t\tc = numpy.where(rr2<r2, 0.0, 1.0)\n\t\treturn c\n\treturn numpy.fromfunction(func, shape)","def circle(x: float, y: float, r: float, quantization: float):\n\n n = math.ceil(2 * math.pi * r / quantization)\n angle = np.array(list(range(n)) + [0]) / n * 2 * math.pi\n return LineCollection([r * (np.cos(angle) + 1j * np.sin(angle)) + complex(x, y)])","def draw_circle(self, color, position, radius, width = 0, anchor= 'topleft'):\n color = spyral.color._determine(color)\n offset = self._calculate_offset(anchor)\n pygame.draw.circle(self._surf, color, position + offset, radius, width)","def __init__( self , center , radius ):\r\n self.center = center\r\n self.radius = radius","def plot_circle(r,**kw):\n try:\n fmt = kw.pop('fmt')\n except:\n fmt='k'\n try:\n label = kw.pop('label')\n except:\n label = None\n x = num.arange(-r,r+0.01,0.01)\n y = num.sqrt(num.fabs(r**2. - x**2.))\n pyplot.plot(x,y,fmt,**kw)\n pyplot.plot(x,-y,fmt,label=label,**kw)","def circle_new(shape = (1024, 1024), radius=0.25, Nrad = None, origin=[0,0]):\r\n if Nrad == None :\r\n pass\r\n else :\r\n radius = max([shape[0], shape[1]]) \r\n radius = np.float(Nrad) / np.float(radius) \r\n # \r\n x, y = make_xy(shape, origin = origin)\r\n r = np.sqrt(x**2 + y**2)\r\n if shape[1] > shape[0]:\r\n rmax = radius * shape[0] / 2\r\n else :\r\n rmax = radius * shape[1] / 2\r\n arrayout = (r <= rmax)\r\n return np.array(arrayout, dtype=np.float64)","def get_circle_radius(self, point, center):\n x, y, z = point[:]\n x0, y0, z0 = center[:]\n return math.sqrt((x-x0)**2 + (y-y0)**2 + (z-z0)**2)","def draw_circle_filled(center_x, center_y, radius, color):\n width = radius\n height = radius\n draw_ellipse_filled(center_x, center_y, width, height, color)","def circle(radius, extent=360):\n turtleTmp.circle(radius, extent)","def _generate_circle_mask(center_y, center_x, radius):\n\n circle = draw.circle(center_y, center_x, radius)\n\n return circle","def Icosphere(radius=1.0, center=(0.0, 0.0, 0.0), nsub=3):\n mesh = Icosahedron()\n mesh.clear_data()\n mesh = mesh.subdivide(nsub=nsub)\n\n # scale to desired radius and translate origin\n dist = np.linalg.norm(mesh.points, axis=1, keepdims=True) # distance from origin\n mesh.points = mesh.points * (radius / dist) + center\n return mesh","def createCircle(self, x, y, radius):\n # TODO (#2398) fix this to be top left coordinates, width, height\n return QtCore.QRectF(\n int(x - radius), int(y - radius), int(radius * 2), int(radius * 2)\n )","def draw_circle(c):\n turtle.circle(c.radius)","def draw_circle(c):\n turtle.circle(c.radius)","def area_of_circle(r):\n a = r**2 * math.pi\n return a","def area_of_circle(r):\n a = r**2 * math.pi\n return a","def draw_circle(self, x, y, radius, color=Color['white']):\n pygame.draw.circle(self.display, color, (x, y), radius)","def make_circle(self):\n A = 2*np.random.rand(self.m, self.n)-1\n b = np.sign(np.sum(A**2, 1) - self.radius)\n return A, b","def draw_circle(self, x0, y0, r, color=None):\n f = 1 - r\n ddF_x = 1\n ddF_y = -2 * r\n x = 0\n y = r\n\n self.set(x0, y0 + r, color)\n self.set(x0, y0 - r, color)\n self.set(x0 + r, y0, color)\n self.set(x0 - r, y0, color)\n\n while x < y:\n if f >= 0:\n y -= 1\n ddF_y += 2\n f += ddF_y\n x += 1\n ddF_x += 2\n f += ddF_x\n\n self.set(x0 + x, y0 + y, color)\n self.set(x0 - x, y0 + y, color)\n self.set(x0 + x, y0 - y, color)\n self.set(x0 - x, y0 - y, color)\n self.set(x0 + y, y0 + x, color)\n self.set(x0 - y, y0 + x, color)\n self.set(x0 + y, y0 - x, color)\n self.set(x0 - y, y0 - x, color)","def get_nice_circle(x, y, radius, color=\"lightsteelblue\", facecolor=\"green\", alpha=.6, ax=None ):\n e = pl.Circle([x, y], radius)\n if ax is None:\n ax = pl.gca()\n ax.add_artist(e)\n e.set_clip_box(ax.bbox)\n e.set_edgecolor( color )\n e.set_linewidth(3)\n e.set_facecolor( facecolor ) # \"none\" not None\n e.set_alpha( alpha )\n return e","def Octahedron(radius=1.0, center=(0.0, 0.0, 0.0)):\n return PlatonicSolid(kind='octahedron', radius=radius, center=center)","def draw_circle(color, position, radius, width=0):\n #print('(color={}, position={}, radius={}, width={})')\n pygame.draw.circle(screen, color, position, radius, width)","def circle():\n xmin=0\n xmax=6.5\n ymin=0.\n ymax=6.5\n\n x = arange(xmin, xmax, 0.005)\n y = x*1.\n [xx, yy] = meshgrid(x, y)\n\n zz=sqrt((xx-3.2475)**2.+(yy-3.2475)**2.)\n zz2=zz*1.\n zz2[(zz <= 3.25)]=1.\n zz2[(zz <= 3.25*0.2)]=0.\n zz2[(zz > 3.25)]=0.\n zz3=zeros(numpy.array(numpy.shape(zz2))/10)\n for i in arange(len(xx)/10):\n for j in arange(len(yy)/10):\n zz3[i,j]=numpy.sum(zz2[(i*10):(i*10+10),(j*10):(j*10+10)])/100.\n\n return zz3","def radius(self,xc=None,yc=None):\n if xc == None:\n xc = self.x1\n if yc == None:\n yc = self.y1\n self.r = sqrt((self.x-xc)**2+(self.y-yc)**2)","def circle(x, r, a, b, x_lim):\n y = (b + np.sqrt(maximum(\n r ** 2 - ((x - a) ** 2) * (x >= x_lim[0]) * (x <= x_lim[1]))\n )) * (x >= x_lim[0]) * (x <= x_lim[1])\n return y","def find_center(r):\n cx=r.corner.x+(r.width/2)\n cy=r.corner.y+(r.height/2)\n return cx,cy","def Radius(self, *args):\n return _Bnd.Bnd_Sphere_Radius(self, *args)","def circle(self,image,radius,i,j,c_x,c_y):\r\n major_axis=radius\r\n minor_axis=radius\r\n self.ellipse(image,major_axis,minor_axis,i,j,c_x,c_y)","def half_circle(n=5000, r=1, noise=0.05):\n phis = np.pi * np.random.rand(n)\n x = [[r * np.sin(phi), r * np.cos(phi)] for phi in phis]\n x = np.array(x)\n x = x + noise * np.random.randn(n, 2)\n return x","def draw_circle(centerx, centery, radius):\r\n global _canvas\r\n global _current_color\r\n if _canvas == None:\r\n raise RuntimeError(\"Canvas is not open yet.\")\r\n else:\r\n circle = Circle()\r\n circle.move(centerx, centery)\r\n circle.setRadius(radius)\r\n _set_not_filled(circle)\r\n _canvas.add(circle)","def pos_on_semicircle(x, r, cxy):\n pos = np.sqrt(r ** 2 - (x - cxy[0]) ** 2) + cxy[1]\n\n return pos","def objects_radius(self, centre, radius):","def circle(self, clear_screen=True, x=50, y=50, radius=40, fill_color='black', outline_color='black'):\n\n if clear_screen:\n self.clear()\n\n x1 = x - radius\n y1 = y - radius\n x2 = x + radius\n y2 = y + radius\n\n return self.draw.ellipse((x1, y1, x2, y2), fill=fill_color, outline=outline_color)","def circle(self, radius, extent=None, steps=None):\n super().circle(radius, extent, steps)","def createSphere( position=(0,0,0), radius=1, colour=(0.6,0.6,0.6), samplesY = 20, samplesXZ = 20 ):\r\n return createEllipsoid( position, (radius,radius,radius), colour, samplesY, samplesXZ )","def create_circle(radius=None, n_instance=None):\n del_theta = np.pi * 2/(n_instance)\n theta_list = np.linspace(0, np.pi * 2, n_instance)\n coordinates = [circular_movement(radius, theta) for theta in theta_list]\n return coordinates","def surfaceIntSphere(r: float) -> float:\n return 4.0 * np.pi * r * r","def circle(radius = 15, resolution = 20, robotHeight = -90, n = 1, dir = 0):\n \n t = np.linspace(0, n*2*m.pi, resolution*n)\n circlePos = []\n for num in t:\n if dir == 0:\n x = m.cos(num)*radius\n y = m.sin(num)*radius\n else:\n x = m.cos(num)*radius\n y = m.sin(num-m.pi)*radius\n\n circlePos.append([x, y, robotHeight, 0, 0, 0, 'mov'])\n\n circlePos.append([0,0,-127,0,0,0,'mov'])\n return circlePos","def circle(center, perp_vect, radius, element_number=10):\n # tl = [0, 0.2, 0.4, 0.6, 0.8]\n tl = np.linspace(0, 1, element_number)\n\n # vector form center to edge of circle\n # u is a unit vector from the centre of the circle to any point on the\n # circumference\n\n # normalized perpendicular vector\n n = perp_vect / np.linalg.norm(perp_vect)\n\n # normalized vector from the centre to point on the circumference\n u = perpendicular_vector(n)\n u /= np.linalg.norm(u)\n\n pts = []\n\n for t in tl:\n # u = np.array([0, 1, 0])\n # n = np.array([1, 0, 0])\n pt = (\n radius * np.cos(t * 2 * np.pi) * u\n + radius * np.sin(t * 2 * np.pi) * np.cross(u, n)\n + center\n )\n\n pt = pt.tolist()\n pts.append(pt)\n\n return pts","def drawCircle(t, x, y, radius):\r\n t.up()\r\n t.goto(x + radius, y)\r\n t.setheading(90)\r\n t.down()\r\n for count in range(120):\r\n t.left(3)\r\n t.forward(2.0 * math.pi * radius / 120.0)","def initP0(self, size, radius):\n return h.circle(size, radius)[:, :, 0]","def area_circle(r):\n return (r ** 2) * math.pi","def __init__(self, c, radius, a0, da):\n Circle.__init__(self, Vector(c).to_2d(), radius)\n self.line = None\n self.a0 = a0\n self.da = da","def plot_circle(self, radius, c=color, ax=None, label=True, fontsize=12, **kwargs):\n if ax is None:\n ax = plt.gca()\n circle = Circle(self.coords, radius, fill=None, ec=c, **kwargs)\n ax.add_artist(circle)\n if label and self.i is not None:\n plt.text(\n *(np.array(self.coords) - [0, 1.5 * radius]),\n self.i,\n c=c,\n ha=\"center\",\n va=\"top\",\n fontsize=fontsize,\n )","def draw_full_circle(x, y, radius):\n iterations = int(2 * radius * pi)\n s = sin(2 * pi / iterations)\n c = cos(2 * pi / iterations)\n\n dx, dy = radius, 0.\n\n glBegin(GL_TRIANGLE_FAN)\n glVertex2f(x, y)\n for _ in range(iterations + 1):\n glVertex2f(x + dx, y + dy)\n dx, dy = (dx * c + dy * s), (dy * c - dx * s)\n glEnd()","def random_uniform_within_circle():\n rho = np.sqrt(np.random.uniform(0, 1))\n phi = np.random.uniform(0, 2 * np.pi)\n x = rho * np.cos(phi)\n y = rho * np.sin(phi)\n return np.array([x, y])","def oncircle(size=None):\n if size is None:\n size = ()\n else:\n try:\n size = tuple(size)\n except TypeError:\n size = (size,)\n # This beats normalizing incircle for all sizes, even though that\n # should be the superior algorithm for compiled code.\n theta = 2.*pi * random(size + (1,))\n return concatenate((cos(theta), sin(theta)), axis=-1)","def create_circle(self, cx, cy, radius, style=None, parent=None):\n if parent is None:\n parent = self.current_parent\n if parent is not None:\n attrs = {'r': str(radius), 'cx': str(cx), 'cy': str(cy)}\n if style:\n attrs['style'] = style\n return etree.SubElement(parent, svgns('circle'), attrs)","def draw_circle_outline(center_x, center_y, radius, color, border_width=1):\n width = radius\n height = radius\n draw_ellipse_outline(center_x, center_y, width, height,\n color, border_width)","def Calc_axe_spheroid(r,c):\n return np.sqrt((r**3)/c)","def createCirclePolygon(h, k, r, dx):\n D = 10.0\n theta = 2 * np.arccos((r-(dx/D))/r)\n npoints = int(360.0/theta)\n x, y = getPointsInCircum(r, n=npoints, h=h, k=k)\n p = Polygon(list(zip(x, y)))\n return p","def fillcircle(draw, centrex, centrey, radius, color=\"#AAAAAAFF\") -> None:\n # convert cartesian centre to pixel centre\n cx, cy = pixelcoord(centrex, centrey)\n # top left and bottom right coordinates, must never reverse\n rect = [(cx-radius, cy-radius), (cx+radius, cy+radius)]\n # draw, same color for outline and fill\n draw.ellipse(rect, color, color)","def points_on_circumference(center=(0, 0), r=50, n=100):\n\treturn [\n (\n center[0]+(cos(2 * pi / n * x) * r), \n center[1] + (sin(2 * pi / n * x) * r) \n\n ) for x in range(0, n + 1)]","def circle_from_points(a, b, c):\n ab = subtract_vectors(b, a)\n cb = subtract_vectors(b, c)\n ba = subtract_vectors(a, b)\n ca = subtract_vectors(a, c)\n ac = subtract_vectors(c, a)\n bc = subtract_vectors(c, b)\n normal = normalize_vector(cross_vectors(ab, ac))\n d = 2 * length_vector_sqrd(cross_vectors(ba, cb))\n A = length_vector_sqrd(cb) * dot_vectors(ba, ca) / d\n B = length_vector_sqrd(ca) * dot_vectors(ab, cb) / d\n C = length_vector_sqrd(ba) * dot_vectors(ac, bc) / d\n Aa = scale_vector(a, A)\n Bb = scale_vector(b, B)\n Cc = scale_vector(c, C)\n center = add_vectorlist([Aa, Bb, Cc])\n radius = distance_point_point(center, a)\n return center, radius, normal","def drawCircle(x, y, r):\n pen1.up()\n pen1.goto(x,y)\n pen1.down()\n pen1.circle(r)","def get_circle(a, b, c):\n vec = [a[0]**2 + a[1]**2, b[0]**2 + b[1]**2, c[0]**2 + c[1]**2]\n x_mat = [vec, [a[1], b[1], c[1]], [1]*3]\n y_mat = [vec, [a[0], b[0], c[0]], [1]*3]\n d_mat = [[a[0], b[0], c[0]], [a[1], b[1], c[1]], [1] * 3]\n d = 2 * det(d_mat)\n x = 1 / d * det(x_mat)\n y = -1 / d * det(y_mat)\n center = [x, y]\n #r = norm(center - a)\n r = norm([center[0]-a[0], center[1]-a[1]])\n return center, r","def plot(self, radius=15, **kwargs):\n self.plot_circle(radius, **kwargs)","def draw_circle(t, circle):\n t.pu()\n t.goto(circle.center.x, circle.center.y)\n t.pd()\n polygon.circle(t, circle.radius)","def circleInfo(r):\n c = 2 * 3.14159 * r\n a = 3.14159 * r * r\n return (c, a)","def area_circle(radius: float) -> float:\r\n if radius < 0:\r\n raise ValueError(\"area_circle() only accepts non-negative values\")\r\n return pi * radius**2","def surface_point(radius):\n z=random.uniform(-1,1)\n chi=random.uniform(0,2*numpy.pi)\n x=numpy.sqrt(1-z*z)*numpy.cos(chi)\n y=numpy.sqrt(1-z*z)*numpy.sin(chi)\n return radius*numpy.array([x,y,z])"],"string":"[\n \"def make_circle(x, y, r):\\n\\tnew_circle = Circle()\\n\\tnew_circle.x = x\\n\\tnew_circle.y = y\\n\\tnew_circle.r = r\\n\\treturn new_circle\",\n \"def create_circle(self, x, y, r, **kwargs):\\n return self.create_oval(*self.circ_to_oval(x, y, r), **kwargs)\",\n \"def _generate_circle(self, center, radius):\\n assert len(center) in [2, 3], 'Center of circle must have 2 or 3 elements'\\n assert radius > 0, 'Radius must be greater than zero'\\n return Point(*center).buffer(radius)\",\n \"def circle(radius, center, dim):\\n kern = np.zeros(shape=(radius*2,radius*2))\\n kern[draw.circle(r=radius, c=radius, radius=radius)] = 1\\n return kern\",\n \"def circle(self, center, radius, color=(255, 255, 255), width=0):\\n center = self._transform(center)\\n pygame.draw.circle(self.screen, color, center, radius, width)\",\n \"def generate_circle(R,center,N=100,t0=0.0,t1=2.0*np.pi):\\r\\n theta = np.linspace(t0,t0+t1,N)\\r\\n y = R*np.sin(theta) + center[1]\\r\\n x = R*np.cos(theta) + center[0]\\r\\n return x,y\",\n \"def get_circle_coords(center, r):\\n circle = [[r, 180* phi/3.14159265] for phi in range(0, 180, 5)]\\n circle = [pol2cart(p[0], p[1]) + (center[0], center[1]) for p in circle]\\n return circle\",\n \"def circle(draw, centrex, centrey, radius, color=\\\"#AAAAAAFF\\\") -> None:\\n # convert cartesian centre to pixel centre\\n cx, cy = pixelcoord(centrex, centrey)\\n # top left and bottom right coordinates\\n rect = [(cx-radius, cy-radius), (cx+radius, cy+radius)]\\n # draw\\n draw.arc(rect, 0, 360, color)\",\n \"def _circle(i, r=.05):\\n\\treturn Circle((i, 0), r, fill=True, color='black')\",\n \"def circle(center, radius, *args, **kwargs):\\n return patch.Circle(center, radius, *args, **kwargs)\",\n \"def circle(t, r):\\n circumference = math.pi * 2 * r\\n n = 60\\n length = circumference / n\\n polygon(t, length, n)\",\n \"def DrawSolidCircle(self, center, radius, axis, color):\\r\\n radius *= self.zoom\\r\\n if radius < 1:\\r\\n radius = 1\\r\\n else: radius = int(radius)\\r\\n\\r\\n pygame.draw.circle(self.surface, (color/2).bytes+[127],\\r\\n center, radius, 0)\\r\\n pygame.draw.circle(self.surface, color.bytes, center, radius, 1)\\r\\n pygame.draw.aaline(self.surface, (255, 0, 0), center,\\r\\n (center[0] - radius*axis[0], center[1] +\\r\\n radius*axis[1]))\",\n \"def circle(self):\\n return circle(self.N, self.o, self.r)\",\n \"def __init__(self,r):\\n self.radius = r\\n self.uc_centered_a = r\\n self.uc_centered_b = r*np.sqrt(3.0)\",\n \"def DrawCircle(self, center, radius, color, drawwidth=1):\\r\\n radius *= self.zoom\\r\\n if radius < 1:\\r\\n radius = 1\\r\\n else: radius = int(radius)\\r\\n\\r\\n pygame.draw.circle(self.surface, color.bytes, center, radius, drawwidth)\",\n \"def circle(r=0):\\n\\tteta = 2*pi*random()\\n\\tx = (r+1)*cos(teta) + L//2\\n\\ty = (r+1)*sin(teta) + L//2\\n\\t\\n\\ti = int(x) + 1\\n\\tj = int(y) + 1\\n\\tprint(r)\\n\\treturn i,j\",\n \"def drawCircle(self, r):\\n assert (type(r) in [int, float]), \\\"parameter r:%s is not a valid number\\\" % `r` \\n x = self._turtle.xcor()\\n y = self._turtle.ycor()\\n \\n # Move the pen into position\\n fstate = self._turtle.pendown()\\n if fstate:\\n self._turtle.penup()\\n self._turtle.setposition(x, y-r)\\n if fstate:\\n self._turtle.pendown()\\n \\n # Draw the circle and fill if necessary\\n self._turtle.circle(r)\\n self.flush()\\n self._turtle.forward(0)\\n \\n # Return the pen to the position\\n if fstate:\\n self._turtle.penup()\\n self._turtle.setposition(x, y)\\n if fstate:\\n self._turtle.pendown()\",\n \"def Circle(radius=0.5, resolution=100):\\n points = np.zeros((resolution, 3))\\n theta = np.linspace(0.0, 2.0 * np.pi, resolution, endpoint=False)\\n points[:, 0] = radius * np.cos(theta)\\n points[:, 1] = radius * np.sin(theta)\\n cells = np.array([np.append(np.array([resolution]), np.arange(resolution))])\\n return wrap(pyvista.PolyData(points, cells))\",\n \"def circle(self, x, y, r, solid = False):\\n px = 0\\n py = r\\n d = 1 - 2 * r\\n err = 0\\n while py >= 0:\\n if solid:\\n for i in range(x - px, x + px + 1):\\n self.pixel(i, y + py, 1)\\n self.pixel(i, y - py, 1)\\n else:\\n self.pixel(x + px, y + py, 1)\\n self.pixel(x + px, y - py, 1)\\n self.pixel(x - px, y + py, 1)\\n self.pixel(x - px, y - py, 1)\\n err = 2 * (d + py) - 1\\n if d < 0 and err <= 0:\\n px += 1\\n d += 2 *px + 1\\n else:\\n err = 2 * (d - px) - 1\\n if d > 0 and err > 0:\\n py -= 1\\n d += 1 - 2 * py\\n else:\\n px += 1\\n d += 2 * (px - py)\\n py -= 1\",\n \"def circle(self, x, y, r, cls=None, style=None):\\n x, y, r = self._meta.units(x, y, r)\\n cls_str = 'class=\\\"%s\\\" ' % cls if cls else ''\\n style_str = 'style=\\\"%s\\\" ' % self._meta.make_style(style) if style else ''\\n self.elements.append(\\\"\\\"\\\"\\n <circle cx=\\\"%s\\\" cy=\\\"%s\\\" r=\\\"%s\\\" %s%s/>\\n \\\"\\\"\\\".strip() % (\\n x, y, r, cls_str, style_str\\n ))\\n return self\",\n \"def circle(self, x, y, r, cls=None, style=None):\\n x, y, r = self._meta.units(x, y, r)\\n cls_str = 'class=\\\"%s\\\" ' % cls if cls else ''\\n style_str = 'style=\\\"%s\\\" ' % self._meta.make_style(style) if style else ''\\n self.elements.append(\\\"\\\"\\\"\\n <circle cx=\\\"%s\\\" cy=\\\"%s\\\" r=\\\"%s\\\" %s%s/>\\n \\\"\\\"\\\".strip() % (\\n x, y, r, cls_str, style_str\\n ))\\n return self\",\n \"def circleCirc(radius):\\n radius = float(radius)\\n return 2*math.pi*radius\",\n \"def circle_area(r):\\n if r < 0:\\n raise ValueError(\\\"Radius cannot be negative\\\")\\n\\n return pi*(r**2)\",\n \"def circle(cls, radius, position, open_circle=False):\\n\\n nb_points = 2*np.pi*radius/1\\n points1 = radius*np.transpose(np.concatenate(([np.cos(2*np.pi*np.arange(0,nb_points+1)/nb_points)],[np.sin(2*np.pi*np.arange(0,nb_points+1)/nb_points)]),axis=0))\\n \\n for y in range(points1.shape[0]):\\n points1[y,:]=points1[y,:]+position\\n \\n circle_obj = cls()\\n circle_obj.coord = [points1]\\n circle_obj.open = open_circle\\n return circle_obj\",\n \"def circle(self, center_x, center_y, radius, color):\\n x = radius - 1\\n y = 0\\n d_x = 1\\n d_y = 1\\n err = d_x - (radius << 1)\\n while x >= y:\\n self.pixel(center_x + x, center_y + y, color)\\n self.pixel(center_x + y, center_y + x, color)\\n self.pixel(center_x - y, center_y + x, color)\\n self.pixel(center_x - x, center_y + y, color)\\n self.pixel(center_x - x, center_y - y, color)\\n self.pixel(center_x - y, center_y - x, color)\\n self.pixel(center_x + y, center_y - x, color)\\n self.pixel(center_x + x, center_y - y, color)\\n if err <= 0:\\n y += 1\\n err += d_y\\n d_y += 2\\n if err > 0:\\n x -= 1\\n d_x += 2\\n err += d_x - (radius << 1)\",\n \"def __drawCircle(self, center, radius, color, drawwidth=1):\\n radius *= self.viewZoom\\n if radius < 1: radius = 1\\n else: radius = int(radius)\\n\\n pygame.draw.circle(self.screen, color, center, radius, drawwidth)\",\n \"def filled_sphere(shape, radius, center=None):\\n\\tr2 = radius*radius\\n\\tif center is None:\\n\\t\\t### set to center of array\\n\\t\\tcenter = (shape[0]-1)/2.0,(shape[1]-1)/2.0,(shape[2]-1)/2.0\\n\\tdef func(i0, i1, i2):\\n\\t\\tii0 = i0 - center[0]\\n\\t\\tii1 = i1 - center[1]\\n\\t\\tii2 = i2 - center[2]\\n\\t\\trr2 = ii0**2 + ii1**2 + ii2**2\\n\\t\\tc = numpy.where(rr2<r2, 0.0, 1.0)\\n\\t\\treturn c\\n\\treturn numpy.fromfunction(func, shape)\",\n \"def circle(radius = 10, angle_resolution = 2.5, layer = 0):\\n D = Device(name = 'circle')\\n t = np.linspace(0, 360, int(np.ceil(360/angle_resolution) + 1)) * pi/180\\n xpts = (radius*cos(t)).tolist()\\n ypts = (radius*sin(t)).tolist()\\n D.add_polygon(points = (xpts, ypts), layer = layer)\\n return D\",\n \"def draw_circle(self, color, center, radius, width):\\n _c = self.T.itrans(center)\\n pg.draw.circle(self.screen, color, _c(), radius, width)\",\n \"def circle(self, p, radius, **kwargs):\\n cx, cy = self._sky2img(p)\\n self._draw.ellipse([cx-radius, cy-radius, cx+radius, cy+radius], **kwargs)\",\n \"def circle(n=5000, r=1, noise=0.05):\\n phis = 2 * np.pi * np.random.rand(n)\\n x = [[r * np.sin(phi), r * np.cos(phi)] for phi in phis]\\n x = np.array(x)\\n x = x + noise * np.random.randn(n, 2)\\n return x\",\n \"def circular_aperture(self, r, scale=True):\\n if scale:\\n radius = r * self.a\\n else:\\n radius = r\\n return CircularAperture(self.coords, float(np.abs(radius)))\",\n \"def circle_point(radius, phi):\\n if radius <= 0:\\n raise AssertionError('Radius mast be grater than 0')\\n x = radius * cos(radians(phi))\\n y = radius * sin(radians(phi))\\n z = 0\\n\\n return x, y, z\",\n \"def __init__(self, center=None, radius=1):\\n if center is None:\\n center = Point()\\n self.center = center\\n self.radius = radius\",\n \"def add_circle(self, r_center, c_center, radius, color=BLUE, image=np.full((640, 480, 3), BLACK)):\\n circle = np.fromfunction(lambda r, c, _: (r - r_center) ** 2 + (c - c_center) ** 2 <= radius ** 2, image.shape)\\n return np.where(circle, color, image)\",\n \"def draw_circle(self, color, position, radius, width=0, anchor='topleft'):\\n offset = self._calculate_offset(anchor)\\n pygame.draw.circle(self._surf, color, (position + offset).floor(),\\n radius, width)\\n self._version += 1\\n spyral.util.scale_surface.clear(self._surf)\\n return self\",\n \"def randomPointOnSphere(r):\\n x = np.random.normal()\\n y = np.random.normal()\\n z = np.random.normal()\\n point = np.array([x, y, z])\\n point *= r/(x**2 + y**2 + z**2)**.5\\n return point\",\n \"def AddCircle(self,centerPnt,radius):\\n\\t\\tcircle=self.Space.AddCircle(centerPnt,radius)\\n\\t\\treturn circle\",\n \"def circle(self, center, rad):\\n self.gc.show_circles(center[0], center[1], rad, facecolor='none', edgecolor=self.color, linewidth=0.5)\",\n \"def filled_circle(shape, radius, center=None):\\n\\tr2 = radius*radius\\n\\tif center is None:\\n\\t\\t### set to center of array\\n\\t\\tcenter = (shape[0]-1)/2.0,(shape[1]-1)/2.0\\n\\tdef func(i0, i1):\\n\\t\\tii0 = i0 - center[0]\\n\\t\\tii1 = i1 - center[1]\\n\\t\\trr2 = ii0**2 + ii1**2\\n\\t\\tc = numpy.where(rr2<r2, 0.0, 1.0)\\n\\t\\treturn c\\n\\treturn numpy.fromfunction(func, shape)\",\n \"def circle(x: float, y: float, r: float, quantization: float):\\n\\n n = math.ceil(2 * math.pi * r / quantization)\\n angle = np.array(list(range(n)) + [0]) / n * 2 * math.pi\\n return LineCollection([r * (np.cos(angle) + 1j * np.sin(angle)) + complex(x, y)])\",\n \"def draw_circle(self, color, position, radius, width = 0, anchor= 'topleft'):\\n color = spyral.color._determine(color)\\n offset = self._calculate_offset(anchor)\\n pygame.draw.circle(self._surf, color, position + offset, radius, width)\",\n \"def __init__( self , center , radius ):\\r\\n self.center = center\\r\\n self.radius = radius\",\n \"def plot_circle(r,**kw):\\n try:\\n fmt = kw.pop('fmt')\\n except:\\n fmt='k'\\n try:\\n label = kw.pop('label')\\n except:\\n label = None\\n x = num.arange(-r,r+0.01,0.01)\\n y = num.sqrt(num.fabs(r**2. - x**2.))\\n pyplot.plot(x,y,fmt,**kw)\\n pyplot.plot(x,-y,fmt,label=label,**kw)\",\n \"def circle_new(shape = (1024, 1024), radius=0.25, Nrad = None, origin=[0,0]):\\r\\n if Nrad == None :\\r\\n pass\\r\\n else :\\r\\n radius = max([shape[0], shape[1]]) \\r\\n radius = np.float(Nrad) / np.float(radius) \\r\\n # \\r\\n x, y = make_xy(shape, origin = origin)\\r\\n r = np.sqrt(x**2 + y**2)\\r\\n if shape[1] > shape[0]:\\r\\n rmax = radius * shape[0] / 2\\r\\n else :\\r\\n rmax = radius * shape[1] / 2\\r\\n arrayout = (r <= rmax)\\r\\n return np.array(arrayout, dtype=np.float64)\",\n \"def get_circle_radius(self, point, center):\\n x, y, z = point[:]\\n x0, y0, z0 = center[:]\\n return math.sqrt((x-x0)**2 + (y-y0)**2 + (z-z0)**2)\",\n \"def draw_circle_filled(center_x, center_y, radius, color):\\n width = radius\\n height = radius\\n draw_ellipse_filled(center_x, center_y, width, height, color)\",\n \"def circle(radius, extent=360):\\n turtleTmp.circle(radius, extent)\",\n \"def _generate_circle_mask(center_y, center_x, radius):\\n\\n circle = draw.circle(center_y, center_x, radius)\\n\\n return circle\",\n \"def Icosphere(radius=1.0, center=(0.0, 0.0, 0.0), nsub=3):\\n mesh = Icosahedron()\\n mesh.clear_data()\\n mesh = mesh.subdivide(nsub=nsub)\\n\\n # scale to desired radius and translate origin\\n dist = np.linalg.norm(mesh.points, axis=1, keepdims=True) # distance from origin\\n mesh.points = mesh.points * (radius / dist) + center\\n return mesh\",\n \"def createCircle(self, x, y, radius):\\n # TODO (#2398) fix this to be top left coordinates, width, height\\n return QtCore.QRectF(\\n int(x - radius), int(y - radius), int(radius * 2), int(radius * 2)\\n )\",\n \"def draw_circle(c):\\n turtle.circle(c.radius)\",\n \"def draw_circle(c):\\n turtle.circle(c.radius)\",\n \"def area_of_circle(r):\\n a = r**2 * math.pi\\n return a\",\n \"def area_of_circle(r):\\n a = r**2 * math.pi\\n return a\",\n \"def draw_circle(self, x, y, radius, color=Color['white']):\\n pygame.draw.circle(self.display, color, (x, y), radius)\",\n \"def make_circle(self):\\n A = 2*np.random.rand(self.m, self.n)-1\\n b = np.sign(np.sum(A**2, 1) - self.radius)\\n return A, b\",\n \"def draw_circle(self, x0, y0, r, color=None):\\n f = 1 - r\\n ddF_x = 1\\n ddF_y = -2 * r\\n x = 0\\n y = r\\n\\n self.set(x0, y0 + r, color)\\n self.set(x0, y0 - r, color)\\n self.set(x0 + r, y0, color)\\n self.set(x0 - r, y0, color)\\n\\n while x < y:\\n if f >= 0:\\n y -= 1\\n ddF_y += 2\\n f += ddF_y\\n x += 1\\n ddF_x += 2\\n f += ddF_x\\n\\n self.set(x0 + x, y0 + y, color)\\n self.set(x0 - x, y0 + y, color)\\n self.set(x0 + x, y0 - y, color)\\n self.set(x0 - x, y0 - y, color)\\n self.set(x0 + y, y0 + x, color)\\n self.set(x0 - y, y0 + x, color)\\n self.set(x0 + y, y0 - x, color)\\n self.set(x0 - y, y0 - x, color)\",\n \"def get_nice_circle(x, y, radius, color=\\\"lightsteelblue\\\", facecolor=\\\"green\\\", alpha=.6, ax=None ):\\n e = pl.Circle([x, y], radius)\\n if ax is None:\\n ax = pl.gca()\\n ax.add_artist(e)\\n e.set_clip_box(ax.bbox)\\n e.set_edgecolor( color )\\n e.set_linewidth(3)\\n e.set_facecolor( facecolor ) # \\\"none\\\" not None\\n e.set_alpha( alpha )\\n return e\",\n \"def Octahedron(radius=1.0, center=(0.0, 0.0, 0.0)):\\n return PlatonicSolid(kind='octahedron', radius=radius, center=center)\",\n \"def draw_circle(color, position, radius, width=0):\\n #print('(color={}, position={}, radius={}, width={})')\\n pygame.draw.circle(screen, color, position, radius, width)\",\n \"def circle():\\n xmin=0\\n xmax=6.5\\n ymin=0.\\n ymax=6.5\\n\\n x = arange(xmin, xmax, 0.005)\\n y = x*1.\\n [xx, yy] = meshgrid(x, y)\\n\\n zz=sqrt((xx-3.2475)**2.+(yy-3.2475)**2.)\\n zz2=zz*1.\\n zz2[(zz <= 3.25)]=1.\\n zz2[(zz <= 3.25*0.2)]=0.\\n zz2[(zz > 3.25)]=0.\\n zz3=zeros(numpy.array(numpy.shape(zz2))/10)\\n for i in arange(len(xx)/10):\\n for j in arange(len(yy)/10):\\n zz3[i,j]=numpy.sum(zz2[(i*10):(i*10+10),(j*10):(j*10+10)])/100.\\n\\n return zz3\",\n \"def radius(self,xc=None,yc=None):\\n if xc == None:\\n xc = self.x1\\n if yc == None:\\n yc = self.y1\\n self.r = sqrt((self.x-xc)**2+(self.y-yc)**2)\",\n \"def circle(x, r, a, b, x_lim):\\n y = (b + np.sqrt(maximum(\\n r ** 2 - ((x - a) ** 2) * (x >= x_lim[0]) * (x <= x_lim[1]))\\n )) * (x >= x_lim[0]) * (x <= x_lim[1])\\n return y\",\n \"def find_center(r):\\n cx=r.corner.x+(r.width/2)\\n cy=r.corner.y+(r.height/2)\\n return cx,cy\",\n \"def Radius(self, *args):\\n return _Bnd.Bnd_Sphere_Radius(self, *args)\",\n \"def circle(self,image,radius,i,j,c_x,c_y):\\r\\n major_axis=radius\\r\\n minor_axis=radius\\r\\n self.ellipse(image,major_axis,minor_axis,i,j,c_x,c_y)\",\n \"def half_circle(n=5000, r=1, noise=0.05):\\n phis = np.pi * np.random.rand(n)\\n x = [[r * np.sin(phi), r * np.cos(phi)] for phi in phis]\\n x = np.array(x)\\n x = x + noise * np.random.randn(n, 2)\\n return x\",\n \"def draw_circle(centerx, centery, radius):\\r\\n global _canvas\\r\\n global _current_color\\r\\n if _canvas == None:\\r\\n raise RuntimeError(\\\"Canvas is not open yet.\\\")\\r\\n else:\\r\\n circle = Circle()\\r\\n circle.move(centerx, centery)\\r\\n circle.setRadius(radius)\\r\\n _set_not_filled(circle)\\r\\n _canvas.add(circle)\",\n \"def pos_on_semicircle(x, r, cxy):\\n pos = np.sqrt(r ** 2 - (x - cxy[0]) ** 2) + cxy[1]\\n\\n return pos\",\n \"def objects_radius(self, centre, radius):\",\n \"def circle(self, clear_screen=True, x=50, y=50, radius=40, fill_color='black', outline_color='black'):\\n\\n if clear_screen:\\n self.clear()\\n\\n x1 = x - radius\\n y1 = y - radius\\n x2 = x + radius\\n y2 = y + radius\\n\\n return self.draw.ellipse((x1, y1, x2, y2), fill=fill_color, outline=outline_color)\",\n \"def circle(self, radius, extent=None, steps=None):\\n super().circle(radius, extent, steps)\",\n \"def createSphere( position=(0,0,0), radius=1, colour=(0.6,0.6,0.6), samplesY = 20, samplesXZ = 20 ):\\r\\n return createEllipsoid( position, (radius,radius,radius), colour, samplesY, samplesXZ )\",\n \"def create_circle(radius=None, n_instance=None):\\n del_theta = np.pi * 2/(n_instance)\\n theta_list = np.linspace(0, np.pi * 2, n_instance)\\n coordinates = [circular_movement(radius, theta) for theta in theta_list]\\n return coordinates\",\n \"def surfaceIntSphere(r: float) -> float:\\n return 4.0 * np.pi * r * r\",\n \"def circle(radius = 15, resolution = 20, robotHeight = -90, n = 1, dir = 0):\\n \\n t = np.linspace(0, n*2*m.pi, resolution*n)\\n circlePos = []\\n for num in t:\\n if dir == 0:\\n x = m.cos(num)*radius\\n y = m.sin(num)*radius\\n else:\\n x = m.cos(num)*radius\\n y = m.sin(num-m.pi)*radius\\n\\n circlePos.append([x, y, robotHeight, 0, 0, 0, 'mov'])\\n\\n circlePos.append([0,0,-127,0,0,0,'mov'])\\n return circlePos\",\n \"def circle(center, perp_vect, radius, element_number=10):\\n # tl = [0, 0.2, 0.4, 0.6, 0.8]\\n tl = np.linspace(0, 1, element_number)\\n\\n # vector form center to edge of circle\\n # u is a unit vector from the centre of the circle to any point on the\\n # circumference\\n\\n # normalized perpendicular vector\\n n = perp_vect / np.linalg.norm(perp_vect)\\n\\n # normalized vector from the centre to point on the circumference\\n u = perpendicular_vector(n)\\n u /= np.linalg.norm(u)\\n\\n pts = []\\n\\n for t in tl:\\n # u = np.array([0, 1, 0])\\n # n = np.array([1, 0, 0])\\n pt = (\\n radius * np.cos(t * 2 * np.pi) * u\\n + radius * np.sin(t * 2 * np.pi) * np.cross(u, n)\\n + center\\n )\\n\\n pt = pt.tolist()\\n pts.append(pt)\\n\\n return pts\",\n \"def drawCircle(t, x, y, radius):\\r\\n t.up()\\r\\n t.goto(x + radius, y)\\r\\n t.setheading(90)\\r\\n t.down()\\r\\n for count in range(120):\\r\\n t.left(3)\\r\\n t.forward(2.0 * math.pi * radius / 120.0)\",\n \"def initP0(self, size, radius):\\n return h.circle(size, radius)[:, :, 0]\",\n \"def area_circle(r):\\n return (r ** 2) * math.pi\",\n \"def __init__(self, c, radius, a0, da):\\n Circle.__init__(self, Vector(c).to_2d(), radius)\\n self.line = None\\n self.a0 = a0\\n self.da = da\",\n \"def plot_circle(self, radius, c=color, ax=None, label=True, fontsize=12, **kwargs):\\n if ax is None:\\n ax = plt.gca()\\n circle = Circle(self.coords, radius, fill=None, ec=c, **kwargs)\\n ax.add_artist(circle)\\n if label and self.i is not None:\\n plt.text(\\n *(np.array(self.coords) - [0, 1.5 * radius]),\\n self.i,\\n c=c,\\n ha=\\\"center\\\",\\n va=\\\"top\\\",\\n fontsize=fontsize,\\n )\",\n \"def draw_full_circle(x, y, radius):\\n iterations = int(2 * radius * pi)\\n s = sin(2 * pi / iterations)\\n c = cos(2 * pi / iterations)\\n\\n dx, dy = radius, 0.\\n\\n glBegin(GL_TRIANGLE_FAN)\\n glVertex2f(x, y)\\n for _ in range(iterations + 1):\\n glVertex2f(x + dx, y + dy)\\n dx, dy = (dx * c + dy * s), (dy * c - dx * s)\\n glEnd()\",\n \"def random_uniform_within_circle():\\n rho = np.sqrt(np.random.uniform(0, 1))\\n phi = np.random.uniform(0, 2 * np.pi)\\n x = rho * np.cos(phi)\\n y = rho * np.sin(phi)\\n return np.array([x, y])\",\n \"def oncircle(size=None):\\n if size is None:\\n size = ()\\n else:\\n try:\\n size = tuple(size)\\n except TypeError:\\n size = (size,)\\n # This beats normalizing incircle for all sizes, even though that\\n # should be the superior algorithm for compiled code.\\n theta = 2.*pi * random(size + (1,))\\n return concatenate((cos(theta), sin(theta)), axis=-1)\",\n \"def create_circle(self, cx, cy, radius, style=None, parent=None):\\n if parent is None:\\n parent = self.current_parent\\n if parent is not None:\\n attrs = {'r': str(radius), 'cx': str(cx), 'cy': str(cy)}\\n if style:\\n attrs['style'] = style\\n return etree.SubElement(parent, svgns('circle'), attrs)\",\n \"def draw_circle_outline(center_x, center_y, radius, color, border_width=1):\\n width = radius\\n height = radius\\n draw_ellipse_outline(center_x, center_y, width, height,\\n color, border_width)\",\n \"def Calc_axe_spheroid(r,c):\\n return np.sqrt((r**3)/c)\",\n \"def createCirclePolygon(h, k, r, dx):\\n D = 10.0\\n theta = 2 * np.arccos((r-(dx/D))/r)\\n npoints = int(360.0/theta)\\n x, y = getPointsInCircum(r, n=npoints, h=h, k=k)\\n p = Polygon(list(zip(x, y)))\\n return p\",\n \"def fillcircle(draw, centrex, centrey, radius, color=\\\"#AAAAAAFF\\\") -> None:\\n # convert cartesian centre to pixel centre\\n cx, cy = pixelcoord(centrex, centrey)\\n # top left and bottom right coordinates, must never reverse\\n rect = [(cx-radius, cy-radius), (cx+radius, cy+radius)]\\n # draw, same color for outline and fill\\n draw.ellipse(rect, color, color)\",\n \"def points_on_circumference(center=(0, 0), r=50, n=100):\\n\\treturn [\\n (\\n center[0]+(cos(2 * pi / n * x) * r), \\n center[1] + (sin(2 * pi / n * x) * r) \\n\\n ) for x in range(0, n + 1)]\",\n \"def circle_from_points(a, b, c):\\n ab = subtract_vectors(b, a)\\n cb = subtract_vectors(b, c)\\n ba = subtract_vectors(a, b)\\n ca = subtract_vectors(a, c)\\n ac = subtract_vectors(c, a)\\n bc = subtract_vectors(c, b)\\n normal = normalize_vector(cross_vectors(ab, ac))\\n d = 2 * length_vector_sqrd(cross_vectors(ba, cb))\\n A = length_vector_sqrd(cb) * dot_vectors(ba, ca) / d\\n B = length_vector_sqrd(ca) * dot_vectors(ab, cb) / d\\n C = length_vector_sqrd(ba) * dot_vectors(ac, bc) / d\\n Aa = scale_vector(a, A)\\n Bb = scale_vector(b, B)\\n Cc = scale_vector(c, C)\\n center = add_vectorlist([Aa, Bb, Cc])\\n radius = distance_point_point(center, a)\\n return center, radius, normal\",\n \"def drawCircle(x, y, r):\\n pen1.up()\\n pen1.goto(x,y)\\n pen1.down()\\n pen1.circle(r)\",\n \"def get_circle(a, b, c):\\n vec = [a[0]**2 + a[1]**2, b[0]**2 + b[1]**2, c[0]**2 + c[1]**2]\\n x_mat = [vec, [a[1], b[1], c[1]], [1]*3]\\n y_mat = [vec, [a[0], b[0], c[0]], [1]*3]\\n d_mat = [[a[0], b[0], c[0]], [a[1], b[1], c[1]], [1] * 3]\\n d = 2 * det(d_mat)\\n x = 1 / d * det(x_mat)\\n y = -1 / d * det(y_mat)\\n center = [x, y]\\n #r = norm(center - a)\\n r = norm([center[0]-a[0], center[1]-a[1]])\\n return center, r\",\n \"def plot(self, radius=15, **kwargs):\\n self.plot_circle(radius, **kwargs)\",\n \"def draw_circle(t, circle):\\n t.pu()\\n t.goto(circle.center.x, circle.center.y)\\n t.pd()\\n polygon.circle(t, circle.radius)\",\n \"def circleInfo(r):\\n c = 2 * 3.14159 * r\\n a = 3.14159 * r * r\\n return (c, a)\",\n \"def area_circle(radius: float) -> float:\\r\\n if radius < 0:\\r\\n raise ValueError(\\\"area_circle() only accepts non-negative values\\\")\\r\\n return pi * radius**2\",\n \"def surface_point(radius):\\n z=random.uniform(-1,1)\\n chi=random.uniform(0,2*numpy.pi)\\n x=numpy.sqrt(1-z*z)*numpy.cos(chi)\\n y=numpy.sqrt(1-z*z)*numpy.sin(chi)\\n return radius*numpy.array([x,y,z])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.769793","0.7630966","0.75068563","0.73866713","0.73565596","0.7355884","0.7321714","0.72842544","0.72638685","0.72363985","0.72306716","0.72283244","0.71910393","0.7182544","0.7088393","0.7056075","0.70258105","0.6998519","0.69953614","0.69949824","0.69949824","0.69793266","0.697089","0.6936297","0.6914057","0.6910311","0.68995893","0.68866396","0.68862146","0.68686074","0.6837757","0.6789712","0.6772269","0.6765891","0.67453176","0.6732436","0.6720182","0.6710668","0.670215","0.6700232","0.66916317","0.6685129","0.66785455","0.6626484","0.66235006","0.6567935","0.6567301","0.6559519","0.6545657","0.6537419","0.6490755","0.6490329","0.6490329","0.6469824","0.6469824","0.6462379","0.6443092","0.64390135","0.6433608","0.6427514","0.64111865","0.6397197","0.63958365","0.63657314","0.6357481","0.6346355","0.6331287","0.63257945","0.63210917","0.6308456","0.62812346","0.6280265","0.62736493","0.6273244","0.6268905","0.6262728","0.6260079","0.6259824","0.625363","0.6253238","0.62522626","0.6246306","0.62437713","0.62372017","0.62315536","0.6231516","0.6214043","0.62027353","0.6190812","0.61891115","0.61877894","0.61543167","0.6153773","0.61527866","0.6143822","0.61323243","0.61201566","0.6115602","0.6097838","0.609685"],"string":"[\n \"0.769793\",\n \"0.7630966\",\n \"0.75068563\",\n \"0.73866713\",\n \"0.73565596\",\n \"0.7355884\",\n \"0.7321714\",\n \"0.72842544\",\n \"0.72638685\",\n \"0.72363985\",\n \"0.72306716\",\n \"0.72283244\",\n \"0.71910393\",\n \"0.7182544\",\n \"0.7088393\",\n \"0.7056075\",\n \"0.70258105\",\n \"0.6998519\",\n \"0.69953614\",\n \"0.69949824\",\n \"0.69949824\",\n \"0.69793266\",\n \"0.697089\",\n \"0.6936297\",\n \"0.6914057\",\n \"0.6910311\",\n \"0.68995893\",\n \"0.68866396\",\n \"0.68862146\",\n \"0.68686074\",\n \"0.6837757\",\n \"0.6789712\",\n \"0.6772269\",\n \"0.6765891\",\n \"0.67453176\",\n \"0.6732436\",\n \"0.6720182\",\n \"0.6710668\",\n \"0.670215\",\n \"0.6700232\",\n \"0.66916317\",\n \"0.6685129\",\n \"0.66785455\",\n \"0.6626484\",\n \"0.66235006\",\n \"0.6567935\",\n \"0.6567301\",\n \"0.6559519\",\n \"0.6545657\",\n \"0.6537419\",\n \"0.6490755\",\n \"0.6490329\",\n \"0.6490329\",\n \"0.6469824\",\n \"0.6469824\",\n \"0.6462379\",\n \"0.6443092\",\n \"0.64390135\",\n \"0.6433608\",\n \"0.6427514\",\n \"0.64111865\",\n \"0.6397197\",\n \"0.63958365\",\n \"0.63657314\",\n \"0.6357481\",\n \"0.6346355\",\n \"0.6331287\",\n \"0.63257945\",\n \"0.63210917\",\n \"0.6308456\",\n \"0.62812346\",\n \"0.6280265\",\n \"0.62736493\",\n \"0.6273244\",\n \"0.6268905\",\n \"0.6262728\",\n \"0.6260079\",\n \"0.6259824\",\n \"0.625363\",\n \"0.6253238\",\n \"0.62522626\",\n \"0.6246306\",\n \"0.62437713\",\n \"0.62372017\",\n \"0.62315536\",\n \"0.6231516\",\n \"0.6214043\",\n \"0.62027353\",\n \"0.6190812\",\n \"0.61891115\",\n \"0.61877894\",\n \"0.61543167\",\n \"0.6153773\",\n \"0.61527866\",\n \"0.6143822\",\n \"0.61323243\",\n \"0.61201566\",\n \"0.6115602\",\n \"0.6097838\",\n \"0.609685\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73965037"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":790,"cells":{"query":{"kind":"string","value":"Create a centered square (or cube) with edge length of d."},"document":{"kind":"string","value":"def model_square(d, nx, ny, nz=1):\n\te = EMData()\n\te.set_size(nx, ny, nz)\n\te.process_inplace(\"testimage.squarecube\", {\"edge_length\":d, \"fill\":1})\n\treturn e"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def my_square(d):asaasasassssssssssssssssssssssssss\n\t return (d ** 3)","def initialize_d(d, square_sides, offset=0):\n return {key:[] for key in range(offset, square_sides ** 2 + offset)}","def square_diamond(sx, sy, size, strong):\n if size == 1:\n return\n\n dsize = size/2\n ex = sx+size-1\n ey = sy+size-1\n # lets get math style\n\n\n # SQUARE STEP\n\n A = sx, sy\n B = ex, sy\n C = sx, ey\n D = ex, ey\n E = sx+dsize, sy+dsize\n F = sx, sy + dsize\n G = sx + dsize, sy\n H = ex, sy + dsize\n I = sx + dsize, ey\n\n def RAND(X):\n return random.randint(-strong, strong)\n\n ### for coasts dont disappear\n\n def normalize(add_z, X):\n if self[X] <= 0:\n if add_z > 0:\n add_z = -5\n else:\n if add_z <= 0:\n add_z = 5\n return add_z\n\n # Generate heights\n # E = (A+B+C+D) / 4 + RAND(d)\n # F = (A + C + E + E) / 4 + RAND(d)\n # G = (A + B + E + E) / 4 + RAND(d)\n # H = (B + D + E + E) / 4 + RAND(d)\n # I = (C + D + E + E) / 4 + RANS(d)\n\n ### E\n\n try:\n\n add_z = ((self[A] + self[B] + self[C] + self[D]) / 4) + RAND(E)\n\n except KeyError, e:\n print A, B, C, D, size, dsize, len(self)\n raise e\n\n\n self[E] = normalize(add_z, E)\n\n ### F\n\n add_z = (self[A] + self[C] + self[E] + self[E]) / 4 + RAND(F)\n\n self[F] = normalize(add_z, F)\n\n ### G\n\n add_z = (self[A] + self[B] + self[E] + self[E]) / 4 + RAND(G)\n\n self[G] = normalize(add_z, G)\n\n ### H\n\n add_z = (self[B] + self[D] + self[E] + self[E]) / 4 + RAND(H)\n\n self[H] = normalize(add_z, H)\n\n ### I\n add_z = (self[C] + self[D] + self[E] + self[E]) / 4 + RAND(I)\n\n self[I] = normalize(add_z, I)\n\n\n # DIAMOND STEP\n\n # get coordinates\n # 0 - x, 1 - y\n\n x, y = 0, 1\n\n dx = (G[x] - A[x]) / 2\n dy = (F[y] - A[y]) / 2\n\n J = A[x] + dx, A[y] + dy\n K = G[x] + dx, G[y] + dy\n L = F[x] + dx, F[y] + dy\n M = E[x] + dx, E[y] + dy\n\n N = A[x], A[y] + dy\n O = A[x] + dx, A[y]\n P = G[x], G[y] + dy\n Q = A[x] + dx, F[y]\n\n # Generate Heights\n # J = (A + G + F + E)/4 + RAND(d)\n # K = (G + B + E + H)/4 + RAND(d)\n # L = (F + E + C + I)/4 + RAND(d)\n # M = (E + H + I + D)/4 + RAND(d)\n\n # J\n add_z = ((self[A] + self[G] + self[F] + self[E]) / 4) + RAND(J)\n self[J] = normalize(add_z, J)\n\n # K\n add_z = ((self[G] + self[B] + self[E] + self[H]) / 4) + RAND(K)\n self[K] = normalize(add_z, K)\n\n # L\n add_z = ((self[F] + self[E] + self[C] + self[I]) / 4) + RAND(L)\n self[L] = normalize(add_z, L)\n\n # M\n add_z = ((self[E] + self[H] + self[I] + self[D]) / 4) + RAND(M)\n self[M] = normalize(add_z, M)\n\n # N = (K + A + J + F)/4 + RAND(d)\n # O = (L + A + G + J)/4 + RAND(d)\n # P = (J + G + K + E)/4 + RAND(d)\n # Q = (F + J + E + L)/4 + RAND(d)\n\n # N\n add_z = ((self[K] + self[A] + self[J] + self[F]) / 4) + RAND(N)\n self[N] = normalize(add_z, N)\n\n # O\n add_z = ((self[L] + self[A] + self[G] + self[J]) / 4) + RAND(O)\n self[O] = normalize(add_z, O)\n\n # P\n add_z = ((self[J] + self[G] + self[K] + self[E]) / 4) + RAND(P)\n self[P] = normalize(add_z, P)\n\n # Q\n add_z = ((self[F] + self[J] + self[E] + self[L]) / 4) + RAND(Q)\n self[Q] = normalize(add_z, Q)\n\n # N = (A + J + F)/3 + RAND(d)\n # O = (A + G + J)/3 + RAND(d)\n\n # N\n add_z = ((self[A] + self[J] + self[F]) / 3) + RAND(N)\n self[N] = normalize(add_z, N)\n\n # O\n add_z = ((self[A] + self[G] + self[J]) / 3) + RAND(N)\n self[O] = normalize(add_z, O)\n\n\n ### Start recurse for diamond alg\n square_diamond(A[0], A[1], dsize, strong)\n square_diamond(G[0], G[1], dsize, strong)\n square_diamond(F[0], F[1], dsize, strong)\n square_diamond(E[0], E[1], dsize, strong)","def make_square(x, size):\n return [ [x, -size/2, size/2],\n\t\t\t [x, size/2, size/2],\n [x, size/2, -size/2],\n\t\t\t [x, -size/2, -size/2]]","def make_box_square(box, offset_scale=0.05):\n\n x_min, y_min, x_max, y_max = box[:4]\n center_x = (x_max + x_min) / 2.\n center_y = (y_max + y_min) / 2.\n width = x_max - x_min\n height = y_max - y_min\n\n if height >= width:\n half_box = height / 2.\n x_min = center_x - half_box\n x_max = center_x + half_box\n if width > height:\n half_box = width / 2.\n y_min = center_y - half_box\n y_max = center_y + half_box\n\n box_side_lenght = (x_max + x_min) / 2.\n offset = offset_scale * box_side_lenght\n x_min = x_min - offset\n x_max = x_max + offset\n y_min = y_min - offset\n y_max = y_max + offset\n return (int(x_min), int(y_min), int(x_max), int(y_max))","def from_diag(d, context = FloatContext):\n n = len(d)\n S = zeros(n,n,context)\n set_diag(S,d)\n return S","def plasm_cube(self, size=0.1, color=WHITE):\n return COLOR(color)(T([1,2,3])(self.coords)(CUBOID([size, size, size])))","def bandcholesky(A, d):\n L, dg = LDL(A, d)\n return matrix(L)*diag(sqrt(dg))","def get_sqd_from_center(boardsize):\n assert boardsize % 2 == 1\n center = boardsize // 2\n return distances_from_pt(sq_distance, (center, center), boardsize)","def square(center, side_length, *args, **kwargs):\n center = np.asarray(center)\n side_length = float(side_length)\n lower_left = center - 0.5*side_length\n return patch.Rectangle(lower_left, side_length, side_length,\n \t\t\t\t\t *args, **kwargs)","def __init__(\n self, d: int, seed: Optional[int] = None, inv_transform: bool = False\n ) -> None:\n self._d = d\n self._seed = seed\n self._inv_transform = inv_transform\n if inv_transform:\n sobol_dim = d\n else:\n # to apply Box-Muller, we need an even number of dimensions\n sobol_dim = 2 * math.ceil(d / 2)\n self._sobol_engine = SobolEngine(dimension=sobol_dim, scramble=True, seed=seed)","def box(original, radius):\n batches = original.size()[0]\n num_elem = h.product(original.size()[1:])\n ei = h.getEi(batches,num_elem)\n \n if len(original.size()) > 2:\n ei = ei.contiguous().view(num_elem, *original.size())\n\n return HBox(original, None, ei * radius).checkSizes()","def draw_square(square_edge):\n\n # create a square filled with zeros with square_edge as size and int as data type\n square = numpy.zeros((square_edge, square_edge), dtype=int)\n\n # square is a \"matix\" of N (square_edge) list\n # we use list index to place the numbers\n current_list = 0\n list_index = square_edge // 2\n\n # magic square logic\n for number in range(1, square_edge**2+1, 1):\n\n # we place our first number\n square[current_list, list_index] = number\n\n # we update how the next number will be placed in the square\n update_current_list = (current_list - 1) % square_edge\n update_list_index = (list_index + 1) % square_edge\n\n # if value of the index are not 0 we will increment current_list\n if square[update_current_list, update_list_index]:\n current_list += 1\n\n # else we simply update the index\n else:\n current_list = update_current_list\n list_index = update_list_index\n\n return square","def get_box(x_tr, y_tr, d=1.):\n xs = np.array([-1., 1., 1., -1., -1.])\n ys = np.array([-1., -1., 1., 1., -1.])\n\n xs = xs*d/2 + x_tr\n ys = ys*d/2 + y_tr\n\n return xs, ys","def center_size(boxes):\n concat = P.Concat(1)\n return concat(((boxes[:, 2:] + boxes[:, :2])/2, # cx, cy\n boxes[:, 2:] - boxes[:, :2])) # w, h","def sqft(d):\n return 2 * (d.l*d.w + d.l*d.h + d.h*d.w)","def sqrtCF(d):\n sqrtD = sqrt(d)\n P = 0\n Q = 1\n while True:\n a = int(floor((P + sqrtD) / Q))\n yield a\n P = a * Q - P\n Q = (d - P*P) // Q # It can be shown that Q evenly divides d - P*P","def C(width = 1, size = (10, 20), layer = 0):\n D = Device(name = 'C')\n w = width/2\n s1, s2 = size\n points = [(-w, -w), (s1, -w), (s1, w), (w, w), (w, s2-w),\n (s1, s2-w), (s1, s2+w), (-w, s2+w), (-w, -w)]\n D.add_polygon(points, layer = layer)\n D.add_port(name = 1, midpoint = (s1, s2), width = width, orientation = 0)\n D.add_port(name = 2, midpoint = (s1, 0), width = width, orientation = 0)\n return D","def cube_area(edge : number) -> number:\n area = 6*edge*edge\n\n return area","def box(original, diameter):\n return Box(original, h.ones(original.size()) * diameter, None).checkSizes()","def Dodecahedron(radius=1.0, center=(0.0, 0.0, 0.0)):\n return PlatonicSolid(kind='dodecahedron', radius=radius, center=center)","def sample(self, d):\n dist = rnd.uniform(0,self.length)\n w = rnd.normal(0,self.width)\n d.pos = np.dot(rotmat(self.angle), [dist, w]) + self.pos\n d.ownpos = self.pos","def center_size(boxes):\n return torch.cat([(boxes[:, :2] + boxes[:, 2:])/2, # cx, cy\n boxes[:, :2] - boxes[:, 2:]], 1) # w, h","def sqnorm(self, d):\n ###TODO\n total = 0.0\n for i in d:\n total = total + (d[i] * d[i])\n return total","def coordinates_of_square(crd):\n col = ord(crd[0]) - ord('a')\n row = int(crd[1]) - 1\n return (col * SQUARE_EDGE + BOARD_MARGIN, (7 - row) * SQUARE_EDGE + BOARD_MARGIN)","def __init__(self, d):\n\t\tself._coords = [0] * d","def diagonal(self):\r\n return math.sqrt((self.width ** 2) + (self.height ** 2))","def propagateInBox((x0,y0,z0), (px,py,pz), d):\n x = x0 + px*d\n y = y0 + py*d\n z = z0 + pz*d\n\n return (x,y,z)","def diagonal(cube_edge: int=128,\n radius: int=10,\n foreground: int=1,\n dtype=np.uint8):\n if 2 * radius > cube_edge:\n raise ValueError(\"Given radius '{}' is larger than than cube edge length {}\"\n .format(radius, cube_edge))\n stack = np.zeros((cube_edge, cube_edge, cube_edge), dtype=bool)\n cylinder = [\n ((0, 0, 0), (cube_edge - 1, cube_edge - 1, cube_edge - 1), radius)\n ]\n stack = add_cylinder_px(stack, *cylinder[0])\n return volume_bool_to_dtype(stack, fg=foreground, dtype=dtype)","def central_composite(d,center=(2,2),alpha='o',face='ccc'):\n \n return build_central_composite(d,center=center,alpha=alpha,face=face)","def diamondSquareFractal3D(size, roughness = .5, perturbance = .5):\n start = time.time()\n count = 0;\n \n #calculate the fractal based on the next highest 2^n + 1\n n = math.log(size-1, 2)\n if n != float(int(n)):\n print \"The size is not valid, choose a side that is a power of 2 + 1.\"\n print \"65, 129, 257, 513, 1025, etc.\"\n return\n \n matrix = numpy.zeros((size, size, size)) - 100\n total = matrix.size\n \n applyCornerValues(matrix, roughness)\n \n \"\"\"\n The algorithm requires calculating the centers, faces and edges \n in a particular order so that neighboring values will be \n available to average:\n \n 1. Calc centers of all size n cubes.\n 2. for each size n cube:\n a. calc faces\n b. calc edges\n c. divide the cube into 8 cubes of size n-1.\n d. repeat from step 1 until n < 3.\n \n Subdivided cubes are populated in the following order so that a\n cube will not be populated until its top, left, and front neighbors\n are done. Values from neighboring cubes are used to calculate \n top, left and front faces and edges\n top. left, front\n top, left, back\n top, right, front\n bottom, left, front\n top, right, back\n bottom, left, back\n bottom, right, front\n bottom, right, back\n \n A queue will be used to manage the cubes waiting to be calculated.\n \"\"\"\n \n from collections import deque\n queue = deque()\n \n # add the whole matrix.\n queue.append([0, 0 ,0 , size-1])\n\n # calc the center for the whole matrix\n pf = perturbanceFactor(size, size, perturbance)\n noiseLevel = roughness * pf\n indexRef = getIndexRef(0, 0, 0, size/2, size)\n setValue(matrix, center, indexRef, noiseLevel)\n\n while len(queue) > 0:\n # pop a square\n whatisit = queue.popleft()\n [row, col, frame, range] = whatisit\n\n midRange = range/2\n # populate the faces and edges of the cube\n [midRow, midCol, midFrame] = populate3D(matrix, \n row, col, frame, midRange, roughness, perturbance)\n\n if midRange >= 2:\n #calc the centers for the next layer\n populateCenters(matrix, row, col, frame, midRange, roughness, perturbance)\n \n #add top left front cube to the queue \n queue.append([row, col, frame, midRange])\n \n #add top left back cube to the queue\n queue.append([row, col, midFrame, midRange])\n\n #add top right front cubes to the queue\n queue.append([row, midCol, frame, midRange])\n\n #add bottom left front cubes to the queue\n queue.append([midRow, col, frame, midRange])\n \n #add top right back cubes to the queue \n queue.append([row, midCol, midFrame, midRange])\n\n #add bottom left back cubes to the queue\n queue.append([midRow, col, midFrame, midRange])\n\n #add bottom right front cubes to the queue\n queue.append([midRow, midCol, frame, midRange])\n \n #add bottom right back cubes to the queue\n queue.append([midRow, midCol, midFrame, midRange])\n\n #print \"result mean =\", matrix.mean(), \"result std = \", matrix.std()\n print \"elapsed seconds =\", time.time() - start\n # return the requested size\n return matrix[0:size, 0:size]","def __init__(self, d):\n self._coords = [0]*d","def surface_area_cube(side_length: float) -> float:\r\n if side_length < 0:\r\n raise ValueError(\"surface_area_cube() only accepts non-negative values\")\r\n return 6 * side_length**2","def onsphere(size=None):\n xy = oncircle(size)\n z = 2.*random(xy.shape[:-1] + (1,)) - 1.\n xy *= sqrt(1. - z*z)\n return concatenate((xy, z), axis=-1)","def __init__(self, a, **kwargs):\r\n center = kwargs.get('center')\r\n density = kwargs.get(\"density\")\r\n spheroid.__init__(self, a, center=center, density=density)","def _fill_cell_rectangle(size = (20, 20), layers = (0, 1, 3),\n densities = (0.5, 0.25, 0.7),\n inverted = (False, False, False)):\n D = Device('fillcell')\n for layer, density, inv in zip(layers, densities, inverted):\n rectangle_size = np.array(size) * sqrt(density)\n # r = D.add_ref(rectangle(size = rectangle_size, layer = layer))\n R = rectangle(size = rectangle_size, layer = layer)\n R.center = (0, 0)\n if inv is True:\n A = rectangle(size = size)\n A.center = (0,0)\n A = A.get_polygons()\n B = R.get_polygons()\n p = gdspy.boolean(A, B, operation = 'not')\n D.add_polygon(p, layer = layer)\n else:\n D.add_ref(R)\n return D","def box(self, x, y, w, h):\n\t\tpass","def rand_sphere(d0):\n p1 = np.random.randn(d0, 3)\n m = np.sqrt(np.sum(p1**2, axis=1))\n\n rad = pow(np.random.rand(d0), 1.0 / 3.0)\n return (p1.T * (rad / m)).T","def zone_from_center_size(x, y, size):\n half_size = size // 2\n size = half_size * 2\n x1 = x - half_size\n x2 = x + half_size -1\n if x1 < 0:\n x1 = 0\n x2 = size - 1\n if x2 >= w:\n x2 = w - 1\n x1 = w - size\n y1 = y - half_size\n y2 = y + half_size\n if y1 < 0:\n y1 = 0\n y2 = size - 1\n if y2 >= h:\n y2 = h - 1\n y1 = h - size\n return [x1, y1, x2, y2]","def sized_normal(self, t, size):\n p = self.lerp(t)\n if self.da < 0:\n v = self.c - p\n else:\n v = p - self.c\n return Line(p, size * v.normalized())","def __init__(self, d=10):\n \n self.d = d\n self.indices = [i for i in range(d)]\n self.weights = [1 / w for w in range(1, d + 1)]","def square(square_x, square_y, square_width, square_height, square_color):\n arcade.draw_rectangle_filled(square_x, square_y, square_width, square_height, square_color)","def make_square(turt,sz):\n for i in range(4):\n turt.forward(sz)\n turt.left(90)","def MakeDifficulties(center, width, n):\n low, high = center-width, center+width\n return numpy.linspace(low, high, n)","def hadamard2(n):\n # set up plot stuff\n fig, ax = plt.subplots(figsize=(10, 10))\n ax.set_xticks(range(n + 1)) # set axis ranges\n ax.set_yticks(range(n + 1))\n plt.xticks([]) # remove axis labels\n plt.yticks([])\n ax.set_aspect(aspect=1) # ensure it's a square and not a rectangle\n # invert y axis so the origin is the top left\n ax.set_ylim(ax.get_ylim()[::-1])\n\n def recurse(tlx, tly, brx, bry, flag):\n \"\"\" Given coords for the top left and bottom right of a square, recursively pass a boolean flag\n to see if we should draw it\n \"\"\"\n if(tlx + 1 == brx): # single square (width == 1)\n if flag: # draw black square\n ax.add_patch(Rectangle((tly, brx - 1), 1, 1, color='black'))\n return # no need to recurse anymore\n # here's the recursive part:\n # we go in the order of top left, top right, bottom left, bottom right\n # we negate the flag in the bottom right, and we keep the same flag for the rest\n recurse(tlx, tly, (tlx + brx) // 2, (tly + bry) // 2, flag)\n recurse((tlx + brx) // 2, tly, brx, (tly + bry) // 2, flag)\n recurse(tlx, (tly + bry) // 2, (tlx + brx) // 2, bry, flag)\n recurse((tlx + brx) // 2, (tly + bry) // 2, brx,\n bry, not flag) # invert bottom right\n\n recurse(0, 0, n, n, True) # initial case, pass corners of entire matrix\n plt.show()","def test_euclidean_scale(self):\n\n s = space(curvature=0)\n\n magic = 77773.333773777773733\n for mul in (2, 5, 1/3, 1/11, magic, 1/magic):\n for name, dim in (\n ('sphere_s1', 1),\n ('sphere_v2', 2),\n ('sphere_s2', 2),\n ('sphere_v3', 3)\n ):\n self.assertTrue(isclose(\n getattr(s, name)(1) * mul**dim,\n getattr(s, name)(mul)\n ))","def sized_normal(self, t, size):\n p = self.lerp(t)\n v = size * self.cross.normalized()\n return Line3d(p, v, z_axis=self.z_axis)","def sized_normal(self, t, size):\n return Line(self.lerp(t), size * self.cross_z.normalized())","def latin_hypercube(n_pts, dim):\n X = np.zeros((n_pts, dim))\n centers = (1.0 + 2.0 * np.arange(0.0, n_pts)) / float(2 * n_pts)\n for i in range(dim): # Shuffle the center locataions for each dimension.\n X[:, i] = centers[np.random.permutation(n_pts)]\n\n # Add some perturbations within each box\n pert = np.random.uniform(-1.0, 1.0, (n_pts, dim)) / float(2 * n_pts)\n X += pert\n return X","def __init__(self, n):\n self.row = [0] * n\n self.col = [0] * n\n self.diagonal = 0\n self.antidiagonal = 0\n self.winning = False","def draw_square(turtle, size):\n\n square = range(4)\n\n for line in square:\n alexa.forward(size)\n alexa.left(90)","def cube_area(side_length):\n area = side_length ** 3\n return area","def extra(d):\n return min(d.l * d.w, d.w * d.h, d.h * d.l)","def __init__(self, length):\n self.x = length\n self.y = length\n self._table = self.x * self.y","def spaceDiagonal(self):\n spaceDiagonal = (3**(1/2)) * self.sideLength\n return spaceDiagonal","def gen_square_subsequent_mask(sz: int) -> torch.Tensor:\n return torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)","def cellsize_2d(self):\t\r\n return self.dx * self.dy","def solve_for_edge_dimensionality(n):\n return int(round(np.sqrt(2 * n + 2.25) - 1.5))","def create_diamond(color=COLOR_WHITE):\n a = Point3(-1.0, -1.0, 0.0)\n b = Point3(1.0, -1.0, 0.0)\n c = Point3(1.0, 1.0, 0.0)\n d = Point3(-1.0, 1.0, 0.0)\n e = Point3(0.0, 0.0, 1.0)\n f = Point3(0.0, 0.0, -1.0)\n\n obj = glGenLists(1)\n glNewList(obj, GL_COMPILE)\n glPushMatrix()\n glColor4fv(color)\n glBegin(GL_TRIANGLES)\n drawVertexListCreateNormal([a, b, e])\n drawVertexListCreateNormal([b, c, e])\n drawVertexListCreateNormal([c, d, e])\n drawVertexListCreateNormal([d, a, e])\n drawVertexListCreateNormal([b, a, f])\n drawVertexListCreateNormal([c, b, f])\n drawVertexListCreateNormal([d, c, f])\n drawVertexListCreateNormal([a, d, f])\n glEnd()\n glPopMatrix()\n glEndList()\n return obj","def mesh_uniform(N_e, d, Omega):","def generate_sphere_full():\n \n num_voxels = 31\n c = (15.0, 15.0, 15.0)\n\n data_x = []\n data_y = []\n data_z = []\n data_intensity = []\n\n volume = numpy.zeros((num_voxels, num_voxels, num_voxels))\n\n for x in range(num_voxels):\n for y in range(num_voxels):\n for z in range(num_voxels):\n\n if numpy.sqrt((x-c[0])**2 + (y-c[1])**2 + (z-c[2])**2) - 7.5 < 1.5:\n data_x.append(x)\n data_y.append(y)\n data_z.append(z)\n data_intensity.append(200.0)\n\n volume[x,y,z] = 200.0\n\n\n return data_x, data_y, data_z, data_intensity, volume","def toCartesian(self, y):\r\n return Size - y","def create_cube(color=COLOR_WHITE):\n a = Point3(-1.0, -1.0, -1.0)\n b = Point3(1.0, -1.0, -1.0)\n c = Point3(1.0, -1.0, 1.0)\n d = Point3(-1.0, -1.0, 1.0)\n e = Point3(-1.0, 1.0, -1.0)\n f = Point3(1.0, 1.0, -1.0)\n g = Point3(1.0, 1.0, 1.0)\n h = Point3(-1.0, 1.0, 1.0)\n\n obj = glGenLists(1)\n glNewList(obj, GL_COMPILE)\n glPushMatrix()\n glBegin(GL_QUADS)\n glColor4fv(color)\n drawVertexListCreateNormal([a, b, c, d])\n drawVertexListCreateNormal([b, f, g, c])\n drawVertexListCreateNormal([f, e, h, g])\n drawVertexListCreateNormal([e, a, d, h])\n drawVertexListCreateNormal([d, c, g, h])\n drawVertexListCreateNormal([a, e, f, b])\n glEnd()\n glPopMatrix()\n glEndList()\n return obj","def centre_pad(length):\n if length % 2 == 0:\n side1 = int((size - length) / 2)\n side2 = side1\n else:\n side1 = int((size - length) / 2)\n side2 = side1 + 1\n return side1, side2","def len_square(bound):\n\treturn (8 - 2 * bound)","def generate_square_subsequent_mask(sz: int) -> Tensor:\n return torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)","def create_square_with_diag_grid(self, len_side_a, len_side_b):\n if (len_side_a < 2) or (len_side_b < 2):\n raise ValueError('side length attributes for HexagonalCells should be at least 2.')\n\n self.connections, self.weights = create_grid_square_with_diagonals(len_side_a, len_side_b)\n\n # populate the dictionary from cell coordinates to cell indexes in arrays connection and weights\n for i in range(len_side_a):\n for j in range(len_side_b):\n self.dict_cell_id_to_ind[(i, j)] = j + i*len_side_b","def create_compute_box_size(self):\n def compute_best_size_for(dim):\n size = ((self.element_space[dim]-1)//self.box_space[dim]) + 1\n size += 2 * self.ghost_space[dim]\n while size % Level.BOX_ALIGNMENTS[dim]:\n size += 1\n return size\n\n return Space([compute_best_size_for(dim) for dim in range(self.dimensions)])","def diamond(N, D, rng):\n samples = rng.randn(N, D)\n norm = np.sum(np.abs(samples), axis=1)\n return samples/norm[:,None]","def createTaperedBox( position=(0,0,0), size=(1,1,1), colour=(0.6,0.6,0.6), samplesY = 20, samplesXZ = 20, exponentBottom = 4, exponentTop = 4, exponentSide = 4 ):\r\n\r\n return createEllipsoid( position, (size[0]/2.0,size[1]/2.0,size[2]/2.0), colour, samplesY, samplesXZ, exponentBottom, exponentTop, exponentSide )","def draw_diamond(display, coord, box_size, color, bg_color):\n half = int(box_size * 0.5)\n left, top = coord\n vertices = [\n (left + half, top),\n (left + box_size - 1, top + half),\n (left + half, top + box_size - 1),\n (left, top + half),\n ]\n pygame.draw.polygon(display, color, vertices)\n return","def createBox( size=(1,1,1), position=(0,0,0), colour=(0.6,0.6,0.6) ):\r\n \r\n size = PyUtils.toVector3d(size)\r\n position = PyUtils.toPoint3d(position)\r\n vertices = []\r\n delta = MathLib.Vector3d()\r\n for repeat in range(3):\r\n for x in (-0.5,0.5) :\r\n delta.x = size.x * x\r\n for y in (-0.5,0.5) :\r\n delta.y = size.y * y\r\n for z in (-0.5,0.5) :\r\n delta.z = size.z * z\r\n vertices.append( position + delta )\r\n \r\n faces = [(0,1,3,2),(5,4,6,7), # YZ Faces\r\n (9,13,15,11),(12,8,10,14), # XY Faces\r\n (18,19,23,22),(17,16,20,21)] # XZ Faces\r\n \r\n return create( vertices, faces, colour )","def expand_twelve(vertices):\n box = np.zeros((12, 2), dtype=np.float32)\n p = vertices.shape[0]\n if p == 4:\n box[0, :] = vertices[0, :]\n box[1, :] = [(4 * vertices[0, 0] + vertices[1, 0]) / 5.0, (4 * vertices[0, 1] + vertices[1, 1]) / 5.0]\n box[2, :] = [(3 * vertices[0, 0] + 2 * vertices[1, 0]) / 5.0,\n (3 * vertices[0, 1] + 2 * vertices[1, 1]) / 5.0]\n box[3, :] = [(2 * vertices[0, 0] + 3 * vertices[1, 0]) / 5.0,\n (2 * vertices[0, 1] + 3 * vertices[1, 1]) / 5.0]\n box[4, :] = [(vertices[0, 0] + 4 * vertices[1, 0]) / 5.0, (vertices[0, 1] + 4 * vertices[1, 1]) / 5.0]\n box[5, :] = vertices[1, :]\n box[6, :] = vertices[2, :]\n box[7, :] = [(vertices[3, 0] + 4 * vertices[2, 0]) / 5.0, (vertices[3, 1] + 4 * vertices[2, 1]) / 5.0]\n box[8, :] = [(2 * vertices[3, 0] + 3 * vertices[2, 0]) / 5.0,\n (2 * vertices[3, 1] + 3 * vertices[2, 1]) / 5.0]\n box[9, :] = [(3 * vertices[3, 0] + 2 * vertices[2, 0]) / 5.0,\n (3 * vertices[3, 1] + 2 * vertices[2, 1]) / 5.0]\n box[10, :] = [(4 * vertices[3, 0] + vertices[2, 0]) / 5.0, (4 * vertices[3, 1] + vertices[2, 1]) / 5.0]\n box[11, :] = vertices[3, :]\n elif p == 6:\n box[0, :] = vertices[0, :]\n box[1, :] = [(vertices[0, 0] + vertices[1, 0]) / 2.0, (vertices[0, 1] + vertices[1, 1]) / 2.0]\n box[2, :] = vertices[1, :]\n box[3, :] = [(2 * vertices[1, 0] + vertices[2, 0]) / 3.0, (2 * vertices[1, 1] + vertices[2, 1]) / 3.0]\n box[4, :] = [(vertices[1, 0] + 2 * vertices[2, 0]) / 3.0, (vertices[1, 1] + 2 * vertices[2, 1]) / 3.0]\n box[5, :] = vertices[2, :]\n box[6, :] = vertices[3, :]\n box[7, :] = [(vertices[4, 0] + 2 * vertices[3, 0]) / 3.0, (vertices[4, 1] + 2 * vertices[3, 1]) / 3.0]\n box[8, :] = [(2 * vertices[4, 0] + vertices[3, 0]) / 3.0, (2 * vertices[4, 1] + vertices[3, 1]) / 3.0]\n box[9, :] = vertices[4, :]\n box[10, :] = [(vertices[5, 0] + vertices[4, 0]) / 2.0, (vertices[5, 1] + vertices[4, 1]) / 2.0]\n box[11, :] = vertices[5, :]\n elif p == 8:\n box[0, :] = vertices[0, :]\n box[1, :] = [(vertices[0, 0] + vertices[1, 0]) / 2.0, (vertices[0, 1] + vertices[1, 1]) / 2.0]\n box[2, :] = vertices[1, :]\n box[3, :] = vertices[2, :]\n box[4, :] = [(vertices[2, 0] + vertices[3, 0]) / 2.0, (vertices[2, 1] + vertices[3, 1]) / 2.0]\n box[5, :] = vertices[3, :]\n box[6, :] = vertices[4, :]\n box[7, :] = [(vertices[4, 0] + vertices[5, 0]) / 2.0, (vertices[4, 1] + vertices[5, 1]) / 2.0]\n box[8, :] = vertices[5, :]\n box[9, :] = vertices[6, :]\n box[10, :] = [(vertices[6, 0] + vertices[7, 0]) / 2.0, (vertices[6, 1] + vertices[7, 1]) / 2.0]\n box[11, :] = vertices[7, :]\n elif p == 10:\n box[0, :] = vertices[0, :]\n box[1, :] = vertices[1, :]\n box[2, :] = vertices[2, :]\n box[3, :] = vertices[3, :]\n box[4, :] = [(vertices[3, 0] + vertices[4, 0]) / 2.0, (vertices[3, 1] + vertices[4, 1]) / 2.0]\n box[5, :] = vertices[4, :]\n box[6, :] = vertices[5, :]\n box[7, :] = [(vertices[5, 0] + vertices[6, 0]) / 2.0, (vertices[5, 1] + vertices[6, 1]) / 2.0]\n box[8, :] = vertices[6, :]\n box[9, :] = vertices[7, :]\n box[10, :] = vertices[8, :]\n box[11, :] = vertices[9, :]\n elif p == 12:\n box = vertices\n else:\n raise ValueError(f'Invalid points dimension: {p}')\n\n return box","def create_cube(scale=(1.0,1.0,1.0), st=False, rgba=False, dtype='float32', type='triangles'):\n\n shape = [24, 3]\n rgba_offset = 3\n\n width, height, depth = scale\n # half the dimensions\n width /= 2.0\n height /= 2.0\n depth /= 2.0\n\n vertices = np.array([\n # front\n # top right\n ( width, height, depth,),\n # top left\n (-width, height, depth,),\n # bottom left\n (-width,-height, depth,),\n # bottom right\n ( width,-height, depth,),\n\n # right\n # top right\n ( width, height,-depth),\n # top left\n ( width, height, depth),\n # bottom left\n ( width,-height, depth),\n # bottom right\n ( width,-height,-depth),\n\n # back\n # top right\n (-width, height,-depth),\n # top left\n ( width, height,-depth),\n # bottom left\n ( width,-height,-depth),\n # bottom right\n (-width,-height,-depth),\n\n # left\n # top right\n (-width, height, depth),\n # top left\n (-width, height,-depth),\n # bottom left\n (-width,-height,-depth),\n # bottom right\n (-width,-height, depth),\n\n # top\n # top right\n ( width, height,-depth),\n # top left\n (-width, height,-depth),\n # bottom left\n (-width, height, depth),\n # bottom right\n ( width, height, depth),\n\n # bottom\n # top right\n ( width,-height, depth),\n # top left\n (-width,-height, depth),\n # bottom left\n (-width,-height,-depth),\n # bottom right\n ( width,-height,-depth),\n ], dtype=dtype)\n\n st_values = None\n rgba_values = None\n\n if st:\n # default st values\n st_values = np.tile(\n np.array([\n (1.0, 1.0,),\n (0.0, 1.0,),\n (0.0, 0.0,),\n (1.0, 0.0,),\n ], dtype=dtype),\n (6,1,)\n )\n\n if isinstance(st, bool):\n pass\n elif isinstance(st, (int, float)):\n st_values *= st\n elif isinstance(st, (list, tuple, np.ndarray)):\n st = np.array(st, dtype=dtype)\n if st.shape == (2,2,):\n # min / max\n st_values *= st[1] - st[0]\n st_values += st[0]\n elif st.shape == (4,2,):\n # per face st values specified manually\n st_values[:] = np.tile(st, (6,1,))\n elif st.shape == (6,2,):\n # st values specified manually\n st_values[:] = st\n else:\n raise ValueError('Invalid shape for st')\n else:\n raise ValueError('Invalid value for st')\n\n shape[-1] += st_values.shape[-1]\n rgba_offset += st_values.shape[-1]\n\n if rgba:\n # default rgba values\n rgba_values = np.tile(np.array([1.0, 1.0, 1.0, 1.0], dtype=dtype), (24,1,))\n\n if isinstance(rgba, bool):\n pass\n elif isinstance(rgba, (int, float)):\n # int / float expands to RGBA with all values == value\n rgba_values *= rgba \n elif isinstance(rgba, (list, tuple, np.ndarray)):\n rgba = np.array(rgba, dtype=dtype)\n\n if rgba.shape == (3,):\n rgba_values = np.tile(rgba, (24,1,))\n elif rgba.shape == (4,):\n rgba_values[:] = np.tile(rgba, (24,1,))\n elif rgba.shape == (4,3,):\n rgba_values = np.tile(rgba, (6,1,))\n elif rgba.shape == (4,4,):\n rgba_values = np.tile(rgba, (6,1,))\n elif rgba.shape == (6,3,):\n rgba_values = np.repeat(rgba, 4, axis=0)\n elif rgba.shape == (6,4,):\n rgba_values = np.repeat(rgba, 4, axis=0)\n elif rgba.shape == (24,3,):\n rgba_values = rgba\n elif rgba.shape == (24,4,):\n rgba_values = rgba\n else:\n raise ValueError('Invalid shape for rgba')\n else:\n raise ValueError('Invalid value for rgba')\n\n shape[-1] += rgba_values.shape[-1]\n\n data = np.empty(shape, dtype=dtype)\n data[:,:3] = vertices\n if st_values is not None:\n data[:,3:5] = st_values\n if rgba_values is not None:\n data[:,rgba_offset:] = rgba_values\n\n if type == 'triangles':\n # counter clockwise\n # top right -> top left -> bottom left\n # top right -> bottom left -> bottom right\n indices = np.tile(np.array([0, 1, 2, 0, 2, 3], dtype='int'), (6,1))\n for face in range(6):\n indices[face] += (face * 4)\n indices.shape = (-1,)\n elif type == 'triangle_strip':\n raise NotImplementedError\n elif type == 'triangle_fan':\n raise NotImplementedError\n elif type == 'quads':\n raise NotImplementedError\n elif type == 'quad_strip':\n raise NotImplementedError\n else:\n raise ValueError('Unknown type')\n\n return data, indices","def cube(n):\n return n**3","def _center_seed(self, shape, seed):\n board = np.zeros(shape, dtype=int) # Start with a blank board\n\n # Find coordinates to place the seed array\n # Note: this assumes the seed <= the size of the game board\n x0 = shape[0]//2 - seed.shape[0]//2\n y0 = shape[1]//2 - seed.shape[1]//2\n x1 = x0 + seed.shape[0]\n y1 = y0 + seed.shape[1]\n\n # Place the seed on the board\n board[x0:x1, y0:y1] = seed\n\n return board","def generate_square_vertices(geom):\n unit = geom.pix_x.unit\n width = geom.pixel_width.to_value(unit) / 2\n x = geom.pix_x.to_value(unit)\n y = geom.pix_y.to_value(unit)\n\n x_offset = width[:, np.newaxis] * np.array([-1, -1, 1, 1])\n y_offset = width[:, np.newaxis] * np.array([1, -1, -1, 1])\n\n x = x[:, np.newaxis] + x_offset\n y = y[:, np.newaxis] + y_offset\n return x, y","def cube(self):\n\n dims = self.voxels.shape\n max_dim = max(dims)\n \n x_target = (max_dim - dims[0]) / 2\n y_target = (max_dim - dims[1]) / 2\n z_target = (max_dim - dims[2]) / 2\n\n self.voxels = np.pad(self.voxels,\n ((int(np.ceil(x_target)), int(np.floor(x_target))),\n (int(np.ceil(y_target)), int(np.floor(y_target))),\n (int(np.ceil(z_target)), int(np.floor(z_target)))),\n 'constant',\n constant_values=(0))\n\n self.point_position = self.point_position + [np.ceil(z_target),\n np.ceil(y_target),\n np.ceil(x_target)]\n\n return(self)","def solid(t, coord, ii, n_pixels, random_values):\n\n\n return (100,100,100)","def build(self, box, size):\n centroid = box.centroid\n edge1_midpoint = get_midpoint_of_edge(\n box.vertices[0], box.vertices[1])\n distance_to_edge1 = centroid.distance_to_xyz(edge1_midpoint)\n quadrat_indexes1 = range(int(distance_to_edge1 / size) + 1)\n\n edge2_midpoint = get_midpoint_of_edge(\n box.vertices[0], box.vertices[3])\n distance_to_edge2 = centroid.distance_to_xyz(edge2_midpoint)\n quadrat_indexes2 = range(int(distance_to_edge2 / size) + 1)\n\n quadrats = list()\n for i in quadrat_indexes2:\n for j in quadrat_indexes1:\n if i == 0 and j == 0:\n quadrats.append(Quadrat((0, 0), size, centroid))\n else:\n top_right = Vertex(centroid.x + i * size,\n centroid.y + (j * size), centroid.z)\n top_left = Vertex(centroid.x + i * size,\n centroid.y - (j * size), centroid.z)\n bottom_right = Vertex(\n centroid.x - i * size, centroid.y + (j * size), centroid.z)\n bottom_left = Vertex(\n centroid.x - i * size, centroid.y - (j * size), centroid.z)\n four_quadrats = [Quadrat((i, j), size, top_right),\n Quadrat((i, -1 * j), size, top_left),\n Quadrat((-1 * i, j), size, bottom_right),\n Quadrat((-1 * i, -1 * j), size, bottom_left)]\n quadrats_inside = list(\n filter(lambda x: box.contains(x.midpoint), four_quadrats))\n quadrats += quadrats_inside\n # print(\"There are this many quadrats: \" + str(len(quadrats)))\n return tuple(set(quadrats))","def faceDiagonal(self):\n faceDiagonal = (2**(1/2)) * self.sideLength\n return faceDiagonal","def diagonal(d, axis=0):\n assert d.ndim == 1\n n = d.shape[0]\n times = lambda x: d * x\n trans = lambda x: _hermitian(d) * x\n times, trans = apply_along_axis(times, trans, axis)\n return Operator(times=times, trans=trans, shape=(n,n))","def generate_box(n: int, include_origin=True) -> np.array:\n box = np.array((), dtype=np.int8)\n for i in range(-n, n + 1):\n for j in range(-n, n + 1):\n for k in range(-n, n + 1):\n if not (i == 0 and j == 0 and k == 0 and not include_origin):\n box = np.append(box, np.array((i, j, k)))\n\n if include_origin:\n shape = ((2 * n + 1) ** 3, 3)\n else:\n shape = ((2 * n + 1) ** 3 - 1, 3)\n return np.reshape(box, shape)","def square(self):\n return self.x * self.x + self.y * self.y","def Icosahedron(radius=1.0, center=(0.0, 0.0, 0.0)):\n return PlatonicSolid(kind='icosahedron', radius=radius, center=center)","def centering_matrix(n):\n P = eye(n) - 1/float(n) * ones((n,n))\n return P","def make_householder(a):\n\n v = a / (a[0] + np.copysign(np.linalg.norm(a), a[0]))\n v[0] = 1\n H = np.eye(a.shape[0])\n H -= (2 / np.dot(v, v)) * np.dot(v[:, None], v[None, :])\n return H","def draw_square(display, coord, box_size, color, bg_color):\n left, top = coord\n half = int(box_size * 0.5)\n quarter = int(box_size * 0.25)\n pygame.draw.rect(\n display, color, (left + quarter, top + quarter, half, half))\n return","def CubeGraph(n):\n theta = float(pi/n)\n\n d = {'':[]}\n dn={}\n p = {'':(float(0),float(0))}\n pn={}\n\n # construct recursively the adjacency dict and the positions\n for i in range(n):\n ci = float(cos(i*theta))\n si = float(sin(i*theta))\n for v,e in d.iteritems():\n v0 = v+'0'\n v1 = v+'1'\n l0 = [v1]\n l1 = [v0]\n for m in e:\n l0.append(m+'0')\n l1.append(m+'1')\n dn[v0] = l0\n dn[v1] = l1\n x,y = p[v]\n pn[v0] = (x, y)\n pn[v1] = (x+ci, y+si)\n d,dn = dn,{}\n p,pn = pn,{}\n\n # construct the graph\n r = Graph(name=\"%d-Cube\"%n)\n r.add_vertices(d.keys())\n for u,L in d.iteritems():\n for v in L:\n r.add_edge(u,v)\n r.set_pos(p)\n\n return r","def dctmtx(n):\n x,y = np.meshgrid(range(n), range(n))\n D = np.sqrt(2.0/n) * np.cos(np.pi * (2*x+1) * y / (2*n))\n D[0] /= np.sqrt(2)\n return D","def draw_random_u(d):\n mu = np.zeros(d)\n cov = np.eye(d)\n u = multivariate_normal.rvs(mean=mu, cov=cov)\n return u / np.linalg.norm(u)","def square_circumference(a):\n return (4*a)","def d2xy(n, d):\n x, y, rx, ry, s, t = 0, 0, 0, 0, 0, d\n s = 1\n while s < n:\n rx = 1 & (t // 2)\n ry = 1 & (t ^ rx)\n x, y = rot(s, x, y, rx, ry)\n x += s * rx\n y += s * ry\n t = t // 4\n s *= 2\n return x, y","def print_square(size):\n if not isinstance(size, int):\n raise ValueError(\"size must be an integer\")\n if size < 0:\n raise ValueError(\"size must be >= 0\")\n if size == 0:\n return\n for row in range(int(size)):\n for col in range(int(size)):\n print(\"{:s}\".format(\"#\"), end=\"\")\n print()","def eye(size):\n return Matrix.diagonal(size, 1.0)","def test_shaped_instance(self, seed):\n dim = Dimension(\"yolo\", \"norm\", 0.9, shape=(3, 2))\n samples = dim.sample(seed=seed)\n assert len(samples) == 1\n assert_eq(dists.norm.rvs(0.9, size=(3, 2)), samples[0])\n\n assert dim.shape == (3, 2)\n\n dim = Dimension(\"yolo\", \"norm\", 0.9, shape=4)\n samples = dim.sample(seed=seed)\n assert len(samples) == 1\n assert_eq(dists.norm.rvs(0.9, size=4), samples[0])\n\n assert dim.shape == (4,)","def centered_stencil(self):\n\n # compute the shape of the new stencil\n shp = self.arr.shape\n shp = tuple(max(i, j-(i+1))*2 + 1 for i, j in zip(self.center, shp))\n # print(\"New Shape :\", shp)\n # generate the stencil in the right shape\n S = np.zeros(shp)\n # embed the stencil into the bigger stencil in order to place the center\n # into the center\n slc = []\n for c, shp_arr, shp_s in zip(self.center, self.arr.shape, shp):\n if c < shp_arr/2:\n slc.append(slice(shp_s - shp_arr, None))\n else:\n slc.append(slice(0, -(shp_s - shp_arr)))\n\n # print(slc)\n S[slc] = self.arr[:]\n # print(\"The Stencil\")\n # print(self.arr)\n # print(\"Centered stencil\")\n # print(S)\n return S","def cmdscale(D):\n # Number of points\n n = len(D)\n\n # Centering matrix\n H = np.eye(n) - np.ones((n, n))/n\n\n # YY^T\n B = -H.dot(D**2).dot(H)/2\n\n # Diagonalize\n evals, evecs = np.linalg.eigh(B)\n\n # Sort by eigenvalue in descending order\n idx = np.argsort(evals)[::-1]\n evals = evals[idx]\n evecs = evecs[:, idx]\n\n # Compute the coordinates using positive-eigenvalued components only\n w, = np.where(evals > 0)\n L = np.diag(np.sqrt(evals[w]))\n V = evecs[:, w]\n Y = V.dot(L)\n\n return Y, evals[evals > 0]","def make_box(self, scale, color):\n box = Box()\n box.set_color(c=color)\n box.set_location(0, 0, 0)\n box.set_size(scale, scale, scale)\n return box","def make_box(self, scale, color):\n box = Box()\n box.set_color(c=color)\n box.set_location(0, 0, 0)\n box.set_size(scale, scale, scale)\n return box"],"string":"[\n \"def my_square(d):asaasasassssssssssssssssssssssssss\\n\\t return (d ** 3)\",\n \"def initialize_d(d, square_sides, offset=0):\\n return {key:[] for key in range(offset, square_sides ** 2 + offset)}\",\n \"def square_diamond(sx, sy, size, strong):\\n if size == 1:\\n return\\n\\n dsize = size/2\\n ex = sx+size-1\\n ey = sy+size-1\\n # lets get math style\\n\\n\\n # SQUARE STEP\\n\\n A = sx, sy\\n B = ex, sy\\n C = sx, ey\\n D = ex, ey\\n E = sx+dsize, sy+dsize\\n F = sx, sy + dsize\\n G = sx + dsize, sy\\n H = ex, sy + dsize\\n I = sx + dsize, ey\\n\\n def RAND(X):\\n return random.randint(-strong, strong)\\n\\n ### for coasts dont disappear\\n\\n def normalize(add_z, X):\\n if self[X] <= 0:\\n if add_z > 0:\\n add_z = -5\\n else:\\n if add_z <= 0:\\n add_z = 5\\n return add_z\\n\\n # Generate heights\\n # E = (A+B+C+D) / 4 + RAND(d)\\n # F = (A + C + E + E) / 4 + RAND(d)\\n # G = (A + B + E + E) / 4 + RAND(d)\\n # H = (B + D + E + E) / 4 + RAND(d)\\n # I = (C + D + E + E) / 4 + RANS(d)\\n\\n ### E\\n\\n try:\\n\\n add_z = ((self[A] + self[B] + self[C] + self[D]) / 4) + RAND(E)\\n\\n except KeyError, e:\\n print A, B, C, D, size, dsize, len(self)\\n raise e\\n\\n\\n self[E] = normalize(add_z, E)\\n\\n ### F\\n\\n add_z = (self[A] + self[C] + self[E] + self[E]) / 4 + RAND(F)\\n\\n self[F] = normalize(add_z, F)\\n\\n ### G\\n\\n add_z = (self[A] + self[B] + self[E] + self[E]) / 4 + RAND(G)\\n\\n self[G] = normalize(add_z, G)\\n\\n ### H\\n\\n add_z = (self[B] + self[D] + self[E] + self[E]) / 4 + RAND(H)\\n\\n self[H] = normalize(add_z, H)\\n\\n ### I\\n add_z = (self[C] + self[D] + self[E] + self[E]) / 4 + RAND(I)\\n\\n self[I] = normalize(add_z, I)\\n\\n\\n # DIAMOND STEP\\n\\n # get coordinates\\n # 0 - x, 1 - y\\n\\n x, y = 0, 1\\n\\n dx = (G[x] - A[x]) / 2\\n dy = (F[y] - A[y]) / 2\\n\\n J = A[x] + dx, A[y] + dy\\n K = G[x] + dx, G[y] + dy\\n L = F[x] + dx, F[y] + dy\\n M = E[x] + dx, E[y] + dy\\n\\n N = A[x], A[y] + dy\\n O = A[x] + dx, A[y]\\n P = G[x], G[y] + dy\\n Q = A[x] + dx, F[y]\\n\\n # Generate Heights\\n # J = (A + G + F + E)/4 + RAND(d)\\n # K = (G + B + E + H)/4 + RAND(d)\\n # L = (F + E + C + I)/4 + RAND(d)\\n # M = (E + H + I + D)/4 + RAND(d)\\n\\n # J\\n add_z = ((self[A] + self[G] + self[F] + self[E]) / 4) + RAND(J)\\n self[J] = normalize(add_z, J)\\n\\n # K\\n add_z = ((self[G] + self[B] + self[E] + self[H]) / 4) + RAND(K)\\n self[K] = normalize(add_z, K)\\n\\n # L\\n add_z = ((self[F] + self[E] + self[C] + self[I]) / 4) + RAND(L)\\n self[L] = normalize(add_z, L)\\n\\n # M\\n add_z = ((self[E] + self[H] + self[I] + self[D]) / 4) + RAND(M)\\n self[M] = normalize(add_z, M)\\n\\n # N = (K + A + J + F)/4 + RAND(d)\\n # O = (L + A + G + J)/4 + RAND(d)\\n # P = (J + G + K + E)/4 + RAND(d)\\n # Q = (F + J + E + L)/4 + RAND(d)\\n\\n # N\\n add_z = ((self[K] + self[A] + self[J] + self[F]) / 4) + RAND(N)\\n self[N] = normalize(add_z, N)\\n\\n # O\\n add_z = ((self[L] + self[A] + self[G] + self[J]) / 4) + RAND(O)\\n self[O] = normalize(add_z, O)\\n\\n # P\\n add_z = ((self[J] + self[G] + self[K] + self[E]) / 4) + RAND(P)\\n self[P] = normalize(add_z, P)\\n\\n # Q\\n add_z = ((self[F] + self[J] + self[E] + self[L]) / 4) + RAND(Q)\\n self[Q] = normalize(add_z, Q)\\n\\n # N = (A + J + F)/3 + RAND(d)\\n # O = (A + G + J)/3 + RAND(d)\\n\\n # N\\n add_z = ((self[A] + self[J] + self[F]) / 3) + RAND(N)\\n self[N] = normalize(add_z, N)\\n\\n # O\\n add_z = ((self[A] + self[G] + self[J]) / 3) + RAND(N)\\n self[O] = normalize(add_z, O)\\n\\n\\n ### Start recurse for diamond alg\\n square_diamond(A[0], A[1], dsize, strong)\\n square_diamond(G[0], G[1], dsize, strong)\\n square_diamond(F[0], F[1], dsize, strong)\\n square_diamond(E[0], E[1], dsize, strong)\",\n \"def make_square(x, size):\\n return [ [x, -size/2, size/2],\\n\\t\\t\\t [x, size/2, size/2],\\n [x, size/2, -size/2],\\n\\t\\t\\t [x, -size/2, -size/2]]\",\n \"def make_box_square(box, offset_scale=0.05):\\n\\n x_min, y_min, x_max, y_max = box[:4]\\n center_x = (x_max + x_min) / 2.\\n center_y = (y_max + y_min) / 2.\\n width = x_max - x_min\\n height = y_max - y_min\\n\\n if height >= width:\\n half_box = height / 2.\\n x_min = center_x - half_box\\n x_max = center_x + half_box\\n if width > height:\\n half_box = width / 2.\\n y_min = center_y - half_box\\n y_max = center_y + half_box\\n\\n box_side_lenght = (x_max + x_min) / 2.\\n offset = offset_scale * box_side_lenght\\n x_min = x_min - offset\\n x_max = x_max + offset\\n y_min = y_min - offset\\n y_max = y_max + offset\\n return (int(x_min), int(y_min), int(x_max), int(y_max))\",\n \"def from_diag(d, context = FloatContext):\\n n = len(d)\\n S = zeros(n,n,context)\\n set_diag(S,d)\\n return S\",\n \"def plasm_cube(self, size=0.1, color=WHITE):\\n return COLOR(color)(T([1,2,3])(self.coords)(CUBOID([size, size, size])))\",\n \"def bandcholesky(A, d):\\n L, dg = LDL(A, d)\\n return matrix(L)*diag(sqrt(dg))\",\n \"def get_sqd_from_center(boardsize):\\n assert boardsize % 2 == 1\\n center = boardsize // 2\\n return distances_from_pt(sq_distance, (center, center), boardsize)\",\n \"def square(center, side_length, *args, **kwargs):\\n center = np.asarray(center)\\n side_length = float(side_length)\\n lower_left = center - 0.5*side_length\\n return patch.Rectangle(lower_left, side_length, side_length,\\n \\t\\t\\t\\t\\t *args, **kwargs)\",\n \"def __init__(\\n self, d: int, seed: Optional[int] = None, inv_transform: bool = False\\n ) -> None:\\n self._d = d\\n self._seed = seed\\n self._inv_transform = inv_transform\\n if inv_transform:\\n sobol_dim = d\\n else:\\n # to apply Box-Muller, we need an even number of dimensions\\n sobol_dim = 2 * math.ceil(d / 2)\\n self._sobol_engine = SobolEngine(dimension=sobol_dim, scramble=True, seed=seed)\",\n \"def box(original, radius):\\n batches = original.size()[0]\\n num_elem = h.product(original.size()[1:])\\n ei = h.getEi(batches,num_elem)\\n \\n if len(original.size()) > 2:\\n ei = ei.contiguous().view(num_elem, *original.size())\\n\\n return HBox(original, None, ei * radius).checkSizes()\",\n \"def draw_square(square_edge):\\n\\n # create a square filled with zeros with square_edge as size and int as data type\\n square = numpy.zeros((square_edge, square_edge), dtype=int)\\n\\n # square is a \\\"matix\\\" of N (square_edge) list\\n # we use list index to place the numbers\\n current_list = 0\\n list_index = square_edge // 2\\n\\n # magic square logic\\n for number in range(1, square_edge**2+1, 1):\\n\\n # we place our first number\\n square[current_list, list_index] = number\\n\\n # we update how the next number will be placed in the square\\n update_current_list = (current_list - 1) % square_edge\\n update_list_index = (list_index + 1) % square_edge\\n\\n # if value of the index are not 0 we will increment current_list\\n if square[update_current_list, update_list_index]:\\n current_list += 1\\n\\n # else we simply update the index\\n else:\\n current_list = update_current_list\\n list_index = update_list_index\\n\\n return square\",\n \"def get_box(x_tr, y_tr, d=1.):\\n xs = np.array([-1., 1., 1., -1., -1.])\\n ys = np.array([-1., -1., 1., 1., -1.])\\n\\n xs = xs*d/2 + x_tr\\n ys = ys*d/2 + y_tr\\n\\n return xs, ys\",\n \"def center_size(boxes):\\n concat = P.Concat(1)\\n return concat(((boxes[:, 2:] + boxes[:, :2])/2, # cx, cy\\n boxes[:, 2:] - boxes[:, :2])) # w, h\",\n \"def sqft(d):\\n return 2 * (d.l*d.w + d.l*d.h + d.h*d.w)\",\n \"def sqrtCF(d):\\n sqrtD = sqrt(d)\\n P = 0\\n Q = 1\\n while True:\\n a = int(floor((P + sqrtD) / Q))\\n yield a\\n P = a * Q - P\\n Q = (d - P*P) // Q # It can be shown that Q evenly divides d - P*P\",\n \"def C(width = 1, size = (10, 20), layer = 0):\\n D = Device(name = 'C')\\n w = width/2\\n s1, s2 = size\\n points = [(-w, -w), (s1, -w), (s1, w), (w, w), (w, s2-w),\\n (s1, s2-w), (s1, s2+w), (-w, s2+w), (-w, -w)]\\n D.add_polygon(points, layer = layer)\\n D.add_port(name = 1, midpoint = (s1, s2), width = width, orientation = 0)\\n D.add_port(name = 2, midpoint = (s1, 0), width = width, orientation = 0)\\n return D\",\n \"def cube_area(edge : number) -> number:\\n area = 6*edge*edge\\n\\n return area\",\n \"def box(original, diameter):\\n return Box(original, h.ones(original.size()) * diameter, None).checkSizes()\",\n \"def Dodecahedron(radius=1.0, center=(0.0, 0.0, 0.0)):\\n return PlatonicSolid(kind='dodecahedron', radius=radius, center=center)\",\n \"def sample(self, d):\\n dist = rnd.uniform(0,self.length)\\n w = rnd.normal(0,self.width)\\n d.pos = np.dot(rotmat(self.angle), [dist, w]) + self.pos\\n d.ownpos = self.pos\",\n \"def center_size(boxes):\\n return torch.cat([(boxes[:, :2] + boxes[:, 2:])/2, # cx, cy\\n boxes[:, :2] - boxes[:, 2:]], 1) # w, h\",\n \"def sqnorm(self, d):\\n ###TODO\\n total = 0.0\\n for i in d:\\n total = total + (d[i] * d[i])\\n return total\",\n \"def coordinates_of_square(crd):\\n col = ord(crd[0]) - ord('a')\\n row = int(crd[1]) - 1\\n return (col * SQUARE_EDGE + BOARD_MARGIN, (7 - row) * SQUARE_EDGE + BOARD_MARGIN)\",\n \"def __init__(self, d):\\n\\t\\tself._coords = [0] * d\",\n \"def diagonal(self):\\r\\n return math.sqrt((self.width ** 2) + (self.height ** 2))\",\n \"def propagateInBox((x0,y0,z0), (px,py,pz), d):\\n x = x0 + px*d\\n y = y0 + py*d\\n z = z0 + pz*d\\n\\n return (x,y,z)\",\n \"def diagonal(cube_edge: int=128,\\n radius: int=10,\\n foreground: int=1,\\n dtype=np.uint8):\\n if 2 * radius > cube_edge:\\n raise ValueError(\\\"Given radius '{}' is larger than than cube edge length {}\\\"\\n .format(radius, cube_edge))\\n stack = np.zeros((cube_edge, cube_edge, cube_edge), dtype=bool)\\n cylinder = [\\n ((0, 0, 0), (cube_edge - 1, cube_edge - 1, cube_edge - 1), radius)\\n ]\\n stack = add_cylinder_px(stack, *cylinder[0])\\n return volume_bool_to_dtype(stack, fg=foreground, dtype=dtype)\",\n \"def central_composite(d,center=(2,2),alpha='o',face='ccc'):\\n \\n return build_central_composite(d,center=center,alpha=alpha,face=face)\",\n \"def diamondSquareFractal3D(size, roughness = .5, perturbance = .5):\\n start = time.time()\\n count = 0;\\n \\n #calculate the fractal based on the next highest 2^n + 1\\n n = math.log(size-1, 2)\\n if n != float(int(n)):\\n print \\\"The size is not valid, choose a side that is a power of 2 + 1.\\\"\\n print \\\"65, 129, 257, 513, 1025, etc.\\\"\\n return\\n \\n matrix = numpy.zeros((size, size, size)) - 100\\n total = matrix.size\\n \\n applyCornerValues(matrix, roughness)\\n \\n \\\"\\\"\\\"\\n The algorithm requires calculating the centers, faces and edges \\n in a particular order so that neighboring values will be \\n available to average:\\n \\n 1. Calc centers of all size n cubes.\\n 2. for each size n cube:\\n a. calc faces\\n b. calc edges\\n c. divide the cube into 8 cubes of size n-1.\\n d. repeat from step 1 until n < 3.\\n \\n Subdivided cubes are populated in the following order so that a\\n cube will not be populated until its top, left, and front neighbors\\n are done. Values from neighboring cubes are used to calculate \\n top, left and front faces and edges\\n top. left, front\\n top, left, back\\n top, right, front\\n bottom, left, front\\n top, right, back\\n bottom, left, back\\n bottom, right, front\\n bottom, right, back\\n \\n A queue will be used to manage the cubes waiting to be calculated.\\n \\\"\\\"\\\"\\n \\n from collections import deque\\n queue = deque()\\n \\n # add the whole matrix.\\n queue.append([0, 0 ,0 , size-1])\\n\\n # calc the center for the whole matrix\\n pf = perturbanceFactor(size, size, perturbance)\\n noiseLevel = roughness * pf\\n indexRef = getIndexRef(0, 0, 0, size/2, size)\\n setValue(matrix, center, indexRef, noiseLevel)\\n\\n while len(queue) > 0:\\n # pop a square\\n whatisit = queue.popleft()\\n [row, col, frame, range] = whatisit\\n\\n midRange = range/2\\n # populate the faces and edges of the cube\\n [midRow, midCol, midFrame] = populate3D(matrix, \\n row, col, frame, midRange, roughness, perturbance)\\n\\n if midRange >= 2:\\n #calc the centers for the next layer\\n populateCenters(matrix, row, col, frame, midRange, roughness, perturbance)\\n \\n #add top left front cube to the queue \\n queue.append([row, col, frame, midRange])\\n \\n #add top left back cube to the queue\\n queue.append([row, col, midFrame, midRange])\\n\\n #add top right front cubes to the queue\\n queue.append([row, midCol, frame, midRange])\\n\\n #add bottom left front cubes to the queue\\n queue.append([midRow, col, frame, midRange])\\n \\n #add top right back cubes to the queue \\n queue.append([row, midCol, midFrame, midRange])\\n\\n #add bottom left back cubes to the queue\\n queue.append([midRow, col, midFrame, midRange])\\n\\n #add bottom right front cubes to the queue\\n queue.append([midRow, midCol, frame, midRange])\\n \\n #add bottom right back cubes to the queue\\n queue.append([midRow, midCol, midFrame, midRange])\\n\\n #print \\\"result mean =\\\", matrix.mean(), \\\"result std = \\\", matrix.std()\\n print \\\"elapsed seconds =\\\", time.time() - start\\n # return the requested size\\n return matrix[0:size, 0:size]\",\n \"def __init__(self, d):\\n self._coords = [0]*d\",\n \"def surface_area_cube(side_length: float) -> float:\\r\\n if side_length < 0:\\r\\n raise ValueError(\\\"surface_area_cube() only accepts non-negative values\\\")\\r\\n return 6 * side_length**2\",\n \"def onsphere(size=None):\\n xy = oncircle(size)\\n z = 2.*random(xy.shape[:-1] + (1,)) - 1.\\n xy *= sqrt(1. - z*z)\\n return concatenate((xy, z), axis=-1)\",\n \"def __init__(self, a, **kwargs):\\r\\n center = kwargs.get('center')\\r\\n density = kwargs.get(\\\"density\\\")\\r\\n spheroid.__init__(self, a, center=center, density=density)\",\n \"def _fill_cell_rectangle(size = (20, 20), layers = (0, 1, 3),\\n densities = (0.5, 0.25, 0.7),\\n inverted = (False, False, False)):\\n D = Device('fillcell')\\n for layer, density, inv in zip(layers, densities, inverted):\\n rectangle_size = np.array(size) * sqrt(density)\\n # r = D.add_ref(rectangle(size = rectangle_size, layer = layer))\\n R = rectangle(size = rectangle_size, layer = layer)\\n R.center = (0, 0)\\n if inv is True:\\n A = rectangle(size = size)\\n A.center = (0,0)\\n A = A.get_polygons()\\n B = R.get_polygons()\\n p = gdspy.boolean(A, B, operation = 'not')\\n D.add_polygon(p, layer = layer)\\n else:\\n D.add_ref(R)\\n return D\",\n \"def box(self, x, y, w, h):\\n\\t\\tpass\",\n \"def rand_sphere(d0):\\n p1 = np.random.randn(d0, 3)\\n m = np.sqrt(np.sum(p1**2, axis=1))\\n\\n rad = pow(np.random.rand(d0), 1.0 / 3.0)\\n return (p1.T * (rad / m)).T\",\n \"def zone_from_center_size(x, y, size):\\n half_size = size // 2\\n size = half_size * 2\\n x1 = x - half_size\\n x2 = x + half_size -1\\n if x1 < 0:\\n x1 = 0\\n x2 = size - 1\\n if x2 >= w:\\n x2 = w - 1\\n x1 = w - size\\n y1 = y - half_size\\n y2 = y + half_size\\n if y1 < 0:\\n y1 = 0\\n y2 = size - 1\\n if y2 >= h:\\n y2 = h - 1\\n y1 = h - size\\n return [x1, y1, x2, y2]\",\n \"def sized_normal(self, t, size):\\n p = self.lerp(t)\\n if self.da < 0:\\n v = self.c - p\\n else:\\n v = p - self.c\\n return Line(p, size * v.normalized())\",\n \"def __init__(self, d=10):\\n \\n self.d = d\\n self.indices = [i for i in range(d)]\\n self.weights = [1 / w for w in range(1, d + 1)]\",\n \"def square(square_x, square_y, square_width, square_height, square_color):\\n arcade.draw_rectangle_filled(square_x, square_y, square_width, square_height, square_color)\",\n \"def make_square(turt,sz):\\n for i in range(4):\\n turt.forward(sz)\\n turt.left(90)\",\n \"def MakeDifficulties(center, width, n):\\n low, high = center-width, center+width\\n return numpy.linspace(low, high, n)\",\n \"def hadamard2(n):\\n # set up plot stuff\\n fig, ax = plt.subplots(figsize=(10, 10))\\n ax.set_xticks(range(n + 1)) # set axis ranges\\n ax.set_yticks(range(n + 1))\\n plt.xticks([]) # remove axis labels\\n plt.yticks([])\\n ax.set_aspect(aspect=1) # ensure it's a square and not a rectangle\\n # invert y axis so the origin is the top left\\n ax.set_ylim(ax.get_ylim()[::-1])\\n\\n def recurse(tlx, tly, brx, bry, flag):\\n \\\"\\\"\\\" Given coords for the top left and bottom right of a square, recursively pass a boolean flag\\n to see if we should draw it\\n \\\"\\\"\\\"\\n if(tlx + 1 == brx): # single square (width == 1)\\n if flag: # draw black square\\n ax.add_patch(Rectangle((tly, brx - 1), 1, 1, color='black'))\\n return # no need to recurse anymore\\n # here's the recursive part:\\n # we go in the order of top left, top right, bottom left, bottom right\\n # we negate the flag in the bottom right, and we keep the same flag for the rest\\n recurse(tlx, tly, (tlx + brx) // 2, (tly + bry) // 2, flag)\\n recurse((tlx + brx) // 2, tly, brx, (tly + bry) // 2, flag)\\n recurse(tlx, (tly + bry) // 2, (tlx + brx) // 2, bry, flag)\\n recurse((tlx + brx) // 2, (tly + bry) // 2, brx,\\n bry, not flag) # invert bottom right\\n\\n recurse(0, 0, n, n, True) # initial case, pass corners of entire matrix\\n plt.show()\",\n \"def test_euclidean_scale(self):\\n\\n s = space(curvature=0)\\n\\n magic = 77773.333773777773733\\n for mul in (2, 5, 1/3, 1/11, magic, 1/magic):\\n for name, dim in (\\n ('sphere_s1', 1),\\n ('sphere_v2', 2),\\n ('sphere_s2', 2),\\n ('sphere_v3', 3)\\n ):\\n self.assertTrue(isclose(\\n getattr(s, name)(1) * mul**dim,\\n getattr(s, name)(mul)\\n ))\",\n \"def sized_normal(self, t, size):\\n p = self.lerp(t)\\n v = size * self.cross.normalized()\\n return Line3d(p, v, z_axis=self.z_axis)\",\n \"def sized_normal(self, t, size):\\n return Line(self.lerp(t), size * self.cross_z.normalized())\",\n \"def latin_hypercube(n_pts, dim):\\n X = np.zeros((n_pts, dim))\\n centers = (1.0 + 2.0 * np.arange(0.0, n_pts)) / float(2 * n_pts)\\n for i in range(dim): # Shuffle the center locataions for each dimension.\\n X[:, i] = centers[np.random.permutation(n_pts)]\\n\\n # Add some perturbations within each box\\n pert = np.random.uniform(-1.0, 1.0, (n_pts, dim)) / float(2 * n_pts)\\n X += pert\\n return X\",\n \"def __init__(self, n):\\n self.row = [0] * n\\n self.col = [0] * n\\n self.diagonal = 0\\n self.antidiagonal = 0\\n self.winning = False\",\n \"def draw_square(turtle, size):\\n\\n square = range(4)\\n\\n for line in square:\\n alexa.forward(size)\\n alexa.left(90)\",\n \"def cube_area(side_length):\\n area = side_length ** 3\\n return area\",\n \"def extra(d):\\n return min(d.l * d.w, d.w * d.h, d.h * d.l)\",\n \"def __init__(self, length):\\n self.x = length\\n self.y = length\\n self._table = self.x * self.y\",\n \"def spaceDiagonal(self):\\n spaceDiagonal = (3**(1/2)) * self.sideLength\\n return spaceDiagonal\",\n \"def gen_square_subsequent_mask(sz: int) -> torch.Tensor:\\n return torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)\",\n \"def cellsize_2d(self):\\t\\r\\n return self.dx * self.dy\",\n \"def solve_for_edge_dimensionality(n):\\n return int(round(np.sqrt(2 * n + 2.25) - 1.5))\",\n \"def create_diamond(color=COLOR_WHITE):\\n a = Point3(-1.0, -1.0, 0.0)\\n b = Point3(1.0, -1.0, 0.0)\\n c = Point3(1.0, 1.0, 0.0)\\n d = Point3(-1.0, 1.0, 0.0)\\n e = Point3(0.0, 0.0, 1.0)\\n f = Point3(0.0, 0.0, -1.0)\\n\\n obj = glGenLists(1)\\n glNewList(obj, GL_COMPILE)\\n glPushMatrix()\\n glColor4fv(color)\\n glBegin(GL_TRIANGLES)\\n drawVertexListCreateNormal([a, b, e])\\n drawVertexListCreateNormal([b, c, e])\\n drawVertexListCreateNormal([c, d, e])\\n drawVertexListCreateNormal([d, a, e])\\n drawVertexListCreateNormal([b, a, f])\\n drawVertexListCreateNormal([c, b, f])\\n drawVertexListCreateNormal([d, c, f])\\n drawVertexListCreateNormal([a, d, f])\\n glEnd()\\n glPopMatrix()\\n glEndList()\\n return obj\",\n \"def mesh_uniform(N_e, d, Omega):\",\n \"def generate_sphere_full():\\n \\n num_voxels = 31\\n c = (15.0, 15.0, 15.0)\\n\\n data_x = []\\n data_y = []\\n data_z = []\\n data_intensity = []\\n\\n volume = numpy.zeros((num_voxels, num_voxels, num_voxels))\\n\\n for x in range(num_voxels):\\n for y in range(num_voxels):\\n for z in range(num_voxels):\\n\\n if numpy.sqrt((x-c[0])**2 + (y-c[1])**2 + (z-c[2])**2) - 7.5 < 1.5:\\n data_x.append(x)\\n data_y.append(y)\\n data_z.append(z)\\n data_intensity.append(200.0)\\n\\n volume[x,y,z] = 200.0\\n\\n\\n return data_x, data_y, data_z, data_intensity, volume\",\n \"def toCartesian(self, y):\\r\\n return Size - y\",\n \"def create_cube(color=COLOR_WHITE):\\n a = Point3(-1.0, -1.0, -1.0)\\n b = Point3(1.0, -1.0, -1.0)\\n c = Point3(1.0, -1.0, 1.0)\\n d = Point3(-1.0, -1.0, 1.0)\\n e = Point3(-1.0, 1.0, -1.0)\\n f = Point3(1.0, 1.0, -1.0)\\n g = Point3(1.0, 1.0, 1.0)\\n h = Point3(-1.0, 1.0, 1.0)\\n\\n obj = glGenLists(1)\\n glNewList(obj, GL_COMPILE)\\n glPushMatrix()\\n glBegin(GL_QUADS)\\n glColor4fv(color)\\n drawVertexListCreateNormal([a, b, c, d])\\n drawVertexListCreateNormal([b, f, g, c])\\n drawVertexListCreateNormal([f, e, h, g])\\n drawVertexListCreateNormal([e, a, d, h])\\n drawVertexListCreateNormal([d, c, g, h])\\n drawVertexListCreateNormal([a, e, f, b])\\n glEnd()\\n glPopMatrix()\\n glEndList()\\n return obj\",\n \"def centre_pad(length):\\n if length % 2 == 0:\\n side1 = int((size - length) / 2)\\n side2 = side1\\n else:\\n side1 = int((size - length) / 2)\\n side2 = side1 + 1\\n return side1, side2\",\n \"def len_square(bound):\\n\\treturn (8 - 2 * bound)\",\n \"def generate_square_subsequent_mask(sz: int) -> Tensor:\\n return torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)\",\n \"def create_square_with_diag_grid(self, len_side_a, len_side_b):\\n if (len_side_a < 2) or (len_side_b < 2):\\n raise ValueError('side length attributes for HexagonalCells should be at least 2.')\\n\\n self.connections, self.weights = create_grid_square_with_diagonals(len_side_a, len_side_b)\\n\\n # populate the dictionary from cell coordinates to cell indexes in arrays connection and weights\\n for i in range(len_side_a):\\n for j in range(len_side_b):\\n self.dict_cell_id_to_ind[(i, j)] = j + i*len_side_b\",\n \"def create_compute_box_size(self):\\n def compute_best_size_for(dim):\\n size = ((self.element_space[dim]-1)//self.box_space[dim]) + 1\\n size += 2 * self.ghost_space[dim]\\n while size % Level.BOX_ALIGNMENTS[dim]:\\n size += 1\\n return size\\n\\n return Space([compute_best_size_for(dim) for dim in range(self.dimensions)])\",\n \"def diamond(N, D, rng):\\n samples = rng.randn(N, D)\\n norm = np.sum(np.abs(samples), axis=1)\\n return samples/norm[:,None]\",\n \"def createTaperedBox( position=(0,0,0), size=(1,1,1), colour=(0.6,0.6,0.6), samplesY = 20, samplesXZ = 20, exponentBottom = 4, exponentTop = 4, exponentSide = 4 ):\\r\\n\\r\\n return createEllipsoid( position, (size[0]/2.0,size[1]/2.0,size[2]/2.0), colour, samplesY, samplesXZ, exponentBottom, exponentTop, exponentSide )\",\n \"def draw_diamond(display, coord, box_size, color, bg_color):\\n half = int(box_size * 0.5)\\n left, top = coord\\n vertices = [\\n (left + half, top),\\n (left + box_size - 1, top + half),\\n (left + half, top + box_size - 1),\\n (left, top + half),\\n ]\\n pygame.draw.polygon(display, color, vertices)\\n return\",\n \"def createBox( size=(1,1,1), position=(0,0,0), colour=(0.6,0.6,0.6) ):\\r\\n \\r\\n size = PyUtils.toVector3d(size)\\r\\n position = PyUtils.toPoint3d(position)\\r\\n vertices = []\\r\\n delta = MathLib.Vector3d()\\r\\n for repeat in range(3):\\r\\n for x in (-0.5,0.5) :\\r\\n delta.x = size.x * x\\r\\n for y in (-0.5,0.5) :\\r\\n delta.y = size.y * y\\r\\n for z in (-0.5,0.5) :\\r\\n delta.z = size.z * z\\r\\n vertices.append( position + delta )\\r\\n \\r\\n faces = [(0,1,3,2),(5,4,6,7), # YZ Faces\\r\\n (9,13,15,11),(12,8,10,14), # XY Faces\\r\\n (18,19,23,22),(17,16,20,21)] # XZ Faces\\r\\n \\r\\n return create( vertices, faces, colour )\",\n \"def expand_twelve(vertices):\\n box = np.zeros((12, 2), dtype=np.float32)\\n p = vertices.shape[0]\\n if p == 4:\\n box[0, :] = vertices[0, :]\\n box[1, :] = [(4 * vertices[0, 0] + vertices[1, 0]) / 5.0, (4 * vertices[0, 1] + vertices[1, 1]) / 5.0]\\n box[2, :] = [(3 * vertices[0, 0] + 2 * vertices[1, 0]) / 5.0,\\n (3 * vertices[0, 1] + 2 * vertices[1, 1]) / 5.0]\\n box[3, :] = [(2 * vertices[0, 0] + 3 * vertices[1, 0]) / 5.0,\\n (2 * vertices[0, 1] + 3 * vertices[1, 1]) / 5.0]\\n box[4, :] = [(vertices[0, 0] + 4 * vertices[1, 0]) / 5.0, (vertices[0, 1] + 4 * vertices[1, 1]) / 5.0]\\n box[5, :] = vertices[1, :]\\n box[6, :] = vertices[2, :]\\n box[7, :] = [(vertices[3, 0] + 4 * vertices[2, 0]) / 5.0, (vertices[3, 1] + 4 * vertices[2, 1]) / 5.0]\\n box[8, :] = [(2 * vertices[3, 0] + 3 * vertices[2, 0]) / 5.0,\\n (2 * vertices[3, 1] + 3 * vertices[2, 1]) / 5.0]\\n box[9, :] = [(3 * vertices[3, 0] + 2 * vertices[2, 0]) / 5.0,\\n (3 * vertices[3, 1] + 2 * vertices[2, 1]) / 5.0]\\n box[10, :] = [(4 * vertices[3, 0] + vertices[2, 0]) / 5.0, (4 * vertices[3, 1] + vertices[2, 1]) / 5.0]\\n box[11, :] = vertices[3, :]\\n elif p == 6:\\n box[0, :] = vertices[0, :]\\n box[1, :] = [(vertices[0, 0] + vertices[1, 0]) / 2.0, (vertices[0, 1] + vertices[1, 1]) / 2.0]\\n box[2, :] = vertices[1, :]\\n box[3, :] = [(2 * vertices[1, 0] + vertices[2, 0]) / 3.0, (2 * vertices[1, 1] + vertices[2, 1]) / 3.0]\\n box[4, :] = [(vertices[1, 0] + 2 * vertices[2, 0]) / 3.0, (vertices[1, 1] + 2 * vertices[2, 1]) / 3.0]\\n box[5, :] = vertices[2, :]\\n box[6, :] = vertices[3, :]\\n box[7, :] = [(vertices[4, 0] + 2 * vertices[3, 0]) / 3.0, (vertices[4, 1] + 2 * vertices[3, 1]) / 3.0]\\n box[8, :] = [(2 * vertices[4, 0] + vertices[3, 0]) / 3.0, (2 * vertices[4, 1] + vertices[3, 1]) / 3.0]\\n box[9, :] = vertices[4, :]\\n box[10, :] = [(vertices[5, 0] + vertices[4, 0]) / 2.0, (vertices[5, 1] + vertices[4, 1]) / 2.0]\\n box[11, :] = vertices[5, :]\\n elif p == 8:\\n box[0, :] = vertices[0, :]\\n box[1, :] = [(vertices[0, 0] + vertices[1, 0]) / 2.0, (vertices[0, 1] + vertices[1, 1]) / 2.0]\\n box[2, :] = vertices[1, :]\\n box[3, :] = vertices[2, :]\\n box[4, :] = [(vertices[2, 0] + vertices[3, 0]) / 2.0, (vertices[2, 1] + vertices[3, 1]) / 2.0]\\n box[5, :] = vertices[3, :]\\n box[6, :] = vertices[4, :]\\n box[7, :] = [(vertices[4, 0] + vertices[5, 0]) / 2.0, (vertices[4, 1] + vertices[5, 1]) / 2.0]\\n box[8, :] = vertices[5, :]\\n box[9, :] = vertices[6, :]\\n box[10, :] = [(vertices[6, 0] + vertices[7, 0]) / 2.0, (vertices[6, 1] + vertices[7, 1]) / 2.0]\\n box[11, :] = vertices[7, :]\\n elif p == 10:\\n box[0, :] = vertices[0, :]\\n box[1, :] = vertices[1, :]\\n box[2, :] = vertices[2, :]\\n box[3, :] = vertices[3, :]\\n box[4, :] = [(vertices[3, 0] + vertices[4, 0]) / 2.0, (vertices[3, 1] + vertices[4, 1]) / 2.0]\\n box[5, :] = vertices[4, :]\\n box[6, :] = vertices[5, :]\\n box[7, :] = [(vertices[5, 0] + vertices[6, 0]) / 2.0, (vertices[5, 1] + vertices[6, 1]) / 2.0]\\n box[8, :] = vertices[6, :]\\n box[9, :] = vertices[7, :]\\n box[10, :] = vertices[8, :]\\n box[11, :] = vertices[9, :]\\n elif p == 12:\\n box = vertices\\n else:\\n raise ValueError(f'Invalid points dimension: {p}')\\n\\n return box\",\n \"def create_cube(scale=(1.0,1.0,1.0), st=False, rgba=False, dtype='float32', type='triangles'):\\n\\n shape = [24, 3]\\n rgba_offset = 3\\n\\n width, height, depth = scale\\n # half the dimensions\\n width /= 2.0\\n height /= 2.0\\n depth /= 2.0\\n\\n vertices = np.array([\\n # front\\n # top right\\n ( width, height, depth,),\\n # top left\\n (-width, height, depth,),\\n # bottom left\\n (-width,-height, depth,),\\n # bottom right\\n ( width,-height, depth,),\\n\\n # right\\n # top right\\n ( width, height,-depth),\\n # top left\\n ( width, height, depth),\\n # bottom left\\n ( width,-height, depth),\\n # bottom right\\n ( width,-height,-depth),\\n\\n # back\\n # top right\\n (-width, height,-depth),\\n # top left\\n ( width, height,-depth),\\n # bottom left\\n ( width,-height,-depth),\\n # bottom right\\n (-width,-height,-depth),\\n\\n # left\\n # top right\\n (-width, height, depth),\\n # top left\\n (-width, height,-depth),\\n # bottom left\\n (-width,-height,-depth),\\n # bottom right\\n (-width,-height, depth),\\n\\n # top\\n # top right\\n ( width, height,-depth),\\n # top left\\n (-width, height,-depth),\\n # bottom left\\n (-width, height, depth),\\n # bottom right\\n ( width, height, depth),\\n\\n # bottom\\n # top right\\n ( width,-height, depth),\\n # top left\\n (-width,-height, depth),\\n # bottom left\\n (-width,-height,-depth),\\n # bottom right\\n ( width,-height,-depth),\\n ], dtype=dtype)\\n\\n st_values = None\\n rgba_values = None\\n\\n if st:\\n # default st values\\n st_values = np.tile(\\n np.array([\\n (1.0, 1.0,),\\n (0.0, 1.0,),\\n (0.0, 0.0,),\\n (1.0, 0.0,),\\n ], dtype=dtype),\\n (6,1,)\\n )\\n\\n if isinstance(st, bool):\\n pass\\n elif isinstance(st, (int, float)):\\n st_values *= st\\n elif isinstance(st, (list, tuple, np.ndarray)):\\n st = np.array(st, dtype=dtype)\\n if st.shape == (2,2,):\\n # min / max\\n st_values *= st[1] - st[0]\\n st_values += st[0]\\n elif st.shape == (4,2,):\\n # per face st values specified manually\\n st_values[:] = np.tile(st, (6,1,))\\n elif st.shape == (6,2,):\\n # st values specified manually\\n st_values[:] = st\\n else:\\n raise ValueError('Invalid shape for st')\\n else:\\n raise ValueError('Invalid value for st')\\n\\n shape[-1] += st_values.shape[-1]\\n rgba_offset += st_values.shape[-1]\\n\\n if rgba:\\n # default rgba values\\n rgba_values = np.tile(np.array([1.0, 1.0, 1.0, 1.0], dtype=dtype), (24,1,))\\n\\n if isinstance(rgba, bool):\\n pass\\n elif isinstance(rgba, (int, float)):\\n # int / float expands to RGBA with all values == value\\n rgba_values *= rgba \\n elif isinstance(rgba, (list, tuple, np.ndarray)):\\n rgba = np.array(rgba, dtype=dtype)\\n\\n if rgba.shape == (3,):\\n rgba_values = np.tile(rgba, (24,1,))\\n elif rgba.shape == (4,):\\n rgba_values[:] = np.tile(rgba, (24,1,))\\n elif rgba.shape == (4,3,):\\n rgba_values = np.tile(rgba, (6,1,))\\n elif rgba.shape == (4,4,):\\n rgba_values = np.tile(rgba, (6,1,))\\n elif rgba.shape == (6,3,):\\n rgba_values = np.repeat(rgba, 4, axis=0)\\n elif rgba.shape == (6,4,):\\n rgba_values = np.repeat(rgba, 4, axis=0)\\n elif rgba.shape == (24,3,):\\n rgba_values = rgba\\n elif rgba.shape == (24,4,):\\n rgba_values = rgba\\n else:\\n raise ValueError('Invalid shape for rgba')\\n else:\\n raise ValueError('Invalid value for rgba')\\n\\n shape[-1] += rgba_values.shape[-1]\\n\\n data = np.empty(shape, dtype=dtype)\\n data[:,:3] = vertices\\n if st_values is not None:\\n data[:,3:5] = st_values\\n if rgba_values is not None:\\n data[:,rgba_offset:] = rgba_values\\n\\n if type == 'triangles':\\n # counter clockwise\\n # top right -> top left -> bottom left\\n # top right -> bottom left -> bottom right\\n indices = np.tile(np.array([0, 1, 2, 0, 2, 3], dtype='int'), (6,1))\\n for face in range(6):\\n indices[face] += (face * 4)\\n indices.shape = (-1,)\\n elif type == 'triangle_strip':\\n raise NotImplementedError\\n elif type == 'triangle_fan':\\n raise NotImplementedError\\n elif type == 'quads':\\n raise NotImplementedError\\n elif type == 'quad_strip':\\n raise NotImplementedError\\n else:\\n raise ValueError('Unknown type')\\n\\n return data, indices\",\n \"def cube(n):\\n return n**3\",\n \"def _center_seed(self, shape, seed):\\n board = np.zeros(shape, dtype=int) # Start with a blank board\\n\\n # Find coordinates to place the seed array\\n # Note: this assumes the seed <= the size of the game board\\n x0 = shape[0]//2 - seed.shape[0]//2\\n y0 = shape[1]//2 - seed.shape[1]//2\\n x1 = x0 + seed.shape[0]\\n y1 = y0 + seed.shape[1]\\n\\n # Place the seed on the board\\n board[x0:x1, y0:y1] = seed\\n\\n return board\",\n \"def generate_square_vertices(geom):\\n unit = geom.pix_x.unit\\n width = geom.pixel_width.to_value(unit) / 2\\n x = geom.pix_x.to_value(unit)\\n y = geom.pix_y.to_value(unit)\\n\\n x_offset = width[:, np.newaxis] * np.array([-1, -1, 1, 1])\\n y_offset = width[:, np.newaxis] * np.array([1, -1, -1, 1])\\n\\n x = x[:, np.newaxis] + x_offset\\n y = y[:, np.newaxis] + y_offset\\n return x, y\",\n \"def cube(self):\\n\\n dims = self.voxels.shape\\n max_dim = max(dims)\\n \\n x_target = (max_dim - dims[0]) / 2\\n y_target = (max_dim - dims[1]) / 2\\n z_target = (max_dim - dims[2]) / 2\\n\\n self.voxels = np.pad(self.voxels,\\n ((int(np.ceil(x_target)), int(np.floor(x_target))),\\n (int(np.ceil(y_target)), int(np.floor(y_target))),\\n (int(np.ceil(z_target)), int(np.floor(z_target)))),\\n 'constant',\\n constant_values=(0))\\n\\n self.point_position = self.point_position + [np.ceil(z_target),\\n np.ceil(y_target),\\n np.ceil(x_target)]\\n\\n return(self)\",\n \"def solid(t, coord, ii, n_pixels, random_values):\\n\\n\\n return (100,100,100)\",\n \"def build(self, box, size):\\n centroid = box.centroid\\n edge1_midpoint = get_midpoint_of_edge(\\n box.vertices[0], box.vertices[1])\\n distance_to_edge1 = centroid.distance_to_xyz(edge1_midpoint)\\n quadrat_indexes1 = range(int(distance_to_edge1 / size) + 1)\\n\\n edge2_midpoint = get_midpoint_of_edge(\\n box.vertices[0], box.vertices[3])\\n distance_to_edge2 = centroid.distance_to_xyz(edge2_midpoint)\\n quadrat_indexes2 = range(int(distance_to_edge2 / size) + 1)\\n\\n quadrats = list()\\n for i in quadrat_indexes2:\\n for j in quadrat_indexes1:\\n if i == 0 and j == 0:\\n quadrats.append(Quadrat((0, 0), size, centroid))\\n else:\\n top_right = Vertex(centroid.x + i * size,\\n centroid.y + (j * size), centroid.z)\\n top_left = Vertex(centroid.x + i * size,\\n centroid.y - (j * size), centroid.z)\\n bottom_right = Vertex(\\n centroid.x - i * size, centroid.y + (j * size), centroid.z)\\n bottom_left = Vertex(\\n centroid.x - i * size, centroid.y - (j * size), centroid.z)\\n four_quadrats = [Quadrat((i, j), size, top_right),\\n Quadrat((i, -1 * j), size, top_left),\\n Quadrat((-1 * i, j), size, bottom_right),\\n Quadrat((-1 * i, -1 * j), size, bottom_left)]\\n quadrats_inside = list(\\n filter(lambda x: box.contains(x.midpoint), four_quadrats))\\n quadrats += quadrats_inside\\n # print(\\\"There are this many quadrats: \\\" + str(len(quadrats)))\\n return tuple(set(quadrats))\",\n \"def faceDiagonal(self):\\n faceDiagonal = (2**(1/2)) * self.sideLength\\n return faceDiagonal\",\n \"def diagonal(d, axis=0):\\n assert d.ndim == 1\\n n = d.shape[0]\\n times = lambda x: d * x\\n trans = lambda x: _hermitian(d) * x\\n times, trans = apply_along_axis(times, trans, axis)\\n return Operator(times=times, trans=trans, shape=(n,n))\",\n \"def generate_box(n: int, include_origin=True) -> np.array:\\n box = np.array((), dtype=np.int8)\\n for i in range(-n, n + 1):\\n for j in range(-n, n + 1):\\n for k in range(-n, n + 1):\\n if not (i == 0 and j == 0 and k == 0 and not include_origin):\\n box = np.append(box, np.array((i, j, k)))\\n\\n if include_origin:\\n shape = ((2 * n + 1) ** 3, 3)\\n else:\\n shape = ((2 * n + 1) ** 3 - 1, 3)\\n return np.reshape(box, shape)\",\n \"def square(self):\\n return self.x * self.x + self.y * self.y\",\n \"def Icosahedron(radius=1.0, center=(0.0, 0.0, 0.0)):\\n return PlatonicSolid(kind='icosahedron', radius=radius, center=center)\",\n \"def centering_matrix(n):\\n P = eye(n) - 1/float(n) * ones((n,n))\\n return P\",\n \"def make_householder(a):\\n\\n v = a / (a[0] + np.copysign(np.linalg.norm(a), a[0]))\\n v[0] = 1\\n H = np.eye(a.shape[0])\\n H -= (2 / np.dot(v, v)) * np.dot(v[:, None], v[None, :])\\n return H\",\n \"def draw_square(display, coord, box_size, color, bg_color):\\n left, top = coord\\n half = int(box_size * 0.5)\\n quarter = int(box_size * 0.25)\\n pygame.draw.rect(\\n display, color, (left + quarter, top + quarter, half, half))\\n return\",\n \"def CubeGraph(n):\\n theta = float(pi/n)\\n\\n d = {'':[]}\\n dn={}\\n p = {'':(float(0),float(0))}\\n pn={}\\n\\n # construct recursively the adjacency dict and the positions\\n for i in range(n):\\n ci = float(cos(i*theta))\\n si = float(sin(i*theta))\\n for v,e in d.iteritems():\\n v0 = v+'0'\\n v1 = v+'1'\\n l0 = [v1]\\n l1 = [v0]\\n for m in e:\\n l0.append(m+'0')\\n l1.append(m+'1')\\n dn[v0] = l0\\n dn[v1] = l1\\n x,y = p[v]\\n pn[v0] = (x, y)\\n pn[v1] = (x+ci, y+si)\\n d,dn = dn,{}\\n p,pn = pn,{}\\n\\n # construct the graph\\n r = Graph(name=\\\"%d-Cube\\\"%n)\\n r.add_vertices(d.keys())\\n for u,L in d.iteritems():\\n for v in L:\\n r.add_edge(u,v)\\n r.set_pos(p)\\n\\n return r\",\n \"def dctmtx(n):\\n x,y = np.meshgrid(range(n), range(n))\\n D = np.sqrt(2.0/n) * np.cos(np.pi * (2*x+1) * y / (2*n))\\n D[0] /= np.sqrt(2)\\n return D\",\n \"def draw_random_u(d):\\n mu = np.zeros(d)\\n cov = np.eye(d)\\n u = multivariate_normal.rvs(mean=mu, cov=cov)\\n return u / np.linalg.norm(u)\",\n \"def square_circumference(a):\\n return (4*a)\",\n \"def d2xy(n, d):\\n x, y, rx, ry, s, t = 0, 0, 0, 0, 0, d\\n s = 1\\n while s < n:\\n rx = 1 & (t // 2)\\n ry = 1 & (t ^ rx)\\n x, y = rot(s, x, y, rx, ry)\\n x += s * rx\\n y += s * ry\\n t = t // 4\\n s *= 2\\n return x, y\",\n \"def print_square(size):\\n if not isinstance(size, int):\\n raise ValueError(\\\"size must be an integer\\\")\\n if size < 0:\\n raise ValueError(\\\"size must be >= 0\\\")\\n if size == 0:\\n return\\n for row in range(int(size)):\\n for col in range(int(size)):\\n print(\\\"{:s}\\\".format(\\\"#\\\"), end=\\\"\\\")\\n print()\",\n \"def eye(size):\\n return Matrix.diagonal(size, 1.0)\",\n \"def test_shaped_instance(self, seed):\\n dim = Dimension(\\\"yolo\\\", \\\"norm\\\", 0.9, shape=(3, 2))\\n samples = dim.sample(seed=seed)\\n assert len(samples) == 1\\n assert_eq(dists.norm.rvs(0.9, size=(3, 2)), samples[0])\\n\\n assert dim.shape == (3, 2)\\n\\n dim = Dimension(\\\"yolo\\\", \\\"norm\\\", 0.9, shape=4)\\n samples = dim.sample(seed=seed)\\n assert len(samples) == 1\\n assert_eq(dists.norm.rvs(0.9, size=4), samples[0])\\n\\n assert dim.shape == (4,)\",\n \"def centered_stencil(self):\\n\\n # compute the shape of the new stencil\\n shp = self.arr.shape\\n shp = tuple(max(i, j-(i+1))*2 + 1 for i, j in zip(self.center, shp))\\n # print(\\\"New Shape :\\\", shp)\\n # generate the stencil in the right shape\\n S = np.zeros(shp)\\n # embed the stencil into the bigger stencil in order to place the center\\n # into the center\\n slc = []\\n for c, shp_arr, shp_s in zip(self.center, self.arr.shape, shp):\\n if c < shp_arr/2:\\n slc.append(slice(shp_s - shp_arr, None))\\n else:\\n slc.append(slice(0, -(shp_s - shp_arr)))\\n\\n # print(slc)\\n S[slc] = self.arr[:]\\n # print(\\\"The Stencil\\\")\\n # print(self.arr)\\n # print(\\\"Centered stencil\\\")\\n # print(S)\\n return S\",\n \"def cmdscale(D):\\n # Number of points\\n n = len(D)\\n\\n # Centering matrix\\n H = np.eye(n) - np.ones((n, n))/n\\n\\n # YY^T\\n B = -H.dot(D**2).dot(H)/2\\n\\n # Diagonalize\\n evals, evecs = np.linalg.eigh(B)\\n\\n # Sort by eigenvalue in descending order\\n idx = np.argsort(evals)[::-1]\\n evals = evals[idx]\\n evecs = evecs[:, idx]\\n\\n # Compute the coordinates using positive-eigenvalued components only\\n w, = np.where(evals > 0)\\n L = np.diag(np.sqrt(evals[w]))\\n V = evecs[:, w]\\n Y = V.dot(L)\\n\\n return Y, evals[evals > 0]\",\n \"def make_box(self, scale, color):\\n box = Box()\\n box.set_color(c=color)\\n box.set_location(0, 0, 0)\\n box.set_size(scale, scale, scale)\\n return box\",\n \"def make_box(self, scale, color):\\n box = Box()\\n box.set_color(c=color)\\n box.set_location(0, 0, 0)\\n box.set_size(scale, scale, scale)\\n return box\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5763527","0.5631988","0.55919534","0.5325949","0.52789533","0.52779573","0.5240144","0.5220922","0.5193584","0.5174843","0.5162735","0.5129172","0.5129167","0.51191884","0.5118387","0.51183224","0.50997084","0.50534856","0.50478446","0.50203365","0.4996185","0.49504822","0.49483687","0.49390522","0.49352452","0.4905792","0.49013817","0.4879734","0.4877485","0.4870109","0.48628297","0.48516265","0.48506096","0.48316988","0.4829679","0.48289815","0.48227805","0.48082832","0.4802107","0.47995335","0.47992527","0.4755421","0.47532874","0.475253","0.47493905","0.47401658","0.473692","0.47350475","0.47298098","0.47209114","0.47191516","0.47134316","0.47046056","0.4701363","0.4700699","0.46998116","0.4688682","0.46793416","0.467599","0.46735117","0.46701315","0.46675038","0.46663028","0.4658024","0.46557057","0.46556953","0.46537572","0.4649176","0.46477684","0.4646615","0.46447897","0.46428654","0.4640011","0.46386576","0.46255088","0.4610281","0.45973548","0.45943734","0.4591791","0.45902467","0.45866218","0.45795867","0.45747194","0.45737323","0.4567216","0.4565515","0.456326","0.4559941","0.45588025","0.45535576","0.45518452","0.45427582","0.45420173","0.45408636","0.4539642","0.45319277","0.45318568","0.45316955","0.45273712","0.45273712"],"string":"[\n \"0.5763527\",\n \"0.5631988\",\n \"0.55919534\",\n \"0.5325949\",\n \"0.52789533\",\n \"0.52779573\",\n \"0.5240144\",\n \"0.5220922\",\n \"0.5193584\",\n \"0.5174843\",\n \"0.5162735\",\n \"0.5129172\",\n \"0.5129167\",\n \"0.51191884\",\n \"0.5118387\",\n \"0.51183224\",\n \"0.50997084\",\n \"0.50534856\",\n \"0.50478446\",\n \"0.50203365\",\n \"0.4996185\",\n \"0.49504822\",\n \"0.49483687\",\n \"0.49390522\",\n \"0.49352452\",\n \"0.4905792\",\n \"0.49013817\",\n \"0.4879734\",\n \"0.4877485\",\n \"0.4870109\",\n \"0.48628297\",\n \"0.48516265\",\n \"0.48506096\",\n \"0.48316988\",\n \"0.4829679\",\n \"0.48289815\",\n \"0.48227805\",\n \"0.48082832\",\n \"0.4802107\",\n \"0.47995335\",\n \"0.47992527\",\n \"0.4755421\",\n \"0.47532874\",\n \"0.475253\",\n \"0.47493905\",\n \"0.47401658\",\n \"0.473692\",\n \"0.47350475\",\n \"0.47298098\",\n \"0.47209114\",\n \"0.47191516\",\n \"0.47134316\",\n \"0.47046056\",\n \"0.4701363\",\n \"0.4700699\",\n \"0.46998116\",\n \"0.4688682\",\n \"0.46793416\",\n \"0.467599\",\n \"0.46735117\",\n \"0.46701315\",\n \"0.46675038\",\n \"0.46663028\",\n \"0.4658024\",\n \"0.46557057\",\n \"0.46556953\",\n \"0.46537572\",\n \"0.4649176\",\n \"0.46477684\",\n \"0.4646615\",\n \"0.46447897\",\n \"0.46428654\",\n \"0.4640011\",\n \"0.46386576\",\n \"0.46255088\",\n \"0.4610281\",\n \"0.45973548\",\n \"0.45943734\",\n \"0.4591791\",\n \"0.45902467\",\n \"0.45866218\",\n \"0.45795867\",\n \"0.45747194\",\n \"0.45737323\",\n \"0.4567216\",\n \"0.4565515\",\n \"0.456326\",\n \"0.4559941\",\n \"0.45588025\",\n \"0.45535576\",\n \"0.45518452\",\n \"0.45427582\",\n \"0.45420173\",\n \"0.45408636\",\n \"0.4539642\",\n \"0.45319277\",\n \"0.45318568\",\n \"0.45316955\",\n \"0.45273712\",\n \"0.45273712\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.63994324"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":791,"cells":{"query":{"kind":"string","value":"Create a centered Gaussian image having standard deviation \"sigma\"."},"document":{"kind":"string","value":"def model_gauss(xsigma, nx, ny=1, nz=1, ysigma=None, zsigma=None, xcenter=None, ycenter=None, zcenter=None):\n\te = EMData()\n\te.set_size(nx, ny, nz)\n\tif( ysigma == None ) : ysigma = xsigma\n\tif( zsigma == None ) : zsigma = xsigma\n\tif( xcenter == None ) : xcenter = nx//2\n\tif( ycenter == None ) : ycenter = ny//2\n\tif( zcenter == None ) : zcenter = nz//2\n\te.process_inplace(\"testimage.puregaussian\", {\"x_sigma\":xsigma,\"y_sigma\":ysigma,\"z_sigma\":zsigma,\"x_center\":xcenter,\"y_center\":ycenter,\"z_center\":zcenter} )\n\treturn e"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def make_gaussian(size, sigma=10, center=None):\n\n x = np.arange(0, size[1], 1, float)\n y = np.arange(0, size[0], 1, float)\n y = y[:, np.newaxis]\n\n if center is None:\n x0 = y0 = size[0] // 2\n else:\n x0 = center[0]\n y0 = center[1]\n\n return np.exp(-4 * np.log(2) * ((x - x0) ** 2 + (y - y0) ** 2) / sigma ** 2)","def makeGaussian(height, width, sigma=3, center=None):\n x = np.arange(0, width, 1, float)\n y = np.arange(0, height, 1, float)[:, np.newaxis]\n if center is None:\n x0 = width // 2\n y0 = height // 2\n else:\n x0 = center[0]\n y0 = center[1]\n\n return np.exp(-4 * np.log(2) * ((x - x0) ** 2 + (y - y0) ** 2) / sigma ** 2)","def gaussian(x, amplitude=1.0, center=0.0, sigma=1.0):\n \n return (amplitude/(np.sqrt(2.*np.pi)*sigma)) * exp(-np.power((1.0*x-center)/(sigma), 2.)/2.)","def makeGaussian(size, fwhm, sigma, center=None):\n\n x = np.arange(0, size, 1, float)\n y = x[:,np.newaxis]\n\n if center is None:\n x0 = y0 = size // 2\n else:\n x0 = center[0]\n y0 = center[1]\n \n #return (np.exp(-4*np.log(2) * ((x-x0)**2 + (y-y0)**2) / fwhm**2)) #approximation using exponenial functions\n return ((1/(2*np.pi*sigma**2))*np.exp(-((xx)**2 + (yy)**2)/(2*sigma**2))) # symmetric 2D Gaussian distribution","def gaussian(x, mean, sigma):\n return np.exp(-np.square(x-mean)/(2*np.square(sigma))) / (np.sqrt(2*np.pi*sigma**2))","def GaussianKernel(sigma: float = 1., width: int = 0):\n assert not ((width is None or width == 0) and\n (sigma is None or sigma == 0)), \\\n \"GaussianKernel :: both sigma ({}) & width ({}) are not valid\".format(\n sigma, width)\n\n if width is None or width == 0:\n width = int(2.0 * 3.0 * sigma + 1.0)\n if width % 2 == 0:\n width += 1\n\n if sigma is None or sigma == 0:\n sigma = (width - 1)/6.\n half = width//2\n x, y = np.meshgrid(np.linspace(-half, half, width),\n np.linspace(-half, half, width), indexing='xy')\n w = np.exp(- (x**2 + y**2) / (2.*(sigma**2)))\n w /= np.sum(w)\n return torch.from_numpy(w.astype(np.float32)).view(1, 1, width, width)","def gauss_kern(sigma, size):\r\n size = int(np.floor(size/2))\r\n sizey = size\r\n x, y = scipy.mgrid[-size:size+1, -sizey:sizey+1]\r\n g = scipy.exp(-(x**2+y**2) / (2*(sigma)**2))\r\n return np.ravel(g / g.max())","def gaussian(size,sigma):\n a,b=np.ogrid[-size/2:size/2,-size/2:size/2]\n mask = a**2+b**2\n mask = np.exp(-mask.astype('float')/(2*float(sigma**2)))\n return mask","def _gaussian_distribution(self, x: ndarray, mu: float, sigma: float) -> ndarray:\n return 1 / (np.sqrt(2 * np.pi) * sigma) * np.exp(\n -np.power(\n (x - mu) / sigma, 2) / 2)","def gaussian(x, mu, sigma):\n return (np.exp(-(x - mu)**2 / 2.0 / sigma**2) /\n np.sqrt(2.0 * np.pi) / sigma)","def add_gaussian_noise(image, min_sigma, max_sigma):\n assert(max_sigma >= min_sigma)\n sigma = np.random.uniform(min_sigma, max_sigma)\n noise = np.random.normal(loc=0, scale=sigma, size=image.shape)\n noisy_im = np.floor((image + noise) * 255) / 255\n noisy_im = np.clip(noisy_im, 0, 1)\n return noisy_im","def Gaussian(x, mu=0, sigma=26.4, A=1, y0=0):\r\n #width = sigma*(2*np.sqrt(2*np.log(2)))\r\n b = 1/(sigma*np.sqrt(2*np.pi))\r\n f = b*np.power(np.e, -(((x-mu)**2)/(2*sigma**2)))\r\n return A*f + y0","def add_gaussian_noise(image, min_sigma, max_sigma):\r\n sigma = np.random.uniform(min_sigma, max_sigma)\r\n corrupted_im = image + np.random.normal(0, sigma, image.shape)\r\n rounded_im = (np.round(corrupted_im * MAX_PIXEL_NUMBER)) / MAX_PIXEL_NUMBER\r\n return np.clip(rounded_im, 0, 1).astype(np.float64)","def generate_gaussian():\n amp = 10 * numpy.random.chisquare(3)\n width = numpy.random.chisquare(3)\n mean = numpy.random.uniform(-10 + width, 10 - width)\n x = numpy.linspace(-10, 10, 500)\n y = amp * numpy.exp(- (x - mean) ** 2 / width ** 2)\n add_noise(y, 0.1)\n return x, y","def create_gaussian_filter(size, sigma):\n h = size[0] #height of the template\n w = size[1] #width of the template \n if h % 2 == 0: h += 1 #add 1 if dimensions are even\n if w % 2 == 0: w += 1\n x = math.floor(h/2)\n y = math.floor(w/2) \n sum = 0\n #create our template\n template = np.zeros((h,w))\n #fill the template in with the numbers from Gaussian distribution\n for i in range(h):\n for j in range(w):\n template[i,j] = math.exp(-((((j-x)**2)+((i-y)**2))/(2*(sigma**2))))\n sum = sum + template[i,j]\n #normalise the numbers\n gaussian_filter = template/sum\n return gaussian_filter","def makeGaussian(size, fwhm, center=None):\n\n x = sp.arange(0, size, 1, float)\n y = x[:,sp.newaxis]\n\n if center is None:\n x0 = y0 = size // 2\n else:\n x0 = center[0]\n y0 = center[1]\n\n return sp.exp(-4*sp.log(2) * ((x-x0)**2 + (y-y0)**2) / fwhm**2)","def gaussian_black(z, mu: 'normal' = 0, sigma: (0.4,1) = 0.7):\n return 1/(np.sqrt(2*np.pi)*sigma)*np.exp(-np.power((z - mu)/sigma, 2)/2)","def gaussian(mu, sigma, start, end):\r\n \r\n val = np.linspace(start, end, 100)\r\n a = 1/(sigma*np.pi)\r\n b = - 0.5 * np.power((mu - val)/sigma, 2)\r\n return a*np.exp(b)","def gauss_kern(size, sigma=1.0):\n h1 = size[0]\n h2 = size[1]\n x, y = np.mgrid[0:h2, 0:h1]\n x = x-h2/2\n y = y-h1/2\n g = np.exp( -( x**2 + y**2 ) / (2*sigma**2) );\n return g / g.sum()","def gaussianKernel(size, sigma=1):\n\n colourers.info(f'Creating gaussian kernel of size {size} with sigma of {sigma}')\n size = int(size) // 2\n x, y = np.mgrid[-size:size+1, -size:size+1]\n normal = 1 / (2.0 * np.pi * sigma**2)\n g = np.exp(-((x**2 + y**2) / (2.0 * sigma ** 2))) * normal\n return g","def Gaussian(x, mu, sigma, a):\n amplitude = a / ( sigma * np.sqrt(2 * np.pi) )\n u = (x - mu) / sigma\n return amplitude * np.exp( -0.5 * (u**2) )","def makeGaussian(size, fwhm=3, center=None):\n\n x = np.arange(0, size, 1, float)\n y = x[:, np.newaxis]\n\n if center is None:\n x0 = y0 = size // 2\n else:\n x0 = center[0]\n y0 = center[1]\n\n return np.exp(-4 * np.log(2) * ((x - x0)**2 + (y - y0)**2) / fwhm**2)","def makeGaussian(size, fwhm = 3, center=None):\n\n x = np.arange(0, size, 1, float)\n y = x[:,np.newaxis]\n\n if center is None:\n x0 = y0 = size // 2\n else:\n x0 = center[0]\n y0 = center[1]\n\n return np.exp(-4*np.log(2) * ((x-x0)**2 + (y-y0)**2) / fwhm**2)","def makeGaussian(size, fwhm = 3, center=None):\n\n x = np.arange(0, size, 1, float)\n y = x[:,np.newaxis]\n\n if center is None:\n x0 = y0 = size // 2\n else:\n x0 = center[0]\n y0 = center[1]\n\n return np.exp(-4*np.log(2) * ((x-x0)**2 + (y-y0)**2) / fwhm**2)","def gaussian(x, sigma):\n try: r = np.exp(-0.5*(x/sigma)**2) \n except: r = np.zeros(len(x))\n return r","def set_gaussian(self, X, sigma=0):\n sigma = float(sigma)\n if sigma < 0:\n raise ValueError('sigma should be positive')\n self.set_euclidian(X)\n d = self.weights\n\n if sigma == 0:\n sigma = (d ** 2).mean()\n\n w = np.exp(- (d ** 2) / (2 * sigma))\n self.weights = w","def gaussianPSF(shape, sigma):\n psf = dg.drawGaussiansXY(shape,\n numpy.array([0.5*shape[0]]),\n numpy.array([0.5*shape[1]]),\n sigma = sigma)\n return psf/numpy.sum(psf)","def makeGaussianKernel(sigma: float) -> np.ndarray:\n\n # Your code here.\n kernel_size = 8*sigma+1\n kernel = np.zeros([kernel_size,kernel_size], dtype=float)\n center = kernel_size//2\n \n \n s = 2*(sigma**2)\n sum_val = 0\n for i in range(0,kernel_size):\n for j in range(0,kernel_size):\n x = i-center\n y = j-center\n kernel[i,j] = np.exp(-(x**2+y**2) / s)\n sum_val += kernel[i,j]\n #/(np.pi * s)\n sum_val = 1/sum_val\n print(\"here is the kernel\", kernel*sum_val)\n return kernel*sum_val","def _gaussian(self, c, sigma):\n d = 2*sigma*sigma\n ax = exp(-power(self._xx-self._xx.T[c], 2)/d)\n ay = exp(-power(self._yy-self._yy.T[c], 2)/d)\n return (ax * ay).T # the external product gives a matrix","def gaussian_kernel(training_ex, landmark, sigma=0.1):\n return np.exp(-(np.linalg.norm(training_ex - landmark) ** 2 / (2 * (sigma ** 2))))","def _FWHMGauss(sigma, pixel=12):\n return sigma*2*np.sqrt(2*np.log(2))*pixel","def Gaussian(x,t,sigma):\n return np.exp(-(x-t)**2/(2*sigma**2))","def gaussian(centre, k, intensity, xpos):\r\n\treturn intensity * np.exp(- np.power(k * (xpos - centre), 2))","def GaussianKernel(shape=(3, 3), sigma=0.5):\r\n radius_x, radius_y = [(radius-1.)/2. for radius in shape]\r\n y_range, x_range = np.ogrid[-radius_y:radius_y+1, -radius_x:radius_x+1]\r\n h = np.exp(- (x_range*x_range + y_range*y_range) / (2.*sigma*sigma))\r\n h[h < np.finfo(h.dtype).eps*h.max()] = 0\r\n sumofh = h.sum()\r\n if sumofh != 0:\r\n h /= sumofh\r\n return h","def make_gaussian(size, fwhm=3, center=None):\n\n x = np.arange(0, size, 1, float)\n y = x[:, np.newaxis]\n\n if center is None:\n x0 = y0 = size // 2\n else:\n x0 = center[0]\n y0 = center[1]\n\n k = np.exp(-4 * np.log(2) * ((x - x0) ** 2 + (y - y0) ** 2) / fwhm ** 2)\n return k / np.sum(k)","def random_gaussian(img, mu=0.0, sigma=4.0):\n\n out = np.copy(img.astype(np.float))\n rows, cols, depth = img.shape\n noise = np.random.normal(mu, sigma, (rows, cols))\n for dim in range(depth):\n out[:, :, dim] = img[:, :, dim] + noise\n out[out > 255] = 255\n out[out < 0] = 0\n out = out.astype(np.uint8)\n\n return out","def gaussianBlur(img,ksize=(5,5),sigma=10):\n #kernel = cv2.getGaussianKernel(ksize,sigma)\n dst = np.zeros_like(img)\n cv2.GaussianBlur(src=img,dst=dst,ksize=ksize,sigmaX=0)\n return dst","def gaussian(t, params):\n DeprecationWarning(\"Using standard width. Better use gaussian_sigma.\")\n params['sigma'] = Qty(\n value=params['t_final'].get_value()/6,\n min_val=params['t_final'].get_value()/8,\n max_val=params['t_final'].get_value()/4,\n unit=params['t_final'].unit\n )\n return gaussian_sigma(t, params)","def generate_gaussian_kernel(shape=(3,3),sigma=0.8):\n m,n = [(ss-1.)/2. for ss in shape]\n y,x = np.ogrid[-m:m+1,-n:n+1]\n h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\n sumh = h.sum()\n if sumh != 0:\n h /= sumh\n return h","def gaussian(height, center_x, center_y, width_x, width_y):\n width_x = float(width_x)\n width_y = float(width_y)\n return lambda x,y: height*np.exp(\n -(((center_x-x)/width_x)**2+((center_y-y)/width_y)**2)/2)","def gaussian(height, center_x, center_y, width_x, width_y):\n width_x = float(width_x)\n width_y = float(width_y)\n return lambda x,y: height*np.exp(\n -(((center_x-x)/width_x)**2+((center_y-y)/width_y)**2)/2)","def gaussian(height, center_x, center_y, width_x, width_y):\n width_x = float(width_x)\n width_y = float(width_y)\n return lambda x,y: height*np.exp(\n -(((center_x-x)/width_x)**2+((center_y-y)/width_y)**2)/2)","def calculateGaussian(x, mean, stdev):\n\t\t\texponent = math.exp(-(math.pow(x-mean,2)/(2*math.pow(stdev,2))))\n\t\t\tvalue= (1 / (math.sqrt(2*math.pi) * stdev)) * exponent\n\t\t\tif value==0:\n\t\t\t\treturn np.nan\n\t\t\telse:\n\t\t\t\treturn math.log(value)","def gaussian_kernel(sigma, truncate=4.0):\n\n sigma = float(sigma)\n radius = int(truncate * sigma + 0.5)\n\n x, y = np.mgrid[-radius:radius + 1, -radius:radius + 1]\n sigma = sigma**2\n\n k = 2 * np.exp(-0.5 * (x**2 + y**2) / sigma)\n k = k / np.sum(k)\n\n return k","def gaussian(height, center_x, center_y, width_x, width_y):\n width_x = float(width_x)\n width_y = float(width_y)\n return lambda x,y: height*exp(\n -(((center_x-x)/width_x)**2+((center_y-y)/width_y)**2)/2)","def Gaussian(x, t, sigma):\n return np.exp(-(x - t)**2 / (2 * sigma**2))","def gaussian(z, mean=0, stdev=None, sigma=1):\n sigma = stdev if stdev is not None else sigma\n norm = stats.norm(loc=mean, scale=sigma)\n pdf = norm.pdf(z, loc=mean, scale=sigma)\n return z, pdf","def _generate_gaussian_kernel(self, size: int, sigma: float = 1.0, mu: float = 0.0) -> ndarray:\n # create the 1D array of equally spaced distance point of given size\n self.kernel_1d = np.linspace(-(size//2), size//2, size)\n # get the gaussian distribution of the 1D array\n self.kernel_1d = self._gaussian_distribution(\n self.kernel_1d, mu, sigma)\n\n # Compute the outer product of kernel1D tranpose and kernel1D\n self.kernel_2d = np.outer(self.kernel_1d.T, self.kernel_1d)\n # normalize the the outer product to suish the values between 0.0-1.0\n self.kernel_2d *= 1.0/self.kernel_2d.max()\n return self.kernel_2d","def gaussian(height, center_x, center_y, width_x, width_y):\n width_x = float(width_x)\n width_y = float(width_y)\n return lambda x,y: height*np.exp(-(((center_x-x)/width_x)**2+((center_y-y)/width_y)**2)/2)","def gaussian_filter(size,sigma=-1):\n\n if sigma == -1:\n sigma = np.sqrt(size)\n\n filter = np.zeros((size,size))\n\n for i,j in it.product(range(size),range(size)):\n x = j-size//2\n y = i-size//2\n filter[i,j] = 1/(2*np.pi*sigma**2) * np.exp(-(x**2+y**2)/(2*sigma**2))\n\n filter = filter/filter[0,0]\n filter = filter/filter.sum()\n\n return filter","def gaussian_kernel(size, sigma): \n \n kernel = np.zeros((size, size))\n\n #####################################\n # START YOUR CODE HERE #\n #####################################\n k = (size - 1) / 2\n sigma_sq = sigma ** 2\n pi_sigma = 1/(2 * np.pi * sigma_sq)\n for i in range(size):\n for j in range(size):\n kernel[i, j] = pi_sigma * np.exp(-0.5 * ((i-k)**2 + (j-k)**2) / (sigma_sq))\n ######################################\n # END OF YOUR CODE #\n ######################################\n\n return kernel","def normal_pmf(x: np.array, mean: float, sigma: float) -> np.array:\n x = np.exp(-1 / 2 * ((x - mean) / sigma) ** 2)\n x /= np.sqrt(2 * np.pi * sigma ** 2)\n x /= x.sum()\n return x","def Gaussiankernel(size, sigma=1): \n size = int(size) // 2\n # create x grid and y grid\n x, y = np.mgrid[-size:size+1, -size:size+1] \n # gaussian distribution formula\n normal = 1 / np.sqrt(2.0 * np.pi * sigma**2)\n g = np.exp(-((x**2 + y**2) / (2.0*sigma**2))) * normal\n \n return g/g.sum()","def gaussian_k(x0, y0, sigma, height, width):\n y = np.arange(0, width, 1, float)\n x = np.arange(0, height, 1, float)[:, np.newaxis]\n return np.exp(-((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2))","def gaussian2d(x, amplitude=1.0, center_x=0.0, sigma_x=1.0, center_y=0.0, sigma_y=1.0, rota=0.0):\n \n if len(x) == 1:\n y = x\n else:\n (x, y) = x\n \n if not sigma_y:\n sigma_y = sigma_x\n \n if not center_y:\n center_y = center_x\n \n if rota:\n center_x = center_x*np.cos(np.deg2rad(rota)) - center_y*np.sin(np.deg2rad(rota))\n center_y = center_x*np.sin(np.deg2rad(rota)) + center_y*np.cos(np.deg2rad(rota)) \n \n x = x*np.cos(np.deg2rad(rota)) - y*np.sin(np.deg2rad(rota))\n y = x*np.sin(np.deg2rad(rota)) + y*np.cos(np.deg2rad(rota))\n \n norm = 2.*np.pi*sigma_x*sigma_y\n #exp_x = np.power((x - center_x)/(sigma_x), 2.)\n #exp_y = np.power((y - center_y)/(sigma_y), 2.)\n g = amplitude*np.exp(-(((center_x - x)/sigma_x)**2 + \\\n ((center_y - y)/sigma_y)**2)/2.)\n \n return g #(amplitude/norm)*np.exp(-(exp_x + exp_y)/2.)","def get_gauss_kernel(sigma, samples):\n p = ny.ceil (2*ny.sqrt(2*ny.log(2))*sigma)\n r = ny.linspace(-p, p, samples)\n x,y = ny.meshgrid(r, r)\n b=bivariate_normal(x,y,sigma,sigma)\n A=(1/ny.sum(b))\n B=A*b\n return x,y,B","def gaussian_white(z, mu: 'normal' = 0, sigma: (0.4, 1) = 0.7):\n return 1 - gaussian_black(z, mu, sigma)","def gaussian_blurring(self,input_image,kernel_size,sigma):\n #Applying Gaussian Blur filter\n output_image=cv2.GaussianBlur(input_image,kernel_size,sigma)\n return output_image","def make_gaussian(shape, var):\n h,w = shape\n x = np.arange(w, dtype=float)\n y = np.arange(h, dtype=float)[:,np.newaxis]\n x0 = w // 2\n y0 = h // 2\n mat = np.exp(-0.5 * (pow(x-x0, 2) + pow(y-y0, 2)) / var)\n normalized_img = np.zeros((h, w))\n cv2.normalize(mat, normalized_img, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX)\n return normalized_img","def apply_gaussian(X, sigma):\n return np.array([ndimage.gaussian_filter(x, sigma) for x in X])","def initiategaussian(sd, x0):\n y = np.exp(-x**2/(2*sd**2))\n return y","def gaussian(\n x: float,\n measurement: 'mstats.ValueUncertainty',\n max_sigma: float = 10.0\n) -> float:\n center = measurement.value\n width = measurement.uncertainty\n width = max(width, 1e-6)\n\n if x <= (center - max_sigma * width) or x >= (center + max_sigma * width):\n # Don't calculate values outside a \"reasonable\" 10 sigma range\n return 0.0\n\n coefficient = 1 / math.sqrt(2.0 * math.pi * width * width)\n exponent = -0.5 * ((float(x) - center) ** 2) / (width * width)\n\n return coefficient * math.exp(exponent)","def gaussian(x, *parameters):\n position, sigma, amplitude, background = parameters\n return amplitude * np.exp(-(x - position)**2 / (2.0 * sigma**2)) + background","def get_sigma_psf(self):\n pass","def MVgaussian(size,mu1=0,mu2=0, sigma1=3,sigma2 = 1):\n kernel = np.zeros((size, size), dtype=np.float32)\n \n size = int(size) // 2\n X = np.arange(-size,size+1)\n Y = np.arange(-size,size+1)\n \n for x in X:\n for y in Y:\n Gx = np.exp(-((x-mu1)**2)/(2*(sigma1**2)))\n Gy = np.exp(-((y-mu2)**2)/(2*(sigma2**2)))\n Gx = math.exp(-(math.pow(x-mu1,2))/(2*math.pow(sigma1,2)))\n Gy = math.exp(-(math.pow(y-mu2,2))/(2*math.pow(sigma2,2)))\n kernel[x+size,y+size] = Gx*Gy\n return kernel","def gaussian_kernel(size, sigma):\n\n m, n = [(s - 1.) / 2. for s in size]\n y, x = np.ogrid[-m:m+1, -n:n+1]\n h = np.exp(-(x*x + y*y) / (2. * sigma * sigma))\n h[h < np.finfo(h.dtype).eps*h.max()] = 0\n sumh = h.sum()\n if sumh != 0: h /= sumh\n return h","def model_gauss_noise(sigma, nx, ny=1, nz=1):\n\te = EMData()\n\te.set_size(nx, ny, nz)\n\te.process_inplace(\"testimage.noise.gauss\", {\"sigma\":sigma})\n\treturn e","def gaussian(window_size, sigma):\n gauss = torch.Tensor([math.exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])\n return gauss/gauss.sum()","def gauss_kern(sigma,h):\n h1 = h\n h2 = h\n x, y = np.mgrid[0:h2, 0:h1]\n x = x-h2/2\n y = y-h1/2\n # sigma = 10.0\n g = np.exp( -( x**2 + y**2 ) / (2*sigma**2) )\n return g / g.sum()","def sigmanorm(y):\n y = y.copy()\n y -= y.mean() # set to zero mean\n y /= y.std() # rescale to units of sigma\n return y","def add_gaussian(self, high, center, stdev):\n if callable(high):\n high = high()\n assert stdev > 0\n self.Y += high * np.exp(-0.5 * (self.Xnum-center)**2 / stdev**2)","def _FSpecialGauss(size, sigma):\n radius = size // 2\n offset = 0.0\n start, stop = -radius, radius + 1\n if size % 2 == 0:\n offset = 0.5\n stop -= 1\n x, y = np.mgrid[offset + start:stop, offset + start:stop]\n assert len(x) == size\n g = np.exp(-((x**2 + y**2)/(2.0 * sigma**2)))\n return g / g.sum()","def gaussian_kernel_2d(mean, std_inv, size):\n if type(mean) is torch.Tensor:\n device = mean.device\n elif type(std_inv) is torch.Tensor:\n device = std_inv.device\n else:\n device = \"cpu\"\n\n # repeat the size for width, height if single number\n if isinstance(size, numbers.Number):\n width = height = size\n else:\n width, height = size\n\n # expand std to (2, 2) matrix\n if isinstance(std_inv, numbers.Number):\n std_inv = torch.tensor([[std_inv, 0], [0, std_inv]], device=device)\n elif std_inv.dim() == 0:\n std_inv = torch.diag(std_inv.repeat(2))\n elif std_inv.dim() == 1:\n assert len(std_inv) == 2\n std_inv = torch.diag(std_inv)\n\n # Enforce PSD of covariance matrix\n covariance_inv = std_inv.transpose(0, 1) @ std_inv\n covariance_inv = covariance_inv.float()\n\n # make a grid (width, height, 2)\n X = torch.cat(\n [\n t.unsqueeze(-1)\n for t in reversed(\n torch.meshgrid(\n [torch.arange(s, device=device) for s in [width, height]]\n )\n )\n ],\n dim=-1,\n )\n X = X.float()\n\n # center the gaussian in (0, 0) and then shift to mean\n X -= torch.tensor([(width - 1) / 2, (height - 1) / 2], device=device).float()\n X -= mean.float()\n\n # does not use the normalize constant of gaussian distribution\n Y = torch.exp((-1 / 2) * torch.einsum(\"xyi,ij,xyj->xy\", [X, covariance_inv, X]))\n\n # normalize\n # TODO could compute the correct normalization (1/2pi det ...)\n # and send warning if there is a significant diff\n # -> part of the gaussian is outside the kernel\n Z = Y / Y.sum()\n return Z","def gaussian_template(\n wavelengths: np.ndarray,\n mean: Union[float, np.ndarray],\n std: Union[float, np.ndarray] = 30.0,\n) -> np.ndarray:\n y = norm.pdf(wavelengths, mean, std)\n return y / np.max(y, axis=-1, keepdims=True)","def gaussian(mu, wid, x):\n return np.exp(-((x - mu) / (0.6005612 * wid))**2)","def gaussian_kernel(size, sigma):\n\n kernel = np.zeros((size, size))\n\n ### YOUR CODE HERE\n k = (size-1)/2\n factor = 1/(2*np.pi*sigma**2)\n for i in range(size):\n for j in range(size):\n exponent = -((i-k)**2 +(j-k)**2)/(2*sigma**2)\n kernel[i,j] = factor*np.exp(exponent)\n ### END YOUR CODE\n\n return kernel","def gaussian_filter(shape=(3,3),sigma=0.5):\n m,n = [(ss-1.)/2. for ss in shape]\n y,x = np.ogrid[-m:m+1,-n:n+1]\n h = np.exp( -(x*x + y*y) / (2.*sigma*sigma))\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\n sumh = h.sum()\n if sumh != 0:\n h /= sumh\n return h","def random_gaussian_noise(image ):\n sigma = image.std()\n for ch in range(image.shape[2] ):\n sigma = min(sigma, image[:,:,ch].std() )\n image = random_noise(image, var = sigma**2)\n return image","def matlab_style_gauss2D(shape=(3,3),sigma=0.5):\n m,n = [(ss-1.)/2. for ss in shape]\n y,x = np.ogrid[-m:m+1,-n:n+1]\n h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\n sumh = h.sum()\n if sumh != 0:\n h /= sumh\n return h","def matlab_style_gauss2D(shape=(3,3),sigma=0.5):\n m,n = [(ss-1.)/2. for ss in shape]\n y,x = np.ogrid[-m:m+1,-n:n+1]\n h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\n sumh = h.sum()\n if sumh != 0:\n h /= sumh\n return h","def uni_gaussian(X, mu, sigma2):\n p = (1 / sqrt(2 * pi * sigma2))\n p = p * exp(-power(X - mu, 2) / (2 * sigma2))\n\n def prod(x, y):\n return x * y\n p = array([[reduce(prod, el)] for el in p])\n\n return p","def GaussianKernel(radius, std):\n size = 2 * radius + 1\n weight = torch.ones(size, size)\n weight.requires_grad = False\n for i in range(-radius, radius+1):\n for j in range(-radius, radius+1):\n dis = (i * i) + (j * j)\n weight[i+radius][j+radius] = np.exp(-dis / (2 * std * std))\n weight = weight / weight.sum()\n return weight","def kernel_gaussiano(image: np.ndarray, sigma: float, kind: str = 'low') -> np.ndarray:\n U, V = fourier_meshgrid(image)\n D = fourier_distance(U, V)\n H = np.exp( (-1.0 * D) / (2.0 * sigma**2) )\n \n if kind == 'high' or kind == 'highpass':\n H = 1.0 - H\n \n return H","def get_mask_gauss(mask_dist_src, sigma):\n return P.Exp()(-2.772588722 * (mask_dist_src ** 2) / (sigma ** 2))","def gauss_kernels(size, sigma=1.0):\n if size < 3:\n size = 3\n\n m = size / 2\n x, y = np.mgrid[-m:m + 1, -m:m + 1]\n kernel = np.exp(-(x * x + y * y) / (2 * sigma * sigma))\n kernel_sum = kernel.sum()\n\n if not sum == 0:\n kernel = kernel / kernel_sum\n\n return kernel","def fspecial_gaussian(shape=(3, 3), sigma=0.5):\n m, n = [(ss - 1.) / 2. for ss in shape]\n y, x = np.ogrid[-m:m + 1, -n:n + 1]\n h = np.exp(-(x * x + y * y) / (2. * sigma * sigma))\n h[h < np.finfo(h.dtype).eps * h.max()] = 0\n sumh = h.sum()\n if sumh != 0:\n h /= sumh\n return h","def flatten(img,sigma=20.) :\n\n for i in range(img.shape[0]) :\n img[i] /= np.median(img[i])\n for i in range(img.shape[1]) :\n img[:,i] /= np.median(img[:,i])\n\n hw=int(3*sigma)\n u=np.linspace(-hw,hw,2*hw+1)\n x=np.tile(u,(2*hw+1,1))\n y=x.T\n k=np.exp(-x**2/2/sigma**2-y**2/2/sigma**2)\n k /= np.sum(k)\n smooth=convolve2d(img,k,weight=None)\n img /= smooth\n\n return img","def _calc_sigma(self):\n if self.data is None:\n self._initial_blur()\n previous = self.init_sigma\n incr = 0\n self.sigmas = [(previous, incr)]\n for i in range(1, self.scale_per_octave + 3):\n sigma_abs = self.init_sigma * (self.dest_sigma / self.init_sigma) ** (1.0 * i / (self.scale_per_octave))\n increase = previous * sqrt((self.dest_sigma / self.init_sigma) ** (2.0 / self.scale_per_octave) - 1.0)\n self.sigmas.append((sigma_abs, increase))\n previous = sigma_abs\n logger.debug(\"Sigma= %s\" % self.sigmas)","def create_gaussian_array(self):\n\n # Fill array of size l x w with Gaussian Noise.\n terrain_length = int(ceil(self.length/self.resolution))\n terrain_width = int(ceil(self.width/self.resolution))\n gaussian_array = np.random.normal(self.mu, self.sigma, (terrain_length,terrain_width))\n\n # Filter the array to smoothen the variation of the noise\n gaussian_array = gaussian_filter(gaussian_array, self.sigma_filter)\n\n return gaussian_array","def gaussian(self, amp_step, sigma_step):\n l = len(self.overlaid_x_axis)\n x = np.linspace(0, l, l) - l/2 # centre of data\n\n # This is new code to 'guess' the size of the Gaussian from the\n # existing data rather than from hard-coded numbers.\n # TODO: test this! Possibly link up to the get_windowed_data function\n # as it uses a lot of the same functionality\n trigger = self.pv_monitor.arrays[self.controls.Arrays.WAVEFORMS][0]\n trace = self.pv_monitor.arrays[self.controls.Arrays.WAVEFORMS][1]\n amplitude = max(trace) + amp_step\n diff = np.diff(trigger)\n stepvalue = 0.5\n if min(diff) > -1 * stepvalue or max(diff) < stepvalue:\n raise RangeError\n else:\n maxtrig = next(x for x in diff if x > stepvalue)\n mintrig = next(x for x in diff if x < -1 * stepvalue)\n edges = [np.where(diff == maxtrig)[0][0],\n np.where(diff == mintrig)[0][0]]\n half_trigger_length = (edges[1]-edges[0])\n sigma = half_trigger_length/4 + sigma_step\n\n gauss = self.ax2.plot(amplitude * np.exp(-x**2 / (2 * sigma**2)), 'r')\n self.overlaid_lines.append(gauss)\n self.draw()","def multi_gaussian(X, mu, sigma):\n m, n = X.shape\n X = X - mu\n\n factor = X.dot(inv(sigma))\n factor = multiply(factor, X)\n factor = - (1 / 2) * sum(factor, axis=1, keepdims=True)\n\n p = 1 / (power(2 * pi, n / 2) * sqrt(det(sigma)))\n p = p * exp(factor)\n\n return p","def _sigma_1(gam, eps):\n s1 = 4 * r0**2 * alpha / eps / mec2_unit\n s2 = 1 + (1./3. - eps/gam) * (1 - eps/gam)\n s3 = np.log(2 * gam * (gam - eps) / eps) - 1./2.\n s3[np.where(gam < eps)] = 0.0\n return s1 * s2 * s3","def gaussian_kernel(sigma, bin_width, truncate=10):\n\n nbins = sigma * truncate // bin_width\n bins = np.arange(-bin_width * nbins, bin_width * (nbins + 1), bin_width)\n return np.exp(-0.5 * bins**2 / (sigma**2)) * 1 / sigma / np.sqrt(2 * np.pi)","def gauss_2d(N, sigma = 0.25):\r\n x, y = make_xy(N)\r\n sigma_pixel = sigma * np.float(N)\r\n arrayout = np.exp(-(x**2 + y**2) / sigma_pixel**2) / (np.pi * sigma_pixel**2)\r\n return arrayout","def add_gaussian(self, high, center, stdev):\n indexes = dtloc2pos(center, self.X)\n\n if isinstance(stdev, timedelta):\n stdev = stdev.total_seconds() / self.step.total_seconds()\n elif not isinstance(stdev, (float, int)):\n raise TypeError(f\"Unknown type center {type(center)}\")\n\n for index in indexes:\n super().add_gaussian(\n high=high,\n center=index,\n stdev=stdev,\n )","def gaussian_sigma(t, params):\n t_final = tf.cast(params['t_final'].get_value(), dtype=tf.float64)\n sigma = tf.cast(params['sigma'].get_value(), dtype=tf.float64)\n gauss = tf.exp(-(t - t_final / 2) ** 2 / (2 * sigma ** 2))\n norm = (tf.sqrt(2 * np.pi * sigma ** 2)\n * tf.math.erf(t_final / (np.sqrt(8) * sigma))\n - t_final * tf.exp(-t_final ** 2 / (8 * sigma ** 2)))\n offset = tf.exp(-t_final ** 2 / (8 * sigma ** 2))\n return (gauss - offset) / norm","def make_DOG(inner_sigma, x):\n y = x\n outer_sigma = inner_sigma*5\n X, Y = np.meshgrid(x, y)\n inner_gaussian = 1./(2.*np.pi*inner_sigma) * np.exp(-(X**2 + Y**2)/2./inner_sigma**2) \n outer_gaussian = 1./(2.*np.pi*outer_sigma) * np.exp(-(X**2 + Y**2)/2./outer_sigma**2) \n return inner_gaussian - outer_gaussian/2 #weaker surround works better with our weights, which don't account for bursts ","def gaussian(var):\n stddev = np.sqrt(var)\n return stats.norm(0, stddev)","def get_sigma(self):\n\n if not hasattr(self, 'sigma_zw'):\n self.get_sigma_zw()\n if not hasattr(self, 'sigmaw'):\n self.get_sigmaw()\n if not hasattr(self, 'sigmaz'):\n self.get_sigmaz()\n out = np.hstack((\n np.vstack((self.sigma_w, self.sigma_zw.reshape((-1, 1)))),\n np.vstack((self.sigma_zw, self.sigma_z))\n ))\n return out","def norm(imagestack, mean, std):\n \n new_im = (imagestack - mean)/std \n \n return new_im"],"string":"[\n \"def make_gaussian(size, sigma=10, center=None):\\n\\n x = np.arange(0, size[1], 1, float)\\n y = np.arange(0, size[0], 1, float)\\n y = y[:, np.newaxis]\\n\\n if center is None:\\n x0 = y0 = size[0] // 2\\n else:\\n x0 = center[0]\\n y0 = center[1]\\n\\n return np.exp(-4 * np.log(2) * ((x - x0) ** 2 + (y - y0) ** 2) / sigma ** 2)\",\n \"def makeGaussian(height, width, sigma=3, center=None):\\n x = np.arange(0, width, 1, float)\\n y = np.arange(0, height, 1, float)[:, np.newaxis]\\n if center is None:\\n x0 = width // 2\\n y0 = height // 2\\n else:\\n x0 = center[0]\\n y0 = center[1]\\n\\n return np.exp(-4 * np.log(2) * ((x - x0) ** 2 + (y - y0) ** 2) / sigma ** 2)\",\n \"def gaussian(x, amplitude=1.0, center=0.0, sigma=1.0):\\n \\n return (amplitude/(np.sqrt(2.*np.pi)*sigma)) * exp(-np.power((1.0*x-center)/(sigma), 2.)/2.)\",\n \"def makeGaussian(size, fwhm, sigma, center=None):\\n\\n x = np.arange(0, size, 1, float)\\n y = x[:,np.newaxis]\\n\\n if center is None:\\n x0 = y0 = size // 2\\n else:\\n x0 = center[0]\\n y0 = center[1]\\n \\n #return (np.exp(-4*np.log(2) * ((x-x0)**2 + (y-y0)**2) / fwhm**2)) #approximation using exponenial functions\\n return ((1/(2*np.pi*sigma**2))*np.exp(-((xx)**2 + (yy)**2)/(2*sigma**2))) # symmetric 2D Gaussian distribution\",\n \"def gaussian(x, mean, sigma):\\n return np.exp(-np.square(x-mean)/(2*np.square(sigma))) / (np.sqrt(2*np.pi*sigma**2))\",\n \"def GaussianKernel(sigma: float = 1., width: int = 0):\\n assert not ((width is None or width == 0) and\\n (sigma is None or sigma == 0)), \\\\\\n \\\"GaussianKernel :: both sigma ({}) & width ({}) are not valid\\\".format(\\n sigma, width)\\n\\n if width is None or width == 0:\\n width = int(2.0 * 3.0 * sigma + 1.0)\\n if width % 2 == 0:\\n width += 1\\n\\n if sigma is None or sigma == 0:\\n sigma = (width - 1)/6.\\n half = width//2\\n x, y = np.meshgrid(np.linspace(-half, half, width),\\n np.linspace(-half, half, width), indexing='xy')\\n w = np.exp(- (x**2 + y**2) / (2.*(sigma**2)))\\n w /= np.sum(w)\\n return torch.from_numpy(w.astype(np.float32)).view(1, 1, width, width)\",\n \"def gauss_kern(sigma, size):\\r\\n size = int(np.floor(size/2))\\r\\n sizey = size\\r\\n x, y = scipy.mgrid[-size:size+1, -sizey:sizey+1]\\r\\n g = scipy.exp(-(x**2+y**2) / (2*(sigma)**2))\\r\\n return np.ravel(g / g.max())\",\n \"def gaussian(size,sigma):\\n a,b=np.ogrid[-size/2:size/2,-size/2:size/2]\\n mask = a**2+b**2\\n mask = np.exp(-mask.astype('float')/(2*float(sigma**2)))\\n return mask\",\n \"def _gaussian_distribution(self, x: ndarray, mu: float, sigma: float) -> ndarray:\\n return 1 / (np.sqrt(2 * np.pi) * sigma) * np.exp(\\n -np.power(\\n (x - mu) / sigma, 2) / 2)\",\n \"def gaussian(x, mu, sigma):\\n return (np.exp(-(x - mu)**2 / 2.0 / sigma**2) /\\n np.sqrt(2.0 * np.pi) / sigma)\",\n \"def add_gaussian_noise(image, min_sigma, max_sigma):\\n assert(max_sigma >= min_sigma)\\n sigma = np.random.uniform(min_sigma, max_sigma)\\n noise = np.random.normal(loc=0, scale=sigma, size=image.shape)\\n noisy_im = np.floor((image + noise) * 255) / 255\\n noisy_im = np.clip(noisy_im, 0, 1)\\n return noisy_im\",\n \"def Gaussian(x, mu=0, sigma=26.4, A=1, y0=0):\\r\\n #width = sigma*(2*np.sqrt(2*np.log(2)))\\r\\n b = 1/(sigma*np.sqrt(2*np.pi))\\r\\n f = b*np.power(np.e, -(((x-mu)**2)/(2*sigma**2)))\\r\\n return A*f + y0\",\n \"def add_gaussian_noise(image, min_sigma, max_sigma):\\r\\n sigma = np.random.uniform(min_sigma, max_sigma)\\r\\n corrupted_im = image + np.random.normal(0, sigma, image.shape)\\r\\n rounded_im = (np.round(corrupted_im * MAX_PIXEL_NUMBER)) / MAX_PIXEL_NUMBER\\r\\n return np.clip(rounded_im, 0, 1).astype(np.float64)\",\n \"def generate_gaussian():\\n amp = 10 * numpy.random.chisquare(3)\\n width = numpy.random.chisquare(3)\\n mean = numpy.random.uniform(-10 + width, 10 - width)\\n x = numpy.linspace(-10, 10, 500)\\n y = amp * numpy.exp(- (x - mean) ** 2 / width ** 2)\\n add_noise(y, 0.1)\\n return x, y\",\n \"def create_gaussian_filter(size, sigma):\\n h = size[0] #height of the template\\n w = size[1] #width of the template \\n if h % 2 == 0: h += 1 #add 1 if dimensions are even\\n if w % 2 == 0: w += 1\\n x = math.floor(h/2)\\n y = math.floor(w/2) \\n sum = 0\\n #create our template\\n template = np.zeros((h,w))\\n #fill the template in with the numbers from Gaussian distribution\\n for i in range(h):\\n for j in range(w):\\n template[i,j] = math.exp(-((((j-x)**2)+((i-y)**2))/(2*(sigma**2))))\\n sum = sum + template[i,j]\\n #normalise the numbers\\n gaussian_filter = template/sum\\n return gaussian_filter\",\n \"def makeGaussian(size, fwhm, center=None):\\n\\n x = sp.arange(0, size, 1, float)\\n y = x[:,sp.newaxis]\\n\\n if center is None:\\n x0 = y0 = size // 2\\n else:\\n x0 = center[0]\\n y0 = center[1]\\n\\n return sp.exp(-4*sp.log(2) * ((x-x0)**2 + (y-y0)**2) / fwhm**2)\",\n \"def gaussian_black(z, mu: 'normal' = 0, sigma: (0.4,1) = 0.7):\\n return 1/(np.sqrt(2*np.pi)*sigma)*np.exp(-np.power((z - mu)/sigma, 2)/2)\",\n \"def gaussian(mu, sigma, start, end):\\r\\n \\r\\n val = np.linspace(start, end, 100)\\r\\n a = 1/(sigma*np.pi)\\r\\n b = - 0.5 * np.power((mu - val)/sigma, 2)\\r\\n return a*np.exp(b)\",\n \"def gauss_kern(size, sigma=1.0):\\n h1 = size[0]\\n h2 = size[1]\\n x, y = np.mgrid[0:h2, 0:h1]\\n x = x-h2/2\\n y = y-h1/2\\n g = np.exp( -( x**2 + y**2 ) / (2*sigma**2) );\\n return g / g.sum()\",\n \"def gaussianKernel(size, sigma=1):\\n\\n colourers.info(f'Creating gaussian kernel of size {size} with sigma of {sigma}')\\n size = int(size) // 2\\n x, y = np.mgrid[-size:size+1, -size:size+1]\\n normal = 1 / (2.0 * np.pi * sigma**2)\\n g = np.exp(-((x**2 + y**2) / (2.0 * sigma ** 2))) * normal\\n return g\",\n \"def Gaussian(x, mu, sigma, a):\\n amplitude = a / ( sigma * np.sqrt(2 * np.pi) )\\n u = (x - mu) / sigma\\n return amplitude * np.exp( -0.5 * (u**2) )\",\n \"def makeGaussian(size, fwhm=3, center=None):\\n\\n x = np.arange(0, size, 1, float)\\n y = x[:, np.newaxis]\\n\\n if center is None:\\n x0 = y0 = size // 2\\n else:\\n x0 = center[0]\\n y0 = center[1]\\n\\n return np.exp(-4 * np.log(2) * ((x - x0)**2 + (y - y0)**2) / fwhm**2)\",\n \"def makeGaussian(size, fwhm = 3, center=None):\\n\\n x = np.arange(0, size, 1, float)\\n y = x[:,np.newaxis]\\n\\n if center is None:\\n x0 = y0 = size // 2\\n else:\\n x0 = center[0]\\n y0 = center[1]\\n\\n return np.exp(-4*np.log(2) * ((x-x0)**2 + (y-y0)**2) / fwhm**2)\",\n \"def makeGaussian(size, fwhm = 3, center=None):\\n\\n x = np.arange(0, size, 1, float)\\n y = x[:,np.newaxis]\\n\\n if center is None:\\n x0 = y0 = size // 2\\n else:\\n x0 = center[0]\\n y0 = center[1]\\n\\n return np.exp(-4*np.log(2) * ((x-x0)**2 + (y-y0)**2) / fwhm**2)\",\n \"def gaussian(x, sigma):\\n try: r = np.exp(-0.5*(x/sigma)**2) \\n except: r = np.zeros(len(x))\\n return r\",\n \"def set_gaussian(self, X, sigma=0):\\n sigma = float(sigma)\\n if sigma < 0:\\n raise ValueError('sigma should be positive')\\n self.set_euclidian(X)\\n d = self.weights\\n\\n if sigma == 0:\\n sigma = (d ** 2).mean()\\n\\n w = np.exp(- (d ** 2) / (2 * sigma))\\n self.weights = w\",\n \"def gaussianPSF(shape, sigma):\\n psf = dg.drawGaussiansXY(shape,\\n numpy.array([0.5*shape[0]]),\\n numpy.array([0.5*shape[1]]),\\n sigma = sigma)\\n return psf/numpy.sum(psf)\",\n \"def makeGaussianKernel(sigma: float) -> np.ndarray:\\n\\n # Your code here.\\n kernel_size = 8*sigma+1\\n kernel = np.zeros([kernel_size,kernel_size], dtype=float)\\n center = kernel_size//2\\n \\n \\n s = 2*(sigma**2)\\n sum_val = 0\\n for i in range(0,kernel_size):\\n for j in range(0,kernel_size):\\n x = i-center\\n y = j-center\\n kernel[i,j] = np.exp(-(x**2+y**2) / s)\\n sum_val += kernel[i,j]\\n #/(np.pi * s)\\n sum_val = 1/sum_val\\n print(\\\"here is the kernel\\\", kernel*sum_val)\\n return kernel*sum_val\",\n \"def _gaussian(self, c, sigma):\\n d = 2*sigma*sigma\\n ax = exp(-power(self._xx-self._xx.T[c], 2)/d)\\n ay = exp(-power(self._yy-self._yy.T[c], 2)/d)\\n return (ax * ay).T # the external product gives a matrix\",\n \"def gaussian_kernel(training_ex, landmark, sigma=0.1):\\n return np.exp(-(np.linalg.norm(training_ex - landmark) ** 2 / (2 * (sigma ** 2))))\",\n \"def _FWHMGauss(sigma, pixel=12):\\n return sigma*2*np.sqrt(2*np.log(2))*pixel\",\n \"def Gaussian(x,t,sigma):\\n return np.exp(-(x-t)**2/(2*sigma**2))\",\n \"def gaussian(centre, k, intensity, xpos):\\r\\n\\treturn intensity * np.exp(- np.power(k * (xpos - centre), 2))\",\n \"def GaussianKernel(shape=(3, 3), sigma=0.5):\\r\\n radius_x, radius_y = [(radius-1.)/2. for radius in shape]\\r\\n y_range, x_range = np.ogrid[-radius_y:radius_y+1, -radius_x:radius_x+1]\\r\\n h = np.exp(- (x_range*x_range + y_range*y_range) / (2.*sigma*sigma))\\r\\n h[h < np.finfo(h.dtype).eps*h.max()] = 0\\r\\n sumofh = h.sum()\\r\\n if sumofh != 0:\\r\\n h /= sumofh\\r\\n return h\",\n \"def make_gaussian(size, fwhm=3, center=None):\\n\\n x = np.arange(0, size, 1, float)\\n y = x[:, np.newaxis]\\n\\n if center is None:\\n x0 = y0 = size // 2\\n else:\\n x0 = center[0]\\n y0 = center[1]\\n\\n k = np.exp(-4 * np.log(2) * ((x - x0) ** 2 + (y - y0) ** 2) / fwhm ** 2)\\n return k / np.sum(k)\",\n \"def random_gaussian(img, mu=0.0, sigma=4.0):\\n\\n out = np.copy(img.astype(np.float))\\n rows, cols, depth = img.shape\\n noise = np.random.normal(mu, sigma, (rows, cols))\\n for dim in range(depth):\\n out[:, :, dim] = img[:, :, dim] + noise\\n out[out > 255] = 255\\n out[out < 0] = 0\\n out = out.astype(np.uint8)\\n\\n return out\",\n \"def gaussianBlur(img,ksize=(5,5),sigma=10):\\n #kernel = cv2.getGaussianKernel(ksize,sigma)\\n dst = np.zeros_like(img)\\n cv2.GaussianBlur(src=img,dst=dst,ksize=ksize,sigmaX=0)\\n return dst\",\n \"def gaussian(t, params):\\n DeprecationWarning(\\\"Using standard width. Better use gaussian_sigma.\\\")\\n params['sigma'] = Qty(\\n value=params['t_final'].get_value()/6,\\n min_val=params['t_final'].get_value()/8,\\n max_val=params['t_final'].get_value()/4,\\n unit=params['t_final'].unit\\n )\\n return gaussian_sigma(t, params)\",\n \"def generate_gaussian_kernel(shape=(3,3),sigma=0.8):\\n m,n = [(ss-1.)/2. for ss in shape]\\n y,x = np.ogrid[-m:m+1,-n:n+1]\\n h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )\\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\\n sumh = h.sum()\\n if sumh != 0:\\n h /= sumh\\n return h\",\n \"def gaussian(height, center_x, center_y, width_x, width_y):\\n width_x = float(width_x)\\n width_y = float(width_y)\\n return lambda x,y: height*np.exp(\\n -(((center_x-x)/width_x)**2+((center_y-y)/width_y)**2)/2)\",\n \"def gaussian(height, center_x, center_y, width_x, width_y):\\n width_x = float(width_x)\\n width_y = float(width_y)\\n return lambda x,y: height*np.exp(\\n -(((center_x-x)/width_x)**2+((center_y-y)/width_y)**2)/2)\",\n \"def gaussian(height, center_x, center_y, width_x, width_y):\\n width_x = float(width_x)\\n width_y = float(width_y)\\n return lambda x,y: height*np.exp(\\n -(((center_x-x)/width_x)**2+((center_y-y)/width_y)**2)/2)\",\n \"def calculateGaussian(x, mean, stdev):\\n\\t\\t\\texponent = math.exp(-(math.pow(x-mean,2)/(2*math.pow(stdev,2))))\\n\\t\\t\\tvalue= (1 / (math.sqrt(2*math.pi) * stdev)) * exponent\\n\\t\\t\\tif value==0:\\n\\t\\t\\t\\treturn np.nan\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn math.log(value)\",\n \"def gaussian_kernel(sigma, truncate=4.0):\\n\\n sigma = float(sigma)\\n radius = int(truncate * sigma + 0.5)\\n\\n x, y = np.mgrid[-radius:radius + 1, -radius:radius + 1]\\n sigma = sigma**2\\n\\n k = 2 * np.exp(-0.5 * (x**2 + y**2) / sigma)\\n k = k / np.sum(k)\\n\\n return k\",\n \"def gaussian(height, center_x, center_y, width_x, width_y):\\n width_x = float(width_x)\\n width_y = float(width_y)\\n return lambda x,y: height*exp(\\n -(((center_x-x)/width_x)**2+((center_y-y)/width_y)**2)/2)\",\n \"def Gaussian(x, t, sigma):\\n return np.exp(-(x - t)**2 / (2 * sigma**2))\",\n \"def gaussian(z, mean=0, stdev=None, sigma=1):\\n sigma = stdev if stdev is not None else sigma\\n norm = stats.norm(loc=mean, scale=sigma)\\n pdf = norm.pdf(z, loc=mean, scale=sigma)\\n return z, pdf\",\n \"def _generate_gaussian_kernel(self, size: int, sigma: float = 1.0, mu: float = 0.0) -> ndarray:\\n # create the 1D array of equally spaced distance point of given size\\n self.kernel_1d = np.linspace(-(size//2), size//2, size)\\n # get the gaussian distribution of the 1D array\\n self.kernel_1d = self._gaussian_distribution(\\n self.kernel_1d, mu, sigma)\\n\\n # Compute the outer product of kernel1D tranpose and kernel1D\\n self.kernel_2d = np.outer(self.kernel_1d.T, self.kernel_1d)\\n # normalize the the outer product to suish the values between 0.0-1.0\\n self.kernel_2d *= 1.0/self.kernel_2d.max()\\n return self.kernel_2d\",\n \"def gaussian(height, center_x, center_y, width_x, width_y):\\n width_x = float(width_x)\\n width_y = float(width_y)\\n return lambda x,y: height*np.exp(-(((center_x-x)/width_x)**2+((center_y-y)/width_y)**2)/2)\",\n \"def gaussian_filter(size,sigma=-1):\\n\\n if sigma == -1:\\n sigma = np.sqrt(size)\\n\\n filter = np.zeros((size,size))\\n\\n for i,j in it.product(range(size),range(size)):\\n x = j-size//2\\n y = i-size//2\\n filter[i,j] = 1/(2*np.pi*sigma**2) * np.exp(-(x**2+y**2)/(2*sigma**2))\\n\\n filter = filter/filter[0,0]\\n filter = filter/filter.sum()\\n\\n return filter\",\n \"def gaussian_kernel(size, sigma): \\n \\n kernel = np.zeros((size, size))\\n\\n #####################################\\n # START YOUR CODE HERE #\\n #####################################\\n k = (size - 1) / 2\\n sigma_sq = sigma ** 2\\n pi_sigma = 1/(2 * np.pi * sigma_sq)\\n for i in range(size):\\n for j in range(size):\\n kernel[i, j] = pi_sigma * np.exp(-0.5 * ((i-k)**2 + (j-k)**2) / (sigma_sq))\\n ######################################\\n # END OF YOUR CODE #\\n ######################################\\n\\n return kernel\",\n \"def normal_pmf(x: np.array, mean: float, sigma: float) -> np.array:\\n x = np.exp(-1 / 2 * ((x - mean) / sigma) ** 2)\\n x /= np.sqrt(2 * np.pi * sigma ** 2)\\n x /= x.sum()\\n return x\",\n \"def Gaussiankernel(size, sigma=1): \\n size = int(size) // 2\\n # create x grid and y grid\\n x, y = np.mgrid[-size:size+1, -size:size+1] \\n # gaussian distribution formula\\n normal = 1 / np.sqrt(2.0 * np.pi * sigma**2)\\n g = np.exp(-((x**2 + y**2) / (2.0*sigma**2))) * normal\\n \\n return g/g.sum()\",\n \"def gaussian_k(x0, y0, sigma, height, width):\\n y = np.arange(0, width, 1, float)\\n x = np.arange(0, height, 1, float)[:, np.newaxis]\\n return np.exp(-((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2))\",\n \"def gaussian2d(x, amplitude=1.0, center_x=0.0, sigma_x=1.0, center_y=0.0, sigma_y=1.0, rota=0.0):\\n \\n if len(x) == 1:\\n y = x\\n else:\\n (x, y) = x\\n \\n if not sigma_y:\\n sigma_y = sigma_x\\n \\n if not center_y:\\n center_y = center_x\\n \\n if rota:\\n center_x = center_x*np.cos(np.deg2rad(rota)) - center_y*np.sin(np.deg2rad(rota))\\n center_y = center_x*np.sin(np.deg2rad(rota)) + center_y*np.cos(np.deg2rad(rota)) \\n \\n x = x*np.cos(np.deg2rad(rota)) - y*np.sin(np.deg2rad(rota))\\n y = x*np.sin(np.deg2rad(rota)) + y*np.cos(np.deg2rad(rota))\\n \\n norm = 2.*np.pi*sigma_x*sigma_y\\n #exp_x = np.power((x - center_x)/(sigma_x), 2.)\\n #exp_y = np.power((y - center_y)/(sigma_y), 2.)\\n g = amplitude*np.exp(-(((center_x - x)/sigma_x)**2 + \\\\\\n ((center_y - y)/sigma_y)**2)/2.)\\n \\n return g #(amplitude/norm)*np.exp(-(exp_x + exp_y)/2.)\",\n \"def get_gauss_kernel(sigma, samples):\\n p = ny.ceil (2*ny.sqrt(2*ny.log(2))*sigma)\\n r = ny.linspace(-p, p, samples)\\n x,y = ny.meshgrid(r, r)\\n b=bivariate_normal(x,y,sigma,sigma)\\n A=(1/ny.sum(b))\\n B=A*b\\n return x,y,B\",\n \"def gaussian_white(z, mu: 'normal' = 0, sigma: (0.4, 1) = 0.7):\\n return 1 - gaussian_black(z, mu, sigma)\",\n \"def gaussian_blurring(self,input_image,kernel_size,sigma):\\n #Applying Gaussian Blur filter\\n output_image=cv2.GaussianBlur(input_image,kernel_size,sigma)\\n return output_image\",\n \"def make_gaussian(shape, var):\\n h,w = shape\\n x = np.arange(w, dtype=float)\\n y = np.arange(h, dtype=float)[:,np.newaxis]\\n x0 = w // 2\\n y0 = h // 2\\n mat = np.exp(-0.5 * (pow(x-x0, 2) + pow(y-y0, 2)) / var)\\n normalized_img = np.zeros((h, w))\\n cv2.normalize(mat, normalized_img, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX)\\n return normalized_img\",\n \"def apply_gaussian(X, sigma):\\n return np.array([ndimage.gaussian_filter(x, sigma) for x in X])\",\n \"def initiategaussian(sd, x0):\\n y = np.exp(-x**2/(2*sd**2))\\n return y\",\n \"def gaussian(\\n x: float,\\n measurement: 'mstats.ValueUncertainty',\\n max_sigma: float = 10.0\\n) -> float:\\n center = measurement.value\\n width = measurement.uncertainty\\n width = max(width, 1e-6)\\n\\n if x <= (center - max_sigma * width) or x >= (center + max_sigma * width):\\n # Don't calculate values outside a \\\"reasonable\\\" 10 sigma range\\n return 0.0\\n\\n coefficient = 1 / math.sqrt(2.0 * math.pi * width * width)\\n exponent = -0.5 * ((float(x) - center) ** 2) / (width * width)\\n\\n return coefficient * math.exp(exponent)\",\n \"def gaussian(x, *parameters):\\n position, sigma, amplitude, background = parameters\\n return amplitude * np.exp(-(x - position)**2 / (2.0 * sigma**2)) + background\",\n \"def get_sigma_psf(self):\\n pass\",\n \"def MVgaussian(size,mu1=0,mu2=0, sigma1=3,sigma2 = 1):\\n kernel = np.zeros((size, size), dtype=np.float32)\\n \\n size = int(size) // 2\\n X = np.arange(-size,size+1)\\n Y = np.arange(-size,size+1)\\n \\n for x in X:\\n for y in Y:\\n Gx = np.exp(-((x-mu1)**2)/(2*(sigma1**2)))\\n Gy = np.exp(-((y-mu2)**2)/(2*(sigma2**2)))\\n Gx = math.exp(-(math.pow(x-mu1,2))/(2*math.pow(sigma1,2)))\\n Gy = math.exp(-(math.pow(y-mu2,2))/(2*math.pow(sigma2,2)))\\n kernel[x+size,y+size] = Gx*Gy\\n return kernel\",\n \"def gaussian_kernel(size, sigma):\\n\\n m, n = [(s - 1.) / 2. for s in size]\\n y, x = np.ogrid[-m:m+1, -n:n+1]\\n h = np.exp(-(x*x + y*y) / (2. * sigma * sigma))\\n h[h < np.finfo(h.dtype).eps*h.max()] = 0\\n sumh = h.sum()\\n if sumh != 0: h /= sumh\\n return h\",\n \"def model_gauss_noise(sigma, nx, ny=1, nz=1):\\n\\te = EMData()\\n\\te.set_size(nx, ny, nz)\\n\\te.process_inplace(\\\"testimage.noise.gauss\\\", {\\\"sigma\\\":sigma})\\n\\treturn e\",\n \"def gaussian(window_size, sigma):\\n gauss = torch.Tensor([math.exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])\\n return gauss/gauss.sum()\",\n \"def gauss_kern(sigma,h):\\n h1 = h\\n h2 = h\\n x, y = np.mgrid[0:h2, 0:h1]\\n x = x-h2/2\\n y = y-h1/2\\n # sigma = 10.0\\n g = np.exp( -( x**2 + y**2 ) / (2*sigma**2) )\\n return g / g.sum()\",\n \"def sigmanorm(y):\\n y = y.copy()\\n y -= y.mean() # set to zero mean\\n y /= y.std() # rescale to units of sigma\\n return y\",\n \"def add_gaussian(self, high, center, stdev):\\n if callable(high):\\n high = high()\\n assert stdev > 0\\n self.Y += high * np.exp(-0.5 * (self.Xnum-center)**2 / stdev**2)\",\n \"def _FSpecialGauss(size, sigma):\\n radius = size // 2\\n offset = 0.0\\n start, stop = -radius, radius + 1\\n if size % 2 == 0:\\n offset = 0.5\\n stop -= 1\\n x, y = np.mgrid[offset + start:stop, offset + start:stop]\\n assert len(x) == size\\n g = np.exp(-((x**2 + y**2)/(2.0 * sigma**2)))\\n return g / g.sum()\",\n \"def gaussian_kernel_2d(mean, std_inv, size):\\n if type(mean) is torch.Tensor:\\n device = mean.device\\n elif type(std_inv) is torch.Tensor:\\n device = std_inv.device\\n else:\\n device = \\\"cpu\\\"\\n\\n # repeat the size for width, height if single number\\n if isinstance(size, numbers.Number):\\n width = height = size\\n else:\\n width, height = size\\n\\n # expand std to (2, 2) matrix\\n if isinstance(std_inv, numbers.Number):\\n std_inv = torch.tensor([[std_inv, 0], [0, std_inv]], device=device)\\n elif std_inv.dim() == 0:\\n std_inv = torch.diag(std_inv.repeat(2))\\n elif std_inv.dim() == 1:\\n assert len(std_inv) == 2\\n std_inv = torch.diag(std_inv)\\n\\n # Enforce PSD of covariance matrix\\n covariance_inv = std_inv.transpose(0, 1) @ std_inv\\n covariance_inv = covariance_inv.float()\\n\\n # make a grid (width, height, 2)\\n X = torch.cat(\\n [\\n t.unsqueeze(-1)\\n for t in reversed(\\n torch.meshgrid(\\n [torch.arange(s, device=device) for s in [width, height]]\\n )\\n )\\n ],\\n dim=-1,\\n )\\n X = X.float()\\n\\n # center the gaussian in (0, 0) and then shift to mean\\n X -= torch.tensor([(width - 1) / 2, (height - 1) / 2], device=device).float()\\n X -= mean.float()\\n\\n # does not use the normalize constant of gaussian distribution\\n Y = torch.exp((-1 / 2) * torch.einsum(\\\"xyi,ij,xyj->xy\\\", [X, covariance_inv, X]))\\n\\n # normalize\\n # TODO could compute the correct normalization (1/2pi det ...)\\n # and send warning if there is a significant diff\\n # -> part of the gaussian is outside the kernel\\n Z = Y / Y.sum()\\n return Z\",\n \"def gaussian_template(\\n wavelengths: np.ndarray,\\n mean: Union[float, np.ndarray],\\n std: Union[float, np.ndarray] = 30.0,\\n) -> np.ndarray:\\n y = norm.pdf(wavelengths, mean, std)\\n return y / np.max(y, axis=-1, keepdims=True)\",\n \"def gaussian(mu, wid, x):\\n return np.exp(-((x - mu) / (0.6005612 * wid))**2)\",\n \"def gaussian_kernel(size, sigma):\\n\\n kernel = np.zeros((size, size))\\n\\n ### YOUR CODE HERE\\n k = (size-1)/2\\n factor = 1/(2*np.pi*sigma**2)\\n for i in range(size):\\n for j in range(size):\\n exponent = -((i-k)**2 +(j-k)**2)/(2*sigma**2)\\n kernel[i,j] = factor*np.exp(exponent)\\n ### END YOUR CODE\\n\\n return kernel\",\n \"def gaussian_filter(shape=(3,3),sigma=0.5):\\n m,n = [(ss-1.)/2. for ss in shape]\\n y,x = np.ogrid[-m:m+1,-n:n+1]\\n h = np.exp( -(x*x + y*y) / (2.*sigma*sigma))\\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\\n sumh = h.sum()\\n if sumh != 0:\\n h /= sumh\\n return h\",\n \"def random_gaussian_noise(image ):\\n sigma = image.std()\\n for ch in range(image.shape[2] ):\\n sigma = min(sigma, image[:,:,ch].std() )\\n image = random_noise(image, var = sigma**2)\\n return image\",\n \"def matlab_style_gauss2D(shape=(3,3),sigma=0.5):\\n m,n = [(ss-1.)/2. for ss in shape]\\n y,x = np.ogrid[-m:m+1,-n:n+1]\\n h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )\\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\\n sumh = h.sum()\\n if sumh != 0:\\n h /= sumh\\n return h\",\n \"def matlab_style_gauss2D(shape=(3,3),sigma=0.5):\\n m,n = [(ss-1.)/2. for ss in shape]\\n y,x = np.ogrid[-m:m+1,-n:n+1]\\n h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )\\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\\n sumh = h.sum()\\n if sumh != 0:\\n h /= sumh\\n return h\",\n \"def uni_gaussian(X, mu, sigma2):\\n p = (1 / sqrt(2 * pi * sigma2))\\n p = p * exp(-power(X - mu, 2) / (2 * sigma2))\\n\\n def prod(x, y):\\n return x * y\\n p = array([[reduce(prod, el)] for el in p])\\n\\n return p\",\n \"def GaussianKernel(radius, std):\\n size = 2 * radius + 1\\n weight = torch.ones(size, size)\\n weight.requires_grad = False\\n for i in range(-radius, radius+1):\\n for j in range(-radius, radius+1):\\n dis = (i * i) + (j * j)\\n weight[i+radius][j+radius] = np.exp(-dis / (2 * std * std))\\n weight = weight / weight.sum()\\n return weight\",\n \"def kernel_gaussiano(image: np.ndarray, sigma: float, kind: str = 'low') -> np.ndarray:\\n U, V = fourier_meshgrid(image)\\n D = fourier_distance(U, V)\\n H = np.exp( (-1.0 * D) / (2.0 * sigma**2) )\\n \\n if kind == 'high' or kind == 'highpass':\\n H = 1.0 - H\\n \\n return H\",\n \"def get_mask_gauss(mask_dist_src, sigma):\\n return P.Exp()(-2.772588722 * (mask_dist_src ** 2) / (sigma ** 2))\",\n \"def gauss_kernels(size, sigma=1.0):\\n if size < 3:\\n size = 3\\n\\n m = size / 2\\n x, y = np.mgrid[-m:m + 1, -m:m + 1]\\n kernel = np.exp(-(x * x + y * y) / (2 * sigma * sigma))\\n kernel_sum = kernel.sum()\\n\\n if not sum == 0:\\n kernel = kernel / kernel_sum\\n\\n return kernel\",\n \"def fspecial_gaussian(shape=(3, 3), sigma=0.5):\\n m, n = [(ss - 1.) / 2. for ss in shape]\\n y, x = np.ogrid[-m:m + 1, -n:n + 1]\\n h = np.exp(-(x * x + y * y) / (2. * sigma * sigma))\\n h[h < np.finfo(h.dtype).eps * h.max()] = 0\\n sumh = h.sum()\\n if sumh != 0:\\n h /= sumh\\n return h\",\n \"def flatten(img,sigma=20.) :\\n\\n for i in range(img.shape[0]) :\\n img[i] /= np.median(img[i])\\n for i in range(img.shape[1]) :\\n img[:,i] /= np.median(img[:,i])\\n\\n hw=int(3*sigma)\\n u=np.linspace(-hw,hw,2*hw+1)\\n x=np.tile(u,(2*hw+1,1))\\n y=x.T\\n k=np.exp(-x**2/2/sigma**2-y**2/2/sigma**2)\\n k /= np.sum(k)\\n smooth=convolve2d(img,k,weight=None)\\n img /= smooth\\n\\n return img\",\n \"def _calc_sigma(self):\\n if self.data is None:\\n self._initial_blur()\\n previous = self.init_sigma\\n incr = 0\\n self.sigmas = [(previous, incr)]\\n for i in range(1, self.scale_per_octave + 3):\\n sigma_abs = self.init_sigma * (self.dest_sigma / self.init_sigma) ** (1.0 * i / (self.scale_per_octave))\\n increase = previous * sqrt((self.dest_sigma / self.init_sigma) ** (2.0 / self.scale_per_octave) - 1.0)\\n self.sigmas.append((sigma_abs, increase))\\n previous = sigma_abs\\n logger.debug(\\\"Sigma= %s\\\" % self.sigmas)\",\n \"def create_gaussian_array(self):\\n\\n # Fill array of size l x w with Gaussian Noise.\\n terrain_length = int(ceil(self.length/self.resolution))\\n terrain_width = int(ceil(self.width/self.resolution))\\n gaussian_array = np.random.normal(self.mu, self.sigma, (terrain_length,terrain_width))\\n\\n # Filter the array to smoothen the variation of the noise\\n gaussian_array = gaussian_filter(gaussian_array, self.sigma_filter)\\n\\n return gaussian_array\",\n \"def gaussian(self, amp_step, sigma_step):\\n l = len(self.overlaid_x_axis)\\n x = np.linspace(0, l, l) - l/2 # centre of data\\n\\n # This is new code to 'guess' the size of the Gaussian from the\\n # existing data rather than from hard-coded numbers.\\n # TODO: test this! Possibly link up to the get_windowed_data function\\n # as it uses a lot of the same functionality\\n trigger = self.pv_monitor.arrays[self.controls.Arrays.WAVEFORMS][0]\\n trace = self.pv_monitor.arrays[self.controls.Arrays.WAVEFORMS][1]\\n amplitude = max(trace) + amp_step\\n diff = np.diff(trigger)\\n stepvalue = 0.5\\n if min(diff) > -1 * stepvalue or max(diff) < stepvalue:\\n raise RangeError\\n else:\\n maxtrig = next(x for x in diff if x > stepvalue)\\n mintrig = next(x for x in diff if x < -1 * stepvalue)\\n edges = [np.where(diff == maxtrig)[0][0],\\n np.where(diff == mintrig)[0][0]]\\n half_trigger_length = (edges[1]-edges[0])\\n sigma = half_trigger_length/4 + sigma_step\\n\\n gauss = self.ax2.plot(amplitude * np.exp(-x**2 / (2 * sigma**2)), 'r')\\n self.overlaid_lines.append(gauss)\\n self.draw()\",\n \"def multi_gaussian(X, mu, sigma):\\n m, n = X.shape\\n X = X - mu\\n\\n factor = X.dot(inv(sigma))\\n factor = multiply(factor, X)\\n factor = - (1 / 2) * sum(factor, axis=1, keepdims=True)\\n\\n p = 1 / (power(2 * pi, n / 2) * sqrt(det(sigma)))\\n p = p * exp(factor)\\n\\n return p\",\n \"def _sigma_1(gam, eps):\\n s1 = 4 * r0**2 * alpha / eps / mec2_unit\\n s2 = 1 + (1./3. - eps/gam) * (1 - eps/gam)\\n s3 = np.log(2 * gam * (gam - eps) / eps) - 1./2.\\n s3[np.where(gam < eps)] = 0.0\\n return s1 * s2 * s3\",\n \"def gaussian_kernel(sigma, bin_width, truncate=10):\\n\\n nbins = sigma * truncate // bin_width\\n bins = np.arange(-bin_width * nbins, bin_width * (nbins + 1), bin_width)\\n return np.exp(-0.5 * bins**2 / (sigma**2)) * 1 / sigma / np.sqrt(2 * np.pi)\",\n \"def gauss_2d(N, sigma = 0.25):\\r\\n x, y = make_xy(N)\\r\\n sigma_pixel = sigma * np.float(N)\\r\\n arrayout = np.exp(-(x**2 + y**2) / sigma_pixel**2) / (np.pi * sigma_pixel**2)\\r\\n return arrayout\",\n \"def add_gaussian(self, high, center, stdev):\\n indexes = dtloc2pos(center, self.X)\\n\\n if isinstance(stdev, timedelta):\\n stdev = stdev.total_seconds() / self.step.total_seconds()\\n elif not isinstance(stdev, (float, int)):\\n raise TypeError(f\\\"Unknown type center {type(center)}\\\")\\n\\n for index in indexes:\\n super().add_gaussian(\\n high=high,\\n center=index,\\n stdev=stdev,\\n )\",\n \"def gaussian_sigma(t, params):\\n t_final = tf.cast(params['t_final'].get_value(), dtype=tf.float64)\\n sigma = tf.cast(params['sigma'].get_value(), dtype=tf.float64)\\n gauss = tf.exp(-(t - t_final / 2) ** 2 / (2 * sigma ** 2))\\n norm = (tf.sqrt(2 * np.pi * sigma ** 2)\\n * tf.math.erf(t_final / (np.sqrt(8) * sigma))\\n - t_final * tf.exp(-t_final ** 2 / (8 * sigma ** 2)))\\n offset = tf.exp(-t_final ** 2 / (8 * sigma ** 2))\\n return (gauss - offset) / norm\",\n \"def make_DOG(inner_sigma, x):\\n y = x\\n outer_sigma = inner_sigma*5\\n X, Y = np.meshgrid(x, y)\\n inner_gaussian = 1./(2.*np.pi*inner_sigma) * np.exp(-(X**2 + Y**2)/2./inner_sigma**2) \\n outer_gaussian = 1./(2.*np.pi*outer_sigma) * np.exp(-(X**2 + Y**2)/2./outer_sigma**2) \\n return inner_gaussian - outer_gaussian/2 #weaker surround works better with our weights, which don't account for bursts \",\n \"def gaussian(var):\\n stddev = np.sqrt(var)\\n return stats.norm(0, stddev)\",\n \"def get_sigma(self):\\n\\n if not hasattr(self, 'sigma_zw'):\\n self.get_sigma_zw()\\n if not hasattr(self, 'sigmaw'):\\n self.get_sigmaw()\\n if not hasattr(self, 'sigmaz'):\\n self.get_sigmaz()\\n out = np.hstack((\\n np.vstack((self.sigma_w, self.sigma_zw.reshape((-1, 1)))),\\n np.vstack((self.sigma_zw, self.sigma_z))\\n ))\\n return out\",\n \"def norm(imagestack, mean, std):\\n \\n new_im = (imagestack - mean)/std \\n \\n return new_im\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7622218","0.76156443","0.73544854","0.71752834","0.69022816","0.68148255","0.67936516","0.6789439","0.6705065","0.66758764","0.6667239","0.66651136","0.6639735","0.65926224","0.65801823","0.6565116","0.65491575","0.6542619","0.6531326","0.6530046","0.6523103","0.6519727","0.6508179","0.6508179","0.64856905","0.6451151","0.64402145","0.64362067","0.6431242","0.63854766","0.6370809","0.6363925","0.6360078","0.6356346","0.6353183","0.6352357","0.6350569","0.6305891","0.62952286","0.6293415","0.6293415","0.6293415","0.6286197","0.6280724","0.6278903","0.6274311","0.6270234","0.6259633","0.62589765","0.62472814","0.6244438","0.62303096","0.62300014","0.6224764","0.62183625","0.6211588","0.62019575","0.6186674","0.617777","0.6151976","0.61454535","0.61311007","0.60970306","0.6093216","0.6083889","0.60780966","0.60717964","0.6068253","0.6053314","0.6047031","0.6042388","0.60368204","0.60364133","0.6024456","0.6022659","0.6016426","0.601408","0.601242","0.60078555","0.60078555","0.5995167","0.59921455","0.5991372","0.5983936","0.5974868","0.5973256","0.59709895","0.5959234","0.5956632","0.59550893","0.5944672","0.59417605","0.59401304","0.5937216","0.59348196","0.59280026","0.59212506","0.591163","0.5907433","0.5906628"],"string":"[\n \"0.7622218\",\n \"0.76156443\",\n \"0.73544854\",\n \"0.71752834\",\n \"0.69022816\",\n \"0.68148255\",\n \"0.67936516\",\n \"0.6789439\",\n \"0.6705065\",\n \"0.66758764\",\n \"0.6667239\",\n \"0.66651136\",\n \"0.6639735\",\n \"0.65926224\",\n \"0.65801823\",\n \"0.6565116\",\n \"0.65491575\",\n \"0.6542619\",\n \"0.6531326\",\n \"0.6530046\",\n \"0.6523103\",\n \"0.6519727\",\n \"0.6508179\",\n \"0.6508179\",\n \"0.64856905\",\n \"0.6451151\",\n \"0.64402145\",\n \"0.64362067\",\n \"0.6431242\",\n \"0.63854766\",\n \"0.6370809\",\n \"0.6363925\",\n \"0.6360078\",\n \"0.6356346\",\n \"0.6353183\",\n \"0.6352357\",\n \"0.6350569\",\n \"0.6305891\",\n \"0.62952286\",\n \"0.6293415\",\n \"0.6293415\",\n \"0.6293415\",\n \"0.6286197\",\n \"0.6280724\",\n \"0.6278903\",\n \"0.6274311\",\n \"0.6270234\",\n \"0.6259633\",\n \"0.62589765\",\n \"0.62472814\",\n \"0.6244438\",\n \"0.62303096\",\n \"0.62300014\",\n \"0.6224764\",\n \"0.62183625\",\n \"0.6211588\",\n \"0.62019575\",\n \"0.6186674\",\n \"0.617777\",\n \"0.6151976\",\n \"0.61454535\",\n \"0.61311007\",\n \"0.60970306\",\n \"0.6093216\",\n \"0.6083889\",\n \"0.60780966\",\n \"0.60717964\",\n \"0.6068253\",\n \"0.6053314\",\n \"0.6047031\",\n \"0.6042388\",\n \"0.60368204\",\n \"0.60364133\",\n \"0.6024456\",\n \"0.6022659\",\n \"0.6016426\",\n \"0.601408\",\n \"0.601242\",\n \"0.60078555\",\n \"0.60078555\",\n \"0.5995167\",\n \"0.59921455\",\n \"0.5991372\",\n \"0.5983936\",\n \"0.5974868\",\n \"0.5973256\",\n \"0.59709895\",\n \"0.5959234\",\n \"0.5956632\",\n \"0.59550893\",\n \"0.5944672\",\n \"0.59417605\",\n \"0.59401304\",\n \"0.5937216\",\n \"0.59348196\",\n \"0.59280026\",\n \"0.59212506\",\n \"0.591163\",\n \"0.5907433\",\n \"0.5906628\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6426071"},"document_rank":{"kind":"string","value":"29"}}},{"rowIdx":792,"cells":{"query":{"kind":"string","value":"create a cylinder along z axis"},"document":{"kind":"string","value":"def model_cylinder(radius, nx, ny, nz):\n\te = EMData()\n\te.set_size(nx, ny, nz)\n\te.process_inplace(\"testimage.cylinder\", {\"radius\":radius})\n\treturn e"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def cylinder(*args, axis: Union[List[float, float, float], bool]=None, caching: bool=True,\n degree: Union[int, bool]=3, endSweep: Union[float, bool]=2, heightRatio:\n Union[float, bool]=2.0, nodeState: Union[int, bool]=0, pivot: Union[List[float,\n float, float], bool]=None, radius: Union[float, bool]=1.0, sections: Union[int,\n bool]=8, spans: Union[int, bool]=1, startSweep: Union[float, bool]=0, tolerance:\n Union[float, bool]=0.01, useTolerance: bool=False, constructionHistory: bool=True,\n name: AnyStr=\"\", object: bool=True, polygon: int=0, q=True, query=True, e=True,\n edit=True, **kwargs)->Union[List[AnyStr], Any]:\n pass","def makecylinder(model=[0,0,0,1,0,0,1],height = 1,density=10):\n # extract info from cylinder model\n radius = model[6]\n X,Y,Z = model[:3]\n # get 3d points to make an upright cylinder centered to the origin\n n = np.arange(0,360,int(360/density))\n height = np.arange(0,height,height/density)\n n = np.deg2rad(n)\n x,z = np.meshgrid(n,height)\n x = x.flatten()\n z = z.flatten()\n cyl = np.vstack([np.cos(x)*radius,np.sin(x)*radius,z]).T\n # rotate and translate the cylinder to fit the model\n rotation = rotation_matrix_from_vectors([0,0,1],model[3:6])\n rotated_cylinder = np.matmul(rotation,cyl.T).T + np.array([X,Y,Z])\n return rotated_cylinder","def z_cylinder(shape: (int, int, int), radius: int, dtype=np.uint8):\n z_depth = shape[2]\n xhalf_atom = shape[0] // 2\n yhalf_atom = shape[1] // 2\n cylinders = [\n ((xhalf_atom, yhalf_atom, 0),\n (xhalf_atom, yhalf_atom, z_depth - 1),\n radius)\n ]\n data_mask = create_cylinders_volume(shape, cylinders, foreground=1, dtype=dtype)\n return data_mask, cylinders","def polyCylinder(*args, axis: Union[List[float, float, float], bool]=None, caching: bool=True,\n constructionHistory: bool=True, createUVs: Union[int, bool]=2, height:\n Union[float, bool]=2.0, name: AnyStr=\"\", nodeState: Union[int, bool]=0,\n object: bool=True, radius: Union[float, bool]=1.0, roundCap: bool=False,\n subdivisionsAxis: Union[int, bool]=20, subdivisionsCaps: Union[int, bool]=0,\n subdivisionsHeight: Union[int, bool]=1, subdivisionsX: Union[int, bool]=20,\n subdivisionsY: Union[int, bool]=1, subdivisionsZ: Union[int, bool]=1, texture:\n Union[int, bool]=2, q=True, query=True, e=True, edit=True,\n **kwargs)->Union[List[AnyStr], Any]:\n pass","def Cylinder(\n center=(0.0, 0.0, 0.0),\n direction=(1.0, 0.0, 0.0),\n radius=0.5,\n height=1.0,\n resolution=100,\n capping=True,\n):\n cylinderSource = _vtk.vtkCylinderSource()\n cylinderSource.SetRadius(radius)\n cylinderSource.SetHeight(height)\n cylinderSource.SetCapping(capping)\n cylinderSource.SetResolution(resolution)\n cylinderSource.Update()\n surf = wrap(cylinderSource.GetOutput())\n surf.rotate_z(-90, inplace=True)\n translate(surf, center, direction)\n return surf","def cylinderize(self, z0=0.0, z1=1.0, support=(0.0, 1.0)):\n from .geometry import tensor_product, line_segment\n return tensor_product(line_segment(z0, z1, support=support), self)","def createCylinder( basePoint=(0,-1,0), tipPoint=(0,1,0), radius = 1.0, colour=(0.6,0.6,0.6), samples = 20 ):\r\n \r\n basePoint = PyUtils.toPoint3d(basePoint)\r\n tipPoint = PyUtils.toPoint3d(tipPoint)\r\n baseToTipVector = Vector3d(basePoint,tipPoint)\r\n if baseToTipVector.isZeroVector() :\r\n raise ValueError( 'Invalid points for cylinder: base and tip are equal!' )\r\n baseToTipUnitVector = baseToTipVector.unit()\r\n xUnitVector = baseToTipUnitVector.crossProductWith( Vector3d(0,0,1) )\r\n if xUnitVector.length() < 0.5 :\r\n xUnitVector = baseToTipUnitVector.crossProductWith( Vector3d(0,-1,0) )\r\n xUnitVector.toUnit()\r\n yUnitVector = baseToTipUnitVector.crossProductWith( Vector3d(-1,0,0) )\r\n if yUnitVector.length() < 0.5 :\r\n yUnitVector = baseToTipUnitVector.crossProductWith( Vector3d(0,1,0) )\r\n yUnitVector.toUnit()\r\n\r\n vertices = []\r\n for i in range(samples):\r\n theta = i * 2 * math.pi / float(samples)\r\n vertices.append( basePoint + xUnitVector * math.cos(theta) * radius + yUnitVector * math.sin(theta) * radius )\r\n for i in range(samples):\r\n theta = i * 2 * math.pi / float(samples)\r\n vertices.append( tipPoint + xUnitVector * math.cos(theta) * radius + yUnitVector * math.sin(theta) * radius )\r\n for i in range(samples):\r\n theta = i * 2 * math.pi / float(samples)\r\n vertices.append( basePoint + xUnitVector * math.cos(theta) * radius + yUnitVector * math.sin(theta) * radius )\r\n vertices.append( tipPoint + xUnitVector * math.cos(theta) * radius + yUnitVector * math.sin(theta) * radius )\r\n \r\n faces = [ range(0,samples), range(samples,2*samples) ]\r\n for i in range(0,2*samples,2) :\r\n base = 2*samples\r\n size = 2*samples\r\n faces.append( (base+i, base+i+1, base+(i+3)%size, base+(i+2)%size ) )\r\n \r\n return create( vertices, faces, colour )","def project_to_cylinder(points):\n r, phi, z = cs.cart2cyl(points[:, 0], points[:, 1], points[:, 2])\n # NOTE: z is unchanged\n # print(np.array_equal(z, points[:, 2]))\n # project onto unit cylinder\n x_cyl = points[:, 0] / r\n y_cyl = points[:, 1] / r\n z_cyl = (z - z.min()) / (z.max() - z.min())\n points_cyl = np.stack((x_cyl, y_cyl, z_cyl), axis=1)\n return points_cyl, r, phi, z","def cylinder(downCirc = -120, upCirc = -70,radius = 15, resolution = 20):\n t = np.linspace(0, 2*m.pi, resolution)\n cylinderPos = []\n for num in t:\n x = -m.cos(num)*radius\n y = m.sin(num)*radius\n\n cylinderPos.append([x, y, downCirc, 0, 0, 0, 'mov'])\n\n for num in t:\n x = -m.cos(num)*radius\n y = m.sin(num)*radius\n\n cylinderPos.append([x, y, upCirc, 0, 0, 0, 'mov'])\n\n cylinderPos.append([0,0,-127,0,0,0,'mov'])\n return cylinderPos","def cylinder(objname, coord1, coord2, radius, rgba, rgba2=None):\r\n if not rgba2:\r\n rgba2 = rgba\r\n return \\\r\n f'\\n{objname} = ' \\\r\n f'[CYLINDER, {coord1[0]}, {coord1[1]}, {coord1[2]}, ' \\\r\n f'{coord2[0]}, {coord2[1]}, {coord2[2]}, {radius}, ' \\\r\n f'{rgba[0]}, {rgba[1]}, {rgba[2]}, ' \\\r\n f'{rgba2[0]}, {rgba2[1]}, {rgba2[2]}] \\n'","def cyl(diameter, height):\n global _cmds, fragments\n radius = diameter / 2\n _cmds = (\n f\"cylinder(h={height},\"\n f\"r1={radius},r2={radius},\"\n f\"center=false,$fn={fragments});\\n\\n\"\n ) + _cmds","def create_cylinder(self, pt1, pt2, radius):\n ## Compute the cylinder axis and center.\n axis = [pt1[i] - pt2[i] for i in range(0,3)]\n length = vtk.vtkMath.Normalize(axis)\n center = [(pt1[i] + pt2[i])/2.0 for i in range(0,3)]\n\n # Determine angle to rotate cylinder into given axis.\n vec = [ 0.0, 1.0, 0.0 ]\n rotate_axis = 3*[0.0]\n tmp_cross = 3*[0.0]\n vtk.vtkMath.Cross(vec, axis, rotate_axis)\n radangle = math.atan2(vtk.vtkMath.Norm(rotate_axis), vtk.vtkMath.Dot(axis,vec))\n degangle = vtk.vtkMath.DegreesFromRadians(radangle)\n\n # Create cylinder.\n cylinder = vtk.vtkCylinderSource()\n cylinder.SetCenter(0.0,0.0,0.0);\n cylinder.SetHeight(length);\n cylinder.SetRadius(radius);\n cylinder.SetResolution(32)\n cylinder.Update()\n\n # Transform.\n transformer = vtk.vtkTransform()\n transformer.Translate(center[0], center[1], center[2])\n transformer.RotateWXYZ(degangle,rotate_axis)\n\n # Get the polydata (polygon mesh) for the transformed cylinder.\n polyDataTransformer = vtk.vtkTransformPolyDataFilter()\n polyDataTransformer.SetInputData(cylinder.GetOutput())\n polyDataTransformer.SetTransform(transformer)\n polyDataTransformer.Update()\n return polyDataTransformer.GetOutput()","def cylinder(geometry,\n network,\n propname,\n seed='seed',\n **params):\n prob_fn = getattr(spst,params['name'])\n P = prob_fn(params['shape'],loc=params['loc'],scale=params['scale'])\n value=P.ppf(network.get_throat_data(prop=seed,locations=geometry))\n network.set_throat_data(locations=geometry,prop=propname,data=value)","def vertical_cylinders(xy_size: int, z_depth: int, dtype=np.uint8):\n shape = (xy_size, xy_size, z_depth)\n image_size_px = shape[0] // 3\n z_depth = shape[2]\n half_atom = image_size_px // 2\n quarter_atom = image_size_px // 4\n cylinders = [\n # center cylinder, z-aligned, 64x64 radius = 16\n ((image_size_px + half_atom, image_size_px + half_atom, 0),\n (image_size_px + half_atom, image_size_px + half_atom, z_depth - 1),\n image_size_px // 4),\n # first tile overlapping to other tiles, z-aligned, 64x64 radius = 16\n ((image_size_px - quarter_atom, image_size_px - quarter_atom, 0),\n (image_size_px - quarter_atom, image_size_px - quarter_atom, z_depth - 1),\n image_size_px // 4),\n # lower middle tile overlapping to other tiles, z-aligned, 64x64 radius = 8\n ((image_size_px * 2 + quarter_atom, image_size_px + half_atom, 0),\n (image_size_px * 2 + quarter_atom, image_size_px + half_atom, z_depth - 1),\n image_size_px // 8),\n ]\n data_mask = create_cylinders_volume(shape, cylinders, foreground=1, dtype=dtype)\n return data_mask, cylinders","def cylinder_on_axis(radius: int=5, axis: int=0, shape: (int, int, int)=(256, 256, 256)):\n assert axis < len(shape), \"Invalid axis index specified, must be < len(shape)\"\n r = phantom_base.scale_radius_to_basecoord(radius, shape)\n\n def cylinder_axis(t):\n u = -1 + (t * 2), 0, 0\n # roll the elements to have the formula on the target axis\n return tuple(np.roll(u, axis)) + (r,)\n\n # TODO: we need to scale step_count appropriately for the input shape; maybe half?\n nsteps = max(shape)\n return phantom_base.trace_function(cylinder_axis, shape=shape, step_count=nsteps)","def createCone( basePoint=(0,-1,0), tipPoint=(0,1,0), radius = 1.0, colour=(0.6,0.6,0.6), samples = 20 ):\r\n \r\n basePoint = PyUtils.toPoint3d(basePoint)\r\n tipPoint = PyUtils.toPoint3d(tipPoint)\r\n baseToTipVector = Vector3d(basePoint,tipPoint)\r\n if baseToTipVector.isZeroVector() :\r\n raise ValueError( 'Invalid points for cylinder: base and tip are equal!' )\r\n baseToTipUnitVector = baseToTipVector.unit()\r\n xUnitVector = baseToTipUnitVector.crossProductWith( Vector3d(0,0,1) )\r\n if xUnitVector.length() < 0.5 :\r\n xUnitVector = baseToTipUnitVector.crossProductWith( Vector3d(0,-1,0) )\r\n xUnitVector.toUnit()\r\n yUnitVector = baseToTipUnitVector.crossProductWith( Vector3d(-1,0,0) )\r\n if yUnitVector.length() < 0.5 :\r\n yUnitVector = baseToTipUnitVector.crossProductWith( Vector3d(0,1,0) )\r\n yUnitVector.toUnit()\r\n\r\n vertices = []\r\n for i in range(samples):\r\n theta = i * 2 * math.pi / float(samples)\r\n vertices.append( basePoint + xUnitVector * math.cos(theta) * radius + yUnitVector * math.sin(theta) * radius )\r\n for i in range(samples):\r\n theta = i * 2 * math.pi / float(samples)\r\n vertices.append( basePoint + xUnitVector * math.cos(theta) * radius + yUnitVector * math.sin(theta) * radius )\r\n vertices.append( tipPoint )\r\n \r\n faces = [ range(0,samples) ]\r\n for i in range(0,samples) :\r\n base = samples\r\n size = samples\r\n faces.append( (base+i, base+(i+1)%size, 2*samples ) )\r\n \r\n return create( vertices, faces, colour )","def HollowCylinder(self,center=(0,0,0),inner_radius=1.0,outer_radius=2.,\n element_type='hex',isotropic=True,nrad=5,ncirc=10, nlong=20,length=10):\n\n if element_type != \"hex\":\n raise NotImplementedError('Generating {} mesh of cylinder is not supported yet'.format(element_type))\n\n if not isinstance(center,tuple):\n raise ValueError(\"The center for the base of the cylinder should be given in a tuple with three elements (x,y,z)\")\n\n self.__reset__()\n\n nlong = int(nlong)\n if nlong==0:\n nlong = 1\n\n mesh = Mesh()\n mesh.HollowCircle(center=(center[0],center[1]), inner_radius=inner_radius,\n outer_radius=outer_radius, element_type=\"quad\",\n isotropic=isotropic, nrad=nrad, ncirc=ncirc)\n\n self.Extrude(base_mesh=mesh, length=length, nlong=nlong)\n self.points += center[2]","def template_cylinder_annulus(height, outer_radius, inner_radius=0):\n\n img = _template_sphere_disc(dim=2, outer_radius=outer_radius,\n inner_radius=inner_radius)\n img = np.tile(np.atleast_3d(img), reps=height)\n return img","def cylinder_volume(radius: number, height: number) -> number:\n volume = pi*radius*radius*height\n return volume","def OneElementCylinder(self,radius=1, length=100, nz=10, element_type=\"hex\"):\n\n if element_type == \"hex\":\n elements = np.arange(0,8)[:,None]\n for i in range(1,nz):\n elements = np.concatenate((elements,np.arange(4*i,4*i+8)[:,None]),axis=1)\n elements = elements.T.copy()\n\n theta = np.array([225,315,45,135])*np.pi/180\n\n xs = np.tile(radius*np.cos(theta),nz+1)\n ys = np.tile(radius*np.sin(theta),nz+1)\n\n points = np.array([xs,ys]).T.copy()\n points = np.concatenate((points,np.zeros((4*(nz+1),1))),axis=1)\n\n zs = np.linspace(0,length, nz+1)[1:]\n zs = np.repeat(zs,4)\n points[4:,-1] = zs\n\n if element_type == \"hex\":\n self.element_type = element_type\n self.elements = elements\n self.points = points\n self.GetBoundaryFacesHex()\n self.GetBoundaryEdgesHex()\n self.GetFacesHex()\n self.GetEdgesHex()\n self.nelem = self.elements.shape[0]\n self.nnode = self.points.shape[0]\n\n elif element_type == \"tet\":\n # USE MESHPY\n raise NotImplementedError('Not implemented yet')\n else:\n raise ValueError('element type not suppported')","def __init__(self,x_c,z_c,cyl_rad):\n self.x_c = x_c\n self.z_c = z_c\n self.cyl_rad = cyl_rad","def __init__(self,x_c,z_c,cyl_rad):\n self.x_c = x_c\n self.z_c = z_c\n self.cyl_rad = cyl_rad","def __init__(self,x_c,z_c,cyl_rad):\n self.x_c = x_c\n self.z_c = z_c\n self.cyl_rad = cyl_rad","def __init__(self,x_c,z_c,cyl_rad):\n self.x_c = x_c\n self.z_c = z_c\n self.cyl_rad = cyl_rad","def __init__(self,x_c,z_c,cyl_rad):\n self.x_c = x_c\n self.z_c = z_c\n self.cyl_rad = cyl_rad","def add_cylinder_between(pt1, pt2, r, name=None):\n pt1 = np.array(pt1)\n pt2 = np.array(pt2)\n\n d = pt2 - pt1\n\n # Add cylinder at the correct location\n dist = np.linalg.norm(d)\n loc = (pt1[0] + d[0] / 2, pt1[1] + d[1] / 2, pt1[2] + d[2] / 2)\n bpy.ops.mesh.primitive_cylinder_add(radius=r, depth=dist, location=loc)\n\n cylinder_obj = bpy.context.object\n\n if name is not None:\n cylinder_obj.name = name\n\n # Further rotate it accordingly\n phi = np.arctan2(d[1], d[0])\n theta = np.arccos(d[2] / dist)\n cylinder_obj.rotation_euler[1] = theta\n cylinder_obj.rotation_euler[2] = phi\n\n # Scene update necessary, as matrix_world is updated lazily\n bpy.context.scene.update()\n\n return cylinder_obj","def build_cylinders(self, vtk_fname, zscale=1, radius=20):\r\n centers = []\r\n heights = []\r\n for w in self.wells:\r\n b = w['LOC']\r\n t = [b[0], b[1], 1]\r\n b_surf = self.cell_verts(b)\r\n t_surf = self.cell_verts(t)\r\n minZ = t_surf[0][2]\r\n b_v = self.centroid(b_surf)\r\n for i in range(len(b_surf)):\r\n t_v = t_surf[i]\r\n if t_v[2] < minZ:\r\n minZ = t_v[2]\r\n cyl_height = abs(b_v[2] - minZ)\r\n cyl_center = [b_v[0], b_v[1], minZ + cyl_height/2]\r\n centers.append(cyl_center)\r\n heights.append(cyl_height)\r\n # Write center, height data to string\r\n c_s = '['\r\n for c in centers:\r\n c_s += '[' + ', '.join(map(str, c)) + '],\\n'\r\n c_s = c_s[:-2] + ']\\n\\n'\r\n h_s = '['\r\n for h in heights:\r\n h_s += str(h) + ',\\n'\r\n h_s = h_s[:-2] + ']\\n\\n'\r\n # Write Paraview script\r\n with io.open('well_cylinders_test.py', 'w', newline='\\r\\n') as f:\r\n f.write(\"from paraview.simple import *\\n\")\r\n f.write(\"paraview.simple._DisableFirstRenderCameraReset()\\n\\n\")\r\n f.write(\"# To use, open grid vtk files in Paraview, then run this script in Python Shell\\n\")\r\n f.write(\"c = \" + c_s)\r\n f.write(\"h = \" + h_s)\r\n f.write(\"for i in range(len(h)):\\n\")\r\n f.write(\" cylinder = Cylinder()\\n\")\r\n f.write(\" fn = FindSource('\" + vtk_fname + \"_*')\\n\")\r\n f.write(\" rv = GetActiveViewOrCreate('RenderView')\\n\")\r\n f.write(\" tf = Transform(Input=cylinder)\\n\")\r\n f.write(\" tf.Transform = 'Transform'\\n\")\r\n f.write(\" tf.Transform.Translate = [c[i][0], c[i][1], c[i][2] * \" + str(zscale) + \"]\\n\")\r\n f.write(\" tf.Transform.Rotate = [90.0, 0.0, 0.0]\\n\")\r\n f.write(\" tf.Transform.Scale = [1.0, \" + str(zscale) + \", 1.0]\\n\")\r\n f.write(\" transformDisplay = Show(tf, rv, 'GeometryRepresentation')\\n\")\r\n f.write(\" cylinder.Radius = \" + str(radius) + \"\\n\")\r\n f.write(\" cylinder.Height = h[i]\\n\")\r\n f.write(\" rv.Update()\")","def Cylinder(self, center=(0.,0.,0.), radius=1., length=10., nrad=16, ncirc=40, nlong=50, element_type=\"hex\", algorithm=\"standard\"):\n\n if element_type != \"hex\":\n raise NotImplementedError('Generating {} mesh of cylinder is not supported yet'.format(element_type))\n\n if not isinstance(center,tuple):\n raise ValueError(\"The center for the base of the cylinder should be given in a tuple with three elements (x,y,z)\")\n\n self.__reset__()\n\n nlong = int(nlong)\n if nlong==0:\n nlong = 1\n\n mesh = Mesh()\n mesh.Circle(center=(center[0],center[1]), radius=radius, nrad=nrad, ncirc=ncirc, element_type=\"quad\", algorithm=algorithm)\n\n self.Extrude(base_mesh=mesh, length=length, nlong=nlong)\n self.points += center[2]","def plot_eccentricity(self, z=0):\n p = figure(\n title=\"Cut in plane Z=\" + str(z),\n x_axis_label=\"X axis\",\n y_axis_label=\"Y axis\",\n )\n for j in range(0, self.ntheta):\n p.circle(self.xre[z][j], self.yre[z][j], color=\"red\")\n p.circle(self.xri[z][j], self.yri[z][j], color=\"blue\")\n p.circle(0, 0, color=\"blue\")\n p.circle(self.xi, self.yi, color=\"red\")\n p.circle(0, 0, color=\"black\")\n return p","def unit_z(cls):\n return cls(0, 0, 1)","def build_cylinder(self, n_phis):\n index = glGenLists(1)\n phis = [float(i)*2.0*numpy.pi/float(n_phis) for i in range(n_phis+1)]\n phi_pairs = zip(phis, phis[1:])\n glNewList(index, GL_COMPILE)\n glBegin(GL_QUADS)\n for phi1,phi2 in phi_pairs:\n dot1 = min(max(numpy.cos(phi1), 0.0), 1.0)\n dot2 = min(max(numpy.cos(phi2), 0.0), 1.0)\n glTexCoord1f(dot1)\n glVertex3f(-0.5, numpy.sin(phi1), numpy.cos(phi1))\n glTexCoord1f(dot1)\n glVertex3f(0.5, numpy.sin(phi1), numpy.cos(phi1))\n glTexCoord1f(dot2)\n glVertex3f(0.5, numpy.sin(phi2), numpy.cos(phi2))\n glTexCoord1f(dot2)\n glVertex3f(-0.5, numpy.sin(phi2), numpy.cos(phi2))\n glEnd()\n glEndList()\n return index","def in_cylinder(x, y, z, min_z, max_z, max_r):\n r = np.sqrt(x ** 2 + y ** 2)\n m = r < max_r\n m = m & (z < max_z)\n m = m & (z >= min_z)\n return m","def tube(outside_diam, inside_diam, height):\n global _cmds, fragments\n r1 = outside_diam / 2\n r2 = inside_diam / 2\n _cmds = (\n \"difference(){\\n\"\n f\"cylinder(h={height},r1={r1},r2={r1},\"\n f\"center=false,$fn={fragments});\\n\"\n f\"cylinder(h={height*3},r1={r2},r2={r2},\"\n f\"center=true,$fn={fragments});\\n\"\n \"}\\n\") + _cmds","def cross_z(self):\n return Vector((self.v.y, -self.v.x))","def rotateZ(self, *args, **kwargs):\n ...","def cylinder_volume(diameter, length):\n return 3.14159 * ((diameter/2) ** 2) * length","def cross_z(self):\n return self.v.cross(Vector((0, 0, 1)))","def wheel(self):\n return FilletedSolid(\n built_from=\n TranslatedShape(shape_in=\n RotatedShape(shape_in=\n Cylinder(radius=\n self.wheels_properties[1],\n height=300.,\n position=self.position),\n rotation_point=self.position,\n vector=Vector(1, 0, 0),\n angle=radians(90)),\n displacement=Vector(self.wheels_properties[0],\n 300.,\n -self.positions[1][0])),\n radius=30.)","def p2pCyl(startPoint, endPoint, radius=10, modName=\"Cyl\", plus=0, Seg=3,\n\t\t\t color=\"red\", Opacity=1, RotY=0, Tx=0):\n\t\tcylinderSource = vtk.vtkCylinderSource()\n\t\tcylinderSource.SetRadius(radius)\n\t\tcylinderSource.SetResolution(Seg)\n\n\t\trng = vtk.vtkMinimalStandardRandomSequence()\n\t\trng.SetSeed(8775070) # For testing 8775070\n\n\t\t# Compute a basis\n\t\tnormalizedX = [0] * 3\n\t\tnormalizedY = [0] * 3\n\t\tnormalizedZ = [0] * 3\n\n\t\t# The X axis is a vector from start to end\n\t\tvtk.vtkMath.Subtract(endPoint, startPoint, normalizedX)\n\t\tlength = vtk.vtkMath.Norm(normalizedX) + plus\n\t\t# length = 20\n\t\tvtk.vtkMath.Normalize(normalizedX)\n\n\t\t# The Xn axis is an arbitrary vector cross X\n\t\tarbitrary = [0] * 3\n\t\tfor i in range(0, 3):\n\t\t\trng.Next()\n\t\t\tarbitrary[i] = rng.GetRangeValue(-10, 10)\n\t\tvtk.vtkMath.Cross(normalizedX, arbitrary, normalizedZ)\n\t\tvtk.vtkMath.Normalize(normalizedZ)\n\n\t\t# The Zn axis is Xn cross X\n\t\tvtk.vtkMath.Cross(normalizedZ, normalizedX, normalizedY)\n\t\tmatrix = vtk.vtkMatrix4x4()\n\t\t# Create the direction cosine matrix\n\t\tmatrix.Identity()\n\t\tfor i in range(0, 3):\n\t\t\tmatrix.SetElement(i, 0, normalizedX[i])\n\t\t\tmatrix.SetElement(i, 1, normalizedY[i])\n\t\t\tmatrix.SetElement(i, 2, normalizedZ[i])\n\t\t# Apply the transforms\n\t\ttransform = vtk.vtkTransform()\n\t\ttransform.Translate(startPoint) # translate to starting point\n\t\ttransform.Concatenate(matrix) # apply direction cosines\n\t\ttransform.RotateZ(-90.0) # align cylinder to x axis\n\t\ttransform.Scale(1.0, length, 1.0) # scale along the height vector\n\t\ttransform.Translate(0, .5, 0) # translate to start of cylinder\n\t\ttransform.RotateY(RotY)\n\t\ttransform.Translate(Tx, 0, 0)\n\n\t\t# Transform the polydata\n\t\ttransformPD = vtk.vtkTransformPolyDataFilter()\n\t\ttransformPD.SetTransform(transform)\n\t\ttransformPD.SetInputConnection(cylinderSource.GetOutputPort())\n\n\t\tstlMapper = vtk.vtkPolyDataMapper()\n\t\tstlMapper.SetInputConnection(transformPD.GetOutputPort())\n\t\tvtkNode = slicer.modules.models.logic().AddModel(transformPD.GetOutputPort())\n\t\tvtkNode.SetName(modName)\n\t\tmodelDisplay = vtkNode.GetDisplayNode()\n\t\tmodelDisplay.SetColor(Helper.myColor(color))\n\t\tmodelDisplay.SetOpacity(Opacity)\n\t\tmodelDisplay.SetBackfaceCulling(0)\n\t\t# modelDisplay.SetVisibility(1)\n\t\tmodelDisplay.SetVisibility2D(True)\n\t\t# modelDisplay.SetSliceDisplayModeToProjection()\n\t\t# dn.SetVisibility2D(True)\n\t\treturn","def _zforce_xyz(self,x,y,z):\n return -2.*np.pi*self._rhoc_M * self.a**3*self._b*self._c * \\\n _forceInt(x, y, z, self._a2, self._b2*self._a2, self._c2*self._a2, self.n, 2)","def ArcCylinder(self, center=(0.,0.,0.), radius=1., start_angle=0, end_angle=np.pi/2.,\n length=10., nrad=16, ncirc=40, nlong=50, element_type=\"hex\"):\n\n if element_type != \"hex\":\n raise NotImplementedError('Generating {} mesh of cylinder is not supported yet'.format(element_type))\n\n if not isinstance(center,tuple):\n raise ValueError(\"The center for the base of the cylinder should be given in a tuple with three elements (x,y,z)\")\n\n self.__reset__()\n\n nlong = int(nlong)\n if nlong==0:\n nlong = 1\n\n mesh = Mesh()\n mesh.Arc(center=(center[0],center[1]), radius=radius, start_angle=start_angle,\n end_angle=end_angle, nrad=nrad, ncirc=ncirc, element_type=\"quad\")\n\n self.Extrude(base_mesh=mesh, length=length, nlong=nlong)\n self.points += center[2]","def origami_H2_2cyl(w1,h1,t1,w2,h2,t2):\n assert((w2 < w1) and (t1 < w1) and (t2 < w2))\n\n # v for volumes and z for z\n v1 = h1*w1\n v2 = h2*w2\n z1 = (h1-1)*w1 + 1\n z2 = v1 + (h2-1)*w2 + 1\n\n # the horizontal permutation\n x = [None] + range(2,v1+v2+1) + [1]\n for i in range(h1):\n x[(i+1)*w1] = i*w1 + 1\n for i in range(h2):\n x[v1 + (i+1)*w2] = v1 + i*w2 + 1\n\n # the vertical permutation\n y = ([None] +\n range(w1+1,v1+1) + [None]*w1 +\n range(v1+w2+1,v1+v2+1) + [None]*w2)\n\n for i in range(w2):\n # up-left of the first cylinder\n # print \"U1L) z1 + (t1+i)%w1 -> 1+v1+i: \", z1+(t1+i)%w1, 1+v1+i\n y[z1+(t1+i)%w1] = 1+v1+i\n for i in range(w2):\n # up of the second cylinder\n # print \"U2) z2+(t2+i)%w2 -> 1 + (t1+i)%w1: \", z2+(t2+i)%w2, 1+(t1+i)%w1\n y[z2+(t2+i)%w2] = 1+i\n for i in range(w1-w2):\n # up-right of the first cylinder\n # print \"U1R) z1+w2+(t1+i) -> 1+i: \", z1+(w2+t1+i)%w1, 1+w2+i\n y[z1+(w2+t1+i)%w1] = 1+w2+i\n\n return Origami(x[1:],y[1:])","def render_capsule(self, l, r):\n # draw cylinder\n glPushMatrix()\n glScalef(l, r, r)\n glCallList(self.cylinder)\n glPopMatrix()\n # draw +x hemisphere\n glPushMatrix()\n glTranslatef(l/2.0, 0, 0)\n glScalef(r, r, r)\n glCallList(self.hemisphere)\n glPopMatrix()\n # draw -x hemisphere\n glPushMatrix()\n glRotatef(180.0, 0, 0, 1)\n glTranslatef(l/2.0, 0, 0)\n glScalef(r, r, r)\n glCallList(self.hemisphere)\n glPopMatrix()","def createDetectorCylinder(self,uNum=1):\n cellsCreated = 0\n sNum = self.SurfaceStartNum\n cNum = self.CellStartNum\n detectorCells = list()\n s = '{:5d} rcc 0 0 0 0 0 217.7 {}\\n'.format(self.SurfaceStartNum,self.geoParam['CylinderRadius'])\n c = ''\n keyList = sorted(self.surfGeo.keys(), key = lambda x: float(x))\n for key in keyList:\n sPrev = sNum\n sNum += 1\n cNum += 1\n s += self.surfForStr.format(sNum,key)\n m = self.material[self.surfGeo[key]]\n if cNum == self.CellStartNum+1:\n c+= '{:5d} {:d} -{:4.3f} -{:d} u={:d}\\n'.format(cNum,m['mt'],m['rho'],sNum,uNum)\n else:\n c += self.cellForStr.format(cNum,m['mt'],m['rho'],sPrev,sNum,uNum)\n # List of cells for the detector\n if self.surfGeo[key] is 'Detector':\n detectorCells.append(cNum)\n cellsCreated += 1\n # Last cell up to universe boundary\n m = self.material['Moderator']\n c += '{:5d} {:d} -{:4.3f} {:d} u={:d}\\n'.format(cNum+1,m['mt'],m['rho'],sNum,uNum)\n cellsCreated += 1\n return s,c,detectorCells,cellsCreated","def rot_z(angle):\n sangle = math.sin(angle)\n cangle = math.cos(angle)\n rz = np.array([[cangle, sangle, 0.0],\n [-sangle, cangle, 0.0],\n [0.0, 0.0, 1.0]])\n return rz","def make_cylindrical(self, grid, axis='z'):\n if grid.shape != self.shape:\n raise NotImplementedError('make_cylindrical: grid shape mismatch'\n ' not supported')\n gt_flat = grid.t.flat\n self.r = self.x.copy()\n self.t = self.x.copy()\n r_flat = self.r.flat\n t_flat = self.t.flat\n\n if axis == 'z':\n x_flat = self.x.flat\n y_flat = self.y.flat\n for i in range(self.x.size):\n gt = gt_flat[i]\n x = x_flat[i]\n y = y_flat[i]\n magnitude = hypot(x, y)\n rel_theta = atan2(y, x) - gt\n r_flat[i] = magnitude * cos(rel_theta)\n t_flat[i] = magnitude * sin(rel_theta)\n self.x = None\n self.y = None\n\n elif axis == 'x':\n y_flat = self.y.flat\n z_flat = self.z.flat\n for i in range(self.y.size):\n gt = gt_flat[i]\n y = y_flat[i]\n z = z_flat[i]\n magnitude = hypot(y, z)\n rel_theta = atan2(z, y) - gt\n r_flat[i] = magnitude * cos(rel_theta)\n t_flat[i] = magnitude * sin(rel_theta)\n self.z = self.x\n self.x = None\n self.y = None\n\n else:\n raise ValueError(\"axis must be 'z' or 'x'\")","def create_cylinders_volume(\n shape: (int, int, int),\n cylinders_list: list,\n foreground=1,\n dtype=np.uint8):\n volume = np.zeros(shape, dtype=bool)\n for point1, point2, radius in cylinders_list:\n volume = add_cylinder_px(volume, point1, point2, radius)\n return volume_bool_to_dtype(volume, fg=foreground, dtype=dtype)","def c(self, z, y, r, t):\n \n u = np.zeros( self.m ) \n \n return u","def rotateZ(self, ang):\n if ang != 0.0:\n x = self.getX() * cos(ang) - self.getY() * sin(ang)\n y = self.getX() * sin(ang) + self.getY() * cos(ang)\n z = self.getZ()\n self.setX(x)\n self.setY(y)\n self.setZ(z)","def rotateZ(self, angle):\r\n rad = angle * math.pi / 180\r\n cosa = math.cos(rad)\r\n sina = math.sin(rad)\r\n x = self.x * cosa - self.y * sina\r\n y = self.x * sina + self.y * cosa\r\n return Point3D(x, y, self.z)","def cylindervert_addedmass(R, z1, z2, rho, Ca=1, AxCa=1,\n m_f=0, z_f=0, m_mg=0, z_mg=0):\n if z1<z2:\n raise Exception('z1 should be above z2')\n if z1<0:\n # Fully submerged\n ztop = z1\n A0=0\n nAx=2\n else:\n # Partially submerged\n ztop = 0 \n A0 = np.pi*R**2 # undisplaced waterplane area of platform (m^2)\n nAx=1\n\n h = ztop-z2 # submerged height\n z_b = (ztop+z2)/2 # coordinates of the center of buoyancy of the undisplaced platform (m)\n V0 = np.pi*R**2*h # undisplaced volume of platform (m^3)\n\n M=np.zeros((6,6))\n M[0,0] = Ca * rho*V0\n M[1,1] = Ca * rho*V0\n M[2,2] = nAx*AxCa * 2/3*rho*np.pi * R**3 # rho*V0* D/(3*h)\n M[4,0] = M[0,0]*z_b # TODO empirical\n M[3,1] = -M[0,0]*z_b # TODO empirical\n M[0,4] = M[0,0]*z_b # TODO empirical\n M[1,3] = -M[0,0]*z_b # TODO empirical\n\n T1 =Ca*rho*np.pi*R**2 * h**3 /3 # Ca * rho*V0 * h**2/3 # TODO a bit empirical\n M[3,3] = T1\n M[4,4] = T1\n return M","def showSimpleCylinders(self): \n #from morphforge.morphology.util import TriMeshBuilderVerySimple\n import sys\n sys.path.append('/usr/share/pyshared/')\n \n #import morphforge\n from morphforge.morphology.mesh import MeshBuilderRings\n MonkeyPatchMayaVi()\n #import enthought.mayavi.mlab as mlab\n from mayavi import mlab\n \n assert len(self.morphs)==1\n mesh = MeshBuilderRings().build(self.morphs[0])\n \n \n @mlab.show\n def _showSimpleCylinders():\n\t \n #c = TriMeshBuilderVerySimple(self.morphs[0])\n #mlab.triangular_mesh(c.x, c.y, c.z, c.triangles, colormap=self.colormap)\n mlab.triangular_mesh(mesh.vertices[:,0], mesh.vertices[:,1], mesh.vertices[:,2], mesh.triangles, colormap=self.colormap)\n \n _showSimpleCylinders()","def rotation3D_z(angle: float) -> np.array:\n c = np.cos(angle)\n s = np.sin(angle)\n return np.array([[c, -s, 0.0], [s, c, 0.0], [0.0, 0.0, 1.0]])","def surface_area_cylinder(radius: float, height: float) -> float:\r\n if radius < 0 or height < 0:\r\n raise ValueError(\"surface_area_cylinder() only accepts non-negative values\")\r\n return 2 * pi * radius * (height + radius)","def rotateZ(self, angle):\n rad = angle * math.pi / 180\n cosa = math.cos(rad)\n sina = math.sin(rad)\n x = self.x * cosa - self.y * sina\n y = self.x * sina + self.y * cosa\n return Point3D(x, y, self.z)","def rotateZ(self, angle):\n rad = angle * math.pi / 180\n cosa = math.cos(rad)\n sina = math.sin(rad)\n x = self.x * cosa - self.y * sina\n y = self.x * sina + self.y * cosa\n return Point3D(x, y, self.z)","def rotateZ(self, angle):\n rad = angle * math.pi / 180\n cosa = math.cos(rad)\n sina = math.sin(rad)\n x = self.x * cosa - self.y * sina\n y = self.x * sina + self.y * cosa\n return Point3D(x, y, self.z)","def rotateZ(self, angle):\n rad = angle * math.pi / 180\n cosa = math.cos(rad)\n sina = math.sin(rad)\n x = self.x * cosa - self.y * sina\n y = self.x * sina + self.y * cosa\n return Point3D(x, y, self.z)","def test_cylindrical(self):\n # Rotate around the z axis\n r = Joint.cylindrical(np.array([0, 0, 1]))\n t_mat = r(np.array([np.pi / 2, 1.0]))\n\n rot_vec = np.dot(t_mat[:3, :3], np.array([1, 0, 0]))\n\n self.assertTrue(np.allclose(\n rot_vec, np.array([0, 1, 0]), rtol=1e-5, atol=1e-5))\n self.assertTrue(np.allclose(t_mat[2, 3], 1))","def rotateZ(self, angle):\n rad = math.radians(angle)\n cosa = math.cos(rad)\n sina = math.sin(rad)\n x = self.x * cosa - self.y * sina\n y = self.x * sina + self.y * cosa\n return Point3D(x, y, self.z)","def rotateZ(self, angle):\n\t\trad = angle * math.pi / 180\n\t\tcosa = math.cos(rad)\n\t\tsina = math.sin(rad)\n\t\tx = self.x * cosa - self.y * sina\n\t\ty = self.x * sina + self.y * cosa\n\t\treturn Point3D(x, y, self.z)","def test_circular_scatter_z():\n area = [0, 1000, 0, 1000]\n size = 1000\n z_value = 500\n x, y, z = gridder.circular_scatter(area, size, z=z_value, random=False)\n assert z.size == size\n npt.assert_allclose(z, z_value + np.zeros(size))","def RunCylinder(l,p,cylinderPositions):\n # Creating input and output deck names\n posString = ''\n for pos in cylinderPositions:\n posString += '{:2.1f}-'.format(pos[0])\n posString = posString.rstrip('-')\n inp='Cyl_{}LiF_{}_{}.mcnp'.format(int(l*100),p,posString)\n name='OUTCyl_{}LiF_{}_{}.'.format(int(l*100),p,posString)\n print inp\n # Creating and running the model\n m = CylinderRPM()\n m.createSurfaceGeo()\n m.setMaterial(l,p)\n m.createDetectorCylinder()\n m.createInputDeck(cylinderPositions,inp,name)\n m.runModel()","def squeeze(self, z, mode):\n phi = angle(z)\n r = abs(z)\n self.circuit.squeeze(r, phi, mode)","def generate(self):\n inside = self.crystal.is_inside(self.x,self.y,self.z)\n X = np.vstack((self.x[inside],self.y[inside],self.z[inside]))\n return self.rot.rotate(X)","def cart2polar3d(cartesian):\n radius = np.linalg.norm(cartesian)\n theta = np.cos","def polyCone(*args, axis: Union[List[float, float, float], bool]=None, caching: bool=True,\n constructionHistory: bool=True, createUVs: Union[int, bool]=2, height: Union[float,\n bool]=2.0, name: AnyStr=\"\", nodeState: Union[int, bool]=0, object: bool=True,\n radius: Union[float, bool]=1.0, roundCap: bool=False, subdivisionsAxis: Union[int,\n bool]=20, subdivisionsCap: Union[int, bool]=0, subdivisionsHeight: Union[int,\n bool]=1, subdivisionsX: Union[int, bool]=20, subdivisionsY: Union[int, bool]=1,\n subdivisionsZ: Union[int, bool]=0, texture: bool=True, q=True, query=True, e=True,\n edit=True, **kwargs)->Union[List[AnyStr], Any]:\n pass","def _add_cylinder_basic(self, point1, point2, radius):\n\n self._cylinder_end_nodes.append(point1)\n self._cylinder_end_nodes.append(point2)\n self._cylinder_end_nodes_radiuses.append(radius)\n self._cylinder_end_nodes_radiuses.append(radius)\n self.object_number += 1","def zaxis ( self ) :\n return self.__zaxis","def rotz(t):\n c = np.cos(t)\n s = np.sin(t)\n return np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]])","def create_triangle( xsize, ysize, zsize, place=(0,0,0), rotate=(1,0,0,0) ):\n \n b1 = create_box( (xsize,ysize,zsize), place=(0,0,0 ) )\n ms = max( xsize, zsize )\n angle = math.atan( zsize / xsize ) * 180 / math.pi\n b2 = create_box( (2*ms, 2*ysize, 2*ms), place=(0,-EPS,0), rotate=(0,1,0,-angle) )\n tr = create_cut( b1, b2 )\n return relocate( tr, place, rotate )","def _derZ(self, w, x, y, z):\n raise NotImplementedError()","def add_vehicle_z_axis(df):\n x = pd.Series(\n np.zeros(df.shape[0]),\n index=df[\"timestamp\"],\n name=\"T_vehicle_attitude_0__NF_body_z_axis_x\",\n )\n y = pd.Series(\n np.zeros(df.shape[0]),\n index=df[\"timestamp\"],\n name=\"T_vehicle_attitude_0__NF_body_z_axis_y\",\n )\n z = pd.Series(\n np.ones(df.shape[0]),\n index=df[\"timestamp\"],\n name=\"T_vehicle_attitude_0__NF_body_z_axis_z\",\n )\n x, y, z = mpd.series_quatrot(\n x,\n y,\n z,\n df[\"T_vehicle_attitude_0__F_q_0\"],\n df[\"T_vehicle_attitude_0__F_q_1\"],\n df[\"T_vehicle_attitude_0__F_q_2\"],\n df[\"T_vehicle_attitude_0__F_q_3\"],\n )\n\n df[x.name] = x.values\n df[y.name] = y.values\n df[z.name] = z.values","def __init__(self, pos, heading=vector(0,0,1)):\n # list of vPython 3D shapes that make up this player\n self.parts = []\n \n self.pos = vector(pos)\n # Direction in which robot is moving, normalized to unit length\n self.heading = norm(heading)\n\n self.radius = 1.0\n\n self.velocity = vector(0,0,0)\n\n face = cylinder(pos=self.pos, axis = (0,1.5,0), radius=.75,\n color=color.white, material = materials.chrome)\n self.parts += [face] \n\n self.head = sphere(pos=self.pos, radius = .75, color = color.white, material = materials.chrome)\n self.parts += [self.head]\n\n \n left_eye = sphere(pos=self.pos+vector(.35,.4,.6), \n radius=0.36, color=color.blue, material = materials.emissive)\n self.parts += [left_eye]\n right_eye = sphere(pos=self.pos+vector(-.35,.4,.6),\n radius=0.36, color=color.blue, material = materials.emissive)\n self.parts += [right_eye]\n\n neck = cylinder(pos=self.pos+vector(0,-1,0), axis = (0,.5,0), radius = .05, color=color.white)\n self.parts += [neck]\n\n self.body = cylinder(pos=self.pos+vector(0,-1.75,0),axis = (0,.75,-.2), radius = .35, color=color.white, material = materials.chrome)\n self.parts += [self.body]\n\n bottom = sphere(pos=self.pos+vector(0,-1.75,0), radius =.35, color = color.white, material = materials.chrome)\n self.parts += [bottom]\n\n right_shoulder = sphere(pos = self.pos+vector(-.35,-1,0), radius = .20, color = color.blue, material = materials.chrome)\n self.parts += [right_shoulder]\n\n left_shoulder = sphere(pos= self.pos+vector(.35,-1,0), radius = .20, color = color.blue, material = materials.chrome)\n self.parts += [left_shoulder]\n\n right_arm = cone(pos = self.pos+vector(-.36, -1.1, 0), axis = (-.2, -.7, -.4), radius = .12, color = color.white, material = materials.chrome)\n self.parts += [right_arm]\n\n left_arm = cone(pos = self.pos+vector(.36, -1.1, 0), axis = (.2, -.7, -.4), radius = .12, color = color.white, material = materials.chrome)\n self.parts += [left_arm]\n\n right_leg = cone(pos = self.pos+vector(-.32, -2.85, 0), axis = (.1, .8, .1), radius = .2, color = color.white, material = materials.chrome)\n self.parts += [right_leg]\n\n left_leg = cone(pos = self.pos+vector(.32,-2.15,.8), axis = (-.1, .1, -.8), radius = .2, color = color.white, material = materials.chrome)\n self.parts += [left_leg]","def rotate_z(self, angle: float):\n self.vertices = list(\n Matrix44.z_rotate(angle).transform_vertices(self.vertices)\n )\n return self","def _derZ(self, x, y, z):\n raise NotImplementedError()","def test_rotate_around_v3_z_axis(self):\n from pedemath.vec3 import rotate_around_vector_v3\n\n vec_a = Vec3(3, 4, 5)\n vec_b = Vec3(0, 0, 1)\n\n result = rotate_around_vector_v3(vec_a, math.pi, vec_b)\n expected = Vec3(-3, -4, 5)\n\n self.assertAlmostEqual(result.x, expected.x)\n self.assertAlmostEqual(result.y, expected.y)\n self.assertAlmostEqual(result.z, expected.z)","def cross(self):\n return self.v.cross(self.z_axis)","def draw_car():\r\n\twheel_radius = .5\r\n\twheel_thickness = .4\r\n\r\n\tglPushMatrix()\r\n\r\n\t# shift the car up so the base lies at the origin\r\n\tglTranslatef(0,wheel_radius,0)\r\n\t\r\n\tdraw_car_body()\r\n\r\n\t# draw the car wheels\r\n\t# assume the car is facing down the -z axis\r\n\t# front left, front right, back left, back right\r\n\tww = wheel_thickness/2\r\n\twheel_centers = [(-.5-ww,0,-1),(.5+ww,0,-1),(-.5-ww,0,1),(.5+ww,0,1)]\r\n\tfor i in range(4):\r\n\t\tglPushMatrix()\r\n\t\tapply(glTranslatef,wheel_centers[i])\r\n\t\tglRotatef(90,0,1,0)\r\n\t\tglScalef(wheel_radius,wheel_radius,wheel_radius)\r\n\t\tdraw_wheel()\r\n\t\tglPopMatrix()\r\n\r\n\tglPopMatrix()","def plot_surface_3D(self, length = 30, fps = 30, **kwargs):\n fig = utils.get_figure(scale = 3)\n ax = fig.add_subplot(111, projection = '3d')\n\n # surface_x = self.xi_1_mesh\n # surface_y = self.xi_2_mesh\n # surface_x, surface_y, surface_z = self.surface()\n xyz = self.surface()\n\n # surface_x, surface_y = np.meshgrid(surface_x, surface_y)\n\n # print(np.shape(surface_x))\n # print(np.shape(surface_y))\n # print(np.shape(surface_z))\n\n control_points_x = np.array([control_point[0] for control_point in self.control_net.values()])\n control_points_y = np.array([control_point[1] for control_point in self.control_net.values()])\n control_points_z = np.array([control_point[2] for control_point in self.control_net.values()])\n\n # x_min = min(np.min(surface_x), np.min(control_points_x))\n # x_max = max(np.max(surface_x), np.max(control_points_x))\n # x_range = np.abs(x_max - x_min)\n #\n # y_min = min(np.min(surface_y), np.min(control_points_y))\n # y_max = max(np.max(surface_y), np.max(control_points_y))\n # y_range = np.abs(y_max - y_min)\n #\n # z_min = min(np.min(surface_z), np.min(control_points_z))\n # z_max = max(np.max(surface_z), np.max(control_points_z))\n # z_range = np.abs(z_max - z_min)\n #\n # ax.set_xlim(x_min - 0.05 * x_range, x_max + 0.05 * x_range)\n # ax.set_ylim(y_min - 0.05 * y_range, y_max + 0.05 * y_range)\n # ax.set_zlim(z_min - 0.05 * z_range, z_max + 0.05 * z_range)\n\n ax.scatter(control_points_x, control_points_y, control_points_z, depthshade = False, **CONTROL_POLYGON_KWARGS)\n\n # print(np.max(surface_x), np.max(surface_y), np.max(surface_z))\n # print(np.min(surface_x), np.min(surface_y), np.min(surface_z))\n # print(surface_x)\n # print(surface_y)\n # print(surface_z)\n xyz = np.reshape(xyz, (-1, 3))\n print(xyz.shape)\n x, y, z = xyz[:, 0], xyz[:, 1], xyz[:, 2]\n ax.scatter(x, y, z)\n # ax.plot_trisurf(\n # x, y, z,\n # cmap = plt.get_cmap('viridis'),\n # linewidth = 0,\n # antialiased = True,\n # )\n # ax.plot_surface(surface_x, surface_y, surface_z, rstride = 1, cstride = 1)\n # ax.plot_trisurf(surface_x, surface_y, surface_z)\n # ax.plot_trisurf(surface_x, surface_y, surface_z, **CURVE_KWARGS)\n\n ax.axis('off')\n\n ax.view_init(elev = 45, azim = 0) # note that this resets ax.dist to 10, so we can't use it below\n ax.dist = 7.5 # default is 10, so zoom in a little because there's no axis to take up the rest of the space\n\n plt.show()\n utils.save_current_figure(**kwargs)\n\n ### ANIMATION ###\n\n frames = length * fps\n\n writer = anim.writers['ffmpeg'](fps = fps, bitrate = 2000) # don't need a very high bitrate\n\n def animate(frame):\n print(frame, frames, frame / frames)\n ax.azim = 360 * frame / frames # one full rotation\n return [] # must return the list of artists we modified (i.e., nothing, since all we did is rotate the view)\n\n ani = anim.FuncAnimation(fig, animate, frames = frames, blit = True)\n ani.save(f\"{os.path.join(kwargs['target_dir'], kwargs['name'])}.mp4\", writer = writer)\n\n plt.close()","def drawCurve3D(xlist, ylist, zlist):\n dislin.curv3d(xlist,ylist,zlist,len(xlist))","def rotate_z(self, angle):\n angle *= np.pi / 180\n return self.transform(np.matrix([[np.cos(angle), -np.sin(angle), 0],\n [np.sin(angle), np.cos(angle), 0],\n [0, 0, 1]]))","def drawSphere3D(x0,y0,z0, radius, hres, vres):\n dislin.sphe3d(x0,y0,z0, radius, hres, vres)","def cbar( self , y , t = 0 ):\n \n u = self.c( self.zbar, y , self.rbar , t )\n \n return u","def rotz(t):\n c = np.cos(t)\n s = np.sin(t)\n return np.array([[c, -s, 0],\n [s, c, 0],\n [0, 0, 1]])","def rotz(t):\n c = np.cos(t)\n s = np.sin(t)\n return np.array([[c, -s, 0],\n [s, c, 0],\n [0, 0, 1]])","def rotz(t):\n c = np.cos(t)\n s = np.sin(t)\n return np.array([[c, -s, 0],\n [s, c, 0],\n [0, 0, 1]])","def rotz(t):\n c = np.cos(t)\n s = np.sin(t)\n return np.array([[c, -s, 0],\n [s, c, 0],\n [0, 0, 1]])","def rz(self, angle: float, center: Onion[Iterable[Onion[float, int]], Point3D] = None) -> 'Component':\n return self.rotate(rz=angle, center=center)","def cylinder_area(radius: number, height: number) -> number:\n area = 2*pi*radius*(radius+height)\n return area","def cylindrical2cartesian(cylinder):\n cart = np.zeros(cylinder.shape)\n cart[:, 0] = cylinder[:, 0] * np.cos(cylinder[:, 1])\n cart[:, 1] = cylinder[:, 0] * np.sin(cylinder[:, 1])\n cart[:, 2] = cylinder[:, 2]\n return cart","def unoriented_cube():\n faces = get_oriented_cube_faces()\n for face in faces:\n np.random.shuffle(face)\n poly = Polyhedron(get_cube_points(), faces, faces_are_convex=True)\n poly.sort_faces()\n return poly","def add_cylinder_px(volume,\n point1: (int, int, int),\n point2: (int, int, int),\n radius: int):\n p1, p2, r = cylinders_px_to_basecoord([(point1, point2, radius)], volume.shape)[0]\n return add_cylinder_basecoord(volume, p1, p2, r)","def plane(*args, length: float=0.0, name: AnyStr=\"\", position: List[float, float, float]=None,\n rotation: List[float, float, float]=None, size: float=0.0, width: float=0.0,\n **kwargs)->AnyStr:\n pass","def get_z_axis(self, pipette_id: str) -> MotorAxis:\n mount = self.get(pipette_id).mount\n return MotorAxis.LEFT_Z if mount == MountType.LEFT else MotorAxis.RIGHT_Z","def cz(self, q0, q1, ctrl=None):\n self.__add_quantum_gate(kind=CONTROLLED_Z, qid=[q0,q1], ctrl=ctrl)\n return self","def tapered_cylinder_geom(R1, R2, H):\r\n m = (R2-R1)/H\r\n if R1 == R2: # cylinder\r\n V = abs(np.pi*R1*R1*H)\r\n h_c = H/2.0\r\n elif R1==0: \r\n V = abs(1.0/3.0*np.pi*R2*R2*H)\r\n h_c = 3.0/4.0*H\r\n else:\r\n V = abs(np.pi/3.0/m*(R2**3 - R1**3))\r\n h_c = H*(R1**2 + 2*R1*R2 + 3*R2**2)/4.0/(R1**2 + R1*R2 + R2**2) # ( coneV*1./4.*coneH - coneVtip*(1./4.*(coneH-H) + H) )/ taperV\r\n return V, h_c","def full_3d(self, quantity):\n # The data just tells you what integer grid point you are on. Not what actual x,y coordinate you\n # are at\n x = np.arange(0, self.period, self.dx)\n y = np.arange(0, self.period, self.dy)\n z = np.arange(0, self.height + self.dz, self.dz)\n points = np.array(list(itertools.product(z, x, y)))\n # Get the scalar\n scalar = self.get_scalar_quantity(quantity)\n labels = ('X [um]', 'Y [um]', 'Z [um]', quantity)\n # Now plot!\n self.scatter3d(points[:, 1], points[:, 2], points[\n :, 0], scalar.flatten(), labels, 'full_3d')","def vel(z, c = cp.cc.c_light_cm_s/1e5):\n # return z*c/(1+z)\n return c*((1+z)**2-1)/((1+z)**2+1)","def distance_from_cylinder(self, points, params, sqrt=False):\n # axis: 3 x 1, center: 1 x 3\n axis, center, radius = params\n center = center.reshape((1, 3))\n axis = axis.reshape((3, 1))\n\n v = points - center\n prj = (v @ axis) ** 2\n\n # this is going negative at some point! fix it. Numerical issues.\n # voilating pythagoras\n dist_from_surface = torch.sum(v * v, 1) - prj[:, 0]\n dist_from_surface = torch.clamp(dist_from_surface, min=1e-5)\n\n distance = torch.sqrt(dist_from_surface) - radius\n # distance.register_hook(self.print_norm)\n distance = distance ** 2\n\n if sqrt:\n distance = guard_sqrt(distance)\n\n if torch.sum(torch.isnan(distance)):\n import ipdb;\n ipdb.set_trace()\n if self.reduce:\n distance = torch.mean(distance)\n\n return distance"],"string":"[\n \"def cylinder(*args, axis: Union[List[float, float, float], bool]=None, caching: bool=True,\\n degree: Union[int, bool]=3, endSweep: Union[float, bool]=2, heightRatio:\\n Union[float, bool]=2.0, nodeState: Union[int, bool]=0, pivot: Union[List[float,\\n float, float], bool]=None, radius: Union[float, bool]=1.0, sections: Union[int,\\n bool]=8, spans: Union[int, bool]=1, startSweep: Union[float, bool]=0, tolerance:\\n Union[float, bool]=0.01, useTolerance: bool=False, constructionHistory: bool=True,\\n name: AnyStr=\\\"\\\", object: bool=True, polygon: int=0, q=True, query=True, e=True,\\n edit=True, **kwargs)->Union[List[AnyStr], Any]:\\n pass\",\n \"def makecylinder(model=[0,0,0,1,0,0,1],height = 1,density=10):\\n # extract info from cylinder model\\n radius = model[6]\\n X,Y,Z = model[:3]\\n # get 3d points to make an upright cylinder centered to the origin\\n n = np.arange(0,360,int(360/density))\\n height = np.arange(0,height,height/density)\\n n = np.deg2rad(n)\\n x,z = np.meshgrid(n,height)\\n x = x.flatten()\\n z = z.flatten()\\n cyl = np.vstack([np.cos(x)*radius,np.sin(x)*radius,z]).T\\n # rotate and translate the cylinder to fit the model\\n rotation = rotation_matrix_from_vectors([0,0,1],model[3:6])\\n rotated_cylinder = np.matmul(rotation,cyl.T).T + np.array([X,Y,Z])\\n return rotated_cylinder\",\n \"def z_cylinder(shape: (int, int, int), radius: int, dtype=np.uint8):\\n z_depth = shape[2]\\n xhalf_atom = shape[0] // 2\\n yhalf_atom = shape[1] // 2\\n cylinders = [\\n ((xhalf_atom, yhalf_atom, 0),\\n (xhalf_atom, yhalf_atom, z_depth - 1),\\n radius)\\n ]\\n data_mask = create_cylinders_volume(shape, cylinders, foreground=1, dtype=dtype)\\n return data_mask, cylinders\",\n \"def polyCylinder(*args, axis: Union[List[float, float, float], bool]=None, caching: bool=True,\\n constructionHistory: bool=True, createUVs: Union[int, bool]=2, height:\\n Union[float, bool]=2.0, name: AnyStr=\\\"\\\", nodeState: Union[int, bool]=0,\\n object: bool=True, radius: Union[float, bool]=1.0, roundCap: bool=False,\\n subdivisionsAxis: Union[int, bool]=20, subdivisionsCaps: Union[int, bool]=0,\\n subdivisionsHeight: Union[int, bool]=1, subdivisionsX: Union[int, bool]=20,\\n subdivisionsY: Union[int, bool]=1, subdivisionsZ: Union[int, bool]=1, texture:\\n Union[int, bool]=2, q=True, query=True, e=True, edit=True,\\n **kwargs)->Union[List[AnyStr], Any]:\\n pass\",\n \"def Cylinder(\\n center=(0.0, 0.0, 0.0),\\n direction=(1.0, 0.0, 0.0),\\n radius=0.5,\\n height=1.0,\\n resolution=100,\\n capping=True,\\n):\\n cylinderSource = _vtk.vtkCylinderSource()\\n cylinderSource.SetRadius(radius)\\n cylinderSource.SetHeight(height)\\n cylinderSource.SetCapping(capping)\\n cylinderSource.SetResolution(resolution)\\n cylinderSource.Update()\\n surf = wrap(cylinderSource.GetOutput())\\n surf.rotate_z(-90, inplace=True)\\n translate(surf, center, direction)\\n return surf\",\n \"def cylinderize(self, z0=0.0, z1=1.0, support=(0.0, 1.0)):\\n from .geometry import tensor_product, line_segment\\n return tensor_product(line_segment(z0, z1, support=support), self)\",\n \"def createCylinder( basePoint=(0,-1,0), tipPoint=(0,1,0), radius = 1.0, colour=(0.6,0.6,0.6), samples = 20 ):\\r\\n \\r\\n basePoint = PyUtils.toPoint3d(basePoint)\\r\\n tipPoint = PyUtils.toPoint3d(tipPoint)\\r\\n baseToTipVector = Vector3d(basePoint,tipPoint)\\r\\n if baseToTipVector.isZeroVector() :\\r\\n raise ValueError( 'Invalid points for cylinder: base and tip are equal!' )\\r\\n baseToTipUnitVector = baseToTipVector.unit()\\r\\n xUnitVector = baseToTipUnitVector.crossProductWith( Vector3d(0,0,1) )\\r\\n if xUnitVector.length() < 0.5 :\\r\\n xUnitVector = baseToTipUnitVector.crossProductWith( Vector3d(0,-1,0) )\\r\\n xUnitVector.toUnit()\\r\\n yUnitVector = baseToTipUnitVector.crossProductWith( Vector3d(-1,0,0) )\\r\\n if yUnitVector.length() < 0.5 :\\r\\n yUnitVector = baseToTipUnitVector.crossProductWith( Vector3d(0,1,0) )\\r\\n yUnitVector.toUnit()\\r\\n\\r\\n vertices = []\\r\\n for i in range(samples):\\r\\n theta = i * 2 * math.pi / float(samples)\\r\\n vertices.append( basePoint + xUnitVector * math.cos(theta) * radius + yUnitVector * math.sin(theta) * radius )\\r\\n for i in range(samples):\\r\\n theta = i * 2 * math.pi / float(samples)\\r\\n vertices.append( tipPoint + xUnitVector * math.cos(theta) * radius + yUnitVector * math.sin(theta) * radius )\\r\\n for i in range(samples):\\r\\n theta = i * 2 * math.pi / float(samples)\\r\\n vertices.append( basePoint + xUnitVector * math.cos(theta) * radius + yUnitVector * math.sin(theta) * radius )\\r\\n vertices.append( tipPoint + xUnitVector * math.cos(theta) * radius + yUnitVector * math.sin(theta) * radius )\\r\\n \\r\\n faces = [ range(0,samples), range(samples,2*samples) ]\\r\\n for i in range(0,2*samples,2) :\\r\\n base = 2*samples\\r\\n size = 2*samples\\r\\n faces.append( (base+i, base+i+1, base+(i+3)%size, base+(i+2)%size ) )\\r\\n \\r\\n return create( vertices, faces, colour )\",\n \"def project_to_cylinder(points):\\n r, phi, z = cs.cart2cyl(points[:, 0], points[:, 1], points[:, 2])\\n # NOTE: z is unchanged\\n # print(np.array_equal(z, points[:, 2]))\\n # project onto unit cylinder\\n x_cyl = points[:, 0] / r\\n y_cyl = points[:, 1] / r\\n z_cyl = (z - z.min()) / (z.max() - z.min())\\n points_cyl = np.stack((x_cyl, y_cyl, z_cyl), axis=1)\\n return points_cyl, r, phi, z\",\n \"def cylinder(downCirc = -120, upCirc = -70,radius = 15, resolution = 20):\\n t = np.linspace(0, 2*m.pi, resolution)\\n cylinderPos = []\\n for num in t:\\n x = -m.cos(num)*radius\\n y = m.sin(num)*radius\\n\\n cylinderPos.append([x, y, downCirc, 0, 0, 0, 'mov'])\\n\\n for num in t:\\n x = -m.cos(num)*radius\\n y = m.sin(num)*radius\\n\\n cylinderPos.append([x, y, upCirc, 0, 0, 0, 'mov'])\\n\\n cylinderPos.append([0,0,-127,0,0,0,'mov'])\\n return cylinderPos\",\n \"def cylinder(objname, coord1, coord2, radius, rgba, rgba2=None):\\r\\n if not rgba2:\\r\\n rgba2 = rgba\\r\\n return \\\\\\r\\n f'\\\\n{objname} = ' \\\\\\r\\n f'[CYLINDER, {coord1[0]}, {coord1[1]}, {coord1[2]}, ' \\\\\\r\\n f'{coord2[0]}, {coord2[1]}, {coord2[2]}, {radius}, ' \\\\\\r\\n f'{rgba[0]}, {rgba[1]}, {rgba[2]}, ' \\\\\\r\\n f'{rgba2[0]}, {rgba2[1]}, {rgba2[2]}] \\\\n'\",\n \"def cyl(diameter, height):\\n global _cmds, fragments\\n radius = diameter / 2\\n _cmds = (\\n f\\\"cylinder(h={height},\\\"\\n f\\\"r1={radius},r2={radius},\\\"\\n f\\\"center=false,$fn={fragments});\\\\n\\\\n\\\"\\n ) + _cmds\",\n \"def create_cylinder(self, pt1, pt2, radius):\\n ## Compute the cylinder axis and center.\\n axis = [pt1[i] - pt2[i] for i in range(0,3)]\\n length = vtk.vtkMath.Normalize(axis)\\n center = [(pt1[i] + pt2[i])/2.0 for i in range(0,3)]\\n\\n # Determine angle to rotate cylinder into given axis.\\n vec = [ 0.0, 1.0, 0.0 ]\\n rotate_axis = 3*[0.0]\\n tmp_cross = 3*[0.0]\\n vtk.vtkMath.Cross(vec, axis, rotate_axis)\\n radangle = math.atan2(vtk.vtkMath.Norm(rotate_axis), vtk.vtkMath.Dot(axis,vec))\\n degangle = vtk.vtkMath.DegreesFromRadians(radangle)\\n\\n # Create cylinder.\\n cylinder = vtk.vtkCylinderSource()\\n cylinder.SetCenter(0.0,0.0,0.0);\\n cylinder.SetHeight(length);\\n cylinder.SetRadius(radius);\\n cylinder.SetResolution(32)\\n cylinder.Update()\\n\\n # Transform.\\n transformer = vtk.vtkTransform()\\n transformer.Translate(center[0], center[1], center[2])\\n transformer.RotateWXYZ(degangle,rotate_axis)\\n\\n # Get the polydata (polygon mesh) for the transformed cylinder.\\n polyDataTransformer = vtk.vtkTransformPolyDataFilter()\\n polyDataTransformer.SetInputData(cylinder.GetOutput())\\n polyDataTransformer.SetTransform(transformer)\\n polyDataTransformer.Update()\\n return polyDataTransformer.GetOutput()\",\n \"def cylinder(geometry,\\n network,\\n propname,\\n seed='seed',\\n **params):\\n prob_fn = getattr(spst,params['name'])\\n P = prob_fn(params['shape'],loc=params['loc'],scale=params['scale'])\\n value=P.ppf(network.get_throat_data(prop=seed,locations=geometry))\\n network.set_throat_data(locations=geometry,prop=propname,data=value)\",\n \"def vertical_cylinders(xy_size: int, z_depth: int, dtype=np.uint8):\\n shape = (xy_size, xy_size, z_depth)\\n image_size_px = shape[0] // 3\\n z_depth = shape[2]\\n half_atom = image_size_px // 2\\n quarter_atom = image_size_px // 4\\n cylinders = [\\n # center cylinder, z-aligned, 64x64 radius = 16\\n ((image_size_px + half_atom, image_size_px + half_atom, 0),\\n (image_size_px + half_atom, image_size_px + half_atom, z_depth - 1),\\n image_size_px // 4),\\n # first tile overlapping to other tiles, z-aligned, 64x64 radius = 16\\n ((image_size_px - quarter_atom, image_size_px - quarter_atom, 0),\\n (image_size_px - quarter_atom, image_size_px - quarter_atom, z_depth - 1),\\n image_size_px // 4),\\n # lower middle tile overlapping to other tiles, z-aligned, 64x64 radius = 8\\n ((image_size_px * 2 + quarter_atom, image_size_px + half_atom, 0),\\n (image_size_px * 2 + quarter_atom, image_size_px + half_atom, z_depth - 1),\\n image_size_px // 8),\\n ]\\n data_mask = create_cylinders_volume(shape, cylinders, foreground=1, dtype=dtype)\\n return data_mask, cylinders\",\n \"def cylinder_on_axis(radius: int=5, axis: int=0, shape: (int, int, int)=(256, 256, 256)):\\n assert axis < len(shape), \\\"Invalid axis index specified, must be < len(shape)\\\"\\n r = phantom_base.scale_radius_to_basecoord(radius, shape)\\n\\n def cylinder_axis(t):\\n u = -1 + (t * 2), 0, 0\\n # roll the elements to have the formula on the target axis\\n return tuple(np.roll(u, axis)) + (r,)\\n\\n # TODO: we need to scale step_count appropriately for the input shape; maybe half?\\n nsteps = max(shape)\\n return phantom_base.trace_function(cylinder_axis, shape=shape, step_count=nsteps)\",\n \"def createCone( basePoint=(0,-1,0), tipPoint=(0,1,0), radius = 1.0, colour=(0.6,0.6,0.6), samples = 20 ):\\r\\n \\r\\n basePoint = PyUtils.toPoint3d(basePoint)\\r\\n tipPoint = PyUtils.toPoint3d(tipPoint)\\r\\n baseToTipVector = Vector3d(basePoint,tipPoint)\\r\\n if baseToTipVector.isZeroVector() :\\r\\n raise ValueError( 'Invalid points for cylinder: base and tip are equal!' )\\r\\n baseToTipUnitVector = baseToTipVector.unit()\\r\\n xUnitVector = baseToTipUnitVector.crossProductWith( Vector3d(0,0,1) )\\r\\n if xUnitVector.length() < 0.5 :\\r\\n xUnitVector = baseToTipUnitVector.crossProductWith( Vector3d(0,-1,0) )\\r\\n xUnitVector.toUnit()\\r\\n yUnitVector = baseToTipUnitVector.crossProductWith( Vector3d(-1,0,0) )\\r\\n if yUnitVector.length() < 0.5 :\\r\\n yUnitVector = baseToTipUnitVector.crossProductWith( Vector3d(0,1,0) )\\r\\n yUnitVector.toUnit()\\r\\n\\r\\n vertices = []\\r\\n for i in range(samples):\\r\\n theta = i * 2 * math.pi / float(samples)\\r\\n vertices.append( basePoint + xUnitVector * math.cos(theta) * radius + yUnitVector * math.sin(theta) * radius )\\r\\n for i in range(samples):\\r\\n theta = i * 2 * math.pi / float(samples)\\r\\n vertices.append( basePoint + xUnitVector * math.cos(theta) * radius + yUnitVector * math.sin(theta) * radius )\\r\\n vertices.append( tipPoint )\\r\\n \\r\\n faces = [ range(0,samples) ]\\r\\n for i in range(0,samples) :\\r\\n base = samples\\r\\n size = samples\\r\\n faces.append( (base+i, base+(i+1)%size, 2*samples ) )\\r\\n \\r\\n return create( vertices, faces, colour )\",\n \"def HollowCylinder(self,center=(0,0,0),inner_radius=1.0,outer_radius=2.,\\n element_type='hex',isotropic=True,nrad=5,ncirc=10, nlong=20,length=10):\\n\\n if element_type != \\\"hex\\\":\\n raise NotImplementedError('Generating {} mesh of cylinder is not supported yet'.format(element_type))\\n\\n if not isinstance(center,tuple):\\n raise ValueError(\\\"The center for the base of the cylinder should be given in a tuple with three elements (x,y,z)\\\")\\n\\n self.__reset__()\\n\\n nlong = int(nlong)\\n if nlong==0:\\n nlong = 1\\n\\n mesh = Mesh()\\n mesh.HollowCircle(center=(center[0],center[1]), inner_radius=inner_radius,\\n outer_radius=outer_radius, element_type=\\\"quad\\\",\\n isotropic=isotropic, nrad=nrad, ncirc=ncirc)\\n\\n self.Extrude(base_mesh=mesh, length=length, nlong=nlong)\\n self.points += center[2]\",\n \"def template_cylinder_annulus(height, outer_radius, inner_radius=0):\\n\\n img = _template_sphere_disc(dim=2, outer_radius=outer_radius,\\n inner_radius=inner_radius)\\n img = np.tile(np.atleast_3d(img), reps=height)\\n return img\",\n \"def cylinder_volume(radius: number, height: number) -> number:\\n volume = pi*radius*radius*height\\n return volume\",\n \"def OneElementCylinder(self,radius=1, length=100, nz=10, element_type=\\\"hex\\\"):\\n\\n if element_type == \\\"hex\\\":\\n elements = np.arange(0,8)[:,None]\\n for i in range(1,nz):\\n elements = np.concatenate((elements,np.arange(4*i,4*i+8)[:,None]),axis=1)\\n elements = elements.T.copy()\\n\\n theta = np.array([225,315,45,135])*np.pi/180\\n\\n xs = np.tile(radius*np.cos(theta),nz+1)\\n ys = np.tile(radius*np.sin(theta),nz+1)\\n\\n points = np.array([xs,ys]).T.copy()\\n points = np.concatenate((points,np.zeros((4*(nz+1),1))),axis=1)\\n\\n zs = np.linspace(0,length, nz+1)[1:]\\n zs = np.repeat(zs,4)\\n points[4:,-1] = zs\\n\\n if element_type == \\\"hex\\\":\\n self.element_type = element_type\\n self.elements = elements\\n self.points = points\\n self.GetBoundaryFacesHex()\\n self.GetBoundaryEdgesHex()\\n self.GetFacesHex()\\n self.GetEdgesHex()\\n self.nelem = self.elements.shape[0]\\n self.nnode = self.points.shape[0]\\n\\n elif element_type == \\\"tet\\\":\\n # USE MESHPY\\n raise NotImplementedError('Not implemented yet')\\n else:\\n raise ValueError('element type not suppported')\",\n \"def __init__(self,x_c,z_c,cyl_rad):\\n self.x_c = x_c\\n self.z_c = z_c\\n self.cyl_rad = cyl_rad\",\n \"def __init__(self,x_c,z_c,cyl_rad):\\n self.x_c = x_c\\n self.z_c = z_c\\n self.cyl_rad = cyl_rad\",\n \"def __init__(self,x_c,z_c,cyl_rad):\\n self.x_c = x_c\\n self.z_c = z_c\\n self.cyl_rad = cyl_rad\",\n \"def __init__(self,x_c,z_c,cyl_rad):\\n self.x_c = x_c\\n self.z_c = z_c\\n self.cyl_rad = cyl_rad\",\n \"def __init__(self,x_c,z_c,cyl_rad):\\n self.x_c = x_c\\n self.z_c = z_c\\n self.cyl_rad = cyl_rad\",\n \"def add_cylinder_between(pt1, pt2, r, name=None):\\n pt1 = np.array(pt1)\\n pt2 = np.array(pt2)\\n\\n d = pt2 - pt1\\n\\n # Add cylinder at the correct location\\n dist = np.linalg.norm(d)\\n loc = (pt1[0] + d[0] / 2, pt1[1] + d[1] / 2, pt1[2] + d[2] / 2)\\n bpy.ops.mesh.primitive_cylinder_add(radius=r, depth=dist, location=loc)\\n\\n cylinder_obj = bpy.context.object\\n\\n if name is not None:\\n cylinder_obj.name = name\\n\\n # Further rotate it accordingly\\n phi = np.arctan2(d[1], d[0])\\n theta = np.arccos(d[2] / dist)\\n cylinder_obj.rotation_euler[1] = theta\\n cylinder_obj.rotation_euler[2] = phi\\n\\n # Scene update necessary, as matrix_world is updated lazily\\n bpy.context.scene.update()\\n\\n return cylinder_obj\",\n \"def build_cylinders(self, vtk_fname, zscale=1, radius=20):\\r\\n centers = []\\r\\n heights = []\\r\\n for w in self.wells:\\r\\n b = w['LOC']\\r\\n t = [b[0], b[1], 1]\\r\\n b_surf = self.cell_verts(b)\\r\\n t_surf = self.cell_verts(t)\\r\\n minZ = t_surf[0][2]\\r\\n b_v = self.centroid(b_surf)\\r\\n for i in range(len(b_surf)):\\r\\n t_v = t_surf[i]\\r\\n if t_v[2] < minZ:\\r\\n minZ = t_v[2]\\r\\n cyl_height = abs(b_v[2] - minZ)\\r\\n cyl_center = [b_v[0], b_v[1], minZ + cyl_height/2]\\r\\n centers.append(cyl_center)\\r\\n heights.append(cyl_height)\\r\\n # Write center, height data to string\\r\\n c_s = '['\\r\\n for c in centers:\\r\\n c_s += '[' + ', '.join(map(str, c)) + '],\\\\n'\\r\\n c_s = c_s[:-2] + ']\\\\n\\\\n'\\r\\n h_s = '['\\r\\n for h in heights:\\r\\n h_s += str(h) + ',\\\\n'\\r\\n h_s = h_s[:-2] + ']\\\\n\\\\n'\\r\\n # Write Paraview script\\r\\n with io.open('well_cylinders_test.py', 'w', newline='\\\\r\\\\n') as f:\\r\\n f.write(\\\"from paraview.simple import *\\\\n\\\")\\r\\n f.write(\\\"paraview.simple._DisableFirstRenderCameraReset()\\\\n\\\\n\\\")\\r\\n f.write(\\\"# To use, open grid vtk files in Paraview, then run this script in Python Shell\\\\n\\\")\\r\\n f.write(\\\"c = \\\" + c_s)\\r\\n f.write(\\\"h = \\\" + h_s)\\r\\n f.write(\\\"for i in range(len(h)):\\\\n\\\")\\r\\n f.write(\\\" cylinder = Cylinder()\\\\n\\\")\\r\\n f.write(\\\" fn = FindSource('\\\" + vtk_fname + \\\"_*')\\\\n\\\")\\r\\n f.write(\\\" rv = GetActiveViewOrCreate('RenderView')\\\\n\\\")\\r\\n f.write(\\\" tf = Transform(Input=cylinder)\\\\n\\\")\\r\\n f.write(\\\" tf.Transform = 'Transform'\\\\n\\\")\\r\\n f.write(\\\" tf.Transform.Translate = [c[i][0], c[i][1], c[i][2] * \\\" + str(zscale) + \\\"]\\\\n\\\")\\r\\n f.write(\\\" tf.Transform.Rotate = [90.0, 0.0, 0.0]\\\\n\\\")\\r\\n f.write(\\\" tf.Transform.Scale = [1.0, \\\" + str(zscale) + \\\", 1.0]\\\\n\\\")\\r\\n f.write(\\\" transformDisplay = Show(tf, rv, 'GeometryRepresentation')\\\\n\\\")\\r\\n f.write(\\\" cylinder.Radius = \\\" + str(radius) + \\\"\\\\n\\\")\\r\\n f.write(\\\" cylinder.Height = h[i]\\\\n\\\")\\r\\n f.write(\\\" rv.Update()\\\")\",\n \"def Cylinder(self, center=(0.,0.,0.), radius=1., length=10., nrad=16, ncirc=40, nlong=50, element_type=\\\"hex\\\", algorithm=\\\"standard\\\"):\\n\\n if element_type != \\\"hex\\\":\\n raise NotImplementedError('Generating {} mesh of cylinder is not supported yet'.format(element_type))\\n\\n if not isinstance(center,tuple):\\n raise ValueError(\\\"The center for the base of the cylinder should be given in a tuple with three elements (x,y,z)\\\")\\n\\n self.__reset__()\\n\\n nlong = int(nlong)\\n if nlong==0:\\n nlong = 1\\n\\n mesh = Mesh()\\n mesh.Circle(center=(center[0],center[1]), radius=radius, nrad=nrad, ncirc=ncirc, element_type=\\\"quad\\\", algorithm=algorithm)\\n\\n self.Extrude(base_mesh=mesh, length=length, nlong=nlong)\\n self.points += center[2]\",\n \"def plot_eccentricity(self, z=0):\\n p = figure(\\n title=\\\"Cut in plane Z=\\\" + str(z),\\n x_axis_label=\\\"X axis\\\",\\n y_axis_label=\\\"Y axis\\\",\\n )\\n for j in range(0, self.ntheta):\\n p.circle(self.xre[z][j], self.yre[z][j], color=\\\"red\\\")\\n p.circle(self.xri[z][j], self.yri[z][j], color=\\\"blue\\\")\\n p.circle(0, 0, color=\\\"blue\\\")\\n p.circle(self.xi, self.yi, color=\\\"red\\\")\\n p.circle(0, 0, color=\\\"black\\\")\\n return p\",\n \"def unit_z(cls):\\n return cls(0, 0, 1)\",\n \"def build_cylinder(self, n_phis):\\n index = glGenLists(1)\\n phis = [float(i)*2.0*numpy.pi/float(n_phis) for i in range(n_phis+1)]\\n phi_pairs = zip(phis, phis[1:])\\n glNewList(index, GL_COMPILE)\\n glBegin(GL_QUADS)\\n for phi1,phi2 in phi_pairs:\\n dot1 = min(max(numpy.cos(phi1), 0.0), 1.0)\\n dot2 = min(max(numpy.cos(phi2), 0.0), 1.0)\\n glTexCoord1f(dot1)\\n glVertex3f(-0.5, numpy.sin(phi1), numpy.cos(phi1))\\n glTexCoord1f(dot1)\\n glVertex3f(0.5, numpy.sin(phi1), numpy.cos(phi1))\\n glTexCoord1f(dot2)\\n glVertex3f(0.5, numpy.sin(phi2), numpy.cos(phi2))\\n glTexCoord1f(dot2)\\n glVertex3f(-0.5, numpy.sin(phi2), numpy.cos(phi2))\\n glEnd()\\n glEndList()\\n return index\",\n \"def in_cylinder(x, y, z, min_z, max_z, max_r):\\n r = np.sqrt(x ** 2 + y ** 2)\\n m = r < max_r\\n m = m & (z < max_z)\\n m = m & (z >= min_z)\\n return m\",\n \"def tube(outside_diam, inside_diam, height):\\n global _cmds, fragments\\n r1 = outside_diam / 2\\n r2 = inside_diam / 2\\n _cmds = (\\n \\\"difference(){\\\\n\\\"\\n f\\\"cylinder(h={height},r1={r1},r2={r1},\\\"\\n f\\\"center=false,$fn={fragments});\\\\n\\\"\\n f\\\"cylinder(h={height*3},r1={r2},r2={r2},\\\"\\n f\\\"center=true,$fn={fragments});\\\\n\\\"\\n \\\"}\\\\n\\\") + _cmds\",\n \"def cross_z(self):\\n return Vector((self.v.y, -self.v.x))\",\n \"def rotateZ(self, *args, **kwargs):\\n ...\",\n \"def cylinder_volume(diameter, length):\\n return 3.14159 * ((diameter/2) ** 2) * length\",\n \"def cross_z(self):\\n return self.v.cross(Vector((0, 0, 1)))\",\n \"def wheel(self):\\n return FilletedSolid(\\n built_from=\\n TranslatedShape(shape_in=\\n RotatedShape(shape_in=\\n Cylinder(radius=\\n self.wheels_properties[1],\\n height=300.,\\n position=self.position),\\n rotation_point=self.position,\\n vector=Vector(1, 0, 0),\\n angle=radians(90)),\\n displacement=Vector(self.wheels_properties[0],\\n 300.,\\n -self.positions[1][0])),\\n radius=30.)\",\n \"def p2pCyl(startPoint, endPoint, radius=10, modName=\\\"Cyl\\\", plus=0, Seg=3,\\n\\t\\t\\t color=\\\"red\\\", Opacity=1, RotY=0, Tx=0):\\n\\t\\tcylinderSource = vtk.vtkCylinderSource()\\n\\t\\tcylinderSource.SetRadius(radius)\\n\\t\\tcylinderSource.SetResolution(Seg)\\n\\n\\t\\trng = vtk.vtkMinimalStandardRandomSequence()\\n\\t\\trng.SetSeed(8775070) # For testing 8775070\\n\\n\\t\\t# Compute a basis\\n\\t\\tnormalizedX = [0] * 3\\n\\t\\tnormalizedY = [0] * 3\\n\\t\\tnormalizedZ = [0] * 3\\n\\n\\t\\t# The X axis is a vector from start to end\\n\\t\\tvtk.vtkMath.Subtract(endPoint, startPoint, normalizedX)\\n\\t\\tlength = vtk.vtkMath.Norm(normalizedX) + plus\\n\\t\\t# length = 20\\n\\t\\tvtk.vtkMath.Normalize(normalizedX)\\n\\n\\t\\t# The Xn axis is an arbitrary vector cross X\\n\\t\\tarbitrary = [0] * 3\\n\\t\\tfor i in range(0, 3):\\n\\t\\t\\trng.Next()\\n\\t\\t\\tarbitrary[i] = rng.GetRangeValue(-10, 10)\\n\\t\\tvtk.vtkMath.Cross(normalizedX, arbitrary, normalizedZ)\\n\\t\\tvtk.vtkMath.Normalize(normalizedZ)\\n\\n\\t\\t# The Zn axis is Xn cross X\\n\\t\\tvtk.vtkMath.Cross(normalizedZ, normalizedX, normalizedY)\\n\\t\\tmatrix = vtk.vtkMatrix4x4()\\n\\t\\t# Create the direction cosine matrix\\n\\t\\tmatrix.Identity()\\n\\t\\tfor i in range(0, 3):\\n\\t\\t\\tmatrix.SetElement(i, 0, normalizedX[i])\\n\\t\\t\\tmatrix.SetElement(i, 1, normalizedY[i])\\n\\t\\t\\tmatrix.SetElement(i, 2, normalizedZ[i])\\n\\t\\t# Apply the transforms\\n\\t\\ttransform = vtk.vtkTransform()\\n\\t\\ttransform.Translate(startPoint) # translate to starting point\\n\\t\\ttransform.Concatenate(matrix) # apply direction cosines\\n\\t\\ttransform.RotateZ(-90.0) # align cylinder to x axis\\n\\t\\ttransform.Scale(1.0, length, 1.0) # scale along the height vector\\n\\t\\ttransform.Translate(0, .5, 0) # translate to start of cylinder\\n\\t\\ttransform.RotateY(RotY)\\n\\t\\ttransform.Translate(Tx, 0, 0)\\n\\n\\t\\t# Transform the polydata\\n\\t\\ttransformPD = vtk.vtkTransformPolyDataFilter()\\n\\t\\ttransformPD.SetTransform(transform)\\n\\t\\ttransformPD.SetInputConnection(cylinderSource.GetOutputPort())\\n\\n\\t\\tstlMapper = vtk.vtkPolyDataMapper()\\n\\t\\tstlMapper.SetInputConnection(transformPD.GetOutputPort())\\n\\t\\tvtkNode = slicer.modules.models.logic().AddModel(transformPD.GetOutputPort())\\n\\t\\tvtkNode.SetName(modName)\\n\\t\\tmodelDisplay = vtkNode.GetDisplayNode()\\n\\t\\tmodelDisplay.SetColor(Helper.myColor(color))\\n\\t\\tmodelDisplay.SetOpacity(Opacity)\\n\\t\\tmodelDisplay.SetBackfaceCulling(0)\\n\\t\\t# modelDisplay.SetVisibility(1)\\n\\t\\tmodelDisplay.SetVisibility2D(True)\\n\\t\\t# modelDisplay.SetSliceDisplayModeToProjection()\\n\\t\\t# dn.SetVisibility2D(True)\\n\\t\\treturn\",\n \"def _zforce_xyz(self,x,y,z):\\n return -2.*np.pi*self._rhoc_M * self.a**3*self._b*self._c * \\\\\\n _forceInt(x, y, z, self._a2, self._b2*self._a2, self._c2*self._a2, self.n, 2)\",\n \"def ArcCylinder(self, center=(0.,0.,0.), radius=1., start_angle=0, end_angle=np.pi/2.,\\n length=10., nrad=16, ncirc=40, nlong=50, element_type=\\\"hex\\\"):\\n\\n if element_type != \\\"hex\\\":\\n raise NotImplementedError('Generating {} mesh of cylinder is not supported yet'.format(element_type))\\n\\n if not isinstance(center,tuple):\\n raise ValueError(\\\"The center for the base of the cylinder should be given in a tuple with three elements (x,y,z)\\\")\\n\\n self.__reset__()\\n\\n nlong = int(nlong)\\n if nlong==0:\\n nlong = 1\\n\\n mesh = Mesh()\\n mesh.Arc(center=(center[0],center[1]), radius=radius, start_angle=start_angle,\\n end_angle=end_angle, nrad=nrad, ncirc=ncirc, element_type=\\\"quad\\\")\\n\\n self.Extrude(base_mesh=mesh, length=length, nlong=nlong)\\n self.points += center[2]\",\n \"def origami_H2_2cyl(w1,h1,t1,w2,h2,t2):\\n assert((w2 < w1) and (t1 < w1) and (t2 < w2))\\n\\n # v for volumes and z for z\\n v1 = h1*w1\\n v2 = h2*w2\\n z1 = (h1-1)*w1 + 1\\n z2 = v1 + (h2-1)*w2 + 1\\n\\n # the horizontal permutation\\n x = [None] + range(2,v1+v2+1) + [1]\\n for i in range(h1):\\n x[(i+1)*w1] = i*w1 + 1\\n for i in range(h2):\\n x[v1 + (i+1)*w2] = v1 + i*w2 + 1\\n\\n # the vertical permutation\\n y = ([None] +\\n range(w1+1,v1+1) + [None]*w1 +\\n range(v1+w2+1,v1+v2+1) + [None]*w2)\\n\\n for i in range(w2):\\n # up-left of the first cylinder\\n # print \\\"U1L) z1 + (t1+i)%w1 -> 1+v1+i: \\\", z1+(t1+i)%w1, 1+v1+i\\n y[z1+(t1+i)%w1] = 1+v1+i\\n for i in range(w2):\\n # up of the second cylinder\\n # print \\\"U2) z2+(t2+i)%w2 -> 1 + (t1+i)%w1: \\\", z2+(t2+i)%w2, 1+(t1+i)%w1\\n y[z2+(t2+i)%w2] = 1+i\\n for i in range(w1-w2):\\n # up-right of the first cylinder\\n # print \\\"U1R) z1+w2+(t1+i) -> 1+i: \\\", z1+(w2+t1+i)%w1, 1+w2+i\\n y[z1+(w2+t1+i)%w1] = 1+w2+i\\n\\n return Origami(x[1:],y[1:])\",\n \"def render_capsule(self, l, r):\\n # draw cylinder\\n glPushMatrix()\\n glScalef(l, r, r)\\n glCallList(self.cylinder)\\n glPopMatrix()\\n # draw +x hemisphere\\n glPushMatrix()\\n glTranslatef(l/2.0, 0, 0)\\n glScalef(r, r, r)\\n glCallList(self.hemisphere)\\n glPopMatrix()\\n # draw -x hemisphere\\n glPushMatrix()\\n glRotatef(180.0, 0, 0, 1)\\n glTranslatef(l/2.0, 0, 0)\\n glScalef(r, r, r)\\n glCallList(self.hemisphere)\\n glPopMatrix()\",\n \"def createDetectorCylinder(self,uNum=1):\\n cellsCreated = 0\\n sNum = self.SurfaceStartNum\\n cNum = self.CellStartNum\\n detectorCells = list()\\n s = '{:5d} rcc 0 0 0 0 0 217.7 {}\\\\n'.format(self.SurfaceStartNum,self.geoParam['CylinderRadius'])\\n c = ''\\n keyList = sorted(self.surfGeo.keys(), key = lambda x: float(x))\\n for key in keyList:\\n sPrev = sNum\\n sNum += 1\\n cNum += 1\\n s += self.surfForStr.format(sNum,key)\\n m = self.material[self.surfGeo[key]]\\n if cNum == self.CellStartNum+1:\\n c+= '{:5d} {:d} -{:4.3f} -{:d} u={:d}\\\\n'.format(cNum,m['mt'],m['rho'],sNum,uNum)\\n else:\\n c += self.cellForStr.format(cNum,m['mt'],m['rho'],sPrev,sNum,uNum)\\n # List of cells for the detector\\n if self.surfGeo[key] is 'Detector':\\n detectorCells.append(cNum)\\n cellsCreated += 1\\n # Last cell up to universe boundary\\n m = self.material['Moderator']\\n c += '{:5d} {:d} -{:4.3f} {:d} u={:d}\\\\n'.format(cNum+1,m['mt'],m['rho'],sNum,uNum)\\n cellsCreated += 1\\n return s,c,detectorCells,cellsCreated\",\n \"def rot_z(angle):\\n sangle = math.sin(angle)\\n cangle = math.cos(angle)\\n rz = np.array([[cangle, sangle, 0.0],\\n [-sangle, cangle, 0.0],\\n [0.0, 0.0, 1.0]])\\n return rz\",\n \"def make_cylindrical(self, grid, axis='z'):\\n if grid.shape != self.shape:\\n raise NotImplementedError('make_cylindrical: grid shape mismatch'\\n ' not supported')\\n gt_flat = grid.t.flat\\n self.r = self.x.copy()\\n self.t = self.x.copy()\\n r_flat = self.r.flat\\n t_flat = self.t.flat\\n\\n if axis == 'z':\\n x_flat = self.x.flat\\n y_flat = self.y.flat\\n for i in range(self.x.size):\\n gt = gt_flat[i]\\n x = x_flat[i]\\n y = y_flat[i]\\n magnitude = hypot(x, y)\\n rel_theta = atan2(y, x) - gt\\n r_flat[i] = magnitude * cos(rel_theta)\\n t_flat[i] = magnitude * sin(rel_theta)\\n self.x = None\\n self.y = None\\n\\n elif axis == 'x':\\n y_flat = self.y.flat\\n z_flat = self.z.flat\\n for i in range(self.y.size):\\n gt = gt_flat[i]\\n y = y_flat[i]\\n z = z_flat[i]\\n magnitude = hypot(y, z)\\n rel_theta = atan2(z, y) - gt\\n r_flat[i] = magnitude * cos(rel_theta)\\n t_flat[i] = magnitude * sin(rel_theta)\\n self.z = self.x\\n self.x = None\\n self.y = None\\n\\n else:\\n raise ValueError(\\\"axis must be 'z' or 'x'\\\")\",\n \"def create_cylinders_volume(\\n shape: (int, int, int),\\n cylinders_list: list,\\n foreground=1,\\n dtype=np.uint8):\\n volume = np.zeros(shape, dtype=bool)\\n for point1, point2, radius in cylinders_list:\\n volume = add_cylinder_px(volume, point1, point2, radius)\\n return volume_bool_to_dtype(volume, fg=foreground, dtype=dtype)\",\n \"def c(self, z, y, r, t):\\n \\n u = np.zeros( self.m ) \\n \\n return u\",\n \"def rotateZ(self, ang):\\n if ang != 0.0:\\n x = self.getX() * cos(ang) - self.getY() * sin(ang)\\n y = self.getX() * sin(ang) + self.getY() * cos(ang)\\n z = self.getZ()\\n self.setX(x)\\n self.setY(y)\\n self.setZ(z)\",\n \"def rotateZ(self, angle):\\r\\n rad = angle * math.pi / 180\\r\\n cosa = math.cos(rad)\\r\\n sina = math.sin(rad)\\r\\n x = self.x * cosa - self.y * sina\\r\\n y = self.x * sina + self.y * cosa\\r\\n return Point3D(x, y, self.z)\",\n \"def cylindervert_addedmass(R, z1, z2, rho, Ca=1, AxCa=1,\\n m_f=0, z_f=0, m_mg=0, z_mg=0):\\n if z1<z2:\\n raise Exception('z1 should be above z2')\\n if z1<0:\\n # Fully submerged\\n ztop = z1\\n A0=0\\n nAx=2\\n else:\\n # Partially submerged\\n ztop = 0 \\n A0 = np.pi*R**2 # undisplaced waterplane area of platform (m^2)\\n nAx=1\\n\\n h = ztop-z2 # submerged height\\n z_b = (ztop+z2)/2 # coordinates of the center of buoyancy of the undisplaced platform (m)\\n V0 = np.pi*R**2*h # undisplaced volume of platform (m^3)\\n\\n M=np.zeros((6,6))\\n M[0,0] = Ca * rho*V0\\n M[1,1] = Ca * rho*V0\\n M[2,2] = nAx*AxCa * 2/3*rho*np.pi * R**3 # rho*V0* D/(3*h)\\n M[4,0] = M[0,0]*z_b # TODO empirical\\n M[3,1] = -M[0,0]*z_b # TODO empirical\\n M[0,4] = M[0,0]*z_b # TODO empirical\\n M[1,3] = -M[0,0]*z_b # TODO empirical\\n\\n T1 =Ca*rho*np.pi*R**2 * h**3 /3 # Ca * rho*V0 * h**2/3 # TODO a bit empirical\\n M[3,3] = T1\\n M[4,4] = T1\\n return M\",\n \"def showSimpleCylinders(self): \\n #from morphforge.morphology.util import TriMeshBuilderVerySimple\\n import sys\\n sys.path.append('/usr/share/pyshared/')\\n \\n #import morphforge\\n from morphforge.morphology.mesh import MeshBuilderRings\\n MonkeyPatchMayaVi()\\n #import enthought.mayavi.mlab as mlab\\n from mayavi import mlab\\n \\n assert len(self.morphs)==1\\n mesh = MeshBuilderRings().build(self.morphs[0])\\n \\n \\n @mlab.show\\n def _showSimpleCylinders():\\n\\t \\n #c = TriMeshBuilderVerySimple(self.morphs[0])\\n #mlab.triangular_mesh(c.x, c.y, c.z, c.triangles, colormap=self.colormap)\\n mlab.triangular_mesh(mesh.vertices[:,0], mesh.vertices[:,1], mesh.vertices[:,2], mesh.triangles, colormap=self.colormap)\\n \\n _showSimpleCylinders()\",\n \"def rotation3D_z(angle: float) -> np.array:\\n c = np.cos(angle)\\n s = np.sin(angle)\\n return np.array([[c, -s, 0.0], [s, c, 0.0], [0.0, 0.0, 1.0]])\",\n \"def surface_area_cylinder(radius: float, height: float) -> float:\\r\\n if radius < 0 or height < 0:\\r\\n raise ValueError(\\\"surface_area_cylinder() only accepts non-negative values\\\")\\r\\n return 2 * pi * radius * (height + radius)\",\n \"def rotateZ(self, angle):\\n rad = angle * math.pi / 180\\n cosa = math.cos(rad)\\n sina = math.sin(rad)\\n x = self.x * cosa - self.y * sina\\n y = self.x * sina + self.y * cosa\\n return Point3D(x, y, self.z)\",\n \"def rotateZ(self, angle):\\n rad = angle * math.pi / 180\\n cosa = math.cos(rad)\\n sina = math.sin(rad)\\n x = self.x * cosa - self.y * sina\\n y = self.x * sina + self.y * cosa\\n return Point3D(x, y, self.z)\",\n \"def rotateZ(self, angle):\\n rad = angle * math.pi / 180\\n cosa = math.cos(rad)\\n sina = math.sin(rad)\\n x = self.x * cosa - self.y * sina\\n y = self.x * sina + self.y * cosa\\n return Point3D(x, y, self.z)\",\n \"def rotateZ(self, angle):\\n rad = angle * math.pi / 180\\n cosa = math.cos(rad)\\n sina = math.sin(rad)\\n x = self.x * cosa - self.y * sina\\n y = self.x * sina + self.y * cosa\\n return Point3D(x, y, self.z)\",\n \"def test_cylindrical(self):\\n # Rotate around the z axis\\n r = Joint.cylindrical(np.array([0, 0, 1]))\\n t_mat = r(np.array([np.pi / 2, 1.0]))\\n\\n rot_vec = np.dot(t_mat[:3, :3], np.array([1, 0, 0]))\\n\\n self.assertTrue(np.allclose(\\n rot_vec, np.array([0, 1, 0]), rtol=1e-5, atol=1e-5))\\n self.assertTrue(np.allclose(t_mat[2, 3], 1))\",\n \"def rotateZ(self, angle):\\n rad = math.radians(angle)\\n cosa = math.cos(rad)\\n sina = math.sin(rad)\\n x = self.x * cosa - self.y * sina\\n y = self.x * sina + self.y * cosa\\n return Point3D(x, y, self.z)\",\n \"def rotateZ(self, angle):\\n\\t\\trad = angle * math.pi / 180\\n\\t\\tcosa = math.cos(rad)\\n\\t\\tsina = math.sin(rad)\\n\\t\\tx = self.x * cosa - self.y * sina\\n\\t\\ty = self.x * sina + self.y * cosa\\n\\t\\treturn Point3D(x, y, self.z)\",\n \"def test_circular_scatter_z():\\n area = [0, 1000, 0, 1000]\\n size = 1000\\n z_value = 500\\n x, y, z = gridder.circular_scatter(area, size, z=z_value, random=False)\\n assert z.size == size\\n npt.assert_allclose(z, z_value + np.zeros(size))\",\n \"def RunCylinder(l,p,cylinderPositions):\\n # Creating input and output deck names\\n posString = ''\\n for pos in cylinderPositions:\\n posString += '{:2.1f}-'.format(pos[0])\\n posString = posString.rstrip('-')\\n inp='Cyl_{}LiF_{}_{}.mcnp'.format(int(l*100),p,posString)\\n name='OUTCyl_{}LiF_{}_{}.'.format(int(l*100),p,posString)\\n print inp\\n # Creating and running the model\\n m = CylinderRPM()\\n m.createSurfaceGeo()\\n m.setMaterial(l,p)\\n m.createDetectorCylinder()\\n m.createInputDeck(cylinderPositions,inp,name)\\n m.runModel()\",\n \"def squeeze(self, z, mode):\\n phi = angle(z)\\n r = abs(z)\\n self.circuit.squeeze(r, phi, mode)\",\n \"def generate(self):\\n inside = self.crystal.is_inside(self.x,self.y,self.z)\\n X = np.vstack((self.x[inside],self.y[inside],self.z[inside]))\\n return self.rot.rotate(X)\",\n \"def cart2polar3d(cartesian):\\n radius = np.linalg.norm(cartesian)\\n theta = np.cos\",\n \"def polyCone(*args, axis: Union[List[float, float, float], bool]=None, caching: bool=True,\\n constructionHistory: bool=True, createUVs: Union[int, bool]=2, height: Union[float,\\n bool]=2.0, name: AnyStr=\\\"\\\", nodeState: Union[int, bool]=0, object: bool=True,\\n radius: Union[float, bool]=1.0, roundCap: bool=False, subdivisionsAxis: Union[int,\\n bool]=20, subdivisionsCap: Union[int, bool]=0, subdivisionsHeight: Union[int,\\n bool]=1, subdivisionsX: Union[int, bool]=20, subdivisionsY: Union[int, bool]=1,\\n subdivisionsZ: Union[int, bool]=0, texture: bool=True, q=True, query=True, e=True,\\n edit=True, **kwargs)->Union[List[AnyStr], Any]:\\n pass\",\n \"def _add_cylinder_basic(self, point1, point2, radius):\\n\\n self._cylinder_end_nodes.append(point1)\\n self._cylinder_end_nodes.append(point2)\\n self._cylinder_end_nodes_radiuses.append(radius)\\n self._cylinder_end_nodes_radiuses.append(radius)\\n self.object_number += 1\",\n \"def zaxis ( self ) :\\n return self.__zaxis\",\n \"def rotz(t):\\n c = np.cos(t)\\n s = np.sin(t)\\n return np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]])\",\n \"def create_triangle( xsize, ysize, zsize, place=(0,0,0), rotate=(1,0,0,0) ):\\n \\n b1 = create_box( (xsize,ysize,zsize), place=(0,0,0 ) )\\n ms = max( xsize, zsize )\\n angle = math.atan( zsize / xsize ) * 180 / math.pi\\n b2 = create_box( (2*ms, 2*ysize, 2*ms), place=(0,-EPS,0), rotate=(0,1,0,-angle) )\\n tr = create_cut( b1, b2 )\\n return relocate( tr, place, rotate )\",\n \"def _derZ(self, w, x, y, z):\\n raise NotImplementedError()\",\n \"def add_vehicle_z_axis(df):\\n x = pd.Series(\\n np.zeros(df.shape[0]),\\n index=df[\\\"timestamp\\\"],\\n name=\\\"T_vehicle_attitude_0__NF_body_z_axis_x\\\",\\n )\\n y = pd.Series(\\n np.zeros(df.shape[0]),\\n index=df[\\\"timestamp\\\"],\\n name=\\\"T_vehicle_attitude_0__NF_body_z_axis_y\\\",\\n )\\n z = pd.Series(\\n np.ones(df.shape[0]),\\n index=df[\\\"timestamp\\\"],\\n name=\\\"T_vehicle_attitude_0__NF_body_z_axis_z\\\",\\n )\\n x, y, z = mpd.series_quatrot(\\n x,\\n y,\\n z,\\n df[\\\"T_vehicle_attitude_0__F_q_0\\\"],\\n df[\\\"T_vehicle_attitude_0__F_q_1\\\"],\\n df[\\\"T_vehicle_attitude_0__F_q_2\\\"],\\n df[\\\"T_vehicle_attitude_0__F_q_3\\\"],\\n )\\n\\n df[x.name] = x.values\\n df[y.name] = y.values\\n df[z.name] = z.values\",\n \"def __init__(self, pos, heading=vector(0,0,1)):\\n # list of vPython 3D shapes that make up this player\\n self.parts = []\\n \\n self.pos = vector(pos)\\n # Direction in which robot is moving, normalized to unit length\\n self.heading = norm(heading)\\n\\n self.radius = 1.0\\n\\n self.velocity = vector(0,0,0)\\n\\n face = cylinder(pos=self.pos, axis = (0,1.5,0), radius=.75,\\n color=color.white, material = materials.chrome)\\n self.parts += [face] \\n\\n self.head = sphere(pos=self.pos, radius = .75, color = color.white, material = materials.chrome)\\n self.parts += [self.head]\\n\\n \\n left_eye = sphere(pos=self.pos+vector(.35,.4,.6), \\n radius=0.36, color=color.blue, material = materials.emissive)\\n self.parts += [left_eye]\\n right_eye = sphere(pos=self.pos+vector(-.35,.4,.6),\\n radius=0.36, color=color.blue, material = materials.emissive)\\n self.parts += [right_eye]\\n\\n neck = cylinder(pos=self.pos+vector(0,-1,0), axis = (0,.5,0), radius = .05, color=color.white)\\n self.parts += [neck]\\n\\n self.body = cylinder(pos=self.pos+vector(0,-1.75,0),axis = (0,.75,-.2), radius = .35, color=color.white, material = materials.chrome)\\n self.parts += [self.body]\\n\\n bottom = sphere(pos=self.pos+vector(0,-1.75,0), radius =.35, color = color.white, material = materials.chrome)\\n self.parts += [bottom]\\n\\n right_shoulder = sphere(pos = self.pos+vector(-.35,-1,0), radius = .20, color = color.blue, material = materials.chrome)\\n self.parts += [right_shoulder]\\n\\n left_shoulder = sphere(pos= self.pos+vector(.35,-1,0), radius = .20, color = color.blue, material = materials.chrome)\\n self.parts += [left_shoulder]\\n\\n right_arm = cone(pos = self.pos+vector(-.36, -1.1, 0), axis = (-.2, -.7, -.4), radius = .12, color = color.white, material = materials.chrome)\\n self.parts += [right_arm]\\n\\n left_arm = cone(pos = self.pos+vector(.36, -1.1, 0), axis = (.2, -.7, -.4), radius = .12, color = color.white, material = materials.chrome)\\n self.parts += [left_arm]\\n\\n right_leg = cone(pos = self.pos+vector(-.32, -2.85, 0), axis = (.1, .8, .1), radius = .2, color = color.white, material = materials.chrome)\\n self.parts += [right_leg]\\n\\n left_leg = cone(pos = self.pos+vector(.32,-2.15,.8), axis = (-.1, .1, -.8), radius = .2, color = color.white, material = materials.chrome)\\n self.parts += [left_leg]\",\n \"def rotate_z(self, angle: float):\\n self.vertices = list(\\n Matrix44.z_rotate(angle).transform_vertices(self.vertices)\\n )\\n return self\",\n \"def _derZ(self, x, y, z):\\n raise NotImplementedError()\",\n \"def test_rotate_around_v3_z_axis(self):\\n from pedemath.vec3 import rotate_around_vector_v3\\n\\n vec_a = Vec3(3, 4, 5)\\n vec_b = Vec3(0, 0, 1)\\n\\n result = rotate_around_vector_v3(vec_a, math.pi, vec_b)\\n expected = Vec3(-3, -4, 5)\\n\\n self.assertAlmostEqual(result.x, expected.x)\\n self.assertAlmostEqual(result.y, expected.y)\\n self.assertAlmostEqual(result.z, expected.z)\",\n \"def cross(self):\\n return self.v.cross(self.z_axis)\",\n \"def draw_car():\\r\\n\\twheel_radius = .5\\r\\n\\twheel_thickness = .4\\r\\n\\r\\n\\tglPushMatrix()\\r\\n\\r\\n\\t# shift the car up so the base lies at the origin\\r\\n\\tglTranslatef(0,wheel_radius,0)\\r\\n\\t\\r\\n\\tdraw_car_body()\\r\\n\\r\\n\\t# draw the car wheels\\r\\n\\t# assume the car is facing down the -z axis\\r\\n\\t# front left, front right, back left, back right\\r\\n\\tww = wheel_thickness/2\\r\\n\\twheel_centers = [(-.5-ww,0,-1),(.5+ww,0,-1),(-.5-ww,0,1),(.5+ww,0,1)]\\r\\n\\tfor i in range(4):\\r\\n\\t\\tglPushMatrix()\\r\\n\\t\\tapply(glTranslatef,wheel_centers[i])\\r\\n\\t\\tglRotatef(90,0,1,0)\\r\\n\\t\\tglScalef(wheel_radius,wheel_radius,wheel_radius)\\r\\n\\t\\tdraw_wheel()\\r\\n\\t\\tglPopMatrix()\\r\\n\\r\\n\\tglPopMatrix()\",\n \"def plot_surface_3D(self, length = 30, fps = 30, **kwargs):\\n fig = utils.get_figure(scale = 3)\\n ax = fig.add_subplot(111, projection = '3d')\\n\\n # surface_x = self.xi_1_mesh\\n # surface_y = self.xi_2_mesh\\n # surface_x, surface_y, surface_z = self.surface()\\n xyz = self.surface()\\n\\n # surface_x, surface_y = np.meshgrid(surface_x, surface_y)\\n\\n # print(np.shape(surface_x))\\n # print(np.shape(surface_y))\\n # print(np.shape(surface_z))\\n\\n control_points_x = np.array([control_point[0] for control_point in self.control_net.values()])\\n control_points_y = np.array([control_point[1] for control_point in self.control_net.values()])\\n control_points_z = np.array([control_point[2] for control_point in self.control_net.values()])\\n\\n # x_min = min(np.min(surface_x), np.min(control_points_x))\\n # x_max = max(np.max(surface_x), np.max(control_points_x))\\n # x_range = np.abs(x_max - x_min)\\n #\\n # y_min = min(np.min(surface_y), np.min(control_points_y))\\n # y_max = max(np.max(surface_y), np.max(control_points_y))\\n # y_range = np.abs(y_max - y_min)\\n #\\n # z_min = min(np.min(surface_z), np.min(control_points_z))\\n # z_max = max(np.max(surface_z), np.max(control_points_z))\\n # z_range = np.abs(z_max - z_min)\\n #\\n # ax.set_xlim(x_min - 0.05 * x_range, x_max + 0.05 * x_range)\\n # ax.set_ylim(y_min - 0.05 * y_range, y_max + 0.05 * y_range)\\n # ax.set_zlim(z_min - 0.05 * z_range, z_max + 0.05 * z_range)\\n\\n ax.scatter(control_points_x, control_points_y, control_points_z, depthshade = False, **CONTROL_POLYGON_KWARGS)\\n\\n # print(np.max(surface_x), np.max(surface_y), np.max(surface_z))\\n # print(np.min(surface_x), np.min(surface_y), np.min(surface_z))\\n # print(surface_x)\\n # print(surface_y)\\n # print(surface_z)\\n xyz = np.reshape(xyz, (-1, 3))\\n print(xyz.shape)\\n x, y, z = xyz[:, 0], xyz[:, 1], xyz[:, 2]\\n ax.scatter(x, y, z)\\n # ax.plot_trisurf(\\n # x, y, z,\\n # cmap = plt.get_cmap('viridis'),\\n # linewidth = 0,\\n # antialiased = True,\\n # )\\n # ax.plot_surface(surface_x, surface_y, surface_z, rstride = 1, cstride = 1)\\n # ax.plot_trisurf(surface_x, surface_y, surface_z)\\n # ax.plot_trisurf(surface_x, surface_y, surface_z, **CURVE_KWARGS)\\n\\n ax.axis('off')\\n\\n ax.view_init(elev = 45, azim = 0) # note that this resets ax.dist to 10, so we can't use it below\\n ax.dist = 7.5 # default is 10, so zoom in a little because there's no axis to take up the rest of the space\\n\\n plt.show()\\n utils.save_current_figure(**kwargs)\\n\\n ### ANIMATION ###\\n\\n frames = length * fps\\n\\n writer = anim.writers['ffmpeg'](fps = fps, bitrate = 2000) # don't need a very high bitrate\\n\\n def animate(frame):\\n print(frame, frames, frame / frames)\\n ax.azim = 360 * frame / frames # one full rotation\\n return [] # must return the list of artists we modified (i.e., nothing, since all we did is rotate the view)\\n\\n ani = anim.FuncAnimation(fig, animate, frames = frames, blit = True)\\n ani.save(f\\\"{os.path.join(kwargs['target_dir'], kwargs['name'])}.mp4\\\", writer = writer)\\n\\n plt.close()\",\n \"def drawCurve3D(xlist, ylist, zlist):\\n dislin.curv3d(xlist,ylist,zlist,len(xlist))\",\n \"def rotate_z(self, angle):\\n angle *= np.pi / 180\\n return self.transform(np.matrix([[np.cos(angle), -np.sin(angle), 0],\\n [np.sin(angle), np.cos(angle), 0],\\n [0, 0, 1]]))\",\n \"def drawSphere3D(x0,y0,z0, radius, hres, vres):\\n dislin.sphe3d(x0,y0,z0, radius, hres, vres)\",\n \"def cbar( self , y , t = 0 ):\\n \\n u = self.c( self.zbar, y , self.rbar , t )\\n \\n return u\",\n \"def rotz(t):\\n c = np.cos(t)\\n s = np.sin(t)\\n return np.array([[c, -s, 0],\\n [s, c, 0],\\n [0, 0, 1]])\",\n \"def rotz(t):\\n c = np.cos(t)\\n s = np.sin(t)\\n return np.array([[c, -s, 0],\\n [s, c, 0],\\n [0, 0, 1]])\",\n \"def rotz(t):\\n c = np.cos(t)\\n s = np.sin(t)\\n return np.array([[c, -s, 0],\\n [s, c, 0],\\n [0, 0, 1]])\",\n \"def rotz(t):\\n c = np.cos(t)\\n s = np.sin(t)\\n return np.array([[c, -s, 0],\\n [s, c, 0],\\n [0, 0, 1]])\",\n \"def rz(self, angle: float, center: Onion[Iterable[Onion[float, int]], Point3D] = None) -> 'Component':\\n return self.rotate(rz=angle, center=center)\",\n \"def cylinder_area(radius: number, height: number) -> number:\\n area = 2*pi*radius*(radius+height)\\n return area\",\n \"def cylindrical2cartesian(cylinder):\\n cart = np.zeros(cylinder.shape)\\n cart[:, 0] = cylinder[:, 0] * np.cos(cylinder[:, 1])\\n cart[:, 1] = cylinder[:, 0] * np.sin(cylinder[:, 1])\\n cart[:, 2] = cylinder[:, 2]\\n return cart\",\n \"def unoriented_cube():\\n faces = get_oriented_cube_faces()\\n for face in faces:\\n np.random.shuffle(face)\\n poly = Polyhedron(get_cube_points(), faces, faces_are_convex=True)\\n poly.sort_faces()\\n return poly\",\n \"def add_cylinder_px(volume,\\n point1: (int, int, int),\\n point2: (int, int, int),\\n radius: int):\\n p1, p2, r = cylinders_px_to_basecoord([(point1, point2, radius)], volume.shape)[0]\\n return add_cylinder_basecoord(volume, p1, p2, r)\",\n \"def plane(*args, length: float=0.0, name: AnyStr=\\\"\\\", position: List[float, float, float]=None,\\n rotation: List[float, float, float]=None, size: float=0.0, width: float=0.0,\\n **kwargs)->AnyStr:\\n pass\",\n \"def get_z_axis(self, pipette_id: str) -> MotorAxis:\\n mount = self.get(pipette_id).mount\\n return MotorAxis.LEFT_Z if mount == MountType.LEFT else MotorAxis.RIGHT_Z\",\n \"def cz(self, q0, q1, ctrl=None):\\n self.__add_quantum_gate(kind=CONTROLLED_Z, qid=[q0,q1], ctrl=ctrl)\\n return self\",\n \"def tapered_cylinder_geom(R1, R2, H):\\r\\n m = (R2-R1)/H\\r\\n if R1 == R2: # cylinder\\r\\n V = abs(np.pi*R1*R1*H)\\r\\n h_c = H/2.0\\r\\n elif R1==0: \\r\\n V = abs(1.0/3.0*np.pi*R2*R2*H)\\r\\n h_c = 3.0/4.0*H\\r\\n else:\\r\\n V = abs(np.pi/3.0/m*(R2**3 - R1**3))\\r\\n h_c = H*(R1**2 + 2*R1*R2 + 3*R2**2)/4.0/(R1**2 + R1*R2 + R2**2) # ( coneV*1./4.*coneH - coneVtip*(1./4.*(coneH-H) + H) )/ taperV\\r\\n return V, h_c\",\n \"def full_3d(self, quantity):\\n # The data just tells you what integer grid point you are on. Not what actual x,y coordinate you\\n # are at\\n x = np.arange(0, self.period, self.dx)\\n y = np.arange(0, self.period, self.dy)\\n z = np.arange(0, self.height + self.dz, self.dz)\\n points = np.array(list(itertools.product(z, x, y)))\\n # Get the scalar\\n scalar = self.get_scalar_quantity(quantity)\\n labels = ('X [um]', 'Y [um]', 'Z [um]', quantity)\\n # Now plot!\\n self.scatter3d(points[:, 1], points[:, 2], points[\\n :, 0], scalar.flatten(), labels, 'full_3d')\",\n \"def vel(z, c = cp.cc.c_light_cm_s/1e5):\\n # return z*c/(1+z)\\n return c*((1+z)**2-1)/((1+z)**2+1)\",\n \"def distance_from_cylinder(self, points, params, sqrt=False):\\n # axis: 3 x 1, center: 1 x 3\\n axis, center, radius = params\\n center = center.reshape((1, 3))\\n axis = axis.reshape((3, 1))\\n\\n v = points - center\\n prj = (v @ axis) ** 2\\n\\n # this is going negative at some point! fix it. Numerical issues.\\n # voilating pythagoras\\n dist_from_surface = torch.sum(v * v, 1) - prj[:, 0]\\n dist_from_surface = torch.clamp(dist_from_surface, min=1e-5)\\n\\n distance = torch.sqrt(dist_from_surface) - radius\\n # distance.register_hook(self.print_norm)\\n distance = distance ** 2\\n\\n if sqrt:\\n distance = guard_sqrt(distance)\\n\\n if torch.sum(torch.isnan(distance)):\\n import ipdb;\\n ipdb.set_trace()\\n if self.reduce:\\n distance = torch.mean(distance)\\n\\n return distance\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.73577124","0.7329743","0.72503877","0.6993126","0.68925846","0.68604624","0.68472177","0.67055696","0.66552156","0.6639137","0.6544904","0.6456661","0.633183","0.62076217","0.61787236","0.61410314","0.6091397","0.6059696","0.6049184","0.6046165","0.60013103","0.60013103","0.60013103","0.60013103","0.60013103","0.5951592","0.59418696","0.59176785","0.58725595","0.5872051","0.5790854","0.57749695","0.5764138","0.5744195","0.5742941","0.5731307","0.571015","0.5691538","0.56867766","0.56848395","0.56839186","0.5657884","0.56461626","0.56399894","0.5626941","0.5617542","0.5617503","0.56159866","0.5600786","0.55727893","0.5569838","0.55664855","0.55486387","0.5545254","0.5520897","0.5520897","0.5520897","0.5520897","0.5469066","0.5443069","0.5437509","0.5420359","0.54102385","0.5407999","0.5387315","0.53830725","0.537215","0.53713644","0.5341841","0.53344786","0.53306925","0.5325854","0.5318335","0.5317186","0.5308981","0.5303932","0.52995473","0.52964514","0.5286888","0.52833354","0.52759016","0.5265466","0.5261938","0.52573335","0.5250805","0.5250805","0.5250805","0.5250805","0.52478427","0.52472705","0.524603","0.52399266","0.52372414","0.5230161","0.5229741","0.5217988","0.5207454","0.5204966","0.5204629","0.5203862"],"string":"[\n \"0.73577124\",\n \"0.7329743\",\n \"0.72503877\",\n \"0.6993126\",\n \"0.68925846\",\n \"0.68604624\",\n \"0.68472177\",\n \"0.67055696\",\n \"0.66552156\",\n \"0.6639137\",\n \"0.6544904\",\n \"0.6456661\",\n \"0.633183\",\n \"0.62076217\",\n \"0.61787236\",\n \"0.61410314\",\n \"0.6091397\",\n \"0.6059696\",\n \"0.6049184\",\n \"0.6046165\",\n \"0.60013103\",\n \"0.60013103\",\n \"0.60013103\",\n \"0.60013103\",\n \"0.60013103\",\n \"0.5951592\",\n \"0.59418696\",\n \"0.59176785\",\n \"0.58725595\",\n \"0.5872051\",\n \"0.5790854\",\n \"0.57749695\",\n \"0.5764138\",\n \"0.5744195\",\n \"0.5742941\",\n \"0.5731307\",\n \"0.571015\",\n \"0.5691538\",\n \"0.56867766\",\n \"0.56848395\",\n \"0.56839186\",\n \"0.5657884\",\n \"0.56461626\",\n \"0.56399894\",\n \"0.5626941\",\n \"0.5617542\",\n \"0.5617503\",\n \"0.56159866\",\n \"0.5600786\",\n \"0.55727893\",\n \"0.5569838\",\n \"0.55664855\",\n \"0.55486387\",\n \"0.5545254\",\n \"0.5520897\",\n \"0.5520897\",\n \"0.5520897\",\n \"0.5520897\",\n \"0.5469066\",\n \"0.5443069\",\n \"0.5437509\",\n \"0.5420359\",\n \"0.54102385\",\n \"0.5407999\",\n \"0.5387315\",\n \"0.53830725\",\n \"0.537215\",\n \"0.53713644\",\n \"0.5341841\",\n \"0.53344786\",\n \"0.53306925\",\n \"0.5325854\",\n \"0.5318335\",\n \"0.5317186\",\n \"0.5308981\",\n \"0.5303932\",\n \"0.52995473\",\n \"0.52964514\",\n \"0.5286888\",\n \"0.52833354\",\n \"0.52759016\",\n \"0.5265466\",\n \"0.5261938\",\n \"0.52573335\",\n \"0.5250805\",\n \"0.5250805\",\n \"0.5250805\",\n \"0.5250805\",\n \"0.52478427\",\n \"0.52472705\",\n \"0.524603\",\n \"0.52399266\",\n \"0.52372414\",\n \"0.5230161\",\n \"0.5229741\",\n \"0.5217988\",\n \"0.5207454\",\n \"0.5204966\",\n \"0.5204629\",\n \"0.5203862\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.62607145"},"document_rank":{"kind":"string","value":"13"}}},{"rowIdx":793,"cells":{"query":{"kind":"string","value":"Create an image of noise having standard deviation \"sigma\", and average 0."},"document":{"kind":"string","value":"def model_gauss_noise(sigma, nx, ny=1, nz=1):\n\te = EMData()\n\te.set_size(nx, ny, nz)\n\te.process_inplace(\"testimage.noise.gauss\", {\"sigma\":sigma})\n\treturn e"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def add_gaussian_noise(image, min_sigma, max_sigma):\n assert(max_sigma >= min_sigma)\n sigma = np.random.uniform(min_sigma, max_sigma)\n noise = np.random.normal(loc=0, scale=sigma, size=image.shape)\n noisy_im = np.floor((image + noise) * 255) / 255\n noisy_im = np.clip(noisy_im, 0, 1)\n return noisy_im","def addNoise(img, sigma=2.0, mean=0):\n img2 = np.random.normal(mean, sigma, size=img.shape)\n\n img2 += img\n img2 = np.uint8(img2.clip(0, 255))\n return img2","def add_gaussian_noise(image, min_sigma, max_sigma):\r\n sigma = np.random.uniform(min_sigma, max_sigma)\r\n corrupted_im = image + np.random.normal(0, sigma, image.shape)\r\n rounded_im = (np.round(corrupted_im * MAX_PIXEL_NUMBER)) / MAX_PIXEL_NUMBER\r\n return np.clip(rounded_im, 0, 1).astype(np.float64)","def random_gaussian_noise(image ):\n sigma = image.std()\n for ch in range(image.shape[2] ):\n sigma = min(sigma, image[:,:,ch].std() )\n image = random_noise(image, var = sigma**2)\n return image","def addNoise(self, sigma=1.0):\n noise = numpy.random.normal(loc=0, scale=sigma, size=(self.ny, self.nx))\n self.image += noise\n return","def EST_NOISE(images):\n num = images.shape[0]\n m_e_bar = sum(images)/num\n m_sigma = np.sqrt(sum((images - m_e_bar)**2)/(num - 1))\n \n return m_sigma","def calcu_noise_sigma(ts_data):\r\n ts_data_len = len(ts_data)\r\n if ts_data_len < 3:\r\n raise Exception('ts_data_len should be at least 3')\r\n y_ip1 = ts_data[2:]\r\n y_i = ts_data[1:-1]\r\n y_im1 = ts_data[:-2]\r\n sigma_n2 = (2.0 / (3 * len(y_ip1))) * sum(\r\n (0.5 * y_im1 - y_i + 0.5 * y_ip1)**2)\r\n sigma_n = np.sqrt(sigma_n2)\r\n # sigma_n = 1e-5 if sigma_n < 1e-5 else sigma_n\r\n minimal_sigma_n = 1e-6\r\n sigma_n = minimal_sigma_n if sigma_n < minimal_sigma_n else sigma_n\r\n return sigma_n","def additive_gaussian_noise(img, seed=None, std=(0, 0.4)):\n if seed is None:\n random_state = np.random.RandomState(None)\n else:\n random_state = np.random.RandomState(seed)\n sigma = std[0] + random_state.rand() * std[1]\n gaussian_noise = random_state.randn(*img.shape) * sigma\n noisy_img = img + gaussian_noise\n # noisy_img = np.clip(noisy_img, 0, 1)\n return noisy_img","def awgn(input, noise_std):\n\tif not isinstance(noise_std, (list, tuple)):\n\t\tsigma = noise_std\n\telse: # uniform sampling of sigma\n\t\tsigma = noise_std[0] + \\\n\t\t (noise_std[1] - noise_std[0])*torch.rand(len(input),1,1,1, device=input.device)\n\treturn input + torch.randn_like(input) * (sigma/255)","def awgn(input, noise_std):\n\tif not isinstance(noise_std, (list, tuple)):\n\t\tsigma = noise_std\n\telse: # uniform sampling of sigma\n\t\tsigma = noise_std[0] + \\\n\t\t (noise_std[1] - noise_std[0])*torch.rand(len(input),1,1,1, device=input.device)\n\treturn input + torch.randn_like(input) * (sigma/255)","def add_noise(Y, sigma):\r\n return Y + np.random.normal(0, sigma, Y.shape)","def noise(self, stddev):\n #add noise to weights\n pass","def generate_gaussian():\n amp = 10 * numpy.random.chisquare(3)\n width = numpy.random.chisquare(3)\n mean = numpy.random.uniform(-10 + width, 10 - width)\n x = numpy.linspace(-10, 10, 500)\n y = amp * numpy.exp(- (x - mean) ** 2 / width ** 2)\n add_noise(y, 0.1)\n return x, y","def GenerateGaussianNoise(PSD):\n\n Noise = np.zeros((N_fd),complex)\n # Generate noise from PSD \n Real = np.random.randn(N_fd)*np.sqrt(PSD/(4.*dF))\n Imag = np.random.randn(N_fd)*np.sqrt(PSD/(4.*dF))\n Noise = Real + 1j*Imag\n\n return Noise","def make_noise_image(shape, distribution='gaussian', mean=None, stddev=None,\n seed=None):\n if mean is None:\n raise ValueError('\"mean\" must be input')\n\n rng = np.random.default_rng(seed)\n\n if distribution == 'gaussian':\n if stddev is None:\n raise ValueError('\"stddev\" must be input for Gaussian noise')\n image = rng.normal(loc=mean, scale=stddev, size=shape)\n elif distribution == 'poisson':\n image = rng.poisson(lam=mean, size=shape)\n else:\n raise ValueError(f'Invalid distribution: {distribution}. Use either '\n '\"gaussian\" or \"poisson\".')\n\n return image","def gauss_noise(_spec, stdev):\n t, f = _spec.shape\n awgn = np.random.normal(0, stdev, (t, f))\n # gauss = gauss.reshape(t, f)\n _spec_noise = _spec + awgn\n return _spec_noise","def make_4gaussians_image(noise=True):\n table = QTable()\n table['amplitude'] = [50, 70, 150, 210]\n table['x_mean'] = [160, 25, 150, 90]\n table['y_mean'] = [70, 40, 25, 60]\n table['x_stddev'] = [15.2, 5.1, 3.0, 8.1]\n table['y_stddev'] = [2.6, 2.5, 3.0, 4.7]\n table['theta'] = np.radians(np.array([145.0, 20.0, 0.0, 60.0]))\n\n shape = (100, 200)\n data = make_gaussian_sources_image(shape, table) + 5.0\n\n if noise:\n rng = np.random.RandomState(12345)\n data += rng.normal(loc=0.0, scale=5.0, size=shape)\n\n return data","def gnoise(mag, sigma, mu):\n noise = np.random.normal(mu,sigma,n)\n mag = mag + noise\n return mag, noise","def addNoise (image,noise_type=\"gauss\",var = .01):\n row,col,ch= image.shape\n if noise_type == \"gauss\": \n mean = 0.0\n #var = 0.001\n sigma = var**0.5\n gauss = np.array(image.shape)\n gauss = np.random.normal(mean,sigma,(row,col,ch))\n gauss = gauss.reshape(row,col,ch)\n #print(gauss)\n noisy = image + gauss*255\n return noisy.astype('uint8')\n elif noise_type == \"s&p\":\n s_vs_p = 0.5\n amount = 0.09\n out = image\n # Generate Salt '1' noise\n num_salt = np.ceil(amount * image.size * s_vs_p)\n coords = [np.random.randint(0, i - 1, int(num_salt))\n for i in image.shape]\n out[coords] = 255\n # Generate Pepper '0' noise\n num_pepper = np.ceil(amount* image.size * (1. - s_vs_p))\n coords = [np.random.randint(0, i - 1, int(num_pepper))\n for i in image.shape]\n out[coords] = 0\n return out\n elif noise_type == \"poisson\":\n vals = len(np.unique(image))\n vals = 2 ** np.ceil(np.log2(vals))\n noisy = np.random.poisson(image * vals) / float(vals)\n return noisy\n elif noise_type ==\"speckle\":\n gauss = np.random.randn(row,col,ch)\n gauss = gauss.reshape(row,col,ch) \n noisy = image + image * gauss\n return noisy\n else:\n return image","def gaussian_noise(tensor, mean, stddev):\n noise = Variable(tensor.data.new(tensor.size()).normal_(mean, std))\n return tensor + noise","def add_gaussian_noise(x, std):\n noise = x.new_zeros(x.size()).normal_(std=std)\n return x + noise","def random_gaussian_noise(self, img, p = 0.5):\n if self.decision(p):\n mean = 30.0\n std = 80.0\n img = img + np.random.normal(mean, std, img.shape)\n\n img = np.clip(img, 0, 255).astype('uint8')\n return img","def add_noise(image):\n image += 10e-10 * np.random.randn(image.shape[0], image.shape[1], 1)\n \n return image","def gaussian_noise(shape, mean, std):\n return tf.random.normal(shape=shape, mean=mean, stddev=std, dtype=tf.float32)","def naive_gaussian_noise(true_depth: np.ndarray) -> np.ndarray:\n return true_depth + np.random.normal(0, 0.0012 + 0.0019 * np.square(true_depth - 0.4))","def random_gaussian(img, mu=0.0, sigma=4.0):\n\n out = np.copy(img.astype(np.float))\n rows, cols, depth = img.shape\n noise = np.random.normal(mu, sigma, (rows, cols))\n for dim in range(depth):\n out[:, :, dim] = img[:, :, dim] + noise\n out[out > 255] = 255\n out[out < 0] = 0\n out = out.astype(np.uint8)\n\n return out","def gaussian_noise(self, tensor):\n return tensor.new_empty(tensor.size()).normal_(std=self._discreteness)","def noise(x: np.ndarray) -> np.ndarray:\n\n return np.random.normal(loc=MEAN, scale=1e-2, size=1)","def add_noise(image, noise, rate=0.05):\n\n if noise == \"gaussian\":\n row, col = image.shape\n var = ndimage.laplace(image).var()\n sigma = (var*rate) ** 0.5\n print(var, sigma)\n gauss = np.random.normal(loc=0, scale=sigma, size=(row, col)) * rate\n noisy = image + gauss\n # noisy = image + gauss\n return noisy\n\n elif noise == \"salt_pepper\":\n output = image.copy()\n black = 0\n white = 255\n probs = np.random.random(image.shape[:2])\n output[probs < (rate / 2)] = black\n output[probs > 1 - (rate / 2)] = white\n\n return output\n\n else:\n return image","def generate_noisy_window(d, sigma= 1):\n return np.random.normal(scale=sigma, size=d)","def noise(self):\n # Extract parameters\n pzs = self.params[0]\n # retrieve number of galaxies in each bins\n ngals = np.array([pz.gals_per_steradian for pz in pzs])\n if isinstance(self.config[\"sigma_e\"], list):\n sigma_e = np.array([s for s in self.config[\"sigma_e\"]])\n else:\n sigma_e = self.config[\"sigma_e\"]\n return sigma_e ** 2 / ngals","def _gaussian_for_learn_denosing_model(image):\n return add_gaussian_noise(image, 0, 0.2)","def softing_noise(image, kn):\n\n s_noise = cv2.GaussianBlur(image, (kn, kn), 0)\n\n return s_noise","def add_image_noise(\n self, mu=0, sigma=0.005, only_positive=False, random_seed=None):\n if random_seed is not None:\n np.random.seed(random_seed)\n shape = self.signal.axes_manager.shape\n noise = normal(mu, sigma, shape)\n if only_positive:\n self._image_noise = np.absolute(noise)\n else:\n self._image_noise = noise","def make_noisy_images(image):\r\n return apply_poisson_noise(image, random_state=12345)","def estimate_noise_std(r_data):\n\n s_noise = 0.2 * np.max(abs(r_data))\n return s_noise","def gaussian_black(z, mu: 'normal' = 0, sigma: (0.4,1) = 0.7):\n return 1/(np.sqrt(2*np.pi)*sigma)*np.exp(-np.power((z - mu)/sigma, 2)/2)","def add_noise(pointcloud, stddev=0.01):\n result = pointcloud\n result[0:2, :] = np.random.normal(pointcloud[0:2, :], stddev)\n return result","def gaussian_noise(inputs: torch.Tensor, stddev=None, sigma_max=0.06, channel_wise=1):\n if stddev is None:\n stddev = torch.rand(channel_wise) * sigma_max\n stddev = torch.tensor(stddev, device=inputs.device)\n if DATA_FORMAT == 'channels_first':\n stddev = stddev.reshape([1, -1] + [1] * (inputs.ndim - 2))\n else:\n stddev = stddev.reshape([1] * (inputs.ndim - 1) + [-1])\n noise_map = torch.randn_like(inputs) * stddev\n return noise_map","def add_gaussian_noise(X, mu=0, sigma=0.1):\n noise = np.random.normal(0.0, sigma, size=X.size)\n return X + noise.reshape(X.shape)","def gaussian_white(z, mu: 'normal' = 0, sigma: (0.4, 1) = 0.7):\n return 1 - gaussian_black(z, mu, sigma)","def add_gaussian_noise(self, samples):\n\n if 'sigma' in self.gaussian_component:\n sigma = self.gaussian_component['sigma']\n return samples + self.random_state.normal(size=samples.shape) * sigma\n if 'sigmas' in self.gaussian_component:\n sigmas = self.gaussian_component['sigmas']\n return samples + self.random_state.normal(size=samples.shape) * sigmas\n\n return samples","def make_100gaussians_image(noise=True):\n n_sources = 100\n flux_range = [500, 1000]\n xmean_range = [0, 500]\n ymean_range = [0, 300]\n xstddev_range = [1, 5]\n ystddev_range = [1, 5]\n params = {'flux': flux_range,\n 'x_mean': xmean_range,\n 'y_mean': ymean_range,\n 'x_stddev': xstddev_range,\n 'y_stddev': ystddev_range,\n 'theta': [0, 2 * np.pi]}\n\n rng = np.random.RandomState(12345)\n sources = QTable()\n for param_name, (lower, upper) in params.items():\n # Generate a column for every item in param_ranges, even if it\n # is not in the model (e.g., flux). However, such columns will\n # be ignored when rendering the image.\n sources[param_name] = rng.uniform(lower, upper, n_sources)\n xstd = sources['x_stddev']\n ystd = sources['y_stddev']\n sources['amplitude'] = sources['flux'] / (2.0 * np.pi * xstd * ystd)\n\n shape = (300, 500)\n data = make_gaussian_sources_image(shape, sources) + 5.0\n\n if noise:\n rng = np.random.RandomState(12345)\n data += rng.normal(loc=0.0, scale=2.0, size=shape)\n\n return data","def _gauss_noise(self, shape):\n\n n = np.random.normal(0, 1, shape)\n return self.e*n","def add_noise(image, type=\"s&p\"):\n # Get the width and height of the image\n w, h = image.size\n\n # Add salt and pepper noise\n if type == \"s&p\":\n # Choose a random amount of noise (lower number = more noise)\n salt = np.random.randint(100, 400)\n # Generate an array to determine location of noise\n noise = np.random.randint(salt+1, size=(h, w))\n\n # Find the index of the salt and pepper (respectively location with max/min random value)\n idx_salt = noise == salt\n idx_pepper = noise == 0\n\n # Create a numpy array from the initial image and add the salt and pepper\n np_img = np.array(image)\n np_img[idx_salt, :] = 255\n np_img[idx_pepper, :] = 0\n\n return Image.fromarray(np.uint8(np_img))\n\n # Add gaussian noise to image\n if type == \"gauss\":\n # Get the number of channels\n c = len(image.getbands())\n\n # Get a random value for the mean and the standard deviation of the noise\n mean = np.random.randint(-4, 5)\n std = np.random.randint(5)\n\n # Generate the noise array\n noise = np.random.normal(mean, std, (h, w, c))\n\n # Add noise to the image\n return Image.fromarray(np.uint8(np.array(image) + noise))\n\n else:\n # If the name of the given noise is not correct\n return image","def generate_fake_noise(inputs, size):\n return np.random.normal(-0.0289923828125, 1.9391296947313124, (inputs, size)).astype(np.float32)","def Noise(self, eps, size):\n return eps * (np.random.uniform(size=size) * 2 - 1)","def sigmanorm(y):\n y = y.copy()\n y -= y.mean() # set to zero mean\n y /= y.std() # rescale to units of sigma\n return y","def noise_generator(n, mean, std, fractindex):\n if fractindex not in VALID_FRACT:\n raise ValueError(\"results: status must be one of %r.\" % VALID_FRACT)\n \n stdev = std\n \n b = 2*fractindex-1\n print('beta: ', b)\n \n bdis = np.zeros(n)\n\n bdis[0] = 1\n for i in range(1,n):\n bdis[i] = bdis[i-1] * (0.5 * b + (i-1))/i # note that b is the shape parementer (b)\n\n plt.plot(bdis)\n plt.show\n\n wnt = np.random.normal(mean, stdev, size = n)\n print('WhiteNoise Stdev: ', np.std(wnt))\n plt.plot(wnt)\n plt.show()\n\n bdis_freq = np.fft.fft(bdis)\n wnt_freq = np.fft.fft(wnt)\n\n bdis_freq = bdis_freq[1:n+1]\n wnt_freq = wnt_freq[1:n+1]\n\n freq_total = bdis_freq * wnt_freq\n \n NumUniquePts = n/2 + 1\n NumUniquePts = int(NumUniquePts)\n j = np.arange(1, NumUniquePts)\n \n if fractindex > 1.0:\n j = j\n elif fractindex <= 1.0:\n j = j**0.5\n \n ft_half1 = freq_total[1:NumUniquePts]/j\n\n real = np.real(freq_total[1:NumUniquePts+1])\n real = np.flip(real, axis=0)\n\n imaginary = np.imag(freq_total[1:NumUniquePts+1])\n imaginary = np.flip(imaginary, axis=0)\n imaginary = 1j * imaginary\n\n ft_half2 = real - imaginary\n\n ft = np.hstack((ft_half1, ft_half2))\n \n x = np.fft.ifft(ft)\n x = np.real(x[:n])\n\n mean_diff = mean - np.mean(x)\n x = mean_diff + x\n print(np.mean(x))\n print(np.std(x))\n plt.plot(x)\n plt.show()\n \n return x","def noise(self):\n # Extract parameters\n pzs = self.params[0]\n ngals = np.array([pz.gals_per_steradian for pz in pzs])\n return 1.0 / ngals","def kernel_sigma(n_kernels):\n sigmas = [0.001] # exact match small variance means exact match ?\n if n_kernels == 1:\n return sigmas\n return sigmas + [0.1] * (n_kernels - 1)","def drop_and_noise(image, sigma_d, percentage=0.8):\n M, N = image.shape[:2]\n n = N * M\n p = m.floor(percentage * n)\n image = np.cast[np.float32](image)\n\n missing_pixels_ind = np.random.permutation(n)[:p]\n\n mask = np.ones((M * N,), dtype=np.bool)\n mask[missing_pixels_ind] = 0\n mask = mask.reshape((M, N, 1))\n\n maskf = np.cast[np.float32](mask)\n y_clean = image * maskf\n\n noise = np.random.normal(loc=0, scale=sigma_d, size=image.shape) * maskf\n y = y_clean + noise\n\n return y, mask","def add_noise(arr, sigma):\n dims = arr.shape\n arr += sigma * noise(*dims)","def makeGaussian(height, width, sigma=3, center=None):\n x = np.arange(0, width, 1, float)\n y = np.arange(0, height, 1, float)[:, np.newaxis]\n if center is None:\n x0 = width // 2\n y0 = height // 2\n else:\n x0 = center[0]\n y0 = center[1]\n\n return np.exp(-4 * np.log(2) * ((x - x0) ** 2 + (y - y0) ** 2) / sigma ** 2)","def real_blur_and_noise(image, kernel, sigma_d):\n degraded = filter_image(image, kernel, mode=\"valid\", boundary=\"fill\")\n noise = np.random.normal(0.0, sigma_d, degraded.shape).astype(np.float32)\n degraded = degraded + noise\n initial = np.pad(degraded, ((kernel.shape[0] // 2, kernel.shape[0] // 2),\n (kernel.shape[1] // 2, kernel.shape[1] // 2),\n (0, 0)), mode=\"edge\")\n initial = edgeTaper(initial, kernel)\n return initial","def flatten(img,sigma=20.) :\n\n for i in range(img.shape[0]) :\n img[i] /= np.median(img[i])\n for i in range(img.shape[1]) :\n img[:,i] /= np.median(img[:,i])\n\n hw=int(3*sigma)\n u=np.linspace(-hw,hw,2*hw+1)\n x=np.tile(u,(2*hw+1,1))\n y=x.T\n k=np.exp(-x**2/2/sigma**2-y**2/2/sigma**2)\n k /= np.sum(k)\n smooth=convolve2d(img,k,weight=None)\n img /= smooth\n\n return img","def _FWHMGauss(sigma, pixel=12):\n return sigma*2*np.sqrt(2*np.log(2))*pixel","def create_gaussian_array(self):\n\n # Fill array of size l x w with Gaussian Noise.\n terrain_length = int(ceil(self.length/self.resolution))\n terrain_width = int(ceil(self.width/self.resolution))\n gaussian_array = np.random.normal(self.mu, self.sigma, (terrain_length,terrain_width))\n\n # Filter the array to smoothen the variation of the noise\n gaussian_array = gaussian_filter(gaussian_array, self.sigma_filter)\n\n return gaussian_array","def _get_noise(self, shape, dtype=None):\n return np.random.normal(self._bias, self._scale, shape).astype(dtype)","def make_time_series_noise(dates, mean=0., std=1.):\n # TODO: implement Gaussian noise with mean/std\n # TODO: add kwarg to parametrize noise from clouds/shadows\n pass","def initiategaussian(sd, x0):\n y = np.exp(-x**2/(2*sd**2))\n return y","def add_imageNoise(img):\n if not np.all(img >= 0):\n print 'make sure the image pixel values are positive definite'\n sys.exit()\n noise = st.poisson.rvs(1.,loc = -1.,scale=1.,size=img.shape)*np.sqrt(img)\n return noise","def gaussian(x, sigma):\n try: r = np.exp(-0.5*(x/sigma)**2) \n except: r = np.zeros(len(x))\n return r","def noise_generator(self, power=None, SNR=None, size=None):\r\n alpha = self.db2power(SNR)\r\n sigma = np.sqrt(power / alpha) # 计算噪声标准差\r\n # 产生噪声\r\n noise_data = np.sqrt(0.5) * (np.random.normal(0, sigma, size=size) + np.random.normal(0, sigma, size=size) * 1j)\r\n noise_data = noise_data.astype(np.complex64)\r\n return noise_data","def get_sigma_psf(self):\n pass","def _calc_sigma(self):\n if self.data is None:\n self._initial_blur()\n previous = self.init_sigma\n incr = 0\n self.sigmas = [(previous, incr)]\n for i in range(1, self.scale_per_octave + 3):\n sigma_abs = self.init_sigma * (self.dest_sigma / self.init_sigma) ** (1.0 * i / (self.scale_per_octave))\n increase = previous * sqrt((self.dest_sigma / self.init_sigma) ** (2.0 / self.scale_per_octave) - 1.0)\n self.sigmas.append((sigma_abs, increase))\n previous = sigma_abs\n logger.debug(\"Sigma= %s\" % self.sigmas)","def task_gaussian_noise(input_array, noise_factor):\n return(np.random.normal(0, noise_factor, input_array.shape))","def make_gaussian(size, sigma=10, center=None):\n\n x = np.arange(0, size[1], 1, float)\n y = np.arange(0, size[0], 1, float)\n y = y[:, np.newaxis]\n\n if center is None:\n x0 = y0 = size[0] // 2\n else:\n x0 = center[0]\n y0 = center[1]\n\n return np.exp(-4 * np.log(2) * ((x - x0) ** 2 + (y - y0) ** 2) / sigma ** 2)","def make_noise(self, num):\n return np.random.randn(num, self.seq_length, self.noise_dim)","def generate_noise_image(content_image, noise_ratio = CONFIG.NOISE_RATIO):\n\tnoise_img = np.random.uniform(-20,20,(1, CONFIG.IMAGE_HEIGHT, CONFIG.IMAGE_WIDTH, CONFIG.COLOR_CHANNELS)).astype('float32')\n\n\t# Setting the resulting image to be the weighted average of the content image and noise_image\n\tresult_img = noise_img * noise_ratio + content_image * (1 - noise_ratio)\n\n\treturn result_img","def noisePreset() :\n s.noisePreset()","def generate_noise(\n self,\n noise_iteration=(1, 1),\n noise_size=(1, 1),\n noise_value=(0, 128),\n noise_background=(255, 255),\n noise_sparsity=(0.4, 0.6),\n noise_concentration=(0.4, 0.6),\n xsize=1500,\n ysize=1500,\n ):\n\n # generate random iterations\n iterations = random.randint(noise_iteration[0], noise_iteration[1])\n\n # generate background value\n background_value = random.randint(noise_background[0], noise_background[1])\n\n # initialize blank noise mask\n img_mask = np.full((ysize, xsize), fill_value=background_value, dtype=\"int\")\n\n # any invalid noise type will reset noise type to 0\n if self.noise_type not in [1, 2, 3, 4, 5]:\n noise_type = random.randint(1, 5)\n else:\n noise_type = self.noise_type\n\n # random location with no sides if no side is chosen\n if self.noise_side not in self.sides:\n noise_side = random.choice(self.sides)\n else:\n noise_side = self.noise_side\n\n # loop each iterations\n for _ in range(iterations):\n\n # divider to rescale noise mask to larger size\n y_divider = random.randint(noise_size[0], noise_size[1])\n x_divider = random.randint(noise_size[0], noise_size[1])\n\n # generate noise mask for current iteration\n img_mask_temporary = self.generate_mask_main(\n noise_type,\n noise_side,\n noise_value,\n noise_background,\n noise_sparsity,\n noise_concentration,\n int(xsize / x_divider),\n int(ysize / y_divider),\n )\n img_mask_temporary = cv2.resize(\n img_mask_temporary.astype(\"uint8\"),\n (xsize, ysize),\n interpolation=cv2.INTER_CUBIC,\n )\n\n # merge noise mask in each iteration by getting their min value\n img_mask = np.minimum(img_mask_temporary, img_mask)\n\n # output needs uint8 type\n img_mask = img_mask.astype(\"uint8\")\n\n return img_mask","def ICA_Denoise(Y, ica_model, noise_std):\n\n # TODO: YOUR CODE HERE","def threshold(image, sigma):\n image[image<1] = 0\n mean = np.mean(image[image>=1])\n std = np.std(image[image>=1])\n if (sigma == 0):\n thresh = mean + 0.5*std\n else:\n thresh = min(mean + 0.5*std, 30/sigma)\n image[image>thresh] = 255\n image[image<=thresh] = 0\n numAfterThresh = (image>0).sum()\n\n print(\"Average: {:.2f}\".format(mean))\n print(\"Std dev: {:.2f}\".format(std))\n print(\"Threshold: {:.2f}\".format(thresh))\n print(\"Number after threshold: {}\".format(numAfterThresh))\n return image","def _addNoise(self):\n self.dispNoise = self.dispRaw.copy()\n self.dispNoise[:, 0] += self.sigmaEast * numpy.random.randn(self.numStations)\n self.dispNoise[:, 1] += self.sigmaNorth * numpy.random.randn(self.numStations)\n self.dispNoise[:, 2] += self.sigmaUp * numpy.random.randn(self.numStations)\n return","def _sigma_1(gam, eps):\n s1 = 4 * r0**2 * alpha / eps / mec2_unit\n s2 = 1 + (1./3. - eps/gam) * (1 - eps/gam)\n s3 = np.log(2 * gam * (gam - eps) / eps) - 1./2.\n s3[np.where(gam < eps)] = 0.0\n return s1 * s2 * s3","def _get_noise(self, shape, dtype=None):","def salt_and_pepper_noise(image, prob):\n output = np.zeros(image.shape,np.uint8)\n thres = 1 - prob \n for i in range(image.shape[0]):\n for j in range(image.shape[1]):\n rdn = random.random()\n if rdn < prob:\n output[i][j] = 0\n elif rdn > thres:\n output[i][j] = 255\n else:\n output[i][j] = image[i][j]\n return output","def get_sigma(rate, dt=1 * units.ns):\n return stats.norm.isf(rate * dt)","def create_gaussian_filter(size, sigma):\n h = size[0] #height of the template\n w = size[1] #width of the template \n if h % 2 == 0: h += 1 #add 1 if dimensions are even\n if w % 2 == 0: w += 1\n x = math.floor(h/2)\n y = math.floor(w/2) \n sum = 0\n #create our template\n template = np.zeros((h,w))\n #fill the template in with the numbers from Gaussian distribution\n for i in range(h):\n for j in range(w):\n template[i,j] = math.exp(-((((j-x)**2)+((i-y)**2))/(2*(sigma**2))))\n sum = sum + template[i,j]\n #normalise the numbers\n gaussian_filter = template/sum\n return gaussian_filter","def addSTDdevIndices(img):\n\t\t\timg = img.addBands(img.normalizedDifference(['green','swir1']).rename(['ND_green_swir1'])); # NDSI, MNDWI\n\t\t\timg = img.addBands(img.normalizedDifference(['nir','red']).rename(['ND_nir_red'])); # NDVI\n\t\t\timg = img.addBands(img.normalizedDifference(['nir','swir2']).rename(['ND_nir_swir2'])); # NBR, MNDVI\n\t\t\t\n\t\t\treturn img;","def add_gaussian_noise(images: list, var: list, random_var: float=None, gauss_noise: list=None):\n if random_var is None:\n random_var = np.random.uniform(var[0], var[1])\n mean = 0\n new_images = []\n gauss_noise_out = []\n for i,image in enumerate(images):\n row, col, c = image.shape\n if gauss_noise is None or \\\n (gauss_noise is not None and row*col*c !=\n gauss_noise[i].shape[0]*gauss_noise[i].shape[1] * gauss_noise[i].shape[2]):\n gauss = np.random.normal(mean, random_var * 127.5, (row, col, c))\n else:\n gauss = gauss_noise[i]\n gauss_noise_out.append(gauss)\n gauss = gauss.reshape(row, col, c)\n image1 = np.clip(image + gauss, 0., 255.)\n new_images.append(image1)\n return new_images, random_var, gauss_noise_out","def gaussian(size,sigma):\n a,b=np.ogrid[-size/2:size/2,-size/2:size/2]\n mask = a**2+b**2\n mask = np.exp(-mask.astype('float')/(2*float(sigma**2)))\n return mask","def add_noise(np_image, amount):\n noise = np.random.randn(np_image.shape[0],np_image.shape[1])\n norm_noise = abs(noise/np.max(noise))\n np_image = np_image + norm_noise*np.max(np_image)*amount\n \n return np_image","def test_denoising(image, model, denoise_function,\n noise_range=(0.01, 0.05, 0.1, 0.2), patch_size=(8, 8)):\n h, w = np.shape(image)\n noisy_images = np.zeros((h, w, len(noise_range)))\n denoised_images = []\n cropped_original = crop_image(image, patch_size)\n\n # make the image noisy:\n for i in range(len(noise_range)):\n noisy_images[:, :, i] = image + (\n noise_range[i] * np.random.randn(h, w))\n\n # denoise the image:\n for i in range(len(noise_range)):\n denoised_images.append(\n denoise_image(noisy_images[:, :, i], model, denoise_function,\n noise_range[i], patch_size))\n\n # calculate the MSE for each noise range:\n noisy_mses = {}\n denoised_mses = {}\n for i in range(len(noise_range)):\n print(\"noisy MSE for noise = \" + str(noise_range[i]) + \":\")\n noisy_mse = np.mean((crop_image(noisy_images[:, :, i],\n patch_size) - cropped_original) ** 2)\n noisy_mses[str(noise_range[i])] = noisy_mse\n print(noisy_mse)\n print(\"denoised MSE for noise = \" + str(noise_range[i]) + \":\")\n denoised_mse = np.mean((cropped_original - denoised_images[i]) ** 2)\n denoised_mses[str(noise_range[i])] = denoised_mse\n print(denoised_mse)\n\n plt.figure(figsize=(20, 20))\n plt.axis('off')\n for i in range(len(noise_range)):\n plt.subplot(2, len(noise_range), i + 1, xlabel='Noisy image', xticks=[], yticks=[])\n plt.imshow(noisy_images[:, :, i], cmap='gray')\n plt.subplot(2, len(noise_range), i + 1 + len(noise_range), xlabel='Denoised image', xticks=[], yticks=[])\n plt.imshow(denoised_images[i], cmap='gray')\n plt.show()\n return noisy_mses, denoised_mses","def make_noise(self, num):\n return np.random.randn(num, self.seq_length + 2 * self.seq_pad,\n self.noise_dim)","def estimate_noise(spec, ind_range=None):\n\n # -- set the index range (nb, ends at len(spec)-1 since the derivative has\n # one fewer points than the spectrum).\n ind_range = ind_range if ind_range else [0,spec.shape[0]-1]\n\n # -- compute the derivative and estimate the noise over the range\n noise = (spec[1:]-spec[:-1])[ind_range[0]:ind_range[1]].std(0)/np.sqrt(2.0)\n\n return noise","def white_noise():\n return random.randint(-32767, 32767)","def MVN_Denoise(Y, mvn_model, noise_std):\n return calc_weiner_filter(Y, mvn_model.mean, mvn_model.cov, noise_std)","def make_DOG(inner_sigma, x):\n y = x\n outer_sigma = inner_sigma*5\n X, Y = np.meshgrid(x, y)\n inner_gaussian = 1./(2.*np.pi*inner_sigma) * np.exp(-(X**2 + Y**2)/2./inner_sigma**2) \n outer_gaussian = 1./(2.*np.pi*outer_sigma) * np.exp(-(X**2 + Y**2)/2./outer_sigma**2) \n return inner_gaussian - outer_gaussian/2 #weaker surround works better with our weights, which don't account for bursts ","def create_synthetic_noise_dataset(cfg):\n from colorednoise import powerlaw_psd_gaussian\n\n betas = np.linspace(cfg['data.mix_synthetic_noise.min_beta'],\n cfg['data.mix_synthetic_noise.max_beta'],\n num=cfg['data.mix_synthetic_noise.num_samples'])\n sample_rate = cfg['data.sample_rate']\n segment_length = 2 * cfg['data.len_min']\n wavs = [powerlaw_psd_gaussian(beta, sample_rate * segment_length)\n for beta in betas]\n wavs = [audio.normalize(wav, low=-1, high=1) for wav in wavs]\n return NoiseDataset(wavs)","def _get_add_noise(stddev, seed: int = None):\n if distutils.version.LooseVersion(\n tf.__version__) < distutils.version.LooseVersion('2.0.0'):\n\n # The seed should be only used for testing purpose.\n if seed is not None:\n tf.random.set_seed(seed)\n\n def add_noise(v):\n return v + tf.random.normal(\n tf.shape(input=v), stddev=stddev, dtype=v.dtype)\n else:\n random_normal = tf.random_normal_initializer(stddev=stddev, seed=seed)\n\n def add_noise(v):\n return v + tf.cast(random_normal(tf.shape(input=v)), dtype=v.dtype)\n\n return add_noise","def sampleGaussian(self, mu, log_sigma):\n # reparameterization trick\n epsilon = tf.random_normal(tf.shape(log_sigma), name=\"epsilon\")\n return mu + epsilon * tf.exp(log_sigma) # N(mu, I * sigma**2)","def standardize(image, mean=[0.48462227599918, 0.45624044862054, 0.40588363755159], std=[0.22889466674951, 0.22446679341259, 0.22495548344775]):\n image = image.astype(np.float32) / 255.0\n image = np.divide(np.subtract(image, mean), std)\n return image","def get_image_with_poisson_noise(image):\r\n img = tf_norm_crop_resize_image(image, resize_dim=(64,64))\r\n noisy_img = np.clip(make_noisy_images(img*255.)/255., 0., 1.)\r\n return noisy_img","def add_gaussian_noise(self, stdev, iteration = 1, method = 'absolute', normalize = \"on\"):\n\n self.number_of_replicate = iteration\n for fragment in self.fragments_for_mdv_calculation:\n number_of_mass_data = max(self.mdv[fragment].keys()) + 1\n noise = numpy.zeros((number_of_mass_data, iteration))\n for i in range(iteration):\n for number in range(number_of_mass_data):\n if method == 'relative':\n noise[number, i] = (numpy.random.randn() * stdev + 1) * self.mdv[fragment][number]['ratio']\n else:\n noise[number, i] = (numpy.random.randn() * stdev) + self.mdv[fragment][number]['ratio']\n if noise[number, i] < 0.0:\n noise[number, i] = 0.0\n #各フラグメント毎に総和を１にする。\n if normalize == \"on\":\n sumvalue = sum(noise[:,i])\n noise[:,i] = noise[:,i] / sumvalue\n for number in range(number_of_mass_data):\n self.mdv[fragment][number]['ratio']= sum(noise[number,:])/iteration\n #self.mdv[fragment][number]['std'] = numpy.std(noise[number,:])\n self.mdv[fragment][number]['data'] = numpy.array(noise[number,:])","def sigma(h,T):\r\n P = 1/(1+np.exp(-2*h/T)) #from the mean field results\r\n ll = len(h)\r\n rand = np.random.rand(ll)\r\n pos = np.where(P-rand>0)[0]\r\n r = -np.ones(ll)\r\n r[pos] = +1\r\n return r","def scipy_smooth(img, sigma=5):\n return ndimage.gaussian_filter(img, sigma=sigma)","def GSM_Denoise(Y, gsm_model, noise_std):\n X = np.empty(Y.shape)\n k = gsm_model.mix.shape[0]\n I = np.identity(gsm_model.cov[0, :].shape[0])\n for i in range(k):\n mvn = multivariate_normal(cov=(gsm_model.cov[i, :] + ((noise_std**2) * I)))\n upper_arg = gsm_model.mix[i] * (mvn.logpdf(Y[:, i]))\n lower_arg = 0\n for j in range(k):\n inner_mvn = multivariate_normal(cov=(gsm_model.cov[j] + ((noise_std**2) * I)))\n lower_arg += gsm_model.mix[j] * (inner_mvn.logpdf(Y[:, i]))\n c_i = upper_arg / lower_arg\n weiner_i = calc_weiner_filter(Y, np.zeros(Y.shape[0]), gsm_model.cov[i, :], noise_std)\n X += c_i * weiner_i\n return X","def kernel_gaussiano(image: np.ndarray, sigma: float, kind: str = 'low') -> np.ndarray:\n U, V = fourier_meshgrid(image)\n D = fourier_distance(U, V)\n H = np.exp( (-1.0 * D) / (2.0 * sigma**2) )\n \n if kind == 'high' or kind == 'highpass':\n H = 1.0 - H\n \n return H"],"string":"[\n \"def add_gaussian_noise(image, min_sigma, max_sigma):\\n assert(max_sigma >= min_sigma)\\n sigma = np.random.uniform(min_sigma, max_sigma)\\n noise = np.random.normal(loc=0, scale=sigma, size=image.shape)\\n noisy_im = np.floor((image + noise) * 255) / 255\\n noisy_im = np.clip(noisy_im, 0, 1)\\n return noisy_im\",\n \"def addNoise(img, sigma=2.0, mean=0):\\n img2 = np.random.normal(mean, sigma, size=img.shape)\\n\\n img2 += img\\n img2 = np.uint8(img2.clip(0, 255))\\n return img2\",\n \"def add_gaussian_noise(image, min_sigma, max_sigma):\\r\\n sigma = np.random.uniform(min_sigma, max_sigma)\\r\\n corrupted_im = image + np.random.normal(0, sigma, image.shape)\\r\\n rounded_im = (np.round(corrupted_im * MAX_PIXEL_NUMBER)) / MAX_PIXEL_NUMBER\\r\\n return np.clip(rounded_im, 0, 1).astype(np.float64)\",\n \"def random_gaussian_noise(image ):\\n sigma = image.std()\\n for ch in range(image.shape[2] ):\\n sigma = min(sigma, image[:,:,ch].std() )\\n image = random_noise(image, var = sigma**2)\\n return image\",\n \"def addNoise(self, sigma=1.0):\\n noise = numpy.random.normal(loc=0, scale=sigma, size=(self.ny, self.nx))\\n self.image += noise\\n return\",\n \"def EST_NOISE(images):\\n num = images.shape[0]\\n m_e_bar = sum(images)/num\\n m_sigma = np.sqrt(sum((images - m_e_bar)**2)/(num - 1))\\n \\n return m_sigma\",\n \"def calcu_noise_sigma(ts_data):\\r\\n ts_data_len = len(ts_data)\\r\\n if ts_data_len < 3:\\r\\n raise Exception('ts_data_len should be at least 3')\\r\\n y_ip1 = ts_data[2:]\\r\\n y_i = ts_data[1:-1]\\r\\n y_im1 = ts_data[:-2]\\r\\n sigma_n2 = (2.0 / (3 * len(y_ip1))) * sum(\\r\\n (0.5 * y_im1 - y_i + 0.5 * y_ip1)**2)\\r\\n sigma_n = np.sqrt(sigma_n2)\\r\\n # sigma_n = 1e-5 if sigma_n < 1e-5 else sigma_n\\r\\n minimal_sigma_n = 1e-6\\r\\n sigma_n = minimal_sigma_n if sigma_n < minimal_sigma_n else sigma_n\\r\\n return sigma_n\",\n \"def additive_gaussian_noise(img, seed=None, std=(0, 0.4)):\\n if seed is None:\\n random_state = np.random.RandomState(None)\\n else:\\n random_state = np.random.RandomState(seed)\\n sigma = std[0] + random_state.rand() * std[1]\\n gaussian_noise = random_state.randn(*img.shape) * sigma\\n noisy_img = img + gaussian_noise\\n # noisy_img = np.clip(noisy_img, 0, 1)\\n return noisy_img\",\n \"def awgn(input, noise_std):\\n\\tif not isinstance(noise_std, (list, tuple)):\\n\\t\\tsigma = noise_std\\n\\telse: # uniform sampling of sigma\\n\\t\\tsigma = noise_std[0] + \\\\\\n\\t\\t (noise_std[1] - noise_std[0])*torch.rand(len(input),1,1,1, device=input.device)\\n\\treturn input + torch.randn_like(input) * (sigma/255)\",\n \"def awgn(input, noise_std):\\n\\tif not isinstance(noise_std, (list, tuple)):\\n\\t\\tsigma = noise_std\\n\\telse: # uniform sampling of sigma\\n\\t\\tsigma = noise_std[0] + \\\\\\n\\t\\t (noise_std[1] - noise_std[0])*torch.rand(len(input),1,1,1, device=input.device)\\n\\treturn input + torch.randn_like(input) * (sigma/255)\",\n \"def add_noise(Y, sigma):\\r\\n return Y + np.random.normal(0, sigma, Y.shape)\",\n \"def noise(self, stddev):\\n #add noise to weights\\n pass\",\n \"def generate_gaussian():\\n amp = 10 * numpy.random.chisquare(3)\\n width = numpy.random.chisquare(3)\\n mean = numpy.random.uniform(-10 + width, 10 - width)\\n x = numpy.linspace(-10, 10, 500)\\n y = amp * numpy.exp(- (x - mean) ** 2 / width ** 2)\\n add_noise(y, 0.1)\\n return x, y\",\n \"def GenerateGaussianNoise(PSD):\\n\\n Noise = np.zeros((N_fd),complex)\\n # Generate noise from PSD \\n Real = np.random.randn(N_fd)*np.sqrt(PSD/(4.*dF))\\n Imag = np.random.randn(N_fd)*np.sqrt(PSD/(4.*dF))\\n Noise = Real + 1j*Imag\\n\\n return Noise\",\n \"def make_noise_image(shape, distribution='gaussian', mean=None, stddev=None,\\n seed=None):\\n if mean is None:\\n raise ValueError('\\\"mean\\\" must be input')\\n\\n rng = np.random.default_rng(seed)\\n\\n if distribution == 'gaussian':\\n if stddev is None:\\n raise ValueError('\\\"stddev\\\" must be input for Gaussian noise')\\n image = rng.normal(loc=mean, scale=stddev, size=shape)\\n elif distribution == 'poisson':\\n image = rng.poisson(lam=mean, size=shape)\\n else:\\n raise ValueError(f'Invalid distribution: {distribution}. Use either '\\n '\\\"gaussian\\\" or \\\"poisson\\\".')\\n\\n return image\",\n \"def gauss_noise(_spec, stdev):\\n t, f = _spec.shape\\n awgn = np.random.normal(0, stdev, (t, f))\\n # gauss = gauss.reshape(t, f)\\n _spec_noise = _spec + awgn\\n return _spec_noise\",\n \"def make_4gaussians_image(noise=True):\\n table = QTable()\\n table['amplitude'] = [50, 70, 150, 210]\\n table['x_mean'] = [160, 25, 150, 90]\\n table['y_mean'] = [70, 40, 25, 60]\\n table['x_stddev'] = [15.2, 5.1, 3.0, 8.1]\\n table['y_stddev'] = [2.6, 2.5, 3.0, 4.7]\\n table['theta'] = np.radians(np.array([145.0, 20.0, 0.0, 60.0]))\\n\\n shape = (100, 200)\\n data = make_gaussian_sources_image(shape, table) + 5.0\\n\\n if noise:\\n rng = np.random.RandomState(12345)\\n data += rng.normal(loc=0.0, scale=5.0, size=shape)\\n\\n return data\",\n \"def gnoise(mag, sigma, mu):\\n noise = np.random.normal(mu,sigma,n)\\n mag = mag + noise\\n return mag, noise\",\n \"def addNoise (image,noise_type=\\\"gauss\\\",var = .01):\\n row,col,ch= image.shape\\n if noise_type == \\\"gauss\\\": \\n mean = 0.0\\n #var = 0.001\\n sigma = var**0.5\\n gauss = np.array(image.shape)\\n gauss = np.random.normal(mean,sigma,(row,col,ch))\\n gauss = gauss.reshape(row,col,ch)\\n #print(gauss)\\n noisy = image + gauss*255\\n return noisy.astype('uint8')\\n elif noise_type == \\\"s&p\\\":\\n s_vs_p = 0.5\\n amount = 0.09\\n out = image\\n # Generate Salt '1' noise\\n num_salt = np.ceil(amount * image.size * s_vs_p)\\n coords = [np.random.randint(0, i - 1, int(num_salt))\\n for i in image.shape]\\n out[coords] = 255\\n # Generate Pepper '0' noise\\n num_pepper = np.ceil(amount* image.size * (1. - s_vs_p))\\n coords = [np.random.randint(0, i - 1, int(num_pepper))\\n for i in image.shape]\\n out[coords] = 0\\n return out\\n elif noise_type == \\\"poisson\\\":\\n vals = len(np.unique(image))\\n vals = 2 ** np.ceil(np.log2(vals))\\n noisy = np.random.poisson(image * vals) / float(vals)\\n return noisy\\n elif noise_type ==\\\"speckle\\\":\\n gauss = np.random.randn(row,col,ch)\\n gauss = gauss.reshape(row,col,ch) \\n noisy = image + image * gauss\\n return noisy\\n else:\\n return image\",\n \"def gaussian_noise(tensor, mean, stddev):\\n noise = Variable(tensor.data.new(tensor.size()).normal_(mean, std))\\n return tensor + noise\",\n \"def add_gaussian_noise(x, std):\\n noise = x.new_zeros(x.size()).normal_(std=std)\\n return x + noise\",\n \"def random_gaussian_noise(self, img, p = 0.5):\\n if self.decision(p):\\n mean = 30.0\\n std = 80.0\\n img = img + np.random.normal(mean, std, img.shape)\\n\\n img = np.clip(img, 0, 255).astype('uint8')\\n return img\",\n \"def add_noise(image):\\n image += 10e-10 * np.random.randn(image.shape[0], image.shape[1], 1)\\n \\n return image\",\n \"def gaussian_noise(shape, mean, std):\\n return tf.random.normal(shape=shape, mean=mean, stddev=std, dtype=tf.float32)\",\n \"def naive_gaussian_noise(true_depth: np.ndarray) -> np.ndarray:\\n return true_depth + np.random.normal(0, 0.0012 + 0.0019 * np.square(true_depth - 0.4))\",\n \"def random_gaussian(img, mu=0.0, sigma=4.0):\\n\\n out = np.copy(img.astype(np.float))\\n rows, cols, depth = img.shape\\n noise = np.random.normal(mu, sigma, (rows, cols))\\n for dim in range(depth):\\n out[:, :, dim] = img[:, :, dim] + noise\\n out[out > 255] = 255\\n out[out < 0] = 0\\n out = out.astype(np.uint8)\\n\\n return out\",\n \"def gaussian_noise(self, tensor):\\n return tensor.new_empty(tensor.size()).normal_(std=self._discreteness)\",\n \"def noise(x: np.ndarray) -> np.ndarray:\\n\\n return np.random.normal(loc=MEAN, scale=1e-2, size=1)\",\n \"def add_noise(image, noise, rate=0.05):\\n\\n if noise == \\\"gaussian\\\":\\n row, col = image.shape\\n var = ndimage.laplace(image).var()\\n sigma = (var*rate) ** 0.5\\n print(var, sigma)\\n gauss = np.random.normal(loc=0, scale=sigma, size=(row, col)) * rate\\n noisy = image + gauss\\n # noisy = image + gauss\\n return noisy\\n\\n elif noise == \\\"salt_pepper\\\":\\n output = image.copy()\\n black = 0\\n white = 255\\n probs = np.random.random(image.shape[:2])\\n output[probs < (rate / 2)] = black\\n output[probs > 1 - (rate / 2)] = white\\n\\n return output\\n\\n else:\\n return image\",\n \"def generate_noisy_window(d, sigma= 1):\\n return np.random.normal(scale=sigma, size=d)\",\n \"def noise(self):\\n # Extract parameters\\n pzs = self.params[0]\\n # retrieve number of galaxies in each bins\\n ngals = np.array([pz.gals_per_steradian for pz in pzs])\\n if isinstance(self.config[\\\"sigma_e\\\"], list):\\n sigma_e = np.array([s for s in self.config[\\\"sigma_e\\\"]])\\n else:\\n sigma_e = self.config[\\\"sigma_e\\\"]\\n return sigma_e ** 2 / ngals\",\n \"def _gaussian_for_learn_denosing_model(image):\\n return add_gaussian_noise(image, 0, 0.2)\",\n \"def softing_noise(image, kn):\\n\\n s_noise = cv2.GaussianBlur(image, (kn, kn), 0)\\n\\n return s_noise\",\n \"def add_image_noise(\\n self, mu=0, sigma=0.005, only_positive=False, random_seed=None):\\n if random_seed is not None:\\n np.random.seed(random_seed)\\n shape = self.signal.axes_manager.shape\\n noise = normal(mu, sigma, shape)\\n if only_positive:\\n self._image_noise = np.absolute(noise)\\n else:\\n self._image_noise = noise\",\n \"def make_noisy_images(image):\\r\\n return apply_poisson_noise(image, random_state=12345)\",\n \"def estimate_noise_std(r_data):\\n\\n s_noise = 0.2 * np.max(abs(r_data))\\n return s_noise\",\n \"def gaussian_black(z, mu: 'normal' = 0, sigma: (0.4,1) = 0.7):\\n return 1/(np.sqrt(2*np.pi)*sigma)*np.exp(-np.power((z - mu)/sigma, 2)/2)\",\n \"def add_noise(pointcloud, stddev=0.01):\\n result = pointcloud\\n result[0:2, :] = np.random.normal(pointcloud[0:2, :], stddev)\\n return result\",\n \"def gaussian_noise(inputs: torch.Tensor, stddev=None, sigma_max=0.06, channel_wise=1):\\n if stddev is None:\\n stddev = torch.rand(channel_wise) * sigma_max\\n stddev = torch.tensor(stddev, device=inputs.device)\\n if DATA_FORMAT == 'channels_first':\\n stddev = stddev.reshape([1, -1] + [1] * (inputs.ndim - 2))\\n else:\\n stddev = stddev.reshape([1] * (inputs.ndim - 1) + [-1])\\n noise_map = torch.randn_like(inputs) * stddev\\n return noise_map\",\n \"def add_gaussian_noise(X, mu=0, sigma=0.1):\\n noise = np.random.normal(0.0, sigma, size=X.size)\\n return X + noise.reshape(X.shape)\",\n \"def gaussian_white(z, mu: 'normal' = 0, sigma: (0.4, 1) = 0.7):\\n return 1 - gaussian_black(z, mu, sigma)\",\n \"def add_gaussian_noise(self, samples):\\n\\n if 'sigma' in self.gaussian_component:\\n sigma = self.gaussian_component['sigma']\\n return samples + self.random_state.normal(size=samples.shape) * sigma\\n if 'sigmas' in self.gaussian_component:\\n sigmas = self.gaussian_component['sigmas']\\n return samples + self.random_state.normal(size=samples.shape) * sigmas\\n\\n return samples\",\n \"def make_100gaussians_image(noise=True):\\n n_sources = 100\\n flux_range = [500, 1000]\\n xmean_range = [0, 500]\\n ymean_range = [0, 300]\\n xstddev_range = [1, 5]\\n ystddev_range = [1, 5]\\n params = {'flux': flux_range,\\n 'x_mean': xmean_range,\\n 'y_mean': ymean_range,\\n 'x_stddev': xstddev_range,\\n 'y_stddev': ystddev_range,\\n 'theta': [0, 2 * np.pi]}\\n\\n rng = np.random.RandomState(12345)\\n sources = QTable()\\n for param_name, (lower, upper) in params.items():\\n # Generate a column for every item in param_ranges, even if it\\n # is not in the model (e.g., flux). However, such columns will\\n # be ignored when rendering the image.\\n sources[param_name] = rng.uniform(lower, upper, n_sources)\\n xstd = sources['x_stddev']\\n ystd = sources['y_stddev']\\n sources['amplitude'] = sources['flux'] / (2.0 * np.pi * xstd * ystd)\\n\\n shape = (300, 500)\\n data = make_gaussian_sources_image(shape, sources) + 5.0\\n\\n if noise:\\n rng = np.random.RandomState(12345)\\n data += rng.normal(loc=0.0, scale=2.0, size=shape)\\n\\n return data\",\n \"def _gauss_noise(self, shape):\\n\\n n = np.random.normal(0, 1, shape)\\n return self.e*n\",\n \"def add_noise(image, type=\\\"s&p\\\"):\\n # Get the width and height of the image\\n w, h = image.size\\n\\n # Add salt and pepper noise\\n if type == \\\"s&p\\\":\\n # Choose a random amount of noise (lower number = more noise)\\n salt = np.random.randint(100, 400)\\n # Generate an array to determine location of noise\\n noise = np.random.randint(salt+1, size=(h, w))\\n\\n # Find the index of the salt and pepper (respectively location with max/min random value)\\n idx_salt = noise == salt\\n idx_pepper = noise == 0\\n\\n # Create a numpy array from the initial image and add the salt and pepper\\n np_img = np.array(image)\\n np_img[idx_salt, :] = 255\\n np_img[idx_pepper, :] = 0\\n\\n return Image.fromarray(np.uint8(np_img))\\n\\n # Add gaussian noise to image\\n if type == \\\"gauss\\\":\\n # Get the number of channels\\n c = len(image.getbands())\\n\\n # Get a random value for the mean and the standard deviation of the noise\\n mean = np.random.randint(-4, 5)\\n std = np.random.randint(5)\\n\\n # Generate the noise array\\n noise = np.random.normal(mean, std, (h, w, c))\\n\\n # Add noise to the image\\n return Image.fromarray(np.uint8(np.array(image) + noise))\\n\\n else:\\n # If the name of the given noise is not correct\\n return image\",\n \"def generate_fake_noise(inputs, size):\\n return np.random.normal(-0.0289923828125, 1.9391296947313124, (inputs, size)).astype(np.float32)\",\n \"def Noise(self, eps, size):\\n return eps * (np.random.uniform(size=size) * 2 - 1)\",\n \"def sigmanorm(y):\\n y = y.copy()\\n y -= y.mean() # set to zero mean\\n y /= y.std() # rescale to units of sigma\\n return y\",\n \"def noise_generator(n, mean, std, fractindex):\\n if fractindex not in VALID_FRACT:\\n raise ValueError(\\\"results: status must be one of %r.\\\" % VALID_FRACT)\\n \\n stdev = std\\n \\n b = 2*fractindex-1\\n print('beta: ', b)\\n \\n bdis = np.zeros(n)\\n\\n bdis[0] = 1\\n for i in range(1,n):\\n bdis[i] = bdis[i-1] * (0.5 * b + (i-1))/i # note that b is the shape parementer (b)\\n\\n plt.plot(bdis)\\n plt.show\\n\\n wnt = np.random.normal(mean, stdev, size = n)\\n print('WhiteNoise Stdev: ', np.std(wnt))\\n plt.plot(wnt)\\n plt.show()\\n\\n bdis_freq = np.fft.fft(bdis)\\n wnt_freq = np.fft.fft(wnt)\\n\\n bdis_freq = bdis_freq[1:n+1]\\n wnt_freq = wnt_freq[1:n+1]\\n\\n freq_total = bdis_freq * wnt_freq\\n \\n NumUniquePts = n/2 + 1\\n NumUniquePts = int(NumUniquePts)\\n j = np.arange(1, NumUniquePts)\\n \\n if fractindex > 1.0:\\n j = j\\n elif fractindex <= 1.0:\\n j = j**0.5\\n \\n ft_half1 = freq_total[1:NumUniquePts]/j\\n\\n real = np.real(freq_total[1:NumUniquePts+1])\\n real = np.flip(real, axis=0)\\n\\n imaginary = np.imag(freq_total[1:NumUniquePts+1])\\n imaginary = np.flip(imaginary, axis=0)\\n imaginary = 1j * imaginary\\n\\n ft_half2 = real - imaginary\\n\\n ft = np.hstack((ft_half1, ft_half2))\\n \\n x = np.fft.ifft(ft)\\n x = np.real(x[:n])\\n\\n mean_diff = mean - np.mean(x)\\n x = mean_diff + x\\n print(np.mean(x))\\n print(np.std(x))\\n plt.plot(x)\\n plt.show()\\n \\n return x\",\n \"def noise(self):\\n # Extract parameters\\n pzs = self.params[0]\\n ngals = np.array([pz.gals_per_steradian for pz in pzs])\\n return 1.0 / ngals\",\n \"def kernel_sigma(n_kernels):\\n sigmas = [0.001] # exact match small variance means exact match ?\\n if n_kernels == 1:\\n return sigmas\\n return sigmas + [0.1] * (n_kernels - 1)\",\n \"def drop_and_noise(image, sigma_d, percentage=0.8):\\n M, N = image.shape[:2]\\n n = N * M\\n p = m.floor(percentage * n)\\n image = np.cast[np.float32](image)\\n\\n missing_pixels_ind = np.random.permutation(n)[:p]\\n\\n mask = np.ones((M * N,), dtype=np.bool)\\n mask[missing_pixels_ind] = 0\\n mask = mask.reshape((M, N, 1))\\n\\n maskf = np.cast[np.float32](mask)\\n y_clean = image * maskf\\n\\n noise = np.random.normal(loc=0, scale=sigma_d, size=image.shape) * maskf\\n y = y_clean + noise\\n\\n return y, mask\",\n \"def add_noise(arr, sigma):\\n dims = arr.shape\\n arr += sigma * noise(*dims)\",\n \"def makeGaussian(height, width, sigma=3, center=None):\\n x = np.arange(0, width, 1, float)\\n y = np.arange(0, height, 1, float)[:, np.newaxis]\\n if center is None:\\n x0 = width // 2\\n y0 = height // 2\\n else:\\n x0 = center[0]\\n y0 = center[1]\\n\\n return np.exp(-4 * np.log(2) * ((x - x0) ** 2 + (y - y0) ** 2) / sigma ** 2)\",\n \"def real_blur_and_noise(image, kernel, sigma_d):\\n degraded = filter_image(image, kernel, mode=\\\"valid\\\", boundary=\\\"fill\\\")\\n noise = np.random.normal(0.0, sigma_d, degraded.shape).astype(np.float32)\\n degraded = degraded + noise\\n initial = np.pad(degraded, ((kernel.shape[0] // 2, kernel.shape[0] // 2),\\n (kernel.shape[1] // 2, kernel.shape[1] // 2),\\n (0, 0)), mode=\\\"edge\\\")\\n initial = edgeTaper(initial, kernel)\\n return initial\",\n \"def flatten(img,sigma=20.) :\\n\\n for i in range(img.shape[0]) :\\n img[i] /= np.median(img[i])\\n for i in range(img.shape[1]) :\\n img[:,i] /= np.median(img[:,i])\\n\\n hw=int(3*sigma)\\n u=np.linspace(-hw,hw,2*hw+1)\\n x=np.tile(u,(2*hw+1,1))\\n y=x.T\\n k=np.exp(-x**2/2/sigma**2-y**2/2/sigma**2)\\n k /= np.sum(k)\\n smooth=convolve2d(img,k,weight=None)\\n img /= smooth\\n\\n return img\",\n \"def _FWHMGauss(sigma, pixel=12):\\n return sigma*2*np.sqrt(2*np.log(2))*pixel\",\n \"def create_gaussian_array(self):\\n\\n # Fill array of size l x w with Gaussian Noise.\\n terrain_length = int(ceil(self.length/self.resolution))\\n terrain_width = int(ceil(self.width/self.resolution))\\n gaussian_array = np.random.normal(self.mu, self.sigma, (terrain_length,terrain_width))\\n\\n # Filter the array to smoothen the variation of the noise\\n gaussian_array = gaussian_filter(gaussian_array, self.sigma_filter)\\n\\n return gaussian_array\",\n \"def _get_noise(self, shape, dtype=None):\\n return np.random.normal(self._bias, self._scale, shape).astype(dtype)\",\n \"def make_time_series_noise(dates, mean=0., std=1.):\\n # TODO: implement Gaussian noise with mean/std\\n # TODO: add kwarg to parametrize noise from clouds/shadows\\n pass\",\n \"def initiategaussian(sd, x0):\\n y = np.exp(-x**2/(2*sd**2))\\n return y\",\n \"def add_imageNoise(img):\\n if not np.all(img >= 0):\\n print 'make sure the image pixel values are positive definite'\\n sys.exit()\\n noise = st.poisson.rvs(1.,loc = -1.,scale=1.,size=img.shape)*np.sqrt(img)\\n return noise\",\n \"def gaussian(x, sigma):\\n try: r = np.exp(-0.5*(x/sigma)**2) \\n except: r = np.zeros(len(x))\\n return r\",\n \"def noise_generator(self, power=None, SNR=None, size=None):\\r\\n alpha = self.db2power(SNR)\\r\\n sigma = np.sqrt(power / alpha) # 计算噪声标准差\\r\\n # 产生噪声\\r\\n noise_data = np.sqrt(0.5) * (np.random.normal(0, sigma, size=size) + np.random.normal(0, sigma, size=size) * 1j)\\r\\n noise_data = noise_data.astype(np.complex64)\\r\\n return noise_data\",\n \"def get_sigma_psf(self):\\n pass\",\n \"def _calc_sigma(self):\\n if self.data is None:\\n self._initial_blur()\\n previous = self.init_sigma\\n incr = 0\\n self.sigmas = [(previous, incr)]\\n for i in range(1, self.scale_per_octave + 3):\\n sigma_abs = self.init_sigma * (self.dest_sigma / self.init_sigma) ** (1.0 * i / (self.scale_per_octave))\\n increase = previous * sqrt((self.dest_sigma / self.init_sigma) ** (2.0 / self.scale_per_octave) - 1.0)\\n self.sigmas.append((sigma_abs, increase))\\n previous = sigma_abs\\n logger.debug(\\\"Sigma= %s\\\" % self.sigmas)\",\n \"def task_gaussian_noise(input_array, noise_factor):\\n return(np.random.normal(0, noise_factor, input_array.shape))\",\n \"def make_gaussian(size, sigma=10, center=None):\\n\\n x = np.arange(0, size[1], 1, float)\\n y = np.arange(0, size[0], 1, float)\\n y = y[:, np.newaxis]\\n\\n if center is None:\\n x0 = y0 = size[0] // 2\\n else:\\n x0 = center[0]\\n y0 = center[1]\\n\\n return np.exp(-4 * np.log(2) * ((x - x0) ** 2 + (y - y0) ** 2) / sigma ** 2)\",\n \"def make_noise(self, num):\\n return np.random.randn(num, self.seq_length, self.noise_dim)\",\n \"def generate_noise_image(content_image, noise_ratio = CONFIG.NOISE_RATIO):\\n\\tnoise_img = np.random.uniform(-20,20,(1, CONFIG.IMAGE_HEIGHT, CONFIG.IMAGE_WIDTH, CONFIG.COLOR_CHANNELS)).astype('float32')\\n\\n\\t# Setting the resulting image to be the weighted average of the content image and noise_image\\n\\tresult_img = noise_img * noise_ratio + content_image * (1 - noise_ratio)\\n\\n\\treturn result_img\",\n \"def noisePreset() :\\n s.noisePreset()\",\n \"def generate_noise(\\n self,\\n noise_iteration=(1, 1),\\n noise_size=(1, 1),\\n noise_value=(0, 128),\\n noise_background=(255, 255),\\n noise_sparsity=(0.4, 0.6),\\n noise_concentration=(0.4, 0.6),\\n xsize=1500,\\n ysize=1500,\\n ):\\n\\n # generate random iterations\\n iterations = random.randint(noise_iteration[0], noise_iteration[1])\\n\\n # generate background value\\n background_value = random.randint(noise_background[0], noise_background[1])\\n\\n # initialize blank noise mask\\n img_mask = np.full((ysize, xsize), fill_value=background_value, dtype=\\\"int\\\")\\n\\n # any invalid noise type will reset noise type to 0\\n if self.noise_type not in [1, 2, 3, 4, 5]:\\n noise_type = random.randint(1, 5)\\n else:\\n noise_type = self.noise_type\\n\\n # random location with no sides if no side is chosen\\n if self.noise_side not in self.sides:\\n noise_side = random.choice(self.sides)\\n else:\\n noise_side = self.noise_side\\n\\n # loop each iterations\\n for _ in range(iterations):\\n\\n # divider to rescale noise mask to larger size\\n y_divider = random.randint(noise_size[0], noise_size[1])\\n x_divider = random.randint(noise_size[0], noise_size[1])\\n\\n # generate noise mask for current iteration\\n img_mask_temporary = self.generate_mask_main(\\n noise_type,\\n noise_side,\\n noise_value,\\n noise_background,\\n noise_sparsity,\\n noise_concentration,\\n int(xsize / x_divider),\\n int(ysize / y_divider),\\n )\\n img_mask_temporary = cv2.resize(\\n img_mask_temporary.astype(\\\"uint8\\\"),\\n (xsize, ysize),\\n interpolation=cv2.INTER_CUBIC,\\n )\\n\\n # merge noise mask in each iteration by getting their min value\\n img_mask = np.minimum(img_mask_temporary, img_mask)\\n\\n # output needs uint8 type\\n img_mask = img_mask.astype(\\\"uint8\\\")\\n\\n return img_mask\",\n \"def ICA_Denoise(Y, ica_model, noise_std):\\n\\n # TODO: YOUR CODE HERE\",\n \"def threshold(image, sigma):\\n image[image<1] = 0\\n mean = np.mean(image[image>=1])\\n std = np.std(image[image>=1])\\n if (sigma == 0):\\n thresh = mean + 0.5*std\\n else:\\n thresh = min(mean + 0.5*std, 30/sigma)\\n image[image>thresh] = 255\\n image[image<=thresh] = 0\\n numAfterThresh = (image>0).sum()\\n\\n print(\\\"Average: {:.2f}\\\".format(mean))\\n print(\\\"Std dev: {:.2f}\\\".format(std))\\n print(\\\"Threshold: {:.2f}\\\".format(thresh))\\n print(\\\"Number after threshold: {}\\\".format(numAfterThresh))\\n return image\",\n \"def _addNoise(self):\\n self.dispNoise = self.dispRaw.copy()\\n self.dispNoise[:, 0] += self.sigmaEast * numpy.random.randn(self.numStations)\\n self.dispNoise[:, 1] += self.sigmaNorth * numpy.random.randn(self.numStations)\\n self.dispNoise[:, 2] += self.sigmaUp * numpy.random.randn(self.numStations)\\n return\",\n \"def _sigma_1(gam, eps):\\n s1 = 4 * r0**2 * alpha / eps / mec2_unit\\n s2 = 1 + (1./3. - eps/gam) * (1 - eps/gam)\\n s3 = np.log(2 * gam * (gam - eps) / eps) - 1./2.\\n s3[np.where(gam < eps)] = 0.0\\n return s1 * s2 * s3\",\n \"def _get_noise(self, shape, dtype=None):\",\n \"def salt_and_pepper_noise(image, prob):\\n output = np.zeros(image.shape,np.uint8)\\n thres = 1 - prob \\n for i in range(image.shape[0]):\\n for j in range(image.shape[1]):\\n rdn = random.random()\\n if rdn < prob:\\n output[i][j] = 0\\n elif rdn > thres:\\n output[i][j] = 255\\n else:\\n output[i][j] = image[i][j]\\n return output\",\n \"def get_sigma(rate, dt=1 * units.ns):\\n return stats.norm.isf(rate * dt)\",\n \"def create_gaussian_filter(size, sigma):\\n h = size[0] #height of the template\\n w = size[1] #width of the template \\n if h % 2 == 0: h += 1 #add 1 if dimensions are even\\n if w % 2 == 0: w += 1\\n x = math.floor(h/2)\\n y = math.floor(w/2) \\n sum = 0\\n #create our template\\n template = np.zeros((h,w))\\n #fill the template in with the numbers from Gaussian distribution\\n for i in range(h):\\n for j in range(w):\\n template[i,j] = math.exp(-((((j-x)**2)+((i-y)**2))/(2*(sigma**2))))\\n sum = sum + template[i,j]\\n #normalise the numbers\\n gaussian_filter = template/sum\\n return gaussian_filter\",\n \"def addSTDdevIndices(img):\\n\\t\\t\\timg = img.addBands(img.normalizedDifference(['green','swir1']).rename(['ND_green_swir1'])); # NDSI, MNDWI\\n\\t\\t\\timg = img.addBands(img.normalizedDifference(['nir','red']).rename(['ND_nir_red'])); # NDVI\\n\\t\\t\\timg = img.addBands(img.normalizedDifference(['nir','swir2']).rename(['ND_nir_swir2'])); # NBR, MNDVI\\n\\t\\t\\t\\n\\t\\t\\treturn img;\",\n \"def add_gaussian_noise(images: list, var: list, random_var: float=None, gauss_noise: list=None):\\n if random_var is None:\\n random_var = np.random.uniform(var[0], var[1])\\n mean = 0\\n new_images = []\\n gauss_noise_out = []\\n for i,image in enumerate(images):\\n row, col, c = image.shape\\n if gauss_noise is None or \\\\\\n (gauss_noise is not None and row*col*c !=\\n gauss_noise[i].shape[0]*gauss_noise[i].shape[1] * gauss_noise[i].shape[2]):\\n gauss = np.random.normal(mean, random_var * 127.5, (row, col, c))\\n else:\\n gauss = gauss_noise[i]\\n gauss_noise_out.append(gauss)\\n gauss = gauss.reshape(row, col, c)\\n image1 = np.clip(image + gauss, 0., 255.)\\n new_images.append(image1)\\n return new_images, random_var, gauss_noise_out\",\n \"def gaussian(size,sigma):\\n a,b=np.ogrid[-size/2:size/2,-size/2:size/2]\\n mask = a**2+b**2\\n mask = np.exp(-mask.astype('float')/(2*float(sigma**2)))\\n return mask\",\n \"def add_noise(np_image, amount):\\n noise = np.random.randn(np_image.shape[0],np_image.shape[1])\\n norm_noise = abs(noise/np.max(noise))\\n np_image = np_image + norm_noise*np.max(np_image)*amount\\n \\n return np_image\",\n \"def test_denoising(image, model, denoise_function,\\n noise_range=(0.01, 0.05, 0.1, 0.2), patch_size=(8, 8)):\\n h, w = np.shape(image)\\n noisy_images = np.zeros((h, w, len(noise_range)))\\n denoised_images = []\\n cropped_original = crop_image(image, patch_size)\\n\\n # make the image noisy:\\n for i in range(len(noise_range)):\\n noisy_images[:, :, i] = image + (\\n noise_range[i] * np.random.randn(h, w))\\n\\n # denoise the image:\\n for i in range(len(noise_range)):\\n denoised_images.append(\\n denoise_image(noisy_images[:, :, i], model, denoise_function,\\n noise_range[i], patch_size))\\n\\n # calculate the MSE for each noise range:\\n noisy_mses = {}\\n denoised_mses = {}\\n for i in range(len(noise_range)):\\n print(\\\"noisy MSE for noise = \\\" + str(noise_range[i]) + \\\":\\\")\\n noisy_mse = np.mean((crop_image(noisy_images[:, :, i],\\n patch_size) - cropped_original) ** 2)\\n noisy_mses[str(noise_range[i])] = noisy_mse\\n print(noisy_mse)\\n print(\\\"denoised MSE for noise = \\\" + str(noise_range[i]) + \\\":\\\")\\n denoised_mse = np.mean((cropped_original - denoised_images[i]) ** 2)\\n denoised_mses[str(noise_range[i])] = denoised_mse\\n print(denoised_mse)\\n\\n plt.figure(figsize=(20, 20))\\n plt.axis('off')\\n for i in range(len(noise_range)):\\n plt.subplot(2, len(noise_range), i + 1, xlabel='Noisy image', xticks=[], yticks=[])\\n plt.imshow(noisy_images[:, :, i], cmap='gray')\\n plt.subplot(2, len(noise_range), i + 1 + len(noise_range), xlabel='Denoised image', xticks=[], yticks=[])\\n plt.imshow(denoised_images[i], cmap='gray')\\n plt.show()\\n return noisy_mses, denoised_mses\",\n \"def make_noise(self, num):\\n return np.random.randn(num, self.seq_length + 2 * self.seq_pad,\\n self.noise_dim)\",\n \"def estimate_noise(spec, ind_range=None):\\n\\n # -- set the index range (nb, ends at len(spec)-1 since the derivative has\\n # one fewer points than the spectrum).\\n ind_range = ind_range if ind_range else [0,spec.shape[0]-1]\\n\\n # -- compute the derivative and estimate the noise over the range\\n noise = (spec[1:]-spec[:-1])[ind_range[0]:ind_range[1]].std(0)/np.sqrt(2.0)\\n\\n return noise\",\n \"def white_noise():\\n return random.randint(-32767, 32767)\",\n \"def MVN_Denoise(Y, mvn_model, noise_std):\\n return calc_weiner_filter(Y, mvn_model.mean, mvn_model.cov, noise_std)\",\n \"def make_DOG(inner_sigma, x):\\n y = x\\n outer_sigma = inner_sigma*5\\n X, Y = np.meshgrid(x, y)\\n inner_gaussian = 1./(2.*np.pi*inner_sigma) * np.exp(-(X**2 + Y**2)/2./inner_sigma**2) \\n outer_gaussian = 1./(2.*np.pi*outer_sigma) * np.exp(-(X**2 + Y**2)/2./outer_sigma**2) \\n return inner_gaussian - outer_gaussian/2 #weaker surround works better with our weights, which don't account for bursts \",\n \"def create_synthetic_noise_dataset(cfg):\\n from colorednoise import powerlaw_psd_gaussian\\n\\n betas = np.linspace(cfg['data.mix_synthetic_noise.min_beta'],\\n cfg['data.mix_synthetic_noise.max_beta'],\\n num=cfg['data.mix_synthetic_noise.num_samples'])\\n sample_rate = cfg['data.sample_rate']\\n segment_length = 2 * cfg['data.len_min']\\n wavs = [powerlaw_psd_gaussian(beta, sample_rate * segment_length)\\n for beta in betas]\\n wavs = [audio.normalize(wav, low=-1, high=1) for wav in wavs]\\n return NoiseDataset(wavs)\",\n \"def _get_add_noise(stddev, seed: int = None):\\n if distutils.version.LooseVersion(\\n tf.__version__) < distutils.version.LooseVersion('2.0.0'):\\n\\n # The seed should be only used for testing purpose.\\n if seed is not None:\\n tf.random.set_seed(seed)\\n\\n def add_noise(v):\\n return v + tf.random.normal(\\n tf.shape(input=v), stddev=stddev, dtype=v.dtype)\\n else:\\n random_normal = tf.random_normal_initializer(stddev=stddev, seed=seed)\\n\\n def add_noise(v):\\n return v + tf.cast(random_normal(tf.shape(input=v)), dtype=v.dtype)\\n\\n return add_noise\",\n \"def sampleGaussian(self, mu, log_sigma):\\n # reparameterization trick\\n epsilon = tf.random_normal(tf.shape(log_sigma), name=\\\"epsilon\\\")\\n return mu + epsilon * tf.exp(log_sigma) # N(mu, I * sigma**2)\",\n \"def standardize(image, mean=[0.48462227599918, 0.45624044862054, 0.40588363755159], std=[0.22889466674951, 0.22446679341259, 0.22495548344775]):\\n image = image.astype(np.float32) / 255.0\\n image = np.divide(np.subtract(image, mean), std)\\n return image\",\n \"def get_image_with_poisson_noise(image):\\r\\n img = tf_norm_crop_resize_image(image, resize_dim=(64,64))\\r\\n noisy_img = np.clip(make_noisy_images(img*255.)/255., 0., 1.)\\r\\n return noisy_img\",\n \"def add_gaussian_noise(self, stdev, iteration = 1, method = 'absolute', normalize = \\\"on\\\"):\\n\\n self.number_of_replicate = iteration\\n for fragment in self.fragments_for_mdv_calculation:\\n number_of_mass_data = max(self.mdv[fragment].keys()) + 1\\n noise = numpy.zeros((number_of_mass_data, iteration))\\n for i in range(iteration):\\n for number in range(number_of_mass_data):\\n if method == 'relative':\\n noise[number, i] = (numpy.random.randn() * stdev + 1) * self.mdv[fragment][number]['ratio']\\n else:\\n noise[number, i] = (numpy.random.randn() * stdev) + self.mdv[fragment][number]['ratio']\\n if noise[number, i] < 0.0:\\n noise[number, i] = 0.0\\n #各フラグメント毎に総和を１にする。\\n if normalize == \\\"on\\\":\\n sumvalue = sum(noise[:,i])\\n noise[:,i] = noise[:,i] / sumvalue\\n for number in range(number_of_mass_data):\\n self.mdv[fragment][number]['ratio']= sum(noise[number,:])/iteration\\n #self.mdv[fragment][number]['std'] = numpy.std(noise[number,:])\\n self.mdv[fragment][number]['data'] = numpy.array(noise[number,:])\",\n \"def sigma(h,T):\\r\\n P = 1/(1+np.exp(-2*h/T)) #from the mean field results\\r\\n ll = len(h)\\r\\n rand = np.random.rand(ll)\\r\\n pos = np.where(P-rand>0)[0]\\r\\n r = -np.ones(ll)\\r\\n r[pos] = +1\\r\\n return r\",\n \"def scipy_smooth(img, sigma=5):\\n return ndimage.gaussian_filter(img, sigma=sigma)\",\n \"def GSM_Denoise(Y, gsm_model, noise_std):\\n X = np.empty(Y.shape)\\n k = gsm_model.mix.shape[0]\\n I = np.identity(gsm_model.cov[0, :].shape[0])\\n for i in range(k):\\n mvn = multivariate_normal(cov=(gsm_model.cov[i, :] + ((noise_std**2) * I)))\\n upper_arg = gsm_model.mix[i] * (mvn.logpdf(Y[:, i]))\\n lower_arg = 0\\n for j in range(k):\\n inner_mvn = multivariate_normal(cov=(gsm_model.cov[j] + ((noise_std**2) * I)))\\n lower_arg += gsm_model.mix[j] * (inner_mvn.logpdf(Y[:, i]))\\n c_i = upper_arg / lower_arg\\n weiner_i = calc_weiner_filter(Y, np.zeros(Y.shape[0]), gsm_model.cov[i, :], noise_std)\\n X += c_i * weiner_i\\n return X\",\n \"def kernel_gaussiano(image: np.ndarray, sigma: float, kind: str = 'low') -> np.ndarray:\\n U, V = fourier_meshgrid(image)\\n D = fourier_distance(U, V)\\n H = np.exp( (-1.0 * D) / (2.0 * sigma**2) )\\n \\n if kind == 'high' or kind == 'highpass':\\n H = 1.0 - H\\n \\n return H\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7620322","0.7568027","0.73862773","0.7340184","0.70870167","0.70391494","0.69299024","0.6861069","0.6838096","0.6838096","0.682284","0.6760456","0.6759536","0.67473197","0.6671933","0.66611886","0.6641475","0.66406924","0.66172576","0.6612591","0.65864265","0.65774983","0.65765923","0.6555375","0.6541537","0.6509458","0.6498063","0.6495391","0.64597684","0.6458453","0.6457465","0.6429288","0.64284694","0.64243513","0.6393034","0.63697535","0.63470703","0.6326588","0.6307213","0.630143","0.62875634","0.6261008","0.62529135","0.6249006","0.61880517","0.61761683","0.616784","0.6163098","0.61541635","0.61332005","0.61054385","0.60982966","0.6074332","0.60614824","0.6053994","0.6049023","0.60473996","0.60408926","0.60152644","0.6011519","0.6009819","0.60087454","0.6001367","0.6001354","0.5985209","0.5972889","0.5961943","0.59462184","0.59460557","0.59436375","0.5942034","0.593967","0.59383345","0.59376675","0.5922431","0.59048975","0.5898023","0.58976614","0.58907855","0.5880775","0.5880528","0.58765113","0.5867165","0.586603","0.5857762","0.5857499","0.58494973","0.5838661","0.5823566","0.58190185","0.58066475","0.5799588","0.5798871","0.57902163","0.5784496","0.5782808","0.5782275","0.577743","0.57729137","0.5764255"],"string":"[\n \"0.7620322\",\n \"0.7568027\",\n \"0.73862773\",\n \"0.7340184\",\n \"0.70870167\",\n \"0.70391494\",\n \"0.69299024\",\n \"0.6861069\",\n \"0.6838096\",\n \"0.6838096\",\n \"0.682284\",\n \"0.6760456\",\n \"0.6759536\",\n \"0.67473197\",\n \"0.6671933\",\n \"0.66611886\",\n \"0.6641475\",\n \"0.66406924\",\n \"0.66172576\",\n \"0.6612591\",\n \"0.65864265\",\n \"0.65774983\",\n \"0.65765923\",\n \"0.6555375\",\n \"0.6541537\",\n \"0.6509458\",\n \"0.6498063\",\n \"0.6495391\",\n \"0.64597684\",\n \"0.6458453\",\n \"0.6457465\",\n \"0.6429288\",\n \"0.64284694\",\n \"0.64243513\",\n \"0.6393034\",\n \"0.63697535\",\n \"0.63470703\",\n \"0.6326588\",\n \"0.6307213\",\n \"0.630143\",\n \"0.62875634\",\n \"0.6261008\",\n \"0.62529135\",\n \"0.6249006\",\n \"0.61880517\",\n \"0.61761683\",\n \"0.616784\",\n \"0.6163098\",\n \"0.61541635\",\n \"0.61332005\",\n \"0.61054385\",\n \"0.60982966\",\n \"0.6074332\",\n \"0.60614824\",\n \"0.6053994\",\n \"0.6049023\",\n \"0.60473996\",\n \"0.60408926\",\n \"0.60152644\",\n \"0.6011519\",\n \"0.6009819\",\n \"0.60087454\",\n \"0.6001367\",\n \"0.6001354\",\n \"0.5985209\",\n \"0.5972889\",\n \"0.5961943\",\n \"0.59462184\",\n \"0.59460557\",\n \"0.59436375\",\n \"0.5942034\",\n \"0.593967\",\n \"0.59383345\",\n \"0.59376675\",\n \"0.5922431\",\n \"0.59048975\",\n \"0.5898023\",\n \"0.58976614\",\n \"0.58907855\",\n \"0.5880775\",\n \"0.5880528\",\n \"0.58765113\",\n \"0.5867165\",\n \"0.586603\",\n \"0.5857762\",\n \"0.5857499\",\n \"0.58494973\",\n \"0.5838661\",\n \"0.5823566\",\n \"0.58190185\",\n \"0.58066475\",\n \"0.5799588\",\n \"0.5798871\",\n \"0.57902163\",\n \"0.5784496\",\n \"0.5782808\",\n \"0.5782275\",\n \"0.577743\",\n \"0.57729137\",\n \"0.5764255\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.68094635"},"document_rank":{"kind":"string","value":"11"}}},{"rowIdx":794,"cells":{"query":{"kind":"string","value":"Create a blank image."},"document":{"kind":"string","value":"def model_blank(nx, ny=1, nz=1, bckg = 0.0):\n\te = EMData()\n\te.set_size(nx, ny, nz)\n\te.to_zero()\n\tif( bckg != 0.0): e+=bckg\n\treturn e"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _blankimage():\n img = TK.PhotoImage(width=1, height=1)\n img.blank()\n return img","def create_empty_image(width=512, height=512):\n blank_img = np.zeros((width, height, 3), np.uint8)\n # Return instance of the class\n return ExtendedImage(blank_img)","def blank_image(height, width):\n all_green = create_uniform_image(height, width, [0, 255, 0])\n return all_green","def gen_empty_img(w=640, h=480):\n return np.zeros((h, w, 3), np.uint8)","def blank(width, height, channels=3, value=0):\n blank_image = np.full((height, width, channels), value, np.uint8)\n return Image(img=blank_image)","def create_white_picture(pic_width, pic_height):\n white_picture = Image.new(\"1\", (pic_width, pic_height), (1))\n return white_picture","def create_blank(width, height, rgb_color=(0, 0, 0)):\r\n # Create black blank image\r\n image = np.zeros((height, width, 3), np.uint8)\r\n\r\n # Since OpenCV uses BGR, convert the color first\r\n color = tuple(reversed(rgb_color))\r\n # Fill image with color\r\n image[:] = color\r\n\r\n return image","def create_blank(width, height, rgb_color=(0, 0, 0)):\n # Create black blank image\n image = np.zeros((height, width, 3), np.uint8)\n\n # Since OpenCV uses BGR, convert the color first\n color = tuple(reversed(rgb_color))\n # Fill image with color\n image[:] = color\n\n return image","def create_blank(width, height, rgb_color=(0, 0, 0)):\n # Create black blank image\n image = np.zeros((height, width, 3), np.uint8)\n\n # Since OpenCV uses BGR, convert the color first\n color = tuple(reversed(rgb_color))\n # Fill image with color\n image[:] = color\n\n return image","def create_blank(w, h, rgb_color=(0, 0, 0)):\n image = np.zeros((h, w), np.uint8)\n color = tuple(reversed(rgb_color))\n image[:] = 0\n return image","def create_blank(w, h, rgb_color=(0, 0, 0)):\n image = np.zeros((h, w), np.uint8)\n color = tuple(reversed(rgb_color))\n image[:] = 0\n return image","def create_image(self):\n\n self._image = 255 * np.ones((self._height, self._width, 3), np.uint8)","def EmptyBitmap(*args, **kwargs):\n val = _gdi_.new_EmptyBitmap(*args, **kwargs)\n return val","def blanck_picture(img):\r\n blank_image = np.zeros((img.shape[0],img.shape[1],3), np.uint8)\r\n blank_image[0:, 0:] = 0, 0, 0\r\n return blank_image","def create_full_pic(self):\n self.create_half_pic()\n mirror_update(self.flag)","def create_blank(cls, n: int, m: int):\n image = np.zeros((n, m, 3), np.uint8)\n\n color = (255, 255, 255)\n # Fill image with color\n image[:] = color\n\n return image","def make_image(self, path):\n\t\treturn None","def create_one_image(attrs=None):\n attrs = attrs or {}\n\n # Set default attribute\n image_info = {\n 'id': str(uuid.uuid4()),\n 'name': 'image-name' + uuid.uuid4().hex,\n 'owner': 'image-owner' + uuid.uuid4().hex,\n 'container_format': '',\n 'disk_format': '',\n 'min_disk': 0,\n 'min_ram': 0,\n 'is_public': True,\n 'protected': False,\n 'properties': {'Alpha': 'a', 'Beta': 'b', 'Gamma': 'g'},\n 'status': 'status' + uuid.uuid4().hex,\n }\n\n # Overwrite default attributes if there are some attributes set\n image_info.update(attrs)\n\n return image.Image(**image_info)","def blanck_picture(img):\r\n\r\n blank_image = np.zeros((img.shape[0],img.shape[1],3), np.uint8)\r\n blank_image[0:img.shape[0], 0:img.shape[1]] = 0, 0, 0","def new_image(self, width, height, background=None, mode=\"RGBA\"):\n self.img = PIL.Image.new(mode, (width, height), background)\n self.width,self.height = width,height\n self.drawer = aggdraw.Draw(self.img)","def generate_image(self):\n pass","def get_blank_img(self):\n if photos_settings.DEBUG:\n return self.get_placeholder_img()\n\n out = {\n 'blank': True,\n 'width': self.max_width,\n 'height': self.max_height,\n 'url': photos_settings.EMPTY_IMAGE_SITE_PREFIX + 'img/empty/%s.png' % (self.name),\n }\n return out","def test_create_image(self):\n pass","def new_test_image():\n warnings.warn(DeprecationWarning(\n \"new_test_image() is deprecated in favour of the get_sample_image() \"\n \"context manager.\"), stacklevel=2)\n image_name = 'test-{}.png'.format(uuid.uuid4())\n image = Image.new('RGBA', size=(50, 50), color=(256, 0, 0))\n ImageDraw.Draw(image)\n byte_io = BytesIO()\n image.save(byte_io, 'png')\n byte_io.seek(0)\n return image_name, ContentFile(byte_io.read(), image_name)","def NullImageProto(msg:str = \"\"):\n return NLImage(width=0, height=0, data=msg)","def _clear(self):\n\n self.image = Image.new(\"RGB\", (self._width, self._height), self._color)","def create_image(self):\n # how many categories?\n aspect_ratio = float(4) / 3\n self.width = int(math.sqrt(aspect_ratio * self.total))\n self.height = int(self.width / aspect_ratio)\n\n img = Image.new(\"RGB\", (self.width, self.height))\n return img","def plot_blank(self):\n self.figure_bmp.SetBitmap(self.controller.plot_blank())","def _ensure_empty_image_ok(self):\n if self.ignore_empty:\n return\n\n if len(self) > 1:\n raise RuntimeError(\n \"Cannot write None image at extension %d\" % len(self))\n if 'ndims' in self[0]._info:\n raise RuntimeError(\"Can only write None images to extension zero, \"\n \"which already exists\")","def make_image(storage, name, width, height, format='JPEG', mode='RGB'):\n im = Image.new(mode, (width, height))\n draw = ImageDraw.Draw(im)\n draw.rectangle([0, 0, width // 2, height // 2], '#F00')\n draw.rectangle([width // 2, 0, width, height // 2], '#0F0')\n draw.rectangle([0, height // 2, width // 2, height], '#00F')\n draw.rectangle([width // 2, height // 2, width, height], '#000')\n draw.rectangle([width // 4, height // 4, 3 * width // 4, 3 * height // 4], '#FFF')\n im_bytes_io = io.BytesIO()\n im.save(im_bytes_io, format)\n im_bytes_io.seek(0)\n storage.save(name, im_bytes_io)","def create_artificial_image(self):\n background = self.BGI.create_background_image()\n star_PSF = self.PSF.create_star_PSF()\n header = self.HDR.create_header()\n\n fits.writeto(self.image_dir + self.image_name + '.fits',\n background + star_PSF, overwrite=True, header=header)","def new_image(x, y, out, data):\n img = Image.new('RGB', (x, y))\n img.putdata(data)\n img.save(out)","def clear(self):\n self.display(Image.new(self.mode, self.size))","def _createimage(self, image):\n return self.cv.create_image(0, 0, image=image)","def get_blank_image(width: int, height: int, n_channels: int, cval=255) -> np.ndarray:\n if n_channels == 0:\n image = np.zeros((height, width)) + 255\n else:\n image = np.zeros((height, width, n_channels)) + cval\n return image.astype(\"uint8\")","def initialise(self):\r\n self.set_image(\"wall.png\")\r\n return self","def __generate_image(self):\n\t\tself.img = np.ones((self.size*self.width+self.border,self.size*self.width+self.border,1), np.uint8)*255\n\t\tfor i in range(len(self.matrix)):\n\t\t\tfor j in range(len(self.matrix)):\n\t\t\t\tif self.matrix[j][i] == 1:\n\t\t\t\t\tself.img = cv2.rectangle(self.img,(i*self.width+int(self.border/2),j*self.width+int(self.border/2))\n\t\t\t\t\t\t,(i*self.width+self.width+int(self.border/2),j*self.width+self.width+int(self.border/2)),(0,0,0),-1)\n\t\tif '.' in self.name:\n\t\t\tcv2.imwrite(self.name,self.img)\n\t\telse:\n\t\t\tcv2.imwrite(self.name+'.jpg',self.img)\n\t\tcv2.imshow(\"Image\",self.img)\n\t\tcv2.waitKey(0)\n\t\tcv2.destroyAllWindows()","def create_base_image(self, builder, template, parameters):","def generateEmptySprite(cls, size, alpha=None, fillWith=None, colorKey=None):\n # BBB: move this away from here, like in a simple module\n surface = pygame.Surface(size, flags=HWSURFACE|HWPALETTE, depth=32)\n if fillWith:\n surface.fill(fillWith)\n if alpha is not None:\n surface.set_alpha(alpha)\n if colorKey:\n surface.set_colorkey(colorKey)\n return surface","def _EmptyBitmapRGBA(*args, **kwargs):\n return _gdi_._EmptyBitmapRGBA(*args, **kwargs)","def no_bin(image, *args, **kwargs):\n return image","def initImg(self):\n self.img = Image.new('RGBA',(self.width,self.height),color='#' + getConfigPart(self.theme,\"bg\"))\n self.draw = ImageDraw.Draw(self.img)","def make_montage(infile):\n\n #create a blank image\n blank = Image.new(\"RGB\", OUTPIC_SIZE ,(25,23,24,255))\n\n #paste the background\n bg = Image.open(BACKGROUND_FILENAME)\n blank.paste(bg,(0,0))\n \n #paste the raw picture\n im = Image.open(infile)\n \n # past on background\n blank.paste(im,OUTPIC_PHOTO_OFFSET)\n\n #paste the stickers layer on the pic (a transparent png)\n stickers = Image.open(STICKERS_FILENAME)\n blank.paste(stickers,(0,0),stickers)\n\n return blank","def draw_nonogram(self):\n image = Image.new(\"RGB\", (self.nonogram_size * 50, self.nonogram_size * 50), (255, 255, 255))\n draw = ImageDraw.Draw(image)\n\n for index, square in enumerate(reduce(lambda x, y: x+y, self.grid), 0):\n\n #print(square)\n x = index % self.nonogram_size\n y = index // self.nonogram_size\n coord = [(x * 50, y * 50), ((x + 1) * 50, (y + 1) * 50)]\n if square == EMPTY:\n draw.rectangle(coord, fill=(255, 255, 255))\n if square == FILLED:\n draw.rectangle(coord, fill=(0, 0, 0))\n return image","def build_filler_images(self):","def maketestimage(self, *args, **kwargs):\n return _image.image_maketestimage(self, *args, **kwargs)","def create_image(path, pxcount):\n img = Image.open(path, 'r').convert('L')\n pixels = img.load()\n for i in range(pxcount):\n x = randint(0, img.size[0]-1)\n y = randint(0, img.size[0]-1)\n if pixels[x, y] == 0:\n pixels[x, y] = 255\n else:\n pixels[x, y] = 0\n return img","def make_image():\n click.echo(\"make_image\")","def EmptyBitmapRGBA(width, height, red=0, green=0, blue=0, alpha=0):\n return _gdi_._EmptyBitmapRGBA(width, height, red, green, blue, alpha)","def make_empty_directories_linux() -> None:\n mkdir(PICTURES_DIR / 'screenshots' / 'grim')\n mkdir(PICTURES_DIR / 'screenshots' / 'swappy')","def imageprepare(self,argv):\r\n\t\tim = Image.open(argv).convert('L')\r\n\t\twidth = float(im.size[0])\r\n\t\theight = float(im.size[1])\r\n\t\tnewImage = Image.new('L', (28, 28), (255)) # creates white canvas of 28x28 pixels\r\n\r\n\t\tif width > height: # check which dimension is bigger\r\n\t\t\t# Width is bigger. Width becomes 20 pixels.\r\n\t\t\tnheight = int(round((20.0 / width * height), 0)) # resize height according to ratio width\r\n\t\t\tif nheight == 0: # rare case but minimum is 1 pixel\r\n\t\t\t\tnheight = 1\r\n\t\t\t\t# resize and sharpen\r\n\t\t\timg = im.resize((20, nheight), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)\r\n\t\t\twtop = int(round(((28 - nheight) / 2), 0)) # caculate horizontal pozition\r\n\t\t\tnewImage.paste(img, (4, wtop)) # paste resized image on white canvas\r\n\t\telse:\r\n\t\t\t# Height is bigger. Heigth becomes 20 pixels.\r\n\t\t\tnwidth = int(round((20.0 / height * width), 0)) # resize width according to ratio height\r\n\t\t\tif (nwidth == 0): # rare case but minimum is 1 pixel\r\n\t\t\t\tnwidth = 1\r\n\t\t\t\t# resize and sharpen\r\n\t\t\timg = im.resize((nwidth, 20), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)\r\n\t\t\twleft = int(round(((28 - nwidth) / 2), 0)) # caculate vertical pozition\r\n\t\t\tnewImage.paste(img, (wleft, 4)) # paste resized image on white canvas\r\n\r\n\t\t# newImage.save(\"sample.png\")\r\n\r\n\t\ttv = list(newImage.getdata()) # get pixel values\r\n\r\n\t\t# normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\r\n\t\ttva = [(255 - x) * 1.0 / 255.0 for x in tv]\r\n\t\treturn tva","def construct_image(imgs):\n\n # todo fill missing pieces and\n\n if len(imgs) == 0:\n return None\n # taking the first\n w, h = imgs[0][1].size\n img_array = order_2d(imgs)\n x_count = len(img_array[0])\n y_count = len(img_array)\n height = h * y_count\n width = w * x_count\n new_im = Image.new('RGB', (width, height))\n for y in range(y_count):\n for x in range(x_count):\n _, im = img_array[y][x]\n new_im.paste(im, (x * w, y * h))\n return new_im","def create_image(self, shapes):\n img = image.IMG()\n img.draw_shapes(shapes)\n img = np.transpose(img.array(), (2, 0, 1))\n return img","def create_image(storage, filename, size=(100, 100), image_mode='RGB', image_format='PNG'):\n data = BytesIO()\n PIL.Image.new(image_mode, size).save(data, image_format)\n data.seek(0)\n if not storage:\n return data\n image_file = ContentFile(data.read())\n return storage.save(filename, image_file)","def test_empty_image(self):\n r = post(self.client, 'upload.up_image_async', {'image': ''},\n args=['questions.Question', 1])\n\n eq_(400, r.status_code)\n json_r = json.loads(r.content)\n eq_('error', json_r['status'])\n eq_('Invalid or no image received.', json_r['message'])\n eq_('You have not selected an image to upload.',\n json_r['errors']['image'][0])","def new(self, size, fill):\n return Image(PIL.Image.new(\"RGB\", size, fill))","def clear(self):\n self.img = PIL.Image.new(self.img.mode, self.img.size, self.background)\n self.drawer = aggdraw.Draw(self.img)","def fill_image(im):\n width, height = im.size\n # Select the larger value of the length and width of the original picture\n # as the radius of the nine palace grid of the new picture\n new_image_len = width if width > height else height\n # Create a white canvas\n new_image = Image.new(im.mode, (new_image_len, new_image_len), color=\"white\")\n # Paste the original image on the canvas at the center\n if width > height:\n new_image.paste(im, (0, int((new_image_len - height) / 2)))\n else:\n new_image.paste(im, (int((new_image_len - width) / 2), 0))\n return new_image","def generate_image(self):\n\n if not has_pillow:\n raise RuntimeError(\"requires https://pypi.org/project/pillow/\")\n\n background = self.get_background()\n foreground = self.get_foreground()\n\n matrix = self.generate_matrix()\n\n image = Image.new(\"RGB\", (420, 420), background)\n draw = ImageDraw.Draw(image)\n\n for (i, row) in enumerate(matrix):\n for (j, bit) in enumerate(row):\n x = 35 + j * 70\n y = 35 + i * 70\n\n if bit:\n draw.rectangle((x, y, x + 70, y + 70), foreground)\n\n return image","def set_default_image(self, image):\n raise NotImplementedError","def create_target_image(self, builder, target, base_image, parameters):","def imBox(self, width, height):\n img = Image.new(\"1\", (width, height))\n draw = ImageDraw.Draw(img)\n bgColor=255\n draw.rectangle((0,0) + img.size,fill=bgColor)\n return img","def to_image(self, width=800, height=600, **kwargs):\n img = _white_image(\n parameters=self.parameters,\n width=width,\n height=height,\n **kwargs\n )\n return img","def create_test_image(dirname, array, normalization=None):\n filename = str(dirname / 'tmp.tif')\n create_test_file(filename, array)\n satellite = 'quickbird'\n image = Image(filename, satellite, normalization_parameters=normalization)\n return image","def create(self, name, image, command, **kwargs):\n return","def draw_empty( text, file, kw ):\n\n prefs = dict(globals()['prefs'])\n for key, data in kw.items():\n prefs[key] = data\n fig = Figure()\n canvas = FigureCanvasAgg( fig )\n dpi = prefs['width'] /prefs['width_inches']\n height_inches = prefs['height'] / float(dpi)\n fig.set_size_inches( prefs['width_inches'], height_inches )\n fig.set_dpi( dpi )\n fig.set_facecolor('white')\n fig.text( .5, .5, text, horizontalalignment='center' )\n if isinstance( file , StringIO.StringIO ) or type(file) == cStringIO_type:\n canvas.draw()\n size = canvas.get_renderer().get_canvas_width_height()\n # Hack: for some unknown reason in py27 this call is returning floats.\n # Convert it to int coordinates so that PIL doesnt complain.\n size = (int(size[0]),int(size[1]))\n buf=canvas.tostring_argb()\n im=PILImage.fromstring('RGBA', size, buf, 'raw', 'RGBA', 0, 1)\n a, r, g, b = im.split()\n im = PILImage.merge( 'RGBA', (r, g, b, a) )\n im.save( file, format = 'PNG' )\n else:\n canvas.print_figure( file, **kw )","def make_image(self, imagename, fitsname, niter=500, antenna='', phasecenter='', start=200, stop=900, del_img=True, overwrite=False): \n self.generate_image(imagename, antenna=antenna, niter=niter, phasecenter=phasecenter, start=start, stop=stop)\n self.to_fits(imagename + '.image', fitsname, overwrite=overwrite)\n if del_img:\n self.remove_image(imagename, del_img=True)","def _make_image(self):\n self.image.fill(0)\n self.image[:, :, PillEater.WALLS] = self.walls\n self.image[:, :, PillEater.FOOD] = self.world_state['food']\n self.image[self.world_state['pillman']['pos'][0], self.world_state['pillman']['pos'][1],\n PillEater.PILLMAN] = 1\n for ghost in self.world_state['ghosts']:\n edibility = self.world_state['power'] / float(self.pill_duration)\n self.image[ghost['pos'][0], ghost['pos'][1], PillEater.GHOSTS] = 1. - edibility\n self.image[ghost['pos'][0], ghost['pos'][1], PillEater.GHOSTS_EDIBLE] = edibility\n for pill in self.world_state['pills']:\n self.image[pill['pos'][0], pill['pos'][1], PillEater.PILL] = 1\n return self.image","def draw_empty( self ):\n prefs = self.prefs\n fig = Figure()\n canvas = FigureCanvasAgg( fig )\n dpi = prefs['width'] /prefs['width_inches']\n height_inches = prefs['height'] / float(dpi)\n fig.set_size_inches( prefs['width_inches'], height_inches )\n fig.set_dpi( dpi )\n fig.set_facecolor('white')\n fig.text( .5, .5, \"No data returned by DB query.\", horizontalalignment='center' )\n self.ax = None\n self.fig = fig\n self.canvas = canvas","def get_image(filename=None, astrotarget=None,\n **kwargs):\n return Image(filename,astrotarget=astrotarget,\n **kwargs)","def test_annotate_blank(self):\n x_size = 500\n y_size = 500\n\n label = li.Label(\"Test Label\", (0.20, 0.20))\n test_im = li.ImageBlank(None, x_size, y_size)\n test_im.annotate(label)\n annotated_im = test_im.data\n\n # Ensure the shape is retained\n shape_expected = (y_size, x_size, 4)\n shape_test = annotated_im.shape\n self.assertEqual(\n shape_test, shape_expected, msg=\"shape not retained after annotation\"\n )\n\n # Test that there are darker text areas\n flattened = annotated_im[..., :3].mean(axis=-1)\n n_black_pixels = (flattened < 10).sum()\n\n self.assertGreater(n_black_pixels, 50, msg=\"Not enough black/text pixels\")\n self.assertLess(n_black_pixels, 5000, msg=\"Too many black/text pixels\")","def make_empty_img_from_img(img, dimensions=3):\n xlen, ylen, zlen = img.GetSize()\n dupe = img[:, :, :]\n for x in xrange(xlen):\n for y in xrange(ylen):\n if dimensions == 3:\n for z in xrange(zlen):\n dupe.SetPixel(x, y, z, 0)\n else:\n dupe.SetPixel(x, y, 0)\n return dupe","def create_image(self, instance_id, name,\r\n description=None, no_reboot=False):\r\n params = {'InstanceId' : instance_id,\r\n 'Name' : name}\r\n if description:\r\n params['Description'] = description\r\n if no_reboot:\r\n params['NoReboot'] = 'true'\r\n img = self.get_object('CreateImage', params, Image, verb='POST')\r\n return img.id","def make_image():\n # get the mask\n twitter_mask = np.array(Image.open('resource/twitter-mask.png'))\n\n wc = WordCloud(background_color='white', max_words=100, mask=twitter_mask, contour_width=3,\n contour_color='steelblue')\n\n # generate word cloud\n wc.generate_from_frequencies(get_word_frequency())\n\n # store to file\n wc.to_file('/tmp/twitter.png')\n\n # show\n frame = cv2.imread('/tmp/twitter.png')\n cv2.imshow('figure', frame)\n cv2.waitKey(60000)\n cv2.destroyAllWindows()","def glance_create_new_image(glance, images_location, image_info, image_name_prefix=None):\n # image raw file path\n image_raw_source = image_info['image_raw_source']\n image_file = os.path.join(images_location, image_raw_source)\n\n if not os.path.isfile(image_file):\n logger.warning(\"image raw file:'%s' not found!\", image_file)\n return None\n\n fimg = None\n try:\n fimg = open(image_file, 'rb')\n except Exception:\n logger.error(\"Opening raw image file:'%s' failed\", image_file)\n return None\n\n try:\n # image name\n image_name = image_info['image_name']\n if image_name_prefix:\n image_name = \"{}{}\".format(image_name_prefix, image_name)\n logger.debug(\"image_name: %s\", image_name)\n\n # image min_disk\n if image_info['image_min_disk'] == 'auto':\n # compute the size of the file -> min disk size in GB\n imagesize = os.fstat(fimg.fileno()).st_size\n image_min_disk = (imagesize/1024/1024/1024)+1\n else:\n image_min_disk = image_info['image_min_disk']\n logger.debug(\"image_min_disk: %s\", image_min_disk)\n\n # image min_ram\n image_min_ram = image_info['image_min_ram']\n logger.debug(\"image_min_ram: %s\", image_min_ram)\n\n # image properties (dictionary)\n image_properties = image_info['image_properties']\n logger.debug(\"image_properies: %s\", image_properties)\n\n logger.debug(\"glance image create (private): '%s'\", image_name)\n image = glance.images.create(name=image_name,\n visibility='private',\n disk_format='raw',\n container_format='bare',\n min_disk=int(image_min_disk),\n min_ram=int(image_min_ram))\n logger.debug(\"glance image upload: '%s' -> '%s'\", fimg.name, image_name)\n glance.images.upload(image.id, fimg)\n\n except Exception:\n logger.exception(\"Creating and uploading Glance image '%s' failed\", image_name)\n return None\n\n return image","def testBinaryImage():\n ALIEN = \"0\"*8 + \"11011011\"*2 + \"0\"*8 + \"00001000\" + \\\n \"01000010\" + \"01111110\" + \"0\"*8\n # this function is imported from cs5png.py\n NUM_ROWS = 8\n NUM_COLS = 8\n binaryIm( ALIEN, NUM_COLS, NUM_ROWS )\n # that should create a file, binary.png, in this\n # directory with the 8x8 image...","def new(mode, size, color=0):\r\n\r\n _check_size(size)\r\n\r\n if color is None:\r\n # don't initialize\r\n _im = Image()._new(mode, size)\r\n return Image(_im)\r\n\r\n if type(color).__name__ == \"str\":\r\n # css3-style specifier\r\n color = ImageColor().getcolor(color, mode)\r\n color = ImageDraw(None)._convert_bgr2rgb(color)\r\n\r\n _im = Image()._new(mode, size, color)\r\n return Image(_im)","def generate_image(self):\n\t\tcenters = self.generate_centers()\n\t\timg = Image.new('RGB', (self.config.image_size, self.config.image_size), color=(0,0,0))\n\t\tshapes = np.random.randint(2, size=len(centers))\n\t\tdrawer = ImageDraw.Draw(img)\n\t\tr = int(0.05 * self.config.image_size)\n\t\tR = []\n\t\tfor i in range(len(centers)):\n\t\t\tcoor = (centers[i][0] - r , centers[i][1] - r, centers[i][0] + r, centers[i][1] + r)\n\t\t\tif shapes[i] < 0.5:\n\t\t\t\tdrawer.rectangle(coor, fill=COLOR[i])\n\t\t\telse:\n\t\t\t\tdrawer.ellipse(coor, fill=COLOR[i])\n\t\t\tR.append([centers[i], i, shapes[i]])\n\t\treturn np.array(img), R","def createBlankPlot(self):\n\n fig = plt.figure(figsize=(8,6),dpi=80)\n fig.set_facecolor('#ededed')\n \n # Format plot\n ax = plt.subplot(111)\n \n fig.canvas.draw()\n \n return fig, ax","def setup():\n img = Image.new('RGB', (10, 20))\n img.putpixel((5, 10), (0, 255, 0))\n img.save('green-dot.tif')\n img.save('green-dot.jpg')\n img.save('green-dot.png')","def test_draw(self):\n image_name = filename(sys._getframe().f_code.co_name)\n result_file, reference_file = get_path(image_name)\n\n ''' This function is to create an empty image with a specific dimension\n with white background, and black/white colored '''\n\n image, canvas = get_image('L', (15,90),'white')\n\n for i in range(len(draw_points) - 1):\n draw(canvas, (draw_points[i + 0], draw_points[i + 1]), 'A')\n\n \"\"\" saving the file and closing it \"\"\"\n\n image.save(result_file)\n image.close()\n\n \"\"\" validate the resultant file against the reference images\"\"\"\n\n validate(reference_file, result_file)","def _create_layer() -> Image:\n data = np.random.random((32, 16))\n return Image(data)","def Empty():\n return Container(name='(empty)',\n metadata={},\n section_sizes={},\n metrics_by_file={})","def get_inline_stmt_emptybitmap(self, bitmap):\n # keep in sync with BitmapMixin.get_preview_obj_bitmap()\n width = 16\n height = 16\n try:\n size = bitmap[6:]\n width, height = [int(item.strip()) for item in size.split(',', 1)]\n except ValueError:\n logging.warn( 'Malformed statement to create an empty bitmap: %s', bitmap )\n stmt = self.tmpl_inline_emptybitmap % { 'width': width, 'height': height }\n return stmt","def test_random_single_image():\n\n shap.image_plot(np.random.randn(3, 20, 20), np.random.randn(3, 20, 20), show=False)","def topil(self) -> Image.Image:\n if self.width == 0 or self.height == 0:\n return None\n return Image.frombytes(\n \"RGBA\", (self.width, self.height), self.data, \"raw\", \"ARGB\", 0, 1\n )","def create_zeroed_image(n):\n\n alist = []\n for i in range(0, n):\n alist.append([])\n for j in range(0,n):\n alist[i].append(0)\n return alist","def _create_image(self):\n if hasattr(self, '_image') and self._image:\n return self._image\n try:\n command = \"tex2im -b transparent -t cyan\"\n subprocess.run([*command.split(), self._formula])\n except Exception as e:\n import traceback\n print(traceback.format_exc())\n return None\n # tex2im converts to out.png by default\n img = Image.open('out.png').convert('RGBA')\n # create a new rgba image to blend the latex with the alpha\n subprocess.run([\"rm\", \"out.png\"])\n return img","def create_image(self):\n img = cv2.imread(self.url)\n self.img = cv2.resize(img, (self.window_x, self.window_y))","def imageprepare(argv):\n im = Image.open(argv).convert('L')\n width = float(im.size[0])\n height = float(im.size[1])\n newImage = Image.new('L', (28, 28), (255)) # creates white canvas of 28x28 pixels\n\n if width > height: # check which dimension is bigger\n # Width is bigger. Width becomes 20 pixels.\n nheight = int(round((20.0 / width * height), 0)) # resize height according to ratio width\n if (nheight == 0): # rare case but minimum is 1 pixel\n nheight = 1\n # resize and sharpen\n img = im.resize((20, nheight), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)\n wtop = int(round(((28 - nheight) / 2), 0)) # calculate horizontal position\n newImage.paste(img, (4, wtop)) # paste resized image on white canvas\n else:\n # Height is bigger. Heigth becomes 20 pixels.\n nwidth = int(round((20.0 / height * width), 0)) # resize width according to ratio height\n if (nwidth == 0): # rare case but minimum is 1 pixel\n nwidth = 1\n # resize and sharpen\n img = im.resize((nwidth, 20), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)\n wleft = int(round(((28 - nwidth) / 2), 0)) # caculate vertical pozition\n newImage.paste(img, (wleft, 4)) # paste resized image on white canvas\n\n # newImage.save(\"sample.png\n\n tv = list(newImage.getdata()) # get pixel values\n\n # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\n tva = [(255 - x) * 1.0 / 255.0 for x in tv]\n print(tva)\n return tva","def make_image(self, mode=\"L\") -> Image:\r\n return Image.fromarray(self.fb, mode=\"L\")","def test_created_invalid_image(self):\n res = self.client.post(IMAGE_URL,\n data={'image': 'no_image'}, format='multipart')\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)","def create_initial_pictures():\n pictures = []\n\n for i in xrange(0, 9):\n pic = Picture()\n pic = Picture.create_random(pic, 1)\n\n pictures.append(pic)\n\n return pictures","def make_dummy_file(file, shape=(15, 2000, 2000), n_ext=2):\n # Primary HDU\n hdulist = [fits.PrimaryHDU()]\n\n # SCI extensions\n for n in range(n_ext):\n hdu = fits.ImageHDU(data=np.random.normal(size=shape), name='SCI_{}'.format(n))\n hdulist.append(hdu)\n\n # Make list\n hdulist = fits.HDUList(hdulist)\n\n # Write the file\n hdulist.writeto(file, overwrite=True)\n\n print(\"{} MB file created at {}\".format(os.path.getsize(file) / 1000000, file))","def genrandimg(args) -> None:\n\n size = (int(args.x), int(args.y))\n fp = Image.new(\"RGB\", size)\n data = []\n\n if not args.c: # If color\n for i in range(size[0]*size[1]):\n r = random.choice([0x00, 0xff])\n data.append((r, r, r)) # Each RGB value is the same random value\n else: # Else black-and-white\n for i in range(size[0]*size[1]):\n r = [random.choice(range(0, 256)) for _ in range(0, 3)]\n r = (r[0], r[1], r[2]) # Choose 3 random numbers for different RGB values\n data.append(r)\n\n fp.putdata(data)\n print(\"Saving to %s...\" % args.o)\n fp.save(args.o)\n fp.close()","def create_image(config, size_mb):\n\n delete_image(config)\n iotests.log(\"truncate %s --size %dMB\" % (config.image_path(), size_mb),\n filters=[iotests.filter_test_dir])\n with open(config.image_path(), \"w\") as fn:\n fn.truncate(size_mb * 1024 * 1024)","def __init__(self, width, height):\n self._image = tk.PhotoImage(master=root, width = width, height = height)\n self.fill((0,0,0))","def no_image(cls):\n def eval_fn(p: Posting):\n if p.img_url is None:\n return f\"I couldn't find any images for this posting.\"\n\n return cls(eval_fn)","def __create_blank_page__(self):\n with open(\"active_weather.basic.exp\"+str(self.box_count)+\".box\",\"w\") as f:\n f.write(\"\")\n\n self.width = 2508\n # self.height = 200\n self.height = 4000\n self.training_page = np.zeros((self.height,self.width),dtype=np.uint8)\n self.training_page.fill(255)\n\n self.row_bitmaps = []\n self.row_characters = []\n\n self.row_pointer = spacing\n self.column_pointer = spacing\n\n\n # self.__box_file_flush__()\n self.box_file_entries = []\n self.used_height = spacing","def create_img(X_train, X_test, y_train, y_test, labels, model, visualizer, upsampled, IMG_OUTPUT_FILEPATH):\n viz = Visualizer(X_train, X_test, y_train, y_test, labels, model, visualizer, upsampled=upsampled)\n viz.evaluate()\n if upsampled == True:\n outpath_ = IMG_OUTPUT_FILEPATH + str(model).split('(')[0] + '/' + visualizer + '_upsampled.png'\n else:\n outpath_ = IMG_OUTPUT_FILEPATH + str(model).split('(')[0] + '/' + visualizer + '.png'\n viz.visualizer.show(outpath=outpath_, clear_figure=True)","def builder_will_create_target_image(self, builder, target, image_id, template, parameters):"],"string":"[\n \"def _blankimage():\\n img = TK.PhotoImage(width=1, height=1)\\n img.blank()\\n return img\",\n \"def create_empty_image(width=512, height=512):\\n blank_img = np.zeros((width, height, 3), np.uint8)\\n # Return instance of the class\\n return ExtendedImage(blank_img)\",\n \"def blank_image(height, width):\\n all_green = create_uniform_image(height, width, [0, 255, 0])\\n return all_green\",\n \"def gen_empty_img(w=640, h=480):\\n return np.zeros((h, w, 3), np.uint8)\",\n \"def blank(width, height, channels=3, value=0):\\n blank_image = np.full((height, width, channels), value, np.uint8)\\n return Image(img=blank_image)\",\n \"def create_white_picture(pic_width, pic_height):\\n white_picture = Image.new(\\\"1\\\", (pic_width, pic_height), (1))\\n return white_picture\",\n \"def create_blank(width, height, rgb_color=(0, 0, 0)):\\r\\n # Create black blank image\\r\\n image = np.zeros((height, width, 3), np.uint8)\\r\\n\\r\\n # Since OpenCV uses BGR, convert the color first\\r\\n color = tuple(reversed(rgb_color))\\r\\n # Fill image with color\\r\\n image[:] = color\\r\\n\\r\\n return image\",\n \"def create_blank(width, height, rgb_color=(0, 0, 0)):\\n # Create black blank image\\n image = np.zeros((height, width, 3), np.uint8)\\n\\n # Since OpenCV uses BGR, convert the color first\\n color = tuple(reversed(rgb_color))\\n # Fill image with color\\n image[:] = color\\n\\n return image\",\n \"def create_blank(width, height, rgb_color=(0, 0, 0)):\\n # Create black blank image\\n image = np.zeros((height, width, 3), np.uint8)\\n\\n # Since OpenCV uses BGR, convert the color first\\n color = tuple(reversed(rgb_color))\\n # Fill image with color\\n image[:] = color\\n\\n return image\",\n \"def create_blank(w, h, rgb_color=(0, 0, 0)):\\n image = np.zeros((h, w), np.uint8)\\n color = tuple(reversed(rgb_color))\\n image[:] = 0\\n return image\",\n \"def create_blank(w, h, rgb_color=(0, 0, 0)):\\n image = np.zeros((h, w), np.uint8)\\n color = tuple(reversed(rgb_color))\\n image[:] = 0\\n return image\",\n \"def create_image(self):\\n\\n self._image = 255 * np.ones((self._height, self._width, 3), np.uint8)\",\n \"def EmptyBitmap(*args, **kwargs):\\n val = _gdi_.new_EmptyBitmap(*args, **kwargs)\\n return val\",\n \"def blanck_picture(img):\\r\\n blank_image = np.zeros((img.shape[0],img.shape[1],3), np.uint8)\\r\\n blank_image[0:, 0:] = 0, 0, 0\\r\\n return blank_image\",\n \"def create_full_pic(self):\\n self.create_half_pic()\\n mirror_update(self.flag)\",\n \"def create_blank(cls, n: int, m: int):\\n image = np.zeros((n, m, 3), np.uint8)\\n\\n color = (255, 255, 255)\\n # Fill image with color\\n image[:] = color\\n\\n return image\",\n \"def make_image(self, path):\\n\\t\\treturn None\",\n \"def create_one_image(attrs=None):\\n attrs = attrs or {}\\n\\n # Set default attribute\\n image_info = {\\n 'id': str(uuid.uuid4()),\\n 'name': 'image-name' + uuid.uuid4().hex,\\n 'owner': 'image-owner' + uuid.uuid4().hex,\\n 'container_format': '',\\n 'disk_format': '',\\n 'min_disk': 0,\\n 'min_ram': 0,\\n 'is_public': True,\\n 'protected': False,\\n 'properties': {'Alpha': 'a', 'Beta': 'b', 'Gamma': 'g'},\\n 'status': 'status' + uuid.uuid4().hex,\\n }\\n\\n # Overwrite default attributes if there are some attributes set\\n image_info.update(attrs)\\n\\n return image.Image(**image_info)\",\n \"def blanck_picture(img):\\r\\n\\r\\n blank_image = np.zeros((img.shape[0],img.shape[1],3), np.uint8)\\r\\n blank_image[0:img.shape[0], 0:img.shape[1]] = 0, 0, 0\",\n \"def new_image(self, width, height, background=None, mode=\\\"RGBA\\\"):\\n self.img = PIL.Image.new(mode, (width, height), background)\\n self.width,self.height = width,height\\n self.drawer = aggdraw.Draw(self.img)\",\n \"def generate_image(self):\\n pass\",\n \"def get_blank_img(self):\\n if photos_settings.DEBUG:\\n return self.get_placeholder_img()\\n\\n out = {\\n 'blank': True,\\n 'width': self.max_width,\\n 'height': self.max_height,\\n 'url': photos_settings.EMPTY_IMAGE_SITE_PREFIX + 'img/empty/%s.png' % (self.name),\\n }\\n return out\",\n \"def test_create_image(self):\\n pass\",\n \"def new_test_image():\\n warnings.warn(DeprecationWarning(\\n \\\"new_test_image() is deprecated in favour of the get_sample_image() \\\"\\n \\\"context manager.\\\"), stacklevel=2)\\n image_name = 'test-{}.png'.format(uuid.uuid4())\\n image = Image.new('RGBA', size=(50, 50), color=(256, 0, 0))\\n ImageDraw.Draw(image)\\n byte_io = BytesIO()\\n image.save(byte_io, 'png')\\n byte_io.seek(0)\\n return image_name, ContentFile(byte_io.read(), image_name)\",\n \"def NullImageProto(msg:str = \\\"\\\"):\\n return NLImage(width=0, height=0, data=msg)\",\n \"def _clear(self):\\n\\n self.image = Image.new(\\\"RGB\\\", (self._width, self._height), self._color)\",\n \"def create_image(self):\\n # how many categories?\\n aspect_ratio = float(4) / 3\\n self.width = int(math.sqrt(aspect_ratio * self.total))\\n self.height = int(self.width / aspect_ratio)\\n\\n img = Image.new(\\\"RGB\\\", (self.width, self.height))\\n return img\",\n \"def plot_blank(self):\\n self.figure_bmp.SetBitmap(self.controller.plot_blank())\",\n \"def _ensure_empty_image_ok(self):\\n if self.ignore_empty:\\n return\\n\\n if len(self) > 1:\\n raise RuntimeError(\\n \\\"Cannot write None image at extension %d\\\" % len(self))\\n if 'ndims' in self[0]._info:\\n raise RuntimeError(\\\"Can only write None images to extension zero, \\\"\\n \\\"which already exists\\\")\",\n \"def make_image(storage, name, width, height, format='JPEG', mode='RGB'):\\n im = Image.new(mode, (width, height))\\n draw = ImageDraw.Draw(im)\\n draw.rectangle([0, 0, width // 2, height // 2], '#F00')\\n draw.rectangle([width // 2, 0, width, height // 2], '#0F0')\\n draw.rectangle([0, height // 2, width // 2, height], '#00F')\\n draw.rectangle([width // 2, height // 2, width, height], '#000')\\n draw.rectangle([width // 4, height // 4, 3 * width // 4, 3 * height // 4], '#FFF')\\n im_bytes_io = io.BytesIO()\\n im.save(im_bytes_io, format)\\n im_bytes_io.seek(0)\\n storage.save(name, im_bytes_io)\",\n \"def create_artificial_image(self):\\n background = self.BGI.create_background_image()\\n star_PSF = self.PSF.create_star_PSF()\\n header = self.HDR.create_header()\\n\\n fits.writeto(self.image_dir + self.image_name + '.fits',\\n background + star_PSF, overwrite=True, header=header)\",\n \"def new_image(x, y, out, data):\\n img = Image.new('RGB', (x, y))\\n img.putdata(data)\\n img.save(out)\",\n \"def clear(self):\\n self.display(Image.new(self.mode, self.size))\",\n \"def _createimage(self, image):\\n return self.cv.create_image(0, 0, image=image)\",\n \"def get_blank_image(width: int, height: int, n_channels: int, cval=255) -> np.ndarray:\\n if n_channels == 0:\\n image = np.zeros((height, width)) + 255\\n else:\\n image = np.zeros((height, width, n_channels)) + cval\\n return image.astype(\\\"uint8\\\")\",\n \"def initialise(self):\\r\\n self.set_image(\\\"wall.png\\\")\\r\\n return self\",\n \"def __generate_image(self):\\n\\t\\tself.img = np.ones((self.size*self.width+self.border,self.size*self.width+self.border,1), np.uint8)*255\\n\\t\\tfor i in range(len(self.matrix)):\\n\\t\\t\\tfor j in range(len(self.matrix)):\\n\\t\\t\\t\\tif self.matrix[j][i] == 1:\\n\\t\\t\\t\\t\\tself.img = cv2.rectangle(self.img,(i*self.width+int(self.border/2),j*self.width+int(self.border/2))\\n\\t\\t\\t\\t\\t\\t,(i*self.width+self.width+int(self.border/2),j*self.width+self.width+int(self.border/2)),(0,0,0),-1)\\n\\t\\tif '.' in self.name:\\n\\t\\t\\tcv2.imwrite(self.name,self.img)\\n\\t\\telse:\\n\\t\\t\\tcv2.imwrite(self.name+'.jpg',self.img)\\n\\t\\tcv2.imshow(\\\"Image\\\",self.img)\\n\\t\\tcv2.waitKey(0)\\n\\t\\tcv2.destroyAllWindows()\",\n \"def create_base_image(self, builder, template, parameters):\",\n \"def generateEmptySprite(cls, size, alpha=None, fillWith=None, colorKey=None):\\n # BBB: move this away from here, like in a simple module\\n surface = pygame.Surface(size, flags=HWSURFACE|HWPALETTE, depth=32)\\n if fillWith:\\n surface.fill(fillWith)\\n if alpha is not None:\\n surface.set_alpha(alpha)\\n if colorKey:\\n surface.set_colorkey(colorKey)\\n return surface\",\n \"def _EmptyBitmapRGBA(*args, **kwargs):\\n return _gdi_._EmptyBitmapRGBA(*args, **kwargs)\",\n \"def no_bin(image, *args, **kwargs):\\n return image\",\n \"def initImg(self):\\n self.img = Image.new('RGBA',(self.width,self.height),color='#' + getConfigPart(self.theme,\\\"bg\\\"))\\n self.draw = ImageDraw.Draw(self.img)\",\n \"def make_montage(infile):\\n\\n #create a blank image\\n blank = Image.new(\\\"RGB\\\", OUTPIC_SIZE ,(25,23,24,255))\\n\\n #paste the background\\n bg = Image.open(BACKGROUND_FILENAME)\\n blank.paste(bg,(0,0))\\n \\n #paste the raw picture\\n im = Image.open(infile)\\n \\n # past on background\\n blank.paste(im,OUTPIC_PHOTO_OFFSET)\\n\\n #paste the stickers layer on the pic (a transparent png)\\n stickers = Image.open(STICKERS_FILENAME)\\n blank.paste(stickers,(0,0),stickers)\\n\\n return blank\",\n \"def draw_nonogram(self):\\n image = Image.new(\\\"RGB\\\", (self.nonogram_size * 50, self.nonogram_size * 50), (255, 255, 255))\\n draw = ImageDraw.Draw(image)\\n\\n for index, square in enumerate(reduce(lambda x, y: x+y, self.grid), 0):\\n\\n #print(square)\\n x = index % self.nonogram_size\\n y = index // self.nonogram_size\\n coord = [(x * 50, y * 50), ((x + 1) * 50, (y + 1) * 50)]\\n if square == EMPTY:\\n draw.rectangle(coord, fill=(255, 255, 255))\\n if square == FILLED:\\n draw.rectangle(coord, fill=(0, 0, 0))\\n return image\",\n \"def build_filler_images(self):\",\n \"def maketestimage(self, *args, **kwargs):\\n return _image.image_maketestimage(self, *args, **kwargs)\",\n \"def create_image(path, pxcount):\\n img = Image.open(path, 'r').convert('L')\\n pixels = img.load()\\n for i in range(pxcount):\\n x = randint(0, img.size[0]-1)\\n y = randint(0, img.size[0]-1)\\n if pixels[x, y] == 0:\\n pixels[x, y] = 255\\n else:\\n pixels[x, y] = 0\\n return img\",\n \"def make_image():\\n click.echo(\\\"make_image\\\")\",\n \"def EmptyBitmapRGBA(width, height, red=0, green=0, blue=0, alpha=0):\\n return _gdi_._EmptyBitmapRGBA(width, height, red, green, blue, alpha)\",\n \"def make_empty_directories_linux() -> None:\\n mkdir(PICTURES_DIR / 'screenshots' / 'grim')\\n mkdir(PICTURES_DIR / 'screenshots' / 'swappy')\",\n \"def imageprepare(self,argv):\\r\\n\\t\\tim = Image.open(argv).convert('L')\\r\\n\\t\\twidth = float(im.size[0])\\r\\n\\t\\theight = float(im.size[1])\\r\\n\\t\\tnewImage = Image.new('L', (28, 28), (255)) # creates white canvas of 28x28 pixels\\r\\n\\r\\n\\t\\tif width > height: # check which dimension is bigger\\r\\n\\t\\t\\t# Width is bigger. Width becomes 20 pixels.\\r\\n\\t\\t\\tnheight = int(round((20.0 / width * height), 0)) # resize height according to ratio width\\r\\n\\t\\t\\tif nheight == 0: # rare case but minimum is 1 pixel\\r\\n\\t\\t\\t\\tnheight = 1\\r\\n\\t\\t\\t\\t# resize and sharpen\\r\\n\\t\\t\\timg = im.resize((20, nheight), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)\\r\\n\\t\\t\\twtop = int(round(((28 - nheight) / 2), 0)) # caculate horizontal pozition\\r\\n\\t\\t\\tnewImage.paste(img, (4, wtop)) # paste resized image on white canvas\\r\\n\\t\\telse:\\r\\n\\t\\t\\t# Height is bigger. Heigth becomes 20 pixels.\\r\\n\\t\\t\\tnwidth = int(round((20.0 / height * width), 0)) # resize width according to ratio height\\r\\n\\t\\t\\tif (nwidth == 0): # rare case but minimum is 1 pixel\\r\\n\\t\\t\\t\\tnwidth = 1\\r\\n\\t\\t\\t\\t# resize and sharpen\\r\\n\\t\\t\\timg = im.resize((nwidth, 20), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)\\r\\n\\t\\t\\twleft = int(round(((28 - nwidth) / 2), 0)) # caculate vertical pozition\\r\\n\\t\\t\\tnewImage.paste(img, (wleft, 4)) # paste resized image on white canvas\\r\\n\\r\\n\\t\\t# newImage.save(\\\"sample.png\\\")\\r\\n\\r\\n\\t\\ttv = list(newImage.getdata()) # get pixel values\\r\\n\\r\\n\\t\\t# normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\\r\\n\\t\\ttva = [(255 - x) * 1.0 / 255.0 for x in tv]\\r\\n\\t\\treturn tva\",\n \"def construct_image(imgs):\\n\\n # todo fill missing pieces and\\n\\n if len(imgs) == 0:\\n return None\\n # taking the first\\n w, h = imgs[0][1].size\\n img_array = order_2d(imgs)\\n x_count = len(img_array[0])\\n y_count = len(img_array)\\n height = h * y_count\\n width = w * x_count\\n new_im = Image.new('RGB', (width, height))\\n for y in range(y_count):\\n for x in range(x_count):\\n _, im = img_array[y][x]\\n new_im.paste(im, (x * w, y * h))\\n return new_im\",\n \"def create_image(self, shapes):\\n img = image.IMG()\\n img.draw_shapes(shapes)\\n img = np.transpose(img.array(), (2, 0, 1))\\n return img\",\n \"def create_image(storage, filename, size=(100, 100), image_mode='RGB', image_format='PNG'):\\n data = BytesIO()\\n PIL.Image.new(image_mode, size).save(data, image_format)\\n data.seek(0)\\n if not storage:\\n return data\\n image_file = ContentFile(data.read())\\n return storage.save(filename, image_file)\",\n \"def test_empty_image(self):\\n r = post(self.client, 'upload.up_image_async', {'image': ''},\\n args=['questions.Question', 1])\\n\\n eq_(400, r.status_code)\\n json_r = json.loads(r.content)\\n eq_('error', json_r['status'])\\n eq_('Invalid or no image received.', json_r['message'])\\n eq_('You have not selected an image to upload.',\\n json_r['errors']['image'][0])\",\n \"def new(self, size, fill):\\n return Image(PIL.Image.new(\\\"RGB\\\", size, fill))\",\n \"def clear(self):\\n self.img = PIL.Image.new(self.img.mode, self.img.size, self.background)\\n self.drawer = aggdraw.Draw(self.img)\",\n \"def fill_image(im):\\n width, height = im.size\\n # Select the larger value of the length and width of the original picture\\n # as the radius of the nine palace grid of the new picture\\n new_image_len = width if width > height else height\\n # Create a white canvas\\n new_image = Image.new(im.mode, (new_image_len, new_image_len), color=\\\"white\\\")\\n # Paste the original image on the canvas at the center\\n if width > height:\\n new_image.paste(im, (0, int((new_image_len - height) / 2)))\\n else:\\n new_image.paste(im, (int((new_image_len - width) / 2), 0))\\n return new_image\",\n \"def generate_image(self):\\n\\n if not has_pillow:\\n raise RuntimeError(\\\"requires https://pypi.org/project/pillow/\\\")\\n\\n background = self.get_background()\\n foreground = self.get_foreground()\\n\\n matrix = self.generate_matrix()\\n\\n image = Image.new(\\\"RGB\\\", (420, 420), background)\\n draw = ImageDraw.Draw(image)\\n\\n for (i, row) in enumerate(matrix):\\n for (j, bit) in enumerate(row):\\n x = 35 + j * 70\\n y = 35 + i * 70\\n\\n if bit:\\n draw.rectangle((x, y, x + 70, y + 70), foreground)\\n\\n return image\",\n \"def set_default_image(self, image):\\n raise NotImplementedError\",\n \"def create_target_image(self, builder, target, base_image, parameters):\",\n \"def imBox(self, width, height):\\n img = Image.new(\\\"1\\\", (width, height))\\n draw = ImageDraw.Draw(img)\\n bgColor=255\\n draw.rectangle((0,0) + img.size,fill=bgColor)\\n return img\",\n \"def to_image(self, width=800, height=600, **kwargs):\\n img = _white_image(\\n parameters=self.parameters,\\n width=width,\\n height=height,\\n **kwargs\\n )\\n return img\",\n \"def create_test_image(dirname, array, normalization=None):\\n filename = str(dirname / 'tmp.tif')\\n create_test_file(filename, array)\\n satellite = 'quickbird'\\n image = Image(filename, satellite, normalization_parameters=normalization)\\n return image\",\n \"def create(self, name, image, command, **kwargs):\\n return\",\n \"def draw_empty( text, file, kw ):\\n\\n prefs = dict(globals()['prefs'])\\n for key, data in kw.items():\\n prefs[key] = data\\n fig = Figure()\\n canvas = FigureCanvasAgg( fig )\\n dpi = prefs['width'] /prefs['width_inches']\\n height_inches = prefs['height'] / float(dpi)\\n fig.set_size_inches( prefs['width_inches'], height_inches )\\n fig.set_dpi( dpi )\\n fig.set_facecolor('white')\\n fig.text( .5, .5, text, horizontalalignment='center' )\\n if isinstance( file , StringIO.StringIO ) or type(file) == cStringIO_type:\\n canvas.draw()\\n size = canvas.get_renderer().get_canvas_width_height()\\n # Hack: for some unknown reason in py27 this call is returning floats.\\n # Convert it to int coordinates so that PIL doesnt complain.\\n size = (int(size[0]),int(size[1]))\\n buf=canvas.tostring_argb()\\n im=PILImage.fromstring('RGBA', size, buf, 'raw', 'RGBA', 0, 1)\\n a, r, g, b = im.split()\\n im = PILImage.merge( 'RGBA', (r, g, b, a) )\\n im.save( file, format = 'PNG' )\\n else:\\n canvas.print_figure( file, **kw )\",\n \"def make_image(self, imagename, fitsname, niter=500, antenna='', phasecenter='', start=200, stop=900, del_img=True, overwrite=False): \\n self.generate_image(imagename, antenna=antenna, niter=niter, phasecenter=phasecenter, start=start, stop=stop)\\n self.to_fits(imagename + '.image', fitsname, overwrite=overwrite)\\n if del_img:\\n self.remove_image(imagename, del_img=True)\",\n \"def _make_image(self):\\n self.image.fill(0)\\n self.image[:, :, PillEater.WALLS] = self.walls\\n self.image[:, :, PillEater.FOOD] = self.world_state['food']\\n self.image[self.world_state['pillman']['pos'][0], self.world_state['pillman']['pos'][1],\\n PillEater.PILLMAN] = 1\\n for ghost in self.world_state['ghosts']:\\n edibility = self.world_state['power'] / float(self.pill_duration)\\n self.image[ghost['pos'][0], ghost['pos'][1], PillEater.GHOSTS] = 1. - edibility\\n self.image[ghost['pos'][0], ghost['pos'][1], PillEater.GHOSTS_EDIBLE] = edibility\\n for pill in self.world_state['pills']:\\n self.image[pill['pos'][0], pill['pos'][1], PillEater.PILL] = 1\\n return self.image\",\n \"def draw_empty( self ):\\n prefs = self.prefs\\n fig = Figure()\\n canvas = FigureCanvasAgg( fig )\\n dpi = prefs['width'] /prefs['width_inches']\\n height_inches = prefs['height'] / float(dpi)\\n fig.set_size_inches( prefs['width_inches'], height_inches )\\n fig.set_dpi( dpi )\\n fig.set_facecolor('white')\\n fig.text( .5, .5, \\\"No data returned by DB query.\\\", horizontalalignment='center' )\\n self.ax = None\\n self.fig = fig\\n self.canvas = canvas\",\n \"def get_image(filename=None, astrotarget=None,\\n **kwargs):\\n return Image(filename,astrotarget=astrotarget,\\n **kwargs)\",\n \"def test_annotate_blank(self):\\n x_size = 500\\n y_size = 500\\n\\n label = li.Label(\\\"Test Label\\\", (0.20, 0.20))\\n test_im = li.ImageBlank(None, x_size, y_size)\\n test_im.annotate(label)\\n annotated_im = test_im.data\\n\\n # Ensure the shape is retained\\n shape_expected = (y_size, x_size, 4)\\n shape_test = annotated_im.shape\\n self.assertEqual(\\n shape_test, shape_expected, msg=\\\"shape not retained after annotation\\\"\\n )\\n\\n # Test that there are darker text areas\\n flattened = annotated_im[..., :3].mean(axis=-1)\\n n_black_pixels = (flattened < 10).sum()\\n\\n self.assertGreater(n_black_pixels, 50, msg=\\\"Not enough black/text pixels\\\")\\n self.assertLess(n_black_pixels, 5000, msg=\\\"Too many black/text pixels\\\")\",\n \"def make_empty_img_from_img(img, dimensions=3):\\n xlen, ylen, zlen = img.GetSize()\\n dupe = img[:, :, :]\\n for x in xrange(xlen):\\n for y in xrange(ylen):\\n if dimensions == 3:\\n for z in xrange(zlen):\\n dupe.SetPixel(x, y, z, 0)\\n else:\\n dupe.SetPixel(x, y, 0)\\n return dupe\",\n \"def create_image(self, instance_id, name,\\r\\n description=None, no_reboot=False):\\r\\n params = {'InstanceId' : instance_id,\\r\\n 'Name' : name}\\r\\n if description:\\r\\n params['Description'] = description\\r\\n if no_reboot:\\r\\n params['NoReboot'] = 'true'\\r\\n img = self.get_object('CreateImage', params, Image, verb='POST')\\r\\n return img.id\",\n \"def make_image():\\n # get the mask\\n twitter_mask = np.array(Image.open('resource/twitter-mask.png'))\\n\\n wc = WordCloud(background_color='white', max_words=100, mask=twitter_mask, contour_width=3,\\n contour_color='steelblue')\\n\\n # generate word cloud\\n wc.generate_from_frequencies(get_word_frequency())\\n\\n # store to file\\n wc.to_file('/tmp/twitter.png')\\n\\n # show\\n frame = cv2.imread('/tmp/twitter.png')\\n cv2.imshow('figure', frame)\\n cv2.waitKey(60000)\\n cv2.destroyAllWindows()\",\n \"def glance_create_new_image(glance, images_location, image_info, image_name_prefix=None):\\n # image raw file path\\n image_raw_source = image_info['image_raw_source']\\n image_file = os.path.join(images_location, image_raw_source)\\n\\n if not os.path.isfile(image_file):\\n logger.warning(\\\"image raw file:'%s' not found!\\\", image_file)\\n return None\\n\\n fimg = None\\n try:\\n fimg = open(image_file, 'rb')\\n except Exception:\\n logger.error(\\\"Opening raw image file:'%s' failed\\\", image_file)\\n return None\\n\\n try:\\n # image name\\n image_name = image_info['image_name']\\n if image_name_prefix:\\n image_name = \\\"{}{}\\\".format(image_name_prefix, image_name)\\n logger.debug(\\\"image_name: %s\\\", image_name)\\n\\n # image min_disk\\n if image_info['image_min_disk'] == 'auto':\\n # compute the size of the file -> min disk size in GB\\n imagesize = os.fstat(fimg.fileno()).st_size\\n image_min_disk = (imagesize/1024/1024/1024)+1\\n else:\\n image_min_disk = image_info['image_min_disk']\\n logger.debug(\\\"image_min_disk: %s\\\", image_min_disk)\\n\\n # image min_ram\\n image_min_ram = image_info['image_min_ram']\\n logger.debug(\\\"image_min_ram: %s\\\", image_min_ram)\\n\\n # image properties (dictionary)\\n image_properties = image_info['image_properties']\\n logger.debug(\\\"image_properies: %s\\\", image_properties)\\n\\n logger.debug(\\\"glance image create (private): '%s'\\\", image_name)\\n image = glance.images.create(name=image_name,\\n visibility='private',\\n disk_format='raw',\\n container_format='bare',\\n min_disk=int(image_min_disk),\\n min_ram=int(image_min_ram))\\n logger.debug(\\\"glance image upload: '%s' -> '%s'\\\", fimg.name, image_name)\\n glance.images.upload(image.id, fimg)\\n\\n except Exception:\\n logger.exception(\\\"Creating and uploading Glance image '%s' failed\\\", image_name)\\n return None\\n\\n return image\",\n \"def testBinaryImage():\\n ALIEN = \\\"0\\\"*8 + \\\"11011011\\\"*2 + \\\"0\\\"*8 + \\\"00001000\\\" + \\\\\\n \\\"01000010\\\" + \\\"01111110\\\" + \\\"0\\\"*8\\n # this function is imported from cs5png.py\\n NUM_ROWS = 8\\n NUM_COLS = 8\\n binaryIm( ALIEN, NUM_COLS, NUM_ROWS )\\n # that should create a file, binary.png, in this\\n # directory with the 8x8 image...\",\n \"def new(mode, size, color=0):\\r\\n\\r\\n _check_size(size)\\r\\n\\r\\n if color is None:\\r\\n # don't initialize\\r\\n _im = Image()._new(mode, size)\\r\\n return Image(_im)\\r\\n\\r\\n if type(color).__name__ == \\\"str\\\":\\r\\n # css3-style specifier\\r\\n color = ImageColor().getcolor(color, mode)\\r\\n color = ImageDraw(None)._convert_bgr2rgb(color)\\r\\n\\r\\n _im = Image()._new(mode, size, color)\\r\\n return Image(_im)\",\n \"def generate_image(self):\\n\\t\\tcenters = self.generate_centers()\\n\\t\\timg = Image.new('RGB', (self.config.image_size, self.config.image_size), color=(0,0,0))\\n\\t\\tshapes = np.random.randint(2, size=len(centers))\\n\\t\\tdrawer = ImageDraw.Draw(img)\\n\\t\\tr = int(0.05 * self.config.image_size)\\n\\t\\tR = []\\n\\t\\tfor i in range(len(centers)):\\n\\t\\t\\tcoor = (centers[i][0] - r , centers[i][1] - r, centers[i][0] + r, centers[i][1] + r)\\n\\t\\t\\tif shapes[i] < 0.5:\\n\\t\\t\\t\\tdrawer.rectangle(coor, fill=COLOR[i])\\n\\t\\t\\telse:\\n\\t\\t\\t\\tdrawer.ellipse(coor, fill=COLOR[i])\\n\\t\\t\\tR.append([centers[i], i, shapes[i]])\\n\\t\\treturn np.array(img), R\",\n \"def createBlankPlot(self):\\n\\n fig = plt.figure(figsize=(8,6),dpi=80)\\n fig.set_facecolor('#ededed')\\n \\n # Format plot\\n ax = plt.subplot(111)\\n \\n fig.canvas.draw()\\n \\n return fig, ax\",\n \"def setup():\\n img = Image.new('RGB', (10, 20))\\n img.putpixel((5, 10), (0, 255, 0))\\n img.save('green-dot.tif')\\n img.save('green-dot.jpg')\\n img.save('green-dot.png')\",\n \"def test_draw(self):\\n image_name = filename(sys._getframe().f_code.co_name)\\n result_file, reference_file = get_path(image_name)\\n\\n ''' This function is to create an empty image with a specific dimension\\n with white background, and black/white colored '''\\n\\n image, canvas = get_image('L', (15,90),'white')\\n\\n for i in range(len(draw_points) - 1):\\n draw(canvas, (draw_points[i + 0], draw_points[i + 1]), 'A')\\n\\n \\\"\\\"\\\" saving the file and closing it \\\"\\\"\\\"\\n\\n image.save(result_file)\\n image.close()\\n\\n \\\"\\\"\\\" validate the resultant file against the reference images\\\"\\\"\\\"\\n\\n validate(reference_file, result_file)\",\n \"def _create_layer() -> Image:\\n data = np.random.random((32, 16))\\n return Image(data)\",\n \"def Empty():\\n return Container(name='(empty)',\\n metadata={},\\n section_sizes={},\\n metrics_by_file={})\",\n \"def get_inline_stmt_emptybitmap(self, bitmap):\\n # keep in sync with BitmapMixin.get_preview_obj_bitmap()\\n width = 16\\n height = 16\\n try:\\n size = bitmap[6:]\\n width, height = [int(item.strip()) for item in size.split(',', 1)]\\n except ValueError:\\n logging.warn( 'Malformed statement to create an empty bitmap: %s', bitmap )\\n stmt = self.tmpl_inline_emptybitmap % { 'width': width, 'height': height }\\n return stmt\",\n \"def test_random_single_image():\\n\\n shap.image_plot(np.random.randn(3, 20, 20), np.random.randn(3, 20, 20), show=False)\",\n \"def topil(self) -> Image.Image:\\n if self.width == 0 or self.height == 0:\\n return None\\n return Image.frombytes(\\n \\\"RGBA\\\", (self.width, self.height), self.data, \\\"raw\\\", \\\"ARGB\\\", 0, 1\\n )\",\n \"def create_zeroed_image(n):\\n\\n alist = []\\n for i in range(0, n):\\n alist.append([])\\n for j in range(0,n):\\n alist[i].append(0)\\n return alist\",\n \"def _create_image(self):\\n if hasattr(self, '_image') and self._image:\\n return self._image\\n try:\\n command = \\\"tex2im -b transparent -t cyan\\\"\\n subprocess.run([*command.split(), self._formula])\\n except Exception as e:\\n import traceback\\n print(traceback.format_exc())\\n return None\\n # tex2im converts to out.png by default\\n img = Image.open('out.png').convert('RGBA')\\n # create a new rgba image to blend the latex with the alpha\\n subprocess.run([\\\"rm\\\", \\\"out.png\\\"])\\n return img\",\n \"def create_image(self):\\n img = cv2.imread(self.url)\\n self.img = cv2.resize(img, (self.window_x, self.window_y))\",\n \"def imageprepare(argv):\\n im = Image.open(argv).convert('L')\\n width = float(im.size[0])\\n height = float(im.size[1])\\n newImage = Image.new('L', (28, 28), (255)) # creates white canvas of 28x28 pixels\\n\\n if width > height: # check which dimension is bigger\\n # Width is bigger. Width becomes 20 pixels.\\n nheight = int(round((20.0 / width * height), 0)) # resize height according to ratio width\\n if (nheight == 0): # rare case but minimum is 1 pixel\\n nheight = 1\\n # resize and sharpen\\n img = im.resize((20, nheight), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)\\n wtop = int(round(((28 - nheight) / 2), 0)) # calculate horizontal position\\n newImage.paste(img, (4, wtop)) # paste resized image on white canvas\\n else:\\n # Height is bigger. Heigth becomes 20 pixels.\\n nwidth = int(round((20.0 / height * width), 0)) # resize width according to ratio height\\n if (nwidth == 0): # rare case but minimum is 1 pixel\\n nwidth = 1\\n # resize and sharpen\\n img = im.resize((nwidth, 20), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)\\n wleft = int(round(((28 - nwidth) / 2), 0)) # caculate vertical pozition\\n newImage.paste(img, (wleft, 4)) # paste resized image on white canvas\\n\\n # newImage.save(\\\"sample.png\\n\\n tv = list(newImage.getdata()) # get pixel values\\n\\n # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\\n tva = [(255 - x) * 1.0 / 255.0 for x in tv]\\n print(tva)\\n return tva\",\n \"def make_image(self, mode=\\\"L\\\") -> Image:\\r\\n return Image.fromarray(self.fb, mode=\\\"L\\\")\",\n \"def test_created_invalid_image(self):\\n res = self.client.post(IMAGE_URL,\\n data={'image': 'no_image'}, format='multipart')\\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)\",\n \"def create_initial_pictures():\\n pictures = []\\n\\n for i in xrange(0, 9):\\n pic = Picture()\\n pic = Picture.create_random(pic, 1)\\n\\n pictures.append(pic)\\n\\n return pictures\",\n \"def make_dummy_file(file, shape=(15, 2000, 2000), n_ext=2):\\n # Primary HDU\\n hdulist = [fits.PrimaryHDU()]\\n\\n # SCI extensions\\n for n in range(n_ext):\\n hdu = fits.ImageHDU(data=np.random.normal(size=shape), name='SCI_{}'.format(n))\\n hdulist.append(hdu)\\n\\n # Make list\\n hdulist = fits.HDUList(hdulist)\\n\\n # Write the file\\n hdulist.writeto(file, overwrite=True)\\n\\n print(\\\"{} MB file created at {}\\\".format(os.path.getsize(file) / 1000000, file))\",\n \"def genrandimg(args) -> None:\\n\\n size = (int(args.x), int(args.y))\\n fp = Image.new(\\\"RGB\\\", size)\\n data = []\\n\\n if not args.c: # If color\\n for i in range(size[0]*size[1]):\\n r = random.choice([0x00, 0xff])\\n data.append((r, r, r)) # Each RGB value is the same random value\\n else: # Else black-and-white\\n for i in range(size[0]*size[1]):\\n r = [random.choice(range(0, 256)) for _ in range(0, 3)]\\n r = (r[0], r[1], r[2]) # Choose 3 random numbers for different RGB values\\n data.append(r)\\n\\n fp.putdata(data)\\n print(\\\"Saving to %s...\\\" % args.o)\\n fp.save(args.o)\\n fp.close()\",\n \"def create_image(config, size_mb):\\n\\n delete_image(config)\\n iotests.log(\\\"truncate %s --size %dMB\\\" % (config.image_path(), size_mb),\\n filters=[iotests.filter_test_dir])\\n with open(config.image_path(), \\\"w\\\") as fn:\\n fn.truncate(size_mb * 1024 * 1024)\",\n \"def __init__(self, width, height):\\n self._image = tk.PhotoImage(master=root, width = width, height = height)\\n self.fill((0,0,0))\",\n \"def no_image(cls):\\n def eval_fn(p: Posting):\\n if p.img_url is None:\\n return f\\\"I couldn't find any images for this posting.\\\"\\n\\n return cls(eval_fn)\",\n \"def __create_blank_page__(self):\\n with open(\\\"active_weather.basic.exp\\\"+str(self.box_count)+\\\".box\\\",\\\"w\\\") as f:\\n f.write(\\\"\\\")\\n\\n self.width = 2508\\n # self.height = 200\\n self.height = 4000\\n self.training_page = np.zeros((self.height,self.width),dtype=np.uint8)\\n self.training_page.fill(255)\\n\\n self.row_bitmaps = []\\n self.row_characters = []\\n\\n self.row_pointer = spacing\\n self.column_pointer = spacing\\n\\n\\n # self.__box_file_flush__()\\n self.box_file_entries = []\\n self.used_height = spacing\",\n \"def create_img(X_train, X_test, y_train, y_test, labels, model, visualizer, upsampled, IMG_OUTPUT_FILEPATH):\\n viz = Visualizer(X_train, X_test, y_train, y_test, labels, model, visualizer, upsampled=upsampled)\\n viz.evaluate()\\n if upsampled == True:\\n outpath_ = IMG_OUTPUT_FILEPATH + str(model).split('(')[0] + '/' + visualizer + '_upsampled.png'\\n else:\\n outpath_ = IMG_OUTPUT_FILEPATH + str(model).split('(')[0] + '/' + visualizer + '.png'\\n viz.visualizer.show(outpath=outpath_, clear_figure=True)\",\n \"def builder_will_create_target_image(self, builder, target, image_id, template, parameters):\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.80658996","0.789249","0.73570555","0.719231","0.71430326","0.7110544","0.69541055","0.69369465","0.69369465","0.69088554","0.69088554","0.6905223","0.6859873","0.6651872","0.65808094","0.65179014","0.6485663","0.64796895","0.6460028","0.6458123","0.63959295","0.637956","0.6311849","0.62752634","0.6267765","0.6159564","0.6155874","0.6090965","0.60397303","0.60341364","0.60111356","0.6009815","0.60005873","0.5974332","0.5942975","0.5905334","0.58878225","0.58763105","0.58750415","0.5872451","0.5860547","0.5835472","0.5834946","0.58275646","0.5824303","0.58068603","0.580354","0.5802434","0.580132","0.57825565","0.5760148","0.5744684","0.5713735","0.5706688","0.5688825","0.5681811","0.56624824","0.5648412","0.5645863","0.5627454","0.56207865","0.5616188","0.56153196","0.5592603","0.55909836","0.55889624","0.55620843","0.55577976","0.5552983","0.553182","0.55315524","0.55224496","0.55197525","0.5512037","0.5502626","0.5500158","0.5494596","0.5487518","0.5453012","0.54524374","0.5430848","0.54251826","0.5413909","0.54118156","0.54110116","0.54093266","0.54067636","0.54019356","0.54000145","0.5391611","0.5386717","0.53854907","0.5377227","0.5370334","0.5367339","0.5361959","0.5355873","0.5354761","0.5353332","0.53503114","0.53497016"],"string":"[\n \"0.80658996\",\n \"0.789249\",\n \"0.73570555\",\n \"0.719231\",\n \"0.71430326\",\n \"0.7110544\",\n \"0.69541055\",\n \"0.69369465\",\n \"0.69369465\",\n \"0.69088554\",\n \"0.69088554\",\n \"0.6905223\",\n \"0.6859873\",\n \"0.6651872\",\n \"0.65808094\",\n \"0.65179014\",\n \"0.6485663\",\n \"0.64796895\",\n \"0.6460028\",\n \"0.6458123\",\n \"0.63959295\",\n \"0.637956\",\n \"0.6311849\",\n \"0.62752634\",\n \"0.6267765\",\n \"0.6159564\",\n \"0.6155874\",\n \"0.6090965\",\n \"0.60397303\",\n \"0.60341364\",\n \"0.60111356\",\n \"0.6009815\",\n \"0.60005873\",\n \"0.5974332\",\n \"0.5942975\",\n \"0.5905334\",\n \"0.58878225\",\n \"0.58763105\",\n \"0.58750415\",\n \"0.5872451\",\n \"0.5860547\",\n \"0.5835472\",\n \"0.5834946\",\n \"0.58275646\",\n \"0.5824303\",\n \"0.58068603\",\n \"0.580354\",\n \"0.5802434\",\n \"0.580132\",\n \"0.57825565\",\n \"0.5760148\",\n \"0.5744684\",\n \"0.5713735\",\n \"0.5706688\",\n \"0.5688825\",\n \"0.5681811\",\n \"0.56624824\",\n \"0.5648412\",\n \"0.5645863\",\n \"0.5627454\",\n \"0.56207865\",\n \"0.5616188\",\n \"0.56153196\",\n \"0.5592603\",\n \"0.55909836\",\n \"0.55889624\",\n \"0.55620843\",\n \"0.55577976\",\n \"0.5552983\",\n \"0.553182\",\n \"0.55315524\",\n \"0.55224496\",\n \"0.55197525\",\n \"0.5512037\",\n \"0.5502626\",\n \"0.5500158\",\n \"0.5494596\",\n \"0.5487518\",\n \"0.5453012\",\n \"0.54524374\",\n \"0.5430848\",\n \"0.54251826\",\n \"0.5413909\",\n \"0.54118156\",\n \"0.54110116\",\n \"0.54093266\",\n \"0.54067636\",\n \"0.54019356\",\n \"0.54000145\",\n \"0.5391611\",\n \"0.5386717\",\n \"0.53854907\",\n \"0.5377227\",\n \"0.5370334\",\n \"0.5367339\",\n \"0.5361959\",\n \"0.5355873\",\n \"0.5354761\",\n \"0.5353332\",\n \"0.53503114\",\n \"0.53497016\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":795,"cells":{"query":{"kind":"string","value":"Parse a Spider filename string and insert parameters."},"document":{"kind":"string","value":"def parse_spider_fname(mystr, *fieldvals):\n\t# helper functions and classes\n\tdef rm_stack_char(mystr):\n\t\t\"Helper function to remove a stack character if it exists\"\n\t\tstackloc = mystr.find(\"@\")\n\t\tif stackloc != -1: \n\t\t\t# there's an '@' somewhere\n\t\t\tif len(mystr) - 1 == stackloc:\n\t\t\t\t# It's at the end of the string\n\t\t\t\treturn mystr[:-1]\n\t\t\telse:\n\t\t\t\t# '@' not at the end, so it's an error\n\t\t\t\traise ValueError, \"Invalid format: misplaced '@'.\"\n\t\telse:\n\t\t\t# no '@' at all\n\t\t\treturn mystr\n\tclass Fieldloc:\n\t\t\"Helper class to store description of a field\"\n\t\tdef __init__(self, begin, end):\n\t\t\tself.begin = begin\n\t\t\tself.end = end\n\t\tdef count(self):\n\t\t\t\"Size of the field (including braces)\"\n\t\t\treturn self.end - self.begin + 1\n\tdef find_fields(mystr):\n\t\t\"Helper function to identify and validate fields in a string\"\n\t\tfields = []\n\t\tloc = 0\n\t\twhile True:\n\t\t\tbegin = mystr.find('{', loc)\n\t\t\tif begin == -1: break\n\t\t\tend = mystr.find('}', begin)\n\t\t\tfield = Fieldloc(begin, end)\n\t\t\t# check validity\n\t\t\tasterisks = mystr[begin+1:end]\n\t\t\tif asterisks.strip(\"*\") != \"\":\n\t\t\t raise ValueError, \"Malformed {*...*} field: %s\" % \\\n\t\t\t\tmystr[begin:end+1]\n\t\t\tfields.append(Fieldloc(begin, end))\n\t\t\tloc = end\n\t\treturn fields\n\t# remove leading whitespace\n\tmystr.strip()\n\t# remove stack character (if it exists)\n\tmystr = rm_stack_char(mystr)\n\t# locate fields to replace\n\tfields = find_fields(mystr)\n\tif len(fields) != len(fieldvals):\n\t\t# wrong number of fields?\n\t\traise ValueError, \"Number of field values provided differs from\" \\\n\t\t\t\"the number of {*...*} fields.\"\n\tnewstrfrags = []\n\tloc = 0\n\tfor i, field in enumerate(fields):\n\t\t# text before the field\n\t\tnewstrfrags.append(mystr[loc:field.begin])\n\t\t# replace the field with the field value\n\t\tfieldsize = field.count() - 2\n\t\tfielddesc = \"%0\" + str(fieldsize) + \"d\"\n\t\tnewstrfrags.append(fielddesc % fieldvals[i])\n\t\tloc = field.end + 1\n\tnewstrfrags.append(mystr[loc:])\n\treturn \"\".join(newstrfrags)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def parse_filename(cls, filename):\n words = filename.split('_')\n return words[0], int(words[1][1:]), int(words[2])","def parseFileName(filename):\n entry = DataEntry(\"\",0,{},{},0,0)\n wordArray = filename.split(\".\")\n entry.publication_name = wordArray[1]\n entry.year = wordArray[0]\n return entry","def parse(source, *, filename=\"[STRING]\", typecheck=True):\n return _Parser(filename, source, typecheck).parse()","def parseFilePath(self, filepath):\n\n li = filepath.split(\"/\") \n last = li[-1].split(\"_\")\n\n self.subjectName = li[-2]\n self.experimenterName = li[-3]\n self.experimentDate = last[-1]\n self.paradigm = last[-2]\n self.subjectName = last[-3]","def parse(filename):\n file_map = {\n '1995-1996.html': ninety_six,\n '2005-2006.html': twenty_six,\n '2014-2015.html': twenty_fifteen\n }\n func = file_map.get(filename, lambda: \"Invalid File\")\n func(filename)","def _parse_filename(filename, metadata):\n\n file_noext = os.path.splitext(filename)[0]\n fname = file_noext.split(\"_\")\n\n metadata[\"scene_id\"] = fname[1]\n metadata[\n \"beam_mode\"] = sat_properties.radarsat_product_characteristics[\n fname[2]]\n metadata[\"product_type\"] = fname[-1]\n try:\n metadata[\n \"product_description\"] = sat_properties.radarsat_1_data_products[\n fname[-1][:3]]['description']\n except Exception:\n metadata[\"product_description\"] = \"\"\n\n metadata[\"scene_mean_time\"] = datetime.datetime.strptime(\n fname[3] + fname[4], \"%Y%m%d%H%M%S\")\n\n return metadata","def ParseFileName(self, fn, fnParser):\n self.filename = fn\n attrs = fnParser.Parse(fn)\n [setattr(self, k, _TryNumeric(v)) for k, v in attrs]","def parse_glider_filename(filename):\n head, tail = os.path.split(filename)\n\n matches = re.search(r\"([\\w\\d\\-]+)-(\\d+)-(\\d+)-(\\d+)-(\\d+)\\.(\\w+)$\", tail)\n\n if matches is not None:\n return {\n 'path': head,\n 'glider': matches.group(1),\n 'year': int(matches.group(2)),\n 'day': int(matches.group(3)),\n 'mission': int(matches.group(4)),\n 'segment': int(matches.group(5)),\n 'type': matches.group(6)\n }\n else:\n raise ValueError(\n \"Filename ({}) not in usual glider format: \"\n \"<glider name>-<year>-<julian day>-\"\n \"<mission>-<segment>.<extenstion>\".format(filename)\n )","def main():\n parse_file(sys.argv[1])","def parse_filename(filename): # , time_fmt=TIME_INFILE_FMT):\n # Split the name up into its \"blocks\"\n parts = filename.split(\"_\")\n hive_str, rpi_str = parts[1:3]\n day_str = parts[3]\n method = parts[5]\n\n # Parse Hive and RPi number\n hive = int(hive_str[-1])\n rpi = int(rpi_str[-1])\n method = method.strip(\".csv\")\n\n # # Parse timestring into a datetime object\n # dt_naive = datetime.strptime(t_str, time_fmt)\n # dt_utc = pytz.utc.localize(dt_naive)\n\n return hive, rpi, method, day_str","def _parse(self, infile):\n raise NotImplementedError()","def parse(self, infile):\r\n raise NotImplementedError()","def init_from_string(self, fs_in, param_string):\n if '(' in param_string:\n name_params_re = re.compile(r'(\\w*)\\((.*)\\)$')\n pieces = name_params_re.match(param_string)\n name = pieces.group(1)\n params = pieces.group(2)\n param_list = params.split(';')\n param_dict = {}\n for param in param_list:\n if '=' not in param:\n raise ValueError('preprocess param %s missing a value.' % param)\n k, v = param.split('=', 1)\n if v.isdigit():\n v = int(v)\n else:\n try:\n v = float(v)\n except ValueError:\n pass\n param_dict[k] = v\n self._name = name\n self.init_highpass(param_dict['highpass_cutoff'],\n param_dict['highpass_order'])\n self.init_channel_numbers(param_dict['channel_numbers'])\n else:\n self.__init__(self, fs_in, param_string)","def insert(self, file_token, *file_tokens, preprocessor):\n tokens = (file_token,) + file_tokens\n for token in tokens:\n preprocessor.insert_file(self._get_filename(token))","def _parse_file_path(self, input_path):\n pass","def parse_parameters(parser):\n parser.add_argument('--parameters-file', '-p', help='parameter filename',\n required=True)\n return parser","def preprocess(self, source, name, filename=None):\n if not name or not os.path.splitext(name)[1] in self.environment.file_extensions:\n return source\n output = StringIO()\n lexer = Lexer(iter(source.splitlines()))\n Parser(lexer, callback=output.write, debug=self.environment.slim_debug).parse()\n\n if self.environment.slim_print:\n print output.getvalue()\n\n return output.getvalue()","def __init__(self, filename=None, label=None, tokens=None):\n if label: # specify from label/tokens, for testing.\n self.label = label\n self.tokens = tokens\n self.postID = -1\n self.likes = -1\n else: # specify from file.\n self.filename = filename\n parsedNames = filename.split(\"#\")\n if 'pop' in parsedNames[0]:\n self.label = 'pop'\n else:\n self.label = 'sod'\n self.postID = parsedNames[1]\n self.likes = parsedNames[2]\n self.tokenize()","def init_from_file(filename, parser=int):\n filename = filename + \".\" + str(PID)\n\n def __parser_couple(s):\n s = s.replace(\"(\", \"\")\n s = s.replace(\")\", \"\")\n ss = s.split(\",\")\n return int(ss[0]), int(ss[1])\n\n p = PTree()\n content = SList([])\n with open(filename, \"r\") as f:\n count_line = 0\n for line in f:\n if line.strip()[0] == '#':\n continue\n # __distribution: PID -> nb of segments\n # __global_index: num seg -> (start, offset)\n if count_line == 0: # Get the distribution\n p.distribution = SList.from_str(line)\n p.start_index = p.distribution.scanl(lambda x, y: x + y, 0)[PID]\n p.nb_segs = p.distribution[PID]\n elif count_line == 1: # Get the global_index\n p.global_index = SList.from_str(line, parser=__parser_couple)\n else: # Get the content\n content.extend(Segment.from_str(line, parser=parser))\n count_line = count_line + 1\n p.content = content\n return p","def setSourceFile(filename):","def __init__(self, line_parser, *filename):\n \n self.line_parser = line_parser\n self.f = fileinput.input(filename)","def _parse(\n self, source: str, name: t.Optional[str], filename: t.Optional[str]\n ) -> nodes.Template:\n return Parser(self, source, name, filename).parse()","def parse(self, fstring):\n pass","def parse_pts(pts_result_file, global_var_list, parsed_results_file):\n fill_value_name(pts_result_file, global_var_list, parsed_results_file)","def parse_filenames(filenames):\n \n for fn in filenames:\n dirname, basename = path.split(fn)\n subject_visit = basename[:7]\n visit = basename[5:7]\n yield dirname, basename, subject_visit, visit","def parse_parameters(filename):\n\n # read in the parameters\n mainInput = ParserClass.Parser(filename)\n if 'LogFile' in mainInput['Inputs']:\n if mainInput['Inputs']['LogFileUsePID']:\n logger = Logging.Logger(mainInput['Inputs']['LogFile']+'_{}'.format(os.getpid()))\n else:\n logger = Logging.Logger(mainInput['Inputs']['LogFile'])\n \n else:\n logger = print\n\n # Generate a filelist to loop over\n filelist = np.loadtxt(mainInput['Inputs']['filelist'],dtype=str,ndmin=1)\n if isinstance(mainInput['Inputs']['data_dir'], type(None)):\n filelist = [filename for filename in filelist]\n else:\n filelist = ['{}/{}'.format(mainInput['Inputs']['data_dir'],\n filename.split('/')[-1]) for filename in filelist]\n \n # Some items should always be a list\n if not isinstance(mainInput['Inputs']['pipeline'], list):\n mainInput['Inputs']['pipeline'] = [mainInput['Inputs']['pipeline']]\n # Get the class names (modulename, classname)\n jobnames = [c for c in mainInput['Inputs']['pipeline']]\n\n logger('Running: '+' '.join(mainInput['Inputs']['pipeline']))\n\n\n prejobnames = [c for c in mainInput['Inputs']['preamble']]\n\n\n # Read the class parameter file\n classInput = ParserClass.Parser(mainInput['Inputs']['classParameters'])\n\n # Initalise the classes : classInput are the kwargs to initiate classes\n jobs = []\n for job in jobnames:\n jobs += [getClass(job)(logger=logger,**classInput[job])]\n\n # Initalise the classes : classInput are the kwargs to initiate classes\n prejobs = []\n for prejob in prejobnames:\n prejobs += [getClass(prejob)(logger=logger,**classInput[prejob])]\n\n\n return jobs,prejobs, filelist, mainInput, classInput, logger","def parse_source_file(self, filepath):\n raise NotImplementedError('Not Implemented')","def parse_file_name(file_name):\n\n elements = file_name.split(\"_\")\n if file_name.find(\"_VI_\") > 0:\n client = elements[0]\n capture_range = \"R1\"\n condition = elements[2]\n polarization = \"VIS\"\n shot = elements[4]\n modality = \"VIS\"\n else:\n client = elements[0]\n capture_range = elements[1]\n condition = elements[2]\n polarization = elements[3]\n shot = elements[4]\n modality = \"THERMAL\"\n \n return client, capture_range, condition, polarization, shot, modality","def parse_infile(self, infile):\n\n if type(infile)==str:\n print('Im a string')\n folder, file = os.path.split(infile)\n elif type(infile) in [list, tuple]:\n if not len(infile) == 2:\n raise(Exception('The infile must be a string or a length 2 sequence'))\n else:\n folder, file = infile\n else:\n raise(Exception('The infile must be a string or a length 2 sequence'))\n \n self.folder = folder\n self.file_ = file","def parser(filename):\n\n regex = re.compile(\n # prolog\n r\"run(?P<run>\\w+)\"\n ##r\"\\-(?P<code_name>((mfdn)|(obscalc-ob))[^\\-]*)\"\n r\"\\-(?P<descriptor>\"\n # descriptor contents\n r\"Z(?P<Z>\\d+)\\-N(?P<N>\\d+)\"\n r\"\\-(?P<interaction>.+)\\-(?P<coulomb>\\d)\"\n r\"\\-(?P<truncation_descriptor>.+)\"\n ## r\"\\-Nmax(?P<Nmax>\\d+)\"\n # epilog\n r\").res\"\n )\n\n conversions = {\n \"Z\" : int,\n \"N\" : int,\n \"interaction\" : str,\n \"coulomb\" : int,\n }\n\n match = regex.match(filename)\n if (match == None):\n raise ValueError(\"bad form for spncci results filename: \" + filename)\n info = match.groupdict()\n\n # convert fields\n for key in conversions:\n conversion = conversions[key]\n info[key] = conversion(info[key]) if (info[key] is not None) else None\n\n return info","def input_parse(filename,params_flag=False):\r\n\r\n input_parameters ={}\r\n with open(filename, 'r') as f:\r\n count = 0\r\n\r\n for line in f:\r\n line=line.strip()\r\n if line:\r\n if line.find('#') == -1:\r\n if not params_flag:\r\n var_name,var_value = line.split(',')[0],\",\".join(line.split(',')[1:]) # handle lines with more than 1 comma\r\n try:\r\n input_parameters[var_name] = float(var_value)\r\n except ValueError: # This occurs when python cannot convert list into a float.\r\n # Evaluate the python expression as a list\r\n input_parameters[var_name] = ast.literal_eval(var_value)\r\n else:\r\n if count==0:\r\n var_name = line.strip('\\n')\r\n input_parameters[var_name] = []\r\n count+=1\r\n else:\r\n try:\r\n input_parameters[var_name].append(float(line.strip('\\n')))\r\n except ValueError: # This occurs when python cannot convert list into a float.\r\n # Evaluate the python expression as a list\r\n input_parameters[var_name].append(ast.literal_eval(line.strip('\\n')))\r\n return input_parameters","def __init__(self, filename, delimiter):\n self.filename = filename\n self.delimiter = delimiter\n \n # open file and process\n self.headers = []\n self.data = []\n self._process_file()","def parse(self, filename):\n infile = file(filename)\n for line in infile:\n self.parseLine(line)","def parseConfig(self, filename):\n parameters = {}\n try:\n f = open(filename)\n except Exception as inst:\n print(type(inst))\n print(inst.args)\n print(inst)\n print('cannot open', filename)\n raise\n else:\n for line in f:\n # Remove text after comment character.\n if self.comment_char in line:\n line, comment = line.split(self.comment_char,\n 1) # Split on comment character, keep only the text before the character\n\n # Find lines with parameters (param=something)\n if self.param_char in line:\n parameter, value = line.split(self.param_char, 1) # Split on parameter character\n parameter = parameter.strip() # Strip spaces\n value = value.strip()\n parameters[parameter] = value # Store parameters in a dictionary\n\n f.close()\n\n return parameters","def split_file(document: str):\n class_name, sep, assignment_name = document.partition(\"-\")\n try:\n assignment_name = assignment_name.split('.')[0].split('_')[0]\n except TypeError:\n pass\n return class_name, assignment_name","def instantiate_for_spirv_args(self, testcase):\n testcase.stdin_shader = self.source\n self.filename = '-'\n return self.filename","def parse_image_filename(filename):\n\n # regexes\n starts_with_six_digits = re.compile(r'^\\d{6}')\n capital_letter = re.compile(r'([A-Z]{1})')\n plus = re.compile(r'\\+')\n\n # split the filename and extention\n filename, extension = os.path.splitext(filename)\n try:\n style_number, color, description = filename.split('_')\n except Exception as e:\n print(e)\n print(filename, extension)\n\n style_number = int(style_number)\n\n # decode the color\n # intCaps -> int/caps\n color = capital_letter.sub(r'/\\1', color).lower()\n # plus+to+space -> plus to space\n color = plus.sub(r' ', color)\n\n # decode the description\n description = plus.sub(r' ', description)\n\n return style_number, color, description","def setFile(self, filename):\n self.prepare() #new call on each new file to process\n self.filename = \"%s\" % filename","def get_params(self, paramFile):\n\n with open(paramFile, 'r') as f:\n titleLine = next(f)\n\n for line in f:\n p, i, v = line.split(\",\")\n\n self.params.update(p, v, i)","def parse_filename(cls, filename):\n #from nose.tools import set_trace; set_trace()\n m = re.match(cls._pattern, os.path.basename(filename))\n basename = m.group(1)\n bandname = cls._bandmap.get(m.group(2), m.group(2))\n return basename, bandname","def __init__(self, fileName,\n openingMark = r'--oo<< *(?P<value>[^ \\n]+) *$',\n closingMark = r'--oo>> *$',\n hereMark = r'--oo== *(?P<value>[^ \\n]+) *$'):\n\n super(AnnotatedSourceFile,self).__init__(fileName)\n self.openingMark = openingMark\n self.closingMark = closingMark\n self.hereMark = hereMark\n\n fragmenter = modelscripts.base.fragments.RegexpFragmenter(\n self.sourceLines,\n openingMark, closingMark, hereMark,\n mainValue = self, firstPosition = 1)\n\n self.fragment = fragmenter.fragment\n \"\"\" The root fragment according to the given mark \"\"\"","def __init__(self, filename):\n\n parser = Parser(filename=filename)\n self.uuid = parser.segregated(parser.read(),'UUID')\n self.id = parser.segregated(parser.read(),'ID')\n self.rate = parser.segregated(parser.read(),'RATE')\n self.gpio = parser.segregated(parser.read(),'GPIO')\n self.ddl = parser.segregated(parser.read(),'DATA_DELIVERY_LOCATION')","def extract_params(self, fname):\n return re.findall(self.regexp_params, os.path.basename(fname))","def parseFile(self, filename):\n\n f = open(filename, \"r\")\n s = f.read()\n f.close()\n\n logging.log(10, 'parsing filename %s: %d lines' % (filename, len(s)))\n\n self.parseString(s)","def parse(self, fp):\n\n # create the plex scanner for fp\n self.create_scanner(fp)\n\n # call parsing logic\n self.stmt_list()\n print('Parsing successful!')","def train_image_parse_function(filename, *argv):\n image = read_image(filename)\n image = tf.image.random_flip_left_right(image)\n\n if FLAGS.augmentation:\n print('data augmentation')\n resized_image = resize_and_random_crop_image(image)\n else:\n resized_image = resize_image(image)\n resized_image = scale_image_value(resized_image)\n\n if len(argv) == 1:\n return resized_image, argv[0]\n elif len(argv) == 2:\n return resized_image, argv[0], argv[1]\n else:\n return resized_image","def __init__(self, filename):\n self.from_file(filename)\n self.parse_cell()\n self.parse_atom()\n self.apply_symops()","def _parse_bids_filename(fname, verbose):\n keys = ['sub', 'ses', 'task', 'acq', 'run', 'proc', 'run', 'space',\n 'rec', 'split', 'kind']\n params = {key: None for key in keys}\n idx_key = 0\n for match in re.finditer(param_regex, op.basename(fname)):\n key, value = match.groups()\n if key not in keys:\n raise KeyError('Unexpected entity \"%s\" found in filename \"%s\"'\n % (key, fname))\n if keys.index(key) < idx_key:\n raise ValueError('Entities in filename not ordered correctly.'\n ' \"%s\" should have occurred earlier in the '\n 'filename \"%s\"' % (key, fname))\n idx_key = keys.index(key)\n params[key] = value\n return params","def insert_file_via_perl(filename, comment=\"Added by Python Job\"):\n for line in lines(['ImportSingleFileIntoPosdaAndReturnId.pl', filename, comment]):\n if line.startswith(\"File id:\"):\n return int(line[8:])\n\n # TODO: pass on the error if there was one\n raise RuntimeError(\"Failed to insert file into posda!\")","def preprocess(\n self,\n source: str,\n name: t.Optional[str] = None,\n filename: t.Optional[str] = None,\n ) -> str:\n return reduce(\n lambda s, e: e.preprocess(s, name, filename),\n self.iter_extensions(),\n str(source),\n )","def load_specs(self, filename):\n self.filename = filename\n # Add loading functionality here","def load_specs(self, filename):\n self.filename = filename\n # Add loading functionality here","def __init__(self, in_file=None, search_str=\"\", buffer_size=None):\n super(FileParser, self).__init__()\n self.buffer_size = buffer_size or 1\n self.search_path = in_file\n self.update(self._parser(search_str=search_str))","def parseParams():\n parser = argparse.ArgumentParser(\n prog=__appname__,\n description=__desc__,\n prefix_chars=\"-/\",\n fromfile_prefix_chars='@',\n add_help=False)\n parser.add_argument('source',\n type=str,\n nargs='?',\n help=\"File containing valid syntax. (Default Standard Input)\"\n )\n parser.add_argument('output',\n type=str,\n help=\"Output Image. (required)\"\n )\n parser.add_argument('-f', '--fmt', '/f',\n dest='fmt',\n default='SVG',\n choices=['SVG', 'PDF', 'EPS', 'PNG'],\n nargs='?',\n help='Format of the Output file. Formats other than SVG require Inkscape in the system path.')\n parser.add_argument('-s', '--syntax', '/s',\n dest='syntax',\n default='EBNF',\n choices=['EBNF', 'DIAGRAM'],\n nargs='?',\n help='Syntax used for the Input.')\n parser.add_argument('-h', '--help', '/h',\n action='help',\n help=\"show this help message and exit.\")\n parser.add_argument('-v', '--version', '/v',\n action='version',\n version=\"{0:s} version {1:s}\".format(__title__, __version__))\n return parser.parse_args()","def parseFile(self,filename):\n\n name = '[0-9a-zA-Z_]+'\n string = '\\\\\"(.+)\\\\\"'\n\n testclass = None\n functionName = None\n\n fin = open(filename, 'r')\n for line in fin:\n # testclass starts\n res = re.match('class ('+name+')', line)\n if res != None:\n testclass = res.group(1)\n\n # end of testclass \n if re.match('};', line) != None:\n testclass = None\n\n # function start\n res = re.match('\\\\s+void ('+name+')\\\\(\\\\)', line)\n if res != None:\n functionName = res.group(1)\n\n elif re.match('\\\\s+}', line) != None:\n functionName = None\n\n if functionName == None:\n continue\n\n # check\n res = re.match('\\s+check.*\\('+string, line)\n if res != None:\n code = res.group(1)\n\n # code..\n res = re.match('\\\\s+'+string, line)\n if res != None:\n code = code + res.group(1)\n\n # assert\n res = re.match('\\\\s+ASSERT_EQUALS\\\\(\\\\\"([^\"]*)\\\\\",', line)\n if res != None and len(code) > 10:\n node = { 'testclass':testclass,\n 'functionName':functionName,\n 'code':code,\n 'expected':res.group(1) }\n self.nodes.append(node)\n code = ''\n\n # close test file\n fin.close()","def read_parse_file(params):\n\tparam_names = []\n\tparam_options = []\n\tif not os.path.isfile(params.parse_file):\n\t\tprint(\"parse file does not exist! ({})\".format(params.parse_file))\n\t\tsys.exit(NO_PARSE)\n\twith open(params.parse_file, 'r') as pf:\n\t\t# first line should be iteration regex\n\t\tsetattr(params, 'iteration_regex', re.compile(pf.readline().strip()))\n\t\tfor line in pf:\n\t\t\tparam_desc = line.split(';')\n\t\t\tparam_names.append(param_desc[0])\n\t\t\tparam_options.append(param_desc[1])\n\n\treturn param_names,param_options","def parse_vid_path(vid_path):\n # ensures all path separators are consistent with OS\n vid_path = os.path.normpath(vid_path)\n i_start = vid_path.rfind(os.path.sep)\n vid_file = vid_path[i_start+1:]\n # cuts out extension and splits by underscores\n tokens = vid_file[:-4].split('_')\n prefix = ''\n\n for i, token in enumerate(tokens):\n if token.isnumeric():\n break\n elif i != 0:\n prefix += '_'\n prefix += token\n \n params = {'prefix' : prefix,\n 'fps' : int(tokens[i]),\n 'exp_time' : read_dash_decimal(tokens[i+1]),\n 'Q_i' : read_dash_decimal(tokens[i+2]),\n 'Q_o' : int(tokens[i+3]),\n 'd' : int(tokens[i+4]),\n 'mag' : int(tokens[i+5]),\n 'num' : int(tokens[i+6])}\n\n return params","def __init__(self, root_directory, filename):\n self.source_name = filename\n extensionless, _ = path.splitext(filename)\n self.binary_name = extensionless + \".sept\"\n self.name = path.relpath(extensionless, root_directory)","def importParameterBoundaryFile(paramfilename):\n try:\n infile = open(paramfilename, \"r\")\n except IOError:\n\t print \"Unable to open file %s\" % (paramfilename)\n\t raise IOError(\"Unable to open parameter boundary file %s\" % (paramfilename))\n lines = infile.readlines()\n infile.close()\n\n # Parse\n paramdict = {}\n for line in lines:\n line = line.strip()\n if len(line) == 0:\n continue\n elif line[0] == '#':\n continue\n else:\n terms = line.split()\n name = terms[0]\n value = float(terms[1])\n parmin = float(terms[2])\n parmax = float(terms[3])\n stepsize = float(terms[4])\n \n paramdict[name] = [value, parmin, parmax, stepsize]\n # ENDIF\n # ENDFOR\n\n return paramdict","def parse(filename):\n if filename.endswith('.tsv'):\n # Horrible filename-based hack; in future examine contents of file here\n return parse_separated(filename, '\\t')\n if filename.endswith('.xlsx'):\n return parse_xlsx(filename)\n # fallback to old assume-csv behavior\n return parse_separated(filename, ',')","def parse_segments(self):\n segs = self.unixtext.split(\"$$\")\n for seg in segs:\n self.segments.append(TextProductSegment(seg, self))","def add_injection_parameters_from_file(self, injection_file, **kwargs):\n self.injection_parameters = self._grab_injection_parameters_from_file(\n injection_file, **kwargs\n )","def __init__(self, filename):\r\n self._results = SpecParser(filename).parse()","def _filename_pre_data(self) -> dict:\n key = []\n remainder = \"\"\n prework = {}\n for i in self.draft_file:\n if i == \"{\":\n remainder = \"\"\n elif i == \"}\":\n key.append(remainder)\n else:\n remainder += i\n list_filename = self.filename.split(\"_\")\n for key, value in zip(key, list_filename):\n prework[key] = value\n self.pre_data = prework","def parse_ir(self, filename):\n with open(filename, 'r') as f:\n configs_data = f.readlines()\n for line in configs_data:\n # remove the \\n char\n cur_line = line[:-1]\n title = re.findall('.+?:', cur_line)\n # remove the : char\n cur_title = title[0][:-1]\n content = re.findall(':.+', cur_line)\n cur_content = content[0][1:]\n exec('self.config_dict[cur_title]=' + cur_content)","def register_filename_format(format_name,parser):\n if format_name == \"ALL\":\n raise ValueError(\"filename format code ALL is reserved\")\n\n filename_format_parser[format_name] = parser","def load_str(str, filename, default_namespace=None, verbose=False):\n paramlist = []\n default_namespace = default_namespace or get_ros_namespace()\n for doc in yaml.load_all(str):\n if NS in doc:\n ns = ns_join(default_namespace, doc.get(NS, None))\n if verbose:\n print(\"reading parameters into namespace [%s]\"%ns)\n del doc[NS]\n else:\n ns = default_namespace\n paramlist.append((doc, ns))\n return paramlist","def process_feature_file(filename: str) -> Dict[str, Any]:\n feature = json.loads(open(filename).read())\n template = feature['query']\n name = feature['name']\n params = feature['params']\n feature_spec = {\n 'name': name,\n 'template': template,\n 'params': params\n }\n return feature_spec","def load_params_from_file(self, input_file):\n\n ### FILL IN ###","def parse_filename(filename, full_output=False):\n\n basename = filename.split(\"/\")[-1]\n teff = float(basename.split(\"t\")[1].split(\"g\")[0])\n logg = float(basename.split(\"g\")[1].split(\"k\")[0])/10.\n feh = float(basename[1:4].replace(\"p\", \"\").replace(\"m\", \"-\"))/10.\n alpha = [0, 0.4][basename[4] == \"a\"]\n parameters = [teff, logg, feh, alpha]\n\n if full_output:\n names = (\"effective_temperature\", \"surface_gravity\", \"metallicity\",\n \"alpha_enhancement\")\n return (parameters, names)\n return parameters","def setup_parse(self, inputstring: str, document: nodes.document) -> None:\n self.inputstring = inputstring\n self.document = document","def main(filename: str, /) -> None:","def __init__(self, filename, default_sep=None):\n self.filename = filename\n if default_sep is not None:\n self.separator = default_sep","def __parseDailyFilename(self, f):\n base = os.path.basename(f)\n\n tokens = base.split('.')\n if len(tokens) < 6:\n # assume it's an old file in the format A2000089etcetc.tif i.e. ?YYYYDDD*\n yr = base[1:5]\n day = base[5:8]\n else:\n # assume it's a file in the newer format ?*.YYYY.DDD.etc format\n varname, yr, day, temporalSummary, res, spatialSummary = tokens[0:6]\n outTemplate = varname + \"{}.{}.{}.\" + \"{}.{}.{}.tif\".format(temporalSummary, res, spatialSummary)\n if self._outTemplate == \"FILLED-OUTPUT{}.{}.{}.TemporalSummary.Res.SpatialSummary.tif\":\n self._outTemplate = outTemplate\n else:\n assert self._outTemplate == outTemplate\n return day, yr","def read_input_file(input_path, split_char='=', cmnt_char='#'):\n # initializes dictionary to hold parameters\n params = {}\n\n # opens file\n file_obj = open(input_path)\n\n # reads in parameters from the lines of the file\n for line in file_obj:\n # removes comments at the end of the line\n line = line.split(cmnt_char, 1)[0]\n # removes spaces padding the line\n line = line.strip()\n key_value = line.split(split_char)\n # checks that there is a key and value (2 items)\n if len(key_value) == 2:\n # loads parameter\n params[key_value[0].strip()] = key_value[1].strip()\n\n return params","def parse_files(files):\n ans = []\n if files:\n for f in files:\n split = f.split(\"=\")\n if len(split) != 2:\n raise Exception(\"invalid file specification '%s'\" % f)\n ans.append((split[0], split[1]))\n return ans","def parseString(self, val):\n \n if not isinstance(val, str):\n raise Exception('Input must be a string!')\n if len(val) < 9:\n raise Exception( 'ESDT Names must be 9 characters!' )\n self.setType( val[:2] )\n self.setTime( val[2] )\n self.setFrequency( val[3] )\n self.setHRes( val[4] )\n self.setVRes( val[5] )\n self.setGroup( val[6:9] )\n tmp = val.split('.')\n if len(tmp) == 4:\n self.setVersion( *tmp[1:] )","def filename_par(filename, searchstr):\n\tstart = filename.find(searchstr) + len(searchstr)\n\tfinish = start + 1\n\twhile unicode(filename[start:].replace(\".\",\"\"))[:finish-start].isnumeric():\n\t\tfinish += 1\n\treturn float(filename[start:finish-1])","def process_file(_dir, _name, pkg_id):\n file_path = os.path.join(_dir, _name)\n if os.path.islink(file_path):\n return\n try:\n fp = open(file_path, 'r')\n first_line = fp.readline()\n if constants.PYTHON in first_line:\n _func = extract_python_kwds\n elif constants.BASH in first_line or constants.SHELL in first_line:\n _func = extract_shell_kwds\n else:\n # Return if file is not a python or a shell script.\n return\n # Populates the keywords in the file.\n _func(fp, pkg_id)\n if _name not in constants.STOP_WORDS:\n insert_keyword(_name.split('.')[0], pkg_id)\n except Exception as e:\n# @todo(Logging, level info)\n print file_path, e","def func_PARSE(self):\n self.parsed_url = parse.urlparse(\"http://{0}:{1}{2}\".format(args.HTTP_HOST, args.HTTP_PORT, self.path).lower())\n self.parsed_param = parse.parse_qs(self.parsed_url[4])","def parse_bids_filename(filename: str) -> dict:\n parsed = {}\n results = list(re.search(bids_pattern, filename).groups())\n parsed[\"ext\"] = results.pop()\n while results:\n item = results.pop()\n if item is not None:\n parsed.update(_parse_segment(item))\n return parsed","def _ParseExtensionsString(self, extensions_string):\n if not extensions_string:\n return\n\n extensions_string = extensions_string.lower()\n extensions = [\n extension.strip() for extension in extensions_string.split(u',')]\n file_entry_filter = file_entry_filters.ExtensionsFileEntryFilter(extensions)\n self._filter_collection.AddFilter(file_entry_filter)","def _parse_parameters(self, parameters_text):\n for mo in re.finditer(self._PARAMETERS_RE, parameters_text):\n self._parameters.append(Parameter(mo.group(\"param_name\"), mo.group(\"default_value\")))","def parse_movie_title(file_name):\n\tmovie_name = os.path.basename(file_name)\n\tmovie_name = parsers.remove_extension(file_name)\n\tmovie_name = movie_name.lower()\n\tmovie_name = parsers.fix_word_seperators(movie_name)\n\tmovie_name = parsers.remove_tags(movie_name)\n\tmovie_name = parsers.remove_resolution(movie_name)\n\tmovie_name = parsers.remove_keywords(movie_name)\n\tmovie_name = parsers.remove_year(movie_name)\n\tmovie_name = parsers.remove_trailing_symbols(movie_name)\n\tmovie_name = parsers.remove_trailing_crap(movie_name)\n\tmovie_name = parsers.fix_the_at_the_end(movie_name)\n\tmovie_name = parsers.fix_a_at_the_end(movie_name)\n\tmovie_name = parsers.remove_double_spaces(movie_name)\n\tmovie_name = movie_name.strip()\n\tmovie_name = parsers.recapitalize(movie_name)\n\treturn movie_name","def from_text(cls, filename, alpha=None, pat=None, pat_args=None,\n auto_fields=None):\n with open(os.path.expanduser(filename), encoding='utf-8') as f:\n return cls(f, alpha, pat, pat_args)","def parse_parameters(filePath):\r\n numThreads, queue, affinity = 0,\"\",\"\"\r\n \r\n for line in open(filePath):\r\n if \"spec.omp2001.size:\" in line:\r\n if get_last_column_number(line)==\"test\":\r\n print(\"IS TEST SIZE!!1 : \" + filePath)\r\n \r\n if \"spec.omp2001.sw_threads:\" in line:\r\n numThreads = int(get_last_column_number(line))\r\n \r\n if \"spec.omp2001.mach:\" in line:\r\n machine = line.split(\" \")[-1]\r\n columns = machine.split(\".\")\r\n \r\n queue = columns[0]\r\n affinity = columns[1]\r\n \r\n return numThreads, queue, affinity","def parse_data(fp):\n pass","def _get_info_from_filename(filename: str) -> dict:\n *parts, suffix = filename.split('.')\n dct = re.match(r'^(?P<name>[A-z0-9.]*)(-(?P<num_rows>[0-9]+))?$', '.'.join(parts)).groupdict()\n return {\n 'name': dct['name'],\n 'num_rows': int(dct['num_rows']) if dct['num_rows'] else None,\n 'format': suffix,\n }","def import_parameters(self, file_name):\n parameters = []\n\n with open(file_name) as in_file:\n parameters = json.load(in_file)\n\n if parameters:\n self.put_parameters(parameters)","def name_parser(string):\n return string.replace('\\n', ' ')","def process(self, filename: str, contents: str) -> None:\n self._current_file_decorators = set()\n self._current_file = filename\n try:\n parsed = ast.parse(contents, filename=filename)\n except Exception as e: # pylint: disable=broad-exception-caught\n # logging errors when parsing file\n logging.exception('Error parsing %s: %s', filename, e)\n else:\n self.visit(parsed)\n finally:\n self._current_file = None\n self._current_file_decorators = set()","def parse_file(self, file_name):\n\n with open(file_name, \"r\") as input_file:\n file_contents = input_file.read()\n\n \"\"\"\n Regex is done on line by line basis - to ensure that irrespective\n of the formatting all docstrings are identified and all variable\n specifications are found.\n \"\"\"\n file_contents = file_contents.replace(\"\\n\", \"NEWLINE\")\n\n docstrings = self.get_docstrings(file_contents)\n variable_declarations = self.select_variable_declarations(docstrings)\n variable_declarations = [x.replace(\"NEWLINE\", \"\\n\") for x in variable_declarations]\n return variable_declarations","def parse(self, script_str):\n lines = script_str.split('\\n')\n for line in lines:\n self.parse_line(line.strip())","def fileparse(filename, node):\n\n fd = open(filename)\n line = fd.readline().strip('\\r\\n')\n\n while line != '':\n node.Add(line, node)\n line = fd.readline().strip('\\r\\n')","def parse_infile_names(self):\n\n rv, slist = UTIL.list_minus_pref_suf(self.infiles,'out.ss_review.','.txt')\n if rv < 0: return\n if rv > 0:\n if self.verb > 1: print('++ trying to get SID from glob form')\n slist = UTIL.list_minus_glob_form(self.infiles, strip='dir')\n else:\n if self.verb > 1: print(\"++ have SIDs from 'out.ss_reiview' form\")\n\n if len(slist) == 0:\n if self.verb > 1: print(\"-- empty SID list\")\n return\n\n # make sure names are unique and not empty\n if not UTIL.vals_are_unique(slist):\n if self.verb > 1: print('-- SIDs not detected: not unique')\n return\n minlen = min([len(ss) for ss in slist])\n if minlen < 1:\n if self.verb > 1: print('-- SIDs not detected: some would be empty')\n return\n\n # we have a subject list\n self.snames = slist\n\n # now go for GID, start by replacing SIDs in infiles\n newfiles = [fname.replace(slist[ind], 'SUBJ') for ind, fname in\n enumerate(self.infiles)]\n\n if UTIL.vals_are_constant(newfiles):\n print('-- no groups detected from filenames')\n return\n\n # okay, try to make a group list\n glist = UTIL.list_minus_glob_form(newfiles)\n\n # cannot have dirs in result\n for gid in glist:\n if gid.find('/') >= 0:\n if self.verb>1: print('-- no GIDs, dirs vary in multiple places')\n return\n\n minlen = min([len(ss) for ss in glist])\n if minlen < 1:\n if self.verb > 1: print('-- GIDs not detected: some would be empty')\n return\n\n if self.verb > 1: print(\"++ have GIDs from infiles\")\n self.gnames = glist","def parse_file(filename, db_factory, load_job_id, error_handler):\n db = db_factory()\n log = parse_file.get_logger()\n log.info('loading from %s', filename)\n try:\n num_cases = 0\n with codecs.open(filename, 'r', encoding='utf-8') as f:\n parser = vtr.Parser()\n for case in parser.parse(f):\n log.info('New case: %s/%s', case['book'], case['number'])\n num_cases += 1\n\n # Store the book\n try:\n db.books.update({'_id': case['book']},\n {'$set': {'year': int(case['book'].split('/')[0]),\n 'number': case['book'].split('/')[1],\n },\n '$addToSet': {'load_jobs': load_job_id,\n },\n },\n upsert=True,\n )\n except Exception as err:\n log.error('Could not store book %s: %s', case['book'], err)\n error_handler(unicode(err))\n\n # Store the case\n case['_id'] = '%s/%s' % (case['book'], case['number'])\n # associate the case record with the job for auditing\n case['load_job_id'] = load_job_id\n # pick a \"date\" for the case\n case['date'] = case.get('hearing_date') or case.get('arrest_date')\n try:\n db.cases.update({'_id': case['_id']},\n case,\n upsert=True,\n )\n except Exception as err:\n log.error('Could not store case %s: %s', case['_id'], err)\n error_handler(unicode(err))\n\n # Add participant info\n\n # index for upsert\n for p in get_encoded_participants(case, error_handler):\n #log.info('new participant: %r', p)\n p['case_id'] = case['_id']\n p['case_number'] = case['number']\n p['date'] = case['date']\n try:\n db.participants.update(\n {'case': case['_id'],\n 'encoding': p['encoding'],\n 'full_name': p['full_name'],\n 'role': p['role'],\n },\n p,\n upsert=True,\n )\n except Exception as err:\n log.error('Could not store participant %s for case %s: %s',\n p['_id'], case['_id'], err)\n error_handler(unicode(err))\n\n # Handle errors that did not result in new case records.\n errors = ['Parse error at %s:%s \"%s\" (%s)' % \\\n (filename, num, line, err)\n for num, line, err in parser.errors\n ]\n for e in errors:\n error_handler(e)\n except (OSError, IOError) as err:\n msg = unicode(err)\n errors = [msg]\n error_handler(msg)\n return {'errors': errors,\n 'num_cases': num_cases,\n }","def __init__(self, f):\n self.index = -1\n self.__tokenized = []\n file = self.__readFile(f)\n while self.__commentHandler(file, COMMENT, NEW_LINE):\n file = self.__commentHandler(file, COMMENT, NEW_LINE)\n file = file.replace(NEW_LINE, \" \")\n while self.__commentHandler(file, MULTI_LINE_COMMENT_START, MULTI_LINE_COMMENT_END):\n file = self.__commentHandler(file, MULTI_LINE_COMMENT_START, MULTI_LINE_COMMENT_END)\n self.__regexMaster(file)","def dispatch(self, filename):\n\n parser = self.find_parser(filename)\n if parser:\n parser.tell({\n 'command': 'parse',\n 'filename': filename\n })\n else:\n log.info('No parser for filename: {}'.format(filename))","def from_text_file(cls, filename):\n raise NotImplementedError()","def parse(self, filename):\r\n return self.fromtree( ElementTree.parse(filename) )"],"string":"[\n \"def parse_filename(cls, filename):\\n words = filename.split('_')\\n return words[0], int(words[1][1:]), int(words[2])\",\n \"def parseFileName(filename):\\n entry = DataEntry(\\\"\\\",0,{},{},0,0)\\n wordArray = filename.split(\\\".\\\")\\n entry.publication_name = wordArray[1]\\n entry.year = wordArray[0]\\n return entry\",\n \"def parse(source, *, filename=\\\"[STRING]\\\", typecheck=True):\\n return _Parser(filename, source, typecheck).parse()\",\n \"def parseFilePath(self, filepath):\\n\\n li = filepath.split(\\\"/\\\") \\n last = li[-1].split(\\\"_\\\")\\n\\n self.subjectName = li[-2]\\n self.experimenterName = li[-3]\\n self.experimentDate = last[-1]\\n self.paradigm = last[-2]\\n self.subjectName = last[-3]\",\n \"def parse(filename):\\n file_map = {\\n '1995-1996.html': ninety_six,\\n '2005-2006.html': twenty_six,\\n '2014-2015.html': twenty_fifteen\\n }\\n func = file_map.get(filename, lambda: \\\"Invalid File\\\")\\n func(filename)\",\n \"def _parse_filename(filename, metadata):\\n\\n file_noext = os.path.splitext(filename)[0]\\n fname = file_noext.split(\\\"_\\\")\\n\\n metadata[\\\"scene_id\\\"] = fname[1]\\n metadata[\\n \\\"beam_mode\\\"] = sat_properties.radarsat_product_characteristics[\\n fname[2]]\\n metadata[\\\"product_type\\\"] = fname[-1]\\n try:\\n metadata[\\n \\\"product_description\\\"] = sat_properties.radarsat_1_data_products[\\n fname[-1][:3]]['description']\\n except Exception:\\n metadata[\\\"product_description\\\"] = \\\"\\\"\\n\\n metadata[\\\"scene_mean_time\\\"] = datetime.datetime.strptime(\\n fname[3] + fname[4], \\\"%Y%m%d%H%M%S\\\")\\n\\n return metadata\",\n \"def ParseFileName(self, fn, fnParser):\\n self.filename = fn\\n attrs = fnParser.Parse(fn)\\n [setattr(self, k, _TryNumeric(v)) for k, v in attrs]\",\n \"def parse_glider_filename(filename):\\n head, tail = os.path.split(filename)\\n\\n matches = re.search(r\\\"([\\\\w\\\\d\\\\-]+)-(\\\\d+)-(\\\\d+)-(\\\\d+)-(\\\\d+)\\\\.(\\\\w+)$\\\", tail)\\n\\n if matches is not None:\\n return {\\n 'path': head,\\n 'glider': matches.group(1),\\n 'year': int(matches.group(2)),\\n 'day': int(matches.group(3)),\\n 'mission': int(matches.group(4)),\\n 'segment': int(matches.group(5)),\\n 'type': matches.group(6)\\n }\\n else:\\n raise ValueError(\\n \\\"Filename ({}) not in usual glider format: \\\"\\n \\\"<glider name>-<year>-<julian day>-\\\"\\n \\\"<mission>-<segment>.<extenstion>\\\".format(filename)\\n )\",\n \"def main():\\n parse_file(sys.argv[1])\",\n \"def parse_filename(filename): # , time_fmt=TIME_INFILE_FMT):\\n # Split the name up into its \\\"blocks\\\"\\n parts = filename.split(\\\"_\\\")\\n hive_str, rpi_str = parts[1:3]\\n day_str = parts[3]\\n method = parts[5]\\n\\n # Parse Hive and RPi number\\n hive = int(hive_str[-1])\\n rpi = int(rpi_str[-1])\\n method = method.strip(\\\".csv\\\")\\n\\n # # Parse timestring into a datetime object\\n # dt_naive = datetime.strptime(t_str, time_fmt)\\n # dt_utc = pytz.utc.localize(dt_naive)\\n\\n return hive, rpi, method, day_str\",\n \"def _parse(self, infile):\\n raise NotImplementedError()\",\n \"def parse(self, infile):\\r\\n raise NotImplementedError()\",\n \"def init_from_string(self, fs_in, param_string):\\n if '(' in param_string:\\n name_params_re = re.compile(r'(\\\\w*)\\\\((.*)\\\\)$')\\n pieces = name_params_re.match(param_string)\\n name = pieces.group(1)\\n params = pieces.group(2)\\n param_list = params.split(';')\\n param_dict = {}\\n for param in param_list:\\n if '=' not in param:\\n raise ValueError('preprocess param %s missing a value.' % param)\\n k, v = param.split('=', 1)\\n if v.isdigit():\\n v = int(v)\\n else:\\n try:\\n v = float(v)\\n except ValueError:\\n pass\\n param_dict[k] = v\\n self._name = name\\n self.init_highpass(param_dict['highpass_cutoff'],\\n param_dict['highpass_order'])\\n self.init_channel_numbers(param_dict['channel_numbers'])\\n else:\\n self.__init__(self, fs_in, param_string)\",\n \"def insert(self, file_token, *file_tokens, preprocessor):\\n tokens = (file_token,) + file_tokens\\n for token in tokens:\\n preprocessor.insert_file(self._get_filename(token))\",\n \"def _parse_file_path(self, input_path):\\n pass\",\n \"def parse_parameters(parser):\\n parser.add_argument('--parameters-file', '-p', help='parameter filename',\\n required=True)\\n return parser\",\n \"def preprocess(self, source, name, filename=None):\\n if not name or not os.path.splitext(name)[1] in self.environment.file_extensions:\\n return source\\n output = StringIO()\\n lexer = Lexer(iter(source.splitlines()))\\n Parser(lexer, callback=output.write, debug=self.environment.slim_debug).parse()\\n\\n if self.environment.slim_print:\\n print output.getvalue()\\n\\n return output.getvalue()\",\n \"def __init__(self, filename=None, label=None, tokens=None):\\n if label: # specify from label/tokens, for testing.\\n self.label = label\\n self.tokens = tokens\\n self.postID = -1\\n self.likes = -1\\n else: # specify from file.\\n self.filename = filename\\n parsedNames = filename.split(\\\"#\\\")\\n if 'pop' in parsedNames[0]:\\n self.label = 'pop'\\n else:\\n self.label = 'sod'\\n self.postID = parsedNames[1]\\n self.likes = parsedNames[2]\\n self.tokenize()\",\n \"def init_from_file(filename, parser=int):\\n filename = filename + \\\".\\\" + str(PID)\\n\\n def __parser_couple(s):\\n s = s.replace(\\\"(\\\", \\\"\\\")\\n s = s.replace(\\\")\\\", \\\"\\\")\\n ss = s.split(\\\",\\\")\\n return int(ss[0]), int(ss[1])\\n\\n p = PTree()\\n content = SList([])\\n with open(filename, \\\"r\\\") as f:\\n count_line = 0\\n for line in f:\\n if line.strip()[0] == '#':\\n continue\\n # __distribution: PID -> nb of segments\\n # __global_index: num seg -> (start, offset)\\n if count_line == 0: # Get the distribution\\n p.distribution = SList.from_str(line)\\n p.start_index = p.distribution.scanl(lambda x, y: x + y, 0)[PID]\\n p.nb_segs = p.distribution[PID]\\n elif count_line == 1: # Get the global_index\\n p.global_index = SList.from_str(line, parser=__parser_couple)\\n else: # Get the content\\n content.extend(Segment.from_str(line, parser=parser))\\n count_line = count_line + 1\\n p.content = content\\n return p\",\n \"def setSourceFile(filename):\",\n \"def __init__(self, line_parser, *filename):\\n \\n self.line_parser = line_parser\\n self.f = fileinput.input(filename)\",\n \"def _parse(\\n self, source: str, name: t.Optional[str], filename: t.Optional[str]\\n ) -> nodes.Template:\\n return Parser(self, source, name, filename).parse()\",\n \"def parse(self, fstring):\\n pass\",\n \"def parse_pts(pts_result_file, global_var_list, parsed_results_file):\\n fill_value_name(pts_result_file, global_var_list, parsed_results_file)\",\n \"def parse_filenames(filenames):\\n \\n for fn in filenames:\\n dirname, basename = path.split(fn)\\n subject_visit = basename[:7]\\n visit = basename[5:7]\\n yield dirname, basename, subject_visit, visit\",\n \"def parse_parameters(filename):\\n\\n # read in the parameters\\n mainInput = ParserClass.Parser(filename)\\n if 'LogFile' in mainInput['Inputs']:\\n if mainInput['Inputs']['LogFileUsePID']:\\n logger = Logging.Logger(mainInput['Inputs']['LogFile']+'_{}'.format(os.getpid()))\\n else:\\n logger = Logging.Logger(mainInput['Inputs']['LogFile'])\\n \\n else:\\n logger = print\\n\\n # Generate a filelist to loop over\\n filelist = np.loadtxt(mainInput['Inputs']['filelist'],dtype=str,ndmin=1)\\n if isinstance(mainInput['Inputs']['data_dir'], type(None)):\\n filelist = [filename for filename in filelist]\\n else:\\n filelist = ['{}/{}'.format(mainInput['Inputs']['data_dir'],\\n filename.split('/')[-1]) for filename in filelist]\\n \\n # Some items should always be a list\\n if not isinstance(mainInput['Inputs']['pipeline'], list):\\n mainInput['Inputs']['pipeline'] = [mainInput['Inputs']['pipeline']]\\n # Get the class names (modulename, classname)\\n jobnames = [c for c in mainInput['Inputs']['pipeline']]\\n\\n logger('Running: '+' '.join(mainInput['Inputs']['pipeline']))\\n\\n\\n prejobnames = [c for c in mainInput['Inputs']['preamble']]\\n\\n\\n # Read the class parameter file\\n classInput = ParserClass.Parser(mainInput['Inputs']['classParameters'])\\n\\n # Initalise the classes : classInput are the kwargs to initiate classes\\n jobs = []\\n for job in jobnames:\\n jobs += [getClass(job)(logger=logger,**classInput[job])]\\n\\n # Initalise the classes : classInput are the kwargs to initiate classes\\n prejobs = []\\n for prejob in prejobnames:\\n prejobs += [getClass(prejob)(logger=logger,**classInput[prejob])]\\n\\n\\n return jobs,prejobs, filelist, mainInput, classInput, logger\",\n \"def parse_source_file(self, filepath):\\n raise NotImplementedError('Not Implemented')\",\n \"def parse_file_name(file_name):\\n\\n elements = file_name.split(\\\"_\\\")\\n if file_name.find(\\\"_VI_\\\") > 0:\\n client = elements[0]\\n capture_range = \\\"R1\\\"\\n condition = elements[2]\\n polarization = \\\"VIS\\\"\\n shot = elements[4]\\n modality = \\\"VIS\\\"\\n else:\\n client = elements[0]\\n capture_range = elements[1]\\n condition = elements[2]\\n polarization = elements[3]\\n shot = elements[4]\\n modality = \\\"THERMAL\\\"\\n \\n return client, capture_range, condition, polarization, shot, modality\",\n \"def parse_infile(self, infile):\\n\\n if type(infile)==str:\\n print('Im a string')\\n folder, file = os.path.split(infile)\\n elif type(infile) in [list, tuple]:\\n if not len(infile) == 2:\\n raise(Exception('The infile must be a string or a length 2 sequence'))\\n else:\\n folder, file = infile\\n else:\\n raise(Exception('The infile must be a string or a length 2 sequence'))\\n \\n self.folder = folder\\n self.file_ = file\",\n \"def parser(filename):\\n\\n regex = re.compile(\\n # prolog\\n r\\\"run(?P<run>\\\\w+)\\\"\\n ##r\\\"\\\\-(?P<code_name>((mfdn)|(obscalc-ob))[^\\\\-]*)\\\"\\n r\\\"\\\\-(?P<descriptor>\\\"\\n # descriptor contents\\n r\\\"Z(?P<Z>\\\\d+)\\\\-N(?P<N>\\\\d+)\\\"\\n r\\\"\\\\-(?P<interaction>.+)\\\\-(?P<coulomb>\\\\d)\\\"\\n r\\\"\\\\-(?P<truncation_descriptor>.+)\\\"\\n ## r\\\"\\\\-Nmax(?P<Nmax>\\\\d+)\\\"\\n # epilog\\n r\\\").res\\\"\\n )\\n\\n conversions = {\\n \\\"Z\\\" : int,\\n \\\"N\\\" : int,\\n \\\"interaction\\\" : str,\\n \\\"coulomb\\\" : int,\\n }\\n\\n match = regex.match(filename)\\n if (match == None):\\n raise ValueError(\\\"bad form for spncci results filename: \\\" + filename)\\n info = match.groupdict()\\n\\n # convert fields\\n for key in conversions:\\n conversion = conversions[key]\\n info[key] = conversion(info[key]) if (info[key] is not None) else None\\n\\n return info\",\n \"def input_parse(filename,params_flag=False):\\r\\n\\r\\n input_parameters ={}\\r\\n with open(filename, 'r') as f:\\r\\n count = 0\\r\\n\\r\\n for line in f:\\r\\n line=line.strip()\\r\\n if line:\\r\\n if line.find('#') == -1:\\r\\n if not params_flag:\\r\\n var_name,var_value = line.split(',')[0],\\\",\\\".join(line.split(',')[1:]) # handle lines with more than 1 comma\\r\\n try:\\r\\n input_parameters[var_name] = float(var_value)\\r\\n except ValueError: # This occurs when python cannot convert list into a float.\\r\\n # Evaluate the python expression as a list\\r\\n input_parameters[var_name] = ast.literal_eval(var_value)\\r\\n else:\\r\\n if count==0:\\r\\n var_name = line.strip('\\\\n')\\r\\n input_parameters[var_name] = []\\r\\n count+=1\\r\\n else:\\r\\n try:\\r\\n input_parameters[var_name].append(float(line.strip('\\\\n')))\\r\\n except ValueError: # This occurs when python cannot convert list into a float.\\r\\n # Evaluate the python expression as a list\\r\\n input_parameters[var_name].append(ast.literal_eval(line.strip('\\\\n')))\\r\\n return input_parameters\",\n \"def __init__(self, filename, delimiter):\\n self.filename = filename\\n self.delimiter = delimiter\\n \\n # open file and process\\n self.headers = []\\n self.data = []\\n self._process_file()\",\n \"def parse(self, filename):\\n infile = file(filename)\\n for line in infile:\\n self.parseLine(line)\",\n \"def parseConfig(self, filename):\\n parameters = {}\\n try:\\n f = open(filename)\\n except Exception as inst:\\n print(type(inst))\\n print(inst.args)\\n print(inst)\\n print('cannot open', filename)\\n raise\\n else:\\n for line in f:\\n # Remove text after comment character.\\n if self.comment_char in line:\\n line, comment = line.split(self.comment_char,\\n 1) # Split on comment character, keep only the text before the character\\n\\n # Find lines with parameters (param=something)\\n if self.param_char in line:\\n parameter, value = line.split(self.param_char, 1) # Split on parameter character\\n parameter = parameter.strip() # Strip spaces\\n value = value.strip()\\n parameters[parameter] = value # Store parameters in a dictionary\\n\\n f.close()\\n\\n return parameters\",\n \"def split_file(document: str):\\n class_name, sep, assignment_name = document.partition(\\\"-\\\")\\n try:\\n assignment_name = assignment_name.split('.')[0].split('_')[0]\\n except TypeError:\\n pass\\n return class_name, assignment_name\",\n \"def instantiate_for_spirv_args(self, testcase):\\n testcase.stdin_shader = self.source\\n self.filename = '-'\\n return self.filename\",\n \"def parse_image_filename(filename):\\n\\n # regexes\\n starts_with_six_digits = re.compile(r'^\\\\d{6}')\\n capital_letter = re.compile(r'([A-Z]{1})')\\n plus = re.compile(r'\\\\+')\\n\\n # split the filename and extention\\n filename, extension = os.path.splitext(filename)\\n try:\\n style_number, color, description = filename.split('_')\\n except Exception as e:\\n print(e)\\n print(filename, extension)\\n\\n style_number = int(style_number)\\n\\n # decode the color\\n # intCaps -> int/caps\\n color = capital_letter.sub(r'/\\\\1', color).lower()\\n # plus+to+space -> plus to space\\n color = plus.sub(r' ', color)\\n\\n # decode the description\\n description = plus.sub(r' ', description)\\n\\n return style_number, color, description\",\n \"def setFile(self, filename):\\n self.prepare() #new call on each new file to process\\n self.filename = \\\"%s\\\" % filename\",\n \"def get_params(self, paramFile):\\n\\n with open(paramFile, 'r') as f:\\n titleLine = next(f)\\n\\n for line in f:\\n p, i, v = line.split(\\\",\\\")\\n\\n self.params.update(p, v, i)\",\n \"def parse_filename(cls, filename):\\n #from nose.tools import set_trace; set_trace()\\n m = re.match(cls._pattern, os.path.basename(filename))\\n basename = m.group(1)\\n bandname = cls._bandmap.get(m.group(2), m.group(2))\\n return basename, bandname\",\n \"def __init__(self, fileName,\\n openingMark = r'--oo<< *(?P<value>[^ \\\\n]+) *$',\\n closingMark = r'--oo>> *$',\\n hereMark = r'--oo== *(?P<value>[^ \\\\n]+) *$'):\\n\\n super(AnnotatedSourceFile,self).__init__(fileName)\\n self.openingMark = openingMark\\n self.closingMark = closingMark\\n self.hereMark = hereMark\\n\\n fragmenter = modelscripts.base.fragments.RegexpFragmenter(\\n self.sourceLines,\\n openingMark, closingMark, hereMark,\\n mainValue = self, firstPosition = 1)\\n\\n self.fragment = fragmenter.fragment\\n \\\"\\\"\\\" The root fragment according to the given mark \\\"\\\"\\\"\",\n \"def __init__(self, filename):\\n\\n parser = Parser(filename=filename)\\n self.uuid = parser.segregated(parser.read(),'UUID')\\n self.id = parser.segregated(parser.read(),'ID')\\n self.rate = parser.segregated(parser.read(),'RATE')\\n self.gpio = parser.segregated(parser.read(),'GPIO')\\n self.ddl = parser.segregated(parser.read(),'DATA_DELIVERY_LOCATION')\",\n \"def extract_params(self, fname):\\n return re.findall(self.regexp_params, os.path.basename(fname))\",\n \"def parseFile(self, filename):\\n\\n f = open(filename, \\\"r\\\")\\n s = f.read()\\n f.close()\\n\\n logging.log(10, 'parsing filename %s: %d lines' % (filename, len(s)))\\n\\n self.parseString(s)\",\n \"def parse(self, fp):\\n\\n # create the plex scanner for fp\\n self.create_scanner(fp)\\n\\n # call parsing logic\\n self.stmt_list()\\n print('Parsing successful!')\",\n \"def train_image_parse_function(filename, *argv):\\n image = read_image(filename)\\n image = tf.image.random_flip_left_right(image)\\n\\n if FLAGS.augmentation:\\n print('data augmentation')\\n resized_image = resize_and_random_crop_image(image)\\n else:\\n resized_image = resize_image(image)\\n resized_image = scale_image_value(resized_image)\\n\\n if len(argv) == 1:\\n return resized_image, argv[0]\\n elif len(argv) == 2:\\n return resized_image, argv[0], argv[1]\\n else:\\n return resized_image\",\n \"def __init__(self, filename):\\n self.from_file(filename)\\n self.parse_cell()\\n self.parse_atom()\\n self.apply_symops()\",\n \"def _parse_bids_filename(fname, verbose):\\n keys = ['sub', 'ses', 'task', 'acq', 'run', 'proc', 'run', 'space',\\n 'rec', 'split', 'kind']\\n params = {key: None for key in keys}\\n idx_key = 0\\n for match in re.finditer(param_regex, op.basename(fname)):\\n key, value = match.groups()\\n if key not in keys:\\n raise KeyError('Unexpected entity \\\"%s\\\" found in filename \\\"%s\\\"'\\n % (key, fname))\\n if keys.index(key) < idx_key:\\n raise ValueError('Entities in filename not ordered correctly.'\\n ' \\\"%s\\\" should have occurred earlier in the '\\n 'filename \\\"%s\\\"' % (key, fname))\\n idx_key = keys.index(key)\\n params[key] = value\\n return params\",\n \"def insert_file_via_perl(filename, comment=\\\"Added by Python Job\\\"):\\n for line in lines(['ImportSingleFileIntoPosdaAndReturnId.pl', filename, comment]):\\n if line.startswith(\\\"File id:\\\"):\\n return int(line[8:])\\n\\n # TODO: pass on the error if there was one\\n raise RuntimeError(\\\"Failed to insert file into posda!\\\")\",\n \"def preprocess(\\n self,\\n source: str,\\n name: t.Optional[str] = None,\\n filename: t.Optional[str] = None,\\n ) -> str:\\n return reduce(\\n lambda s, e: e.preprocess(s, name, filename),\\n self.iter_extensions(),\\n str(source),\\n )\",\n \"def load_specs(self, filename):\\n self.filename = filename\\n # Add loading functionality here\",\n \"def load_specs(self, filename):\\n self.filename = filename\\n # Add loading functionality here\",\n \"def __init__(self, in_file=None, search_str=\\\"\\\", buffer_size=None):\\n super(FileParser, self).__init__()\\n self.buffer_size = buffer_size or 1\\n self.search_path = in_file\\n self.update(self._parser(search_str=search_str))\",\n \"def parseParams():\\n parser = argparse.ArgumentParser(\\n prog=__appname__,\\n description=__desc__,\\n prefix_chars=\\\"-/\\\",\\n fromfile_prefix_chars='@',\\n add_help=False)\\n parser.add_argument('source',\\n type=str,\\n nargs='?',\\n help=\\\"File containing valid syntax. (Default Standard Input)\\\"\\n )\\n parser.add_argument('output',\\n type=str,\\n help=\\\"Output Image. (required)\\\"\\n )\\n parser.add_argument('-f', '--fmt', '/f',\\n dest='fmt',\\n default='SVG',\\n choices=['SVG', 'PDF', 'EPS', 'PNG'],\\n nargs='?',\\n help='Format of the Output file. Formats other than SVG require Inkscape in the system path.')\\n parser.add_argument('-s', '--syntax', '/s',\\n dest='syntax',\\n default='EBNF',\\n choices=['EBNF', 'DIAGRAM'],\\n nargs='?',\\n help='Syntax used for the Input.')\\n parser.add_argument('-h', '--help', '/h',\\n action='help',\\n help=\\\"show this help message and exit.\\\")\\n parser.add_argument('-v', '--version', '/v',\\n action='version',\\n version=\\\"{0:s} version {1:s}\\\".format(__title__, __version__))\\n return parser.parse_args()\",\n \"def parseFile(self,filename):\\n\\n name = '[0-9a-zA-Z_]+'\\n string = '\\\\\\\\\\\"(.+)\\\\\\\\\\\"'\\n\\n testclass = None\\n functionName = None\\n\\n fin = open(filename, 'r')\\n for line in fin:\\n # testclass starts\\n res = re.match('class ('+name+')', line)\\n if res != None:\\n testclass = res.group(1)\\n\\n # end of testclass \\n if re.match('};', line) != None:\\n testclass = None\\n\\n # function start\\n res = re.match('\\\\\\\\s+void ('+name+')\\\\\\\\(\\\\\\\\)', line)\\n if res != None:\\n functionName = res.group(1)\\n\\n elif re.match('\\\\\\\\s+}', line) != None:\\n functionName = None\\n\\n if functionName == None:\\n continue\\n\\n # check\\n res = re.match('\\\\s+check.*\\\\('+string, line)\\n if res != None:\\n code = res.group(1)\\n\\n # code..\\n res = re.match('\\\\\\\\s+'+string, line)\\n if res != None:\\n code = code + res.group(1)\\n\\n # assert\\n res = re.match('\\\\\\\\s+ASSERT_EQUALS\\\\\\\\(\\\\\\\\\\\"([^\\\"]*)\\\\\\\\\\\",', line)\\n if res != None and len(code) > 10:\\n node = { 'testclass':testclass,\\n 'functionName':functionName,\\n 'code':code,\\n 'expected':res.group(1) }\\n self.nodes.append(node)\\n code = ''\\n\\n # close test file\\n fin.close()\",\n \"def read_parse_file(params):\\n\\tparam_names = []\\n\\tparam_options = []\\n\\tif not os.path.isfile(params.parse_file):\\n\\t\\tprint(\\\"parse file does not exist! ({})\\\".format(params.parse_file))\\n\\t\\tsys.exit(NO_PARSE)\\n\\twith open(params.parse_file, 'r') as pf:\\n\\t\\t# first line should be iteration regex\\n\\t\\tsetattr(params, 'iteration_regex', re.compile(pf.readline().strip()))\\n\\t\\tfor line in pf:\\n\\t\\t\\tparam_desc = line.split(';')\\n\\t\\t\\tparam_names.append(param_desc[0])\\n\\t\\t\\tparam_options.append(param_desc[1])\\n\\n\\treturn param_names,param_options\",\n \"def parse_vid_path(vid_path):\\n # ensures all path separators are consistent with OS\\n vid_path = os.path.normpath(vid_path)\\n i_start = vid_path.rfind(os.path.sep)\\n vid_file = vid_path[i_start+1:]\\n # cuts out extension and splits by underscores\\n tokens = vid_file[:-4].split('_')\\n prefix = ''\\n\\n for i, token in enumerate(tokens):\\n if token.isnumeric():\\n break\\n elif i != 0:\\n prefix += '_'\\n prefix += token\\n \\n params = {'prefix' : prefix,\\n 'fps' : int(tokens[i]),\\n 'exp_time' : read_dash_decimal(tokens[i+1]),\\n 'Q_i' : read_dash_decimal(tokens[i+2]),\\n 'Q_o' : int(tokens[i+3]),\\n 'd' : int(tokens[i+4]),\\n 'mag' : int(tokens[i+5]),\\n 'num' : int(tokens[i+6])}\\n\\n return params\",\n \"def __init__(self, root_directory, filename):\\n self.source_name = filename\\n extensionless, _ = path.splitext(filename)\\n self.binary_name = extensionless + \\\".sept\\\"\\n self.name = path.relpath(extensionless, root_directory)\",\n \"def importParameterBoundaryFile(paramfilename):\\n try:\\n infile = open(paramfilename, \\\"r\\\")\\n except IOError:\\n\\t print \\\"Unable to open file %s\\\" % (paramfilename)\\n\\t raise IOError(\\\"Unable to open parameter boundary file %s\\\" % (paramfilename))\\n lines = infile.readlines()\\n infile.close()\\n\\n # Parse\\n paramdict = {}\\n for line in lines:\\n line = line.strip()\\n if len(line) == 0:\\n continue\\n elif line[0] == '#':\\n continue\\n else:\\n terms = line.split()\\n name = terms[0]\\n value = float(terms[1])\\n parmin = float(terms[2])\\n parmax = float(terms[3])\\n stepsize = float(terms[4])\\n \\n paramdict[name] = [value, parmin, parmax, stepsize]\\n # ENDIF\\n # ENDFOR\\n\\n return paramdict\",\n \"def parse(filename):\\n if filename.endswith('.tsv'):\\n # Horrible filename-based hack; in future examine contents of file here\\n return parse_separated(filename, '\\\\t')\\n if filename.endswith('.xlsx'):\\n return parse_xlsx(filename)\\n # fallback to old assume-csv behavior\\n return parse_separated(filename, ',')\",\n \"def parse_segments(self):\\n segs = self.unixtext.split(\\\"$$\\\")\\n for seg in segs:\\n self.segments.append(TextProductSegment(seg, self))\",\n \"def add_injection_parameters_from_file(self, injection_file, **kwargs):\\n self.injection_parameters = self._grab_injection_parameters_from_file(\\n injection_file, **kwargs\\n )\",\n \"def __init__(self, filename):\\r\\n self._results = SpecParser(filename).parse()\",\n \"def _filename_pre_data(self) -> dict:\\n key = []\\n remainder = \\\"\\\"\\n prework = {}\\n for i in self.draft_file:\\n if i == \\\"{\\\":\\n remainder = \\\"\\\"\\n elif i == \\\"}\\\":\\n key.append(remainder)\\n else:\\n remainder += i\\n list_filename = self.filename.split(\\\"_\\\")\\n for key, value in zip(key, list_filename):\\n prework[key] = value\\n self.pre_data = prework\",\n \"def parse_ir(self, filename):\\n with open(filename, 'r') as f:\\n configs_data = f.readlines()\\n for line in configs_data:\\n # remove the \\\\n char\\n cur_line = line[:-1]\\n title = re.findall('.+?:', cur_line)\\n # remove the : char\\n cur_title = title[0][:-1]\\n content = re.findall(':.+', cur_line)\\n cur_content = content[0][1:]\\n exec('self.config_dict[cur_title]=' + cur_content)\",\n \"def register_filename_format(format_name,parser):\\n if format_name == \\\"ALL\\\":\\n raise ValueError(\\\"filename format code ALL is reserved\\\")\\n\\n filename_format_parser[format_name] = parser\",\n \"def load_str(str, filename, default_namespace=None, verbose=False):\\n paramlist = []\\n default_namespace = default_namespace or get_ros_namespace()\\n for doc in yaml.load_all(str):\\n if NS in doc:\\n ns = ns_join(default_namespace, doc.get(NS, None))\\n if verbose:\\n print(\\\"reading parameters into namespace [%s]\\\"%ns)\\n del doc[NS]\\n else:\\n ns = default_namespace\\n paramlist.append((doc, ns))\\n return paramlist\",\n \"def process_feature_file(filename: str) -> Dict[str, Any]:\\n feature = json.loads(open(filename).read())\\n template = feature['query']\\n name = feature['name']\\n params = feature['params']\\n feature_spec = {\\n 'name': name,\\n 'template': template,\\n 'params': params\\n }\\n return feature_spec\",\n \"def load_params_from_file(self, input_file):\\n\\n ### FILL IN ###\",\n \"def parse_filename(filename, full_output=False):\\n\\n basename = filename.split(\\\"/\\\")[-1]\\n teff = float(basename.split(\\\"t\\\")[1].split(\\\"g\\\")[0])\\n logg = float(basename.split(\\\"g\\\")[1].split(\\\"k\\\")[0])/10.\\n feh = float(basename[1:4].replace(\\\"p\\\", \\\"\\\").replace(\\\"m\\\", \\\"-\\\"))/10.\\n alpha = [0, 0.4][basename[4] == \\\"a\\\"]\\n parameters = [teff, logg, feh, alpha]\\n\\n if full_output:\\n names = (\\\"effective_temperature\\\", \\\"surface_gravity\\\", \\\"metallicity\\\",\\n \\\"alpha_enhancement\\\")\\n return (parameters, names)\\n return parameters\",\n \"def setup_parse(self, inputstring: str, document: nodes.document) -> None:\\n self.inputstring = inputstring\\n self.document = document\",\n \"def main(filename: str, /) -> None:\",\n \"def __init__(self, filename, default_sep=None):\\n self.filename = filename\\n if default_sep is not None:\\n self.separator = default_sep\",\n \"def __parseDailyFilename(self, f):\\n base = os.path.basename(f)\\n\\n tokens = base.split('.')\\n if len(tokens) < 6:\\n # assume it's an old file in the format A2000089etcetc.tif i.e. ?YYYYDDD*\\n yr = base[1:5]\\n day = base[5:8]\\n else:\\n # assume it's a file in the newer format ?*.YYYY.DDD.etc format\\n varname, yr, day, temporalSummary, res, spatialSummary = tokens[0:6]\\n outTemplate = varname + \\\"{}.{}.{}.\\\" + \\\"{}.{}.{}.tif\\\".format(temporalSummary, res, spatialSummary)\\n if self._outTemplate == \\\"FILLED-OUTPUT{}.{}.{}.TemporalSummary.Res.SpatialSummary.tif\\\":\\n self._outTemplate = outTemplate\\n else:\\n assert self._outTemplate == outTemplate\\n return day, yr\",\n \"def read_input_file(input_path, split_char='=', cmnt_char='#'):\\n # initializes dictionary to hold parameters\\n params = {}\\n\\n # opens file\\n file_obj = open(input_path)\\n\\n # reads in parameters from the lines of the file\\n for line in file_obj:\\n # removes comments at the end of the line\\n line = line.split(cmnt_char, 1)[0]\\n # removes spaces padding the line\\n line = line.strip()\\n key_value = line.split(split_char)\\n # checks that there is a key and value (2 items)\\n if len(key_value) == 2:\\n # loads parameter\\n params[key_value[0].strip()] = key_value[1].strip()\\n\\n return params\",\n \"def parse_files(files):\\n ans = []\\n if files:\\n for f in files:\\n split = f.split(\\\"=\\\")\\n if len(split) != 2:\\n raise Exception(\\\"invalid file specification '%s'\\\" % f)\\n ans.append((split[0], split[1]))\\n return ans\",\n \"def parseString(self, val):\\n \\n if not isinstance(val, str):\\n raise Exception('Input must be a string!')\\n if len(val) < 9:\\n raise Exception( 'ESDT Names must be 9 characters!' )\\n self.setType( val[:2] )\\n self.setTime( val[2] )\\n self.setFrequency( val[3] )\\n self.setHRes( val[4] )\\n self.setVRes( val[5] )\\n self.setGroup( val[6:9] )\\n tmp = val.split('.')\\n if len(tmp) == 4:\\n self.setVersion( *tmp[1:] )\",\n \"def filename_par(filename, searchstr):\\n\\tstart = filename.find(searchstr) + len(searchstr)\\n\\tfinish = start + 1\\n\\twhile unicode(filename[start:].replace(\\\".\\\",\\\"\\\"))[:finish-start].isnumeric():\\n\\t\\tfinish += 1\\n\\treturn float(filename[start:finish-1])\",\n \"def process_file(_dir, _name, pkg_id):\\n file_path = os.path.join(_dir, _name)\\n if os.path.islink(file_path):\\n return\\n try:\\n fp = open(file_path, 'r')\\n first_line = fp.readline()\\n if constants.PYTHON in first_line:\\n _func = extract_python_kwds\\n elif constants.BASH in first_line or constants.SHELL in first_line:\\n _func = extract_shell_kwds\\n else:\\n # Return if file is not a python or a shell script.\\n return\\n # Populates the keywords in the file.\\n _func(fp, pkg_id)\\n if _name not in constants.STOP_WORDS:\\n insert_keyword(_name.split('.')[0], pkg_id)\\n except Exception as e:\\n# @todo(Logging, level info)\\n print file_path, e\",\n \"def func_PARSE(self):\\n self.parsed_url = parse.urlparse(\\\"http://{0}:{1}{2}\\\".format(args.HTTP_HOST, args.HTTP_PORT, self.path).lower())\\n self.parsed_param = parse.parse_qs(self.parsed_url[4])\",\n \"def parse_bids_filename(filename: str) -> dict:\\n parsed = {}\\n results = list(re.search(bids_pattern, filename).groups())\\n parsed[\\\"ext\\\"] = results.pop()\\n while results:\\n item = results.pop()\\n if item is not None:\\n parsed.update(_parse_segment(item))\\n return parsed\",\n \"def _ParseExtensionsString(self, extensions_string):\\n if not extensions_string:\\n return\\n\\n extensions_string = extensions_string.lower()\\n extensions = [\\n extension.strip() for extension in extensions_string.split(u',')]\\n file_entry_filter = file_entry_filters.ExtensionsFileEntryFilter(extensions)\\n self._filter_collection.AddFilter(file_entry_filter)\",\n \"def _parse_parameters(self, parameters_text):\\n for mo in re.finditer(self._PARAMETERS_RE, parameters_text):\\n self._parameters.append(Parameter(mo.group(\\\"param_name\\\"), mo.group(\\\"default_value\\\")))\",\n \"def parse_movie_title(file_name):\\n\\tmovie_name = os.path.basename(file_name)\\n\\tmovie_name = parsers.remove_extension(file_name)\\n\\tmovie_name = movie_name.lower()\\n\\tmovie_name = parsers.fix_word_seperators(movie_name)\\n\\tmovie_name = parsers.remove_tags(movie_name)\\n\\tmovie_name = parsers.remove_resolution(movie_name)\\n\\tmovie_name = parsers.remove_keywords(movie_name)\\n\\tmovie_name = parsers.remove_year(movie_name)\\n\\tmovie_name = parsers.remove_trailing_symbols(movie_name)\\n\\tmovie_name = parsers.remove_trailing_crap(movie_name)\\n\\tmovie_name = parsers.fix_the_at_the_end(movie_name)\\n\\tmovie_name = parsers.fix_a_at_the_end(movie_name)\\n\\tmovie_name = parsers.remove_double_spaces(movie_name)\\n\\tmovie_name = movie_name.strip()\\n\\tmovie_name = parsers.recapitalize(movie_name)\\n\\treturn movie_name\",\n \"def from_text(cls, filename, alpha=None, pat=None, pat_args=None,\\n auto_fields=None):\\n with open(os.path.expanduser(filename), encoding='utf-8') as f:\\n return cls(f, alpha, pat, pat_args)\",\n \"def parse_parameters(filePath):\\r\\n numThreads, queue, affinity = 0,\\\"\\\",\\\"\\\"\\r\\n \\r\\n for line in open(filePath):\\r\\n if \\\"spec.omp2001.size:\\\" in line:\\r\\n if get_last_column_number(line)==\\\"test\\\":\\r\\n print(\\\"IS TEST SIZE!!1 : \\\" + filePath)\\r\\n \\r\\n if \\\"spec.omp2001.sw_threads:\\\" in line:\\r\\n numThreads = int(get_last_column_number(line))\\r\\n \\r\\n if \\\"spec.omp2001.mach:\\\" in line:\\r\\n machine = line.split(\\\" \\\")[-1]\\r\\n columns = machine.split(\\\".\\\")\\r\\n \\r\\n queue = columns[0]\\r\\n affinity = columns[1]\\r\\n \\r\\n return numThreads, queue, affinity\",\n \"def parse_data(fp):\\n pass\",\n \"def _get_info_from_filename(filename: str) -> dict:\\n *parts, suffix = filename.split('.')\\n dct = re.match(r'^(?P<name>[A-z0-9.]*)(-(?P<num_rows>[0-9]+))?$', '.'.join(parts)).groupdict()\\n return {\\n 'name': dct['name'],\\n 'num_rows': int(dct['num_rows']) if dct['num_rows'] else None,\\n 'format': suffix,\\n }\",\n \"def import_parameters(self, file_name):\\n parameters = []\\n\\n with open(file_name) as in_file:\\n parameters = json.load(in_file)\\n\\n if parameters:\\n self.put_parameters(parameters)\",\n \"def name_parser(string):\\n return string.replace('\\\\n', ' ')\",\n \"def process(self, filename: str, contents: str) -> None:\\n self._current_file_decorators = set()\\n self._current_file = filename\\n try:\\n parsed = ast.parse(contents, filename=filename)\\n except Exception as e: # pylint: disable=broad-exception-caught\\n # logging errors when parsing file\\n logging.exception('Error parsing %s: %s', filename, e)\\n else:\\n self.visit(parsed)\\n finally:\\n self._current_file = None\\n self._current_file_decorators = set()\",\n \"def parse_file(self, file_name):\\n\\n with open(file_name, \\\"r\\\") as input_file:\\n file_contents = input_file.read()\\n\\n \\\"\\\"\\\"\\n Regex is done on line by line basis - to ensure that irrespective\\n of the formatting all docstrings are identified and all variable\\n specifications are found.\\n \\\"\\\"\\\"\\n file_contents = file_contents.replace(\\\"\\\\n\\\", \\\"NEWLINE\\\")\\n\\n docstrings = self.get_docstrings(file_contents)\\n variable_declarations = self.select_variable_declarations(docstrings)\\n variable_declarations = [x.replace(\\\"NEWLINE\\\", \\\"\\\\n\\\") for x in variable_declarations]\\n return variable_declarations\",\n \"def parse(self, script_str):\\n lines = script_str.split('\\\\n')\\n for line in lines:\\n self.parse_line(line.strip())\",\n \"def fileparse(filename, node):\\n\\n fd = open(filename)\\n line = fd.readline().strip('\\\\r\\\\n')\\n\\n while line != '':\\n node.Add(line, node)\\n line = fd.readline().strip('\\\\r\\\\n')\",\n \"def parse_infile_names(self):\\n\\n rv, slist = UTIL.list_minus_pref_suf(self.infiles,'out.ss_review.','.txt')\\n if rv < 0: return\\n if rv > 0:\\n if self.verb > 1: print('++ trying to get SID from glob form')\\n slist = UTIL.list_minus_glob_form(self.infiles, strip='dir')\\n else:\\n if self.verb > 1: print(\\\"++ have SIDs from 'out.ss_reiview' form\\\")\\n\\n if len(slist) == 0:\\n if self.verb > 1: print(\\\"-- empty SID list\\\")\\n return\\n\\n # make sure names are unique and not empty\\n if not UTIL.vals_are_unique(slist):\\n if self.verb > 1: print('-- SIDs not detected: not unique')\\n return\\n minlen = min([len(ss) for ss in slist])\\n if minlen < 1:\\n if self.verb > 1: print('-- SIDs not detected: some would be empty')\\n return\\n\\n # we have a subject list\\n self.snames = slist\\n\\n # now go for GID, start by replacing SIDs in infiles\\n newfiles = [fname.replace(slist[ind], 'SUBJ') for ind, fname in\\n enumerate(self.infiles)]\\n\\n if UTIL.vals_are_constant(newfiles):\\n print('-- no groups detected from filenames')\\n return\\n\\n # okay, try to make a group list\\n glist = UTIL.list_minus_glob_form(newfiles)\\n\\n # cannot have dirs in result\\n for gid in glist:\\n if gid.find('/') >= 0:\\n if self.verb>1: print('-- no GIDs, dirs vary in multiple places')\\n return\\n\\n minlen = min([len(ss) for ss in glist])\\n if minlen < 1:\\n if self.verb > 1: print('-- GIDs not detected: some would be empty')\\n return\\n\\n if self.verb > 1: print(\\\"++ have GIDs from infiles\\\")\\n self.gnames = glist\",\n \"def parse_file(filename, db_factory, load_job_id, error_handler):\\n db = db_factory()\\n log = parse_file.get_logger()\\n log.info('loading from %s', filename)\\n try:\\n num_cases = 0\\n with codecs.open(filename, 'r', encoding='utf-8') as f:\\n parser = vtr.Parser()\\n for case in parser.parse(f):\\n log.info('New case: %s/%s', case['book'], case['number'])\\n num_cases += 1\\n\\n # Store the book\\n try:\\n db.books.update({'_id': case['book']},\\n {'$set': {'year': int(case['book'].split('/')[0]),\\n 'number': case['book'].split('/')[1],\\n },\\n '$addToSet': {'load_jobs': load_job_id,\\n },\\n },\\n upsert=True,\\n )\\n except Exception as err:\\n log.error('Could not store book %s: %s', case['book'], err)\\n error_handler(unicode(err))\\n\\n # Store the case\\n case['_id'] = '%s/%s' % (case['book'], case['number'])\\n # associate the case record with the job for auditing\\n case['load_job_id'] = load_job_id\\n # pick a \\\"date\\\" for the case\\n case['date'] = case.get('hearing_date') or case.get('arrest_date')\\n try:\\n db.cases.update({'_id': case['_id']},\\n case,\\n upsert=True,\\n )\\n except Exception as err:\\n log.error('Could not store case %s: %s', case['_id'], err)\\n error_handler(unicode(err))\\n\\n # Add participant info\\n\\n # index for upsert\\n for p in get_encoded_participants(case, error_handler):\\n #log.info('new participant: %r', p)\\n p['case_id'] = case['_id']\\n p['case_number'] = case['number']\\n p['date'] = case['date']\\n try:\\n db.participants.update(\\n {'case': case['_id'],\\n 'encoding': p['encoding'],\\n 'full_name': p['full_name'],\\n 'role': p['role'],\\n },\\n p,\\n upsert=True,\\n )\\n except Exception as err:\\n log.error('Could not store participant %s for case %s: %s',\\n p['_id'], case['_id'], err)\\n error_handler(unicode(err))\\n\\n # Handle errors that did not result in new case records.\\n errors = ['Parse error at %s:%s \\\"%s\\\" (%s)' % \\\\\\n (filename, num, line, err)\\n for num, line, err in parser.errors\\n ]\\n for e in errors:\\n error_handler(e)\\n except (OSError, IOError) as err:\\n msg = unicode(err)\\n errors = [msg]\\n error_handler(msg)\\n return {'errors': errors,\\n 'num_cases': num_cases,\\n }\",\n \"def __init__(self, f):\\n self.index = -1\\n self.__tokenized = []\\n file = self.__readFile(f)\\n while self.__commentHandler(file, COMMENT, NEW_LINE):\\n file = self.__commentHandler(file, COMMENT, NEW_LINE)\\n file = file.replace(NEW_LINE, \\\" \\\")\\n while self.__commentHandler(file, MULTI_LINE_COMMENT_START, MULTI_LINE_COMMENT_END):\\n file = self.__commentHandler(file, MULTI_LINE_COMMENT_START, MULTI_LINE_COMMENT_END)\\n self.__regexMaster(file)\",\n \"def dispatch(self, filename):\\n\\n parser = self.find_parser(filename)\\n if parser:\\n parser.tell({\\n 'command': 'parse',\\n 'filename': filename\\n })\\n else:\\n log.info('No parser for filename: {}'.format(filename))\",\n \"def from_text_file(cls, filename):\\n raise NotImplementedError()\",\n \"def parse(self, filename):\\r\\n return self.fromtree( ElementTree.parse(filename) )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5459443","0.5448573","0.5429191","0.52798533","0.5271916","0.5247543","0.52416116","0.5215298","0.51574737","0.5120585","0.5072528","0.5036985","0.5022814","0.4980182","0.49415386","0.4940157","0.49290508","0.49222475","0.49006563","0.48977634","0.48940113","0.48884514","0.48853","0.48508862","0.4831189","0.4828288","0.48280442","0.48250598","0.47992033","0.47972468","0.47771767","0.4773233","0.47715867","0.47603905","0.47384235","0.47292638","0.47249687","0.4703285","0.46953326","0.469194","0.4672991","0.4664157","0.4663032","0.46629053","0.46596935","0.465447","0.4654159","0.46506816","0.46425876","0.46396428","0.46296486","0.46296486","0.46249697","0.4622102","0.46015215","0.45960686","0.45929492","0.45894977","0.45848167","0.45807895","0.4567585","0.4559083","0.45490068","0.4545571","0.45451957","0.45289043","0.45219862","0.45218718","0.4517237","0.45171303","0.45155457","0.4512571","0.4508418","0.44733927","0.44696665","0.4468715","0.44625285","0.44496748","0.4448394","0.4446431","0.4444349","0.44441938","0.44403014","0.4435095","0.44340175","0.4433671","0.44309166","0.4430827","0.4424339","0.4423634","0.4415864","0.44147328","0.4407466","0.44026887","0.44003782","0.43977904","0.43969172","0.43946016","0.43860486","0.43853277"],"string":"[\n \"0.5459443\",\n \"0.5448573\",\n \"0.5429191\",\n \"0.52798533\",\n \"0.5271916\",\n \"0.5247543\",\n \"0.52416116\",\n \"0.5215298\",\n \"0.51574737\",\n \"0.5120585\",\n \"0.5072528\",\n \"0.5036985\",\n \"0.5022814\",\n \"0.4980182\",\n \"0.49415386\",\n \"0.4940157\",\n \"0.49290508\",\n \"0.49222475\",\n \"0.49006563\",\n \"0.48977634\",\n \"0.48940113\",\n \"0.48884514\",\n \"0.48853\",\n \"0.48508862\",\n \"0.4831189\",\n \"0.4828288\",\n \"0.48280442\",\n \"0.48250598\",\n \"0.47992033\",\n \"0.47972468\",\n \"0.47771767\",\n \"0.4773233\",\n \"0.47715867\",\n \"0.47603905\",\n \"0.47384235\",\n \"0.47292638\",\n \"0.47249687\",\n \"0.4703285\",\n \"0.46953326\",\n \"0.469194\",\n \"0.4672991\",\n \"0.4664157\",\n \"0.4663032\",\n \"0.46629053\",\n \"0.46596935\",\n \"0.465447\",\n \"0.4654159\",\n \"0.46506816\",\n \"0.46425876\",\n \"0.46396428\",\n \"0.46296486\",\n \"0.46296486\",\n \"0.46249697\",\n \"0.4622102\",\n \"0.46015215\",\n \"0.45960686\",\n \"0.45929492\",\n \"0.45894977\",\n \"0.45848167\",\n \"0.45807895\",\n \"0.4567585\",\n \"0.4559083\",\n \"0.45490068\",\n \"0.4545571\",\n \"0.45451957\",\n \"0.45289043\",\n \"0.45219862\",\n \"0.45218718\",\n \"0.4517237\",\n \"0.45171303\",\n \"0.45155457\",\n \"0.4512571\",\n \"0.4508418\",\n \"0.44733927\",\n \"0.44696665\",\n \"0.4468715\",\n \"0.44625285\",\n \"0.44496748\",\n \"0.4448394\",\n \"0.4446431\",\n \"0.4444349\",\n \"0.44441938\",\n \"0.44403014\",\n \"0.4435095\",\n \"0.44340175\",\n \"0.4433671\",\n \"0.44309166\",\n \"0.4430827\",\n \"0.4424339\",\n \"0.4423634\",\n \"0.4415864\",\n \"0.44147328\",\n \"0.4407466\",\n \"0.44026887\",\n \"0.44003782\",\n \"0.43977904\",\n \"0.43969172\",\n \"0.43946016\",\n \"0.43860486\",\n \"0.43853277\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5895396"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":796,"cells":{"query":{"kind":"string","value":"Print the data in slice iz, row ix of an image to standard out."},"document":{"kind":"string","value":"def print_row(input, ix=0, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice, x = %d row)\" % (iz, ix)\n\tline = []\n\tfor iy in xrange(ny):\n\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\tif ((iy + 1) % 5 == 0): line.append(\"\\n \")\n\tline.append(\"\\n\")\n\tprint \"\".join(line)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def print_image_row(input, iy=0, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice, y = %d col)\" % (iz, iy)\n\tline = []\n\tfor ix in xrange(nx):\n\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\tif ((ix + 1) % 5 == 0): line.append(\"\\n \")\n\tline.append(\"\\n\")\n\tprint \"\".join(line)","def print_image_slice(input, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice)\" % (iz)\n\tline = []\n\tfor iy in xrange(ny-1,-1,-1):\n\t\tline.append(\"Row \")\n\t\tline.append(\"%4i \" % iy)\n\t\tfor ix in xrange(nx):\n\t\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\t\tif ((ix + 1) % 5 == 0): \n\t\t\t\tline.append(\"\\n \")\n\t\t\t\tline.append(\" \")\n\t \tline.append(\"\\n\")\n\t \tif(nx%5 != 0): line.append(\"\\n\")\n\tprint \"\".join(line)","def print_slice(input, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice)\" % (iz)\n\tline = []\n\tfor iy in xrange(ny):\n\t\tline.append(\"Row \")\n\t\tline.append(\"%4i \" % iy)\n\t\tfor ix in xrange(nx):\n\t\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\t\tif ((ix + 1) % 5 == 0): \n\t\t\t\tline.append(\"\\n \")\n\t\t\t\tline.append(\" \")\n\t \tline.append(\"\\n\")\n\t \tif(nx%5 != 0): line.append(\"\\n\")\n\tprint \"\".join(line)","def print_image(data, index):\n image = data[index]\n for line in image:\n print(line)","def print_image_col(input, ix=0, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice, x = %d row)\" % (iz, ix)\n\tline = []\n\tfor iy in xrange(ny):\n\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\tif ((iy + 1) % 5 == 0): line.append(\"\\n \")\n\tline.append(\"\\n\")\n\tprint \"\".join(line)","def print_image(input):\n\timage=get_image(input)\n\tnz = image.get_zsize()\n\tfor iz in xrange(nz): print_slice(input, iz)","def dump_row(input, fname, ix=0, iz=0):\n\tfout = open(fname, \"w\")\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tfout.write(\"# z = %d slice, x = %d row)\\n\" % (iz, ix))\n\tline = []\n\tfor iy in xrange(ny):\n\t\tfout.write(\"%d\\t%12.5g\\n\" % (iy, image.get_value_at(ix,iy,iz)))\n\tfout.close()","def print_img_at_idx(self, idx):\n\n img = self.images[idx]\n print_img(img)","def print_col(input, iy=0, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice, y = %d col)\" % (iz, iy)\n\tline = []\n\tfor ix in xrange(nx):\n\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\tif ((ix + 1) % 5 == 0): line.append(\"\\n \")\n\tline.append(\"\\n\")\n\tprint \"\".join(line)","def print_img_data(img,title='Image'):\n print(f'________ {title} _______')\n if len(image.shape) == 2:\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\n\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0])}\\n')\n elif len(image.shape) == 3:\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\n\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0,0])}\\n')","def print_image_slice_3d(input, num=0,direction=\"z\"):\n\t#print \"print slice at 3 directions\"\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tif(direction==\"x\"):\n\t\t#print \"xxxxx\"\n\t\tix=num\n\t\tprint \"(x = %d slice)\" % (ix)\n\t\tline = []\n\t\tfor iz in xrange(nz-1,-1,-1):\n\t\t\tline.append(\"Z \")\n\t\t\tline.append(\"%4i \" % iz)\n\t\t\tfor iy in xrange(ny):\n\t\t\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\t\t\tif ((iy + 1) % 5 == 0): \n\t\t\t\t\tline.append(\"\\n \")\n\t\t\t\t\tline.append(\" \")\n\t \t\tline.append(\"\\n\")\n\t \t\tif(ny%5 != 0): line.append(\"\\n\")\n\t\tprint \"\".join(line)\n\telif(direction==\"y\"):\n\t\t#print \"yyy\"\n\t\tiy=num\n\t\tprint \"(y = %d slice)\" % (iy)\n\t\tline = []\n\t\tfor iz in xrange(nz-1,-1,-1):\n\t\t\tline.append(\"Z \")\n\t\t\tline.append(\"%4i \" % iz)\n\t\t\tfor ix in xrange(nx):\n\t\t\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\t\t\tif ((ix + 1) % 5 == 0): \n\t\t\t\t\tline.append(\"\\n \")\n\t\t\t\t\tline.append(\" \")\n\t \t\tline.append(\"\\n\")\n\t \t\tif(nx%5 != 0): line.append(\"\\n\")\n\t\tprint \"\".join(line)\n\telse:\n\t\t#print \"zzzz\"\n\t\tiz=num\n\t\tprint \"(z = %d slice)\" % (iz)\n\t\tline = []\n\t\tfor iy in xrange(ny-1,-1,-1):\n\t\t\tline.append(\"Row \")\n\t\t\tline.append(\"%4i \" % iy)\n\t\t\tfor ix in xrange(nx):\n\t\t\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\t\t\tif ((ix + 1) % 5 == 0): \n\t\t\t\t\tline.append(\"\\n \")\n\t\t\t\t\tline.append(\" \")\n\t \t\tline.append(\"\\n\")\n\t \t\tif(nx%5 != 0): line.append(\"\\n\")\n\t\tprint \"\".join(line)","def show(self):\n m = [xo_convert(int(x)) for x in np.nditer(self.arr)]\n print(\"{} | {} | {}\".format(*m[:3]))\n print(\"--+---+--\")\n print(\"{} | {} | {}\".format(*m[3:6]))\n print(\"--+---+--\")\n print(\"{} | {} | {}\".format(*m[6:]))\n print()","def printMat(image):\n for row in range(image.rows):\n print \"[\",\n for col in range(image.cols):\n print cv.mGet(image, row, col),\n print \"]\"\n print \"\"","def print_image(img):\n for k in img:\n print(\"\".join([str(x) for x in k] ))","def print_slice(complex_slice, name = None):\n if name: print(\"***********\" + name + \"**********\")\n slice = complex_slice.clone()\n slice = slice.detach()\n slice = slice.cpu()\n slice = slice.permute(0, 2, 3, 1)\n slice = slice.squeeze()\n slice_image_abs = fastmri.complex_abs(slice)\n plt.imshow(slice_image_abs, cmap = 'gray')\n plt.show()","def pretty_print(image_example):\n print numpy.array_str(image_example, precision=1, max_line_width=142)","def print_real_image(r_img, name = None, batch_size = BATCH_SIZE):\n if name: print(\"***********\" + name + \"**********\")\n to_print = []\n c_img = r_img.clone()\n for i in range(batch_size):\n img = c_img[i,:,:]\n img = torch.squeeze(img)\n img = img.detach()\n img = img.cpu()\n plt.imshow(img, cmap = 'gray')\n plt.show()","def print_image(self, width, data):\n if len(data) % width:\n raise ValueError('Bad image format, length of data must be '\n 'divisible by width.')\n height = len(data) / width\n\n # send line-by-line\n for row in range(height):\n self.send_command('print_bitmap', 1, width)\n self.port.write(data[row*width:(row+1)*width], is_text=False)\n self.port.fed_dots(1)","def disp(self, idx=100): # r_[0:5,1e2:1e9:1e2,-10:0]):\r\n\r\n filenameprefix=self.name_prefix\r\n\r\n def printdatarow(dat, iteration):\r\n \"\"\"print data of iteration i\"\"\"\r\n i = np.where(dat.f[:, 0] == iteration)[0][0]\r\n j = np.where(dat.std[:, 0] == iteration)[0][0]\r\n print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) +\r\n ' %5.1e' % (dat.f[i,3]) +\r\n ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:]))\r\n\r\n dat = CMADataLogger(filenameprefix).load()\r\n ndata = dat.f.shape[0]\r\n\r\n # map index to iteration number, is difficult if not all iteration numbers exist\r\n # idx = idx[np.where(map(lambda x: x in dat.f[:,0], idx))[0]] # TODO: takes pretty long\r\n # otherwise:\r\n if idx is None:\r\n idx = 100\r\n if np.isscalar(idx):\r\n # idx = np.arange(0, ndata, idx)\r\n if idx:\r\n idx = np.r_[0, 1, idx:ndata-3:idx, -3:0]\r\n else:\r\n idx = np.r_[0, 1, -3:0]\r\n\r\n idx = array(idx)\r\n idx = idx[idx<ndata]\r\n idx = idx[-idx<=ndata]\r\n iters = dat.f[idx, 0]\r\n idxbest = np.argmin(dat.f[:,5])\r\n iterbest = dat.f[idxbest, 0]\r\n\r\n if len(iters) == 1:\r\n printdatarow(dat, iters[0])\r\n else:\r\n self.disp_header()\r\n for i in iters:\r\n printdatarow(dat, i)\r\n self.disp_header()\r\n printdatarow(dat, iterbest)\r\n sys.stdout.flush()","def disp(self, idx=100): # r_[0:5,1e2:1e9:1e2,-10:0]):\r\n\r\n filenameprefix=self.name_prefix\r\n\r\n def printdatarow(dat, iteration):\r\n \"\"\"print data of iteration i\"\"\"\r\n i = np.where(dat.f[:, 0] == iteration)[0][0]\r\n j = np.where(dat.std[:, 0] == iteration)[0][0]\r\n print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) +\r\n ' %5.1e' % (dat.f[i,3]) +\r\n ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:]))\r\n\r\n dat = CMADataLogger(filenameprefix).load()\r\n ndata = dat.f.shape[0]\r\n\r\n # map index to iteration number, is difficult if not all iteration numbers exist\r\n # idx = idx[np.where(map(lambda x: x in dat.f[:,0], idx))[0]] # TODO: takes pretty long\r\n # otherwise:\r\n if idx is None:\r\n idx = 100\r\n if np.isscalar(idx):\r\n # idx = np.arange(0, ndata, idx)\r\n if idx:\r\n idx = np.r_[0, 1, idx:ndata-3:idx, -3:0]\r\n else:\r\n idx = np.r_[0, 1, -3:0]\r\n\r\n idx = array(idx)\r\n idx = idx[idx<=ndata] # TODO: shouldn't this be \"<\"?\r\n idx = idx[-idx<=ndata]\r\n iters = dat.f[idx, 0]\r\n idxbest = np.argmin(dat.f[:,5])\r\n iterbest = dat.f[idxbest, 0]\r\n if len(iters) == 1:\r\n printdatarow(dat, iters[0])\r\n else:\r\n self.disp_header()\r\n for i in iters:\r\n printdatarow(dat, i)\r\n self.disp_header()\r\n printdatarow(dat, iterbest)\r\n sys.stdout.flush()","def printImage(imageObject):\n # TODO\n pass","def imshow(image):\n iio.imshow(dtype.im2uint(image))","def explore_data(dataset, start, end, rows_and_columns=False):\r\n for i in range(start,end):\r\n print(dataset[i],end=\"\\n\")","def ndarray_print(nd):\n try:\n x = nd.tolist()\n except (TypeError, NotImplementedError):\n x = nd.tobytes()\n if isinstance(nd, ndarray):\n offset = nd.offset\n flags = nd.flags\n else:\n offset = 'unknown'\n flags = 'unknown'\n print(\n \"ndarray(%s, shape=%s, strides=%s, suboffsets=%s, offset=%s, format='%s', itemsize=%s, flags=%s)\"\n % (x, nd.shape, nd.strides, nd.suboffsets, offset, nd.format, nd.\n itemsize, flags))\n sys.stdout.flush()","def print_image(indiv,name):\n routine = gp.compile(indiv,pset)\n output = gen_beat_output(routine)\n bits = np.array(map(bitlist,output)[0:24000]).transpose()\n plt.style.use('classic')\n plt.imshow(bits,interpolation='nearest',aspect='auto',cmap=plt.get_cmap('Greys'))\n plt.savefig(name+\".png\",dpi=150)","def show_image(image):\n print('-' * (len(image) + 4))\n for line in image:\n print('| ', end='')\n for ch in line:\n char = '#' if ch is True else '.'\n print(char, end='')\n print(' |')\n print('-' * (len(image) + 4))","def print_info(self, i):\n\n im_size = self.image_size(i)\n print 'The path of the image is: {}'.format(self.image_path_at(i))\n print 'width: {}, height: {}'.format(im_size[0], im_size[1])\n \n attr_i = self.gtdb['attr'][i, :]\n print 'The attributes are: {}'.format(','.join([self._classes[i] for i in np.where(attr_i==1)[0]]))","def printdatarow(dat, iteration):\r\n i = np.where(dat.f[:, 0] == iteration)[0][0]\r\n j = np.where(dat.std[:, 0] == iteration)[0][0]\r\n print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) +\r\n ' %5.1e' % (dat.f[i,3]) +\r\n ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:]))","def printdatarow(dat, iteration):\r\n i = np.where(dat.f[:, 0] == iteration)[0][0]\r\n j = np.where(dat.std[:, 0] == iteration)[0][0]\r\n print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) +\r\n ' %5.1e' % (dat.f[i,3]) +\r\n ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:]))","def print_image(self, pil_image):\n bw_image = pil_image.convert('1', dither=Image.FLOYDSTEINBERG)\n width = bw_image.width\n height = bw_image.height\n img_bytes = list(bw_image.getdata())\n self.print_bitmap(width, height, img_bytes)","def show_slice(file_path, x, y, z):\n img = nib.load(file_path)\n img = img.get_fdata()\n print(\"The scan has dimensions {}.\".format(img.shape))\n slice_0 = img[x, :, :]\n slice_1 = img[:, y, :]\n slice_2 = img[:, :, z]\n slices = [slice_0, slice_1, slice_2]\n fig, axes = plt.subplots(1, len(slices), figsize=[12, 4])\n for i, sli in enumerate(slices):\n axes[i].imshow(sli.T, cmap=\"gray\", origin=\"lower\")","def print_array(x, idx=slice(None), message=None, message_prefix=\"SHIM - \",\n file=sys.stdout):\n return set_subtensor(x[idx],\n print(x[idx],\n message=message,\n message_prefix=message_prefix,\n file=file\n )\n )","def ndarray_print(nd):\n try:\n x = nd.tolist()\n except (TypeError, NotImplementedError):\n x = nd.tobytes()\n if isinstance(nd, ndarray):\n offset = nd.offset\n flags = nd.flags\n else:\n offset = 'unknown'\n flags = 'unknown'\n print(\"ndarray(%s, shape=%s, strides=%s, suboffsets=%s, offset=%s, \"\n \"format='%s', itemsize=%s, flags=%s)\" %\n (x, nd.shape, nd.strides, nd.suboffsets, offset,\n nd.format, nd.itemsize, flags))\n sys.stdout.flush()","def display_data():\n data = read_data_from_file()\n data_size = len(data)\n print('Data size - ' + str(data_size))\n\n\n for i in range(data_size-1, 0, -1):\n image = data[i]\n cv2.imshow('window', image[0])\n print(str(image[1]))\n cv2.waitKey(50)","def collatz_print(w, i, j, v):\n\tw.write(str(i) + \" \" + str(j) + \" \" + str(v) + \"\\n\")","def _showdata(self, prec=4):\n print('nh {0:d} nslices {1:d} nbl {2:d} ncp {3:d} nca {4:d} '.format(\n self.nh, self.nslices, self.nbl, self.ncp, self.nca), end=\"\")\n print(\"observables in np arrays with {:d} rows\".format(self.nslices))\n\n if len(self.observables) == 4:\n print('nca', self.nca)\n else:\n print()\n np.set_printoptions(precision=prec)\n\n print(self.fp.shape, \"fp (degrees, but stored internally in radians):\\n\",\n self.fp*self.degree, \"\\n\")\n print(self.fa.shape, \"fa:\\n\", self.fa, \"\\n\")\n\n print(self.cp.shape, \"cp (degrees, but stored internally in radians):\\n\",\n self.cp*self.degree, \"\\n\")\n if len(self.observables) == 4:\n print(self.ca.shape, \"ca:\\n\", self.ca, \"\\n\")\n # print(self.info4oif_dict)\n\n print(\"hole centers array shape:\", self.ctrs.shape)\n\n print(len(self.bholes), \"baseline hole indices\\n\", self.bholes)\n print(self.bls.shape, \"baselines:\\n\", self.bls)\n\n print(self.tholes.shape, \"triple hole indices:\\n\", self.tholes)\n print(self.tuv.shape, \"triple uv vectors:\\n\", self.tuv)\n\n print(self.qholes.shape, \"quad hole indices:\\n\", self.qholes)\n print(self.quvw.shape, \"quad uvw vectors:\\n\", self.quvw)","def _print_image(self, line, size):\n i = 0\n cont = 0\n\n self._write(self.__class__.__imageSize['1x1'])\n buffer = bytearray([int((size[0] / size[1]) / 8), 0, size[1], 0])\n\n self._write(buffer)\n buffer = bytearray()\n\n while i < len(line):\n hex_string = int(line[i:i + 8], 2)\n buffer.append(hex_string)\n i += 8\n cont += 1\n if cont % 4 == 0:\n self._write(buffer)\n buffer = bytearray()\n cont = 0","def print_row(row,writer,x):\n sys.stdout.write(unichr(0x2503))\n for n in xrange(row.shape[0]-1):\n writer(row[n],Width,(x,n))\n sys.stdout.write(unichr(0x2502))\n if row.shape[0] > 0:\n writer(row[-1],Width,(x,row.shape[0]-1))\n sys.stdout.write(unichr(0x2503) + '\\n')","def print(self):\n self.__print_local(self.dataset, 0)","def print_wrapped(data, ncols=3):\r\n nrows = len(data)\r\n labels = data.index\r\n n_split_rows = int(np.ceil(nrows / ncols))\r\n for r in range(0, nrows, ncols):\r\n for c in range(ncols):\r\n try:\r\n numstr = '{}'.format(data[r + c])\r\n tabs = [' '] * (20 - len(labels[r + c]) - len(numstr))\r\n print(labels[r + c] + \"\".join(tabs) + numstr, end='\\t')\r\n except:\r\n pass\r\n print()","def display(self):\n for row in self.tile_rows:\n print(row)","def display(self, image):\n assert(image.mode == '1')\n assert(image.size[0] == self.width)\n assert(image.size[1] == self.height)\n\n page = 0xB0\n pix = list(image.getdata())\n step = self.width * 8\n for y in xrange(0, self.pages * step, step):\n\n # move to given page, then reset the column address\n self.command(page, 0x02, 0x10)\n page += 1\n\n buf = []\n for x in xrange(self.width):\n byte = 0\n for n in xrange(0, step, self.width):\n byte |= (pix[x + y + n] & 0x01) << 8\n byte >>= 1\n\n buf.append(byte)\n\n self.data(buf)","def display(self, image):\n assert(image.mode == '1')\n assert(image.size[0] == self.width)\n assert(image.size[1] == self.height)\n\n page = 0xB0\n pix = list(image.getdata())\n step = self.width * 8\n for y in xrange(0, self.pages * step, step):\n\n # move to given page, then reset the column address\n self.command(page, 0x02, 0x10)\n page += 1\n\n buf = []\n for x in xrange(self.width):\n byte = 0\n for n in xrange(0, step, self.width):\n byte |= (pix[x + y + n] & 0x01) << 8\n byte >>= 1\n\n buf.append(byte)\n\n self.data(buf)","def print_image(img):\r\n # On affiche l'image\r\n plt.figure(figsize=(20, 5))\r\n plt.subplot(1, 2, 1)\r\n plt.imshow(img)\r\n # On affiche l'histogramme\r\n plt.subplot(1, 2, 2)\r\n plt.hist(img.flatten(), bins=range(256))\r\n plt.show()","def display_napari(pos_img):\n global data\n global img_queue\n if pos_img is None:\n return\n # read image and z position\n image = np.reshape(pos_img[2:],(clip[0], clip[1]))\n z_pos = pos_img[1]\n color = pos_img[0]\n\n # write image into correct slice of data and update display\n data[z_pos] = np.squeeze(image)\n layer = viewer.layers[color]\n layer.data = data\n #print(\"updating \", z_pos, color)\n\n img_queue.task_done()","def pprint(self, data):\n self._assert(data)\n data = self._render(data) # make elements ascii\n fmats = self._fmats(data) # get array of padding formats)\n for row in data:\n print(fmats.format(*row))","def info_np(img):\n import numpy as np\n\n print ('Dimensions: ' + str(np.shape(img)))\n print ('Min value: ' + str(np.min(img)))\n print ('Avg value: ' + str(np.average(img)))\n print ('Med value: ' + str(np.median(img)))\n print ('Max value: ' + str(np.max(img)))\n print ('Std dev: ' + str(np.std(img)))\n print ('Sum: ' + str(np.sum(img)))","def display(self):\n for row in range(self.height):\n for col in range(self.width):\n char = '#' if self.pixels[row * self.width + col] else '.'\n print(char, end='')\n print()\n print()","def print_seg_row_col(sp) : \n s, r, c = src_from_rc8x8(sp.peak_y_raw, sp.peak_x_raw)\n print('seg: %d, row: %.1f, col: %.1f' % (s, r, c))","def collatz_print (w, i, j, v) :\n w.write(str(i) + \" \" + str(j) + \" \" + str(v) + \"\\n\")","def show_ipv(data: np.ndarray):\n import ipyvolume as ipv\n return ipv.quickvolshow(data)","def show_np(mat):\n for x in range(15):\n for y in range(15):\n if (x == 7) and (y == 7):\n print(\"\\033[%d;%d;%dm**\\033[0m\" % (0, 33, 41), end='')\n elif mat[x, y, 0] > 0:\n print(\"\\033[%d;%d;%dm \\033[0m\" % (0, 31, 41), end='')\n elif mat[x, y, 1] > 0:\n print(\"\\033[%d;%d;%dm \\033[0m\" % (0, 32, 42), end='')\n else:\n print(\" \", end='')\n print(\"\")","def pixdump( source, start=None, end=None, length=None, width=64, height=None, palette=None ):\n\n for line in pixdump_iter( source, start, end, length, width, height, palette ):\n print( line )","def show(self):\r\n \r\n clear() \r\n print \" \" + \"-\" * self.__width + \" \"\r\n \r\n for row in self.__buffer:\r\n rowData = \"\".join(str(i) for i in row)\r\n print \"|\" + rowData + \"|\"\r\n\r\n print \" \" + \"-\" * self.__width + \" \"\r\n self.clearBuffer()","def _convert_and_print_image(self, im):\n pixLine = \"\"\n imLeft = \"\"\n imRight = \"\"\n switch = 0\n imgSize = [0, 0]\n\n if im.size[0] > 512:\n print (\"WARNING: Image is wider than 512 and could be truncated at print time \")\n if im.size[1] > 255:\n raise ValueError(\"Image Height larger than 255\")\n\n imBorder = self._check_image_size(im.size[0])\n for i in range(imBorder[0]):\n imLeft += \"0\"\n for i in range(imBorder[1]):\n imRight += \"0\"\n\n for y in range(im.size[1]):\n imgSize[1] += 1\n pixLine += imLeft\n imgSize[0] += imBorder[0]\n for x in range(im.size[0]):\n imgSize[0] += 1\n RGB = im.getpixel((x, y))\n imColor = (RGB[0] + RGB[1] + RGB[2])\n imPattern = \"1X0\"\n patternLen = len(imPattern)\n switch = (switch - 1) * (-1)\n for x in range(patternLen):\n if imColor <= (255 * 3 / patternLen * (x + 1)):\n if imPattern[x] == \"X\":\n pixLine += \"%d\" % switch\n else:\n pixLine += imPattern[x]\n break\n elif imColor > (255 * 3 / patternLen * patternLen) and imColor <= (255 * 3):\n pixLine += imPattern[-1]\n break\n pixLine += imRight\n imgSize[0] += imBorder[1]\n\n self._print_image(pixLine, imgSize)","def show(self, data):\n if isinstance(data, (numpy.ndarray, h5py.Dataset)):\n isAtomic = len(data.shape) == 0\n isCurve = len(data.shape) == 1 and numpy.issubdtype(data.dtype, numpy.number)\n isImage = len(data.shape) == 2 and numpy.issubdtype(data.dtype, numpy.number)\n if isAtomic:\n self.showAsString(data)\n elif isCurve:\n self.show1d(data)\n elif isImage:\n self.show2d(data)\n else:\n self.showAsString(data)\n else:\n self.showAsString(data)","def display(self):\n \"\"\" Coordinates for position are x-axis (LR) and y-axis (NS) \"\"\"\n for coordY in range(self.y):\n print()\n for column in range(self.height):\n for coordLR in range(self.x):\n print(\" \", end=\"\")\n for row in range(self.width):\n print(\"#\", end=\"\")\n print()","def info(self):\n\n print(\"pixellisation:\", self.pixel)\n print(\"number of components:\", self.ncomp)\n print(\"number of pixels:\", self.data.shape[:] if self.ncomp == 1 else self.data.shape[1:])\n print(\"nside:\", self.nside)\n print(\"geometry:\", self.geometry)\n print(\"coordinates:\", self.coordinate)","def print_azeltables(inviews, ic):\n for i in range(0, len(inviews)):\n print \" \"\n print \"Az/El for inview %s to %s\" % (inviews[i][0], inviews[i][1])\n azels = ic.compute_azels(inviews[i][0], inviews[i][1], 15)\n for j in range(0, len(azels)):\n print \"At %s, azimuth=%8.2f, elevation=%8.2f\" % \\\n (azels[j][0], azels[j][1], azels[j][2])","def printImage(currentImage):\n\tprint currentImage + ' is set to be printed...'","def __printRow(self, i):\n if i < len(self.__data) and i >= self.__firstShownLine and \\\n i < self.__firstShownLine + self.height - 2:\n text = self.__formatString % self.__data[i]\n self._window.addnstr(i - self.__firstShownLine + 1, 1, text,\n self.width - 2)","def print_bitmap(self, w, h, image):\n\n bitmap = self._pack_bitmap(w, h, image)\n\n row_bytes = (w + 7) // 8 # Round up to next byte boundary\n\n if row_bytes >= 48:\n row_bytes_clipped = 48\n else:\n row_bytes_clipped = row_bytes # 384 pixels max width\n\n # Est. max rows to write at once, assuming 256 byte printer buffer.\n if self._dtr_enabled:\n chunk_height_limit = 255 # Buffer doesn't matter, handshake!\n else:\n chunk_height_limit = 256 // row_bytes_clipped\n if chunk_height_limit > self._max_chunk_height:\n chunk_height_limit = self._max_chunk_height\n elif chunk_height_limit < 1:\n chunk_height_limit = 1\n\n row_start = 0\n i = 0\n while row_start < h:\n # Issue up to chunkHeightLimit rows at a time:\n chunk_height = h - row_start\n if chunk_height > chunk_height_limit:\n chunk_height = chunk_height_limit\n\n self.write(self.ASCII_DC2, '*', chunk_height, row_bytes_clipped)\n\n y = 0\n while y < chunk_height:\n x = 0\n while x < row_bytes_clipped:\n self.timeout_wait()\n self._send_to_printer(int(bitmap[i]))\n x += 1\n i += 1\n\n y += 1\n\n i += row_bytes - row_bytes_clipped\n\n self.timeout_set(chunk_height * self._dot_print_time)\n\n row_start += chunk_height_limit\n\n self._prev_byte = '\\n'","def myprint(dataset, indent=0):\n dont_print = ['Pixel Data', 'File Meta Information Version']\n\n indent_string = \" \" * indent\n next_indent_string = \" \" * (indent + 1)\n\n for data_element in dataset:\n if data_element.VR == \"SQ\": # a sequence\n print(indent_string, data_element.name)\n for sequence_item in data_element.value:\n myprint(sequence_item, indent + 1)\n print(next_indent_string + \"---------\")\n else:\n if data_element.name in dont_print:\n print(\"\"\"<item not printed -- in the \"don't print\" list>\"\"\")\n else:\n repr_value = repr(data_element.value)\n if len(repr_value) > 50:\n repr_value = repr_value[:50] + \"...\"\n print(\"{0:s} {1:s} = {2:s}\".format(indent_string,\n data_element.name,\n repr_value))","def display(self):\n for row0 in range(self.y):\n print()\n for row in range(self.height):\n for column0 in range(self.x):\n print(\" \", end=\"\")\n for column in range(self.width):\n print(\"#\", end=\"\")\n print()","def display(array):\n plt.figure()\n plt.imshow(array)\n plt.show()","def display_cropped_img(i):\n image = PIL.Image.open(testing_img_paths[i])\n image = image.crop(box=(313,99,825,611))\n image = image.resize((256,256))\n display(image)","def view_image(train_dataloader):\n for (x, target) in train_dataloader:\n np.save(\"img.npy\", x)\n print(x.shape)\n exit(0)","def printdata(self,whichstream_,firsti_,lasti_,firstj_,lastj_,firstk_,lastk_,c_,qo_,a_,qc_,bc_,bx_,vartype_,cones_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n res = self.__obj.printdata(whichstream_,firsti_,lasti_,firstj_,lastj_,firstk_,lastk_,c_,qo_,a_,qc_,bc_,bx_,vartype_,cones_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def display(self, image):\n assert(image.mode == '1')\n assert(image.size[0] == self.width)\n assert(image.size[1] == self.height)\n\n self.command(\n const.COLUMNADDR, 0x00, self.width-1, # Column start/end address\n const.PAGEADDR, 0x00, self.pages-1) # Page start/end address\n\n pix = list(image.getdata())\n step = self.width * 8\n buf = []\n for y in xrange(0, self.pages * step, step):\n i = y + self.width-1\n while i >= y:\n byte = 0\n for n in xrange(0, step, self.width):\n byte |= (pix[i + n] & 0x01) << 8\n byte >>= 1\n\n buf.append(byte)\n i -= 1\n\n self.data(buf)","def show(self):\n \n \n \n \n \n \n r = 4\n f, axarr = plt.subplots(r, r, figsize=(8,8))\n counter = 0\n for i in range(r):\n for j in range(r):\n temp = self.x[counter,:]\n counter += 1\n img = self.x[counter,:]\n axarr[i][j].imshow(img)\n #######################################################################\n # #\n # #\n # TODO: YOUR CODE HERE #\n # #\n # #\n #######################################################################","def displayData(indices_to_display = None):\n width, height = 20, 20\n nrows, ncols = 10, 10\n if not indices_to_display:\n indices_to_display = random.sample(range(X.shape[0]), nrows*ncols)\n \n big_picture = np.zeros((height*nrows,width*ncols))\n \n irow, icol = 0, 0\n for idx in indices_to_display:\n if icol == ncols:\n irow += 1\n icol = 0\n iimg = getDatumImg(X[idx])\n big_picture[irow*height:irow*height+iimg.shape[0], icol*width:icol*width+iimg.shape[1]] = iimg\n icol += 1\n fig = plt.figure(figsize=(6,6))\n\n big_picture = (big_picture * 255).astype(np.int8)\n img = Image.fromarray(big_picture, mode='L')\n plt.imshow(img, cmap = cm.Greys)","def display_image(X):\n\n\tim = X.reshape(28, 28)\n\ttemp = plt.imshow(im)\n\tplt.show()","def display(self):\n for x, p in zip(self.xs, self.ps):\n print(x, p)","def my_print(self):\n if self.__size > 0:\n for k in range(self.__position[1]):\n print()\n for i in range(self.__size):\n for j in range(self.__position[0]):\n print(\" \", end='')\n print(\"#\" * self.__size)\n else:\n print()","def print_images(i, df):\n \n images_folder_path = \"dataset/petfinder-adoption-prediction/train_images/\"\n plt.imshow(cv2.cvtColor(cv2.imread(images_folder_path+df.filename[i]), cv2.COLOR_BGR2RGB),);\n plt.axis(\"off\");\n plt.show()","def display(self):\n for b in range(self.y):\n print()\n for i in range(self.height):\n print(\" \" * self.x + \"#\" * self.width)","def imshow(self, depth):\n layer = self.cube[depth]\n img = []\n for i in range(self.height):\n img.append([layer[i][j].value for j in range(self.width)])\n plt.imshow(img, cmap='gray')\n plt.show()","def show_input_to_output(img_ns):\n figure()\n \n sp = subplot(1, 2, 1).imshow(img_ns.img)\n sp.axes.grid(False)\n sp.axes.tick_params(bottom=False, left=False, which='both',labelleft=False,labelbottom=False,length=0)\n title(\"Input Image\", fontsize=10);\n outimg = tiles_to_images(img_ns, img_ns.tile_grid, img_ns.tile_catalog, img_ns.tile_size)\n sp = subplot(1, 2, 2).imshow(outimg.astype(np.uint8));\n sp.axes.tick_params(bottom=False, left=False, which='both',labelleft=False,labelbottom=False,length=0)\n title(\"Output Image From Tiles\", fontsize=10);\n sp.axes.grid(False)\n #print(outimg.astype(np.uint8))\n #print(img_ns)\n plt.savefig(img_ns.output_filename + \"_input_to_output.pdf\", bbox_inches=\"tight\")\n plt.close()","def display(self):\n width = 1 + max(len(self.values[s]) for s in self.boxes)\n line = 'x'.join(['-'*(width*3)]*3)\n for r in self.rows:\n print(''.join(self.values[r+c].center(width)+('|' if c in '36' else '')\n for c in self.cols))\n if r in 'CF': print(line)\n print","def display(self):\n result = []\n horizontal_bounds, vertical_bounds = self.get_bounds()\n min_x, max_x = horizontal_bounds\n min_y, max_y = vertical_bounds\n\n # xrange is inclusive in start and exclusive in end, IE [s..e)\n # so we add +1 offset to be safe\n for y in xrange(max_y, min_y - 1, -1):\n # since we have three rows and we're still relying on print,\n # displaying gets a bit dirty\n # will get cleaner once we move to something like HTML\n row_tiles = [self.tile(x, y) for x in xrange(min_x, max_x + 1)]\n\n # now we have to print each of the three rows together.\n # zip to aggregate each of the top, middle, bottom rows\n row_lines = zip(*[str(tile).split(\"\\n\") for tile in row_tiles])\n for line in row_lines:\n result.append(\"\".join(line))\n\n return \"\\n\".join(result)","def print_stats(cars, notcars):\n print(\"Number of car samples: {0}\".format(len(cars)))\n print(\"Number of non car samples: {0}\".format(len(notcars)))\n img = cv2.imread(cars[0])\n print(\"Image shape: {0}x{1}\".format(img.shape[0], img.shape[1]))\n print(\"Image datatype: {}\".format(img.dtype))","def display(self):\n for i in range(self.y):\n print()\n for i in range(self.height):\n for k in range(self.x):\n print(' ', end='')\n for j in range(self.width):\n print('#', end='')\n print()","def print_image_info(input_image):\n print()\n print(\"Basic Information on image: {}\".format(input_image.filename))\n print(\"Format: {}\".format(input_image.format))\n print(\"Mode: {}\".format(input_image.mode))\n print(\"Size: {}\".format(input_image.size))\n print(\"Width: {}\".format(input_image.width))\n print(\"Height: {}\".format(input_image.height))\n print(\"Palette: {}\".format(input_image.palette))\n print()","def show_image(self, pic, prediction=None):\n digitmap = {\n 0: [(0,0), (1,0), (2,0), (3,0), (4,0), (0,1), (4,1), (0,2), (1,2), (2,2), (3,2), (4,2)],\n 1: [(0,2), (1,2), (2,2), (3,2), (4,2)],\n 2: [(0,0), (0,1), (0,2), (1,2), (2,0), (2,1), (2,2), (3,0), (4,0), (4,1), (4,2)],\n 3: [(0,0), (0,1), (0,2), (1,2), (2,0), (2,1), (2,2), (3,2), (4,0), (4,1), (4,2)],\n 4: [(0,0), (0,2), (1,0), (1,2), (2,0), (2,1), (2,2), (3,2), (4,2)],\n 5: [(0,0), (0,1), (0,2), (1,0), (2,0), (2,1), (2,2), (3,2), (4,0), (4,1), (4,2)],\n 6: [(0,0), (0,1), (0,2), (1,0), (2,0), (2,1), (2,2), (3,0), (3,2), (4,0), (4,1), (4,2)],\n 7: [(0,0), (0,1), (0,2), (1,2), (2,2), (3,2), (4,2)],\n 8: [(0,0), (1,0), (2,0), (3,0), (4,0), (0,1), (4,1), (0,2), (1,2), (2,2), (3,2), (4,2), (2,1)],\n 9: [(0,0), (0,1), (0,2), (1,0), (1,2), (2,0), (2,1), (2,2), (3,2), (4,0), (4,1), (4,2)]\n }\n\n pic = pic.reshape((28,28)).copy()\n if prediction is not None:\n for pos in digitmap[prediction]:\n pic[pos]=255\n plt.imshow(pic, cmap='gray_r')","def print_vector(self):\n print self.x, self.y, self.z","def display_mask(i):\n ### To display binary masks, comment the folowing line\n # mask = np.argmax(testing_preds[i], axis=-1)\n ### To display probability maps, comment the folowing line\n mask = testing_preds[i,:,:,-1]\n mask = np.expand_dims(mask, axis=-1)\n img = PIL.ImageOps.autocontrast(keras.preprocessing.image.array_to_img(mask))\n display(img)","def printData (data):\n print(str(len(data)) + '\\t' + str(data))","def print_data(place):\n raise NotImplementedError","def print(self):\n print(\" 0 1 2 3 4 5 6 7 8 \")\n print(\" -------------------------\")\n for x in range(0, 9):\n print(f\"{x} | \", end=\"\")\n for y in range(0, 9):\n if self.field[x][y] == -1:\n print(\"* \", end=\"\")\n else:\n print(f\"{self.field[x][y]} \", end=\"\")\n if y % 3 == 2:\n print(\"| \", end=\"\")\n print(\"\")\n if x % 3 == 2:\n print(\" -------------------------\")","def show_all(img, overlay=None, axis='z'):\n xlen, ylen, zlen = img.GetSize()\n all_images = []\n all_overlays = []\n if axis == 'z':\n all_images = [img[:, :, z] for z in xrange(zlen)]\n if overlay:\n all_overlays = [overlay[:, :, z] for z in xrange(zlen)]\n elif axis == 'y':\n all_images = [img[:, y, :] for y in xrange(ylen)]\n if overlay:\n all_overlays = [overlay[:, y, :] for y in xrange(ylen)]\n elif axis == 'x':\n all_images = [img[x, :, :] for x in xrange(xlen)]\n if overlay:\n all_overlays = [overlay[x, :, :] for x in xrange(xlen)]\n else:\n raise Exception('invalid axis')\n\n for i, image in enumerate(all_images):\n if overlay:\n show_one(sitk.LabelOverlay(image, all_overlays[i]))\n else:\n show_one(image)\n plt.show()","def print(self):\n # IMPLEMENT ME\n for i in range(self.height):\n for j in range(self.width):\n print(self.board[i][j], end=\" \")\n print()\n print()","def my_print(self):\n if self.__size is not 0:\n for ite in range(self.__position[1]):\n print()\n for ite in range(self.__size):\n print(\" \" * self.__position[0], end=\"\")\n print(\"#\" * self.size)\n else:\n print()","def printDataRange(matrix):\n print(\"Samples\\tMin\\tMax\\tMedian\\t10th\\t90th\")\n for i, sample in enumerate(matrix.matrix.sample_labels):\n start = matrix.matrix.sample_boundaries[i]\n end = matrix.matrix.sample_boundaries[i + 1]\n sample_matrix = matrix.matrix.matrix[..., start:end]\n print(\"{0}\\t{1}\\t{2}\\t{3}\\t{4}\\t{5}\".format(sample, np.amin(sample_matrix),\n np.amax(sample_matrix),\n np.ma.median(sample_matrix),\n np.percentile(sample_matrix, 10),\n np.percentile(sample_matrix, 90)))","def print_cell_information(obj_ase_cell):\n # print the lattice vectors\n print('a1=',obj_ase_cell.cell[0,:])\n print('a2=',obj_ase_cell.cell[1,:])\n print('a3=',obj_ase_cell.cell[2,:])\n for i,a in enumerate(obj_ase_cell):\n print(i,a.symbol,a.position)","def print(self):\n for row in self.board:\n print(row)","def dumpResults(x,y,lon,lat):\n for i in range(0,len(x)):\n print(x[i],y[i],\"lonlat\",lon[i],lat[i])\n return","def display(self):\n for i in range(self.__y):\n print()\n for i in range(self.__height):\n print(\" \" * self.__x + \"#\" * self.__width)","def display(self):\n width = self.width\n height = self.height\n x = self.x\n y = self.y\n for d_y in range(y):\n print()\n for h in range(height):\n if x != 0:\n print(\" \" * x, end=\"\")\n print(\"#\" * width)","def display(self):\n for i in range(0, len(self.top_row)):\n self.top_row[i].display()\n for i in range(0, len(self.bottom_row)):\n self.bottom_row[i].display()\n for i in range(0, len(self.left_col)):\n self.left_col[i].display()\n for i in range(0, len(self.right_col)):\n self.right_col[i].display()","def showTensorImg(ts, title):\n img = np.transpose(ts, (1, 2, 0))\n showImg(img, title)\n return"],"string":"[\n \"def print_image_row(input, iy=0, iz=0):\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tprint \\\"(z = %d slice, y = %d col)\\\" % (iz, iy)\\n\\tline = []\\n\\tfor ix in xrange(nx):\\n\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\tif ((ix + 1) % 5 == 0): line.append(\\\"\\\\n \\\")\\n\\tline.append(\\\"\\\\n\\\")\\n\\tprint \\\"\\\".join(line)\",\n \"def print_image_slice(input, iz=0):\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tprint \\\"(z = %d slice)\\\" % (iz)\\n\\tline = []\\n\\tfor iy in xrange(ny-1,-1,-1):\\n\\t\\tline.append(\\\"Row \\\")\\n\\t\\tline.append(\\\"%4i \\\" % iy)\\n\\t\\tfor ix in xrange(nx):\\n\\t\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\t\\tif ((ix + 1) % 5 == 0): \\n\\t\\t\\t\\tline.append(\\\"\\\\n \\\")\\n\\t\\t\\t\\tline.append(\\\" \\\")\\n\\t \\tline.append(\\\"\\\\n\\\")\\n\\t \\tif(nx%5 != 0): line.append(\\\"\\\\n\\\")\\n\\tprint \\\"\\\".join(line)\",\n \"def print_slice(input, iz=0):\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tprint \\\"(z = %d slice)\\\" % (iz)\\n\\tline = []\\n\\tfor iy in xrange(ny):\\n\\t\\tline.append(\\\"Row \\\")\\n\\t\\tline.append(\\\"%4i \\\" % iy)\\n\\t\\tfor ix in xrange(nx):\\n\\t\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\t\\tif ((ix + 1) % 5 == 0): \\n\\t\\t\\t\\tline.append(\\\"\\\\n \\\")\\n\\t\\t\\t\\tline.append(\\\" \\\")\\n\\t \\tline.append(\\\"\\\\n\\\")\\n\\t \\tif(nx%5 != 0): line.append(\\\"\\\\n\\\")\\n\\tprint \\\"\\\".join(line)\",\n \"def print_image(data, index):\\n image = data[index]\\n for line in image:\\n print(line)\",\n \"def print_image_col(input, ix=0, iz=0):\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tprint \\\"(z = %d slice, x = %d row)\\\" % (iz, ix)\\n\\tline = []\\n\\tfor iy in xrange(ny):\\n\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\tif ((iy + 1) % 5 == 0): line.append(\\\"\\\\n \\\")\\n\\tline.append(\\\"\\\\n\\\")\\n\\tprint \\\"\\\".join(line)\",\n \"def print_image(input):\\n\\timage=get_image(input)\\n\\tnz = image.get_zsize()\\n\\tfor iz in xrange(nz): print_slice(input, iz)\",\n \"def dump_row(input, fname, ix=0, iz=0):\\n\\tfout = open(fname, \\\"w\\\")\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tfout.write(\\\"# z = %d slice, x = %d row)\\\\n\\\" % (iz, ix))\\n\\tline = []\\n\\tfor iy in xrange(ny):\\n\\t\\tfout.write(\\\"%d\\\\t%12.5g\\\\n\\\" % (iy, image.get_value_at(ix,iy,iz)))\\n\\tfout.close()\",\n \"def print_img_at_idx(self, idx):\\n\\n img = self.images[idx]\\n print_img(img)\",\n \"def print_col(input, iy=0, iz=0):\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tprint \\\"(z = %d slice, y = %d col)\\\" % (iz, iy)\\n\\tline = []\\n\\tfor ix in xrange(nx):\\n\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\tif ((ix + 1) % 5 == 0): line.append(\\\"\\\\n \\\")\\n\\tline.append(\\\"\\\\n\\\")\\n\\tprint \\\"\\\".join(line)\",\n \"def print_img_data(img,title='Image'):\\n print(f'________ {title} _______')\\n if len(image.shape) == 2:\\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\\\\n\\\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0])}\\\\n')\\n elif len(image.shape) == 3:\\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\\\\n\\\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0,0])}\\\\n')\",\n \"def print_image_slice_3d(input, num=0,direction=\\\"z\\\"):\\n\\t#print \\\"print slice at 3 directions\\\"\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tif(direction==\\\"x\\\"):\\n\\t\\t#print \\\"xxxxx\\\"\\n\\t\\tix=num\\n\\t\\tprint \\\"(x = %d slice)\\\" % (ix)\\n\\t\\tline = []\\n\\t\\tfor iz in xrange(nz-1,-1,-1):\\n\\t\\t\\tline.append(\\\"Z \\\")\\n\\t\\t\\tline.append(\\\"%4i \\\" % iz)\\n\\t\\t\\tfor iy in xrange(ny):\\n\\t\\t\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\t\\t\\tif ((iy + 1) % 5 == 0): \\n\\t\\t\\t\\t\\tline.append(\\\"\\\\n \\\")\\n\\t\\t\\t\\t\\tline.append(\\\" \\\")\\n\\t \\t\\tline.append(\\\"\\\\n\\\")\\n\\t \\t\\tif(ny%5 != 0): line.append(\\\"\\\\n\\\")\\n\\t\\tprint \\\"\\\".join(line)\\n\\telif(direction==\\\"y\\\"):\\n\\t\\t#print \\\"yyy\\\"\\n\\t\\tiy=num\\n\\t\\tprint \\\"(y = %d slice)\\\" % (iy)\\n\\t\\tline = []\\n\\t\\tfor iz in xrange(nz-1,-1,-1):\\n\\t\\t\\tline.append(\\\"Z \\\")\\n\\t\\t\\tline.append(\\\"%4i \\\" % iz)\\n\\t\\t\\tfor ix in xrange(nx):\\n\\t\\t\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\t\\t\\tif ((ix + 1) % 5 == 0): \\n\\t\\t\\t\\t\\tline.append(\\\"\\\\n \\\")\\n\\t\\t\\t\\t\\tline.append(\\\" \\\")\\n\\t \\t\\tline.append(\\\"\\\\n\\\")\\n\\t \\t\\tif(nx%5 != 0): line.append(\\\"\\\\n\\\")\\n\\t\\tprint \\\"\\\".join(line)\\n\\telse:\\n\\t\\t#print \\\"zzzz\\\"\\n\\t\\tiz=num\\n\\t\\tprint \\\"(z = %d slice)\\\" % (iz)\\n\\t\\tline = []\\n\\t\\tfor iy in xrange(ny-1,-1,-1):\\n\\t\\t\\tline.append(\\\"Row \\\")\\n\\t\\t\\tline.append(\\\"%4i \\\" % iy)\\n\\t\\t\\tfor ix in xrange(nx):\\n\\t\\t\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\t\\t\\tif ((ix + 1) % 5 == 0): \\n\\t\\t\\t\\t\\tline.append(\\\"\\\\n \\\")\\n\\t\\t\\t\\t\\tline.append(\\\" \\\")\\n\\t \\t\\tline.append(\\\"\\\\n\\\")\\n\\t \\t\\tif(nx%5 != 0): line.append(\\\"\\\\n\\\")\\n\\t\\tprint \\\"\\\".join(line)\",\n \"def show(self):\\n m = [xo_convert(int(x)) for x in np.nditer(self.arr)]\\n print(\\\"{} | {} | {}\\\".format(*m[:3]))\\n print(\\\"--+---+--\\\")\\n print(\\\"{} | {} | {}\\\".format(*m[3:6]))\\n print(\\\"--+---+--\\\")\\n print(\\\"{} | {} | {}\\\".format(*m[6:]))\\n print()\",\n \"def printMat(image):\\n for row in range(image.rows):\\n print \\\"[\\\",\\n for col in range(image.cols):\\n print cv.mGet(image, row, col),\\n print \\\"]\\\"\\n print \\\"\\\"\",\n \"def print_image(img):\\n for k in img:\\n print(\\\"\\\".join([str(x) for x in k] ))\",\n \"def print_slice(complex_slice, name = None):\\n if name: print(\\\"***********\\\" + name + \\\"**********\\\")\\n slice = complex_slice.clone()\\n slice = slice.detach()\\n slice = slice.cpu()\\n slice = slice.permute(0, 2, 3, 1)\\n slice = slice.squeeze()\\n slice_image_abs = fastmri.complex_abs(slice)\\n plt.imshow(slice_image_abs, cmap = 'gray')\\n plt.show()\",\n \"def pretty_print(image_example):\\n print numpy.array_str(image_example, precision=1, max_line_width=142)\",\n \"def print_real_image(r_img, name = None, batch_size = BATCH_SIZE):\\n if name: print(\\\"***********\\\" + name + \\\"**********\\\")\\n to_print = []\\n c_img = r_img.clone()\\n for i in range(batch_size):\\n img = c_img[i,:,:]\\n img = torch.squeeze(img)\\n img = img.detach()\\n img = img.cpu()\\n plt.imshow(img, cmap = 'gray')\\n plt.show()\",\n \"def print_image(self, width, data):\\n if len(data) % width:\\n raise ValueError('Bad image format, length of data must be '\\n 'divisible by width.')\\n height = len(data) / width\\n\\n # send line-by-line\\n for row in range(height):\\n self.send_command('print_bitmap', 1, width)\\n self.port.write(data[row*width:(row+1)*width], is_text=False)\\n self.port.fed_dots(1)\",\n \"def disp(self, idx=100): # r_[0:5,1e2:1e9:1e2,-10:0]):\\r\\n\\r\\n filenameprefix=self.name_prefix\\r\\n\\r\\n def printdatarow(dat, iteration):\\r\\n \\\"\\\"\\\"print data of iteration i\\\"\\\"\\\"\\r\\n i = np.where(dat.f[:, 0] == iteration)[0][0]\\r\\n j = np.where(dat.std[:, 0] == iteration)[0][0]\\r\\n print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) +\\r\\n ' %5.1e' % (dat.f[i,3]) +\\r\\n ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:]))\\r\\n\\r\\n dat = CMADataLogger(filenameprefix).load()\\r\\n ndata = dat.f.shape[0]\\r\\n\\r\\n # map index to iteration number, is difficult if not all iteration numbers exist\\r\\n # idx = idx[np.where(map(lambda x: x in dat.f[:,0], idx))[0]] # TODO: takes pretty long\\r\\n # otherwise:\\r\\n if idx is None:\\r\\n idx = 100\\r\\n if np.isscalar(idx):\\r\\n # idx = np.arange(0, ndata, idx)\\r\\n if idx:\\r\\n idx = np.r_[0, 1, idx:ndata-3:idx, -3:0]\\r\\n else:\\r\\n idx = np.r_[0, 1, -3:0]\\r\\n\\r\\n idx = array(idx)\\r\\n idx = idx[idx<ndata]\\r\\n idx = idx[-idx<=ndata]\\r\\n iters = dat.f[idx, 0]\\r\\n idxbest = np.argmin(dat.f[:,5])\\r\\n iterbest = dat.f[idxbest, 0]\\r\\n\\r\\n if len(iters) == 1:\\r\\n printdatarow(dat, iters[0])\\r\\n else:\\r\\n self.disp_header()\\r\\n for i in iters:\\r\\n printdatarow(dat, i)\\r\\n self.disp_header()\\r\\n printdatarow(dat, iterbest)\\r\\n sys.stdout.flush()\",\n \"def disp(self, idx=100): # r_[0:5,1e2:1e9:1e2,-10:0]):\\r\\n\\r\\n filenameprefix=self.name_prefix\\r\\n\\r\\n def printdatarow(dat, iteration):\\r\\n \\\"\\\"\\\"print data of iteration i\\\"\\\"\\\"\\r\\n i = np.where(dat.f[:, 0] == iteration)[0][0]\\r\\n j = np.where(dat.std[:, 0] == iteration)[0][0]\\r\\n print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) +\\r\\n ' %5.1e' % (dat.f[i,3]) +\\r\\n ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:]))\\r\\n\\r\\n dat = CMADataLogger(filenameprefix).load()\\r\\n ndata = dat.f.shape[0]\\r\\n\\r\\n # map index to iteration number, is difficult if not all iteration numbers exist\\r\\n # idx = idx[np.where(map(lambda x: x in dat.f[:,0], idx))[0]] # TODO: takes pretty long\\r\\n # otherwise:\\r\\n if idx is None:\\r\\n idx = 100\\r\\n if np.isscalar(idx):\\r\\n # idx = np.arange(0, ndata, idx)\\r\\n if idx:\\r\\n idx = np.r_[0, 1, idx:ndata-3:idx, -3:0]\\r\\n else:\\r\\n idx = np.r_[0, 1, -3:0]\\r\\n\\r\\n idx = array(idx)\\r\\n idx = idx[idx<=ndata] # TODO: shouldn't this be \\\"<\\\"?\\r\\n idx = idx[-idx<=ndata]\\r\\n iters = dat.f[idx, 0]\\r\\n idxbest = np.argmin(dat.f[:,5])\\r\\n iterbest = dat.f[idxbest, 0]\\r\\n if len(iters) == 1:\\r\\n printdatarow(dat, iters[0])\\r\\n else:\\r\\n self.disp_header()\\r\\n for i in iters:\\r\\n printdatarow(dat, i)\\r\\n self.disp_header()\\r\\n printdatarow(dat, iterbest)\\r\\n sys.stdout.flush()\",\n \"def printImage(imageObject):\\n # TODO\\n pass\",\n \"def imshow(image):\\n iio.imshow(dtype.im2uint(image))\",\n \"def explore_data(dataset, start, end, rows_and_columns=False):\\r\\n for i in range(start,end):\\r\\n print(dataset[i],end=\\\"\\\\n\\\")\",\n \"def ndarray_print(nd):\\n try:\\n x = nd.tolist()\\n except (TypeError, NotImplementedError):\\n x = nd.tobytes()\\n if isinstance(nd, ndarray):\\n offset = nd.offset\\n flags = nd.flags\\n else:\\n offset = 'unknown'\\n flags = 'unknown'\\n print(\\n \\\"ndarray(%s, shape=%s, strides=%s, suboffsets=%s, offset=%s, format='%s', itemsize=%s, flags=%s)\\\"\\n % (x, nd.shape, nd.strides, nd.suboffsets, offset, nd.format, nd.\\n itemsize, flags))\\n sys.stdout.flush()\",\n \"def print_image(indiv,name):\\n routine = gp.compile(indiv,pset)\\n output = gen_beat_output(routine)\\n bits = np.array(map(bitlist,output)[0:24000]).transpose()\\n plt.style.use('classic')\\n plt.imshow(bits,interpolation='nearest',aspect='auto',cmap=plt.get_cmap('Greys'))\\n plt.savefig(name+\\\".png\\\",dpi=150)\",\n \"def show_image(image):\\n print('-' * (len(image) + 4))\\n for line in image:\\n print('| ', end='')\\n for ch in line:\\n char = '#' if ch is True else '.'\\n print(char, end='')\\n print(' |')\\n print('-' * (len(image) + 4))\",\n \"def print_info(self, i):\\n\\n im_size = self.image_size(i)\\n print 'The path of the image is: {}'.format(self.image_path_at(i))\\n print 'width: {}, height: {}'.format(im_size[0], im_size[1])\\n \\n attr_i = self.gtdb['attr'][i, :]\\n print 'The attributes are: {}'.format(','.join([self._classes[i] for i in np.where(attr_i==1)[0]]))\",\n \"def printdatarow(dat, iteration):\\r\\n i = np.where(dat.f[:, 0] == iteration)[0][0]\\r\\n j = np.where(dat.std[:, 0] == iteration)[0][0]\\r\\n print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) +\\r\\n ' %5.1e' % (dat.f[i,3]) +\\r\\n ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:]))\",\n \"def printdatarow(dat, iteration):\\r\\n i = np.where(dat.f[:, 0] == iteration)[0][0]\\r\\n j = np.where(dat.std[:, 0] == iteration)[0][0]\\r\\n print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) +\\r\\n ' %5.1e' % (dat.f[i,3]) +\\r\\n ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:]))\",\n \"def print_image(self, pil_image):\\n bw_image = pil_image.convert('1', dither=Image.FLOYDSTEINBERG)\\n width = bw_image.width\\n height = bw_image.height\\n img_bytes = list(bw_image.getdata())\\n self.print_bitmap(width, height, img_bytes)\",\n \"def show_slice(file_path, x, y, z):\\n img = nib.load(file_path)\\n img = img.get_fdata()\\n print(\\\"The scan has dimensions {}.\\\".format(img.shape))\\n slice_0 = img[x, :, :]\\n slice_1 = img[:, y, :]\\n slice_2 = img[:, :, z]\\n slices = [slice_0, slice_1, slice_2]\\n fig, axes = plt.subplots(1, len(slices), figsize=[12, 4])\\n for i, sli in enumerate(slices):\\n axes[i].imshow(sli.T, cmap=\\\"gray\\\", origin=\\\"lower\\\")\",\n \"def print_array(x, idx=slice(None), message=None, message_prefix=\\\"SHIM - \\\",\\n file=sys.stdout):\\n return set_subtensor(x[idx],\\n print(x[idx],\\n message=message,\\n message_prefix=message_prefix,\\n file=file\\n )\\n )\",\n \"def ndarray_print(nd):\\n try:\\n x = nd.tolist()\\n except (TypeError, NotImplementedError):\\n x = nd.tobytes()\\n if isinstance(nd, ndarray):\\n offset = nd.offset\\n flags = nd.flags\\n else:\\n offset = 'unknown'\\n flags = 'unknown'\\n print(\\\"ndarray(%s, shape=%s, strides=%s, suboffsets=%s, offset=%s, \\\"\\n \\\"format='%s', itemsize=%s, flags=%s)\\\" %\\n (x, nd.shape, nd.strides, nd.suboffsets, offset,\\n nd.format, nd.itemsize, flags))\\n sys.stdout.flush()\",\n \"def display_data():\\n data = read_data_from_file()\\n data_size = len(data)\\n print('Data size - ' + str(data_size))\\n\\n\\n for i in range(data_size-1, 0, -1):\\n image = data[i]\\n cv2.imshow('window', image[0])\\n print(str(image[1]))\\n cv2.waitKey(50)\",\n \"def collatz_print(w, i, j, v):\\n\\tw.write(str(i) + \\\" \\\" + str(j) + \\\" \\\" + str(v) + \\\"\\\\n\\\")\",\n \"def _showdata(self, prec=4):\\n print('nh {0:d} nslices {1:d} nbl {2:d} ncp {3:d} nca {4:d} '.format(\\n self.nh, self.nslices, self.nbl, self.ncp, self.nca), end=\\\"\\\")\\n print(\\\"observables in np arrays with {:d} rows\\\".format(self.nslices))\\n\\n if len(self.observables) == 4:\\n print('nca', self.nca)\\n else:\\n print()\\n np.set_printoptions(precision=prec)\\n\\n print(self.fp.shape, \\\"fp (degrees, but stored internally in radians):\\\\n\\\",\\n self.fp*self.degree, \\\"\\\\n\\\")\\n print(self.fa.shape, \\\"fa:\\\\n\\\", self.fa, \\\"\\\\n\\\")\\n\\n print(self.cp.shape, \\\"cp (degrees, but stored internally in radians):\\\\n\\\",\\n self.cp*self.degree, \\\"\\\\n\\\")\\n if len(self.observables) == 4:\\n print(self.ca.shape, \\\"ca:\\\\n\\\", self.ca, \\\"\\\\n\\\")\\n # print(self.info4oif_dict)\\n\\n print(\\\"hole centers array shape:\\\", self.ctrs.shape)\\n\\n print(len(self.bholes), \\\"baseline hole indices\\\\n\\\", self.bholes)\\n print(self.bls.shape, \\\"baselines:\\\\n\\\", self.bls)\\n\\n print(self.tholes.shape, \\\"triple hole indices:\\\\n\\\", self.tholes)\\n print(self.tuv.shape, \\\"triple uv vectors:\\\\n\\\", self.tuv)\\n\\n print(self.qholes.shape, \\\"quad hole indices:\\\\n\\\", self.qholes)\\n print(self.quvw.shape, \\\"quad uvw vectors:\\\\n\\\", self.quvw)\",\n \"def _print_image(self, line, size):\\n i = 0\\n cont = 0\\n\\n self._write(self.__class__.__imageSize['1x1'])\\n buffer = bytearray([int((size[0] / size[1]) / 8), 0, size[1], 0])\\n\\n self._write(buffer)\\n buffer = bytearray()\\n\\n while i < len(line):\\n hex_string = int(line[i:i + 8], 2)\\n buffer.append(hex_string)\\n i += 8\\n cont += 1\\n if cont % 4 == 0:\\n self._write(buffer)\\n buffer = bytearray()\\n cont = 0\",\n \"def print_row(row,writer,x):\\n sys.stdout.write(unichr(0x2503))\\n for n in xrange(row.shape[0]-1):\\n writer(row[n],Width,(x,n))\\n sys.stdout.write(unichr(0x2502))\\n if row.shape[0] > 0:\\n writer(row[-1],Width,(x,row.shape[0]-1))\\n sys.stdout.write(unichr(0x2503) + '\\\\n')\",\n \"def print(self):\\n self.__print_local(self.dataset, 0)\",\n \"def print_wrapped(data, ncols=3):\\r\\n nrows = len(data)\\r\\n labels = data.index\\r\\n n_split_rows = int(np.ceil(nrows / ncols))\\r\\n for r in range(0, nrows, ncols):\\r\\n for c in range(ncols):\\r\\n try:\\r\\n numstr = '{}'.format(data[r + c])\\r\\n tabs = [' '] * (20 - len(labels[r + c]) - len(numstr))\\r\\n print(labels[r + c] + \\\"\\\".join(tabs) + numstr, end='\\\\t')\\r\\n except:\\r\\n pass\\r\\n print()\",\n \"def display(self):\\n for row in self.tile_rows:\\n print(row)\",\n \"def display(self, image):\\n assert(image.mode == '1')\\n assert(image.size[0] == self.width)\\n assert(image.size[1] == self.height)\\n\\n page = 0xB0\\n pix = list(image.getdata())\\n step = self.width * 8\\n for y in xrange(0, self.pages * step, step):\\n\\n # move to given page, then reset the column address\\n self.command(page, 0x02, 0x10)\\n page += 1\\n\\n buf = []\\n for x in xrange(self.width):\\n byte = 0\\n for n in xrange(0, step, self.width):\\n byte |= (pix[x + y + n] & 0x01) << 8\\n byte >>= 1\\n\\n buf.append(byte)\\n\\n self.data(buf)\",\n \"def display(self, image):\\n assert(image.mode == '1')\\n assert(image.size[0] == self.width)\\n assert(image.size[1] == self.height)\\n\\n page = 0xB0\\n pix = list(image.getdata())\\n step = self.width * 8\\n for y in xrange(0, self.pages * step, step):\\n\\n # move to given page, then reset the column address\\n self.command(page, 0x02, 0x10)\\n page += 1\\n\\n buf = []\\n for x in xrange(self.width):\\n byte = 0\\n for n in xrange(0, step, self.width):\\n byte |= (pix[x + y + n] & 0x01) << 8\\n byte >>= 1\\n\\n buf.append(byte)\\n\\n self.data(buf)\",\n \"def print_image(img):\\r\\n # On affiche l'image\\r\\n plt.figure(figsize=(20, 5))\\r\\n plt.subplot(1, 2, 1)\\r\\n plt.imshow(img)\\r\\n # On affiche l'histogramme\\r\\n plt.subplot(1, 2, 2)\\r\\n plt.hist(img.flatten(), bins=range(256))\\r\\n plt.show()\",\n \"def display_napari(pos_img):\\n global data\\n global img_queue\\n if pos_img is None:\\n return\\n # read image and z position\\n image = np.reshape(pos_img[2:],(clip[0], clip[1]))\\n z_pos = pos_img[1]\\n color = pos_img[0]\\n\\n # write image into correct slice of data and update display\\n data[z_pos] = np.squeeze(image)\\n layer = viewer.layers[color]\\n layer.data = data\\n #print(\\\"updating \\\", z_pos, color)\\n\\n img_queue.task_done()\",\n \"def pprint(self, data):\\n self._assert(data)\\n data = self._render(data) # make elements ascii\\n fmats = self._fmats(data) # get array of padding formats)\\n for row in data:\\n print(fmats.format(*row))\",\n \"def info_np(img):\\n import numpy as np\\n\\n print ('Dimensions: ' + str(np.shape(img)))\\n print ('Min value: ' + str(np.min(img)))\\n print ('Avg value: ' + str(np.average(img)))\\n print ('Med value: ' + str(np.median(img)))\\n print ('Max value: ' + str(np.max(img)))\\n print ('Std dev: ' + str(np.std(img)))\\n print ('Sum: ' + str(np.sum(img)))\",\n \"def display(self):\\n for row in range(self.height):\\n for col in range(self.width):\\n char = '#' if self.pixels[row * self.width + col] else '.'\\n print(char, end='')\\n print()\\n print()\",\n \"def print_seg_row_col(sp) : \\n s, r, c = src_from_rc8x8(sp.peak_y_raw, sp.peak_x_raw)\\n print('seg: %d, row: %.1f, col: %.1f' % (s, r, c))\",\n \"def collatz_print (w, i, j, v) :\\n w.write(str(i) + \\\" \\\" + str(j) + \\\" \\\" + str(v) + \\\"\\\\n\\\")\",\n \"def show_ipv(data: np.ndarray):\\n import ipyvolume as ipv\\n return ipv.quickvolshow(data)\",\n \"def show_np(mat):\\n for x in range(15):\\n for y in range(15):\\n if (x == 7) and (y == 7):\\n print(\\\"\\\\033[%d;%d;%dm**\\\\033[0m\\\" % (0, 33, 41), end='')\\n elif mat[x, y, 0] > 0:\\n print(\\\"\\\\033[%d;%d;%dm \\\\033[0m\\\" % (0, 31, 41), end='')\\n elif mat[x, y, 1] > 0:\\n print(\\\"\\\\033[%d;%d;%dm \\\\033[0m\\\" % (0, 32, 42), end='')\\n else:\\n print(\\\" \\\", end='')\\n print(\\\"\\\")\",\n \"def pixdump( source, start=None, end=None, length=None, width=64, height=None, palette=None ):\\n\\n for line in pixdump_iter( source, start, end, length, width, height, palette ):\\n print( line )\",\n \"def show(self):\\r\\n \\r\\n clear() \\r\\n print \\\" \\\" + \\\"-\\\" * self.__width + \\\" \\\"\\r\\n \\r\\n for row in self.__buffer:\\r\\n rowData = \\\"\\\".join(str(i) for i in row)\\r\\n print \\\"|\\\" + rowData + \\\"|\\\"\\r\\n\\r\\n print \\\" \\\" + \\\"-\\\" * self.__width + \\\" \\\"\\r\\n self.clearBuffer()\",\n \"def _convert_and_print_image(self, im):\\n pixLine = \\\"\\\"\\n imLeft = \\\"\\\"\\n imRight = \\\"\\\"\\n switch = 0\\n imgSize = [0, 0]\\n\\n if im.size[0] > 512:\\n print (\\\"WARNING: Image is wider than 512 and could be truncated at print time \\\")\\n if im.size[1] > 255:\\n raise ValueError(\\\"Image Height larger than 255\\\")\\n\\n imBorder = self._check_image_size(im.size[0])\\n for i in range(imBorder[0]):\\n imLeft += \\\"0\\\"\\n for i in range(imBorder[1]):\\n imRight += \\\"0\\\"\\n\\n for y in range(im.size[1]):\\n imgSize[1] += 1\\n pixLine += imLeft\\n imgSize[0] += imBorder[0]\\n for x in range(im.size[0]):\\n imgSize[0] += 1\\n RGB = im.getpixel((x, y))\\n imColor = (RGB[0] + RGB[1] + RGB[2])\\n imPattern = \\\"1X0\\\"\\n patternLen = len(imPattern)\\n switch = (switch - 1) * (-1)\\n for x in range(patternLen):\\n if imColor <= (255 * 3 / patternLen * (x + 1)):\\n if imPattern[x] == \\\"X\\\":\\n pixLine += \\\"%d\\\" % switch\\n else:\\n pixLine += imPattern[x]\\n break\\n elif imColor > (255 * 3 / patternLen * patternLen) and imColor <= (255 * 3):\\n pixLine += imPattern[-1]\\n break\\n pixLine += imRight\\n imgSize[0] += imBorder[1]\\n\\n self._print_image(pixLine, imgSize)\",\n \"def show(self, data):\\n if isinstance(data, (numpy.ndarray, h5py.Dataset)):\\n isAtomic = len(data.shape) == 0\\n isCurve = len(data.shape) == 1 and numpy.issubdtype(data.dtype, numpy.number)\\n isImage = len(data.shape) == 2 and numpy.issubdtype(data.dtype, numpy.number)\\n if isAtomic:\\n self.showAsString(data)\\n elif isCurve:\\n self.show1d(data)\\n elif isImage:\\n self.show2d(data)\\n else:\\n self.showAsString(data)\\n else:\\n self.showAsString(data)\",\n \"def display(self):\\n \\\"\\\"\\\" Coordinates for position are x-axis (LR) and y-axis (NS) \\\"\\\"\\\"\\n for coordY in range(self.y):\\n print()\\n for column in range(self.height):\\n for coordLR in range(self.x):\\n print(\\\" \\\", end=\\\"\\\")\\n for row in range(self.width):\\n print(\\\"#\\\", end=\\\"\\\")\\n print()\",\n \"def info(self):\\n\\n print(\\\"pixellisation:\\\", self.pixel)\\n print(\\\"number of components:\\\", self.ncomp)\\n print(\\\"number of pixels:\\\", self.data.shape[:] if self.ncomp == 1 else self.data.shape[1:])\\n print(\\\"nside:\\\", self.nside)\\n print(\\\"geometry:\\\", self.geometry)\\n print(\\\"coordinates:\\\", self.coordinate)\",\n \"def print_azeltables(inviews, ic):\\n for i in range(0, len(inviews)):\\n print \\\" \\\"\\n print \\\"Az/El for inview %s to %s\\\" % (inviews[i][0], inviews[i][1])\\n azels = ic.compute_azels(inviews[i][0], inviews[i][1], 15)\\n for j in range(0, len(azels)):\\n print \\\"At %s, azimuth=%8.2f, elevation=%8.2f\\\" % \\\\\\n (azels[j][0], azels[j][1], azels[j][2])\",\n \"def printImage(currentImage):\\n\\tprint currentImage + ' is set to be printed...'\",\n \"def __printRow(self, i):\\n if i < len(self.__data) and i >= self.__firstShownLine and \\\\\\n i < self.__firstShownLine + self.height - 2:\\n text = self.__formatString % self.__data[i]\\n self._window.addnstr(i - self.__firstShownLine + 1, 1, text,\\n self.width - 2)\",\n \"def print_bitmap(self, w, h, image):\\n\\n bitmap = self._pack_bitmap(w, h, image)\\n\\n row_bytes = (w + 7) // 8 # Round up to next byte boundary\\n\\n if row_bytes >= 48:\\n row_bytes_clipped = 48\\n else:\\n row_bytes_clipped = row_bytes # 384 pixels max width\\n\\n # Est. max rows to write at once, assuming 256 byte printer buffer.\\n if self._dtr_enabled:\\n chunk_height_limit = 255 # Buffer doesn't matter, handshake!\\n else:\\n chunk_height_limit = 256 // row_bytes_clipped\\n if chunk_height_limit > self._max_chunk_height:\\n chunk_height_limit = self._max_chunk_height\\n elif chunk_height_limit < 1:\\n chunk_height_limit = 1\\n\\n row_start = 0\\n i = 0\\n while row_start < h:\\n # Issue up to chunkHeightLimit rows at a time:\\n chunk_height = h - row_start\\n if chunk_height > chunk_height_limit:\\n chunk_height = chunk_height_limit\\n\\n self.write(self.ASCII_DC2, '*', chunk_height, row_bytes_clipped)\\n\\n y = 0\\n while y < chunk_height:\\n x = 0\\n while x < row_bytes_clipped:\\n self.timeout_wait()\\n self._send_to_printer(int(bitmap[i]))\\n x += 1\\n i += 1\\n\\n y += 1\\n\\n i += row_bytes - row_bytes_clipped\\n\\n self.timeout_set(chunk_height * self._dot_print_time)\\n\\n row_start += chunk_height_limit\\n\\n self._prev_byte = '\\\\n'\",\n \"def myprint(dataset, indent=0):\\n dont_print = ['Pixel Data', 'File Meta Information Version']\\n\\n indent_string = \\\" \\\" * indent\\n next_indent_string = \\\" \\\" * (indent + 1)\\n\\n for data_element in dataset:\\n if data_element.VR == \\\"SQ\\\": # a sequence\\n print(indent_string, data_element.name)\\n for sequence_item in data_element.value:\\n myprint(sequence_item, indent + 1)\\n print(next_indent_string + \\\"---------\\\")\\n else:\\n if data_element.name in dont_print:\\n print(\\\"\\\"\\\"<item not printed -- in the \\\"don't print\\\" list>\\\"\\\"\\\")\\n else:\\n repr_value = repr(data_element.value)\\n if len(repr_value) > 50:\\n repr_value = repr_value[:50] + \\\"...\\\"\\n print(\\\"{0:s} {1:s} = {2:s}\\\".format(indent_string,\\n data_element.name,\\n repr_value))\",\n \"def display(self):\\n for row0 in range(self.y):\\n print()\\n for row in range(self.height):\\n for column0 in range(self.x):\\n print(\\\" \\\", end=\\\"\\\")\\n for column in range(self.width):\\n print(\\\"#\\\", end=\\\"\\\")\\n print()\",\n \"def display(array):\\n plt.figure()\\n plt.imshow(array)\\n plt.show()\",\n \"def display_cropped_img(i):\\n image = PIL.Image.open(testing_img_paths[i])\\n image = image.crop(box=(313,99,825,611))\\n image = image.resize((256,256))\\n display(image)\",\n \"def view_image(train_dataloader):\\n for (x, target) in train_dataloader:\\n np.save(\\\"img.npy\\\", x)\\n print(x.shape)\\n exit(0)\",\n \"def printdata(self,whichstream_,firsti_,lasti_,firstj_,lastj_,firstk_,lastk_,c_,qo_,a_,qc_,bc_,bx_,vartype_,cones_): # 3\\n if not isinstance(whichstream_,streamtype): raise TypeError(\\\"Argument whichstream has wrong type\\\")\\n res = self.__obj.printdata(whichstream_,firsti_,lasti_,firstj_,lastj_,firstk_,lastk_,c_,qo_,a_,qc_,bc_,bx_,vartype_,cones_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def display(self, image):\\n assert(image.mode == '1')\\n assert(image.size[0] == self.width)\\n assert(image.size[1] == self.height)\\n\\n self.command(\\n const.COLUMNADDR, 0x00, self.width-1, # Column start/end address\\n const.PAGEADDR, 0x00, self.pages-1) # Page start/end address\\n\\n pix = list(image.getdata())\\n step = self.width * 8\\n buf = []\\n for y in xrange(0, self.pages * step, step):\\n i = y + self.width-1\\n while i >= y:\\n byte = 0\\n for n in xrange(0, step, self.width):\\n byte |= (pix[i + n] & 0x01) << 8\\n byte >>= 1\\n\\n buf.append(byte)\\n i -= 1\\n\\n self.data(buf)\",\n \"def show(self):\\n \\n \\n \\n \\n \\n \\n r = 4\\n f, axarr = plt.subplots(r, r, figsize=(8,8))\\n counter = 0\\n for i in range(r):\\n for j in range(r):\\n temp = self.x[counter,:]\\n counter += 1\\n img = self.x[counter,:]\\n axarr[i][j].imshow(img)\\n #######################################################################\\n # #\\n # #\\n # TODO: YOUR CODE HERE #\\n # #\\n # #\\n #######################################################################\",\n \"def displayData(indices_to_display = None):\\n width, height = 20, 20\\n nrows, ncols = 10, 10\\n if not indices_to_display:\\n indices_to_display = random.sample(range(X.shape[0]), nrows*ncols)\\n \\n big_picture = np.zeros((height*nrows,width*ncols))\\n \\n irow, icol = 0, 0\\n for idx in indices_to_display:\\n if icol == ncols:\\n irow += 1\\n icol = 0\\n iimg = getDatumImg(X[idx])\\n big_picture[irow*height:irow*height+iimg.shape[0], icol*width:icol*width+iimg.shape[1]] = iimg\\n icol += 1\\n fig = plt.figure(figsize=(6,6))\\n\\n big_picture = (big_picture * 255).astype(np.int8)\\n img = Image.fromarray(big_picture, mode='L')\\n plt.imshow(img, cmap = cm.Greys)\",\n \"def display_image(X):\\n\\n\\tim = X.reshape(28, 28)\\n\\ttemp = plt.imshow(im)\\n\\tplt.show()\",\n \"def display(self):\\n for x, p in zip(self.xs, self.ps):\\n print(x, p)\",\n \"def my_print(self):\\n if self.__size > 0:\\n for k in range(self.__position[1]):\\n print()\\n for i in range(self.__size):\\n for j in range(self.__position[0]):\\n print(\\\" \\\", end='')\\n print(\\\"#\\\" * self.__size)\\n else:\\n print()\",\n \"def print_images(i, df):\\n \\n images_folder_path = \\\"dataset/petfinder-adoption-prediction/train_images/\\\"\\n plt.imshow(cv2.cvtColor(cv2.imread(images_folder_path+df.filename[i]), cv2.COLOR_BGR2RGB),);\\n plt.axis(\\\"off\\\");\\n plt.show()\",\n \"def display(self):\\n for b in range(self.y):\\n print()\\n for i in range(self.height):\\n print(\\\" \\\" * self.x + \\\"#\\\" * self.width)\",\n \"def imshow(self, depth):\\n layer = self.cube[depth]\\n img = []\\n for i in range(self.height):\\n img.append([layer[i][j].value for j in range(self.width)])\\n plt.imshow(img, cmap='gray')\\n plt.show()\",\n \"def show_input_to_output(img_ns):\\n figure()\\n \\n sp = subplot(1, 2, 1).imshow(img_ns.img)\\n sp.axes.grid(False)\\n sp.axes.tick_params(bottom=False, left=False, which='both',labelleft=False,labelbottom=False,length=0)\\n title(\\\"Input Image\\\", fontsize=10);\\n outimg = tiles_to_images(img_ns, img_ns.tile_grid, img_ns.tile_catalog, img_ns.tile_size)\\n sp = subplot(1, 2, 2).imshow(outimg.astype(np.uint8));\\n sp.axes.tick_params(bottom=False, left=False, which='both',labelleft=False,labelbottom=False,length=0)\\n title(\\\"Output Image From Tiles\\\", fontsize=10);\\n sp.axes.grid(False)\\n #print(outimg.astype(np.uint8))\\n #print(img_ns)\\n plt.savefig(img_ns.output_filename + \\\"_input_to_output.pdf\\\", bbox_inches=\\\"tight\\\")\\n plt.close()\",\n \"def display(self):\\n width = 1 + max(len(self.values[s]) for s in self.boxes)\\n line = 'x'.join(['-'*(width*3)]*3)\\n for r in self.rows:\\n print(''.join(self.values[r+c].center(width)+('|' if c in '36' else '')\\n for c in self.cols))\\n if r in 'CF': print(line)\\n print\",\n \"def display(self):\\n result = []\\n horizontal_bounds, vertical_bounds = self.get_bounds()\\n min_x, max_x = horizontal_bounds\\n min_y, max_y = vertical_bounds\\n\\n # xrange is inclusive in start and exclusive in end, IE [s..e)\\n # so we add +1 offset to be safe\\n for y in xrange(max_y, min_y - 1, -1):\\n # since we have three rows and we're still relying on print,\\n # displaying gets a bit dirty\\n # will get cleaner once we move to something like HTML\\n row_tiles = [self.tile(x, y) for x in xrange(min_x, max_x + 1)]\\n\\n # now we have to print each of the three rows together.\\n # zip to aggregate each of the top, middle, bottom rows\\n row_lines = zip(*[str(tile).split(\\\"\\\\n\\\") for tile in row_tiles])\\n for line in row_lines:\\n result.append(\\\"\\\".join(line))\\n\\n return \\\"\\\\n\\\".join(result)\",\n \"def print_stats(cars, notcars):\\n print(\\\"Number of car samples: {0}\\\".format(len(cars)))\\n print(\\\"Number of non car samples: {0}\\\".format(len(notcars)))\\n img = cv2.imread(cars[0])\\n print(\\\"Image shape: {0}x{1}\\\".format(img.shape[0], img.shape[1]))\\n print(\\\"Image datatype: {}\\\".format(img.dtype))\",\n \"def display(self):\\n for i in range(self.y):\\n print()\\n for i in range(self.height):\\n for k in range(self.x):\\n print(' ', end='')\\n for j in range(self.width):\\n print('#', end='')\\n print()\",\n \"def print_image_info(input_image):\\n print()\\n print(\\\"Basic Information on image: {}\\\".format(input_image.filename))\\n print(\\\"Format: {}\\\".format(input_image.format))\\n print(\\\"Mode: {}\\\".format(input_image.mode))\\n print(\\\"Size: {}\\\".format(input_image.size))\\n print(\\\"Width: {}\\\".format(input_image.width))\\n print(\\\"Height: {}\\\".format(input_image.height))\\n print(\\\"Palette: {}\\\".format(input_image.palette))\\n print()\",\n \"def show_image(self, pic, prediction=None):\\n digitmap = {\\n 0: [(0,0), (1,0), (2,0), (3,0), (4,0), (0,1), (4,1), (0,2), (1,2), (2,2), (3,2), (4,2)],\\n 1: [(0,2), (1,2), (2,2), (3,2), (4,2)],\\n 2: [(0,0), (0,1), (0,2), (1,2), (2,0), (2,1), (2,2), (3,0), (4,0), (4,1), (4,2)],\\n 3: [(0,0), (0,1), (0,2), (1,2), (2,0), (2,1), (2,2), (3,2), (4,0), (4,1), (4,2)],\\n 4: [(0,0), (0,2), (1,0), (1,2), (2,0), (2,1), (2,2), (3,2), (4,2)],\\n 5: [(0,0), (0,1), (0,2), (1,0), (2,0), (2,1), (2,2), (3,2), (4,0), (4,1), (4,2)],\\n 6: [(0,0), (0,1), (0,2), (1,0), (2,0), (2,1), (2,2), (3,0), (3,2), (4,0), (4,1), (4,2)],\\n 7: [(0,0), (0,1), (0,2), (1,2), (2,2), (3,2), (4,2)],\\n 8: [(0,0), (1,0), (2,0), (3,0), (4,0), (0,1), (4,1), (0,2), (1,2), (2,2), (3,2), (4,2), (2,1)],\\n 9: [(0,0), (0,1), (0,2), (1,0), (1,2), (2,0), (2,1), (2,2), (3,2), (4,0), (4,1), (4,2)]\\n }\\n\\n pic = pic.reshape((28,28)).copy()\\n if prediction is not None:\\n for pos in digitmap[prediction]:\\n pic[pos]=255\\n plt.imshow(pic, cmap='gray_r')\",\n \"def print_vector(self):\\n print self.x, self.y, self.z\",\n \"def display_mask(i):\\n ### To display binary masks, comment the folowing line\\n # mask = np.argmax(testing_preds[i], axis=-1)\\n ### To display probability maps, comment the folowing line\\n mask = testing_preds[i,:,:,-1]\\n mask = np.expand_dims(mask, axis=-1)\\n img = PIL.ImageOps.autocontrast(keras.preprocessing.image.array_to_img(mask))\\n display(img)\",\n \"def printData (data):\\n print(str(len(data)) + '\\\\t' + str(data))\",\n \"def print_data(place):\\n raise NotImplementedError\",\n \"def print(self):\\n print(\\\" 0 1 2 3 4 5 6 7 8 \\\")\\n print(\\\" -------------------------\\\")\\n for x in range(0, 9):\\n print(f\\\"{x} | \\\", end=\\\"\\\")\\n for y in range(0, 9):\\n if self.field[x][y] == -1:\\n print(\\\"* \\\", end=\\\"\\\")\\n else:\\n print(f\\\"{self.field[x][y]} \\\", end=\\\"\\\")\\n if y % 3 == 2:\\n print(\\\"| \\\", end=\\\"\\\")\\n print(\\\"\\\")\\n if x % 3 == 2:\\n print(\\\" -------------------------\\\")\",\n \"def show_all(img, overlay=None, axis='z'):\\n xlen, ylen, zlen = img.GetSize()\\n all_images = []\\n all_overlays = []\\n if axis == 'z':\\n all_images = [img[:, :, z] for z in xrange(zlen)]\\n if overlay:\\n all_overlays = [overlay[:, :, z] for z in xrange(zlen)]\\n elif axis == 'y':\\n all_images = [img[:, y, :] for y in xrange(ylen)]\\n if overlay:\\n all_overlays = [overlay[:, y, :] for y in xrange(ylen)]\\n elif axis == 'x':\\n all_images = [img[x, :, :] for x in xrange(xlen)]\\n if overlay:\\n all_overlays = [overlay[x, :, :] for x in xrange(xlen)]\\n else:\\n raise Exception('invalid axis')\\n\\n for i, image in enumerate(all_images):\\n if overlay:\\n show_one(sitk.LabelOverlay(image, all_overlays[i]))\\n else:\\n show_one(image)\\n plt.show()\",\n \"def print(self):\\n # IMPLEMENT ME\\n for i in range(self.height):\\n for j in range(self.width):\\n print(self.board[i][j], end=\\\" \\\")\\n print()\\n print()\",\n \"def my_print(self):\\n if self.__size is not 0:\\n for ite in range(self.__position[1]):\\n print()\\n for ite in range(self.__size):\\n print(\\\" \\\" * self.__position[0], end=\\\"\\\")\\n print(\\\"#\\\" * self.size)\\n else:\\n print()\",\n \"def printDataRange(matrix):\\n print(\\\"Samples\\\\tMin\\\\tMax\\\\tMedian\\\\t10th\\\\t90th\\\")\\n for i, sample in enumerate(matrix.matrix.sample_labels):\\n start = matrix.matrix.sample_boundaries[i]\\n end = matrix.matrix.sample_boundaries[i + 1]\\n sample_matrix = matrix.matrix.matrix[..., start:end]\\n print(\\\"{0}\\\\t{1}\\\\t{2}\\\\t{3}\\\\t{4}\\\\t{5}\\\".format(sample, np.amin(sample_matrix),\\n np.amax(sample_matrix),\\n np.ma.median(sample_matrix),\\n np.percentile(sample_matrix, 10),\\n np.percentile(sample_matrix, 90)))\",\n \"def print_cell_information(obj_ase_cell):\\n # print the lattice vectors\\n print('a1=',obj_ase_cell.cell[0,:])\\n print('a2=',obj_ase_cell.cell[1,:])\\n print('a3=',obj_ase_cell.cell[2,:])\\n for i,a in enumerate(obj_ase_cell):\\n print(i,a.symbol,a.position)\",\n \"def print(self):\\n for row in self.board:\\n print(row)\",\n \"def dumpResults(x,y,lon,lat):\\n for i in range(0,len(x)):\\n print(x[i],y[i],\\\"lonlat\\\",lon[i],lat[i])\\n return\",\n \"def display(self):\\n for i in range(self.__y):\\n print()\\n for i in range(self.__height):\\n print(\\\" \\\" * self.__x + \\\"#\\\" * self.__width)\",\n \"def display(self):\\n width = self.width\\n height = self.height\\n x = self.x\\n y = self.y\\n for d_y in range(y):\\n print()\\n for h in range(height):\\n if x != 0:\\n print(\\\" \\\" * x, end=\\\"\\\")\\n print(\\\"#\\\" * width)\",\n \"def display(self):\\n for i in range(0, len(self.top_row)):\\n self.top_row[i].display()\\n for i in range(0, len(self.bottom_row)):\\n self.bottom_row[i].display()\\n for i in range(0, len(self.left_col)):\\n self.left_col[i].display()\\n for i in range(0, len(self.right_col)):\\n self.right_col[i].display()\",\n \"def showTensorImg(ts, title):\\n img = np.transpose(ts, (1, 2, 0))\\n showImg(img, title)\\n return\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.75990057","0.7544907","0.74450433","0.73937047","0.7349031","0.7347187","0.7033798","0.68403655","0.6811467","0.66571254","0.6630757","0.6279358","0.6275334","0.61232245","0.60401046","0.5973948","0.5973784","0.5965042","0.59583265","0.59162855","0.5894761","0.5819436","0.5811356","0.58048147","0.57793635","0.57645047","0.5754731","0.57335156","0.57335156","0.5726376","0.5715056","0.57111317","0.57064384","0.56567574","0.5577068","0.55756503","0.55694","0.55646145","0.55592704","0.55557805","0.5544266","0.5533631","0.5533631","0.55232507","0.55178654","0.5515087","0.5503338","0.5490018","0.5488094","0.5487511","0.5482174","0.54544526","0.5454091","0.54539174","0.5419365","0.54145783","0.53863937","0.53683513","0.5363214","0.5358269","0.5357344","0.5318814","0.53133845","0.53115493","0.53113484","0.5308694","0.52823716","0.5281389","0.52733016","0.5255574","0.5242868","0.52380145","0.52352977","0.5234517","0.5230952","0.52300847","0.5228469","0.5226477","0.52176017","0.52133596","0.51984113","0.51942176","0.51879305","0.5177211","0.51671976","0.51668286","0.51616746","0.5159799","0.515941","0.51542795","0.51455265","0.51432824","0.51390004","0.5137368","0.51322716","0.5130539","0.51247895","0.5123635","0.51206356","0.51191825"],"string":"[\n \"0.75990057\",\n \"0.7544907\",\n \"0.74450433\",\n \"0.73937047\",\n \"0.7349031\",\n \"0.7347187\",\n \"0.7033798\",\n \"0.68403655\",\n \"0.6811467\",\n \"0.66571254\",\n \"0.6630757\",\n \"0.6279358\",\n \"0.6275334\",\n \"0.61232245\",\n \"0.60401046\",\n \"0.5973948\",\n \"0.5973784\",\n \"0.5965042\",\n \"0.59583265\",\n \"0.59162855\",\n \"0.5894761\",\n \"0.5819436\",\n \"0.5811356\",\n \"0.58048147\",\n \"0.57793635\",\n \"0.57645047\",\n \"0.5754731\",\n \"0.57335156\",\n \"0.57335156\",\n \"0.5726376\",\n \"0.5715056\",\n \"0.57111317\",\n \"0.57064384\",\n \"0.56567574\",\n \"0.5577068\",\n \"0.55756503\",\n \"0.55694\",\n \"0.55646145\",\n \"0.55592704\",\n \"0.55557805\",\n \"0.5544266\",\n \"0.5533631\",\n \"0.5533631\",\n \"0.55232507\",\n \"0.55178654\",\n \"0.5515087\",\n \"0.5503338\",\n \"0.5490018\",\n \"0.5488094\",\n \"0.5487511\",\n \"0.5482174\",\n \"0.54544526\",\n \"0.5454091\",\n \"0.54539174\",\n \"0.5419365\",\n \"0.54145783\",\n \"0.53863937\",\n \"0.53683513\",\n \"0.5363214\",\n \"0.5358269\",\n \"0.5357344\",\n \"0.5318814\",\n \"0.53133845\",\n \"0.53115493\",\n \"0.53113484\",\n \"0.5308694\",\n \"0.52823716\",\n \"0.5281389\",\n \"0.52733016\",\n \"0.5255574\",\n \"0.5242868\",\n \"0.52380145\",\n \"0.52352977\",\n \"0.5234517\",\n \"0.5230952\",\n \"0.52300847\",\n \"0.5228469\",\n \"0.5226477\",\n \"0.52176017\",\n \"0.52133596\",\n \"0.51984113\",\n \"0.51942176\",\n \"0.51879305\",\n \"0.5177211\",\n \"0.51671976\",\n \"0.51668286\",\n \"0.51616746\",\n \"0.5159799\",\n \"0.515941\",\n \"0.51542795\",\n \"0.51455265\",\n \"0.51432824\",\n \"0.51390004\",\n \"0.5137368\",\n \"0.51322716\",\n \"0.5130539\",\n \"0.51247895\",\n \"0.5123635\",\n \"0.51206356\",\n \"0.51191825\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.76172984"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":797,"cells":{"query":{"kind":"string","value":"Print the data in slice iz, column iy of an image to standard out."},"document":{"kind":"string","value":"def print_col(input, iy=0, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice, y = %d col)\" % (iz, iy)\n\tline = []\n\tfor ix in xrange(nx):\n\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\tif ((ix + 1) % 5 == 0): line.append(\"\\n \")\n\tline.append(\"\\n\")\n\tprint \"\".join(line)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def print_image_col(input, ix=0, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice, x = %d row)\" % (iz, ix)\n\tline = []\n\tfor iy in xrange(ny):\n\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\tif ((iy + 1) % 5 == 0): line.append(\"\\n \")\n\tline.append(\"\\n\")\n\tprint \"\".join(line)","def print_image_slice(input, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice)\" % (iz)\n\tline = []\n\tfor iy in xrange(ny-1,-1,-1):\n\t\tline.append(\"Row \")\n\t\tline.append(\"%4i \" % iy)\n\t\tfor ix in xrange(nx):\n\t\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\t\tif ((ix + 1) % 5 == 0): \n\t\t\t\tline.append(\"\\n \")\n\t\t\t\tline.append(\" \")\n\t \tline.append(\"\\n\")\n\t \tif(nx%5 != 0): line.append(\"\\n\")\n\tprint \"\".join(line)","def print_image_row(input, iy=0, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice, y = %d col)\" % (iz, iy)\n\tline = []\n\tfor ix in xrange(nx):\n\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\tif ((ix + 1) % 5 == 0): line.append(\"\\n \")\n\tline.append(\"\\n\")\n\tprint \"\".join(line)","def print_slice(input, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice)\" % (iz)\n\tline = []\n\tfor iy in xrange(ny):\n\t\tline.append(\"Row \")\n\t\tline.append(\"%4i \" % iy)\n\t\tfor ix in xrange(nx):\n\t\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\t\tif ((ix + 1) % 5 == 0): \n\t\t\t\tline.append(\"\\n \")\n\t\t\t\tline.append(\" \")\n\t \tline.append(\"\\n\")\n\t \tif(nx%5 != 0): line.append(\"\\n\")\n\tprint \"\".join(line)","def print_row(input, ix=0, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice, x = %d row)\" % (iz, ix)\n\tline = []\n\tfor iy in xrange(ny):\n\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\tif ((iy + 1) % 5 == 0): line.append(\"\\n \")\n\tline.append(\"\\n\")\n\tprint \"\".join(line)","def print_image(input):\n\timage=get_image(input)\n\tnz = image.get_zsize()\n\tfor iz in xrange(nz): print_slice(input, iz)","def print_image(data, index):\n image = data[index]\n for line in image:\n print(line)","def dump_row(input, fname, ix=0, iz=0):\n\tfout = open(fname, \"w\")\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tfout.write(\"# z = %d slice, x = %d row)\\n\" % (iz, ix))\n\tline = []\n\tfor iy in xrange(ny):\n\t\tfout.write(\"%d\\t%12.5g\\n\" % (iy, image.get_value_at(ix,iy,iz)))\n\tfout.close()","def print_image_slice_3d(input, num=0,direction=\"z\"):\n\t#print \"print slice at 3 directions\"\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tif(direction==\"x\"):\n\t\t#print \"xxxxx\"\n\t\tix=num\n\t\tprint \"(x = %d slice)\" % (ix)\n\t\tline = []\n\t\tfor iz in xrange(nz-1,-1,-1):\n\t\t\tline.append(\"Z \")\n\t\t\tline.append(\"%4i \" % iz)\n\t\t\tfor iy in xrange(ny):\n\t\t\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\t\t\tif ((iy + 1) % 5 == 0): \n\t\t\t\t\tline.append(\"\\n \")\n\t\t\t\t\tline.append(\" \")\n\t \t\tline.append(\"\\n\")\n\t \t\tif(ny%5 != 0): line.append(\"\\n\")\n\t\tprint \"\".join(line)\n\telif(direction==\"y\"):\n\t\t#print \"yyy\"\n\t\tiy=num\n\t\tprint \"(y = %d slice)\" % (iy)\n\t\tline = []\n\t\tfor iz in xrange(nz-1,-1,-1):\n\t\t\tline.append(\"Z \")\n\t\t\tline.append(\"%4i \" % iz)\n\t\t\tfor ix in xrange(nx):\n\t\t\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\t\t\tif ((ix + 1) % 5 == 0): \n\t\t\t\t\tline.append(\"\\n \")\n\t\t\t\t\tline.append(\" \")\n\t \t\tline.append(\"\\n\")\n\t \t\tif(nx%5 != 0): line.append(\"\\n\")\n\t\tprint \"\".join(line)\n\telse:\n\t\t#print \"zzzz\"\n\t\tiz=num\n\t\tprint \"(z = %d slice)\" % (iz)\n\t\tline = []\n\t\tfor iy in xrange(ny-1,-1,-1):\n\t\t\tline.append(\"Row \")\n\t\t\tline.append(\"%4i \" % iy)\n\t\t\tfor ix in xrange(nx):\n\t\t\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\t\t\tif ((ix + 1) % 5 == 0): \n\t\t\t\t\tline.append(\"\\n \")\n\t\t\t\t\tline.append(\" \")\n\t \t\tline.append(\"\\n\")\n\t \t\tif(nx%5 != 0): line.append(\"\\n\")\n\t\tprint \"\".join(line)","def print_img_data(img,title='Image'):\n print(f'________ {title} _______')\n if len(image.shape) == 2:\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\n\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0])}\\n')\n elif len(image.shape) == 3:\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\n\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0,0])}\\n')","def print_img_at_idx(self, idx):\n\n img = self.images[idx]\n print_img(img)","def printMat(image):\n for row in range(image.rows):\n print \"[\",\n for col in range(image.cols):\n print cv.mGet(image, row, col),\n print \"]\"\n print \"\"","def show(self):\n m = [xo_convert(int(x)) for x in np.nditer(self.arr)]\n print(\"{} | {} | {}\".format(*m[:3]))\n print(\"--+---+--\")\n print(\"{} | {} | {}\".format(*m[3:6]))\n print(\"--+---+--\")\n print(\"{} | {} | {}\".format(*m[6:]))\n print()","def print_image(img):\n for k in img:\n print(\"\".join([str(x) for x in k] ))","def pretty_print(image_example):\n print numpy.array_str(image_example, precision=1, max_line_width=142)","def print_slice(complex_slice, name = None):\n if name: print(\"***********\" + name + \"**********\")\n slice = complex_slice.clone()\n slice = slice.detach()\n slice = slice.cpu()\n slice = slice.permute(0, 2, 3, 1)\n slice = slice.squeeze()\n slice_image_abs = fastmri.complex_abs(slice)\n plt.imshow(slice_image_abs, cmap = 'gray')\n plt.show()","def print_seg_row_col(sp) : \n s, r, c = src_from_rc8x8(sp.peak_y_raw, sp.peak_x_raw)\n print('seg: %d, row: %.1f, col: %.1f' % (s, r, c))","def explore_data(dataset, start, end, rows_and_columns=False):\r\n for i in range(start,end):\r\n print(dataset[i],end=\"\\n\")","def show_slice(file_path, x, y, z):\n img = nib.load(file_path)\n img = img.get_fdata()\n print(\"The scan has dimensions {}.\".format(img.shape))\n slice_0 = img[x, :, :]\n slice_1 = img[:, y, :]\n slice_2 = img[:, :, z]\n slices = [slice_0, slice_1, slice_2]\n fig, axes = plt.subplots(1, len(slices), figsize=[12, 4])\n for i, sli in enumerate(slices):\n axes[i].imshow(sli.T, cmap=\"gray\", origin=\"lower\")","def imshow(image):\n iio.imshow(dtype.im2uint(image))","def show_image(image):\n print('-' * (len(image) + 4))\n for line in image:\n print('| ', end='')\n for ch in line:\n char = '#' if ch is True else '.'\n print(char, end='')\n print(' |')\n print('-' * (len(image) + 4))","def print_info(self, i):\n\n im_size = self.image_size(i)\n print 'The path of the image is: {}'.format(self.image_path_at(i))\n print 'width: {}, height: {}'.format(im_size[0], im_size[1])\n \n attr_i = self.gtdb['attr'][i, :]\n print 'The attributes are: {}'.format(','.join([self._classes[i] for i in np.where(attr_i==1)[0]]))","def print_image(indiv,name):\n routine = gp.compile(indiv,pset)\n output = gen_beat_output(routine)\n bits = np.array(map(bitlist,output)[0:24000]).transpose()\n plt.style.use('classic')\n plt.imshow(bits,interpolation='nearest',aspect='auto',cmap=plt.get_cmap('Greys'))\n plt.savefig(name+\".png\",dpi=150)","def print_image(self, pil_image):\n bw_image = pil_image.convert('1', dither=Image.FLOYDSTEINBERG)\n width = bw_image.width\n height = bw_image.height\n img_bytes = list(bw_image.getdata())\n self.print_bitmap(width, height, img_bytes)","def collatz_print(w, i, j, v):\n\tw.write(str(i) + \" \" + str(j) + \" \" + str(v) + \"\\n\")","def print_image(self, width, data):\n if len(data) % width:\n raise ValueError('Bad image format, length of data must be '\n 'divisible by width.')\n height = len(data) / width\n\n # send line-by-line\n for row in range(height):\n self.send_command('print_bitmap', 1, width)\n self.port.write(data[row*width:(row+1)*width], is_text=False)\n self.port.fed_dots(1)","def printdatarow(dat, iteration):\r\n i = np.where(dat.f[:, 0] == iteration)[0][0]\r\n j = np.where(dat.std[:, 0] == iteration)[0][0]\r\n print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) +\r\n ' %5.1e' % (dat.f[i,3]) +\r\n ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:]))","def printdatarow(dat, iteration):\r\n i = np.where(dat.f[:, 0] == iteration)[0][0]\r\n j = np.where(dat.std[:, 0] == iteration)[0][0]\r\n print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) +\r\n ' %5.1e' % (dat.f[i,3]) +\r\n ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:]))","def collatz_print (w, i, j, v) :\n w.write(str(i) + \" \" + str(j) + \" \" + str(v) + \"\\n\")","def display_data():\n data = read_data_from_file()\n data_size = len(data)\n print('Data size - ' + str(data_size))\n\n\n for i in range(data_size-1, 0, -1):\n image = data[i]\n cv2.imshow('window', image[0])\n print(str(image[1]))\n cv2.waitKey(50)","def printImage(imageObject):\n # TODO\n pass","def _convert_and_print_image(self, im):\n pixLine = \"\"\n imLeft = \"\"\n imRight = \"\"\n switch = 0\n imgSize = [0, 0]\n\n if im.size[0] > 512:\n print (\"WARNING: Image is wider than 512 and could be truncated at print time \")\n if im.size[1] > 255:\n raise ValueError(\"Image Height larger than 255\")\n\n imBorder = self._check_image_size(im.size[0])\n for i in range(imBorder[0]):\n imLeft += \"0\"\n for i in range(imBorder[1]):\n imRight += \"0\"\n\n for y in range(im.size[1]):\n imgSize[1] += 1\n pixLine += imLeft\n imgSize[0] += imBorder[0]\n for x in range(im.size[0]):\n imgSize[0] += 1\n RGB = im.getpixel((x, y))\n imColor = (RGB[0] + RGB[1] + RGB[2])\n imPattern = \"1X0\"\n patternLen = len(imPattern)\n switch = (switch - 1) * (-1)\n for x in range(patternLen):\n if imColor <= (255 * 3 / patternLen * (x + 1)):\n if imPattern[x] == \"X\":\n pixLine += \"%d\" % switch\n else:\n pixLine += imPattern[x]\n break\n elif imColor > (255 * 3 / patternLen * patternLen) and imColor <= (255 * 3):\n pixLine += imPattern[-1]\n break\n pixLine += imRight\n imgSize[0] += imBorder[1]\n\n self._print_image(pixLine, imgSize)","def print_real_image(r_img, name = None, batch_size = BATCH_SIZE):\n if name: print(\"***********\" + name + \"**********\")\n to_print = []\n c_img = r_img.clone()\n for i in range(batch_size):\n img = c_img[i,:,:]\n img = torch.squeeze(img)\n img = img.detach()\n img = img.cpu()\n plt.imshow(img, cmap = 'gray')\n plt.show()","def display(self):\n for row in range(self.height):\n for col in range(self.width):\n char = '#' if self.pixels[row * self.width + col] else '.'\n print(char, end='')\n print()\n print()","def display(self, image):\n assert(image.mode == '1')\n assert(image.size[0] == self.width)\n assert(image.size[1] == self.height)\n\n page = 0xB0\n pix = list(image.getdata())\n step = self.width * 8\n for y in xrange(0, self.pages * step, step):\n\n # move to given page, then reset the column address\n self.command(page, 0x02, 0x10)\n page += 1\n\n buf = []\n for x in xrange(self.width):\n byte = 0\n for n in xrange(0, step, self.width):\n byte |= (pix[x + y + n] & 0x01) << 8\n byte >>= 1\n\n buf.append(byte)\n\n self.data(buf)","def display(self, image):\n assert(image.mode == '1')\n assert(image.size[0] == self.width)\n assert(image.size[1] == self.height)\n\n page = 0xB0\n pix = list(image.getdata())\n step = self.width * 8\n for y in xrange(0, self.pages * step, step):\n\n # move to given page, then reset the column address\n self.command(page, 0x02, 0x10)\n page += 1\n\n buf = []\n for x in xrange(self.width):\n byte = 0\n for n in xrange(0, step, self.width):\n byte |= (pix[x + y + n] & 0x01) << 8\n byte >>= 1\n\n buf.append(byte)\n\n self.data(buf)","def info_np(img):\n import numpy as np\n\n print ('Dimensions: ' + str(np.shape(img)))\n print ('Min value: ' + str(np.min(img)))\n print ('Avg value: ' + str(np.average(img)))\n print ('Med value: ' + str(np.median(img)))\n print ('Max value: ' + str(np.max(img)))\n print ('Std dev: ' + str(np.std(img)))\n print ('Sum: ' + str(np.sum(img)))","def disp(self, idx=100): # r_[0:5,1e2:1e9:1e2,-10:0]):\r\n\r\n filenameprefix=self.name_prefix\r\n\r\n def printdatarow(dat, iteration):\r\n \"\"\"print data of iteration i\"\"\"\r\n i = np.where(dat.f[:, 0] == iteration)[0][0]\r\n j = np.where(dat.std[:, 0] == iteration)[0][0]\r\n print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) +\r\n ' %5.1e' % (dat.f[i,3]) +\r\n ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:]))\r\n\r\n dat = CMADataLogger(filenameprefix).load()\r\n ndata = dat.f.shape[0]\r\n\r\n # map index to iteration number, is difficult if not all iteration numbers exist\r\n # idx = idx[np.where(map(lambda x: x in dat.f[:,0], idx))[0]] # TODO: takes pretty long\r\n # otherwise:\r\n if idx is None:\r\n idx = 100\r\n if np.isscalar(idx):\r\n # idx = np.arange(0, ndata, idx)\r\n if idx:\r\n idx = np.r_[0, 1, idx:ndata-3:idx, -3:0]\r\n else:\r\n idx = np.r_[0, 1, -3:0]\r\n\r\n idx = array(idx)\r\n idx = idx[idx<ndata]\r\n idx = idx[-idx<=ndata]\r\n iters = dat.f[idx, 0]\r\n idxbest = np.argmin(dat.f[:,5])\r\n iterbest = dat.f[idxbest, 0]\r\n\r\n if len(iters) == 1:\r\n printdatarow(dat, iters[0])\r\n else:\r\n self.disp_header()\r\n for i in iters:\r\n printdatarow(dat, i)\r\n self.disp_header()\r\n printdatarow(dat, iterbest)\r\n sys.stdout.flush()","def show_np(mat):\n for x in range(15):\n for y in range(15):\n if (x == 7) and (y == 7):\n print(\"\\033[%d;%d;%dm**\\033[0m\" % (0, 33, 41), end='')\n elif mat[x, y, 0] > 0:\n print(\"\\033[%d;%d;%dm \\033[0m\" % (0, 31, 41), end='')\n elif mat[x, y, 1] > 0:\n print(\"\\033[%d;%d;%dm \\033[0m\" % (0, 32, 42), end='')\n else:\n print(\" \", end='')\n print(\"\")","def _showdata(self, prec=4):\n print('nh {0:d} nslices {1:d} nbl {2:d} ncp {3:d} nca {4:d} '.format(\n self.nh, self.nslices, self.nbl, self.ncp, self.nca), end=\"\")\n print(\"observables in np arrays with {:d} rows\".format(self.nslices))\n\n if len(self.observables) == 4:\n print('nca', self.nca)\n else:\n print()\n np.set_printoptions(precision=prec)\n\n print(self.fp.shape, \"fp (degrees, but stored internally in radians):\\n\",\n self.fp*self.degree, \"\\n\")\n print(self.fa.shape, \"fa:\\n\", self.fa, \"\\n\")\n\n print(self.cp.shape, \"cp (degrees, but stored internally in radians):\\n\",\n self.cp*self.degree, \"\\n\")\n if len(self.observables) == 4:\n print(self.ca.shape, \"ca:\\n\", self.ca, \"\\n\")\n # print(self.info4oif_dict)\n\n print(\"hole centers array shape:\", self.ctrs.shape)\n\n print(len(self.bholes), \"baseline hole indices\\n\", self.bholes)\n print(self.bls.shape, \"baselines:\\n\", self.bls)\n\n print(self.tholes.shape, \"triple hole indices:\\n\", self.tholes)\n print(self.tuv.shape, \"triple uv vectors:\\n\", self.tuv)\n\n print(self.qholes.shape, \"quad hole indices:\\n\", self.qholes)\n print(self.quvw.shape, \"quad uvw vectors:\\n\", self.quvw)","def info(self):\n\n print(\"pixellisation:\", self.pixel)\n print(\"number of components:\", self.ncomp)\n print(\"number of pixels:\", self.data.shape[:] if self.ncomp == 1 else self.data.shape[1:])\n print(\"nside:\", self.nside)\n print(\"geometry:\", self.geometry)\n print(\"coordinates:\", self.coordinate)","def disp(self, idx=100): # r_[0:5,1e2:1e9:1e2,-10:0]):\r\n\r\n filenameprefix=self.name_prefix\r\n\r\n def printdatarow(dat, iteration):\r\n \"\"\"print data of iteration i\"\"\"\r\n i = np.where(dat.f[:, 0] == iteration)[0][0]\r\n j = np.where(dat.std[:, 0] == iteration)[0][0]\r\n print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) +\r\n ' %5.1e' % (dat.f[i,3]) +\r\n ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:]))\r\n\r\n dat = CMADataLogger(filenameprefix).load()\r\n ndata = dat.f.shape[0]\r\n\r\n # map index to iteration number, is difficult if not all iteration numbers exist\r\n # idx = idx[np.where(map(lambda x: x in dat.f[:,0], idx))[0]] # TODO: takes pretty long\r\n # otherwise:\r\n if idx is None:\r\n idx = 100\r\n if np.isscalar(idx):\r\n # idx = np.arange(0, ndata, idx)\r\n if idx:\r\n idx = np.r_[0, 1, idx:ndata-3:idx, -3:0]\r\n else:\r\n idx = np.r_[0, 1, -3:0]\r\n\r\n idx = array(idx)\r\n idx = idx[idx<=ndata] # TODO: shouldn't this be \"<\"?\r\n idx = idx[-idx<=ndata]\r\n iters = dat.f[idx, 0]\r\n idxbest = np.argmin(dat.f[:,5])\r\n iterbest = dat.f[idxbest, 0]\r\n if len(iters) == 1:\r\n printdatarow(dat, iters[0])\r\n else:\r\n self.disp_header()\r\n for i in iters:\r\n printdatarow(dat, i)\r\n self.disp_header()\r\n printdatarow(dat, iterbest)\r\n sys.stdout.flush()","def print_image(img):\r\n # On affiche l'image\r\n plt.figure(figsize=(20, 5))\r\n plt.subplot(1, 2, 1)\r\n plt.imshow(img)\r\n # On affiche l'histogramme\r\n plt.subplot(1, 2, 2)\r\n plt.hist(img.flatten(), bins=range(256))\r\n plt.show()","def display(self):\n \"\"\" Coordinates for position are x-axis (LR) and y-axis (NS) \"\"\"\n for coordY in range(self.y):\n print()\n for column in range(self.height):\n for coordLR in range(self.x):\n print(\" \", end=\"\")\n for row in range(self.width):\n print(\"#\", end=\"\")\n print()","def ndarray_print(nd):\n try:\n x = nd.tolist()\n except (TypeError, NotImplementedError):\n x = nd.tobytes()\n if isinstance(nd, ndarray):\n offset = nd.offset\n flags = nd.flags\n else:\n offset = 'unknown'\n flags = 'unknown'\n print(\n \"ndarray(%s, shape=%s, strides=%s, suboffsets=%s, offset=%s, format='%s', itemsize=%s, flags=%s)\"\n % (x, nd.shape, nd.strides, nd.suboffsets, offset, nd.format, nd.\n itemsize, flags))\n sys.stdout.flush()","def print_azeltables(inviews, ic):\n for i in range(0, len(inviews)):\n print \" \"\n print \"Az/El for inview %s to %s\" % (inviews[i][0], inviews[i][1])\n azels = ic.compute_azels(inviews[i][0], inviews[i][1], 15)\n for j in range(0, len(azels)):\n print \"At %s, azimuth=%8.2f, elevation=%8.2f\" % \\\n (azels[j][0], azels[j][1], azels[j][2])","def _print_image(self, line, size):\n i = 0\n cont = 0\n\n self._write(self.__class__.__imageSize['1x1'])\n buffer = bytearray([int((size[0] / size[1]) / 8), 0, size[1], 0])\n\n self._write(buffer)\n buffer = bytearray()\n\n while i < len(line):\n hex_string = int(line[i:i + 8], 2)\n buffer.append(hex_string)\n i += 8\n cont += 1\n if cont % 4 == 0:\n self._write(buffer)\n buffer = bytearray()\n cont = 0","def show(self, data):\n if isinstance(data, (numpy.ndarray, h5py.Dataset)):\n isAtomic = len(data.shape) == 0\n isCurve = len(data.shape) == 1 and numpy.issubdtype(data.dtype, numpy.number)\n isImage = len(data.shape) == 2 and numpy.issubdtype(data.dtype, numpy.number)\n if isAtomic:\n self.showAsString(data)\n elif isCurve:\n self.show1d(data)\n elif isImage:\n self.show2d(data)\n else:\n self.showAsString(data)\n else:\n self.showAsString(data)","def print_bitmap(self, w, h, image):\n\n bitmap = self._pack_bitmap(w, h, image)\n\n row_bytes = (w + 7) // 8 # Round up to next byte boundary\n\n if row_bytes >= 48:\n row_bytes_clipped = 48\n else:\n row_bytes_clipped = row_bytes # 384 pixels max width\n\n # Est. max rows to write at once, assuming 256 byte printer buffer.\n if self._dtr_enabled:\n chunk_height_limit = 255 # Buffer doesn't matter, handshake!\n else:\n chunk_height_limit = 256 // row_bytes_clipped\n if chunk_height_limit > self._max_chunk_height:\n chunk_height_limit = self._max_chunk_height\n elif chunk_height_limit < 1:\n chunk_height_limit = 1\n\n row_start = 0\n i = 0\n while row_start < h:\n # Issue up to chunkHeightLimit rows at a time:\n chunk_height = h - row_start\n if chunk_height > chunk_height_limit:\n chunk_height = chunk_height_limit\n\n self.write(self.ASCII_DC2, '*', chunk_height, row_bytes_clipped)\n\n y = 0\n while y < chunk_height:\n x = 0\n while x < row_bytes_clipped:\n self.timeout_wait()\n self._send_to_printer(int(bitmap[i]))\n x += 1\n i += 1\n\n y += 1\n\n i += row_bytes - row_bytes_clipped\n\n self.timeout_set(chunk_height * self._dot_print_time)\n\n row_start += chunk_height_limit\n\n self._prev_byte = '\\n'","def display(self):\n for row0 in range(self.y):\n print()\n for row in range(self.height):\n for column0 in range(self.x):\n print(\" \", end=\"\")\n for column in range(self.width):\n print(\"#\", end=\"\")\n print()","def print_wrapped(data, ncols=3):\r\n nrows = len(data)\r\n labels = data.index\r\n n_split_rows = int(np.ceil(nrows / ncols))\r\n for r in range(0, nrows, ncols):\r\n for c in range(ncols):\r\n try:\r\n numstr = '{}'.format(data[r + c])\r\n tabs = [' '] * (20 - len(labels[r + c]) - len(numstr))\r\n print(labels[r + c] + \"\".join(tabs) + numstr, end='\\t')\r\n except:\r\n pass\r\n print()","def ndarray_print(nd):\n try:\n x = nd.tolist()\n except (TypeError, NotImplementedError):\n x = nd.tobytes()\n if isinstance(nd, ndarray):\n offset = nd.offset\n flags = nd.flags\n else:\n offset = 'unknown'\n flags = 'unknown'\n print(\"ndarray(%s, shape=%s, strides=%s, suboffsets=%s, offset=%s, \"\n \"format='%s', itemsize=%s, flags=%s)\" %\n (x, nd.shape, nd.strides, nd.suboffsets, offset,\n nd.format, nd.itemsize, flags))\n sys.stdout.flush()","def display_cropped_img(i):\n image = PIL.Image.open(testing_img_paths[i])\n image = image.crop(box=(313,99,825,611))\n image = image.resize((256,256))\n display(image)","def display(self, image):\n assert(image.mode == '1')\n assert(image.size[0] == self.width)\n assert(image.size[1] == self.height)\n\n self.command(\n const.COLUMNADDR, 0x00, self.width-1, # Column start/end address\n const.PAGEADDR, 0x00, self.pages-1) # Page start/end address\n\n pix = list(image.getdata())\n step = self.width * 8\n buf = []\n for y in xrange(0, self.pages * step, step):\n i = y + self.width-1\n while i >= y:\n byte = 0\n for n in xrange(0, step, self.width):\n byte |= (pix[i + n] & 0x01) << 8\n byte >>= 1\n\n buf.append(byte)\n i -= 1\n\n self.data(buf)","def display(self):\n for row in self.tile_rows:\n print(row)","def display(self):\n for i in range(self.y):\n print()\n for i in range(self.height):\n for k in range(self.x):\n print(' ', end='')\n for j in range(self.width):\n print('#', end='')\n print()","def show_ipv(data: np.ndarray):\n import ipyvolume as ipv\n return ipv.quickvolshow(data)","def imshow(self, depth):\n layer = self.cube[depth]\n img = []\n for i in range(self.height):\n img.append([layer[i][j].value for j in range(self.width)])\n plt.imshow(img, cmap='gray')\n plt.show()","def display(self):\n for b in range(self.y):\n print()\n for i in range(self.height):\n print(\" \" * self.x + \"#\" * self.width)","def display(self):\n width = 1 + max(len(self.values[s]) for s in self.boxes)\n line = 'x'.join(['-'*(width*3)]*3)\n for r in self.rows:\n print(''.join(self.values[r+c].center(width)+('|' if c in '36' else '')\n for c in self.cols))\n if r in 'CF': print(line)\n print","def display_mask(i):\n ### To display binary masks, comment the folowing line\n # mask = np.argmax(testing_preds[i], axis=-1)\n ### To display probability maps, comment the folowing line\n mask = testing_preds[i,:,:,-1]\n mask = np.expand_dims(mask, axis=-1)\n img = PIL.ImageOps.autocontrast(keras.preprocessing.image.array_to_img(mask))\n display(img)","def show_input_to_output(img_ns):\n figure()\n \n sp = subplot(1, 2, 1).imshow(img_ns.img)\n sp.axes.grid(False)\n sp.axes.tick_params(bottom=False, left=False, which='both',labelleft=False,labelbottom=False,length=0)\n title(\"Input Image\", fontsize=10);\n outimg = tiles_to_images(img_ns, img_ns.tile_grid, img_ns.tile_catalog, img_ns.tile_size)\n sp = subplot(1, 2, 2).imshow(outimg.astype(np.uint8));\n sp.axes.tick_params(bottom=False, left=False, which='both',labelleft=False,labelbottom=False,length=0)\n title(\"Output Image From Tiles\", fontsize=10);\n sp.axes.grid(False)\n #print(outimg.astype(np.uint8))\n #print(img_ns)\n plt.savefig(img_ns.output_filename + \"_input_to_output.pdf\", bbox_inches=\"tight\")\n plt.close()","def display(array):\n plt.figure()\n plt.imshow(array)\n plt.show()","def header(self):\n print 'dimensions',self.data.shape\n print 'llcorner', self.xllcorner, self.yllcorner\n print 'cell size', self.cellsize","def show_image(self, pic, prediction=None):\n digitmap = {\n 0: [(0,0), (1,0), (2,0), (3,0), (4,0), (0,1), (4,1), (0,2), (1,2), (2,2), (3,2), (4,2)],\n 1: [(0,2), (1,2), (2,2), (3,2), (4,2)],\n 2: [(0,0), (0,1), (0,2), (1,2), (2,0), (2,1), (2,2), (3,0), (4,0), (4,1), (4,2)],\n 3: [(0,0), (0,1), (0,2), (1,2), (2,0), (2,1), (2,2), (3,2), (4,0), (4,1), (4,2)],\n 4: [(0,0), (0,2), (1,0), (1,2), (2,0), (2,1), (2,2), (3,2), (4,2)],\n 5: [(0,0), (0,1), (0,2), (1,0), (2,0), (2,1), (2,2), (3,2), (4,0), (4,1), (4,2)],\n 6: [(0,0), (0,1), (0,2), (1,0), (2,0), (2,1), (2,2), (3,0), (3,2), (4,0), (4,1), (4,2)],\n 7: [(0,0), (0,1), (0,2), (1,2), (2,2), (3,2), (4,2)],\n 8: [(0,0), (1,0), (2,0), (3,0), (4,0), (0,1), (4,1), (0,2), (1,2), (2,2), (3,2), (4,2), (2,1)],\n 9: [(0,0), (0,1), (0,2), (1,0), (1,2), (2,0), (2,1), (2,2), (3,2), (4,0), (4,1), (4,2)]\n }\n\n pic = pic.reshape((28,28)).copy()\n if prediction is not None:\n for pos in digitmap[prediction]:\n pic[pos]=255\n plt.imshow(pic, cmap='gray_r')","def display(self):\n width = self.width\n height = self.height\n x = self.x\n y = self.y\n for d_y in range(y):\n print()\n for h in range(height):\n if x != 0:\n print(\" \" * x, end=\"\")\n print(\"#\" * width)","def print(self):\n self.__print_local(self.dataset, 0)","def display_napari(pos_img):\n global data\n global img_queue\n if pos_img is None:\n return\n # read image and z position\n image = np.reshape(pos_img[2:],(clip[0], clip[1]))\n z_pos = pos_img[1]\n color = pos_img[0]\n\n # write image into correct slice of data and update display\n data[z_pos] = np.squeeze(image)\n layer = viewer.layers[color]\n layer.data = data\n #print(\"updating \", z_pos, color)\n\n img_queue.task_done()","def output(self):\n width = \" \" # 6 spaces formatting\n print(\"\\n\\n\")\n for row in range(self._length, -1, -1):\n if row != 0:\n print(row, end = width)\n for col in range(0, self._length):\n #print(self.board[col][row - 1], end = width)\n self.board[col][row-1].output(width)\n print(\"\\n\\n\")\n else:\n print(width, end=\" \")\n for col in self.columns:\n print(col, end = width)\n print(\"\\n\\n\")","def pprint(self, data):\n self._assert(data)\n data = self._render(data) # make elements ascii\n fmats = self._fmats(data) # get array of padding formats)\n for row in data:\n print(fmats.format(*row))","def info(self):\n\n\t\tprint(\"Pixels on a side: {0}\".format(self.data.shape[0]))\n\t\tprint(\"Pixel size: {0}\".format(self.resolution))\n\t\tprint(\"Total angular size: {0}\".format(self.side_angle))\n\t\tprint(\"lmin={0:.1e} ; lmax={1:.1e}\".format(self.lmin,self.lmax))","def displayData(indices_to_display = None):\n width, height = 20, 20\n nrows, ncols = 10, 10\n if not indices_to_display:\n indices_to_display = random.sample(range(X.shape[0]), nrows*ncols)\n \n big_picture = np.zeros((height*nrows,width*ncols))\n \n irow, icol = 0, 0\n for idx in indices_to_display:\n if icol == ncols:\n irow += 1\n icol = 0\n iimg = getDatumImg(X[idx])\n big_picture[irow*height:irow*height+iimg.shape[0], icol*width:icol*width+iimg.shape[1]] = iimg\n icol += 1\n fig = plt.figure(figsize=(6,6))\n\n big_picture = (big_picture * 255).astype(np.int8)\n img = Image.fromarray(big_picture, mode='L')\n plt.imshow(img, cmap = cm.Greys)","def render(filename,i):\n print('running render')\n A = np.genfromtxt(filename,skip_header=1,dtype=float,delimiter=',')\n img = np.array(A[i,:],copy=True)\n print(img.shape)\n img = img.reshape(28,28)\n img = 255 - img\n print(img.shape)\n plt.imshow(img, cmap=\"gray\", vmin=0, vmax=255)\n plt.savefig(\"img\" + str(i)+\"render\"+ \".png\")","def display(self):\n for i in range(self.__y):\n print()\n for i in range(self.__height):\n print(\" \" * self.__x + \"#\" * self.__width)","def write_window(img, ds, window):\n new_img = np.array([img[:, :, i] for i in range(img.shape[2])])\n ds.write(new_img, window=window)","def print_images(i, df):\n \n images_folder_path = \"dataset/petfinder-adoption-prediction/train_images/\"\n plt.imshow(cv2.cvtColor(cv2.imread(images_folder_path+df.filename[i]), cv2.COLOR_BGR2RGB),);\n plt.axis(\"off\");\n plt.show()","def print_images_out_statistics(self):\n self._print_images_statistics(self._images_out_folder, self._pose_class_names)","def view_image(train_dataloader):\n for (x, target) in train_dataloader:\n np.save(\"img.npy\", x)\n print(x.shape)\n exit(0)","def print(self):\n print(\" 0 1 2 3 4 5 6 7 8 \")\n print(\" -------------------------\")\n for x in range(0, 9):\n print(f\"{x} | \", end=\"\")\n for y in range(0, 9):\n if self.field[x][y] == -1:\n print(\"* \", end=\"\")\n else:\n print(f\"{self.field[x][y]} \", end=\"\")\n if y % 3 == 2:\n print(\"| \", end=\"\")\n print(\"\")\n if x % 3 == 2:\n print(\" -------------------------\")","def print_data(place):\n raise NotImplementedError","def display(self):\n [print() for i in range(self.__y)]\n for i in range(self.__height):\n [print(\" \", end=\"\") for i in range(self.__x)]\n for j in range(self.__width):\n print(\"#\", end=\"\")\n print()","def print_image_info(input_image):\n print()\n print(\"Basic Information on image: {}\".format(input_image.filename))\n print(\"Format: {}\".format(input_image.format))\n print(\"Mode: {}\".format(input_image.mode))\n print(\"Size: {}\".format(input_image.size))\n print(\"Width: {}\".format(input_image.width))\n print(\"Height: {}\".format(input_image.height))\n print(\"Palette: {}\".format(input_image.palette))\n print()","def __str__(self):\n result = ''\n for row in range(self.getHeight()):\n for col in range(self.getWidth()):\n result += str(self.data[row][col]) + ' '\n result += '\\n'\n return result","def print(self):\n # IMPLEMENT ME\n for i in range(self.height):\n for j in range(self.width):\n print(self.board[i][j], end=\" \")\n print()\n print()","def pixdump( source, start=None, end=None, length=None, width=64, height=None, palette=None ):\n\n for line in pixdump_iter( source, start, end, length, width, height, palette ):\n print( line )","def print(self):\r\n base = 8 * self.width\r\n print(base * \"-\")\r\n for x in range(self.height):\r\n output = \"\"\r\n for y in range(self.width):\r\n output = output + self.board[x][y] + \"|\"\r\n print(\"|\" + output)\r\n print(base * \"-\")","def show_slices(slices):\n fig, axes = plt.subplots(1, len(slices))\n for i, slice in enumerate(slices):\n axes[i].imshow(slice.T, cmap=\"gray\", origin=\"lower\")","def display(self):\n result = []\n horizontal_bounds, vertical_bounds = self.get_bounds()\n min_x, max_x = horizontal_bounds\n min_y, max_y = vertical_bounds\n\n # xrange is inclusive in start and exclusive in end, IE [s..e)\n # so we add +1 offset to be safe\n for y in xrange(max_y, min_y - 1, -1):\n # since we have three rows and we're still relying on print,\n # displaying gets a bit dirty\n # will get cleaner once we move to something like HTML\n row_tiles = [self.tile(x, y) for x in xrange(min_x, max_x + 1)]\n\n # now we have to print each of the three rows together.\n # zip to aggregate each of the top, middle, bottom rows\n row_lines = zip(*[str(tile).split(\"\\n\") for tile in row_tiles])\n for line in row_lines:\n result.append(\"\".join(line))\n\n return \"\\n\".join(result)","def imdisplay(filename, representation):\n\n image = read_image(filename, representation)\n plt.imshow(image, cmap=\"gray\")\n plt.show()","def disImg(data=None,colorbar=False):\n size = np.sqrt(len(data[4:]))\n xmm = data[0]\n ymm = data[1]\n pl.matshow(data[4:].reshape(size,size),fignum=False)\n if colorbar == True:\n pl.colorbar()\n pl.xlim(0,size-1)\n pl.ylim(0,size-1)\n pl.xlabel('Pixels')\n pl.ylabel('Pixels')\n pl.grid(color='yellow')","def visualize_output(\n self,\n img: np.ndarray,\n output_data: Any):\n raise NotImplementedError","def imdisplay(filename, representation):\n image = read_image(filename, representation)\n\n if representation == GRAY_OUT:\n plt.imshow(image, cmap='gray')\n else:\n plt.imshow(image)\n\n plt.show()","def show(self):\r\n \r\n clear() \r\n print \" \" + \"-\" * self.__width + \" \"\r\n \r\n for row in self.__buffer:\r\n rowData = \"\".join(str(i) for i in row)\r\n print \"|\" + rowData + \"|\"\r\n\r\n print \" \" + \"-\" * self.__width + \" \"\r\n self.clearBuffer()","def display_image(X):\n\n\tim = X.reshape(28, 28)\n\ttemp = plt.imshow(im)\n\tplt.show()","def print_vector(self):\n print self.x, self.y, self.z","def print_array(x, idx=slice(None), message=None, message_prefix=\"SHIM - \",\n file=sys.stdout):\n return set_subtensor(x[idx],\n print(x[idx],\n message=message,\n message_prefix=message_prefix,\n file=file\n )\n )","def show(self):\n \n \n \n \n \n \n r = 4\n f, axarr = plt.subplots(r, r, figsize=(8,8))\n counter = 0\n for i in range(r):\n for j in range(r):\n temp = self.x[counter,:]\n counter += 1\n img = self.x[counter,:]\n axarr[i][j].imshow(img)\n #######################################################################\n # #\n # #\n # TODO: YOUR CODE HERE #\n # #\n # #\n #######################################################################","def print_cell_information(obj_ase_cell):\n # print the lattice vectors\n print('a1=',obj_ase_cell.cell[0,:])\n print('a2=',obj_ase_cell.cell[1,:])\n print('a3=',obj_ase_cell.cell[2,:])\n for i,a in enumerate(obj_ase_cell):\n print(i,a.symbol,a.position)","def print_row(row,writer,x):\n sys.stdout.write(unichr(0x2503))\n for n in xrange(row.shape[0]-1):\n writer(row[n],Width,(x,n))\n sys.stdout.write(unichr(0x2502))\n if row.shape[0] > 0:\n writer(row[-1],Width,(x,row.shape[0]-1))\n sys.stdout.write(unichr(0x2503) + '\\n')","def show_slices(self, slices):\n fig, axes = plt.subplots(1, len(slices))\n for i, slice in enumerate(slices):\n axes[i].imshow(slice.T, cmap=\"gray\", origin=\"lower\")"],"string":"[\n \"def print_image_col(input, ix=0, iz=0):\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tprint \\\"(z = %d slice, x = %d row)\\\" % (iz, ix)\\n\\tline = []\\n\\tfor iy in xrange(ny):\\n\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\tif ((iy + 1) % 5 == 0): line.append(\\\"\\\\n \\\")\\n\\tline.append(\\\"\\\\n\\\")\\n\\tprint \\\"\\\".join(line)\",\n \"def print_image_slice(input, iz=0):\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tprint \\\"(z = %d slice)\\\" % (iz)\\n\\tline = []\\n\\tfor iy in xrange(ny-1,-1,-1):\\n\\t\\tline.append(\\\"Row \\\")\\n\\t\\tline.append(\\\"%4i \\\" % iy)\\n\\t\\tfor ix in xrange(nx):\\n\\t\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\t\\tif ((ix + 1) % 5 == 0): \\n\\t\\t\\t\\tline.append(\\\"\\\\n \\\")\\n\\t\\t\\t\\tline.append(\\\" \\\")\\n\\t \\tline.append(\\\"\\\\n\\\")\\n\\t \\tif(nx%5 != 0): line.append(\\\"\\\\n\\\")\\n\\tprint \\\"\\\".join(line)\",\n \"def print_image_row(input, iy=0, iz=0):\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tprint \\\"(z = %d slice, y = %d col)\\\" % (iz, iy)\\n\\tline = []\\n\\tfor ix in xrange(nx):\\n\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\tif ((ix + 1) % 5 == 0): line.append(\\\"\\\\n \\\")\\n\\tline.append(\\\"\\\\n\\\")\\n\\tprint \\\"\\\".join(line)\",\n \"def print_slice(input, iz=0):\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tprint \\\"(z = %d slice)\\\" % (iz)\\n\\tline = []\\n\\tfor iy in xrange(ny):\\n\\t\\tline.append(\\\"Row \\\")\\n\\t\\tline.append(\\\"%4i \\\" % iy)\\n\\t\\tfor ix in xrange(nx):\\n\\t\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\t\\tif ((ix + 1) % 5 == 0): \\n\\t\\t\\t\\tline.append(\\\"\\\\n \\\")\\n\\t\\t\\t\\tline.append(\\\" \\\")\\n\\t \\tline.append(\\\"\\\\n\\\")\\n\\t \\tif(nx%5 != 0): line.append(\\\"\\\\n\\\")\\n\\tprint \\\"\\\".join(line)\",\n \"def print_row(input, ix=0, iz=0):\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tprint \\\"(z = %d slice, x = %d row)\\\" % (iz, ix)\\n\\tline = []\\n\\tfor iy in xrange(ny):\\n\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\tif ((iy + 1) % 5 == 0): line.append(\\\"\\\\n \\\")\\n\\tline.append(\\\"\\\\n\\\")\\n\\tprint \\\"\\\".join(line)\",\n \"def print_image(input):\\n\\timage=get_image(input)\\n\\tnz = image.get_zsize()\\n\\tfor iz in xrange(nz): print_slice(input, iz)\",\n \"def print_image(data, index):\\n image = data[index]\\n for line in image:\\n print(line)\",\n \"def dump_row(input, fname, ix=0, iz=0):\\n\\tfout = open(fname, \\\"w\\\")\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tfout.write(\\\"# z = %d slice, x = %d row)\\\\n\\\" % (iz, ix))\\n\\tline = []\\n\\tfor iy in xrange(ny):\\n\\t\\tfout.write(\\\"%d\\\\t%12.5g\\\\n\\\" % (iy, image.get_value_at(ix,iy,iz)))\\n\\tfout.close()\",\n \"def print_image_slice_3d(input, num=0,direction=\\\"z\\\"):\\n\\t#print \\\"print slice at 3 directions\\\"\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tif(direction==\\\"x\\\"):\\n\\t\\t#print \\\"xxxxx\\\"\\n\\t\\tix=num\\n\\t\\tprint \\\"(x = %d slice)\\\" % (ix)\\n\\t\\tline = []\\n\\t\\tfor iz in xrange(nz-1,-1,-1):\\n\\t\\t\\tline.append(\\\"Z \\\")\\n\\t\\t\\tline.append(\\\"%4i \\\" % iz)\\n\\t\\t\\tfor iy in xrange(ny):\\n\\t\\t\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\t\\t\\tif ((iy + 1) % 5 == 0): \\n\\t\\t\\t\\t\\tline.append(\\\"\\\\n \\\")\\n\\t\\t\\t\\t\\tline.append(\\\" \\\")\\n\\t \\t\\tline.append(\\\"\\\\n\\\")\\n\\t \\t\\tif(ny%5 != 0): line.append(\\\"\\\\n\\\")\\n\\t\\tprint \\\"\\\".join(line)\\n\\telif(direction==\\\"y\\\"):\\n\\t\\t#print \\\"yyy\\\"\\n\\t\\tiy=num\\n\\t\\tprint \\\"(y = %d slice)\\\" % (iy)\\n\\t\\tline = []\\n\\t\\tfor iz in xrange(nz-1,-1,-1):\\n\\t\\t\\tline.append(\\\"Z \\\")\\n\\t\\t\\tline.append(\\\"%4i \\\" % iz)\\n\\t\\t\\tfor ix in xrange(nx):\\n\\t\\t\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\t\\t\\tif ((ix + 1) % 5 == 0): \\n\\t\\t\\t\\t\\tline.append(\\\"\\\\n \\\")\\n\\t\\t\\t\\t\\tline.append(\\\" \\\")\\n\\t \\t\\tline.append(\\\"\\\\n\\\")\\n\\t \\t\\tif(nx%5 != 0): line.append(\\\"\\\\n\\\")\\n\\t\\tprint \\\"\\\".join(line)\\n\\telse:\\n\\t\\t#print \\\"zzzz\\\"\\n\\t\\tiz=num\\n\\t\\tprint \\\"(z = %d slice)\\\" % (iz)\\n\\t\\tline = []\\n\\t\\tfor iy in xrange(ny-1,-1,-1):\\n\\t\\t\\tline.append(\\\"Row \\\")\\n\\t\\t\\tline.append(\\\"%4i \\\" % iy)\\n\\t\\t\\tfor ix in xrange(nx):\\n\\t\\t\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\t\\t\\tif ((ix + 1) % 5 == 0): \\n\\t\\t\\t\\t\\tline.append(\\\"\\\\n \\\")\\n\\t\\t\\t\\t\\tline.append(\\\" \\\")\\n\\t \\t\\tline.append(\\\"\\\\n\\\")\\n\\t \\t\\tif(nx%5 != 0): line.append(\\\"\\\\n\\\")\\n\\t\\tprint \\\"\\\".join(line)\",\n \"def print_img_data(img,title='Image'):\\n print(f'________ {title} _______')\\n if len(image.shape) == 2:\\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\\\\n\\\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0])}\\\\n')\\n elif len(image.shape) == 3:\\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\\\\n\\\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0,0])}\\\\n')\",\n \"def print_img_at_idx(self, idx):\\n\\n img = self.images[idx]\\n print_img(img)\",\n \"def printMat(image):\\n for row in range(image.rows):\\n print \\\"[\\\",\\n for col in range(image.cols):\\n print cv.mGet(image, row, col),\\n print \\\"]\\\"\\n print \\\"\\\"\",\n \"def show(self):\\n m = [xo_convert(int(x)) for x in np.nditer(self.arr)]\\n print(\\\"{} | {} | {}\\\".format(*m[:3]))\\n print(\\\"--+---+--\\\")\\n print(\\\"{} | {} | {}\\\".format(*m[3:6]))\\n print(\\\"--+---+--\\\")\\n print(\\\"{} | {} | {}\\\".format(*m[6:]))\\n print()\",\n \"def print_image(img):\\n for k in img:\\n print(\\\"\\\".join([str(x) for x in k] ))\",\n \"def pretty_print(image_example):\\n print numpy.array_str(image_example, precision=1, max_line_width=142)\",\n \"def print_slice(complex_slice, name = None):\\n if name: print(\\\"***********\\\" + name + \\\"**********\\\")\\n slice = complex_slice.clone()\\n slice = slice.detach()\\n slice = slice.cpu()\\n slice = slice.permute(0, 2, 3, 1)\\n slice = slice.squeeze()\\n slice_image_abs = fastmri.complex_abs(slice)\\n plt.imshow(slice_image_abs, cmap = 'gray')\\n plt.show()\",\n \"def print_seg_row_col(sp) : \\n s, r, c = src_from_rc8x8(sp.peak_y_raw, sp.peak_x_raw)\\n print('seg: %d, row: %.1f, col: %.1f' % (s, r, c))\",\n \"def explore_data(dataset, start, end, rows_and_columns=False):\\r\\n for i in range(start,end):\\r\\n print(dataset[i],end=\\\"\\\\n\\\")\",\n \"def show_slice(file_path, x, y, z):\\n img = nib.load(file_path)\\n img = img.get_fdata()\\n print(\\\"The scan has dimensions {}.\\\".format(img.shape))\\n slice_0 = img[x, :, :]\\n slice_1 = img[:, y, :]\\n slice_2 = img[:, :, z]\\n slices = [slice_0, slice_1, slice_2]\\n fig, axes = plt.subplots(1, len(slices), figsize=[12, 4])\\n for i, sli in enumerate(slices):\\n axes[i].imshow(sli.T, cmap=\\\"gray\\\", origin=\\\"lower\\\")\",\n \"def imshow(image):\\n iio.imshow(dtype.im2uint(image))\",\n \"def show_image(image):\\n print('-' * (len(image) + 4))\\n for line in image:\\n print('| ', end='')\\n for ch in line:\\n char = '#' if ch is True else '.'\\n print(char, end='')\\n print(' |')\\n print('-' * (len(image) + 4))\",\n \"def print_info(self, i):\\n\\n im_size = self.image_size(i)\\n print 'The path of the image is: {}'.format(self.image_path_at(i))\\n print 'width: {}, height: {}'.format(im_size[0], im_size[1])\\n \\n attr_i = self.gtdb['attr'][i, :]\\n print 'The attributes are: {}'.format(','.join([self._classes[i] for i in np.where(attr_i==1)[0]]))\",\n \"def print_image(indiv,name):\\n routine = gp.compile(indiv,pset)\\n output = gen_beat_output(routine)\\n bits = np.array(map(bitlist,output)[0:24000]).transpose()\\n plt.style.use('classic')\\n plt.imshow(bits,interpolation='nearest',aspect='auto',cmap=plt.get_cmap('Greys'))\\n plt.savefig(name+\\\".png\\\",dpi=150)\",\n \"def print_image(self, pil_image):\\n bw_image = pil_image.convert('1', dither=Image.FLOYDSTEINBERG)\\n width = bw_image.width\\n height = bw_image.height\\n img_bytes = list(bw_image.getdata())\\n self.print_bitmap(width, height, img_bytes)\",\n \"def collatz_print(w, i, j, v):\\n\\tw.write(str(i) + \\\" \\\" + str(j) + \\\" \\\" + str(v) + \\\"\\\\n\\\")\",\n \"def print_image(self, width, data):\\n if len(data) % width:\\n raise ValueError('Bad image format, length of data must be '\\n 'divisible by width.')\\n height = len(data) / width\\n\\n # send line-by-line\\n for row in range(height):\\n self.send_command('print_bitmap', 1, width)\\n self.port.write(data[row*width:(row+1)*width], is_text=False)\\n self.port.fed_dots(1)\",\n \"def printdatarow(dat, iteration):\\r\\n i = np.where(dat.f[:, 0] == iteration)[0][0]\\r\\n j = np.where(dat.std[:, 0] == iteration)[0][0]\\r\\n print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) +\\r\\n ' %5.1e' % (dat.f[i,3]) +\\r\\n ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:]))\",\n \"def printdatarow(dat, iteration):\\r\\n i = np.where(dat.f[:, 0] == iteration)[0][0]\\r\\n j = np.where(dat.std[:, 0] == iteration)[0][0]\\r\\n print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) +\\r\\n ' %5.1e' % (dat.f[i,3]) +\\r\\n ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:]))\",\n \"def collatz_print (w, i, j, v) :\\n w.write(str(i) + \\\" \\\" + str(j) + \\\" \\\" + str(v) + \\\"\\\\n\\\")\",\n \"def display_data():\\n data = read_data_from_file()\\n data_size = len(data)\\n print('Data size - ' + str(data_size))\\n\\n\\n for i in range(data_size-1, 0, -1):\\n image = data[i]\\n cv2.imshow('window', image[0])\\n print(str(image[1]))\\n cv2.waitKey(50)\",\n \"def printImage(imageObject):\\n # TODO\\n pass\",\n \"def _convert_and_print_image(self, im):\\n pixLine = \\\"\\\"\\n imLeft = \\\"\\\"\\n imRight = \\\"\\\"\\n switch = 0\\n imgSize = [0, 0]\\n\\n if im.size[0] > 512:\\n print (\\\"WARNING: Image is wider than 512 and could be truncated at print time \\\")\\n if im.size[1] > 255:\\n raise ValueError(\\\"Image Height larger than 255\\\")\\n\\n imBorder = self._check_image_size(im.size[0])\\n for i in range(imBorder[0]):\\n imLeft += \\\"0\\\"\\n for i in range(imBorder[1]):\\n imRight += \\\"0\\\"\\n\\n for y in range(im.size[1]):\\n imgSize[1] += 1\\n pixLine += imLeft\\n imgSize[0] += imBorder[0]\\n for x in range(im.size[0]):\\n imgSize[0] += 1\\n RGB = im.getpixel((x, y))\\n imColor = (RGB[0] + RGB[1] + RGB[2])\\n imPattern = \\\"1X0\\\"\\n patternLen = len(imPattern)\\n switch = (switch - 1) * (-1)\\n for x in range(patternLen):\\n if imColor <= (255 * 3 / patternLen * (x + 1)):\\n if imPattern[x] == \\\"X\\\":\\n pixLine += \\\"%d\\\" % switch\\n else:\\n pixLine += imPattern[x]\\n break\\n elif imColor > (255 * 3 / patternLen * patternLen) and imColor <= (255 * 3):\\n pixLine += imPattern[-1]\\n break\\n pixLine += imRight\\n imgSize[0] += imBorder[1]\\n\\n self._print_image(pixLine, imgSize)\",\n \"def print_real_image(r_img, name = None, batch_size = BATCH_SIZE):\\n if name: print(\\\"***********\\\" + name + \\\"**********\\\")\\n to_print = []\\n c_img = r_img.clone()\\n for i in range(batch_size):\\n img = c_img[i,:,:]\\n img = torch.squeeze(img)\\n img = img.detach()\\n img = img.cpu()\\n plt.imshow(img, cmap = 'gray')\\n plt.show()\",\n \"def display(self):\\n for row in range(self.height):\\n for col in range(self.width):\\n char = '#' if self.pixels[row * self.width + col] else '.'\\n print(char, end='')\\n print()\\n print()\",\n \"def display(self, image):\\n assert(image.mode == '1')\\n assert(image.size[0] == self.width)\\n assert(image.size[1] == self.height)\\n\\n page = 0xB0\\n pix = list(image.getdata())\\n step = self.width * 8\\n for y in xrange(0, self.pages * step, step):\\n\\n # move to given page, then reset the column address\\n self.command(page, 0x02, 0x10)\\n page += 1\\n\\n buf = []\\n for x in xrange(self.width):\\n byte = 0\\n for n in xrange(0, step, self.width):\\n byte |= (pix[x + y + n] & 0x01) << 8\\n byte >>= 1\\n\\n buf.append(byte)\\n\\n self.data(buf)\",\n \"def display(self, image):\\n assert(image.mode == '1')\\n assert(image.size[0] == self.width)\\n assert(image.size[1] == self.height)\\n\\n page = 0xB0\\n pix = list(image.getdata())\\n step = self.width * 8\\n for y in xrange(0, self.pages * step, step):\\n\\n # move to given page, then reset the column address\\n self.command(page, 0x02, 0x10)\\n page += 1\\n\\n buf = []\\n for x in xrange(self.width):\\n byte = 0\\n for n in xrange(0, step, self.width):\\n byte |= (pix[x + y + n] & 0x01) << 8\\n byte >>= 1\\n\\n buf.append(byte)\\n\\n self.data(buf)\",\n \"def info_np(img):\\n import numpy as np\\n\\n print ('Dimensions: ' + str(np.shape(img)))\\n print ('Min value: ' + str(np.min(img)))\\n print ('Avg value: ' + str(np.average(img)))\\n print ('Med value: ' + str(np.median(img)))\\n print ('Max value: ' + str(np.max(img)))\\n print ('Std dev: ' + str(np.std(img)))\\n print ('Sum: ' + str(np.sum(img)))\",\n \"def disp(self, idx=100): # r_[0:5,1e2:1e9:1e2,-10:0]):\\r\\n\\r\\n filenameprefix=self.name_prefix\\r\\n\\r\\n def printdatarow(dat, iteration):\\r\\n \\\"\\\"\\\"print data of iteration i\\\"\\\"\\\"\\r\\n i = np.where(dat.f[:, 0] == iteration)[0][0]\\r\\n j = np.where(dat.std[:, 0] == iteration)[0][0]\\r\\n print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) +\\r\\n ' %5.1e' % (dat.f[i,3]) +\\r\\n ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:]))\\r\\n\\r\\n dat = CMADataLogger(filenameprefix).load()\\r\\n ndata = dat.f.shape[0]\\r\\n\\r\\n # map index to iteration number, is difficult if not all iteration numbers exist\\r\\n # idx = idx[np.where(map(lambda x: x in dat.f[:,0], idx))[0]] # TODO: takes pretty long\\r\\n # otherwise:\\r\\n if idx is None:\\r\\n idx = 100\\r\\n if np.isscalar(idx):\\r\\n # idx = np.arange(0, ndata, idx)\\r\\n if idx:\\r\\n idx = np.r_[0, 1, idx:ndata-3:idx, -3:0]\\r\\n else:\\r\\n idx = np.r_[0, 1, -3:0]\\r\\n\\r\\n idx = array(idx)\\r\\n idx = idx[idx<ndata]\\r\\n idx = idx[-idx<=ndata]\\r\\n iters = dat.f[idx, 0]\\r\\n idxbest = np.argmin(dat.f[:,5])\\r\\n iterbest = dat.f[idxbest, 0]\\r\\n\\r\\n if len(iters) == 1:\\r\\n printdatarow(dat, iters[0])\\r\\n else:\\r\\n self.disp_header()\\r\\n for i in iters:\\r\\n printdatarow(dat, i)\\r\\n self.disp_header()\\r\\n printdatarow(dat, iterbest)\\r\\n sys.stdout.flush()\",\n \"def show_np(mat):\\n for x in range(15):\\n for y in range(15):\\n if (x == 7) and (y == 7):\\n print(\\\"\\\\033[%d;%d;%dm**\\\\033[0m\\\" % (0, 33, 41), end='')\\n elif mat[x, y, 0] > 0:\\n print(\\\"\\\\033[%d;%d;%dm \\\\033[0m\\\" % (0, 31, 41), end='')\\n elif mat[x, y, 1] > 0:\\n print(\\\"\\\\033[%d;%d;%dm \\\\033[0m\\\" % (0, 32, 42), end='')\\n else:\\n print(\\\" \\\", end='')\\n print(\\\"\\\")\",\n \"def _showdata(self, prec=4):\\n print('nh {0:d} nslices {1:d} nbl {2:d} ncp {3:d} nca {4:d} '.format(\\n self.nh, self.nslices, self.nbl, self.ncp, self.nca), end=\\\"\\\")\\n print(\\\"observables in np arrays with {:d} rows\\\".format(self.nslices))\\n\\n if len(self.observables) == 4:\\n print('nca', self.nca)\\n else:\\n print()\\n np.set_printoptions(precision=prec)\\n\\n print(self.fp.shape, \\\"fp (degrees, but stored internally in radians):\\\\n\\\",\\n self.fp*self.degree, \\\"\\\\n\\\")\\n print(self.fa.shape, \\\"fa:\\\\n\\\", self.fa, \\\"\\\\n\\\")\\n\\n print(self.cp.shape, \\\"cp (degrees, but stored internally in radians):\\\\n\\\",\\n self.cp*self.degree, \\\"\\\\n\\\")\\n if len(self.observables) == 4:\\n print(self.ca.shape, \\\"ca:\\\\n\\\", self.ca, \\\"\\\\n\\\")\\n # print(self.info4oif_dict)\\n\\n print(\\\"hole centers array shape:\\\", self.ctrs.shape)\\n\\n print(len(self.bholes), \\\"baseline hole indices\\\\n\\\", self.bholes)\\n print(self.bls.shape, \\\"baselines:\\\\n\\\", self.bls)\\n\\n print(self.tholes.shape, \\\"triple hole indices:\\\\n\\\", self.tholes)\\n print(self.tuv.shape, \\\"triple uv vectors:\\\\n\\\", self.tuv)\\n\\n print(self.qholes.shape, \\\"quad hole indices:\\\\n\\\", self.qholes)\\n print(self.quvw.shape, \\\"quad uvw vectors:\\\\n\\\", self.quvw)\",\n \"def info(self):\\n\\n print(\\\"pixellisation:\\\", self.pixel)\\n print(\\\"number of components:\\\", self.ncomp)\\n print(\\\"number of pixels:\\\", self.data.shape[:] if self.ncomp == 1 else self.data.shape[1:])\\n print(\\\"nside:\\\", self.nside)\\n print(\\\"geometry:\\\", self.geometry)\\n print(\\\"coordinates:\\\", self.coordinate)\",\n \"def disp(self, idx=100): # r_[0:5,1e2:1e9:1e2,-10:0]):\\r\\n\\r\\n filenameprefix=self.name_prefix\\r\\n\\r\\n def printdatarow(dat, iteration):\\r\\n \\\"\\\"\\\"print data of iteration i\\\"\\\"\\\"\\r\\n i = np.where(dat.f[:, 0] == iteration)[0][0]\\r\\n j = np.where(dat.std[:, 0] == iteration)[0][0]\\r\\n print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) +\\r\\n ' %5.1e' % (dat.f[i,3]) +\\r\\n ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:]))\\r\\n\\r\\n dat = CMADataLogger(filenameprefix).load()\\r\\n ndata = dat.f.shape[0]\\r\\n\\r\\n # map index to iteration number, is difficult if not all iteration numbers exist\\r\\n # idx = idx[np.where(map(lambda x: x in dat.f[:,0], idx))[0]] # TODO: takes pretty long\\r\\n # otherwise:\\r\\n if idx is None:\\r\\n idx = 100\\r\\n if np.isscalar(idx):\\r\\n # idx = np.arange(0, ndata, idx)\\r\\n if idx:\\r\\n idx = np.r_[0, 1, idx:ndata-3:idx, -3:0]\\r\\n else:\\r\\n idx = np.r_[0, 1, -3:0]\\r\\n\\r\\n idx = array(idx)\\r\\n idx = idx[idx<=ndata] # TODO: shouldn't this be \\\"<\\\"?\\r\\n idx = idx[-idx<=ndata]\\r\\n iters = dat.f[idx, 0]\\r\\n idxbest = np.argmin(dat.f[:,5])\\r\\n iterbest = dat.f[idxbest, 0]\\r\\n if len(iters) == 1:\\r\\n printdatarow(dat, iters[0])\\r\\n else:\\r\\n self.disp_header()\\r\\n for i in iters:\\r\\n printdatarow(dat, i)\\r\\n self.disp_header()\\r\\n printdatarow(dat, iterbest)\\r\\n sys.stdout.flush()\",\n \"def print_image(img):\\r\\n # On affiche l'image\\r\\n plt.figure(figsize=(20, 5))\\r\\n plt.subplot(1, 2, 1)\\r\\n plt.imshow(img)\\r\\n # On affiche l'histogramme\\r\\n plt.subplot(1, 2, 2)\\r\\n plt.hist(img.flatten(), bins=range(256))\\r\\n plt.show()\",\n \"def display(self):\\n \\\"\\\"\\\" Coordinates for position are x-axis (LR) and y-axis (NS) \\\"\\\"\\\"\\n for coordY in range(self.y):\\n print()\\n for column in range(self.height):\\n for coordLR in range(self.x):\\n print(\\\" \\\", end=\\\"\\\")\\n for row in range(self.width):\\n print(\\\"#\\\", end=\\\"\\\")\\n print()\",\n \"def ndarray_print(nd):\\n try:\\n x = nd.tolist()\\n except (TypeError, NotImplementedError):\\n x = nd.tobytes()\\n if isinstance(nd, ndarray):\\n offset = nd.offset\\n flags = nd.flags\\n else:\\n offset = 'unknown'\\n flags = 'unknown'\\n print(\\n \\\"ndarray(%s, shape=%s, strides=%s, suboffsets=%s, offset=%s, format='%s', itemsize=%s, flags=%s)\\\"\\n % (x, nd.shape, nd.strides, nd.suboffsets, offset, nd.format, nd.\\n itemsize, flags))\\n sys.stdout.flush()\",\n \"def print_azeltables(inviews, ic):\\n for i in range(0, len(inviews)):\\n print \\\" \\\"\\n print \\\"Az/El for inview %s to %s\\\" % (inviews[i][0], inviews[i][1])\\n azels = ic.compute_azels(inviews[i][0], inviews[i][1], 15)\\n for j in range(0, len(azels)):\\n print \\\"At %s, azimuth=%8.2f, elevation=%8.2f\\\" % \\\\\\n (azels[j][0], azels[j][1], azels[j][2])\",\n \"def _print_image(self, line, size):\\n i = 0\\n cont = 0\\n\\n self._write(self.__class__.__imageSize['1x1'])\\n buffer = bytearray([int((size[0] / size[1]) / 8), 0, size[1], 0])\\n\\n self._write(buffer)\\n buffer = bytearray()\\n\\n while i < len(line):\\n hex_string = int(line[i:i + 8], 2)\\n buffer.append(hex_string)\\n i += 8\\n cont += 1\\n if cont % 4 == 0:\\n self._write(buffer)\\n buffer = bytearray()\\n cont = 0\",\n \"def show(self, data):\\n if isinstance(data, (numpy.ndarray, h5py.Dataset)):\\n isAtomic = len(data.shape) == 0\\n isCurve = len(data.shape) == 1 and numpy.issubdtype(data.dtype, numpy.number)\\n isImage = len(data.shape) == 2 and numpy.issubdtype(data.dtype, numpy.number)\\n if isAtomic:\\n self.showAsString(data)\\n elif isCurve:\\n self.show1d(data)\\n elif isImage:\\n self.show2d(data)\\n else:\\n self.showAsString(data)\\n else:\\n self.showAsString(data)\",\n \"def print_bitmap(self, w, h, image):\\n\\n bitmap = self._pack_bitmap(w, h, image)\\n\\n row_bytes = (w + 7) // 8 # Round up to next byte boundary\\n\\n if row_bytes >= 48:\\n row_bytes_clipped = 48\\n else:\\n row_bytes_clipped = row_bytes # 384 pixels max width\\n\\n # Est. max rows to write at once, assuming 256 byte printer buffer.\\n if self._dtr_enabled:\\n chunk_height_limit = 255 # Buffer doesn't matter, handshake!\\n else:\\n chunk_height_limit = 256 // row_bytes_clipped\\n if chunk_height_limit > self._max_chunk_height:\\n chunk_height_limit = self._max_chunk_height\\n elif chunk_height_limit < 1:\\n chunk_height_limit = 1\\n\\n row_start = 0\\n i = 0\\n while row_start < h:\\n # Issue up to chunkHeightLimit rows at a time:\\n chunk_height = h - row_start\\n if chunk_height > chunk_height_limit:\\n chunk_height = chunk_height_limit\\n\\n self.write(self.ASCII_DC2, '*', chunk_height, row_bytes_clipped)\\n\\n y = 0\\n while y < chunk_height:\\n x = 0\\n while x < row_bytes_clipped:\\n self.timeout_wait()\\n self._send_to_printer(int(bitmap[i]))\\n x += 1\\n i += 1\\n\\n y += 1\\n\\n i += row_bytes - row_bytes_clipped\\n\\n self.timeout_set(chunk_height * self._dot_print_time)\\n\\n row_start += chunk_height_limit\\n\\n self._prev_byte = '\\\\n'\",\n \"def display(self):\\n for row0 in range(self.y):\\n print()\\n for row in range(self.height):\\n for column0 in range(self.x):\\n print(\\\" \\\", end=\\\"\\\")\\n for column in range(self.width):\\n print(\\\"#\\\", end=\\\"\\\")\\n print()\",\n \"def print_wrapped(data, ncols=3):\\r\\n nrows = len(data)\\r\\n labels = data.index\\r\\n n_split_rows = int(np.ceil(nrows / ncols))\\r\\n for r in range(0, nrows, ncols):\\r\\n for c in range(ncols):\\r\\n try:\\r\\n numstr = '{}'.format(data[r + c])\\r\\n tabs = [' '] * (20 - len(labels[r + c]) - len(numstr))\\r\\n print(labels[r + c] + \\\"\\\".join(tabs) + numstr, end='\\\\t')\\r\\n except:\\r\\n pass\\r\\n print()\",\n \"def ndarray_print(nd):\\n try:\\n x = nd.tolist()\\n except (TypeError, NotImplementedError):\\n x = nd.tobytes()\\n if isinstance(nd, ndarray):\\n offset = nd.offset\\n flags = nd.flags\\n else:\\n offset = 'unknown'\\n flags = 'unknown'\\n print(\\\"ndarray(%s, shape=%s, strides=%s, suboffsets=%s, offset=%s, \\\"\\n \\\"format='%s', itemsize=%s, flags=%s)\\\" %\\n (x, nd.shape, nd.strides, nd.suboffsets, offset,\\n nd.format, nd.itemsize, flags))\\n sys.stdout.flush()\",\n \"def display_cropped_img(i):\\n image = PIL.Image.open(testing_img_paths[i])\\n image = image.crop(box=(313,99,825,611))\\n image = image.resize((256,256))\\n display(image)\",\n \"def display(self, image):\\n assert(image.mode == '1')\\n assert(image.size[0] == self.width)\\n assert(image.size[1] == self.height)\\n\\n self.command(\\n const.COLUMNADDR, 0x00, self.width-1, # Column start/end address\\n const.PAGEADDR, 0x00, self.pages-1) # Page start/end address\\n\\n pix = list(image.getdata())\\n step = self.width * 8\\n buf = []\\n for y in xrange(0, self.pages * step, step):\\n i = y + self.width-1\\n while i >= y:\\n byte = 0\\n for n in xrange(0, step, self.width):\\n byte |= (pix[i + n] & 0x01) << 8\\n byte >>= 1\\n\\n buf.append(byte)\\n i -= 1\\n\\n self.data(buf)\",\n \"def display(self):\\n for row in self.tile_rows:\\n print(row)\",\n \"def display(self):\\n for i in range(self.y):\\n print()\\n for i in range(self.height):\\n for k in range(self.x):\\n print(' ', end='')\\n for j in range(self.width):\\n print('#', end='')\\n print()\",\n \"def show_ipv(data: np.ndarray):\\n import ipyvolume as ipv\\n return ipv.quickvolshow(data)\",\n \"def imshow(self, depth):\\n layer = self.cube[depth]\\n img = []\\n for i in range(self.height):\\n img.append([layer[i][j].value for j in range(self.width)])\\n plt.imshow(img, cmap='gray')\\n plt.show()\",\n \"def display(self):\\n for b in range(self.y):\\n print()\\n for i in range(self.height):\\n print(\\\" \\\" * self.x + \\\"#\\\" * self.width)\",\n \"def display(self):\\n width = 1 + max(len(self.values[s]) for s in self.boxes)\\n line = 'x'.join(['-'*(width*3)]*3)\\n for r in self.rows:\\n print(''.join(self.values[r+c].center(width)+('|' if c in '36' else '')\\n for c in self.cols))\\n if r in 'CF': print(line)\\n print\",\n \"def display_mask(i):\\n ### To display binary masks, comment the folowing line\\n # mask = np.argmax(testing_preds[i], axis=-1)\\n ### To display probability maps, comment the folowing line\\n mask = testing_preds[i,:,:,-1]\\n mask = np.expand_dims(mask, axis=-1)\\n img = PIL.ImageOps.autocontrast(keras.preprocessing.image.array_to_img(mask))\\n display(img)\",\n \"def show_input_to_output(img_ns):\\n figure()\\n \\n sp = subplot(1, 2, 1).imshow(img_ns.img)\\n sp.axes.grid(False)\\n sp.axes.tick_params(bottom=False, left=False, which='both',labelleft=False,labelbottom=False,length=0)\\n title(\\\"Input Image\\\", fontsize=10);\\n outimg = tiles_to_images(img_ns, img_ns.tile_grid, img_ns.tile_catalog, img_ns.tile_size)\\n sp = subplot(1, 2, 2).imshow(outimg.astype(np.uint8));\\n sp.axes.tick_params(bottom=False, left=False, which='both',labelleft=False,labelbottom=False,length=0)\\n title(\\\"Output Image From Tiles\\\", fontsize=10);\\n sp.axes.grid(False)\\n #print(outimg.astype(np.uint8))\\n #print(img_ns)\\n plt.savefig(img_ns.output_filename + \\\"_input_to_output.pdf\\\", bbox_inches=\\\"tight\\\")\\n plt.close()\",\n \"def display(array):\\n plt.figure()\\n plt.imshow(array)\\n plt.show()\",\n \"def header(self):\\n print 'dimensions',self.data.shape\\n print 'llcorner', self.xllcorner, self.yllcorner\\n print 'cell size', self.cellsize\",\n \"def show_image(self, pic, prediction=None):\\n digitmap = {\\n 0: [(0,0), (1,0), (2,0), (3,0), (4,0), (0,1), (4,1), (0,2), (1,2), (2,2), (3,2), (4,2)],\\n 1: [(0,2), (1,2), (2,2), (3,2), (4,2)],\\n 2: [(0,0), (0,1), (0,2), (1,2), (2,0), (2,1), (2,2), (3,0), (4,0), (4,1), (4,2)],\\n 3: [(0,0), (0,1), (0,2), (1,2), (2,0), (2,1), (2,2), (3,2), (4,0), (4,1), (4,2)],\\n 4: [(0,0), (0,2), (1,0), (1,2), (2,0), (2,1), (2,2), (3,2), (4,2)],\\n 5: [(0,0), (0,1), (0,2), (1,0), (2,0), (2,1), (2,2), (3,2), (4,0), (4,1), (4,2)],\\n 6: [(0,0), (0,1), (0,2), (1,0), (2,0), (2,1), (2,2), (3,0), (3,2), (4,0), (4,1), (4,2)],\\n 7: [(0,0), (0,1), (0,2), (1,2), (2,2), (3,2), (4,2)],\\n 8: [(0,0), (1,0), (2,0), (3,0), (4,0), (0,1), (4,1), (0,2), (1,2), (2,2), (3,2), (4,2), (2,1)],\\n 9: [(0,0), (0,1), (0,2), (1,0), (1,2), (2,0), (2,1), (2,2), (3,2), (4,0), (4,1), (4,2)]\\n }\\n\\n pic = pic.reshape((28,28)).copy()\\n if prediction is not None:\\n for pos in digitmap[prediction]:\\n pic[pos]=255\\n plt.imshow(pic, cmap='gray_r')\",\n \"def display(self):\\n width = self.width\\n height = self.height\\n x = self.x\\n y = self.y\\n for d_y in range(y):\\n print()\\n for h in range(height):\\n if x != 0:\\n print(\\\" \\\" * x, end=\\\"\\\")\\n print(\\\"#\\\" * width)\",\n \"def print(self):\\n self.__print_local(self.dataset, 0)\",\n \"def display_napari(pos_img):\\n global data\\n global img_queue\\n if pos_img is None:\\n return\\n # read image and z position\\n image = np.reshape(pos_img[2:],(clip[0], clip[1]))\\n z_pos = pos_img[1]\\n color = pos_img[0]\\n\\n # write image into correct slice of data and update display\\n data[z_pos] = np.squeeze(image)\\n layer = viewer.layers[color]\\n layer.data = data\\n #print(\\\"updating \\\", z_pos, color)\\n\\n img_queue.task_done()\",\n \"def output(self):\\n width = \\\" \\\" # 6 spaces formatting\\n print(\\\"\\\\n\\\\n\\\")\\n for row in range(self._length, -1, -1):\\n if row != 0:\\n print(row, end = width)\\n for col in range(0, self._length):\\n #print(self.board[col][row - 1], end = width)\\n self.board[col][row-1].output(width)\\n print(\\\"\\\\n\\\\n\\\")\\n else:\\n print(width, end=\\\" \\\")\\n for col in self.columns:\\n print(col, end = width)\\n print(\\\"\\\\n\\\\n\\\")\",\n \"def pprint(self, data):\\n self._assert(data)\\n data = self._render(data) # make elements ascii\\n fmats = self._fmats(data) # get array of padding formats)\\n for row in data:\\n print(fmats.format(*row))\",\n \"def info(self):\\n\\n\\t\\tprint(\\\"Pixels on a side: {0}\\\".format(self.data.shape[0]))\\n\\t\\tprint(\\\"Pixel size: {0}\\\".format(self.resolution))\\n\\t\\tprint(\\\"Total angular size: {0}\\\".format(self.side_angle))\\n\\t\\tprint(\\\"lmin={0:.1e} ; lmax={1:.1e}\\\".format(self.lmin,self.lmax))\",\n \"def displayData(indices_to_display = None):\\n width, height = 20, 20\\n nrows, ncols = 10, 10\\n if not indices_to_display:\\n indices_to_display = random.sample(range(X.shape[0]), nrows*ncols)\\n \\n big_picture = np.zeros((height*nrows,width*ncols))\\n \\n irow, icol = 0, 0\\n for idx in indices_to_display:\\n if icol == ncols:\\n irow += 1\\n icol = 0\\n iimg = getDatumImg(X[idx])\\n big_picture[irow*height:irow*height+iimg.shape[0], icol*width:icol*width+iimg.shape[1]] = iimg\\n icol += 1\\n fig = plt.figure(figsize=(6,6))\\n\\n big_picture = (big_picture * 255).astype(np.int8)\\n img = Image.fromarray(big_picture, mode='L')\\n plt.imshow(img, cmap = cm.Greys)\",\n \"def render(filename,i):\\n print('running render')\\n A = np.genfromtxt(filename,skip_header=1,dtype=float,delimiter=',')\\n img = np.array(A[i,:],copy=True)\\n print(img.shape)\\n img = img.reshape(28,28)\\n img = 255 - img\\n print(img.shape)\\n plt.imshow(img, cmap=\\\"gray\\\", vmin=0, vmax=255)\\n plt.savefig(\\\"img\\\" + str(i)+\\\"render\\\"+ \\\".png\\\")\",\n \"def display(self):\\n for i in range(self.__y):\\n print()\\n for i in range(self.__height):\\n print(\\\" \\\" * self.__x + \\\"#\\\" * self.__width)\",\n \"def write_window(img, ds, window):\\n new_img = np.array([img[:, :, i] for i in range(img.shape[2])])\\n ds.write(new_img, window=window)\",\n \"def print_images(i, df):\\n \\n images_folder_path = \\\"dataset/petfinder-adoption-prediction/train_images/\\\"\\n plt.imshow(cv2.cvtColor(cv2.imread(images_folder_path+df.filename[i]), cv2.COLOR_BGR2RGB),);\\n plt.axis(\\\"off\\\");\\n plt.show()\",\n \"def print_images_out_statistics(self):\\n self._print_images_statistics(self._images_out_folder, self._pose_class_names)\",\n \"def view_image(train_dataloader):\\n for (x, target) in train_dataloader:\\n np.save(\\\"img.npy\\\", x)\\n print(x.shape)\\n exit(0)\",\n \"def print(self):\\n print(\\\" 0 1 2 3 4 5 6 7 8 \\\")\\n print(\\\" -------------------------\\\")\\n for x in range(0, 9):\\n print(f\\\"{x} | \\\", end=\\\"\\\")\\n for y in range(0, 9):\\n if self.field[x][y] == -1:\\n print(\\\"* \\\", end=\\\"\\\")\\n else:\\n print(f\\\"{self.field[x][y]} \\\", end=\\\"\\\")\\n if y % 3 == 2:\\n print(\\\"| \\\", end=\\\"\\\")\\n print(\\\"\\\")\\n if x % 3 == 2:\\n print(\\\" -------------------------\\\")\",\n \"def print_data(place):\\n raise NotImplementedError\",\n \"def display(self):\\n [print() for i in range(self.__y)]\\n for i in range(self.__height):\\n [print(\\\" \\\", end=\\\"\\\") for i in range(self.__x)]\\n for j in range(self.__width):\\n print(\\\"#\\\", end=\\\"\\\")\\n print()\",\n \"def print_image_info(input_image):\\n print()\\n print(\\\"Basic Information on image: {}\\\".format(input_image.filename))\\n print(\\\"Format: {}\\\".format(input_image.format))\\n print(\\\"Mode: {}\\\".format(input_image.mode))\\n print(\\\"Size: {}\\\".format(input_image.size))\\n print(\\\"Width: {}\\\".format(input_image.width))\\n print(\\\"Height: {}\\\".format(input_image.height))\\n print(\\\"Palette: {}\\\".format(input_image.palette))\\n print()\",\n \"def __str__(self):\\n result = ''\\n for row in range(self.getHeight()):\\n for col in range(self.getWidth()):\\n result += str(self.data[row][col]) + ' '\\n result += '\\\\n'\\n return result\",\n \"def print(self):\\n # IMPLEMENT ME\\n for i in range(self.height):\\n for j in range(self.width):\\n print(self.board[i][j], end=\\\" \\\")\\n print()\\n print()\",\n \"def pixdump( source, start=None, end=None, length=None, width=64, height=None, palette=None ):\\n\\n for line in pixdump_iter( source, start, end, length, width, height, palette ):\\n print( line )\",\n \"def print(self):\\r\\n base = 8 * self.width\\r\\n print(base * \\\"-\\\")\\r\\n for x in range(self.height):\\r\\n output = \\\"\\\"\\r\\n for y in range(self.width):\\r\\n output = output + self.board[x][y] + \\\"|\\\"\\r\\n print(\\\"|\\\" + output)\\r\\n print(base * \\\"-\\\")\",\n \"def show_slices(slices):\\n fig, axes = plt.subplots(1, len(slices))\\n for i, slice in enumerate(slices):\\n axes[i].imshow(slice.T, cmap=\\\"gray\\\", origin=\\\"lower\\\")\",\n \"def display(self):\\n result = []\\n horizontal_bounds, vertical_bounds = self.get_bounds()\\n min_x, max_x = horizontal_bounds\\n min_y, max_y = vertical_bounds\\n\\n # xrange is inclusive in start and exclusive in end, IE [s..e)\\n # so we add +1 offset to be safe\\n for y in xrange(max_y, min_y - 1, -1):\\n # since we have three rows and we're still relying on print,\\n # displaying gets a bit dirty\\n # will get cleaner once we move to something like HTML\\n row_tiles = [self.tile(x, y) for x in xrange(min_x, max_x + 1)]\\n\\n # now we have to print each of the three rows together.\\n # zip to aggregate each of the top, middle, bottom rows\\n row_lines = zip(*[str(tile).split(\\\"\\\\n\\\") for tile in row_tiles])\\n for line in row_lines:\\n result.append(\\\"\\\".join(line))\\n\\n return \\\"\\\\n\\\".join(result)\",\n \"def imdisplay(filename, representation):\\n\\n image = read_image(filename, representation)\\n plt.imshow(image, cmap=\\\"gray\\\")\\n plt.show()\",\n \"def disImg(data=None,colorbar=False):\\n size = np.sqrt(len(data[4:]))\\n xmm = data[0]\\n ymm = data[1]\\n pl.matshow(data[4:].reshape(size,size),fignum=False)\\n if colorbar == True:\\n pl.colorbar()\\n pl.xlim(0,size-1)\\n pl.ylim(0,size-1)\\n pl.xlabel('Pixels')\\n pl.ylabel('Pixels')\\n pl.grid(color='yellow')\",\n \"def visualize_output(\\n self,\\n img: np.ndarray,\\n output_data: Any):\\n raise NotImplementedError\",\n \"def imdisplay(filename, representation):\\n image = read_image(filename, representation)\\n\\n if representation == GRAY_OUT:\\n plt.imshow(image, cmap='gray')\\n else:\\n plt.imshow(image)\\n\\n plt.show()\",\n \"def show(self):\\r\\n \\r\\n clear() \\r\\n print \\\" \\\" + \\\"-\\\" * self.__width + \\\" \\\"\\r\\n \\r\\n for row in self.__buffer:\\r\\n rowData = \\\"\\\".join(str(i) for i in row)\\r\\n print \\\"|\\\" + rowData + \\\"|\\\"\\r\\n\\r\\n print \\\" \\\" + \\\"-\\\" * self.__width + \\\" \\\"\\r\\n self.clearBuffer()\",\n \"def display_image(X):\\n\\n\\tim = X.reshape(28, 28)\\n\\ttemp = plt.imshow(im)\\n\\tplt.show()\",\n \"def print_vector(self):\\n print self.x, self.y, self.z\",\n \"def print_array(x, idx=slice(None), message=None, message_prefix=\\\"SHIM - \\\",\\n file=sys.stdout):\\n return set_subtensor(x[idx],\\n print(x[idx],\\n message=message,\\n message_prefix=message_prefix,\\n file=file\\n )\\n )\",\n \"def show(self):\\n \\n \\n \\n \\n \\n \\n r = 4\\n f, axarr = plt.subplots(r, r, figsize=(8,8))\\n counter = 0\\n for i in range(r):\\n for j in range(r):\\n temp = self.x[counter,:]\\n counter += 1\\n img = self.x[counter,:]\\n axarr[i][j].imshow(img)\\n #######################################################################\\n # #\\n # #\\n # TODO: YOUR CODE HERE #\\n # #\\n # #\\n #######################################################################\",\n \"def print_cell_information(obj_ase_cell):\\n # print the lattice vectors\\n print('a1=',obj_ase_cell.cell[0,:])\\n print('a2=',obj_ase_cell.cell[1,:])\\n print('a3=',obj_ase_cell.cell[2,:])\\n for i,a in enumerate(obj_ase_cell):\\n print(i,a.symbol,a.position)\",\n \"def print_row(row,writer,x):\\n sys.stdout.write(unichr(0x2503))\\n for n in xrange(row.shape[0]-1):\\n writer(row[n],Width,(x,n))\\n sys.stdout.write(unichr(0x2502))\\n if row.shape[0] > 0:\\n writer(row[-1],Width,(x,row.shape[0]-1))\\n sys.stdout.write(unichr(0x2503) + '\\\\n')\",\n \"def show_slices(self, slices):\\n fig, axes = plt.subplots(1, len(slices))\\n for i, slice in enumerate(slices):\\n axes[i].imshow(slice.T, cmap=\\\"gray\\\", origin=\\\"lower\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77487314","0.750461","0.7496404","0.73919225","0.7150529","0.7141496","0.7027633","0.6667874","0.66633767","0.6654244","0.6348636","0.621092","0.6179944","0.60063565","0.59464717","0.59386533","0.5911687","0.58938134","0.5851181","0.58338124","0.58030814","0.580005","0.5770217","0.57680136","0.57322097","0.5687016","0.56508046","0.56508046","0.56486964","0.5631367","0.56241935","0.56231993","0.56194925","0.5618533","0.5608584","0.5608584","0.56033283","0.55967665","0.5579285","0.55709326","0.55670226","0.5565117","0.55197614","0.5506052","0.54795337","0.5455547","0.54323965","0.5431751","0.5415518","0.54134214","0.54053307","0.5384232","0.53834504","0.53793746","0.53791755","0.53523403","0.5348986","0.53487146","0.5321047","0.5319799","0.53061527","0.52997816","0.52917933","0.5286587","0.5281699","0.5278258","0.52758783","0.5273376","0.52664584","0.52657247","0.525592","0.5242683","0.5238663","0.5227683","0.5221043","0.5217919","0.5201372","0.5196144","0.51943666","0.5190706","0.5189621","0.51893604","0.5183344","0.51832896","0.5175574","0.51731855","0.5168432","0.5162843","0.5161328","0.51551276","0.5155126","0.514921","0.5142674","0.5141334","0.5140754","0.5138634","0.51380163","0.51371795","0.5120729","0.5120167"],"string":"[\n \"0.77487314\",\n \"0.750461\",\n \"0.7496404\",\n \"0.73919225\",\n \"0.7150529\",\n \"0.7141496\",\n \"0.7027633\",\n \"0.6667874\",\n \"0.66633767\",\n \"0.6654244\",\n \"0.6348636\",\n \"0.621092\",\n \"0.6179944\",\n \"0.60063565\",\n \"0.59464717\",\n \"0.59386533\",\n \"0.5911687\",\n \"0.58938134\",\n \"0.5851181\",\n \"0.58338124\",\n \"0.58030814\",\n \"0.580005\",\n \"0.5770217\",\n \"0.57680136\",\n \"0.57322097\",\n \"0.5687016\",\n \"0.56508046\",\n \"0.56508046\",\n \"0.56486964\",\n \"0.5631367\",\n \"0.56241935\",\n \"0.56231993\",\n \"0.56194925\",\n \"0.5618533\",\n \"0.5608584\",\n \"0.5608584\",\n \"0.56033283\",\n \"0.55967665\",\n \"0.5579285\",\n \"0.55709326\",\n \"0.55670226\",\n \"0.5565117\",\n \"0.55197614\",\n \"0.5506052\",\n \"0.54795337\",\n \"0.5455547\",\n \"0.54323965\",\n \"0.5431751\",\n \"0.5415518\",\n \"0.54134214\",\n \"0.54053307\",\n \"0.5384232\",\n \"0.53834504\",\n \"0.53793746\",\n \"0.53791755\",\n \"0.53523403\",\n \"0.5348986\",\n \"0.53487146\",\n \"0.5321047\",\n \"0.5319799\",\n \"0.53061527\",\n \"0.52997816\",\n \"0.52917933\",\n \"0.5286587\",\n \"0.5281699\",\n \"0.5278258\",\n \"0.52758783\",\n \"0.5273376\",\n \"0.52664584\",\n \"0.52657247\",\n \"0.525592\",\n \"0.5242683\",\n \"0.5238663\",\n \"0.5227683\",\n \"0.5221043\",\n \"0.5217919\",\n \"0.5201372\",\n \"0.5196144\",\n \"0.51943666\",\n \"0.5190706\",\n \"0.5189621\",\n \"0.51893604\",\n \"0.5183344\",\n \"0.51832896\",\n \"0.5175574\",\n \"0.51731855\",\n \"0.5168432\",\n \"0.5162843\",\n \"0.5161328\",\n \"0.51551276\",\n \"0.5155126\",\n \"0.514921\",\n \"0.5142674\",\n \"0.5141334\",\n \"0.5140754\",\n \"0.5138634\",\n \"0.51380163\",\n \"0.51371795\",\n \"0.5120729\",\n \"0.5120167\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.75519806"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":798,"cells":{"query":{"kind":"string","value":"Print the data in slice iz of an image to standard out."},"document":{"kind":"string","value":"def print_slice(input, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice)\" % (iz)\n\tline = []\n\tfor iy in xrange(ny):\n\t\tline.append(\"Row \")\n\t\tline.append(\"%4i \" % iy)\n\t\tfor ix in xrange(nx):\n\t\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\t\tif ((ix + 1) % 5 == 0): \n\t\t\t\tline.append(\"\\n \")\n\t\t\t\tline.append(\" \")\n\t \tline.append(\"\\n\")\n\t \tif(nx%5 != 0): line.append(\"\\n\")\n\tprint \"\".join(line)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def print_image_slice(input, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice)\" % (iz)\n\tline = []\n\tfor iy in xrange(ny-1,-1,-1):\n\t\tline.append(\"Row \")\n\t\tline.append(\"%4i \" % iy)\n\t\tfor ix in xrange(nx):\n\t\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\t\tif ((ix + 1) % 5 == 0): \n\t\t\t\tline.append(\"\\n \")\n\t\t\t\tline.append(\" \")\n\t \tline.append(\"\\n\")\n\t \tif(nx%5 != 0): line.append(\"\\n\")\n\tprint \"\".join(line)","def print_image(input):\n\timage=get_image(input)\n\tnz = image.get_zsize()\n\tfor iz in xrange(nz): print_slice(input, iz)","def print_image_row(input, iy=0, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice, y = %d col)\" % (iz, iy)\n\tline = []\n\tfor ix in xrange(nx):\n\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\tif ((ix + 1) % 5 == 0): line.append(\"\\n \")\n\tline.append(\"\\n\")\n\tprint \"\".join(line)","def print_image(data, index):\n image = data[index]\n for line in image:\n print(line)","def print_image_col(input, ix=0, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice, x = %d row)\" % (iz, ix)\n\tline = []\n\tfor iy in xrange(ny):\n\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\tif ((iy + 1) % 5 == 0): line.append(\"\\n \")\n\tline.append(\"\\n\")\n\tprint \"\".join(line)","def print_row(input, ix=0, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice, x = %d row)\" % (iz, ix)\n\tline = []\n\tfor iy in xrange(ny):\n\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\tif ((iy + 1) % 5 == 0): line.append(\"\\n \")\n\tline.append(\"\\n\")\n\tprint \"\".join(line)","def print_col(input, iy=0, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice, y = %d col)\" % (iz, iy)\n\tline = []\n\tfor ix in xrange(nx):\n\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\tif ((ix + 1) % 5 == 0): line.append(\"\\n \")\n\tline.append(\"\\n\")\n\tprint \"\".join(line)","def print_image_slice_3d(input, num=0,direction=\"z\"):\n\t#print \"print slice at 3 directions\"\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tif(direction==\"x\"):\n\t\t#print \"xxxxx\"\n\t\tix=num\n\t\tprint \"(x = %d slice)\" % (ix)\n\t\tline = []\n\t\tfor iz in xrange(nz-1,-1,-1):\n\t\t\tline.append(\"Z \")\n\t\t\tline.append(\"%4i \" % iz)\n\t\t\tfor iy in xrange(ny):\n\t\t\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\t\t\tif ((iy + 1) % 5 == 0): \n\t\t\t\t\tline.append(\"\\n \")\n\t\t\t\t\tline.append(\" \")\n\t \t\tline.append(\"\\n\")\n\t \t\tif(ny%5 != 0): line.append(\"\\n\")\n\t\tprint \"\".join(line)\n\telif(direction==\"y\"):\n\t\t#print \"yyy\"\n\t\tiy=num\n\t\tprint \"(y = %d slice)\" % (iy)\n\t\tline = []\n\t\tfor iz in xrange(nz-1,-1,-1):\n\t\t\tline.append(\"Z \")\n\t\t\tline.append(\"%4i \" % iz)\n\t\t\tfor ix in xrange(nx):\n\t\t\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\t\t\tif ((ix + 1) % 5 == 0): \n\t\t\t\t\tline.append(\"\\n \")\n\t\t\t\t\tline.append(\" \")\n\t \t\tline.append(\"\\n\")\n\t \t\tif(nx%5 != 0): line.append(\"\\n\")\n\t\tprint \"\".join(line)\n\telse:\n\t\t#print \"zzzz\"\n\t\tiz=num\n\t\tprint \"(z = %d slice)\" % (iz)\n\t\tline = []\n\t\tfor iy in xrange(ny-1,-1,-1):\n\t\t\tline.append(\"Row \")\n\t\t\tline.append(\"%4i \" % iy)\n\t\t\tfor ix in xrange(nx):\n\t\t\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\t\t\tif ((ix + 1) % 5 == 0): \n\t\t\t\t\tline.append(\"\\n \")\n\t\t\t\t\tline.append(\" \")\n\t \t\tline.append(\"\\n\")\n\t \t\tif(nx%5 != 0): line.append(\"\\n\")\n\t\tprint \"\".join(line)","def dump_row(input, fname, ix=0, iz=0):\n\tfout = open(fname, \"w\")\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tfout.write(\"# z = %d slice, x = %d row)\\n\" % (iz, ix))\n\tline = []\n\tfor iy in xrange(ny):\n\t\tfout.write(\"%d\\t%12.5g\\n\" % (iy, image.get_value_at(ix,iy,iz)))\n\tfout.close()","def print_img_data(img,title='Image'):\n print(f'________ {title} _______')\n if len(image.shape) == 2:\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\n\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0])}\\n')\n elif len(image.shape) == 3:\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\n\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0,0])}\\n')","def print_img_at_idx(self, idx):\n\n img = self.images[idx]\n print_img(img)","def print_slice(complex_slice, name = None):\n if name: print(\"***********\" + name + \"**********\")\n slice = complex_slice.clone()\n slice = slice.detach()\n slice = slice.cpu()\n slice = slice.permute(0, 2, 3, 1)\n slice = slice.squeeze()\n slice_image_abs = fastmri.complex_abs(slice)\n plt.imshow(slice_image_abs, cmap = 'gray')\n plt.show()","def show(self):\n m = [xo_convert(int(x)) for x in np.nditer(self.arr)]\n print(\"{} | {} | {}\".format(*m[:3]))\n print(\"--+---+--\")\n print(\"{} | {} | {}\".format(*m[3:6]))\n print(\"--+---+--\")\n print(\"{} | {} | {}\".format(*m[6:]))\n print()","def print_real_image(r_img, name = None, batch_size = BATCH_SIZE):\n if name: print(\"***********\" + name + \"**********\")\n to_print = []\n c_img = r_img.clone()\n for i in range(batch_size):\n img = c_img[i,:,:]\n img = torch.squeeze(img)\n img = img.detach()\n img = img.cpu()\n plt.imshow(img, cmap = 'gray')\n plt.show()","def print_image(img):\n for k in img:\n print(\"\".join([str(x) for x in k] ))","def print_image(self, width, data):\n if len(data) % width:\n raise ValueError('Bad image format, length of data must be '\n 'divisible by width.')\n height = len(data) / width\n\n # send line-by-line\n for row in range(height):\n self.send_command('print_bitmap', 1, width)\n self.port.write(data[row*width:(row+1)*width], is_text=False)\n self.port.fed_dots(1)","def print_image(self, pil_image):\n bw_image = pil_image.convert('1', dither=Image.FLOYDSTEINBERG)\n width = bw_image.width\n height = bw_image.height\n img_bytes = list(bw_image.getdata())\n self.print_bitmap(width, height, img_bytes)","def print_image(indiv,name):\n routine = gp.compile(indiv,pset)\n output = gen_beat_output(routine)\n bits = np.array(map(bitlist,output)[0:24000]).transpose()\n plt.style.use('classic')\n plt.imshow(bits,interpolation='nearest',aspect='auto',cmap=plt.get_cmap('Greys'))\n plt.savefig(name+\".png\",dpi=150)","def show_image(image):\n print('-' * (len(image) + 4))\n for line in image:\n print('| ', end='')\n for ch in line:\n char = '#' if ch is True else '.'\n print(char, end='')\n print(' |')\n print('-' * (len(image) + 4))","def printImage(imageObject):\n # TODO\n pass","def pretty_print(image_example):\n print numpy.array_str(image_example, precision=1, max_line_width=142)","def printMat(image):\n for row in range(image.rows):\n print \"[\",\n for col in range(image.cols):\n print cv.mGet(image, row, col),\n print \"]\"\n print \"\"","def imshow(image):\n iio.imshow(dtype.im2uint(image))","def ndarray_print(nd):\n try:\n x = nd.tolist()\n except (TypeError, NotImplementedError):\n x = nd.tobytes()\n if isinstance(nd, ndarray):\n offset = nd.offset\n flags = nd.flags\n else:\n offset = 'unknown'\n flags = 'unknown'\n print(\n \"ndarray(%s, shape=%s, strides=%s, suboffsets=%s, offset=%s, format='%s', itemsize=%s, flags=%s)\"\n % (x, nd.shape, nd.strides, nd.suboffsets, offset, nd.format, nd.\n itemsize, flags))\n sys.stdout.flush()","def print_image(img):\r\n # On affiche l'image\r\n plt.figure(figsize=(20, 5))\r\n plt.subplot(1, 2, 1)\r\n plt.imshow(img)\r\n # On affiche l'histogramme\r\n plt.subplot(1, 2, 2)\r\n plt.hist(img.flatten(), bins=range(256))\r\n plt.show()","def print_info(self, i):\n\n im_size = self.image_size(i)\n print 'The path of the image is: {}'.format(self.image_path_at(i))\n print 'width: {}, height: {}'.format(im_size[0], im_size[1])\n \n attr_i = self.gtdb['attr'][i, :]\n print 'The attributes are: {}'.format(','.join([self._classes[i] for i in np.where(attr_i==1)[0]]))","def ndarray_print(nd):\n try:\n x = nd.tolist()\n except (TypeError, NotImplementedError):\n x = nd.tobytes()\n if isinstance(nd, ndarray):\n offset = nd.offset\n flags = nd.flags\n else:\n offset = 'unknown'\n flags = 'unknown'\n print(\"ndarray(%s, shape=%s, strides=%s, suboffsets=%s, offset=%s, \"\n \"format='%s', itemsize=%s, flags=%s)\" %\n (x, nd.shape, nd.strides, nd.suboffsets, offset,\n nd.format, nd.itemsize, flags))\n sys.stdout.flush()","def display_data():\n data = read_data_from_file()\n data_size = len(data)\n print('Data size - ' + str(data_size))\n\n\n for i in range(data_size-1, 0, -1):\n image = data[i]\n cv2.imshow('window', image[0])\n print(str(image[1]))\n cv2.waitKey(50)","def _print_image(self, line, size):\n i = 0\n cont = 0\n\n self._write(self.__class__.__imageSize['1x1'])\n buffer = bytearray([int((size[0] / size[1]) / 8), 0, size[1], 0])\n\n self._write(buffer)\n buffer = bytearray()\n\n while i < len(line):\n hex_string = int(line[i:i + 8], 2)\n buffer.append(hex_string)\n i += 8\n cont += 1\n if cont % 4 == 0:\n self._write(buffer)\n buffer = bytearray()\n cont = 0","def show_slice(file_path, x, y, z):\n img = nib.load(file_path)\n img = img.get_fdata()\n print(\"The scan has dimensions {}.\".format(img.shape))\n slice_0 = img[x, :, :]\n slice_1 = img[:, y, :]\n slice_2 = img[:, :, z]\n slices = [slice_0, slice_1, slice_2]\n fig, axes = plt.subplots(1, len(slices), figsize=[12, 4])\n for i, sli in enumerate(slices):\n axes[i].imshow(sli.T, cmap=\"gray\", origin=\"lower\")","def show(self, data):\n if isinstance(data, (numpy.ndarray, h5py.Dataset)):\n isAtomic = len(data.shape) == 0\n isCurve = len(data.shape) == 1 and numpy.issubdtype(data.dtype, numpy.number)\n isImage = len(data.shape) == 2 and numpy.issubdtype(data.dtype, numpy.number)\n if isAtomic:\n self.showAsString(data)\n elif isCurve:\n self.show1d(data)\n elif isImage:\n self.show2d(data)\n else:\n self.showAsString(data)\n else:\n self.showAsString(data)","def display(self, image):\n assert(image.mode == '1')\n assert(image.size[0] == self.width)\n assert(image.size[1] == self.height)\n\n page = 0xB0\n pix = list(image.getdata())\n step = self.width * 8\n for y in xrange(0, self.pages * step, step):\n\n # move to given page, then reset the column address\n self.command(page, 0x02, 0x10)\n page += 1\n\n buf = []\n for x in xrange(self.width):\n byte = 0\n for n in xrange(0, step, self.width):\n byte |= (pix[x + y + n] & 0x01) << 8\n byte >>= 1\n\n buf.append(byte)\n\n self.data(buf)","def display(self, image):\n assert(image.mode == '1')\n assert(image.size[0] == self.width)\n assert(image.size[1] == self.height)\n\n page = 0xB0\n pix = list(image.getdata())\n step = self.width * 8\n for y in xrange(0, self.pages * step, step):\n\n # move to given page, then reset the column address\n self.command(page, 0x02, 0x10)\n page += 1\n\n buf = []\n for x in xrange(self.width):\n byte = 0\n for n in xrange(0, step, self.width):\n byte |= (pix[x + y + n] & 0x01) << 8\n byte >>= 1\n\n buf.append(byte)\n\n self.data(buf)","def show_ipv(data: np.ndarray):\n import ipyvolume as ipv\n return ipv.quickvolshow(data)","def explore_data(dataset, start, end, rows_and_columns=False):\r\n for i in range(start,end):\r\n print(dataset[i],end=\"\\n\")","def show_input_to_output(img_ns):\n figure()\n \n sp = subplot(1, 2, 1).imshow(img_ns.img)\n sp.axes.grid(False)\n sp.axes.tick_params(bottom=False, left=False, which='both',labelleft=False,labelbottom=False,length=0)\n title(\"Input Image\", fontsize=10);\n outimg = tiles_to_images(img_ns, img_ns.tile_grid, img_ns.tile_catalog, img_ns.tile_size)\n sp = subplot(1, 2, 2).imshow(outimg.astype(np.uint8));\n sp.axes.tick_params(bottom=False, left=False, which='both',labelleft=False,labelbottom=False,length=0)\n title(\"Output Image From Tiles\", fontsize=10);\n sp.axes.grid(False)\n #print(outimg.astype(np.uint8))\n #print(img_ns)\n plt.savefig(img_ns.output_filename + \"_input_to_output.pdf\", bbox_inches=\"tight\")\n plt.close()","def _showdata(self, prec=4):\n print('nh {0:d} nslices {1:d} nbl {2:d} ncp {3:d} nca {4:d} '.format(\n self.nh, self.nslices, self.nbl, self.ncp, self.nca), end=\"\")\n print(\"observables in np arrays with {:d} rows\".format(self.nslices))\n\n if len(self.observables) == 4:\n print('nca', self.nca)\n else:\n print()\n np.set_printoptions(precision=prec)\n\n print(self.fp.shape, \"fp (degrees, but stored internally in radians):\\n\",\n self.fp*self.degree, \"\\n\")\n print(self.fa.shape, \"fa:\\n\", self.fa, \"\\n\")\n\n print(self.cp.shape, \"cp (degrees, but stored internally in radians):\\n\",\n self.cp*self.degree, \"\\n\")\n if len(self.observables) == 4:\n print(self.ca.shape, \"ca:\\n\", self.ca, \"\\n\")\n # print(self.info4oif_dict)\n\n print(\"hole centers array shape:\", self.ctrs.shape)\n\n print(len(self.bholes), \"baseline hole indices\\n\", self.bholes)\n print(self.bls.shape, \"baselines:\\n\", self.bls)\n\n print(self.tholes.shape, \"triple hole indices:\\n\", self.tholes)\n print(self.tuv.shape, \"triple uv vectors:\\n\", self.tuv)\n\n print(self.qholes.shape, \"quad hole indices:\\n\", self.qholes)\n print(self.quvw.shape, \"quad uvw vectors:\\n\", self.quvw)","def view_image(train_dataloader):\n for (x, target) in train_dataloader:\n np.save(\"img.npy\", x)\n print(x.shape)\n exit(0)","def _convert_and_print_image(self, im):\n pixLine = \"\"\n imLeft = \"\"\n imRight = \"\"\n switch = 0\n imgSize = [0, 0]\n\n if im.size[0] > 512:\n print (\"WARNING: Image is wider than 512 and could be truncated at print time \")\n if im.size[1] > 255:\n raise ValueError(\"Image Height larger than 255\")\n\n imBorder = self._check_image_size(im.size[0])\n for i in range(imBorder[0]):\n imLeft += \"0\"\n for i in range(imBorder[1]):\n imRight += \"0\"\n\n for y in range(im.size[1]):\n imgSize[1] += 1\n pixLine += imLeft\n imgSize[0] += imBorder[0]\n for x in range(im.size[0]):\n imgSize[0] += 1\n RGB = im.getpixel((x, y))\n imColor = (RGB[0] + RGB[1] + RGB[2])\n imPattern = \"1X0\"\n patternLen = len(imPattern)\n switch = (switch - 1) * (-1)\n for x in range(patternLen):\n if imColor <= (255 * 3 / patternLen * (x + 1)):\n if imPattern[x] == \"X\":\n pixLine += \"%d\" % switch\n else:\n pixLine += imPattern[x]\n break\n elif imColor > (255 * 3 / patternLen * patternLen) and imColor <= (255 * 3):\n pixLine += imPattern[-1]\n break\n pixLine += imRight\n imgSize[0] += imBorder[1]\n\n self._print_image(pixLine, imgSize)","def print(self):\n self.__print_local(self.dataset, 0)","def display_napari(pos_img):\n global data\n global img_queue\n if pos_img is None:\n return\n # read image and z position\n image = np.reshape(pos_img[2:],(clip[0], clip[1]))\n z_pos = pos_img[1]\n color = pos_img[0]\n\n # write image into correct slice of data and update display\n data[z_pos] = np.squeeze(image)\n layer = viewer.layers[color]\n layer.data = data\n #print(\"updating \", z_pos, color)\n\n img_queue.task_done()","def pixdump( source, start=None, end=None, length=None, width=64, height=None, palette=None ):\n\n for line in pixdump_iter( source, start, end, length, width, height, palette ):\n print( line )","def info_np(img):\n import numpy as np\n\n print ('Dimensions: ' + str(np.shape(img)))\n print ('Min value: ' + str(np.min(img)))\n print ('Avg value: ' + str(np.average(img)))\n print ('Med value: ' + str(np.median(img)))\n print ('Max value: ' + str(np.max(img)))\n print ('Std dev: ' + str(np.std(img)))\n print ('Sum: ' + str(np.sum(img)))","def print_images_out_statistics(self):\n self._print_images_statistics(self._images_out_folder, self._pose_class_names)","def display(self, image):\n assert(image.mode == '1')\n assert(image.size[0] == self.width)\n assert(image.size[1] == self.height)\n\n self.command(\n const.COLUMNADDR, 0x00, self.width-1, # Column start/end address\n const.PAGEADDR, 0x00, self.pages-1) # Page start/end address\n\n pix = list(image.getdata())\n step = self.width * 8\n buf = []\n for y in xrange(0, self.pages * step, step):\n i = y + self.width-1\n while i >= y:\n byte = 0\n for n in xrange(0, step, self.width):\n byte |= (pix[i + n] & 0x01) << 8\n byte >>= 1\n\n buf.append(byte)\n i -= 1\n\n self.data(buf)","def render(filename,i):\n print('running render')\n A = np.genfromtxt(filename,skip_header=1,dtype=float,delimiter=',')\n img = np.array(A[i,:],copy=True)\n print(img.shape)\n img = img.reshape(28,28)\n img = 255 - img\n print(img.shape)\n plt.imshow(img, cmap=\"gray\", vmin=0, vmax=255)\n plt.savefig(\"img\" + str(i)+\"render\"+ \".png\")","def visualize_output(\n self,\n img: np.ndarray,\n output_data: Any):\n raise NotImplementedError","def disp(self, idx=100): # r_[0:5,1e2:1e9:1e2,-10:0]):\r\n\r\n filenameprefix=self.name_prefix\r\n\r\n def printdatarow(dat, iteration):\r\n \"\"\"print data of iteration i\"\"\"\r\n i = np.where(dat.f[:, 0] == iteration)[0][0]\r\n j = np.where(dat.std[:, 0] == iteration)[0][0]\r\n print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) +\r\n ' %5.1e' % (dat.f[i,3]) +\r\n ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:]))\r\n\r\n dat = CMADataLogger(filenameprefix).load()\r\n ndata = dat.f.shape[0]\r\n\r\n # map index to iteration number, is difficult if not all iteration numbers exist\r\n # idx = idx[np.where(map(lambda x: x in dat.f[:,0], idx))[0]] # TODO: takes pretty long\r\n # otherwise:\r\n if idx is None:\r\n idx = 100\r\n if np.isscalar(idx):\r\n # idx = np.arange(0, ndata, idx)\r\n if idx:\r\n idx = np.r_[0, 1, idx:ndata-3:idx, -3:0]\r\n else:\r\n idx = np.r_[0, 1, -3:0]\r\n\r\n idx = array(idx)\r\n idx = idx[idx<ndata]\r\n idx = idx[-idx<=ndata]\r\n iters = dat.f[idx, 0]\r\n idxbest = np.argmin(dat.f[:,5])\r\n iterbest = dat.f[idxbest, 0]\r\n\r\n if len(iters) == 1:\r\n printdatarow(dat, iters[0])\r\n else:\r\n self.disp_header()\r\n for i in iters:\r\n printdatarow(dat, i)\r\n self.disp_header()\r\n printdatarow(dat, iterbest)\r\n sys.stdout.flush()","def printdatarow(dat, iteration):\r\n i = np.where(dat.f[:, 0] == iteration)[0][0]\r\n j = np.where(dat.std[:, 0] == iteration)[0][0]\r\n print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) +\r\n ' %5.1e' % (dat.f[i,3]) +\r\n ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:]))","def printdatarow(dat, iteration):\r\n i = np.where(dat.f[:, 0] == iteration)[0][0]\r\n j = np.where(dat.std[:, 0] == iteration)[0][0]\r\n print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) +\r\n ' %5.1e' % (dat.f[i,3]) +\r\n ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:]))","def display(array):\n plt.figure()\n plt.imshow(array)\n plt.show()","def print_azeltables(inviews, ic):\n for i in range(0, len(inviews)):\n print \" \"\n print \"Az/El for inview %s to %s\" % (inviews[i][0], inviews[i][1])\n azels = ic.compute_azels(inviews[i][0], inviews[i][1], 15)\n for j in range(0, len(azels)):\n print \"At %s, azimuth=%8.2f, elevation=%8.2f\" % \\\n (azels[j][0], azels[j][1], azels[j][2])","def pprint(self, data):\n self._assert(data)\n data = self._render(data) # make elements ascii\n fmats = self._fmats(data) # get array of padding formats)\n for row in data:\n print(fmats.format(*row))","def disp(self, idx=100): # r_[0:5,1e2:1e9:1e2,-10:0]):\r\n\r\n filenameprefix=self.name_prefix\r\n\r\n def printdatarow(dat, iteration):\r\n \"\"\"print data of iteration i\"\"\"\r\n i = np.where(dat.f[:, 0] == iteration)[0][0]\r\n j = np.where(dat.std[:, 0] == iteration)[0][0]\r\n print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) +\r\n ' %5.1e' % (dat.f[i,3]) +\r\n ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:]))\r\n\r\n dat = CMADataLogger(filenameprefix).load()\r\n ndata = dat.f.shape[0]\r\n\r\n # map index to iteration number, is difficult if not all iteration numbers exist\r\n # idx = idx[np.where(map(lambda x: x in dat.f[:,0], idx))[0]] # TODO: takes pretty long\r\n # otherwise:\r\n if idx is None:\r\n idx = 100\r\n if np.isscalar(idx):\r\n # idx = np.arange(0, ndata, idx)\r\n if idx:\r\n idx = np.r_[0, 1, idx:ndata-3:idx, -3:0]\r\n else:\r\n idx = np.r_[0, 1, -3:0]\r\n\r\n idx = array(idx)\r\n idx = idx[idx<=ndata] # TODO: shouldn't this be \"<\"?\r\n idx = idx[-idx<=ndata]\r\n iters = dat.f[idx, 0]\r\n idxbest = np.argmin(dat.f[:,5])\r\n iterbest = dat.f[idxbest, 0]\r\n if len(iters) == 1:\r\n printdatarow(dat, iters[0])\r\n else:\r\n self.disp_header()\r\n for i in iters:\r\n printdatarow(dat, i)\r\n self.disp_header()\r\n printdatarow(dat, iterbest)\r\n sys.stdout.flush()","def printdata(self,whichstream_,firsti_,lasti_,firstj_,lastj_,firstk_,lastk_,c_,qo_,a_,qc_,bc_,bx_,vartype_,cones_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n res = self.__obj.printdata(whichstream_,firsti_,lasti_,firstj_,lastj_,firstk_,lastk_,c_,qo_,a_,qc_,bc_,bx_,vartype_,cones_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def write_window(img, ds, window):\n new_img = np.array([img[:, :, i] for i in range(img.shape[2])])\n ds.write(new_img, window=window)","def print_image_info(input_image):\n print()\n print(\"Basic Information on image: {}\".format(input_image.filename))\n print(\"Format: {}\".format(input_image.format))\n print(\"Mode: {}\".format(input_image.mode))\n print(\"Size: {}\".format(input_image.size))\n print(\"Width: {}\".format(input_image.width))\n print(\"Height: {}\".format(input_image.height))\n print(\"Palette: {}\".format(input_image.palette))\n print()","def print_data(self, data):\n self.print_indicator = True\n self.imagedata = data\n self.setImage(self.imagedata)\n\n self.indicator_min = -200\n self.indicator_max = 200\n\n if self.video_model != None:\n pos = int(self.video_model.get_pos(datatype = \"motion\"))\n self.indicator = self.view.plot([pos,pos],[self.indicator_min,self.indicator_max],pen=pyqtgraph.mkPen(color=pyqtgraph.hsvColor(2),width=1))","def print_bitmap(self, w, h, image):\n\n bitmap = self._pack_bitmap(w, h, image)\n\n row_bytes = (w + 7) // 8 # Round up to next byte boundary\n\n if row_bytes >= 48:\n row_bytes_clipped = 48\n else:\n row_bytes_clipped = row_bytes # 384 pixels max width\n\n # Est. max rows to write at once, assuming 256 byte printer buffer.\n if self._dtr_enabled:\n chunk_height_limit = 255 # Buffer doesn't matter, handshake!\n else:\n chunk_height_limit = 256 // row_bytes_clipped\n if chunk_height_limit > self._max_chunk_height:\n chunk_height_limit = self._max_chunk_height\n elif chunk_height_limit < 1:\n chunk_height_limit = 1\n\n row_start = 0\n i = 0\n while row_start < h:\n # Issue up to chunkHeightLimit rows at a time:\n chunk_height = h - row_start\n if chunk_height > chunk_height_limit:\n chunk_height = chunk_height_limit\n\n self.write(self.ASCII_DC2, '*', chunk_height, row_bytes_clipped)\n\n y = 0\n while y < chunk_height:\n x = 0\n while x < row_bytes_clipped:\n self.timeout_wait()\n self._send_to_printer(int(bitmap[i]))\n x += 1\n i += 1\n\n y += 1\n\n i += row_bytes - row_bytes_clipped\n\n self.timeout_set(chunk_height * self._dot_print_time)\n\n row_start += chunk_height_limit\n\n self._prev_byte = '\\n'","def printImage(currentImage):\n\tprint currentImage + ' is set to be printed...'","def write_file(self, i, path, fout):\n\n test_file = path + '/' + self.output[i]\n # Write file name\n print(test_file, file=fout, end='\\n\\n')\n\n extension = os.path.splitext(test_file)[1]\n if extension == '.fits' or extension == 'FITS':\n import subprocess\n prog = self.bindir + '/fits2ascii.py -i ' + test_file\n output = subprocess.check_output(prog.split(), shell=False)\n data = output.decode()\n else:\n fin = open(test_file, 'r')\n data = fin.read()\n fin.close()\n #fout.write(data)\n print(data, file=fout)\n print(file=fout, end='\\n')","def print_images(i, df):\n \n images_folder_path = \"dataset/petfinder-adoption-prediction/train_images/\"\n plt.imshow(cv2.cvtColor(cv2.imread(images_folder_path+df.filename[i]), cv2.COLOR_BGR2RGB),);\n plt.axis(\"off\");\n plt.show()","def show_slices(slices):\n fig, axes = plt.subplots(1, len(slices))\n for i, slice in enumerate(slices):\n axes[i].imshow(slice.T, cmap=\"gray\", origin=\"lower\")","def show_slices(self, slices):\n fig, axes = plt.subplots(1, len(slices))\n for i, slice in enumerate(slices):\n axes[i].imshow(slice.T, cmap=\"gray\", origin=\"lower\")","def show_all(img, overlay=None, axis='z'):\n xlen, ylen, zlen = img.GetSize()\n all_images = []\n all_overlays = []\n if axis == 'z':\n all_images = [img[:, :, z] for z in xrange(zlen)]\n if overlay:\n all_overlays = [overlay[:, :, z] for z in xrange(zlen)]\n elif axis == 'y':\n all_images = [img[:, y, :] for y in xrange(ylen)]\n if overlay:\n all_overlays = [overlay[:, y, :] for y in xrange(ylen)]\n elif axis == 'x':\n all_images = [img[x, :, :] for x in xrange(xlen)]\n if overlay:\n all_overlays = [overlay[x, :, :] for x in xrange(xlen)]\n else:\n raise Exception('invalid axis')\n\n for i, image in enumerate(all_images):\n if overlay:\n show_one(sitk.LabelOverlay(image, all_overlays[i]))\n else:\n show_one(image)\n plt.show()","def print_array(x, idx=slice(None), message=None, message_prefix=\"SHIM - \",\n file=sys.stdout):\n return set_subtensor(x[idx],\n print(x[idx],\n message=message,\n message_prefix=message_prefix,\n file=file\n )\n )","def print_images(images,output_dir,image_num=0,pair=False,synth_images=None):\n for i in xrange(images.shape[0]):\n to_print = fix_image(images[i])\n\n if pair and synth_images is not None:\n synth_to_print = fix_image(synth_images[i])\n to_print = np.hstack((to_print,synth_to_print))\n\n #What is the name of the image?\n imsave(os.path.join(output_dir,str(image_num + i) + \".png\"), to_print)","def print_data(place):\n raise NotImplementedError","def imshow(self, depth):\n layer = self.cube[depth]\n img = []\n for i in range(self.height):\n img.append([layer[i][j].value for j in range(self.width)])\n plt.imshow(img, cmap='gray')\n plt.show()","def print_wrapped(data, ncols=3):\r\n nrows = len(data)\r\n labels = data.index\r\n n_split_rows = int(np.ceil(nrows / ncols))\r\n for r in range(0, nrows, ncols):\r\n for c in range(ncols):\r\n try:\r\n numstr = '{}'.format(data[r + c])\r\n tabs = [' '] * (20 - len(labels[r + c]) - len(numstr))\r\n print(labels[r + c] + \"\".join(tabs) + numstr, end='\\t')\r\n except:\r\n pass\r\n print()","def display_mask(i):\n ### To display binary masks, comment the folowing line\n # mask = np.argmax(testing_preds[i], axis=-1)\n ### To display probability maps, comment the folowing line\n mask = testing_preds[i,:,:,-1]\n mask = np.expand_dims(mask, axis=-1)\n img = PIL.ImageOps.autocontrast(keras.preprocessing.image.array_to_img(mask))\n display(img)","def show_slices(slices):\n fig, axes = plt.subplots(1, len(slices))\n for i, slice in enumerate(slices):\n axes[i].imshow(slice.T, cmap=\"gray\", origin=\"lower\")","def info(self):\n\n print(\"pixellisation:\", self.pixel)\n print(\"number of components:\", self.ncomp)\n print(\"number of pixels:\", self.data.shape[:] if self.ncomp == 1 else self.data.shape[1:])\n print(\"nside:\", self.nside)\n print(\"geometry:\", self.geometry)\n print(\"coordinates:\", self.coordinate)","def output(*args):\n print(*args, end='', file=file)","def show(self):\r\n \r\n clear() \r\n print \" \" + \"-\" * self.__width + \" \"\r\n \r\n for row in self.__buffer:\r\n rowData = \"\".join(str(i) for i in row)\r\n print \"|\" + rowData + \"|\"\r\n\r\n print \" \" + \"-\" * self.__width + \" \"\r\n self.clearBuffer()","def display(self, image):\n raise NotImplementedError()","def display_cropped_img(i):\n image = PIL.Image.open(testing_img_paths[i])\n image = image.crop(box=(313,99,825,611))\n image = image.resize((256,256))\n display(image)","def printwf(data):\n print data #replace for Py3\n sys.stdout.flush()\n sys.stderr.flush()","def dumpData(self,out,index):\n #--SCVR\n out.pack('4siBB2sB',\n 'SCVR', 5+len(self.text), index+48, self.type, self.func, self.oper)\n if self.text: out.write(self.text)\n #--Value\n if isinstance(self.value,int):\n out.packSub('INTV','i', self.value)\n else:\n out.packSub('FLTV','f', self.value)","def printData (data):\n print(str(len(data)) + '\\t' + str(data))","def show_one(img):\n dpi = 40\n margin = 0.05\n nda = sitk.GetArrayFromImage(img)\n spacing = img.GetSpacing()\n extent = (0, nda.shape[1] * spacing[1], nda.shape[0] * spacing[0], 0)\n figsize = (5, 5)\n fig = plt.figure(figsize=figsize, dpi=dpi)\n ax = fig.add_axes([margin, margin, 1 - 2 * margin, 1 - 2 * margin])\n\n plt.set_cmap(\"gray\")\n ax.imshow(nda, extent=extent, interpolation=None)","def display(self):\n for row in range(self.height):\n for col in range(self.width):\n char = '#' if self.pixels[row * self.width + col] else '.'\n print(char, end='')\n print()\n print()","def printInterfaces(self, idx = None, flag = None, anchor = \"\"):\n\n if idx is None:\n idx = range(self.atoms.shape[0])\n elif isinstance(idx, (int, np.integer)):\n idx = [idx]\n\n header1 = \"%-2s%6s | %3s %-9s | %5s | %5s | %-5s | %-5s | %4s %-25s | %3s %-11s | %3s %-11s \"\\\n % (\"\", \"Index\", \"\", \"Length\", \"Angle\", \"Angle\", \"Area\", \"Atoms\", \"\", \"Epsilon (*100)\",\\\n \"\", \"Lattice 1\", \"\", \"Lattice 2\")\n header2 = \"%-2s%6s | %6s, %5s | %5s | %5s | %6s | %5s | %7s,%7s,%7s,%6s | \"\\\n \"%3s,%3s,%3s,%3s | %3s,%3s,%3s,%3s\"\\\n % (\"\", \"i\", \"a1\", \"a2\", \"b1/b2\", \"a1/a2\", \"Ang^2\", \"Nr\", \"11\", \"22\", \"12\", \"mas\",\\\n \"a1x\", \"a1y\", \"a2x\", \"a2y\", \"b1x\", \"b1y\", \"b2x\", \"b2y\")\n\n div = \"=\" * len(header1)\n print(\"\\n\" + header1 + \"\\n\" + header2 + \"\\n\" + div)\n\n for i in idx:\n\n la = np.linalg.norm(self.cell_1[i, :, :], axis = 0)\n lb = np.linalg.norm(self.cell_2[i, :, :], axis = 0)\n\n aa = np.dot(self.cell_1[i, :, 0], self.cell_1[i, :, 1]) / (la[0] * la[1])\n aa = np.rad2deg(np.arccos(aa))\n\n ba = np.dot(self.cell_2[i, :, 0], self.cell_2[i, :, 1]) / (lb[0] * lb[1])\n ba = np.rad2deg(np.arccos(ba))\n\n ar = np.abs(np.sin(np.deg2rad(aa))) * la[0] * la[1]\n\n s1 = self.eps_11[i] * 100\n s2 = self.eps_22[i] * 100\n s3 = self.eps_12[i] * 100\n s4 = self.eps_mas[i] * 100\n\n ra = self.rep_1[i, :, :].flatten()\n rb = self.rep_2[i, :, :].flatten()\n\n at = self.atoms[i]\n\n if np.isin(i, flag):\n string = \"%-2s%6.0f * %6.1f,%6.1f * %5.1f * %5.1f * %6.1f * %5.0f * \"\\\n \"%7.2f,%7.2f,%7.2f,%6.2f * %3i,%3i,%3i,%3i * %3i,%3i,%3i,%3i\"\\\n % (anchor, i, la[0], la[1], ba, aa, ar, at, s1, s2, s3, s4,\\\n ra[0], ra[2], ra[1], ra[3], rb[0], rb[2], rb[1], rb[3])\n else:\n string = \"%-2s%6.0f | %6.1f,%6.1f | %5.1f | %5.1f | %6.1f | %5.0f | \"\\\n \"%7.2f,%7.2f,%7.2f,%6.2f | %3i,%3i,%3i,%3i | %3i,%3i,%3i,%3i\"\\\n % (anchor, i, la[0], la[1], ba, aa, ar, at, s1, s2, s3, s4,\\\n ra[0], ra[2], ra[1], ra[3], rb[0], rb[2], rb[1], rb[3])\n\n print(string)\n\n print(div + \"\\n\")","def show_image(self, pic, prediction=None):\n digitmap = {\n 0: [(0,0), (1,0), (2,0), (3,0), (4,0), (0,1), (4,1), (0,2), (1,2), (2,2), (3,2), (4,2)],\n 1: [(0,2), (1,2), (2,2), (3,2), (4,2)],\n 2: [(0,0), (0,1), (0,2), (1,2), (2,0), (2,1), (2,2), (3,0), (4,0), (4,1), (4,2)],\n 3: [(0,0), (0,1), (0,2), (1,2), (2,0), (2,1), (2,2), (3,2), (4,0), (4,1), (4,2)],\n 4: [(0,0), (0,2), (1,0), (1,2), (2,0), (2,1), (2,2), (3,2), (4,2)],\n 5: [(0,0), (0,1), (0,2), (1,0), (2,0), (2,1), (2,2), (3,2), (4,0), (4,1), (4,2)],\n 6: [(0,0), (0,1), (0,2), (1,0), (2,0), (2,1), (2,2), (3,0), (3,2), (4,0), (4,1), (4,2)],\n 7: [(0,0), (0,1), (0,2), (1,2), (2,2), (3,2), (4,2)],\n 8: [(0,0), (1,0), (2,0), (3,0), (4,0), (0,1), (4,1), (0,2), (1,2), (2,2), (3,2), (4,2), (2,1)],\n 9: [(0,0), (0,1), (0,2), (1,0), (1,2), (2,0), (2,1), (2,2), (3,2), (4,0), (4,1), (4,2)]\n }\n\n pic = pic.reshape((28,28)).copy()\n if prediction is not None:\n for pos in digitmap[prediction]:\n pic[pos]=255\n plt.imshow(pic, cmap='gray_r')","def print_out():\n pass","def __writeImageBytes(self, image):\n\n if not image:\n raise Exception(\"image not found\")\n result = []\n for i, b in enumerate(image):\n if i % 39 == 0:\n result.append(\"\\n\")\n result.append(f\"{b:02X}\")\n return \"\".join(result)","def print_stats(cars, notcars):\n print(\"Number of car samples: {0}\".format(len(cars)))\n print(\"Number of non car samples: {0}\".format(len(notcars)))\n img = cv2.imread(cars[0])\n print(\"Image shape: {0}x{1}\".format(img.shape[0], img.shape[1]))\n print(\"Image datatype: {}\".format(img.dtype))","def show_points_on_img(mask,img):\n labeled, num_objects = ndi.label(mask)\n slices = ndi.find_objects(labeled)\n x, y = [], []\n for dy,dx in slices:\n x_center = (dx.start + dx.stop - 1)/2\n x.append(x_center)\n y_center = (dy.start + dy.stop - 1)/2 \n y.append(y_center)\n plt.figure()\n plt.imshow(img)\n plt.autoscale(False)\n plt.plot(x,y, \"o\")","def collatz_print(w, i, j, v):\n\tw.write(str(i) + \" \" + str(j) + \" \" + str(v) + \"\\n\")","def display(self, image=None):\n # By default write the internal buffer to the display.\n if image is None:\n image = self.buffer\n # Set address bounds to entire display.\n self.setAddress()\n # Convert image to array of 16bit 565 RGB data bytes.\n # Unfortunate that this copy has to occur, but the SPI byte writing\n # function needs to take an array of bytes and PIL doesn't natively\n # store images in 16-bit 565 RGB format.\n pixelbytes = list(image_to_data(image))\n # Write data to hardware.\n self.writeData(pixelbytes)","def genout(self):\n ch = self.buffer_output()\n while ch:\n print(ch, end='')\n ch = self.buffer_output()","def display(self):\n for row in self.tile_rows:\n print(row)","def showTensorImg(ts, title):\n img = np.transpose(ts, (1, 2, 0))\n showImg(img, title)\n return","def plot_slice(path: str, image: sitk.Image, slice_no: int) -> None:\n\n slice_ = sitk.GetArrayFromImage(image[:, :, slice_no])\n\n fig = plt.figure()\n # configure axes such that no boarder is plotted\n # refer to https://github.com/matplotlib/matplotlib/issues/7940/ about how to remove axis from plot\n ax = plt.Axes(fig, [0., 0., 1., 1.])\n ax.set_axis_off()\n ax.margins(0)\n ax.tick_params(which='both', direction='in')\n\n # plot image\n ax.imshow(slice_, 'gray', interpolation='none')\n\n fig.add_axes(ax)\n\n extent = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())\n plt.savefig(path, bbox_inches=extent)\n plt.close()","def dump(self, data_points):\n print(data_points)","def my_print(self):\n if self.__size is not 0:\n for ite in range(self.__position[1]):\n print()\n for ite in range(self.__size):\n print(\" \" * self.__position[0], end=\"\")\n print(\"#\" * self.size)\n else:\n print()","def print_adni_qc(outputdir, data, title):\n # extract filename for title\n title = os.path.basename(title)\n\n maximum = np.max(data)\n plt.imshow(data, cmap=plt.cm.jet, interpolation='nearest', vmin=0.15*maximum, vmax=0.75*maximum)\n plt.colorbar()\n plt.title(os.path.basename(title), fontsize=8)\n plt.xticks([])\n plt.yticks([])\n plt.tight_layout()\n plt.savefig(os.path.join(outputdir, '{}.jpg'.format(title)))\n plt.close()","def print_list(data):\n for i, line in enumerate(data):\n print(\"Linha {}: {}\".format(i, line))","def log(self, step, data=''):\n if self.debug:\n print '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++'\n for k in range(0, len(step), 68):\n print '+{:^68.68}+'.format(step[k:k + 68])\n for k in range(0, len(data), 68):\n print '+{:^68.68}+'.format(data[k:k + 68])\n print '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++'\n print","def dump(self) -> NoReturn:\n index = self._head\n while index:\n print(index.data, end=\" \")\n index = index.next"],"string":"[\n \"def print_image_slice(input, iz=0):\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tprint \\\"(z = %d slice)\\\" % (iz)\\n\\tline = []\\n\\tfor iy in xrange(ny-1,-1,-1):\\n\\t\\tline.append(\\\"Row \\\")\\n\\t\\tline.append(\\\"%4i \\\" % iy)\\n\\t\\tfor ix in xrange(nx):\\n\\t\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\t\\tif ((ix + 1) % 5 == 0): \\n\\t\\t\\t\\tline.append(\\\"\\\\n \\\")\\n\\t\\t\\t\\tline.append(\\\" \\\")\\n\\t \\tline.append(\\\"\\\\n\\\")\\n\\t \\tif(nx%5 != 0): line.append(\\\"\\\\n\\\")\\n\\tprint \\\"\\\".join(line)\",\n \"def print_image(input):\\n\\timage=get_image(input)\\n\\tnz = image.get_zsize()\\n\\tfor iz in xrange(nz): print_slice(input, iz)\",\n \"def print_image_row(input, iy=0, iz=0):\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tprint \\\"(z = %d slice, y = %d col)\\\" % (iz, iy)\\n\\tline = []\\n\\tfor ix in xrange(nx):\\n\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\tif ((ix + 1) % 5 == 0): line.append(\\\"\\\\n \\\")\\n\\tline.append(\\\"\\\\n\\\")\\n\\tprint \\\"\\\".join(line)\",\n \"def print_image(data, index):\\n image = data[index]\\n for line in image:\\n print(line)\",\n \"def print_image_col(input, ix=0, iz=0):\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tprint \\\"(z = %d slice, x = %d row)\\\" % (iz, ix)\\n\\tline = []\\n\\tfor iy in xrange(ny):\\n\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\tif ((iy + 1) % 5 == 0): line.append(\\\"\\\\n \\\")\\n\\tline.append(\\\"\\\\n\\\")\\n\\tprint \\\"\\\".join(line)\",\n \"def print_row(input, ix=0, iz=0):\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tprint \\\"(z = %d slice, x = %d row)\\\" % (iz, ix)\\n\\tline = []\\n\\tfor iy in xrange(ny):\\n\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\tif ((iy + 1) % 5 == 0): line.append(\\\"\\\\n \\\")\\n\\tline.append(\\\"\\\\n\\\")\\n\\tprint \\\"\\\".join(line)\",\n \"def print_col(input, iy=0, iz=0):\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tprint \\\"(z = %d slice, y = %d col)\\\" % (iz, iy)\\n\\tline = []\\n\\tfor ix in xrange(nx):\\n\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\tif ((ix + 1) % 5 == 0): line.append(\\\"\\\\n \\\")\\n\\tline.append(\\\"\\\\n\\\")\\n\\tprint \\\"\\\".join(line)\",\n \"def print_image_slice_3d(input, num=0,direction=\\\"z\\\"):\\n\\t#print \\\"print slice at 3 directions\\\"\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tif(direction==\\\"x\\\"):\\n\\t\\t#print \\\"xxxxx\\\"\\n\\t\\tix=num\\n\\t\\tprint \\\"(x = %d slice)\\\" % (ix)\\n\\t\\tline = []\\n\\t\\tfor iz in xrange(nz-1,-1,-1):\\n\\t\\t\\tline.append(\\\"Z \\\")\\n\\t\\t\\tline.append(\\\"%4i \\\" % iz)\\n\\t\\t\\tfor iy in xrange(ny):\\n\\t\\t\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\t\\t\\tif ((iy + 1) % 5 == 0): \\n\\t\\t\\t\\t\\tline.append(\\\"\\\\n \\\")\\n\\t\\t\\t\\t\\tline.append(\\\" \\\")\\n\\t \\t\\tline.append(\\\"\\\\n\\\")\\n\\t \\t\\tif(ny%5 != 0): line.append(\\\"\\\\n\\\")\\n\\t\\tprint \\\"\\\".join(line)\\n\\telif(direction==\\\"y\\\"):\\n\\t\\t#print \\\"yyy\\\"\\n\\t\\tiy=num\\n\\t\\tprint \\\"(y = %d slice)\\\" % (iy)\\n\\t\\tline = []\\n\\t\\tfor iz in xrange(nz-1,-1,-1):\\n\\t\\t\\tline.append(\\\"Z \\\")\\n\\t\\t\\tline.append(\\\"%4i \\\" % iz)\\n\\t\\t\\tfor ix in xrange(nx):\\n\\t\\t\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\t\\t\\tif ((ix + 1) % 5 == 0): \\n\\t\\t\\t\\t\\tline.append(\\\"\\\\n \\\")\\n\\t\\t\\t\\t\\tline.append(\\\" \\\")\\n\\t \\t\\tline.append(\\\"\\\\n\\\")\\n\\t \\t\\tif(nx%5 != 0): line.append(\\\"\\\\n\\\")\\n\\t\\tprint \\\"\\\".join(line)\\n\\telse:\\n\\t\\t#print \\\"zzzz\\\"\\n\\t\\tiz=num\\n\\t\\tprint \\\"(z = %d slice)\\\" % (iz)\\n\\t\\tline = []\\n\\t\\tfor iy in xrange(ny-1,-1,-1):\\n\\t\\t\\tline.append(\\\"Row \\\")\\n\\t\\t\\tline.append(\\\"%4i \\\" % iy)\\n\\t\\t\\tfor ix in xrange(nx):\\n\\t\\t\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\t\\t\\tif ((ix + 1) % 5 == 0): \\n\\t\\t\\t\\t\\tline.append(\\\"\\\\n \\\")\\n\\t\\t\\t\\t\\tline.append(\\\" \\\")\\n\\t \\t\\tline.append(\\\"\\\\n\\\")\\n\\t \\t\\tif(nx%5 != 0): line.append(\\\"\\\\n\\\")\\n\\t\\tprint \\\"\\\".join(line)\",\n \"def dump_row(input, fname, ix=0, iz=0):\\n\\tfout = open(fname, \\\"w\\\")\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tfout.write(\\\"# z = %d slice, x = %d row)\\\\n\\\" % (iz, ix))\\n\\tline = []\\n\\tfor iy in xrange(ny):\\n\\t\\tfout.write(\\\"%d\\\\t%12.5g\\\\n\\\" % (iy, image.get_value_at(ix,iy,iz)))\\n\\tfout.close()\",\n \"def print_img_data(img,title='Image'):\\n print(f'________ {title} _______')\\n if len(image.shape) == 2:\\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\\\\n\\\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0])}\\\\n')\\n elif len(image.shape) == 3:\\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\\\\n\\\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0,0])}\\\\n')\",\n \"def print_img_at_idx(self, idx):\\n\\n img = self.images[idx]\\n print_img(img)\",\n \"def print_slice(complex_slice, name = None):\\n if name: print(\\\"***********\\\" + name + \\\"**********\\\")\\n slice = complex_slice.clone()\\n slice = slice.detach()\\n slice = slice.cpu()\\n slice = slice.permute(0, 2, 3, 1)\\n slice = slice.squeeze()\\n slice_image_abs = fastmri.complex_abs(slice)\\n plt.imshow(slice_image_abs, cmap = 'gray')\\n plt.show()\",\n \"def show(self):\\n m = [xo_convert(int(x)) for x in np.nditer(self.arr)]\\n print(\\\"{} | {} | {}\\\".format(*m[:3]))\\n print(\\\"--+---+--\\\")\\n print(\\\"{} | {} | {}\\\".format(*m[3:6]))\\n print(\\\"--+---+--\\\")\\n print(\\\"{} | {} | {}\\\".format(*m[6:]))\\n print()\",\n \"def print_real_image(r_img, name = None, batch_size = BATCH_SIZE):\\n if name: print(\\\"***********\\\" + name + \\\"**********\\\")\\n to_print = []\\n c_img = r_img.clone()\\n for i in range(batch_size):\\n img = c_img[i,:,:]\\n img = torch.squeeze(img)\\n img = img.detach()\\n img = img.cpu()\\n plt.imshow(img, cmap = 'gray')\\n plt.show()\",\n \"def print_image(img):\\n for k in img:\\n print(\\\"\\\".join([str(x) for x in k] ))\",\n \"def print_image(self, width, data):\\n if len(data) % width:\\n raise ValueError('Bad image format, length of data must be '\\n 'divisible by width.')\\n height = len(data) / width\\n\\n # send line-by-line\\n for row in range(height):\\n self.send_command('print_bitmap', 1, width)\\n self.port.write(data[row*width:(row+1)*width], is_text=False)\\n self.port.fed_dots(1)\",\n \"def print_image(self, pil_image):\\n bw_image = pil_image.convert('1', dither=Image.FLOYDSTEINBERG)\\n width = bw_image.width\\n height = bw_image.height\\n img_bytes = list(bw_image.getdata())\\n self.print_bitmap(width, height, img_bytes)\",\n \"def print_image(indiv,name):\\n routine = gp.compile(indiv,pset)\\n output = gen_beat_output(routine)\\n bits = np.array(map(bitlist,output)[0:24000]).transpose()\\n plt.style.use('classic')\\n plt.imshow(bits,interpolation='nearest',aspect='auto',cmap=plt.get_cmap('Greys'))\\n plt.savefig(name+\\\".png\\\",dpi=150)\",\n \"def show_image(image):\\n print('-' * (len(image) + 4))\\n for line in image:\\n print('| ', end='')\\n for ch in line:\\n char = '#' if ch is True else '.'\\n print(char, end='')\\n print(' |')\\n print('-' * (len(image) + 4))\",\n \"def printImage(imageObject):\\n # TODO\\n pass\",\n \"def pretty_print(image_example):\\n print numpy.array_str(image_example, precision=1, max_line_width=142)\",\n \"def printMat(image):\\n for row in range(image.rows):\\n print \\\"[\\\",\\n for col in range(image.cols):\\n print cv.mGet(image, row, col),\\n print \\\"]\\\"\\n print \\\"\\\"\",\n \"def imshow(image):\\n iio.imshow(dtype.im2uint(image))\",\n \"def ndarray_print(nd):\\n try:\\n x = nd.tolist()\\n except (TypeError, NotImplementedError):\\n x = nd.tobytes()\\n if isinstance(nd, ndarray):\\n offset = nd.offset\\n flags = nd.flags\\n else:\\n offset = 'unknown'\\n flags = 'unknown'\\n print(\\n \\\"ndarray(%s, shape=%s, strides=%s, suboffsets=%s, offset=%s, format='%s', itemsize=%s, flags=%s)\\\"\\n % (x, nd.shape, nd.strides, nd.suboffsets, offset, nd.format, nd.\\n itemsize, flags))\\n sys.stdout.flush()\",\n \"def print_image(img):\\r\\n # On affiche l'image\\r\\n plt.figure(figsize=(20, 5))\\r\\n plt.subplot(1, 2, 1)\\r\\n plt.imshow(img)\\r\\n # On affiche l'histogramme\\r\\n plt.subplot(1, 2, 2)\\r\\n plt.hist(img.flatten(), bins=range(256))\\r\\n plt.show()\",\n \"def print_info(self, i):\\n\\n im_size = self.image_size(i)\\n print 'The path of the image is: {}'.format(self.image_path_at(i))\\n print 'width: {}, height: {}'.format(im_size[0], im_size[1])\\n \\n attr_i = self.gtdb['attr'][i, :]\\n print 'The attributes are: {}'.format(','.join([self._classes[i] for i in np.where(attr_i==1)[0]]))\",\n \"def ndarray_print(nd):\\n try:\\n x = nd.tolist()\\n except (TypeError, NotImplementedError):\\n x = nd.tobytes()\\n if isinstance(nd, ndarray):\\n offset = nd.offset\\n flags = nd.flags\\n else:\\n offset = 'unknown'\\n flags = 'unknown'\\n print(\\\"ndarray(%s, shape=%s, strides=%s, suboffsets=%s, offset=%s, \\\"\\n \\\"format='%s', itemsize=%s, flags=%s)\\\" %\\n (x, nd.shape, nd.strides, nd.suboffsets, offset,\\n nd.format, nd.itemsize, flags))\\n sys.stdout.flush()\",\n \"def display_data():\\n data = read_data_from_file()\\n data_size = len(data)\\n print('Data size - ' + str(data_size))\\n\\n\\n for i in range(data_size-1, 0, -1):\\n image = data[i]\\n cv2.imshow('window', image[0])\\n print(str(image[1]))\\n cv2.waitKey(50)\",\n \"def _print_image(self, line, size):\\n i = 0\\n cont = 0\\n\\n self._write(self.__class__.__imageSize['1x1'])\\n buffer = bytearray([int((size[0] / size[1]) / 8), 0, size[1], 0])\\n\\n self._write(buffer)\\n buffer = bytearray()\\n\\n while i < len(line):\\n hex_string = int(line[i:i + 8], 2)\\n buffer.append(hex_string)\\n i += 8\\n cont += 1\\n if cont % 4 == 0:\\n self._write(buffer)\\n buffer = bytearray()\\n cont = 0\",\n \"def show_slice(file_path, x, y, z):\\n img = nib.load(file_path)\\n img = img.get_fdata()\\n print(\\\"The scan has dimensions {}.\\\".format(img.shape))\\n slice_0 = img[x, :, :]\\n slice_1 = img[:, y, :]\\n slice_2 = img[:, :, z]\\n slices = [slice_0, slice_1, slice_2]\\n fig, axes = plt.subplots(1, len(slices), figsize=[12, 4])\\n for i, sli in enumerate(slices):\\n axes[i].imshow(sli.T, cmap=\\\"gray\\\", origin=\\\"lower\\\")\",\n \"def show(self, data):\\n if isinstance(data, (numpy.ndarray, h5py.Dataset)):\\n isAtomic = len(data.shape) == 0\\n isCurve = len(data.shape) == 1 and numpy.issubdtype(data.dtype, numpy.number)\\n isImage = len(data.shape) == 2 and numpy.issubdtype(data.dtype, numpy.number)\\n if isAtomic:\\n self.showAsString(data)\\n elif isCurve:\\n self.show1d(data)\\n elif isImage:\\n self.show2d(data)\\n else:\\n self.showAsString(data)\\n else:\\n self.showAsString(data)\",\n \"def display(self, image):\\n assert(image.mode == '1')\\n assert(image.size[0] == self.width)\\n assert(image.size[1] == self.height)\\n\\n page = 0xB0\\n pix = list(image.getdata())\\n step = self.width * 8\\n for y in xrange(0, self.pages * step, step):\\n\\n # move to given page, then reset the column address\\n self.command(page, 0x02, 0x10)\\n page += 1\\n\\n buf = []\\n for x in xrange(self.width):\\n byte = 0\\n for n in xrange(0, step, self.width):\\n byte |= (pix[x + y + n] & 0x01) << 8\\n byte >>= 1\\n\\n buf.append(byte)\\n\\n self.data(buf)\",\n \"def display(self, image):\\n assert(image.mode == '1')\\n assert(image.size[0] == self.width)\\n assert(image.size[1] == self.height)\\n\\n page = 0xB0\\n pix = list(image.getdata())\\n step = self.width * 8\\n for y in xrange(0, self.pages * step, step):\\n\\n # move to given page, then reset the column address\\n self.command(page, 0x02, 0x10)\\n page += 1\\n\\n buf = []\\n for x in xrange(self.width):\\n byte = 0\\n for n in xrange(0, step, self.width):\\n byte |= (pix[x + y + n] & 0x01) << 8\\n byte >>= 1\\n\\n buf.append(byte)\\n\\n self.data(buf)\",\n \"def show_ipv(data: np.ndarray):\\n import ipyvolume as ipv\\n return ipv.quickvolshow(data)\",\n \"def explore_data(dataset, start, end, rows_and_columns=False):\\r\\n for i in range(start,end):\\r\\n print(dataset[i],end=\\\"\\\\n\\\")\",\n \"def show_input_to_output(img_ns):\\n figure()\\n \\n sp = subplot(1, 2, 1).imshow(img_ns.img)\\n sp.axes.grid(False)\\n sp.axes.tick_params(bottom=False, left=False, which='both',labelleft=False,labelbottom=False,length=0)\\n title(\\\"Input Image\\\", fontsize=10);\\n outimg = tiles_to_images(img_ns, img_ns.tile_grid, img_ns.tile_catalog, img_ns.tile_size)\\n sp = subplot(1, 2, 2).imshow(outimg.astype(np.uint8));\\n sp.axes.tick_params(bottom=False, left=False, which='both',labelleft=False,labelbottom=False,length=0)\\n title(\\\"Output Image From Tiles\\\", fontsize=10);\\n sp.axes.grid(False)\\n #print(outimg.astype(np.uint8))\\n #print(img_ns)\\n plt.savefig(img_ns.output_filename + \\\"_input_to_output.pdf\\\", bbox_inches=\\\"tight\\\")\\n plt.close()\",\n \"def _showdata(self, prec=4):\\n print('nh {0:d} nslices {1:d} nbl {2:d} ncp {3:d} nca {4:d} '.format(\\n self.nh, self.nslices, self.nbl, self.ncp, self.nca), end=\\\"\\\")\\n print(\\\"observables in np arrays with {:d} rows\\\".format(self.nslices))\\n\\n if len(self.observables) == 4:\\n print('nca', self.nca)\\n else:\\n print()\\n np.set_printoptions(precision=prec)\\n\\n print(self.fp.shape, \\\"fp (degrees, but stored internally in radians):\\\\n\\\",\\n self.fp*self.degree, \\\"\\\\n\\\")\\n print(self.fa.shape, \\\"fa:\\\\n\\\", self.fa, \\\"\\\\n\\\")\\n\\n print(self.cp.shape, \\\"cp (degrees, but stored internally in radians):\\\\n\\\",\\n self.cp*self.degree, \\\"\\\\n\\\")\\n if len(self.observables) == 4:\\n print(self.ca.shape, \\\"ca:\\\\n\\\", self.ca, \\\"\\\\n\\\")\\n # print(self.info4oif_dict)\\n\\n print(\\\"hole centers array shape:\\\", self.ctrs.shape)\\n\\n print(len(self.bholes), \\\"baseline hole indices\\\\n\\\", self.bholes)\\n print(self.bls.shape, \\\"baselines:\\\\n\\\", self.bls)\\n\\n print(self.tholes.shape, \\\"triple hole indices:\\\\n\\\", self.tholes)\\n print(self.tuv.shape, \\\"triple uv vectors:\\\\n\\\", self.tuv)\\n\\n print(self.qholes.shape, \\\"quad hole indices:\\\\n\\\", self.qholes)\\n print(self.quvw.shape, \\\"quad uvw vectors:\\\\n\\\", self.quvw)\",\n \"def view_image(train_dataloader):\\n for (x, target) in train_dataloader:\\n np.save(\\\"img.npy\\\", x)\\n print(x.shape)\\n exit(0)\",\n \"def _convert_and_print_image(self, im):\\n pixLine = \\\"\\\"\\n imLeft = \\\"\\\"\\n imRight = \\\"\\\"\\n switch = 0\\n imgSize = [0, 0]\\n\\n if im.size[0] > 512:\\n print (\\\"WARNING: Image is wider than 512 and could be truncated at print time \\\")\\n if im.size[1] > 255:\\n raise ValueError(\\\"Image Height larger than 255\\\")\\n\\n imBorder = self._check_image_size(im.size[0])\\n for i in range(imBorder[0]):\\n imLeft += \\\"0\\\"\\n for i in range(imBorder[1]):\\n imRight += \\\"0\\\"\\n\\n for y in range(im.size[1]):\\n imgSize[1] += 1\\n pixLine += imLeft\\n imgSize[0] += imBorder[0]\\n for x in range(im.size[0]):\\n imgSize[0] += 1\\n RGB = im.getpixel((x, y))\\n imColor = (RGB[0] + RGB[1] + RGB[2])\\n imPattern = \\\"1X0\\\"\\n patternLen = len(imPattern)\\n switch = (switch - 1) * (-1)\\n for x in range(patternLen):\\n if imColor <= (255 * 3 / patternLen * (x + 1)):\\n if imPattern[x] == \\\"X\\\":\\n pixLine += \\\"%d\\\" % switch\\n else:\\n pixLine += imPattern[x]\\n break\\n elif imColor > (255 * 3 / patternLen * patternLen) and imColor <= (255 * 3):\\n pixLine += imPattern[-1]\\n break\\n pixLine += imRight\\n imgSize[0] += imBorder[1]\\n\\n self._print_image(pixLine, imgSize)\",\n \"def print(self):\\n self.__print_local(self.dataset, 0)\",\n \"def display_napari(pos_img):\\n global data\\n global img_queue\\n if pos_img is None:\\n return\\n # read image and z position\\n image = np.reshape(pos_img[2:],(clip[0], clip[1]))\\n z_pos = pos_img[1]\\n color = pos_img[0]\\n\\n # write image into correct slice of data and update display\\n data[z_pos] = np.squeeze(image)\\n layer = viewer.layers[color]\\n layer.data = data\\n #print(\\\"updating \\\", z_pos, color)\\n\\n img_queue.task_done()\",\n \"def pixdump( source, start=None, end=None, length=None, width=64, height=None, palette=None ):\\n\\n for line in pixdump_iter( source, start, end, length, width, height, palette ):\\n print( line )\",\n \"def info_np(img):\\n import numpy as np\\n\\n print ('Dimensions: ' + str(np.shape(img)))\\n print ('Min value: ' + str(np.min(img)))\\n print ('Avg value: ' + str(np.average(img)))\\n print ('Med value: ' + str(np.median(img)))\\n print ('Max value: ' + str(np.max(img)))\\n print ('Std dev: ' + str(np.std(img)))\\n print ('Sum: ' + str(np.sum(img)))\",\n \"def print_images_out_statistics(self):\\n self._print_images_statistics(self._images_out_folder, self._pose_class_names)\",\n \"def display(self, image):\\n assert(image.mode == '1')\\n assert(image.size[0] == self.width)\\n assert(image.size[1] == self.height)\\n\\n self.command(\\n const.COLUMNADDR, 0x00, self.width-1, # Column start/end address\\n const.PAGEADDR, 0x00, self.pages-1) # Page start/end address\\n\\n pix = list(image.getdata())\\n step = self.width * 8\\n buf = []\\n for y in xrange(0, self.pages * step, step):\\n i = y + self.width-1\\n while i >= y:\\n byte = 0\\n for n in xrange(0, step, self.width):\\n byte |= (pix[i + n] & 0x01) << 8\\n byte >>= 1\\n\\n buf.append(byte)\\n i -= 1\\n\\n self.data(buf)\",\n \"def render(filename,i):\\n print('running render')\\n A = np.genfromtxt(filename,skip_header=1,dtype=float,delimiter=',')\\n img = np.array(A[i,:],copy=True)\\n print(img.shape)\\n img = img.reshape(28,28)\\n img = 255 - img\\n print(img.shape)\\n plt.imshow(img, cmap=\\\"gray\\\", vmin=0, vmax=255)\\n plt.savefig(\\\"img\\\" + str(i)+\\\"render\\\"+ \\\".png\\\")\",\n \"def visualize_output(\\n self,\\n img: np.ndarray,\\n output_data: Any):\\n raise NotImplementedError\",\n \"def disp(self, idx=100): # r_[0:5,1e2:1e9:1e2,-10:0]):\\r\\n\\r\\n filenameprefix=self.name_prefix\\r\\n\\r\\n def printdatarow(dat, iteration):\\r\\n \\\"\\\"\\\"print data of iteration i\\\"\\\"\\\"\\r\\n i = np.where(dat.f[:, 0] == iteration)[0][0]\\r\\n j = np.where(dat.std[:, 0] == iteration)[0][0]\\r\\n print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) +\\r\\n ' %5.1e' % (dat.f[i,3]) +\\r\\n ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:]))\\r\\n\\r\\n dat = CMADataLogger(filenameprefix).load()\\r\\n ndata = dat.f.shape[0]\\r\\n\\r\\n # map index to iteration number, is difficult if not all iteration numbers exist\\r\\n # idx = idx[np.where(map(lambda x: x in dat.f[:,0], idx))[0]] # TODO: takes pretty long\\r\\n # otherwise:\\r\\n if idx is None:\\r\\n idx = 100\\r\\n if np.isscalar(idx):\\r\\n # idx = np.arange(0, ndata, idx)\\r\\n if idx:\\r\\n idx = np.r_[0, 1, idx:ndata-3:idx, -3:0]\\r\\n else:\\r\\n idx = np.r_[0, 1, -3:0]\\r\\n\\r\\n idx = array(idx)\\r\\n idx = idx[idx<ndata]\\r\\n idx = idx[-idx<=ndata]\\r\\n iters = dat.f[idx, 0]\\r\\n idxbest = np.argmin(dat.f[:,5])\\r\\n iterbest = dat.f[idxbest, 0]\\r\\n\\r\\n if len(iters) == 1:\\r\\n printdatarow(dat, iters[0])\\r\\n else:\\r\\n self.disp_header()\\r\\n for i in iters:\\r\\n printdatarow(dat, i)\\r\\n self.disp_header()\\r\\n printdatarow(dat, iterbest)\\r\\n sys.stdout.flush()\",\n \"def printdatarow(dat, iteration):\\r\\n i = np.where(dat.f[:, 0] == iteration)[0][0]\\r\\n j = np.where(dat.std[:, 0] == iteration)[0][0]\\r\\n print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) +\\r\\n ' %5.1e' % (dat.f[i,3]) +\\r\\n ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:]))\",\n \"def printdatarow(dat, iteration):\\r\\n i = np.where(dat.f[:, 0] == iteration)[0][0]\\r\\n j = np.where(dat.std[:, 0] == iteration)[0][0]\\r\\n print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) +\\r\\n ' %5.1e' % (dat.f[i,3]) +\\r\\n ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:]))\",\n \"def display(array):\\n plt.figure()\\n plt.imshow(array)\\n plt.show()\",\n \"def print_azeltables(inviews, ic):\\n for i in range(0, len(inviews)):\\n print \\\" \\\"\\n print \\\"Az/El for inview %s to %s\\\" % (inviews[i][0], inviews[i][1])\\n azels = ic.compute_azels(inviews[i][0], inviews[i][1], 15)\\n for j in range(0, len(azels)):\\n print \\\"At %s, azimuth=%8.2f, elevation=%8.2f\\\" % \\\\\\n (azels[j][0], azels[j][1], azels[j][2])\",\n \"def pprint(self, data):\\n self._assert(data)\\n data = self._render(data) # make elements ascii\\n fmats = self._fmats(data) # get array of padding formats)\\n for row in data:\\n print(fmats.format(*row))\",\n \"def disp(self, idx=100): # r_[0:5,1e2:1e9:1e2,-10:0]):\\r\\n\\r\\n filenameprefix=self.name_prefix\\r\\n\\r\\n def printdatarow(dat, iteration):\\r\\n \\\"\\\"\\\"print data of iteration i\\\"\\\"\\\"\\r\\n i = np.where(dat.f[:, 0] == iteration)[0][0]\\r\\n j = np.where(dat.std[:, 0] == iteration)[0][0]\\r\\n print('%5d' % (int(dat.f[i,0])) + ' %6d' % (int(dat.f[i,1])) + ' %.14e' % (dat.f[i,5]) +\\r\\n ' %5.1e' % (dat.f[i,3]) +\\r\\n ' %6.2e' % (max(dat.std[j,5:])) + ' %6.2e' % min(dat.std[j,5:]))\\r\\n\\r\\n dat = CMADataLogger(filenameprefix).load()\\r\\n ndata = dat.f.shape[0]\\r\\n\\r\\n # map index to iteration number, is difficult if not all iteration numbers exist\\r\\n # idx = idx[np.where(map(lambda x: x in dat.f[:,0], idx))[0]] # TODO: takes pretty long\\r\\n # otherwise:\\r\\n if idx is None:\\r\\n idx = 100\\r\\n if np.isscalar(idx):\\r\\n # idx = np.arange(0, ndata, idx)\\r\\n if idx:\\r\\n idx = np.r_[0, 1, idx:ndata-3:idx, -3:0]\\r\\n else:\\r\\n idx = np.r_[0, 1, -3:0]\\r\\n\\r\\n idx = array(idx)\\r\\n idx = idx[idx<=ndata] # TODO: shouldn't this be \\\"<\\\"?\\r\\n idx = idx[-idx<=ndata]\\r\\n iters = dat.f[idx, 0]\\r\\n idxbest = np.argmin(dat.f[:,5])\\r\\n iterbest = dat.f[idxbest, 0]\\r\\n if len(iters) == 1:\\r\\n printdatarow(dat, iters[0])\\r\\n else:\\r\\n self.disp_header()\\r\\n for i in iters:\\r\\n printdatarow(dat, i)\\r\\n self.disp_header()\\r\\n printdatarow(dat, iterbest)\\r\\n sys.stdout.flush()\",\n \"def printdata(self,whichstream_,firsti_,lasti_,firstj_,lastj_,firstk_,lastk_,c_,qo_,a_,qc_,bc_,bx_,vartype_,cones_): # 3\\n if not isinstance(whichstream_,streamtype): raise TypeError(\\\"Argument whichstream has wrong type\\\")\\n res = self.__obj.printdata(whichstream_,firsti_,lasti_,firstj_,lastj_,firstk_,lastk_,c_,qo_,a_,qc_,bc_,bx_,vartype_,cones_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def write_window(img, ds, window):\\n new_img = np.array([img[:, :, i] for i in range(img.shape[2])])\\n ds.write(new_img, window=window)\",\n \"def print_image_info(input_image):\\n print()\\n print(\\\"Basic Information on image: {}\\\".format(input_image.filename))\\n print(\\\"Format: {}\\\".format(input_image.format))\\n print(\\\"Mode: {}\\\".format(input_image.mode))\\n print(\\\"Size: {}\\\".format(input_image.size))\\n print(\\\"Width: {}\\\".format(input_image.width))\\n print(\\\"Height: {}\\\".format(input_image.height))\\n print(\\\"Palette: {}\\\".format(input_image.palette))\\n print()\",\n \"def print_data(self, data):\\n self.print_indicator = True\\n self.imagedata = data\\n self.setImage(self.imagedata)\\n\\n self.indicator_min = -200\\n self.indicator_max = 200\\n\\n if self.video_model != None:\\n pos = int(self.video_model.get_pos(datatype = \\\"motion\\\"))\\n self.indicator = self.view.plot([pos,pos],[self.indicator_min,self.indicator_max],pen=pyqtgraph.mkPen(color=pyqtgraph.hsvColor(2),width=1))\",\n \"def print_bitmap(self, w, h, image):\\n\\n bitmap = self._pack_bitmap(w, h, image)\\n\\n row_bytes = (w + 7) // 8 # Round up to next byte boundary\\n\\n if row_bytes >= 48:\\n row_bytes_clipped = 48\\n else:\\n row_bytes_clipped = row_bytes # 384 pixels max width\\n\\n # Est. max rows to write at once, assuming 256 byte printer buffer.\\n if self._dtr_enabled:\\n chunk_height_limit = 255 # Buffer doesn't matter, handshake!\\n else:\\n chunk_height_limit = 256 // row_bytes_clipped\\n if chunk_height_limit > self._max_chunk_height:\\n chunk_height_limit = self._max_chunk_height\\n elif chunk_height_limit < 1:\\n chunk_height_limit = 1\\n\\n row_start = 0\\n i = 0\\n while row_start < h:\\n # Issue up to chunkHeightLimit rows at a time:\\n chunk_height = h - row_start\\n if chunk_height > chunk_height_limit:\\n chunk_height = chunk_height_limit\\n\\n self.write(self.ASCII_DC2, '*', chunk_height, row_bytes_clipped)\\n\\n y = 0\\n while y < chunk_height:\\n x = 0\\n while x < row_bytes_clipped:\\n self.timeout_wait()\\n self._send_to_printer(int(bitmap[i]))\\n x += 1\\n i += 1\\n\\n y += 1\\n\\n i += row_bytes - row_bytes_clipped\\n\\n self.timeout_set(chunk_height * self._dot_print_time)\\n\\n row_start += chunk_height_limit\\n\\n self._prev_byte = '\\\\n'\",\n \"def printImage(currentImage):\\n\\tprint currentImage + ' is set to be printed...'\",\n \"def write_file(self, i, path, fout):\\n\\n test_file = path + '/' + self.output[i]\\n # Write file name\\n print(test_file, file=fout, end='\\\\n\\\\n')\\n\\n extension = os.path.splitext(test_file)[1]\\n if extension == '.fits' or extension == 'FITS':\\n import subprocess\\n prog = self.bindir + '/fits2ascii.py -i ' + test_file\\n output = subprocess.check_output(prog.split(), shell=False)\\n data = output.decode()\\n else:\\n fin = open(test_file, 'r')\\n data = fin.read()\\n fin.close()\\n #fout.write(data)\\n print(data, file=fout)\\n print(file=fout, end='\\\\n')\",\n \"def print_images(i, df):\\n \\n images_folder_path = \\\"dataset/petfinder-adoption-prediction/train_images/\\\"\\n plt.imshow(cv2.cvtColor(cv2.imread(images_folder_path+df.filename[i]), cv2.COLOR_BGR2RGB),);\\n plt.axis(\\\"off\\\");\\n plt.show()\",\n \"def show_slices(slices):\\n fig, axes = plt.subplots(1, len(slices))\\n for i, slice in enumerate(slices):\\n axes[i].imshow(slice.T, cmap=\\\"gray\\\", origin=\\\"lower\\\")\",\n \"def show_slices(self, slices):\\n fig, axes = plt.subplots(1, len(slices))\\n for i, slice in enumerate(slices):\\n axes[i].imshow(slice.T, cmap=\\\"gray\\\", origin=\\\"lower\\\")\",\n \"def show_all(img, overlay=None, axis='z'):\\n xlen, ylen, zlen = img.GetSize()\\n all_images = []\\n all_overlays = []\\n if axis == 'z':\\n all_images = [img[:, :, z] for z in xrange(zlen)]\\n if overlay:\\n all_overlays = [overlay[:, :, z] for z in xrange(zlen)]\\n elif axis == 'y':\\n all_images = [img[:, y, :] for y in xrange(ylen)]\\n if overlay:\\n all_overlays = [overlay[:, y, :] for y in xrange(ylen)]\\n elif axis == 'x':\\n all_images = [img[x, :, :] for x in xrange(xlen)]\\n if overlay:\\n all_overlays = [overlay[x, :, :] for x in xrange(xlen)]\\n else:\\n raise Exception('invalid axis')\\n\\n for i, image in enumerate(all_images):\\n if overlay:\\n show_one(sitk.LabelOverlay(image, all_overlays[i]))\\n else:\\n show_one(image)\\n plt.show()\",\n \"def print_array(x, idx=slice(None), message=None, message_prefix=\\\"SHIM - \\\",\\n file=sys.stdout):\\n return set_subtensor(x[idx],\\n print(x[idx],\\n message=message,\\n message_prefix=message_prefix,\\n file=file\\n )\\n )\",\n \"def print_images(images,output_dir,image_num=0,pair=False,synth_images=None):\\n for i in xrange(images.shape[0]):\\n to_print = fix_image(images[i])\\n\\n if pair and synth_images is not None:\\n synth_to_print = fix_image(synth_images[i])\\n to_print = np.hstack((to_print,synth_to_print))\\n\\n #What is the name of the image?\\n imsave(os.path.join(output_dir,str(image_num + i) + \\\".png\\\"), to_print)\",\n \"def print_data(place):\\n raise NotImplementedError\",\n \"def imshow(self, depth):\\n layer = self.cube[depth]\\n img = []\\n for i in range(self.height):\\n img.append([layer[i][j].value for j in range(self.width)])\\n plt.imshow(img, cmap='gray')\\n plt.show()\",\n \"def print_wrapped(data, ncols=3):\\r\\n nrows = len(data)\\r\\n labels = data.index\\r\\n n_split_rows = int(np.ceil(nrows / ncols))\\r\\n for r in range(0, nrows, ncols):\\r\\n for c in range(ncols):\\r\\n try:\\r\\n numstr = '{}'.format(data[r + c])\\r\\n tabs = [' '] * (20 - len(labels[r + c]) - len(numstr))\\r\\n print(labels[r + c] + \\\"\\\".join(tabs) + numstr, end='\\\\t')\\r\\n except:\\r\\n pass\\r\\n print()\",\n \"def display_mask(i):\\n ### To display binary masks, comment the folowing line\\n # mask = np.argmax(testing_preds[i], axis=-1)\\n ### To display probability maps, comment the folowing line\\n mask = testing_preds[i,:,:,-1]\\n mask = np.expand_dims(mask, axis=-1)\\n img = PIL.ImageOps.autocontrast(keras.preprocessing.image.array_to_img(mask))\\n display(img)\",\n \"def show_slices(slices):\\n fig, axes = plt.subplots(1, len(slices))\\n for i, slice in enumerate(slices):\\n axes[i].imshow(slice.T, cmap=\\\"gray\\\", origin=\\\"lower\\\")\",\n \"def info(self):\\n\\n print(\\\"pixellisation:\\\", self.pixel)\\n print(\\\"number of components:\\\", self.ncomp)\\n print(\\\"number of pixels:\\\", self.data.shape[:] if self.ncomp == 1 else self.data.shape[1:])\\n print(\\\"nside:\\\", self.nside)\\n print(\\\"geometry:\\\", self.geometry)\\n print(\\\"coordinates:\\\", self.coordinate)\",\n \"def output(*args):\\n print(*args, end='', file=file)\",\n \"def show(self):\\r\\n \\r\\n clear() \\r\\n print \\\" \\\" + \\\"-\\\" * self.__width + \\\" \\\"\\r\\n \\r\\n for row in self.__buffer:\\r\\n rowData = \\\"\\\".join(str(i) for i in row)\\r\\n print \\\"|\\\" + rowData + \\\"|\\\"\\r\\n\\r\\n print \\\" \\\" + \\\"-\\\" * self.__width + \\\" \\\"\\r\\n self.clearBuffer()\",\n \"def display(self, image):\\n raise NotImplementedError()\",\n \"def display_cropped_img(i):\\n image = PIL.Image.open(testing_img_paths[i])\\n image = image.crop(box=(313,99,825,611))\\n image = image.resize((256,256))\\n display(image)\",\n \"def printwf(data):\\n print data #replace for Py3\\n sys.stdout.flush()\\n sys.stderr.flush()\",\n \"def dumpData(self,out,index):\\n #--SCVR\\n out.pack('4siBB2sB',\\n 'SCVR', 5+len(self.text), index+48, self.type, self.func, self.oper)\\n if self.text: out.write(self.text)\\n #--Value\\n if isinstance(self.value,int):\\n out.packSub('INTV','i', self.value)\\n else:\\n out.packSub('FLTV','f', self.value)\",\n \"def printData (data):\\n print(str(len(data)) + '\\\\t' + str(data))\",\n \"def show_one(img):\\n dpi = 40\\n margin = 0.05\\n nda = sitk.GetArrayFromImage(img)\\n spacing = img.GetSpacing()\\n extent = (0, nda.shape[1] * spacing[1], nda.shape[0] * spacing[0], 0)\\n figsize = (5, 5)\\n fig = plt.figure(figsize=figsize, dpi=dpi)\\n ax = fig.add_axes([margin, margin, 1 - 2 * margin, 1 - 2 * margin])\\n\\n plt.set_cmap(\\\"gray\\\")\\n ax.imshow(nda, extent=extent, interpolation=None)\",\n \"def display(self):\\n for row in range(self.height):\\n for col in range(self.width):\\n char = '#' if self.pixels[row * self.width + col] else '.'\\n print(char, end='')\\n print()\\n print()\",\n \"def printInterfaces(self, idx = None, flag = None, anchor = \\\"\\\"):\\n\\n if idx is None:\\n idx = range(self.atoms.shape[0])\\n elif isinstance(idx, (int, np.integer)):\\n idx = [idx]\\n\\n header1 = \\\"%-2s%6s | %3s %-9s | %5s | %5s | %-5s | %-5s | %4s %-25s | %3s %-11s | %3s %-11s \\\"\\\\\\n % (\\\"\\\", \\\"Index\\\", \\\"\\\", \\\"Length\\\", \\\"Angle\\\", \\\"Angle\\\", \\\"Area\\\", \\\"Atoms\\\", \\\"\\\", \\\"Epsilon (*100)\\\",\\\\\\n \\\"\\\", \\\"Lattice 1\\\", \\\"\\\", \\\"Lattice 2\\\")\\n header2 = \\\"%-2s%6s | %6s, %5s | %5s | %5s | %6s | %5s | %7s,%7s,%7s,%6s | \\\"\\\\\\n \\\"%3s,%3s,%3s,%3s | %3s,%3s,%3s,%3s\\\"\\\\\\n % (\\\"\\\", \\\"i\\\", \\\"a1\\\", \\\"a2\\\", \\\"b1/b2\\\", \\\"a1/a2\\\", \\\"Ang^2\\\", \\\"Nr\\\", \\\"11\\\", \\\"22\\\", \\\"12\\\", \\\"mas\\\",\\\\\\n \\\"a1x\\\", \\\"a1y\\\", \\\"a2x\\\", \\\"a2y\\\", \\\"b1x\\\", \\\"b1y\\\", \\\"b2x\\\", \\\"b2y\\\")\\n\\n div = \\\"=\\\" * len(header1)\\n print(\\\"\\\\n\\\" + header1 + \\\"\\\\n\\\" + header2 + \\\"\\\\n\\\" + div)\\n\\n for i in idx:\\n\\n la = np.linalg.norm(self.cell_1[i, :, :], axis = 0)\\n lb = np.linalg.norm(self.cell_2[i, :, :], axis = 0)\\n\\n aa = np.dot(self.cell_1[i, :, 0], self.cell_1[i, :, 1]) / (la[0] * la[1])\\n aa = np.rad2deg(np.arccos(aa))\\n\\n ba = np.dot(self.cell_2[i, :, 0], self.cell_2[i, :, 1]) / (lb[0] * lb[1])\\n ba = np.rad2deg(np.arccos(ba))\\n\\n ar = np.abs(np.sin(np.deg2rad(aa))) * la[0] * la[1]\\n\\n s1 = self.eps_11[i] * 100\\n s2 = self.eps_22[i] * 100\\n s3 = self.eps_12[i] * 100\\n s4 = self.eps_mas[i] * 100\\n\\n ra = self.rep_1[i, :, :].flatten()\\n rb = self.rep_2[i, :, :].flatten()\\n\\n at = self.atoms[i]\\n\\n if np.isin(i, flag):\\n string = \\\"%-2s%6.0f * %6.1f,%6.1f * %5.1f * %5.1f * %6.1f * %5.0f * \\\"\\\\\\n \\\"%7.2f,%7.2f,%7.2f,%6.2f * %3i,%3i,%3i,%3i * %3i,%3i,%3i,%3i\\\"\\\\\\n % (anchor, i, la[0], la[1], ba, aa, ar, at, s1, s2, s3, s4,\\\\\\n ra[0], ra[2], ra[1], ra[3], rb[0], rb[2], rb[1], rb[3])\\n else:\\n string = \\\"%-2s%6.0f | %6.1f,%6.1f | %5.1f | %5.1f | %6.1f | %5.0f | \\\"\\\\\\n \\\"%7.2f,%7.2f,%7.2f,%6.2f | %3i,%3i,%3i,%3i | %3i,%3i,%3i,%3i\\\"\\\\\\n % (anchor, i, la[0], la[1], ba, aa, ar, at, s1, s2, s3, s4,\\\\\\n ra[0], ra[2], ra[1], ra[3], rb[0], rb[2], rb[1], rb[3])\\n\\n print(string)\\n\\n print(div + \\\"\\\\n\\\")\",\n \"def show_image(self, pic, prediction=None):\\n digitmap = {\\n 0: [(0,0), (1,0), (2,0), (3,0), (4,0), (0,1), (4,1), (0,2), (1,2), (2,2), (3,2), (4,2)],\\n 1: [(0,2), (1,2), (2,2), (3,2), (4,2)],\\n 2: [(0,0), (0,1), (0,2), (1,2), (2,0), (2,1), (2,2), (3,0), (4,0), (4,1), (4,2)],\\n 3: [(0,0), (0,1), (0,2), (1,2), (2,0), (2,1), (2,2), (3,2), (4,0), (4,1), (4,2)],\\n 4: [(0,0), (0,2), (1,0), (1,2), (2,0), (2,1), (2,2), (3,2), (4,2)],\\n 5: [(0,0), (0,1), (0,2), (1,0), (2,0), (2,1), (2,2), (3,2), (4,0), (4,1), (4,2)],\\n 6: [(0,0), (0,1), (0,2), (1,0), (2,0), (2,1), (2,2), (3,0), (3,2), (4,0), (4,1), (4,2)],\\n 7: [(0,0), (0,1), (0,2), (1,2), (2,2), (3,2), (4,2)],\\n 8: [(0,0), (1,0), (2,0), (3,0), (4,0), (0,1), (4,1), (0,2), (1,2), (2,2), (3,2), (4,2), (2,1)],\\n 9: [(0,0), (0,1), (0,2), (1,0), (1,2), (2,0), (2,1), (2,2), (3,2), (4,0), (4,1), (4,2)]\\n }\\n\\n pic = pic.reshape((28,28)).copy()\\n if prediction is not None:\\n for pos in digitmap[prediction]:\\n pic[pos]=255\\n plt.imshow(pic, cmap='gray_r')\",\n \"def print_out():\\n pass\",\n \"def __writeImageBytes(self, image):\\n\\n if not image:\\n raise Exception(\\\"image not found\\\")\\n result = []\\n for i, b in enumerate(image):\\n if i % 39 == 0:\\n result.append(\\\"\\\\n\\\")\\n result.append(f\\\"{b:02X}\\\")\\n return \\\"\\\".join(result)\",\n \"def print_stats(cars, notcars):\\n print(\\\"Number of car samples: {0}\\\".format(len(cars)))\\n print(\\\"Number of non car samples: {0}\\\".format(len(notcars)))\\n img = cv2.imread(cars[0])\\n print(\\\"Image shape: {0}x{1}\\\".format(img.shape[0], img.shape[1]))\\n print(\\\"Image datatype: {}\\\".format(img.dtype))\",\n \"def show_points_on_img(mask,img):\\n labeled, num_objects = ndi.label(mask)\\n slices = ndi.find_objects(labeled)\\n x, y = [], []\\n for dy,dx in slices:\\n x_center = (dx.start + dx.stop - 1)/2\\n x.append(x_center)\\n y_center = (dy.start + dy.stop - 1)/2 \\n y.append(y_center)\\n plt.figure()\\n plt.imshow(img)\\n plt.autoscale(False)\\n plt.plot(x,y, \\\"o\\\")\",\n \"def collatz_print(w, i, j, v):\\n\\tw.write(str(i) + \\\" \\\" + str(j) + \\\" \\\" + str(v) + \\\"\\\\n\\\")\",\n \"def display(self, image=None):\\n # By default write the internal buffer to the display.\\n if image is None:\\n image = self.buffer\\n # Set address bounds to entire display.\\n self.setAddress()\\n # Convert image to array of 16bit 565 RGB data bytes.\\n # Unfortunate that this copy has to occur, but the SPI byte writing\\n # function needs to take an array of bytes and PIL doesn't natively\\n # store images in 16-bit 565 RGB format.\\n pixelbytes = list(image_to_data(image))\\n # Write data to hardware.\\n self.writeData(pixelbytes)\",\n \"def genout(self):\\n ch = self.buffer_output()\\n while ch:\\n print(ch, end='')\\n ch = self.buffer_output()\",\n \"def display(self):\\n for row in self.tile_rows:\\n print(row)\",\n \"def showTensorImg(ts, title):\\n img = np.transpose(ts, (1, 2, 0))\\n showImg(img, title)\\n return\",\n \"def plot_slice(path: str, image: sitk.Image, slice_no: int) -> None:\\n\\n slice_ = sitk.GetArrayFromImage(image[:, :, slice_no])\\n\\n fig = plt.figure()\\n # configure axes such that no boarder is plotted\\n # refer to https://github.com/matplotlib/matplotlib/issues/7940/ about how to remove axis from plot\\n ax = plt.Axes(fig, [0., 0., 1., 1.])\\n ax.set_axis_off()\\n ax.margins(0)\\n ax.tick_params(which='both', direction='in')\\n\\n # plot image\\n ax.imshow(slice_, 'gray', interpolation='none')\\n\\n fig.add_axes(ax)\\n\\n extent = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())\\n plt.savefig(path, bbox_inches=extent)\\n plt.close()\",\n \"def dump(self, data_points):\\n print(data_points)\",\n \"def my_print(self):\\n if self.__size is not 0:\\n for ite in range(self.__position[1]):\\n print()\\n for ite in range(self.__size):\\n print(\\\" \\\" * self.__position[0], end=\\\"\\\")\\n print(\\\"#\\\" * self.size)\\n else:\\n print()\",\n \"def print_adni_qc(outputdir, data, title):\\n # extract filename for title\\n title = os.path.basename(title)\\n\\n maximum = np.max(data)\\n plt.imshow(data, cmap=plt.cm.jet, interpolation='nearest', vmin=0.15*maximum, vmax=0.75*maximum)\\n plt.colorbar()\\n plt.title(os.path.basename(title), fontsize=8)\\n plt.xticks([])\\n plt.yticks([])\\n plt.tight_layout()\\n plt.savefig(os.path.join(outputdir, '{}.jpg'.format(title)))\\n plt.close()\",\n \"def print_list(data):\\n for i, line in enumerate(data):\\n print(\\\"Linha {}: {}\\\".format(i, line))\",\n \"def log(self, step, data=''):\\n if self.debug:\\n print '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++'\\n for k in range(0, len(step), 68):\\n print '+{:^68.68}+'.format(step[k:k + 68])\\n for k in range(0, len(data), 68):\\n print '+{:^68.68}+'.format(data[k:k + 68])\\n print '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++'\\n print\",\n \"def dump(self) -> NoReturn:\\n index = self._head\\n while index:\\n print(index.data, end=\\\" \\\")\\n index = index.next\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7832976","0.7438628","0.72238773","0.70643157","0.70205015","0.6971101","0.659192","0.65685576","0.6525086","0.64007616","0.6349104","0.6105353","0.6058925","0.6016299","0.60077834","0.59170806","0.5827092","0.5820637","0.5805464","0.58032835","0.57411206","0.5733659","0.57312804","0.56618387","0.56552017","0.55842036","0.5539581","0.5538914","0.55242884","0.5521108","0.548123","0.5418916","0.5418916","0.5415355","0.54050523","0.53752536","0.53599876","0.53542846","0.5341357","0.5333343","0.531496","0.53043544","0.5295613","0.5268808","0.5260194","0.52577215","0.52549523","0.52437985","0.5228753","0.5228753","0.5216811","0.52108616","0.5204943","0.51949215","0.51809996","0.5180164","0.51296836","0.51235956","0.5120301","0.50993127","0.509749","0.5094299","0.5094232","0.5091468","0.50899243","0.50882506","0.5086855","0.5067222","0.5065791","0.5056878","0.5055202","0.504992","0.503669","0.5036671","0.503556","0.5034959","0.50338626","0.5002598","0.500236","0.49977767","0.49975577","0.49908924","0.49865043","0.49824145","0.4981608","0.4970869","0.4960709","0.49506357","0.49324876","0.49282718","0.49280524","0.49279028","0.49270505","0.49262777","0.49204618","0.4919497","0.49174082","0.49165586","0.49149203","0.491347"],"string":"[\n \"0.7832976\",\n \"0.7438628\",\n \"0.72238773\",\n \"0.70643157\",\n \"0.70205015\",\n \"0.6971101\",\n \"0.659192\",\n \"0.65685576\",\n \"0.6525086\",\n \"0.64007616\",\n \"0.6349104\",\n \"0.6105353\",\n \"0.6058925\",\n \"0.6016299\",\n \"0.60077834\",\n \"0.59170806\",\n \"0.5827092\",\n \"0.5820637\",\n \"0.5805464\",\n \"0.58032835\",\n \"0.57411206\",\n \"0.5733659\",\n \"0.57312804\",\n \"0.56618387\",\n \"0.56552017\",\n \"0.55842036\",\n \"0.5539581\",\n \"0.5538914\",\n \"0.55242884\",\n \"0.5521108\",\n \"0.548123\",\n \"0.5418916\",\n \"0.5418916\",\n \"0.5415355\",\n \"0.54050523\",\n \"0.53752536\",\n \"0.53599876\",\n \"0.53542846\",\n \"0.5341357\",\n \"0.5333343\",\n \"0.531496\",\n \"0.53043544\",\n \"0.5295613\",\n \"0.5268808\",\n \"0.5260194\",\n \"0.52577215\",\n \"0.52549523\",\n \"0.52437985\",\n \"0.5228753\",\n \"0.5228753\",\n \"0.5216811\",\n \"0.52108616\",\n \"0.5204943\",\n \"0.51949215\",\n \"0.51809996\",\n \"0.5180164\",\n \"0.51296836\",\n \"0.51235956\",\n \"0.5120301\",\n \"0.50993127\",\n \"0.509749\",\n \"0.5094299\",\n \"0.5094232\",\n \"0.5091468\",\n \"0.50899243\",\n \"0.50882506\",\n \"0.5086855\",\n \"0.5067222\",\n \"0.5065791\",\n \"0.5056878\",\n \"0.5055202\",\n \"0.504992\",\n \"0.503669\",\n \"0.5036671\",\n \"0.503556\",\n \"0.5034959\",\n \"0.50338626\",\n \"0.5002598\",\n \"0.500236\",\n \"0.49977767\",\n \"0.49975577\",\n \"0.49908924\",\n \"0.49865043\",\n \"0.49824145\",\n \"0.4981608\",\n \"0.4970869\",\n \"0.4960709\",\n \"0.49506357\",\n \"0.49324876\",\n \"0.49282718\",\n \"0.49280524\",\n \"0.49279028\",\n \"0.49270505\",\n \"0.49262777\",\n \"0.49204618\",\n \"0.4919497\",\n \"0.49174082\",\n \"0.49165586\",\n \"0.49149203\",\n \"0.491347\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7674774"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":799,"cells":{"query":{"kind":"string","value":"Print the data in an image to standard out."},"document":{"kind":"string","value":"def print_image(input):\n\timage=get_image(input)\n\tnz = image.get_zsize()\n\tfor iz in xrange(nz): print_slice(input, iz)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def printImage(imageObject):\n # TODO\n pass","def print_img_data(img,title='Image'):\n print(f'________ {title} _______')\n if len(image.shape) == 2:\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\n\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0])}\\n')\n elif len(image.shape) == 3:\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\n\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0,0])}\\n')","def print_image(data, index):\n image = data[index]\n for line in image:\n print(line)","def print_image(self, width, data):\n if len(data) % width:\n raise ValueError('Bad image format, length of data must be '\n 'divisible by width.')\n height = len(data) / width\n\n # send line-by-line\n for row in range(height):\n self.send_command('print_bitmap', 1, width)\n self.port.write(data[row*width:(row+1)*width], is_text=False)\n self.port.fed_dots(1)","def print_image(self, pil_image):\n bw_image = pil_image.convert('1', dither=Image.FLOYDSTEINBERG)\n width = bw_image.width\n height = bw_image.height\n img_bytes = list(bw_image.getdata())\n self.print_bitmap(width, height, img_bytes)","def print_image(img):\n for k in img:\n print(\"\".join([str(x) for x in k] ))","def print_image(img):\r\n # On affiche l'image\r\n plt.figure(figsize=(20, 5))\r\n plt.subplot(1, 2, 1)\r\n plt.imshow(img)\r\n # On affiche l'histogramme\r\n plt.subplot(1, 2, 2)\r\n plt.hist(img.flatten(), bins=range(256))\r\n plt.show()","def print_image(indiv,name):\n routine = gp.compile(indiv,pset)\n output = gen_beat_output(routine)\n bits = np.array(map(bitlist,output)[0:24000]).transpose()\n plt.style.use('classic')\n plt.imshow(bits,interpolation='nearest',aspect='auto',cmap=plt.get_cmap('Greys'))\n plt.savefig(name+\".png\",dpi=150)","def pretty_print(image_example):\n print numpy.array_str(image_example, precision=1, max_line_width=142)","def print_real_image(r_img, name = None, batch_size = BATCH_SIZE):\n if name: print(\"***********\" + name + \"**********\")\n to_print = []\n c_img = r_img.clone()\n for i in range(batch_size):\n img = c_img[i,:,:]\n img = torch.squeeze(img)\n img = img.detach()\n img = img.cpu()\n plt.imshow(img, cmap = 'gray')\n plt.show()","def print_image_info(input_image):\n print()\n print(\"Basic Information on image: {}\".format(input_image.filename))\n print(\"Format: {}\".format(input_image.format))\n print(\"Mode: {}\".format(input_image.mode))\n print(\"Size: {}\".format(input_image.size))\n print(\"Width: {}\".format(input_image.width))\n print(\"Height: {}\".format(input_image.height))\n print(\"Palette: {}\".format(input_image.palette))\n print()","def printImage(currentImage):\n\tprint currentImage + ' is set to be printed...'","def print_images_out_statistics(self):\n self._print_images_statistics(self._images_out_folder, self._pose_class_names)","def print_img_at_idx(self, idx):\n\n img = self.images[idx]\n print_img(img)","def printMat(image):\n for row in range(image.rows):\n print \"[\",\n for col in range(image.cols):\n print cv.mGet(image, row, col),\n print \"]\"\n print \"\"","def show_image(image):\n print('-' * (len(image) + 4))\n for line in image:\n print('| ', end='')\n for ch in line:\n char = '#' if ch is True else '.'\n print(char, end='')\n print(' |')\n print('-' * (len(image) + 4))","def display(self):\n display(self.image)","def show_input_to_output(img_ns):\n figure()\n \n sp = subplot(1, 2, 1).imshow(img_ns.img)\n sp.axes.grid(False)\n sp.axes.tick_params(bottom=False, left=False, which='both',labelleft=False,labelbottom=False,length=0)\n title(\"Input Image\", fontsize=10);\n outimg = tiles_to_images(img_ns, img_ns.tile_grid, img_ns.tile_catalog, img_ns.tile_size)\n sp = subplot(1, 2, 2).imshow(outimg.astype(np.uint8));\n sp.axes.tick_params(bottom=False, left=False, which='both',labelleft=False,labelbottom=False,length=0)\n title(\"Output Image From Tiles\", fontsize=10);\n sp.axes.grid(False)\n #print(outimg.astype(np.uint8))\n #print(img_ns)\n plt.savefig(img_ns.output_filename + \"_input_to_output.pdf\", bbox_inches=\"tight\")\n plt.close()","def visualize_output(\n self,\n img: np.ndarray,\n output_data: Any):\n raise NotImplementedError","def print_images_in_statistics(self):\n self._print_images_statistics(self._images_in_folder, self._pose_class_names)","def print_stats(cars, notcars):\n print(\"Number of car samples: {0}\".format(len(cars)))\n print(\"Number of non car samples: {0}\".format(len(notcars)))\n img = cv2.imread(cars[0])\n print(\"Image shape: {0}x{1}\".format(img.shape[0], img.shape[1]))\n print(\"Image datatype: {}\".format(img.dtype))","def imshow(image):\n iio.imshow(dtype.im2uint(image))","def _convert_and_print_image(self, im):\n pixLine = \"\"\n imLeft = \"\"\n imRight = \"\"\n switch = 0\n imgSize = [0, 0]\n\n if im.size[0] > 512:\n print (\"WARNING: Image is wider than 512 and could be truncated at print time \")\n if im.size[1] > 255:\n raise ValueError(\"Image Height larger than 255\")\n\n imBorder = self._check_image_size(im.size[0])\n for i in range(imBorder[0]):\n imLeft += \"0\"\n for i in range(imBorder[1]):\n imRight += \"0\"\n\n for y in range(im.size[1]):\n imgSize[1] += 1\n pixLine += imLeft\n imgSize[0] += imBorder[0]\n for x in range(im.size[0]):\n imgSize[0] += 1\n RGB = im.getpixel((x, y))\n imColor = (RGB[0] + RGB[1] + RGB[2])\n imPattern = \"1X0\"\n patternLen = len(imPattern)\n switch = (switch - 1) * (-1)\n for x in range(patternLen):\n if imColor <= (255 * 3 / patternLen * (x + 1)):\n if imPattern[x] == \"X\":\n pixLine += \"%d\" % switch\n else:\n pixLine += imPattern[x]\n break\n elif imColor > (255 * 3 / patternLen * patternLen) and imColor <= (255 * 3):\n pixLine += imPattern[-1]\n break\n pixLine += imRight\n imgSize[0] += imBorder[1]\n\n self._print_image(pixLine, imgSize)","def display(self, image):\n raise NotImplementedError()","def _print_image(self, line, size):\n i = 0\n cont = 0\n\n self._write(self.__class__.__imageSize['1x1'])\n buffer = bytearray([int((size[0] / size[1]) / 8), 0, size[1], 0])\n\n self._write(buffer)\n buffer = bytearray()\n\n while i < len(line):\n hex_string = int(line[i:i + 8], 2)\n buffer.append(hex_string)\n i += 8\n cont += 1\n if cont % 4 == 0:\n self._write(buffer)\n buffer = bytearray()\n cont = 0","def display_image(mat):\n\timg = Image.fromarray(mat)\n\timg.show()","def print_info(self, i):\n\n im_size = self.image_size(i)\n print 'The path of the image is: {}'.format(self.image_path_at(i))\n print 'width: {}, height: {}'.format(im_size[0], im_size[1])\n \n attr_i = self.gtdb['attr'][i, :]\n print 'The attributes are: {}'.format(','.join([self._classes[i] for i in np.where(attr_i==1)[0]]))","def show(self, data):\n if isinstance(data, (numpy.ndarray, h5py.Dataset)):\n isAtomic = len(data.shape) == 0\n isCurve = len(data.shape) == 1 and numpy.issubdtype(data.dtype, numpy.number)\n isImage = len(data.shape) == 2 and numpy.issubdtype(data.dtype, numpy.number)\n if isAtomic:\n self.showAsString(data)\n elif isCurve:\n self.show1d(data)\n elif isImage:\n self.show2d(data)\n else:\n self.showAsString(data)\n else:\n self.showAsString(data)","def display(array):\n plt.figure()\n plt.imshow(array)\n plt.show()","def info_np(img):\n import numpy as np\n\n print ('Dimensions: ' + str(np.shape(img)))\n print ('Min value: ' + str(np.min(img)))\n print ('Avg value: ' + str(np.average(img)))\n print ('Med value: ' + str(np.median(img)))\n print ('Max value: ' + str(np.max(img)))\n print ('Std dev: ' + str(np.std(img)))\n print ('Sum: ' + str(np.sum(img)))","def show_image(image):\r\n plt.imshow(image, cmap='gray')\r\n plt.show()","def showImg(img, binary=True, fName=''):\n img = img[0, 0, :, :]\n\n if binary:\n img = img > 0.5\n\n img = Image.fromarray(np.uint8(img * 255), mode='L')\n\n if fName:\n img.save('assets/' + fName + '.png')\n else:\n img.show()","def display_image(X):\n\n\tim = X.reshape(28, 28)\n\ttemp = plt.imshow(im)\n\tplt.show()","def print_adni_qc(outputdir, data, title):\n # extract filename for title\n title = os.path.basename(title)\n\n maximum = np.max(data)\n plt.imshow(data, cmap=plt.cm.jet, interpolation='nearest', vmin=0.15*maximum, vmax=0.75*maximum)\n plt.colorbar()\n plt.title(os.path.basename(title), fontsize=8)\n plt.xticks([])\n plt.yticks([])\n plt.tight_layout()\n plt.savefig(os.path.join(outputdir, '{}.jpg'.format(title)))\n plt.close()","def show(self) -> None:\n cv.imshow(str(self.__class__), self.output_image)","def display_data():\n data = read_data_from_file()\n data_size = len(data)\n print('Data size - ' + str(data_size))\n\n\n for i in range(data_size-1, 0, -1):\n image = data[i]\n cv2.imshow('window', image[0])\n print(str(image[1]))\n cv2.waitKey(50)","def print_bitmap(self, w, h, image):\n\n bitmap = self._pack_bitmap(w, h, image)\n\n row_bytes = (w + 7) // 8 # Round up to next byte boundary\n\n if row_bytes >= 48:\n row_bytes_clipped = 48\n else:\n row_bytes_clipped = row_bytes # 384 pixels max width\n\n # Est. max rows to write at once, assuming 256 byte printer buffer.\n if self._dtr_enabled:\n chunk_height_limit = 255 # Buffer doesn't matter, handshake!\n else:\n chunk_height_limit = 256 // row_bytes_clipped\n if chunk_height_limit > self._max_chunk_height:\n chunk_height_limit = self._max_chunk_height\n elif chunk_height_limit < 1:\n chunk_height_limit = 1\n\n row_start = 0\n i = 0\n while row_start < h:\n # Issue up to chunkHeightLimit rows at a time:\n chunk_height = h - row_start\n if chunk_height > chunk_height_limit:\n chunk_height = chunk_height_limit\n\n self.write(self.ASCII_DC2, '*', chunk_height, row_bytes_clipped)\n\n y = 0\n while y < chunk_height:\n x = 0\n while x < row_bytes_clipped:\n self.timeout_wait()\n self._send_to_printer(int(bitmap[i]))\n x += 1\n i += 1\n\n y += 1\n\n i += row_bytes - row_bytes_clipped\n\n self.timeout_set(chunk_height * self._dot_print_time)\n\n row_start += chunk_height_limit\n\n self._prev_byte = '\\n'","def show_env(self, img):\n plt.figure(1)\n plt.subplot(111)\n plt.imshow(img, interpolation=\"nearest\")\n plt.show()","def img_disp(name,img):\n cv2.imshow(name,img.astype(int)/255.0)\n cv2.waitKey()","def display(self, image=None):\n # By default write the internal buffer to the display.\n if image is None:\n image = self.buffer\n # Set address bounds to entire display.\n self.setAddress()\n # Convert image to array of 16bit 565 RGB data bytes.\n # Unfortunate that this copy has to occur, but the SPI byte writing\n # function needs to take an array of bytes and PIL doesn't natively\n # store images in 16-bit 565 RGB format.\n pixelbytes = list(image_to_data(image))\n # Write data to hardware.\n self.writeData(pixelbytes)","def show(type,img):\n # print(img)\n cv2.imshow(type, img)\n cv2.waitKey()","def imdisplay(filename, representation):\n image = read_image(filename, representation)\n\n if representation == GRAY_OUT:\n plt.imshow(image, cmap='gray')\n else:\n plt.imshow(image)\n\n plt.show()","def display(self, image):\n assert(image.mode == '1')\n assert(image.size[0] == self.width)\n assert(image.size[1] == self.height)\n\n self.command(\n const.COLUMNADDR, 0x00, self.width-1, # Column start/end address\n const.PAGEADDR, 0x00, self.pages-1) # Page start/end address\n\n pix = list(image.getdata())\n step = self.width * 8\n buf = []\n for y in xrange(0, self.pages * step, step):\n i = y + self.width-1\n while i >= y:\n byte = 0\n for n in xrange(0, step, self.width):\n byte |= (pix[i + n] & 0x01) << 8\n byte >>= 1\n\n buf.append(byte)\n i -= 1\n\n self.data(buf)","def print_image_row(input, iy=0, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice, y = %d col)\" % (iz, iy)\n\tline = []\n\tfor ix in xrange(nx):\n\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\tif ((ix + 1) % 5 == 0): line.append(\"\\n \")\n\tline.append(\"\\n\")\n\tprint \"\".join(line)","def print_image_info(self):\r\n\r\n maxt = np.max(self.times)\r\n\r\n print (\" Duration of Image Stack: %9.3f s (%8.3f min) period = %8.3f s\" % (maxt, maxt/60.0, self.period))\r\n\r\n print (' Image shape: ', self.imageData.shape)\r\n\r\n print (' nFrames: %d framerate: %9.3f\\n' % (self.nFrames, self.framerate))","def print_data(self, data):\n self.print_indicator = True\n self.imagedata = data\n self.setImage(self.imagedata)\n\n self.indicator_min = -200\n self.indicator_max = 200\n\n if self.video_model != None:\n pos = int(self.video_model.get_pos(datatype = \"motion\"))\n self.indicator = self.view.plot([pos,pos],[self.indicator_min,self.indicator_max],pen=pyqtgraph.mkPen(color=pyqtgraph.hsvColor(2),width=1))","def dump_image(image, path_image):\n cv2.imwrite(path_image, image)\n return","def _repr_png_(self):\n mol = self.owner.mol\n keku = IPythonConsole.kekulizeStructures\n size = IPythonConsole.molSize\n opts = IPythonConsole.drawOptions\n return Draw._moltoimg(\n mol, size, self.aix, \"\", returnPNG=True, drawOptions=opts,\n kekulize=keku, highlightBonds=self.bix\n )","def _repr_png_(self):\n mol = self.owner.mol\n keku = IPythonConsole.kekulizeStructures\n size = IPythonConsole.molSize\n opts = IPythonConsole.drawOptions\n return Draw._moltoimg(\n mol, size, self.aix, \"\", returnPNG=True, drawOptions=opts,\n kekulize=keku, highlightBonds=self.bix\n )","def print(self):\n self.__print_local(self.dataset, 0)","def main():\n labels, data = load_image_data()\n print(labels.shape, data.shape)","def print_image_info(image, resize=rsz_default, kernel=kernel_size):\n\tprint \"Image Size: {0}\".format(image.shape)\n\tprint \"Image Max: {0}\".format(image.max())\n\tprint \"Image Min: {0}\".format(image.min())\n\tprint \"Image Mean: {0}\".format(image.mean())\n\tprint \"Image dtype: {0}\\n\".format(image.dtype)\n\timage = to_uint8(image)\n\timage_prep = preprocess(image, resize=resize, kernel=kernel)\n\tcontour = get_contour(image_prep)\n\tM = get_image_moments(contour=contour)\n\tsecond_m = ['m20', 'm11', 'm02', 'm30', 'm21', 'm12', 'm03']\n\tprint \"Zero Order Moment: {0}\".format(M['m00'])\n\tprint \"First Order Moments: {0}, {1}\".format(M['m10'], M['m01'])\n\tprint \"Second Order Moments:\"\n\tsecond_m_str = ''\n\tfor m2 in second_m:\n\t\tsecond_m_str += \"{0},\".format(M[m2])\n\tprint second_m_str[:-1]","def showimage(image):\n mplt.figure()\n mplt.imshow(image)\n mplt.show()","def _print_img_size(self, img):\n width, height = img.size\n print('{}, {}'.format(width, height))","def display(array):\n if isinstance(array, np.ndarray):\n plt.imshow(array)\n plt.show()\n else:\n raise TypeError(\"display() needs a numpy ndarray as parameter, \"\n f\"got {type(array)}\")","def display(image, name=\"Image\"):\n cv2.imshow(name, image)\n cv2.waitKey(0)\n cv2.destroyAllWindows()\n cv2.imwrite(\"{}.png\".format(name), image)","def show(self, exec_rasterize = False):\n\n if (exec_rasterize):\n self.rasterize()\n\n Image.fromarray(self._image).show()","def show_image(self, pic, prediction=None):\n digitmap = {\n 0: [(0,0), (1,0), (2,0), (3,0), (4,0), (0,1), (4,1), (0,2), (1,2), (2,2), (3,2), (4,2)],\n 1: [(0,2), (1,2), (2,2), (3,2), (4,2)],\n 2: [(0,0), (0,1), (0,2), (1,2), (2,0), (2,1), (2,2), (3,0), (4,0), (4,1), (4,2)],\n 3: [(0,0), (0,1), (0,2), (1,2), (2,0), (2,1), (2,2), (3,2), (4,0), (4,1), (4,2)],\n 4: [(0,0), (0,2), (1,0), (1,2), (2,0), (2,1), (2,2), (3,2), (4,2)],\n 5: [(0,0), (0,1), (0,2), (1,0), (2,0), (2,1), (2,2), (3,2), (4,0), (4,1), (4,2)],\n 6: [(0,0), (0,1), (0,2), (1,0), (2,0), (2,1), (2,2), (3,0), (3,2), (4,0), (4,1), (4,2)],\n 7: [(0,0), (0,1), (0,2), (1,2), (2,2), (3,2), (4,2)],\n 8: [(0,0), (1,0), (2,0), (3,0), (4,0), (0,1), (4,1), (0,2), (1,2), (2,2), (3,2), (4,2), (2,1)],\n 9: [(0,0), (0,1), (0,2), (1,0), (1,2), (2,0), (2,1), (2,2), (3,2), (4,0), (4,1), (4,2)]\n }\n\n pic = pic.reshape((28,28)).copy()\n if prediction is not None:\n for pos in digitmap[prediction]:\n pic[pos]=255\n plt.imshow(pic, cmap='gray_r')","def write(self, image):\n raise NotImplementedError()","def visualize_output(\n self,\n img: np.ndarray,\n output_data: List[DetectObject]) -> np.ndarray:\n draw_layer_tag = f'draw_layer_{self.id}_{self.draw_layer_index^1}'\n\n self.layer = dpg.add_draw_layer(\n parent=f'main_window_{self.id}',\n tag=draw_layer_tag,\n show=False\n )\n for out in output_data:\n description = f'{out.clsname} [{out.score*100:.2f}%]'\n # add alpha and convert to RGB\n color = np.array(self.class_colors[out.clsname])\n color = tuple((255*color).astype(np.uint8))\n dpg.draw_rectangle(\n parent=draw_layer_tag,\n pmin=(out.xmin*self.width,\n out.ymin*self.height),\n pmax=(out.xmax*self.width,\n out.ymax*self.height),\n color=color,\n thickness=2\n )\n dpg.draw_text(\n parent=draw_layer_tag,\n text=description,\n pos=(out.xmin*self.width + _PADDING,\n out.ymin*self.height + _PADDING),\n color=color,\n size=_FONT_SIZE\n )\n\n return img","def imdisplay(filename, representation):\n\n image = read_image(filename, representation)\n plt.imshow(image, cmap=\"gray\")\n plt.show()","def print_image_col(input, ix=0, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice, x = %d row)\" % (iz, ix)\n\tline = []\n\tfor iy in xrange(ny):\n\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\tif ((iy + 1) % 5 == 0): line.append(\"\\n \")\n\tline.append(\"\\n\")\n\tprint \"\".join(line)","def print_image_slice(input, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice)\" % (iz)\n\tline = []\n\tfor iy in xrange(ny-1,-1,-1):\n\t\tline.append(\"Row \")\n\t\tline.append(\"%4i \" % iy)\n\t\tfor ix in xrange(nx):\n\t\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\t\tif ((ix + 1) % 5 == 0): \n\t\t\t\tline.append(\"\\n \")\n\t\t\t\tline.append(\" \")\n\t \tline.append(\"\\n\")\n\t \tif(nx%5 != 0): line.append(\"\\n\")\n\tprint \"\".join(line)","def display(self, image):\n assert(image.mode == '1')\n assert(image.size[0] == self.width)\n assert(image.size[1] == self.height)\n\n page = 0xB0\n pix = list(image.getdata())\n step = self.width * 8\n for y in xrange(0, self.pages * step, step):\n\n # move to given page, then reset the column address\n self.command(page, 0x02, 0x10)\n page += 1\n\n buf = []\n for x in xrange(self.width):\n byte = 0\n for n in xrange(0, step, self.width):\n byte |= (pix[x + y + n] & 0x01) << 8\n byte >>= 1\n\n buf.append(byte)\n\n self.data(buf)","def display(self, image):\n assert(image.mode == '1')\n assert(image.size[0] == self.width)\n assert(image.size[1] == self.height)\n\n page = 0xB0\n pix = list(image.getdata())\n step = self.width * 8\n for y in xrange(0, self.pages * step, step):\n\n # move to given page, then reset the column address\n self.command(page, 0x02, 0x10)\n page += 1\n\n buf = []\n for x in xrange(self.width):\n byte = 0\n for n in xrange(0, step, self.width):\n byte |= (pix[x + y + n] & 0x01) << 8\n byte >>= 1\n\n buf.append(byte)\n\n self.data(buf)","def print_images(images,output_dir,image_num=0,pair=False,synth_images=None):\n for i in xrange(images.shape[0]):\n to_print = fix_image(images[i])\n\n if pair and synth_images is not None:\n synth_to_print = fix_image(synth_images[i])\n to_print = np.hstack((to_print,synth_to_print))\n\n #What is the name of the image?\n imsave(os.path.join(output_dir,str(image_num + i) + \".png\"), to_print)","def show_picture(self, data):\n raise NotImplementedError","def show_image(dataset, domain, image_class, image_name):\n\timage_file = io.imread(os.path.join(\"data\", dataset, domain, \"images\", image_class, image_name))\n\tplt.imshow(image_file)\n\tplt.pause(0.001)\n\tplt.figure()","def pixdump( source, start=None, end=None, length=None, width=64, height=None, palette=None ):\n\n for line in pixdump_iter( source, start, end, length, width, height, palette ):\n print( line )","def display(self):\n for row in range(self.height):\n for col in range(self.width):\n char = '#' if self.pixels[row * self.width + col] else '.'\n print(char, end='')\n print()\n print()","def drawData(data, writeLocation = \"../output/data.png\", gamma = 0.25):\n\n im = Image.new(\"RGB\", (data.shape[1], data.shape[0]))\n d = ImageDraw.Draw(im)\n \n _drawData(d, data, gamma)\n \n im.save(writeLocation, \"PNG\")","def print(self):\n print('Name:', self.name)\n print('Camera:', self.camera)\n print('Memory:', self.memory)\n print('Ram:', self.ram)\n print('Price:', self.price)\n print('Image:', self.image)","def showTensorImg(ts, title):\n img = np.transpose(ts, (1, 2, 0))\n showImg(img, title)\n return","def imdisplay(filename, representation):\n img = read_image(filename, representation)\n if representation == GS_REP:\n plt.imshow(img, cmap=plt.cm.gray)\n else:\n plt.imshow(img)","def disImg(data=None,colorbar=False):\n size = np.sqrt(len(data[4:]))\n xmm = data[0]\n ymm = data[1]\n pl.matshow(data[4:].reshape(size,size),fignum=False)\n if colorbar == True:\n pl.colorbar()\n pl.xlim(0,size-1)\n pl.ylim(0,size-1)\n pl.xlabel('Pixels')\n pl.ylabel('Pixels')\n pl.grid(color='yellow')","def info(self):\n\n print(\"pixellisation:\", self.pixel)\n print(\"number of components:\", self.ncomp)\n print(\"number of pixels:\", self.data.shape[:] if self.ncomp == 1 else self.data.shape[1:])\n print(\"nside:\", self.nside)\n print(\"geometry:\", self.geometry)\n print(\"coordinates:\", self.coordinate)","def view_image(train_dataloader):\n for (x, target) in train_dataloader:\n np.save(\"img.npy\", x)\n print(x.shape)\n exit(0)","def show_image(self):\n cv2.imshow(self.config.DISPLAY_NAME, self.image)","def show_digit(self):\n x_train, _, _, _ = self._load_data()\n plt.imshow(x_train[0], cmap=plt.cm.binary)\n plt.show()","def matplotlibDisplay(img, title=\"Image\", colorFlag = 'gray'):\n plt.imshow(img, colorFlag)\n plt.title(title)\n plt.xticks([])\n plt.yticks([])\n plt.show()","def visualize_output(\n self,\n img: np.ndarray,\n output_data: np.ndarray) -> np.ndarray:\n draw_layer_tag = f'draw_layer_{self.id}_{self.draw_layer_index^1}'\n\n self.layer = dpg.add_draw_layer(\n parent=f'main_window_{self.id}',\n tag=draw_layer_tag,\n show=False\n )\n\n best_k = np.argsort(output_data)[-self.top_n:][::-1]\n class_names = np.array(self.class_names)[best_k]\n percentages = softmax(output_data[best_k])\n\n for i, (name, perc) in enumerate(zip(class_names, percentages)):\n label_pos = dpg.get_item_pos(f'cell_bar_{i}_{self.id}')\n dpg.draw_rectangle(\n parent=draw_layer_tag,\n pmin=label_pos,\n pmax=(\n label_pos[0] + _SCORE_COLUMN_WIDTH*perc,\n label_pos[1] + _FONT_SIZE),\n color=(0, 255, 0),\n fill=(0, 255, 0)\n )\n dpg.set_value(f'cell_perc_{i}_{self.id}', f'{perc*100:.2f}%')\n dpg.set_value(f'cell_name_{i}_{self.id}', name)\n\n return img","def show(raster):\n gk.read(raster).show()","def info(self):\n\n\t\tprint(\"Pixels on a side: {0}\".format(self.data.shape[0]))\n\t\tprint(\"Pixel size: {0}\".format(self.resolution))\n\t\tprint(\"Total angular size: {0}\".format(self.side_angle))\n\t\tprint(\"lmin={0:.1e} ; lmax={1:.1e}\".format(self.lmin,self.lmax))","def convert_and_print(img_file, table_path, op_size): \n \n # load up the table and the image\n table_obj = {}\n with open(table_path,\"r\") as f:\n try:\n table_obj = json.load(f)\n except ValueError:\n print(\"problem parsing table JSON, aborting...\")\n return\n image = Image.open(img_file)\n w,h = image.size\n pixels = image.load()\n\n # Calculate character widths after scaling\n chr_width, chr_height = table_obj[Constants.META_KEY][Constants.IMAGE_WIDTH_KEY], table_obj[Constants.META_KEY][Constants.IMAGE_HEIGHT_KEY]\n chr_width = ceil(chr_width / op_size)\n chr_height = ceil(chr_height / op_size)\n # do the conversion and print it\n result = img2Ascii(pixels, table_obj[Constants.TABLE_KEY], w, h, chr_width, chr_height)\n for i in result:\n print(\"\".join(i))","def print_metadata(self, camera, pixel_size, fileout=sys.stdout):\n print(self, end='\\n', file=fileout)\n print(cam_to_string(camera), end='\\n', file=fileout)\n print(\"pixels;{}\".format(pixel_size), file=fileout)","def print_tile(tile: Image.Image):\n width, height = tile.size\n\n pixels = tile.getcolors(width * height)\n\n most_frequent_pixel = pixels[0]\n\n for count, color in pixels:\n if count > most_frequent_pixel[0]:\n most_frequent_pixel = (count, color)\n\n r, g, b = most_frequent_pixel[1]\n\n light = r * 299/1000 + g * 587/1000 + b * 114/1000\n\n char = get_char_from_light(light)\n\n color = get_xterm_color(r, g, b)\n\n print(\"\\u001b[38;5;\" + str(color) + \"m\" + char, end=\"\\033[0m\")","def imshow(img):\n imadd(img)\n plt.ion()\n plt.show()","def testImage():\n width = 200\n height = 200\n image = BitMap( width, height )\n \n # create a loop in order to draw some pixels\n \n for col in range(width):\n if col % 10 == 0: print 'col is', col\n for row in range(height):\n if col % 10 == 0 or row % 10 == 0:\n image.plotPoint( col, row ) \n \n # we have now looped through every image pixel\n # next, we write it out to a file\n \n image.saveFile( \"test.bmp\" )\n #changing the col and row number determines how big the grid is for the picture or how zoomed in it is. Changing the and to or just makes the grid go from dotted grid to lines.","def show_one(img):\n dpi = 40\n margin = 0.05\n nda = sitk.GetArrayFromImage(img)\n spacing = img.GetSpacing()\n extent = (0, nda.shape[1] * spacing[1], nda.shape[0] * spacing[0], 0)\n figsize = (5, 5)\n fig = plt.figure(figsize=figsize, dpi=dpi)\n ax = fig.add_axes([margin, margin, 1 - 2 * margin, 1 - 2 * margin])\n\n plt.set_cmap(\"gray\")\n ax.imshow(nda, extent=extent, interpolation=None)","def image_show(inp, title=None):\n inp = inp.numpy().transpose((1, 2, 0))\n mean = np.array([0.485, 0.456, 0.406])\n std = np.array([0.229, 0.224, 0.225])\n inp = std * inp + mean\n inp = np.clip(inp, 0, 1)\n plt.imshow(inp)\n\n if title is not None:\n plt.title(title)\n plt.pause(0.001)","def imdisplay(filename, representation):\n im = read_image(filename, representation)\n if representation == 1:\n plt.imshow(im, cmap='gray')\n plt.show()\n if representation == 2:\n plt.imshow(im)\n plt.show()","def display_napari(pos_img):\n global data\n global img_queue\n if pos_img is None:\n return\n # read image and z position\n image = np.reshape(pos_img[2:],(clip[0], clip[1]))\n z_pos = pos_img[1]\n color = pos_img[0]\n\n # write image into correct slice of data and update display\n data[z_pos] = np.squeeze(image)\n layer = viewer.layers[color]\n layer.data = data\n #print(\"updating \", z_pos, color)\n\n img_queue.task_done()","def display_image(image):\n image = tf.constant(image)\n image = tf.image.convert_image_dtype(image, tf.uint8)\n return PIL.Image.fromarray(image.numpy())","def print_out():\n pass","def image(self, path):\n im = Image.open(path).convert(\"RGB\")\n # Convert the RGB image in printable image\n self._convert_and_print_image(im)","def im_show(img):\n img = img / 2 + 0.5 # unnormalize\n npimg = img.numpy()\n plt.imshow(np.transpose(npimg, (1, 2, 0)))\n plt.show()","def print_images(i, df):\n \n images_folder_path = \"dataset/petfinder-adoption-prediction/train_images/\"\n plt.imshow(cv2.cvtColor(cv2.imread(images_folder_path+df.filename[i]), cv2.COLOR_BGR2RGB),);\n plt.axis(\"off\");\n plt.show()","def do_info (self, line) :\n\t\tprint\n\t\tprint get_info_string( self.__image )\n\t\tprint","def image_info(img):\n\tprint(img.format)\n\tprint(img.size)\n\tprint(img.mode)","def pprint(self, data):\n self._assert(data)\n data = self._render(data) # make elements ascii\n fmats = self._fmats(data) # get array of padding formats)\n for row in data:\n print(fmats.format(*row))"],"string":"[\n \"def printImage(imageObject):\\n # TODO\\n pass\",\n \"def print_img_data(img,title='Image'):\\n print(f'________ {title} _______')\\n if len(image.shape) == 2:\\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\\\\n\\\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0])}\\\\n')\\n elif len(image.shape) == 3:\\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\\\\n\\\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0,0])}\\\\n')\",\n \"def print_image(data, index):\\n image = data[index]\\n for line in image:\\n print(line)\",\n \"def print_image(self, width, data):\\n if len(data) % width:\\n raise ValueError('Bad image format, length of data must be '\\n 'divisible by width.')\\n height = len(data) / width\\n\\n # send line-by-line\\n for row in range(height):\\n self.send_command('print_bitmap', 1, width)\\n self.port.write(data[row*width:(row+1)*width], is_text=False)\\n self.port.fed_dots(1)\",\n \"def print_image(self, pil_image):\\n bw_image = pil_image.convert('1', dither=Image.FLOYDSTEINBERG)\\n width = bw_image.width\\n height = bw_image.height\\n img_bytes = list(bw_image.getdata())\\n self.print_bitmap(width, height, img_bytes)\",\n \"def print_image(img):\\n for k in img:\\n print(\\\"\\\".join([str(x) for x in k] ))\",\n \"def print_image(img):\\r\\n # On affiche l'image\\r\\n plt.figure(figsize=(20, 5))\\r\\n plt.subplot(1, 2, 1)\\r\\n plt.imshow(img)\\r\\n # On affiche l'histogramme\\r\\n plt.subplot(1, 2, 2)\\r\\n plt.hist(img.flatten(), bins=range(256))\\r\\n plt.show()\",\n \"def print_image(indiv,name):\\n routine = gp.compile(indiv,pset)\\n output = gen_beat_output(routine)\\n bits = np.array(map(bitlist,output)[0:24000]).transpose()\\n plt.style.use('classic')\\n plt.imshow(bits,interpolation='nearest',aspect='auto',cmap=plt.get_cmap('Greys'))\\n plt.savefig(name+\\\".png\\\",dpi=150)\",\n \"def pretty_print(image_example):\\n print numpy.array_str(image_example, precision=1, max_line_width=142)\",\n \"def print_real_image(r_img, name = None, batch_size = BATCH_SIZE):\\n if name: print(\\\"***********\\\" + name + \\\"**********\\\")\\n to_print = []\\n c_img = r_img.clone()\\n for i in range(batch_size):\\n img = c_img[i,:,:]\\n img = torch.squeeze(img)\\n img = img.detach()\\n img = img.cpu()\\n plt.imshow(img, cmap = 'gray')\\n plt.show()\",\n \"def print_image_info(input_image):\\n print()\\n print(\\\"Basic Information on image: {}\\\".format(input_image.filename))\\n print(\\\"Format: {}\\\".format(input_image.format))\\n print(\\\"Mode: {}\\\".format(input_image.mode))\\n print(\\\"Size: {}\\\".format(input_image.size))\\n print(\\\"Width: {}\\\".format(input_image.width))\\n print(\\\"Height: {}\\\".format(input_image.height))\\n print(\\\"Palette: {}\\\".format(input_image.palette))\\n print()\",\n \"def printImage(currentImage):\\n\\tprint currentImage + ' is set to be printed...'\",\n \"def print_images_out_statistics(self):\\n self._print_images_statistics(self._images_out_folder, self._pose_class_names)\",\n \"def print_img_at_idx(self, idx):\\n\\n img = self.images[idx]\\n print_img(img)\",\n \"def printMat(image):\\n for row in range(image.rows):\\n print \\\"[\\\",\\n for col in range(image.cols):\\n print cv.mGet(image, row, col),\\n print \\\"]\\\"\\n print \\\"\\\"\",\n \"def show_image(image):\\n print('-' * (len(image) + 4))\\n for line in image:\\n print('| ', end='')\\n for ch in line:\\n char = '#' if ch is True else '.'\\n print(char, end='')\\n print(' |')\\n print('-' * (len(image) + 4))\",\n \"def display(self):\\n display(self.image)\",\n \"def show_input_to_output(img_ns):\\n figure()\\n \\n sp = subplot(1, 2, 1).imshow(img_ns.img)\\n sp.axes.grid(False)\\n sp.axes.tick_params(bottom=False, left=False, which='both',labelleft=False,labelbottom=False,length=0)\\n title(\\\"Input Image\\\", fontsize=10);\\n outimg = tiles_to_images(img_ns, img_ns.tile_grid, img_ns.tile_catalog, img_ns.tile_size)\\n sp = subplot(1, 2, 2).imshow(outimg.astype(np.uint8));\\n sp.axes.tick_params(bottom=False, left=False, which='both',labelleft=False,labelbottom=False,length=0)\\n title(\\\"Output Image From Tiles\\\", fontsize=10);\\n sp.axes.grid(False)\\n #print(outimg.astype(np.uint8))\\n #print(img_ns)\\n plt.savefig(img_ns.output_filename + \\\"_input_to_output.pdf\\\", bbox_inches=\\\"tight\\\")\\n plt.close()\",\n \"def visualize_output(\\n self,\\n img: np.ndarray,\\n output_data: Any):\\n raise NotImplementedError\",\n \"def print_images_in_statistics(self):\\n self._print_images_statistics(self._images_in_folder, self._pose_class_names)\",\n \"def print_stats(cars, notcars):\\n print(\\\"Number of car samples: {0}\\\".format(len(cars)))\\n print(\\\"Number of non car samples: {0}\\\".format(len(notcars)))\\n img = cv2.imread(cars[0])\\n print(\\\"Image shape: {0}x{1}\\\".format(img.shape[0], img.shape[1]))\\n print(\\\"Image datatype: {}\\\".format(img.dtype))\",\n \"def imshow(image):\\n iio.imshow(dtype.im2uint(image))\",\n \"def _convert_and_print_image(self, im):\\n pixLine = \\\"\\\"\\n imLeft = \\\"\\\"\\n imRight = \\\"\\\"\\n switch = 0\\n imgSize = [0, 0]\\n\\n if im.size[0] > 512:\\n print (\\\"WARNING: Image is wider than 512 and could be truncated at print time \\\")\\n if im.size[1] > 255:\\n raise ValueError(\\\"Image Height larger than 255\\\")\\n\\n imBorder = self._check_image_size(im.size[0])\\n for i in range(imBorder[0]):\\n imLeft += \\\"0\\\"\\n for i in range(imBorder[1]):\\n imRight += \\\"0\\\"\\n\\n for y in range(im.size[1]):\\n imgSize[1] += 1\\n pixLine += imLeft\\n imgSize[0] += imBorder[0]\\n for x in range(im.size[0]):\\n imgSize[0] += 1\\n RGB = im.getpixel((x, y))\\n imColor = (RGB[0] + RGB[1] + RGB[2])\\n imPattern = \\\"1X0\\\"\\n patternLen = len(imPattern)\\n switch = (switch - 1) * (-1)\\n for x in range(patternLen):\\n if imColor <= (255 * 3 / patternLen * (x + 1)):\\n if imPattern[x] == \\\"X\\\":\\n pixLine += \\\"%d\\\" % switch\\n else:\\n pixLine += imPattern[x]\\n break\\n elif imColor > (255 * 3 / patternLen * patternLen) and imColor <= (255 * 3):\\n pixLine += imPattern[-1]\\n break\\n pixLine += imRight\\n imgSize[0] += imBorder[1]\\n\\n self._print_image(pixLine, imgSize)\",\n \"def display(self, image):\\n raise NotImplementedError()\",\n \"def _print_image(self, line, size):\\n i = 0\\n cont = 0\\n\\n self._write(self.__class__.__imageSize['1x1'])\\n buffer = bytearray([int((size[0] / size[1]) / 8), 0, size[1], 0])\\n\\n self._write(buffer)\\n buffer = bytearray()\\n\\n while i < len(line):\\n hex_string = int(line[i:i + 8], 2)\\n buffer.append(hex_string)\\n i += 8\\n cont += 1\\n if cont % 4 == 0:\\n self._write(buffer)\\n buffer = bytearray()\\n cont = 0\",\n \"def display_image(mat):\\n\\timg = Image.fromarray(mat)\\n\\timg.show()\",\n \"def print_info(self, i):\\n\\n im_size = self.image_size(i)\\n print 'The path of the image is: {}'.format(self.image_path_at(i))\\n print 'width: {}, height: {}'.format(im_size[0], im_size[1])\\n \\n attr_i = self.gtdb['attr'][i, :]\\n print 'The attributes are: {}'.format(','.join([self._classes[i] for i in np.where(attr_i==1)[0]]))\",\n \"def show(self, data):\\n if isinstance(data, (numpy.ndarray, h5py.Dataset)):\\n isAtomic = len(data.shape) == 0\\n isCurve = len(data.shape) == 1 and numpy.issubdtype(data.dtype, numpy.number)\\n isImage = len(data.shape) == 2 and numpy.issubdtype(data.dtype, numpy.number)\\n if isAtomic:\\n self.showAsString(data)\\n elif isCurve:\\n self.show1d(data)\\n elif isImage:\\n self.show2d(data)\\n else:\\n self.showAsString(data)\\n else:\\n self.showAsString(data)\",\n \"def display(array):\\n plt.figure()\\n plt.imshow(array)\\n plt.show()\",\n \"def info_np(img):\\n import numpy as np\\n\\n print ('Dimensions: ' + str(np.shape(img)))\\n print ('Min value: ' + str(np.min(img)))\\n print ('Avg value: ' + str(np.average(img)))\\n print ('Med value: ' + str(np.median(img)))\\n print ('Max value: ' + str(np.max(img)))\\n print ('Std dev: ' + str(np.std(img)))\\n print ('Sum: ' + str(np.sum(img)))\",\n \"def show_image(image):\\r\\n plt.imshow(image, cmap='gray')\\r\\n plt.show()\",\n \"def showImg(img, binary=True, fName=''):\\n img = img[0, 0, :, :]\\n\\n if binary:\\n img = img > 0.5\\n\\n img = Image.fromarray(np.uint8(img * 255), mode='L')\\n\\n if fName:\\n img.save('assets/' + fName + '.png')\\n else:\\n img.show()\",\n \"def display_image(X):\\n\\n\\tim = X.reshape(28, 28)\\n\\ttemp = plt.imshow(im)\\n\\tplt.show()\",\n \"def print_adni_qc(outputdir, data, title):\\n # extract filename for title\\n title = os.path.basename(title)\\n\\n maximum = np.max(data)\\n plt.imshow(data, cmap=plt.cm.jet, interpolation='nearest', vmin=0.15*maximum, vmax=0.75*maximum)\\n plt.colorbar()\\n plt.title(os.path.basename(title), fontsize=8)\\n plt.xticks([])\\n plt.yticks([])\\n plt.tight_layout()\\n plt.savefig(os.path.join(outputdir, '{}.jpg'.format(title)))\\n plt.close()\",\n \"def show(self) -> None:\\n cv.imshow(str(self.__class__), self.output_image)\",\n \"def display_data():\\n data = read_data_from_file()\\n data_size = len(data)\\n print('Data size - ' + str(data_size))\\n\\n\\n for i in range(data_size-1, 0, -1):\\n image = data[i]\\n cv2.imshow('window', image[0])\\n print(str(image[1]))\\n cv2.waitKey(50)\",\n \"def print_bitmap(self, w, h, image):\\n\\n bitmap = self._pack_bitmap(w, h, image)\\n\\n row_bytes = (w + 7) // 8 # Round up to next byte boundary\\n\\n if row_bytes >= 48:\\n row_bytes_clipped = 48\\n else:\\n row_bytes_clipped = row_bytes # 384 pixels max width\\n\\n # Est. max rows to write at once, assuming 256 byte printer buffer.\\n if self._dtr_enabled:\\n chunk_height_limit = 255 # Buffer doesn't matter, handshake!\\n else:\\n chunk_height_limit = 256 // row_bytes_clipped\\n if chunk_height_limit > self._max_chunk_height:\\n chunk_height_limit = self._max_chunk_height\\n elif chunk_height_limit < 1:\\n chunk_height_limit = 1\\n\\n row_start = 0\\n i = 0\\n while row_start < h:\\n # Issue up to chunkHeightLimit rows at a time:\\n chunk_height = h - row_start\\n if chunk_height > chunk_height_limit:\\n chunk_height = chunk_height_limit\\n\\n self.write(self.ASCII_DC2, '*', chunk_height, row_bytes_clipped)\\n\\n y = 0\\n while y < chunk_height:\\n x = 0\\n while x < row_bytes_clipped:\\n self.timeout_wait()\\n self._send_to_printer(int(bitmap[i]))\\n x += 1\\n i += 1\\n\\n y += 1\\n\\n i += row_bytes - row_bytes_clipped\\n\\n self.timeout_set(chunk_height * self._dot_print_time)\\n\\n row_start += chunk_height_limit\\n\\n self._prev_byte = '\\\\n'\",\n \"def show_env(self, img):\\n plt.figure(1)\\n plt.subplot(111)\\n plt.imshow(img, interpolation=\\\"nearest\\\")\\n plt.show()\",\n \"def img_disp(name,img):\\n cv2.imshow(name,img.astype(int)/255.0)\\n cv2.waitKey()\",\n \"def display(self, image=None):\\n # By default write the internal buffer to the display.\\n if image is None:\\n image = self.buffer\\n # Set address bounds to entire display.\\n self.setAddress()\\n # Convert image to array of 16bit 565 RGB data bytes.\\n # Unfortunate that this copy has to occur, but the SPI byte writing\\n # function needs to take an array of bytes and PIL doesn't natively\\n # store images in 16-bit 565 RGB format.\\n pixelbytes = list(image_to_data(image))\\n # Write data to hardware.\\n self.writeData(pixelbytes)\",\n \"def show(type,img):\\n # print(img)\\n cv2.imshow(type, img)\\n cv2.waitKey()\",\n \"def imdisplay(filename, representation):\\n image = read_image(filename, representation)\\n\\n if representation == GRAY_OUT:\\n plt.imshow(image, cmap='gray')\\n else:\\n plt.imshow(image)\\n\\n plt.show()\",\n \"def display(self, image):\\n assert(image.mode == '1')\\n assert(image.size[0] == self.width)\\n assert(image.size[1] == self.height)\\n\\n self.command(\\n const.COLUMNADDR, 0x00, self.width-1, # Column start/end address\\n const.PAGEADDR, 0x00, self.pages-1) # Page start/end address\\n\\n pix = list(image.getdata())\\n step = self.width * 8\\n buf = []\\n for y in xrange(0, self.pages * step, step):\\n i = y + self.width-1\\n while i >= y:\\n byte = 0\\n for n in xrange(0, step, self.width):\\n byte |= (pix[i + n] & 0x01) << 8\\n byte >>= 1\\n\\n buf.append(byte)\\n i -= 1\\n\\n self.data(buf)\",\n \"def print_image_row(input, iy=0, iz=0):\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tprint \\\"(z = %d slice, y = %d col)\\\" % (iz, iy)\\n\\tline = []\\n\\tfor ix in xrange(nx):\\n\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\tif ((ix + 1) % 5 == 0): line.append(\\\"\\\\n \\\")\\n\\tline.append(\\\"\\\\n\\\")\\n\\tprint \\\"\\\".join(line)\",\n \"def print_image_info(self):\\r\\n\\r\\n maxt = np.max(self.times)\\r\\n\\r\\n print (\\\" Duration of Image Stack: %9.3f s (%8.3f min) period = %8.3f s\\\" % (maxt, maxt/60.0, self.period))\\r\\n\\r\\n print (' Image shape: ', self.imageData.shape)\\r\\n\\r\\n print (' nFrames: %d framerate: %9.3f\\\\n' % (self.nFrames, self.framerate))\",\n \"def print_data(self, data):\\n self.print_indicator = True\\n self.imagedata = data\\n self.setImage(self.imagedata)\\n\\n self.indicator_min = -200\\n self.indicator_max = 200\\n\\n if self.video_model != None:\\n pos = int(self.video_model.get_pos(datatype = \\\"motion\\\"))\\n self.indicator = self.view.plot([pos,pos],[self.indicator_min,self.indicator_max],pen=pyqtgraph.mkPen(color=pyqtgraph.hsvColor(2),width=1))\",\n \"def dump_image(image, path_image):\\n cv2.imwrite(path_image, image)\\n return\",\n \"def _repr_png_(self):\\n mol = self.owner.mol\\n keku = IPythonConsole.kekulizeStructures\\n size = IPythonConsole.molSize\\n opts = IPythonConsole.drawOptions\\n return Draw._moltoimg(\\n mol, size, self.aix, \\\"\\\", returnPNG=True, drawOptions=opts,\\n kekulize=keku, highlightBonds=self.bix\\n )\",\n \"def _repr_png_(self):\\n mol = self.owner.mol\\n keku = IPythonConsole.kekulizeStructures\\n size = IPythonConsole.molSize\\n opts = IPythonConsole.drawOptions\\n return Draw._moltoimg(\\n mol, size, self.aix, \\\"\\\", returnPNG=True, drawOptions=opts,\\n kekulize=keku, highlightBonds=self.bix\\n )\",\n \"def print(self):\\n self.__print_local(self.dataset, 0)\",\n \"def main():\\n labels, data = load_image_data()\\n print(labels.shape, data.shape)\",\n \"def print_image_info(image, resize=rsz_default, kernel=kernel_size):\\n\\tprint \\\"Image Size: {0}\\\".format(image.shape)\\n\\tprint \\\"Image Max: {0}\\\".format(image.max())\\n\\tprint \\\"Image Min: {0}\\\".format(image.min())\\n\\tprint \\\"Image Mean: {0}\\\".format(image.mean())\\n\\tprint \\\"Image dtype: {0}\\\\n\\\".format(image.dtype)\\n\\timage = to_uint8(image)\\n\\timage_prep = preprocess(image, resize=resize, kernel=kernel)\\n\\tcontour = get_contour(image_prep)\\n\\tM = get_image_moments(contour=contour)\\n\\tsecond_m = ['m20', 'm11', 'm02', 'm30', 'm21', 'm12', 'm03']\\n\\tprint \\\"Zero Order Moment: {0}\\\".format(M['m00'])\\n\\tprint \\\"First Order Moments: {0}, {1}\\\".format(M['m10'], M['m01'])\\n\\tprint \\\"Second Order Moments:\\\"\\n\\tsecond_m_str = ''\\n\\tfor m2 in second_m:\\n\\t\\tsecond_m_str += \\\"{0},\\\".format(M[m2])\\n\\tprint second_m_str[:-1]\",\n \"def showimage(image):\\n mplt.figure()\\n mplt.imshow(image)\\n mplt.show()\",\n \"def _print_img_size(self, img):\\n width, height = img.size\\n print('{}, {}'.format(width, height))\",\n \"def display(array):\\n if isinstance(array, np.ndarray):\\n plt.imshow(array)\\n plt.show()\\n else:\\n raise TypeError(\\\"display() needs a numpy ndarray as parameter, \\\"\\n f\\\"got {type(array)}\\\")\",\n \"def display(image, name=\\\"Image\\\"):\\n cv2.imshow(name, image)\\n cv2.waitKey(0)\\n cv2.destroyAllWindows()\\n cv2.imwrite(\\\"{}.png\\\".format(name), image)\",\n \"def show(self, exec_rasterize = False):\\n\\n if (exec_rasterize):\\n self.rasterize()\\n\\n Image.fromarray(self._image).show()\",\n \"def show_image(self, pic, prediction=None):\\n digitmap = {\\n 0: [(0,0), (1,0), (2,0), (3,0), (4,0), (0,1), (4,1), (0,2), (1,2), (2,2), (3,2), (4,2)],\\n 1: [(0,2), (1,2), (2,2), (3,2), (4,2)],\\n 2: [(0,0), (0,1), (0,2), (1,2), (2,0), (2,1), (2,2), (3,0), (4,0), (4,1), (4,2)],\\n 3: [(0,0), (0,1), (0,2), (1,2), (2,0), (2,1), (2,2), (3,2), (4,0), (4,1), (4,2)],\\n 4: [(0,0), (0,2), (1,0), (1,2), (2,0), (2,1), (2,2), (3,2), (4,2)],\\n 5: [(0,0), (0,1), (0,2), (1,0), (2,0), (2,1), (2,2), (3,2), (4,0), (4,1), (4,2)],\\n 6: [(0,0), (0,1), (0,2), (1,0), (2,0), (2,1), (2,2), (3,0), (3,2), (4,0), (4,1), (4,2)],\\n 7: [(0,0), (0,1), (0,2), (1,2), (2,2), (3,2), (4,2)],\\n 8: [(0,0), (1,0), (2,0), (3,0), (4,0), (0,1), (4,1), (0,2), (1,2), (2,2), (3,2), (4,2), (2,1)],\\n 9: [(0,0), (0,1), (0,2), (1,0), (1,2), (2,0), (2,1), (2,2), (3,2), (4,0), (4,1), (4,2)]\\n }\\n\\n pic = pic.reshape((28,28)).copy()\\n if prediction is not None:\\n for pos in digitmap[prediction]:\\n pic[pos]=255\\n plt.imshow(pic, cmap='gray_r')\",\n \"def write(self, image):\\n raise NotImplementedError()\",\n \"def visualize_output(\\n self,\\n img: np.ndarray,\\n output_data: List[DetectObject]) -> np.ndarray:\\n draw_layer_tag = f'draw_layer_{self.id}_{self.draw_layer_index^1}'\\n\\n self.layer = dpg.add_draw_layer(\\n parent=f'main_window_{self.id}',\\n tag=draw_layer_tag,\\n show=False\\n )\\n for out in output_data:\\n description = f'{out.clsname} [{out.score*100:.2f}%]'\\n # add alpha and convert to RGB\\n color = np.array(self.class_colors[out.clsname])\\n color = tuple((255*color).astype(np.uint8))\\n dpg.draw_rectangle(\\n parent=draw_layer_tag,\\n pmin=(out.xmin*self.width,\\n out.ymin*self.height),\\n pmax=(out.xmax*self.width,\\n out.ymax*self.height),\\n color=color,\\n thickness=2\\n )\\n dpg.draw_text(\\n parent=draw_layer_tag,\\n text=description,\\n pos=(out.xmin*self.width + _PADDING,\\n out.ymin*self.height + _PADDING),\\n color=color,\\n size=_FONT_SIZE\\n )\\n\\n return img\",\n \"def imdisplay(filename, representation):\\n\\n image = read_image(filename, representation)\\n plt.imshow(image, cmap=\\\"gray\\\")\\n plt.show()\",\n \"def print_image_col(input, ix=0, iz=0):\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tprint \\\"(z = %d slice, x = %d row)\\\" % (iz, ix)\\n\\tline = []\\n\\tfor iy in xrange(ny):\\n\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\tif ((iy + 1) % 5 == 0): line.append(\\\"\\\\n \\\")\\n\\tline.append(\\\"\\\\n\\\")\\n\\tprint \\\"\\\".join(line)\",\n \"def print_image_slice(input, iz=0):\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tprint \\\"(z = %d slice)\\\" % (iz)\\n\\tline = []\\n\\tfor iy in xrange(ny-1,-1,-1):\\n\\t\\tline.append(\\\"Row \\\")\\n\\t\\tline.append(\\\"%4i \\\" % iy)\\n\\t\\tfor ix in xrange(nx):\\n\\t\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\t\\tif ((ix + 1) % 5 == 0): \\n\\t\\t\\t\\tline.append(\\\"\\\\n \\\")\\n\\t\\t\\t\\tline.append(\\\" \\\")\\n\\t \\tline.append(\\\"\\\\n\\\")\\n\\t \\tif(nx%5 != 0): line.append(\\\"\\\\n\\\")\\n\\tprint \\\"\\\".join(line)\",\n \"def display(self, image):\\n assert(image.mode == '1')\\n assert(image.size[0] == self.width)\\n assert(image.size[1] == self.height)\\n\\n page = 0xB0\\n pix = list(image.getdata())\\n step = self.width * 8\\n for y in xrange(0, self.pages * step, step):\\n\\n # move to given page, then reset the column address\\n self.command(page, 0x02, 0x10)\\n page += 1\\n\\n buf = []\\n for x in xrange(self.width):\\n byte = 0\\n for n in xrange(0, step, self.width):\\n byte |= (pix[x + y + n] & 0x01) << 8\\n byte >>= 1\\n\\n buf.append(byte)\\n\\n self.data(buf)\",\n \"def display(self, image):\\n assert(image.mode == '1')\\n assert(image.size[0] == self.width)\\n assert(image.size[1] == self.height)\\n\\n page = 0xB0\\n pix = list(image.getdata())\\n step = self.width * 8\\n for y in xrange(0, self.pages * step, step):\\n\\n # move to given page, then reset the column address\\n self.command(page, 0x02, 0x10)\\n page += 1\\n\\n buf = []\\n for x in xrange(self.width):\\n byte = 0\\n for n in xrange(0, step, self.width):\\n byte |= (pix[x + y + n] & 0x01) << 8\\n byte >>= 1\\n\\n buf.append(byte)\\n\\n self.data(buf)\",\n \"def print_images(images,output_dir,image_num=0,pair=False,synth_images=None):\\n for i in xrange(images.shape[0]):\\n to_print = fix_image(images[i])\\n\\n if pair and synth_images is not None:\\n synth_to_print = fix_image(synth_images[i])\\n to_print = np.hstack((to_print,synth_to_print))\\n\\n #What is the name of the image?\\n imsave(os.path.join(output_dir,str(image_num + i) + \\\".png\\\"), to_print)\",\n \"def show_picture(self, data):\\n raise NotImplementedError\",\n \"def show_image(dataset, domain, image_class, image_name):\\n\\timage_file = io.imread(os.path.join(\\\"data\\\", dataset, domain, \\\"images\\\", image_class, image_name))\\n\\tplt.imshow(image_file)\\n\\tplt.pause(0.001)\\n\\tplt.figure()\",\n \"def pixdump( source, start=None, end=None, length=None, width=64, height=None, palette=None ):\\n\\n for line in pixdump_iter( source, start, end, length, width, height, palette ):\\n print( line )\",\n \"def display(self):\\n for row in range(self.height):\\n for col in range(self.width):\\n char = '#' if self.pixels[row * self.width + col] else '.'\\n print(char, end='')\\n print()\\n print()\",\n \"def drawData(data, writeLocation = \\\"../output/data.png\\\", gamma = 0.25):\\n\\n im = Image.new(\\\"RGB\\\", (data.shape[1], data.shape[0]))\\n d = ImageDraw.Draw(im)\\n \\n _drawData(d, data, gamma)\\n \\n im.save(writeLocation, \\\"PNG\\\")\",\n \"def print(self):\\n print('Name:', self.name)\\n print('Camera:', self.camera)\\n print('Memory:', self.memory)\\n print('Ram:', self.ram)\\n print('Price:', self.price)\\n print('Image:', self.image)\",\n \"def showTensorImg(ts, title):\\n img = np.transpose(ts, (1, 2, 0))\\n showImg(img, title)\\n return\",\n \"def imdisplay(filename, representation):\\n img = read_image(filename, representation)\\n if representation == GS_REP:\\n plt.imshow(img, cmap=plt.cm.gray)\\n else:\\n plt.imshow(img)\",\n \"def disImg(data=None,colorbar=False):\\n size = np.sqrt(len(data[4:]))\\n xmm = data[0]\\n ymm = data[1]\\n pl.matshow(data[4:].reshape(size,size),fignum=False)\\n if colorbar == True:\\n pl.colorbar()\\n pl.xlim(0,size-1)\\n pl.ylim(0,size-1)\\n pl.xlabel('Pixels')\\n pl.ylabel('Pixels')\\n pl.grid(color='yellow')\",\n \"def info(self):\\n\\n print(\\\"pixellisation:\\\", self.pixel)\\n print(\\\"number of components:\\\", self.ncomp)\\n print(\\\"number of pixels:\\\", self.data.shape[:] if self.ncomp == 1 else self.data.shape[1:])\\n print(\\\"nside:\\\", self.nside)\\n print(\\\"geometry:\\\", self.geometry)\\n print(\\\"coordinates:\\\", self.coordinate)\",\n \"def view_image(train_dataloader):\\n for (x, target) in train_dataloader:\\n np.save(\\\"img.npy\\\", x)\\n print(x.shape)\\n exit(0)\",\n \"def show_image(self):\\n cv2.imshow(self.config.DISPLAY_NAME, self.image)\",\n \"def show_digit(self):\\n x_train, _, _, _ = self._load_data()\\n plt.imshow(x_train[0], cmap=plt.cm.binary)\\n plt.show()\",\n \"def matplotlibDisplay(img, title=\\\"Image\\\", colorFlag = 'gray'):\\n plt.imshow(img, colorFlag)\\n plt.title(title)\\n plt.xticks([])\\n plt.yticks([])\\n plt.show()\",\n \"def visualize_output(\\n self,\\n img: np.ndarray,\\n output_data: np.ndarray) -> np.ndarray:\\n draw_layer_tag = f'draw_layer_{self.id}_{self.draw_layer_index^1}'\\n\\n self.layer = dpg.add_draw_layer(\\n parent=f'main_window_{self.id}',\\n tag=draw_layer_tag,\\n show=False\\n )\\n\\n best_k = np.argsort(output_data)[-self.top_n:][::-1]\\n class_names = np.array(self.class_names)[best_k]\\n percentages = softmax(output_data[best_k])\\n\\n for i, (name, perc) in enumerate(zip(class_names, percentages)):\\n label_pos = dpg.get_item_pos(f'cell_bar_{i}_{self.id}')\\n dpg.draw_rectangle(\\n parent=draw_layer_tag,\\n pmin=label_pos,\\n pmax=(\\n label_pos[0] + _SCORE_COLUMN_WIDTH*perc,\\n label_pos[1] + _FONT_SIZE),\\n color=(0, 255, 0),\\n fill=(0, 255, 0)\\n )\\n dpg.set_value(f'cell_perc_{i}_{self.id}', f'{perc*100:.2f}%')\\n dpg.set_value(f'cell_name_{i}_{self.id}', name)\\n\\n return img\",\n \"def show(raster):\\n gk.read(raster).show()\",\n \"def info(self):\\n\\n\\t\\tprint(\\\"Pixels on a side: {0}\\\".format(self.data.shape[0]))\\n\\t\\tprint(\\\"Pixel size: {0}\\\".format(self.resolution))\\n\\t\\tprint(\\\"Total angular size: {0}\\\".format(self.side_angle))\\n\\t\\tprint(\\\"lmin={0:.1e} ; lmax={1:.1e}\\\".format(self.lmin,self.lmax))\",\n \"def convert_and_print(img_file, table_path, op_size): \\n \\n # load up the table and the image\\n table_obj = {}\\n with open(table_path,\\\"r\\\") as f:\\n try:\\n table_obj = json.load(f)\\n except ValueError:\\n print(\\\"problem parsing table JSON, aborting...\\\")\\n return\\n image = Image.open(img_file)\\n w,h = image.size\\n pixels = image.load()\\n\\n # Calculate character widths after scaling\\n chr_width, chr_height = table_obj[Constants.META_KEY][Constants.IMAGE_WIDTH_KEY], table_obj[Constants.META_KEY][Constants.IMAGE_HEIGHT_KEY]\\n chr_width = ceil(chr_width / op_size)\\n chr_height = ceil(chr_height / op_size)\\n # do the conversion and print it\\n result = img2Ascii(pixels, table_obj[Constants.TABLE_KEY], w, h, chr_width, chr_height)\\n for i in result:\\n print(\\\"\\\".join(i))\",\n \"def print_metadata(self, camera, pixel_size, fileout=sys.stdout):\\n print(self, end='\\\\n', file=fileout)\\n print(cam_to_string(camera), end='\\\\n', file=fileout)\\n print(\\\"pixels;{}\\\".format(pixel_size), file=fileout)\",\n \"def print_tile(tile: Image.Image):\\n width, height = tile.size\\n\\n pixels = tile.getcolors(width * height)\\n\\n most_frequent_pixel = pixels[0]\\n\\n for count, color in pixels:\\n if count > most_frequent_pixel[0]:\\n most_frequent_pixel = (count, color)\\n\\n r, g, b = most_frequent_pixel[1]\\n\\n light = r * 299/1000 + g * 587/1000 + b * 114/1000\\n\\n char = get_char_from_light(light)\\n\\n color = get_xterm_color(r, g, b)\\n\\n print(\\\"\\\\u001b[38;5;\\\" + str(color) + \\\"m\\\" + char, end=\\\"\\\\033[0m\\\")\",\n \"def imshow(img):\\n imadd(img)\\n plt.ion()\\n plt.show()\",\n \"def testImage():\\n width = 200\\n height = 200\\n image = BitMap( width, height )\\n \\n # create a loop in order to draw some pixels\\n \\n for col in range(width):\\n if col % 10 == 0: print 'col is', col\\n for row in range(height):\\n if col % 10 == 0 or row % 10 == 0:\\n image.plotPoint( col, row ) \\n \\n # we have now looped through every image pixel\\n # next, we write it out to a file\\n \\n image.saveFile( \\\"test.bmp\\\" )\\n #changing the col and row number determines how big the grid is for the picture or how zoomed in it is. Changing the and to or just makes the grid go from dotted grid to lines.\",\n \"def show_one(img):\\n dpi = 40\\n margin = 0.05\\n nda = sitk.GetArrayFromImage(img)\\n spacing = img.GetSpacing()\\n extent = (0, nda.shape[1] * spacing[1], nda.shape[0] * spacing[0], 0)\\n figsize = (5, 5)\\n fig = plt.figure(figsize=figsize, dpi=dpi)\\n ax = fig.add_axes([margin, margin, 1 - 2 * margin, 1 - 2 * margin])\\n\\n plt.set_cmap(\\\"gray\\\")\\n ax.imshow(nda, extent=extent, interpolation=None)\",\n \"def image_show(inp, title=None):\\n inp = inp.numpy().transpose((1, 2, 0))\\n mean = np.array([0.485, 0.456, 0.406])\\n std = np.array([0.229, 0.224, 0.225])\\n inp = std * inp + mean\\n inp = np.clip(inp, 0, 1)\\n plt.imshow(inp)\\n\\n if title is not None:\\n plt.title(title)\\n plt.pause(0.001)\",\n \"def imdisplay(filename, representation):\\n im = read_image(filename, representation)\\n if representation == 1:\\n plt.imshow(im, cmap='gray')\\n plt.show()\\n if representation == 2:\\n plt.imshow(im)\\n plt.show()\",\n \"def display_napari(pos_img):\\n global data\\n global img_queue\\n if pos_img is None:\\n return\\n # read image and z position\\n image = np.reshape(pos_img[2:],(clip[0], clip[1]))\\n z_pos = pos_img[1]\\n color = pos_img[0]\\n\\n # write image into correct slice of data and update display\\n data[z_pos] = np.squeeze(image)\\n layer = viewer.layers[color]\\n layer.data = data\\n #print(\\\"updating \\\", z_pos, color)\\n\\n img_queue.task_done()\",\n \"def display_image(image):\\n image = tf.constant(image)\\n image = tf.image.convert_image_dtype(image, tf.uint8)\\n return PIL.Image.fromarray(image.numpy())\",\n \"def print_out():\\n pass\",\n \"def image(self, path):\\n im = Image.open(path).convert(\\\"RGB\\\")\\n # Convert the RGB image in printable image\\n self._convert_and_print_image(im)\",\n \"def im_show(img):\\n img = img / 2 + 0.5 # unnormalize\\n npimg = img.numpy()\\n plt.imshow(np.transpose(npimg, (1, 2, 0)))\\n plt.show()\",\n \"def print_images(i, df):\\n \\n images_folder_path = \\\"dataset/petfinder-adoption-prediction/train_images/\\\"\\n plt.imshow(cv2.cvtColor(cv2.imread(images_folder_path+df.filename[i]), cv2.COLOR_BGR2RGB),);\\n plt.axis(\\\"off\\\");\\n plt.show()\",\n \"def do_info (self, line) :\\n\\t\\tprint\\n\\t\\tprint get_info_string( self.__image )\\n\\t\\tprint\",\n \"def image_info(img):\\n\\tprint(img.format)\\n\\tprint(img.size)\\n\\tprint(img.mode)\",\n \"def pprint(self, data):\\n self._assert(data)\\n data = self._render(data) # make elements ascii\\n fmats = self._fmats(data) # get array of padding formats)\\n for row in data:\\n print(fmats.format(*row))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.75263417","0.74676603","0.74178773","0.71454406","0.70787203","0.70770824","0.6925922","0.68538165","0.6828833","0.680339","0.678513","0.6639985","0.6579925","0.65075237","0.64939463","0.6484506","0.6398455","0.6392404","0.63622975","0.6340637","0.63262016","0.6307193","0.6285712","0.6285369","0.62540925","0.6243022","0.6193243","0.618649","0.61821467","0.60993254","0.6072938","0.6036931","0.60203135","0.600127","0.59803253","0.59702146","0.59661436","0.59528494","0.59480464","0.5936401","0.5906899","0.5900356","0.58976775","0.5876411","0.5873731","0.5862324","0.5857106","0.58434385","0.58434385","0.5839406","0.58364713","0.58272517","0.57928085","0.57885146","0.57884765","0.57857466","0.5780859","0.5778936","0.5768562","0.5755103","0.5739007","0.57383186","0.57355034","0.5731207","0.5731207","0.57270575","0.5723809","0.5722958","0.57200104","0.5708253","0.5691348","0.5683647","0.5682703","0.56772393","0.5674491","0.5666773","0.56597376","0.5652539","0.56487155","0.5647298","0.5637696","0.5628275","0.56169486","0.56141984","0.5610702","0.5606966","0.56037694","0.55859864","0.5578834","0.5575727","0.5563797","0.55576724","0.55573845","0.5550739","0.55428267","0.55365","0.5532086","0.55081344","0.5500756","0.5498963"],"string":"[\n \"0.75263417\",\n \"0.74676603\",\n \"0.74178773\",\n \"0.71454406\",\n \"0.70787203\",\n \"0.70770824\",\n \"0.6925922\",\n \"0.68538165\",\n \"0.6828833\",\n \"0.680339\",\n \"0.678513\",\n \"0.6639985\",\n \"0.6579925\",\n \"0.65075237\",\n \"0.64939463\",\n \"0.6484506\",\n \"0.6398455\",\n \"0.6392404\",\n \"0.63622975\",\n \"0.6340637\",\n \"0.63262016\",\n \"0.6307193\",\n \"0.6285712\",\n \"0.6285369\",\n \"0.62540925\",\n \"0.6243022\",\n \"0.6193243\",\n \"0.618649\",\n \"0.61821467\",\n \"0.60993254\",\n \"0.6072938\",\n \"0.6036931\",\n \"0.60203135\",\n \"0.600127\",\n \"0.59803253\",\n \"0.59702146\",\n \"0.59661436\",\n \"0.59528494\",\n \"0.59480464\",\n \"0.5936401\",\n \"0.5906899\",\n \"0.5900356\",\n \"0.58976775\",\n \"0.5876411\",\n \"0.5873731\",\n \"0.5862324\",\n \"0.5857106\",\n \"0.58434385\",\n \"0.58434385\",\n \"0.5839406\",\n \"0.58364713\",\n \"0.58272517\",\n \"0.57928085\",\n \"0.57885146\",\n \"0.57884765\",\n \"0.57857466\",\n \"0.5780859\",\n \"0.5778936\",\n \"0.5768562\",\n \"0.5755103\",\n \"0.5739007\",\n \"0.57383186\",\n \"0.57355034\",\n \"0.5731207\",\n \"0.5731207\",\n \"0.57270575\",\n \"0.5723809\",\n \"0.5722958\",\n \"0.57200104\",\n \"0.5708253\",\n \"0.5691348\",\n \"0.5683647\",\n \"0.5682703\",\n \"0.56772393\",\n \"0.5674491\",\n \"0.5666773\",\n \"0.56597376\",\n \"0.5652539\",\n \"0.56487155\",\n \"0.5647298\",\n \"0.5637696\",\n \"0.5628275\",\n \"0.56169486\",\n \"0.56141984\",\n \"0.5610702\",\n \"0.5606966\",\n \"0.56037694\",\n \"0.55859864\",\n \"0.5578834\",\n \"0.5575727\",\n \"0.5563797\",\n \"0.55576724\",\n \"0.55573845\",\n \"0.5550739\",\n \"0.55428267\",\n \"0.55365\",\n \"0.5532086\",\n \"0.55081344\",\n \"0.5500756\",\n \"0.5498963\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7227022"},"document_rank":{"kind":"string","value":"3"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":7,"numItemsPerPage":100,"numTotalItems":95772,"offset":700,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3NjQzNTA2OSwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3NzY0Mzg2NjksImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.mVq7ek6hE6eyT6AOH4vv19K_EKIS6NmqkryPH-TyNIejc5IjTVOikj1Knv-c6QgrJYZ19y-yBiiWDIYB9FWpDQ","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Show employee a level below | def employees_json_id(request, employee_id):
curent_employee = Employee.objects.get(pk=int(employee_id))
if curent_employee.is_manager:
employee_list = Employee.objects.filter(manager=curent_employee)
employees = list()
for employee in employee_list:
manager_dict = model_to_d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handle_level_exp_mouseover(self):\n if self.skill_tree_displaying:\n return\n self.tooltip_focus = self.level_and_exp_rect\n player_panel_renderer.draw_exp_details(self.player_dict['experience'])",
"def getEmployeeLevel(self,number:int):\n allLevels = [1,2,3,4]\n ... | [
"0.6092373",
"0.60603535",
"0.5886119",
"0.5763377",
"0.57354516",
"0.5616083",
"0.5597218",
"0.556029",
"0.55143595",
"0.55038935",
"0.5442811",
"0.54350245",
"0.5397879",
"0.53973335",
"0.5397157",
"0.53344226",
"0.53100413",
"0.529323",
"0.52915984",
"0.52886397",
"0.52444... | 0.0 | -1 |
Get all employees manager as json | def employees_manager(request):
# current_employee = Employee.objects.get(user__pk=request.user.pk)
manager_list = Employee.objects.filter(manager=request.user.employee_user, is_manager=True)
employee = Employee.objects.get(pk=request.user.employee_user.id)
employee_dict = model_to_dict(employee)
em... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_manager_employees(request):\n current_employee = Employee.objects.get(user__pk=request.user.pk)\n manager_employees = Employee.objects.filter(manager=current_employee, development_plan_type=None).all()\n if manager_employees:\n emp_list=[]\n for emp in manager_employees:\n ... | [
"0.80657506",
"0.79710835",
"0.74464977",
"0.7288061",
"0.7011785",
"0.6977914",
"0.6967183",
"0.6929463",
"0.69241434",
"0.6900269",
"0.67404765",
"0.66503465",
"0.64858013",
"0.6479257",
"0.64647675",
"0.6463374",
"0.63625044",
"0.6354452",
"0.6331073",
"0.6328877",
"0.6321... | 0.7887314 | 2 |
Securely download files from user. | def employee_delete_file(request, employee_id, filename):
current_user = Employee.objects.get(user__pk=request.user.pk)
if not current_user.hasAccessTo(employee_id):
logUnauthorizedAccess(
"User tried to delete file he didnt have access to", request, filename
)
return HttpR... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def restricted_download():\n aaa.require(fail_redirect='/login')\n return bottle.static_file('static_file', root='.')",
"def download_files(self):",
"def download_file(self, net_id, request_id, file_name):\n current_user_roles = get_user_roles()\n if current_user_roles[\"STFADM\"] or net_id... | [
"0.6933737",
"0.67762506",
"0.6603779",
"0.65035725",
"0.6269322",
"0.6244334",
"0.6117313",
"0.6104816",
"0.6099837",
"0.60964644",
"0.6042637",
"0.60026896",
"0.59894454",
"0.59013104",
"0.58944786",
"0.5857168",
"0.58530647",
"0.58518803",
"0.5833485",
"0.5799934",
"0.5795... | 0.0 | -1 |
Securely download files from user. | def employee_download_file(request, employee_id, filename):
current_user = Employee.objects.get(user__pk=request.user.pk)
if not current_user.hasAccessTo(employee_id):
logUnauthorizedAccess(
"User tried to download file he didnt have access to", request, filename
)
return H... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def restricted_download():\n aaa.require(fail_redirect='/login')\n return bottle.static_file('static_file', root='.')",
"def download_files(self):",
"def download_file(self, net_id, request_id, file_name):\n current_user_roles = get_user_roles()\n if current_user_roles[\"STFADM\"] or net_id... | [
"0.6934367",
"0.6775749",
"0.6602642",
"0.6503681",
"0.626969",
"0.62442297",
"0.6117034",
"0.6105623",
"0.6099257",
"0.6096529",
"0.6040991",
"0.60035723",
"0.59876895",
"0.5901248",
"0.58960354",
"0.58567363",
"0.5854076",
"0.5851294",
"0.5834396",
"0.5800784",
"0.57954973"... | 0.5542597 | 86 |
View for detail of employee | def employee_detail(request, employee_id):
current_employee = Employee.objects.get(user__pk=request.user.pk)
employee = Employee.objects.get(pk=int(employee_id))
if not current_employee.isEnsoUser() and current_employee.company.pk != employee.company.pk:
raise PermissionDenied()
actions = employ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def employee(employee_id):\n # gather data from db about all employees\n return render_template(\"employee.html\",\n employee_id=employee_id)",
"def employee():\n return Response(render_template('employee/employee.html'))",
"def employees():\n # gather data from db about a... | [
"0.7529937",
"0.7131289",
"0.71110165",
"0.6999811",
"0.6908806",
"0.6881819",
"0.66219467",
"0.6529428",
"0.6462217",
"0.64600676",
"0.6398058",
"0.6358623",
"0.6351768",
"0.63481253",
"0.6347821",
"0.63392276",
"0.6267284",
"0.623935",
"0.6234159",
"0.6226198",
"0.6206023",... | 0.68222797 | 6 |
View for all employees current user is a manager for with empty development plan | def get_manager_employees(request):
current_employee = Employee.objects.get(user__pk=request.user.pk)
manager_employees = Employee.objects.filter(manager=current_employee, development_plan_type=None).all()
if manager_employees:
emp_list=[]
for emp in manager_employees:
emp_data={... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all_user_development_plans_for_manager(request, employee_id):\n current_employee = Employee.objects.get(user__pk=request.user.pk)\n user_development_plans = DevelopmentPlan.objects.filter(employee_relation=current_employee).all()\n employee = Employee.objects.filter(pk=int(employee_id)).first()\n\... | [
"0.7106553",
"0.6753194",
"0.674842",
"0.6595502",
"0.6566336",
"0.63812053",
"0.61806154",
"0.61537486",
"0.612033",
"0.60830206",
"0.60527897",
"0.60266685",
"0.596054",
"0.5893149",
"0.5841044",
"0.57119644",
"0.56820273",
"0.56635785",
"0.5655162",
"0.5615645",
"0.5613845... | 0.73281884 | 0 |
View for detail of employee | def profile_detail(request, employee_id):
current_employee = Employee.objects.filter(user__pk=request.user.pk).first()
employee = Employee.objects.get(pk=int(employee_id))
if not current_employee:
raise PermissionDenied("You don't have any employee assigned to you.", 401)
if not current_employ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def employee(employee_id):\n # gather data from db about all employees\n return render_template(\"employee.html\",\n employee_id=employee_id)",
"def employee():\n return Response(render_template('employee/employee.html'))",
"def employees():\n # gather data from db about a... | [
"0.75270534",
"0.71302915",
"0.71097803",
"0.6996392",
"0.6907323",
"0.6882247",
"0.6820251",
"0.6622057",
"0.65266895",
"0.6458678",
"0.63968426",
"0.6358651",
"0.6349126",
"0.63481754",
"0.6346942",
"0.63395876",
"0.62652826",
"0.62371093",
"0.6237032",
"0.6230674",
"0.6204... | 0.6460907 | 9 |
View for creating employee in company | def create_employee(request, company_id):
company = Company.objects.get(pk=company_id)
current_employee = Employee.objects.get(user__pk=request.user.pk)
if not current_employee.isEnsoUser() and current_employee.company.pk != company.pk:
logUnauthorizedAccess("User tried to create_employee", request... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_leader_model(request, company_id):\n\n errors = {'noactions': []}\n company = Company.objects.get(pk=company_id)\n currentEmpl = Employee.objects.get(user__pk=request.user.pk)\n \"\"\":type : Employee \"\"\"\n\n if not currentEmpl.isEnsoUser() and currentEmpl.company.pk != company.pk:\n ... | [
"0.6837375",
"0.67006433",
"0.66819894",
"0.6620597",
"0.6475171",
"0.64458215",
"0.6421396",
"0.6402227",
"0.6344205",
"0.6299245",
"0.626302",
"0.6249063",
"0.6230102",
"0.6173451",
"0.61211884",
"0.6115985",
"0.60931104",
"0.6089694",
"0.60648185",
"0.60492533",
"0.6024815... | 0.8076184 | 0 |
View for creating many employees in company | def create_many_employees(request, company_id=None):
company = Company.objects.get(pk=company_id)
current_employee = Employee.objects.get(user__pk=request.user.pk)
if not current_employee.isEnsoUser() and current_employee.company.pk != company.pk:
raise PermissionDenied()
if "upload" in request.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_employee(request, company_id):\n\n company = Company.objects.get(pk=company_id)\n current_employee = Employee.objects.get(user__pk=request.user.pk)\n if not current_employee.isEnsoUser() and current_employee.company.pk != company.pk:\n logUnauthorizedAccess(\"User tried to create_employe... | [
"0.72610486",
"0.6829904",
"0.6690067",
"0.6635526",
"0.6073183",
"0.60264933",
"0.6014927",
"0.5973413",
"0.5951959",
"0.5936964",
"0.59301126",
"0.578499",
"0.57461524",
"0.5707354",
"0.56899834",
"0.5680803",
"0.56750655",
"0.56400555",
"0.5560561",
"0.55447835",
"0.554248... | 0.67414385 | 2 |
View for editing employee | def edit_employee(request, employee_id):
employee = Employee.objects.get(pk=int(employee_id))
current_employee = Employee.objects.get(user__pk=request.user.pk)
assert isinstance(employee, Employee)
assert isinstance(current_employee, Employee)
# if not current_employee.isEnsoUser() and current_emp... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def edit_employee(employee_id):\n\n if not g.user:\n flash(\"Please login to access\", \"danger\")\n return redirect(\"/\")\n \n if g.user.is_admin == False:\n flash (\"Unauthorized\", \"danger\")\n return redirect(\"/login\")\n\n employee = Employee.query.get_or_404(employe... | [
"0.7485564",
"0.6978975",
"0.6902276",
"0.6840151",
"0.6783405",
"0.6756372",
"0.66953164",
"0.66938245",
"0.66192317",
"0.65741867",
"0.65598595",
"0.65310377",
"0.6502672",
"0.6400611",
"0.6391832",
"0.6368707",
"0.63276947",
"0.6300049",
"0.6295493",
"0.62654054",
"0.62085... | 0.7715588 | 0 |
View of dashboard containing overview of relevant information | def dashboard(request):
employee = request.user.employee_user.first()
widgets = list()
# development_plans = employee.getDevelopmentPlans()
if employee.is_manager:
widgets.append(dict(
# template="mus/_widget_waiting_developmentplans.html",
data=employee.getMyEmployees(),... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dashboard():",
"def dashboard():\n # Get current user\n user = current_user\n # Get tip of the day\n tip = gdb.gettipofday()\n # Get current user Leaderboard Status\n leaderboard, current_user_info = gdb.getleaderboard(current_user.userID)\n weektopgainers, monthtopgainers = gdb.gettopga... | [
"0.8157949",
"0.7479624",
"0.738469",
"0.7263216",
"0.72455734",
"0.7196145",
"0.7135433",
"0.7082106",
"0.7069867",
"0.7064431",
"0.7064215",
"0.70062995",
"0.6955068",
"0.68793905",
"0.68734276",
"0.6863764",
"0.6852004",
"0.68505263",
"0.6806025",
"0.67434174",
"0.67335117... | 0.6533786 | 25 |
View for list of actions of (current) employee | def action_list(request, employee_id=None):
if employee_id:
employee = Employee.objects.get(pk=employee_id)
current_employee = Employee.objects.get(user__pk=request.user.pk)
if not current_employee.isEnsoUser() and current_employee.company.pk != employee.company.pk:
raise Permiss... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_overview_of_all_employees(self):\n\n print(\"OVERVIEW OF EMPLOYEES\\n\")\n\n employees_ob_list = self.llapi.get_employee_overview()\n \n for employee_ob in employees_ob_list:\n print(employee_ob.print_info_in_line(\"*\"))\n \n print(f\"\\nNAN AI... | [
"0.6613559",
"0.63822025",
"0.6160403",
"0.61327785",
"0.61140096",
"0.5988209",
"0.5908296",
"0.5907843",
"0.5899233",
"0.5892363",
"0.5886298",
"0.58746743",
"0.58155996",
"0.57719344",
"0.5768285",
"0.5741835",
"0.5715916",
"0.57119167",
"0.5709055",
"0.5664173",
"0.561676... | 0.8160547 | 0 |
View for editing action | def action_edit(request, action_id):
employee = request.user.employee_user.first()
action = Action.objects.get(pk=action_id)
if not employee.isEnsoUser() and employee.company.pk != action.employee.company.pk:
raise PermissionDenied()
# if request.method == 'POST':
form = ActionForm(request.P... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def edit(self):\n\n pass",
"def edit(self, **kwargs):\n ...",
"def edit_form():\n return template (\"edit\")",
"def edit():",
"def edit_person(self, pk):",
"def edit(self, *args, **kw):\n\t\t\ttmpl_context.widget = self.edit_form\n\t\t\tpks \t\t= self.provider.get_primary_fields(self.mod... | [
"0.80914015",
"0.78752905",
"0.7775213",
"0.7495747",
"0.74247116",
"0.73104566",
"0.7053273",
"0.7040553",
"0.7038563",
"0.69779855",
"0.6973604",
"0.6886933",
"0.68703216",
"0.68322045",
"0.67837024",
"0.6779167",
"0.67466205",
"0.6741481",
"0.6699304",
"0.6675047",
"0.6622... | 0.74116254 | 5 |
View for detail of action | def action_detail(request, action_id):
employee = request.user.employee_user.first()
action = Action.objects.get(pk=int(action_id))
# if not employee.isEnsoUser() and employee.company.pk != action.employee.company.pk:
if not employee.hasAccessTo(action.employee):
raise PermissionDenied()
if... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n return _action_args_dict[self.action].name",
"def action(self):\n pass",
"def action(self):\n pass",
"def view(self):",
"def get_action(self, context):\n pass",
"def show(self, *args, **kwargs) -> None:\n pass",
"def show(self, *args, **kwargs) ->... | [
"0.67003417",
"0.6600345",
"0.6600345",
"0.6525399",
"0.6474398",
"0.6430569",
"0.6430569",
"0.6430569",
"0.6398873",
"0.6335036",
"0.62369657",
"0.62285584",
"0.6216931",
"0.6206771",
"0.61830616",
"0.61691725",
"0.6162804",
"0.6153047",
"0.61520946",
"0.60394746",
"0.602984... | 0.73323816 | 0 |
View for creating action | def action_add(request, employee_id=None):
if employee_id:
employee = Employee.objects.get(pk=employee_id)
current_employee = Employee.objects.get(user__pk=request.user.pk)
if not current_employee.isEnsoUser() and current_employee.company.pk != employee.company.pk:
raise Permissi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def goto_create(self):\n\n self.create.click()",
"def create(self):",
"def create(self, *args, **kwargs):\n pass",
"def create(self):\n ...",
"def new_result():\n return ActionResult()",
"def create_view(request, title, modelform, **kwargs):\n instance_form = modelform(requ... | [
"0.6654641",
"0.6543866",
"0.6499162",
"0.6441966",
"0.64285094",
"0.63419896",
"0.6320924",
"0.630966",
"0.6293948",
"0.6293948",
"0.62471634",
"0.6235429",
"0.620226",
"0.61985743",
"0.6198156",
"0.6169896",
"0.6168643",
"0.6168643",
"0.6168643",
"0.61534095",
"0.6133898",
... | 0.0 | -1 |
Create a leader model for employees | def create_leader_model(request, company_id):
errors = {'noactions': []}
company = Company.objects.get(pk=company_id)
currentEmpl = Employee.objects.get(user__pk=request.user.pk)
""":type : Employee """
if not currentEmpl.isEnsoUser() and currentEmpl.company.pk != company.pk:
raise Permiss... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handle(self, *args, **kwargs):\n seeder = Seed.seeder()\n seeder.add_entity(User, 20)\n\n seeder.add_entity(EmployeeMptt, 20, {\n 'user': lambda x: User.objects.filter(employeemptt=None).first(),\n 'parent': lambda x: EmployeeMptt.objects.order_by(\"?\").first(),\n ... | [
"0.65373933",
"0.6418469",
"0.6274147",
"0.6274147",
"0.60241544",
"0.5993524",
"0.5734152",
"0.56874007",
"0.5683928",
"0.5643717",
"0.5590358",
"0.55775136",
"0.55295014",
"0.5501056",
"0.54992604",
"0.5479858",
"0.53781486",
"0.535312",
"0.5326933",
"0.52975243",
"0.529675... | 0.63167363 | 2 |
Create LeaderModel and send it as a PDF to the browser | def get_leader_model_pdf(currentEmpl, employees):
lm = LeaderModel()
employee_actions = {}
legend = []
colors = {}
errors = {'noactions': []}
# numbered_actions = {}
for empl in employees:
if not currentEmpl.hasAccessTo(empl):
raise PermissionDenied()
actions ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_leader_model(request, company_id):\n\n errors = {'noactions': []}\n company = Company.objects.get(pk=company_id)\n currentEmpl = Employee.objects.get(user__pk=request.user.pk)\n \"\"\":type : Employee \"\"\"\n\n if not currentEmpl.isEnsoUser() and currentEmpl.company.pk != company.pk:\n ... | [
"0.67373437",
"0.63020295",
"0.6226293",
"0.6087851",
"0.5910283",
"0.58224714",
"0.57705843",
"0.5737507",
"0.5713494",
"0.56827766",
"0.5535205",
"0.55348325",
"0.5490096",
"0.54388666",
"0.5369891",
"0.53277147",
"0.52944934",
"0.52798676",
"0.5266541",
"0.5252111",
"0.524... | 0.71051025 | 0 |
View for employee development plan details | def development_plan_details(request, development_plan_id): #, employee_id ):
# employee = Employee.objects.get(user__pk=request.user.pk)
# employee = Employee.objects.filter(pk=int(employee_id)).first()
development_plan = DevelopmentPlan.objects.get(pk=int(development_plan_id))
current_employee = Empl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_active_development_plan_for_user(request):\n current_employee = Employee.objects.get(user__pk=request.user.pk)\n current_development_plan = DevelopmentPlan.objects.filter(\n employee_relation=current_employee,\n employee_relation__developmentplantoemployeerelation__finished_at__isnull=T... | [
"0.6865501",
"0.6419754",
"0.64083785",
"0.6235996",
"0.6046271",
"0.60325843",
"0.60079944",
"0.5864522",
"0.5858046",
"0.58436126",
"0.5818266",
"0.57469684",
"0.5691461",
"0.5501445",
"0.54863083",
"0.5485314",
"0.5460011",
"0.54553616",
"0.54523605",
"0.5448519",
"0.54469... | 0.68573505 | 1 |
View a list of user's development plans for manager | def get_all_user_development_plans_for_manager(request, employee_id):
current_employee = Employee.objects.get(user__pk=request.user.pk)
user_development_plans = DevelopmentPlan.objects.filter(employee_relation=current_employee).all()
employee = Employee.objects.filter(pk=int(employee_id)).first()
if no... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all_development_plans_for_user(request):\n current_employee = Employee.objects.get(user__pk=request.user.pk)\n user_development_plans = DevelopmentPlan.objects.filter(employee_relation=current_employee).all()\n\n if not current_employee:\n raise PermissionDenied(\"You don't have any employe... | [
"0.7063593",
"0.682502",
"0.6374027",
"0.6217655",
"0.61145437",
"0.610118",
"0.6058615",
"0.59781164",
"0.5932294",
"0.5915161",
"0.59006524",
"0.587544",
"0.5792681",
"0.57351124",
"0.5670701",
"0.5654031",
"0.5596579",
"0.5595037",
"0.55796534",
"0.55656326",
"0.552241",
... | 0.6848171 | 1 |
View a list of development plans for active user | def get_all_development_plans_for_user(request):
current_employee = Employee.objects.get(user__pk=request.user.pk)
user_development_plans = DevelopmentPlan.objects.filter(employee_relation=current_employee).all()
if not current_employee:
raise PermissionDenied("You don't have any employee assigned ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_active_development_plan_for_user(request):\n current_employee = Employee.objects.get(user__pk=request.user.pk)\n current_development_plan = DevelopmentPlan.objects.filter(\n employee_relation=current_employee,\n employee_relation__developmentplantoemployeerelation__finished_at__isnull=T... | [
"0.69843084",
"0.6867608",
"0.6754407",
"0.65818614",
"0.6510022",
"0.64818734",
"0.62669057",
"0.6181612",
"0.6179575",
"0.6045006",
"0.6030298",
"0.60151047",
"0.60096705",
"0.59797555",
"0.5899104",
"0.5885478",
"0.58685434",
"0.5831174",
"0.5770537",
"0.57329553",
"0.5725... | 0.7084471 | 0 |
View active development plan for active user | def get_active_development_plan_for_user(request):
current_employee = Employee.objects.get(user__pk=request.user.pk)
current_development_plan = DevelopmentPlan.objects.filter(
employee_relation=current_employee,
employee_relation__developmentplantoemployeerelation__finished_at__isnull=True).firs... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plan_get(request):\n company = auth_api_key(request)\n plan = get_and_check_plan(request, company)\n return plan",
"def plans(self):\n title = self.context.Title()\n return self.portal_catalog(portal_type='Plan', Subject=title)",
"def get_all_development_plans_for_user(request):\n ... | [
"0.6693725",
"0.66212505",
"0.6312085",
"0.61751384",
"0.61356807",
"0.60612977",
"0.60564137",
"0.59662765",
"0.5901057",
"0.58823967",
"0.5879139",
"0.585081",
"0.58050555",
"0.57663673",
"0.5763864",
"0.57417154",
"0.5737406",
"0.57106596",
"0.57047117",
"0.56790864",
"0.5... | 0.6910045 | 0 |
Get or Update goal by id | def self_goal_by_id(request, goal_id):
current_user = request.user
fields_map = {
'goal_answers': lambda g: [
{
'id': answ.id,
'title': answ.title,
"created_by": answ.created_by.username,
"created_at": answ.created_at,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def goal(self, goal_id):\r\n return goals.Goal(self, goal_id)",
"def goal(self, goal_id):\r\n return Goal(self, goal_id)",
"def getById(self, id_goals):\n lparam = [id_goals]\n rep = AbstractDAO._read(self, R_READBYID, lparam)\n return self.__fetch_to_object(rep, True)",
"d... | [
"0.68321764",
"0.6705152",
"0.6541664",
"0.6067706",
"0.6032877",
"0.6005164",
"0.59921056",
"0.5976328",
"0.59698373",
"0.5885591",
"0.58422995",
"0.58416253",
"0.5819017",
"0.5806884",
"0.58029574",
"0.5794082",
"0.57710695",
"0.57252264",
"0.57099134",
"0.56292725",
"0.562... | 0.67704624 | 1 |
Get or Update goal's answer by id | def set_get_answer(request, answer_id):
current_user = request.user
fields = ["created_by", "title", "created_at", 'id', 'file']
fields_map = {
"created_by": lambda a: a.created_by.username,
"file": lambda a: a.file.url if a.file else ''
}
answ = GoalAnswer.objects.get(pk=answer_i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def self_goal_by_id(request, goal_id):\n current_user = request.user\n\n fields_map = {\n 'goal_answers': lambda g: [\n {\n 'id': answ.id,\n 'title': answ.title,\n \"created_by\": answ.created_by.username,\n \"created_at\": answ.cr... | [
"0.6852361",
"0.6565505",
"0.5933789",
"0.5877702",
"0.58608663",
"0.5800607",
"0.57284534",
"0.5717084",
"0.5700604",
"0.5696132",
"0.5641497",
"0.5632813",
"0.56148297",
"0.5601214",
"0.55787456",
"0.5564735",
"0.5534567",
"0.549483",
"0.5468139",
"0.5458586",
"0.54545426",... | 0.653086 | 2 |
Tokenization/string cleaning for all datasets except for SST. | def clean_str(string):
string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string)
string = re.sub(r"\'re", " \'re", string)
string = re.sub(r"\'d", " \'d", string)
string = re.sub(r"\'ll", " \'ll", string)
string = re.sub(r",", " , ", string)
string = re.sub(r"!", " ! ", string)
string = re.sub(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cleaning (data):",
"def clean_data(self, data):\r\n data=data.lower()\r\n doc=nlp(data, disable=['parser', 'ner'])\r\n \r\n #Removing stopwords, digits and punctuation from data\r\n tokens = [token.lemma_ for token in doc if not (token.is_stop\r\n ... | [
"0.66215855",
"0.61963105",
"0.61640877",
"0.6127651",
"0.6074511",
"0.6014394",
"0.59874463",
"0.5976716",
"0.5957324",
"0.59493726",
"0.59331477",
"0.59058803",
"0.5905406",
"0.59032536",
"0.5874476",
"0.58563834",
"0.584708",
"0.58449286",
"0.58449286",
"0.5819846",
"0.579... | 0.0 | -1 |
Builds a vocabulary mapping from word to index based on the sentences. Returns vocabulary mapping and inverse vocabulary mapping. | def build_vocab(sentences):
# Build vocabulary
word_counts = Counter(itertools.chain(*sentences))
# Mapping from index to word
vocabulary_inv = [x[0] for x in word_counts.most_common()]
# Mapping from word to index
vocabulary = {x: i for i, x in enumerate(vocabulary_inv)}
return [vocabulary,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_vocab(self, sentences):\n\t\t# Build the vocab\n\t\tword_counts = collections.Counter(sentences)\n\n\t\t# Mapping from index to word (get the indices of most common words)\n\t\tvocab_inv = [x[0] for x in word_counts.most_common()] # Do we need this?\n\t\tvocab_inv = list(sorted(vocab_inv))\n\n\t\t# Mapp... | [
"0.796555",
"0.78444034",
"0.76602143",
"0.765326",
"0.7552341",
"0.74616605",
"0.69504786",
"0.69184107",
"0.68647057",
"0.6849758",
"0.6777216",
"0.6661577",
"0.6608296",
"0.6554376",
"0.6551425",
"0.6457603",
"0.6450155",
"0.6442917",
"0.64372295",
"0.6427494",
"0.6417837"... | 0.7715383 | 4 |
Maps sentencs and labels to vectors based on a vocabulary. | def build_input_data(sentences, labels, vocabulary, pos1_sentences, pos2_sentences):
x = np.array([[vocabulary[word] for word in sentence] for sentence in sentences])
y = np.array(labels)
a1 = np.array(pos1_sentences)
a2 = np.array(pos2_sentences)
return [x, y, a1, a2] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_vocab(sentences):\n # Build vocabulary\n word_counts = Counter(itertools.chain(*sentences)) # 实际没用到\n # Mapping from index to word\n vocabulary_inv = [x[0] for x in word_counts.most_common()]\n vocabulary_inv = list(sorted(vocabulary_inv))\n # 加入 <UNK>\n vocabulary_inv.insert(0, '</... | [
"0.7294427",
"0.70792955",
"0.70614845",
"0.700578",
"0.700578",
"0.700578",
"0.699909",
"0.69712275",
"0.6877698",
"0.68140954",
"0.6795766",
"0.6701864",
"0.66283876",
"0.6619812",
"0.6589712",
"0.6576624",
"0.65659446",
"0.6559201",
"0.6493492",
"0.648277",
"0.64260834",
... | 0.58939606 | 88 |
Pads all sentences to the same length. The length is defined by the longest sentence. Returns padded sentences. | def pad_sentences(sentences, padding_word="<PAD/>",sequence_length = 0):
if sequence_length == 0:
sequence_length = max(len(x) for x in sentences)
padded_sentences = []
for i in range(len(sentences)):
sentence = sentences[i]
num_padding = sequence_length - len(sentence)
new_s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pad_sentences(sentences, padding_word=\"<PAD/>\"):\n sequence_length = max(len(x) for x in sentences)\n padded_sentences = []\n for i in range(len(sentences)):\n sentence = sentences[i]\n num_padding = sequence_length - len(sentence)\n new_sentence = sentence + [padding_word] * nu... | [
"0.81174576",
"0.80845124",
"0.8042058",
"0.80319756",
"0.7986996",
"0.76386243",
"0.75553435",
"0.7518657",
"0.7493447",
"0.73408186",
"0.7043601",
"0.7007085",
"0.6957604",
"0.69015884",
"0.6824467",
"0.6684047",
"0.66429424",
"0.6604899",
"0.66040134",
"0.66006076",
"0.660... | 0.7942444 | 5 |
This function takes a csv file as an argument deduplicates the file and writes the deduplicated dataset to a csv file if a path for the output file is provided as the second argument It returns the deduplicated dataframe Parameters , type, return values | def dataDedup_csv(infile, outfile=None):
if fpath.isfile(infile):
dataset = pd.read_csv(infile, sep=',', dtype='unicode')
dedup_dataset = dataset.drop_duplicates()
if outfile!=None:
dedup_dataset.to_csv(outfile,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def strip_duplicates(in_file, out_file, sep_type=\"\", header_rows=0):\n\n util.check_output_dir(out_file)\n\n if header_rows !=0: header=read_header(in_file, num_header_rows=header_rows, sep_type =\"\")\n\n if sep_type==\"\":\n data=pd.read_csv(in_file, skiprows=header_rows, header=None, delim_whi... | [
"0.7729395",
"0.6743207",
"0.6410341",
"0.6338948",
"0.6320352",
"0.6265429",
"0.611104",
"0.60951626",
"0.6093322",
"0.5940891",
"0.5861167",
"0.57967466",
"0.57279533",
"0.5659171",
"0.5623228",
"0.5598873",
"0.5594488",
"0.5593631",
"0.5475531",
"0.54566574",
"0.5449391",
... | 0.78255796 | 0 |
This function checks for the size of a dataframe and splits it into parts containing approximately 1 million records as the default number of records for each dataframe.It also provides the option of writing the split dataframes to the disk. Parameters , type, return values | def dataFrameSplit(df, norec=1000000, outfile= None):
# calculation of the no. of rows of the dataframe
df_rsz = len(df.index)
if df_rsz>norec:
no_splits = np.ceil(df_rsz/norec)
dfarr = np.array_split(df,no_splits)
return dfarr
else:
print(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def split_dataframe(df, size=10*1024*1024):\n \n # size of each row\n row_size = df.memory_usage().sum() / len(df)\n # maximum number of rows in each segment\n row_limit = int(size // row_size)\n # number of segments\n seg_num = (len(df)+row_limit-1)//row_limit\n # split df into segments\n ... | [
"0.73547655",
"0.67736286",
"0.6035461",
"0.59178704",
"0.5877275",
"0.58713686",
"0.584475",
"0.5791403",
"0.57895786",
"0.5737366",
"0.57224447",
"0.57164466",
"0.5714758",
"0.57108814",
"0.5700032",
"0.5689146",
"0.56854934",
"0.5663078",
"0.5655827",
"0.5648846",
"0.56323... | 0.70206773 | 1 |
Configure logging for training log. The format is `log_{log_fname}_{comment}.log` .g. for `train.py`, the log_fname is `log_train.log`. Use `logging.info(...)` to record running log. | def config_logging(comment=None):
# Get current executing script name
import __main__, os
exe_fname=os.path.basename(__main__.__file__)
log_fname = "log_{}".format(exe_fname.split(".")[0])
if comment is not None and str(comment):
log_fname = log_fname + "_" + str(comment)
log_fname = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_logging(config: Any) -> Logger:\n green = \"\\033[32m\"\n reset = \"\\033[0m\"\n logger = setup_logger(\n name=f\"{green}[ignite]{reset}\",\n level=logging.DEBUG if config.debug else logging.INFO,\n format=\"%(name)s: %(message)s\",\n filepath=config.output_dir / \"tr... | [
"0.70399964",
"0.6622312",
"0.65885615",
"0.65304697",
"0.65240383",
"0.6519144",
"0.6495567",
"0.6466729",
"0.64612895",
"0.6407961",
"0.63969386",
"0.63589936",
"0.6348098",
"0.6338948",
"0.63224775",
"0.6312327",
"0.6312132",
"0.63118017",
"0.6299318",
"0.6270785",
"0.6250... | 0.64892864 | 7 |
Load data using PyTorch DataLoader. | def load_data(config, vocab, proportion: float=0.7, max_len: int=256, partition: dict=None, labels: dict=None):
# columns if meta: [0] unique ID, [1] text, [2] metadata, [3] label
# columns if no meta: [0] unique ID, [1] text, [2] label
if config["metadata"]:
unique_id_col = 0
text_col = 1... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def data_loader(data, train=True):\n\n loader_config = {\n 'batch_size':64,\n 'shuffle':train\n }\n \n return torch.utils.data.DataLoader(data, **loader_config)",
"def train_dataloader(self) -> torch.utils.data.DataLoader: \n return torch.utils.data.DataLoader(self.datase... | [
"0.7888709",
"0.785579",
"0.7751118",
"0.7633787",
"0.7525572",
"0.7270675",
"0.7166334",
"0.7075442",
"0.70753306",
"0.70659876",
"0.69723433",
"0.6967991",
"0.69530183",
"0.6943673",
"0.6889306",
"0.68799275",
"0.6872247",
"0.68637246",
"0.6859212",
"0.6836871",
"0.68136996... | 0.0 | -1 |
Padding function for 1D sequences | def __pad__(sequence, max_l):
if max_l - len(sequence) < 0:
sequence = sequence[:max_l]
else:
sequence = np.pad(sequence, (0, max_l - (len(sequence))), 'constant', constant_values=(0))
return sequence | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pad(seq, n):\n return",
"def pad_sequence(xs, length=None, padding=0):\n return PadSequence(length, padding).apply((xs))[0]",
"def pad_sequence(seq):\n seq_split = seq.strip().split(\"1\")\n last = seq_split[0]\n new_seq = last + \"1\"\n inc_added = 0\n out_added = 0\n for i in rang... | [
"0.77374345",
"0.76362944",
"0.737829",
"0.7223377",
"0.7206336",
"0.7144797",
"0.710272",
"0.70930755",
"0.70851046",
"0.705326",
"0.7034367",
"0.700346",
"0.6976378",
"0.6973453",
"0.69480914",
"0.6854283",
"0.68475085",
"0.6833989",
"0.6814044",
"0.6806954",
"0.67819375",
... | 0.6773792 | 22 |
Embed words in a sequence using GLoVE model | def __glove_embed__(sequence, model):
embedded = []
for word in sequence:
embedded.append(model[word])
return embedded | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def embed(self, sequence):\n words = sequence.split(' ')\n vecs = [self._E[self._w2i[i]] if i in self._w2i else self._E[self._w2i[\"UNK\"]]\n for i in words]\n return vecs",
"def embed(self, loader, model):\n print(\" ** Embedding words\")\n\n words = loader.w... | [
"0.6890587",
"0.6337464",
"0.6256114",
"0.6213781",
"0.61906195",
"0.6124841",
"0.6122943",
"0.5976476",
"0.5967347",
"0.59281605",
"0.5920156",
"0.59157413",
"0.591105",
"0.5867188",
"0.58647937",
"0.5861155",
"0.58420885",
"0.58300006",
"0.58148724",
"0.5812145",
"0.5800837... | 0.80186236 | 0 |
Load embeddings either from cache or from scratch | def load_embeddings(config, name, vocab, training_generator, validation_generator):
# Pickle embeddings should be AGNOSTIC to the name. This is because each pickled embedding is specific to the dataset and transformer.
# Applies down the road when/if we attempt active learning
data_name = config['train_fi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _load_embeddings(cache_path):\n with open(cache_path, \"rb\") as fp:\n _cached_embs = pickle.load(fp)\n return _cached_embs",
"def load_embeddings():\n return embedding_utils.PretrainedWordEmbeddings(\n lowercase=FLAGS.lowercase,\n embeddings_path=FLAGS.fasttext_embeddings... | [
"0.7840676",
"0.7315518",
"0.72570604",
"0.7248901",
"0.72128534",
"0.71236014",
"0.7081537",
"0.70751274",
"0.7022085",
"0.6956653",
"0.673481",
"0.6694797",
"0.6664453",
"0.6633258",
"0.65949357",
"0.64013827",
"0.63974303",
"0.6372857",
"0.63583666",
"0.6338655",
"0.632997... | 0.71723187 | 5 |
Get BERT embeddings from a dataloader generator. | def _get_bert_embeddings(data_generator, embedding_model: torch.nn.Module, metadata: False):
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if use_cuda else "cpu")
with torch.set_grad_enabled(False):
embeddings = {'ids': [],
'embeddings': [],
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_embeddings(model, loader, device=torch.device('cpu')):\n embeddings = []\n labels = []\n for item in loader:\n data, label = item\n data = data.view(-1, 1, data.shape[-1])\n data = data.to(device)\n label = label.to(device)\n output = model(data).squeeze(1)\n\n ... | [
"0.6092691",
"0.60436624",
"0.60151154",
"0.59234816",
"0.59059787",
"0.5782936",
"0.5690828",
"0.5666181",
"0.5597089",
"0.5583591",
"0.5570702",
"0.55484855",
"0.55165946",
"0.5495378",
"0.5463972",
"0.5451432",
"0.54469055",
"0.54453945",
"0.5405851",
"0.54010916",
"0.5367... | 0.73473763 | 0 |
Reduced embeddings using PCA. | def get_pca_embeddings(config, name, training_embedding: dict, validation_embedding: dict):
data_name = config['train_file'].split('/')[-1][:-4] # retrieve file name without the extension
train_pca_pkl_f = os.path.join(config['pca_cache'], data_name + '_' + config['embedding_type'] + '_training_embeddings.p')... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pca(embedding, num_components=3, principal_components=None):\n# shape = embedding.get_shape().as_list()\n shape = tf.shape(embedding)\n embedding = tf.reshape(embedding, [-1, shape[3]])\n\n if principal_components is None:\n principal_components = calculate_principal_components(embedding,\n ... | [
"0.6760861",
"0.6653581",
"0.662987",
"0.65325135",
"0.6529479",
"0.6509681",
"0.65022707",
"0.6487274",
"0.64560676",
"0.64380586",
"0.6406564",
"0.63860345",
"0.63095546",
"0.62621355",
"0.6250614",
"0.62483966",
"0.62223923",
"0.6219198",
"0.6173256",
"0.6162128",
"0.60764... | 0.66272324 | 3 |
Provides various metrics between predictions and labels. | def metrics(metric_type: str, preds: list, labels: list):
assert metric_type in ['flat_accuracy', 'f1', 'roc_auc', 'ap'], 'Metrics must be one of the following: \
[\'flat_accuracy\', \'f1\', \'roc_auc\'] \
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calc_metrics(pred, labels):\n pred_flat = np.argmax(pred, axis = 1).flatten()\n labels_flat = labels.flatten()\n \n flat_accuracy = np.sum(pred_flat == labels_flat) / len(labels_flat)\n \n # sklearn takes first parameter as the true label\n precision = precision_score(labels_flat, pred_flat)\n... | [
"0.7634908",
"0.7342889",
"0.72701824",
"0.71024024",
"0.70171285",
"0.70013344",
"0.698941",
"0.6960283",
"0.6859667",
"0.6853133",
"0.68380344",
"0.6824996",
"0.6816467",
"0.6797667",
"0.67882746",
"0.6784857",
"0.67818296",
"0.6759894",
"0.6757059",
"0.6757059",
"0.6755217... | 0.6285268 | 96 |
True if parameter is active, i.e. its value differs from default. | def __bool__(self):
# Do explicit cast to bool, as value can be a NumPy type, resulting in
# an np.bool_ type for the expression (not allowed for __bool__)
return bool(self.value != self.default_value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isActiveFitParam(param):\n return isFitParam(param) and param.isActive()",
"def getBoolParam(self, params, name):\n return params.get(name) in ('True', 'true', '1')",
"def active(self) -> pulumi.Input[bool]:\n return pulumi.get(self, \"active\")",
"def check_active(value):\r\n\tif value ... | [
"0.72914785",
"0.7089384",
"0.6749947",
"0.6713717",
"0.6666445",
"0.6666445",
"0.6666445",
"0.6666445",
"0.66639507",
"0.66464436",
"0.6492625",
"0.6490066",
"0.64801526",
"0.6458125",
"0.64275116",
"0.6371264",
"0.63690764",
"0.6319099",
"0.63014305",
"0.629577",
"0.6269076... | 0.0 | -1 |
String form of parameter value used to convert it to/from a string. | def value_str(self):
return self._to_str(self.value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_string(self, name, value):\r\n \r\n return str(value)",
"def string(self, value):\n # respect {None}\n if value is None:\n # by leaving it alone\n return None\n # my value knows\n return str(value)",
"def get_parameter_string(self, paramete... | [
"0.7203786",
"0.697954",
"0.69234365",
"0.68567574",
"0.6801373",
"0.67694676",
"0.6758182",
"0.6719559",
"0.66809326",
"0.6662741",
"0.6623675",
"0.65918845",
"0.6585053",
"0.65313864",
"0.6505384",
"0.64526814",
"0.64182854",
"0.635534",
"0.6343342",
"0.62921274",
"0.629084... | 0.63790995 | 17 |
Short humanfriendly string representation of parameter object. | def __repr__(self):
return "<katpoint.Parameter %s = %s %s at 0x%x>" % \
(self.name, self.value_str, self.units, id(self)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n\n return \"<ExoParameter>: {0}\".format(self.__dict__)",
"def __str__(self):\n return self.parameters.__str__()",
"def __str__(self):\r\n res = [self.Name + ' parameters:']\r\n for t in self._tracked_properties:\r\n res.append(t + ':' + str(getattr(se... | [
"0.74058735",
"0.7150738",
"0.70581096",
"0.69755995",
"0.6968546",
"0.6926491",
"0.6794039",
"0.67680633",
"0.6709835",
"0.67044413",
"0.66996485",
"0.6644361",
"0.65927416",
"0.6591025",
"0.65706384",
"0.65648234",
"0.65029067",
"0.64802146",
"0.64295065",
"0.6425345",
"0.6... | 0.6784789 | 7 |
Number of parameters in full model. | def __len__(self):
return len(self.params) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def num_params(self):",
"def count_params(self):\n self.N = 0\n for name, param in self.model.named_parameters():\n self.N += param.numel()\n self.N_list.append(self.N)",
"def num_parameters(self) -> int:\n if self._model:\n return self._model.num_parameters()\... | [
"0.8517904",
"0.83146226",
"0.8285639",
"0.8231243",
"0.8113813",
"0.8113813",
"0.8113813",
"0.8045275",
"0.80263823",
"0.8018176",
"0.8015244",
"0.8014556",
"0.7949592",
"0.7944686",
"0.79414004",
"0.79262877",
"0.7919125",
"0.7889194",
"0.78718674",
"0.7828952",
"0.7804306"... | 0.7406236 | 46 |
True if model contains any active (nondefault) parameters. | def __bool__(self):
return any(p for p in self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_active(self) -> bool:\n return any(x is not None for x in self._constraints)",
"def params_required(self) -> bool:\n if self.no_params or self.params_optional:\n return False\n else:\n return True",
"def no_params(self) -> bool:\n result = True\n ... | [
"0.67918104",
"0.67808914",
"0.6769819",
"0.676107",
"0.6671744",
"0.6616392",
"0.6594647",
"0.6546123",
"0.65182585",
"0.6490626",
"0.64229697",
"0.63208073",
"0.62987643",
"0.62911",
"0.6290869",
"0.6289151",
"0.62316024",
"0.6204833",
"0.616754",
"0.61671543",
"0.61343193"... | 0.0 | -1 |
Iterate over parameter objects. | def __iter__(self):
return self.params.values().__iter__() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def iter_params(self):\n for var, val in self._params.iteritems():\n yield var, val",
"def parameters(self):\n for parameters in self:\n for parameter in parameters:\n yield parameter",
"def get_params_iter(self):\n return []",
"def __iter__(self):\n ... | [
"0.82385904",
"0.7981038",
"0.71825755",
"0.7117291",
"0.7055745",
"0.69547874",
"0.6832161",
"0.67801243",
"0.67296636",
"0.66869706",
"0.6610378",
"0.6579612",
"0.64453834",
"0.6407792",
"0.6394474",
"0.6297057",
"0.62369",
"0.6233893",
"0.6227109",
"0.6217013",
"0.62052274... | 0.7283732 | 2 |
Justified (name, value, units, doc) strings for active parameters. | def param_strs(self):
name_len = max(len(p.name) for p in self)
value_len = max(len(p.value_str) for p in self.params.values())
units_len = max(len(p.units) for p in self.params.values())
return [(p.name.ljust(name_len), p.value_str.ljust(value_len),
p.units.ljust(units_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n num_active = len([p for p in self if p])\n summary = \"%s has %d parameters with %d active (non-default)\" % \\\n (self.__class__.__name__, len(self), num_active)\n if num_active == 0:\n return summary\n return summary + ':\\n' + '\\n'.jo... | [
"0.7582478",
"0.6667753",
"0.65951604",
"0.6493384",
"0.64455795",
"0.64114326",
"0.63715106",
"0.62896985",
"0.6242153",
"0.62383384",
"0.62208116",
"0.62207097",
"0.61746264",
"0.6109571",
"0.61072844",
"0.6098375",
"0.60816747",
"0.6051815",
"0.6050969",
"0.60330296",
"0.6... | 0.7280672 | 1 |
Short humanfriendly string representation of model object. | def __repr__(self):
num_active = len([p for p in self if p])
return "<katpoint.%s active_params=%d/%d at 0x%x>" % \
(self.__class__.__name__, num_active, len(self), id(self)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n model = self._meta.verbose_name.title()\n return f\"{model:s}: {self.name:s}\"",
"def __str__(self):\n model = self._meta.verbose_name.title()\n return f\"{model:s}: {self.name:s}\"",
"def __str__(self):\n return super().__str__() + self.model.__str__()",... | [
"0.8028083",
"0.8028083",
"0.79938656",
"0.7976418",
"0.7948468",
"0.77171636",
"0.7662623",
"0.76042145",
"0.7593325",
"0.7579472",
"0.7536077",
"0.7449067",
"0.74469894",
"0.74191",
"0.7378081",
"0.7339475",
"0.73324215",
"0.7328719",
"0.7328719",
"0.730302",
"0.72758746",
... | 0.0 | -1 |
Verbose humanfriendly string representation of model object. | def __str__(self):
num_active = len([p for p in self if p])
summary = "%s has %d parameters with %d active (non-default)" % \
(self.__class__.__name__, len(self), num_active)
if num_active == 0:
return summary
return summary + ':\n' + '\n'.join(('%s = %s %s ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n return super().__str__() + self.model.__str__()",
"def __str__(self):\n model = self._meta.verbose_name.title()\n title = self.extended_object.get_title()\n return f\"{model:s}: {title:s}\"",
"def __str__(self):\n model = self._meta.verbose_name.title()\n... | [
"0.78885835",
"0.7783697",
"0.77635914",
"0.77635914",
"0.7730236",
"0.760641",
"0.75836486",
"0.7574152",
"0.75515556",
"0.7453203",
"0.74457",
"0.7392917",
"0.7392917",
"0.7386357",
"0.73556995",
"0.7335823",
"0.731109",
"0.725926",
"0.72402775",
"0.720554",
"0.71744984",
... | 0.0 | -1 |
Equality comparison operator (parameter values only). | def __eq__(self, other):
return self.description == \
(other.description if isinstance(other, self.__class__) else other) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def values_eq(self, a, b):\r\n return a == b",
"def testEquality(self):\n pass",
"def __eq__(self,*args):\r\n pass",
"def __eq__(self,*args):\r\n pass",
"def __eq__(self,*args):\r\n pass",
"def __eq__(self,*args):\r\n pass",
"def __eq__(self,*args):\r\n pass",
"def __eq__(self,*arg... | [
"0.7495514",
"0.7350638",
"0.71365744",
"0.71365744",
"0.71365744",
"0.71365744",
"0.71365744",
"0.71365744",
"0.71365744",
"0.71365744",
"0.71365744",
"0.71365744",
"0.71365744",
"0.71365744",
"0.71365744",
"0.71365744",
"0.71365744",
"0.71365744",
"0.71365744",
"0.71365744",
... | 0.0 | -1 |
Inequality comparison operator (parameter values only). | def __ne__(self, other):
return not (self == other) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __ne__(self, values):\n self = self.__eq__(values)\n return self.__invert__()",
"def __ne__(self, rhs):\n return not self.__eq__(rhs)",
"def not_equal(lhs, rhs):\n return _make.not_equal(lhs, rhs)",
"def __neq__(self, other): \n return not self == other",
"def test... | [
"0.72408104",
"0.71416056",
"0.69568586",
"0.6941666",
"0.68447745",
"0.6843122",
"0.6838853",
"0.68197155",
"0.6807232",
"0.679406",
"0.6777357",
"0.673857",
"0.673857",
"0.673857",
"0.673857",
"0.6725665",
"0.6725112",
"0.6718812",
"0.67053616",
"0.6701271",
"0.66915655",
... | 0.0 | -1 |
Base hash on description string, just like equality operator. | def __hash__(self):
return hash(self.description) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hash(self) -> str:\r\n ...",
"def __hash__(self):\n\n return hash((str(self.type) + str(self.value)))",
"def __hash__(self):\n return hash(self.text)",
"def hash(self) -> bytes:",
"def hash(self, string):\n return self.__scaffydb.hash(string)",
"def __hash__(self):\n ... | [
"0.72970223",
"0.70238286",
"0.6990711",
"0.68921405",
"0.6877547",
"0.6868672",
"0.6868672",
"0.6868672",
"0.6853107",
"0.6853107",
"0.6853107",
"0.6853107",
"0.6820892",
"0.68152857",
"0.67963797",
"0.67854613",
"0.67854613",
"0.67854613",
"0.6765069",
"0.67579234",
"0.6742... | 0.8038765 | 0 |
Access parameter value by name. | def __getitem__(self, key):
return self.params[key].value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getParam(self, params, name):\n return params.get(name)",
"def getParameter(self, name):",
"def GetValueByName(self, name):",
"def get_param_with_name(self, param_name):\n return self.params[param_name]",
"def get_parameter(self, name: str) -> any:\r\n if name in self.kwargs:\r\n ... | [
"0.81560856",
"0.8143507",
"0.7823986",
"0.7630813",
"0.7557198",
"0.7383771",
"0.7367008",
"0.7312119",
"0.7287928",
"0.7264385",
"0.7236408",
"0.7168084",
"0.7144382",
"0.7120741",
"0.70506805",
"0.7047843",
"0.7025448",
"0.70219696",
"0.70115006",
"0.69442713",
"0.6915904"... | 0.6977442 | 19 |
Modify parameter value by name. | def __setitem__(self, key, value):
self.params[key].value = value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setParameter(self, name, value):",
"def set(self,name,val):\n matches = self.grep_param_names(name)\n if len(matches):\n x = self._get_params()\n x[matches] = val\n self._set_params(x)\n else:\n raise AttributeError, \"no parameter matches %s\"... | [
"0.80584186",
"0.7877263",
"0.7659529",
"0.7543547",
"0.747998",
"0.737759",
"0.7360991",
"0.7008698",
"0.6985977",
"0.69003344",
"0.6785371",
"0.6783321",
"0.6777866",
"0.6752631",
"0.6717762",
"0.6686711",
"0.66651803",
"0.6661193",
"0.66512424",
"0.66512424",
"0.66446614",... | 0.6204177 | 46 |
List of parameter names in the expected order. | def keys(self):
return self.params.keys() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parameter_names(self) -> List[str]:",
"def parameterNames(self, p_int): # real signature unknown; restored from __doc__\n return []",
"def get_param_names(self):\n return list(self.params.keys())",
"def parameter_names(self) -> list:\n parameters = []\n parameters.extend(self.... | [
"0.8899528",
"0.8256276",
"0.8223466",
"0.81843793",
"0.81773764",
"0.76406306",
"0.7631603",
"0.75550413",
"0.75500125",
"0.74934304",
"0.74912333",
"0.74750197",
"0.7323525",
"0.7297529",
"0.7277972",
"0.7231873",
"0.71911895",
"0.71234643",
"0.7113123",
"0.71071076",
"0.70... | 0.64429533 | 62 |
List of parameter values in the expected order ('tolist'). | def values(self):
return [p.value for p in self] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parameters_list(self):\n return [getattr(self.parameters, p) for p in self.parameters_names()]",
"def parameter_values(self) -> List[Tuple[str, Any]]:\n pvs = [(param, getattr(self, variable))\n for variable, param in self.variable_name_to_query_param.items()]\n return [(p,... | [
"0.73683417",
"0.7149095",
"0.7077",
"0.7076585",
"0.7047869",
"0.7043692",
"0.7038208",
"0.70156145",
"0.7003395",
"0.6993167",
"0.6993167",
"0.6947928",
"0.69282645",
"0.68996793",
"0.68764114",
"0.6848381",
"0.6839425",
"0.6823277",
"0.6816432",
"0.68062204",
"0.6788033",
... | 0.6143704 | 58 |
Load model from sequence of floats. | def fromlist(self, floats):
self.header = {}
params = [p for p in self]
min_len = min(len(params), len(floats))
for param, value in zip(params[:min_len], floats[:min_len]):
param.value = value
for param in params[min_len:]:
param.value = param.default_valu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _load(self):\n for k,v in self.parameters.items():\n if isinstance(v,list):\n setattr(self,k,np.array(v,dtype=np.float32))\n else:\n setattr(self,k,v)",
"def parse_model(f_name):\n if os.path.isfile(f_name):\n with open(f_name) as f:\n ... | [
"0.59494925",
"0.5694197",
"0.56045514",
"0.5498191",
"0.5488356",
"0.5477136",
"0.5466593",
"0.54611206",
"0.54511726",
"0.54432213",
"0.54343504",
"0.5339388",
"0.5335935",
"0.5305303",
"0.52712786",
"0.5249649",
"0.5237072",
"0.5233914",
"0.52248335",
"0.5221053",
"0.52194... | 0.5443327 | 9 |
Compact but complete string representation ('tostring'). | def description(self):
active = np.nonzero([bool(p) for p in self])[0]
last_active = active[-1] if len(active) else -1
return ' '.join([p.value_str for p in self][:last_active + 1]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_str(self) -> str:",
"def toString():",
"def safeToString():",
"def __str__(self):\n\n if not self:\n return '\\0'\n\n parts = []\n for name, value in self:\n if value is None:\n item = name\n else:\n item = '%s=%s' % (... | [
"0.755594",
"0.74858665",
"0.73231876",
"0.72323847",
"0.72257227",
"0.7008717",
"0.69522905",
"0.69489115",
"0.69489115",
"0.6871342",
"0.68474585",
"0.68474585",
"0.6772113",
"0.6723712",
"0.66784674",
"0.6602459",
"0.6586125",
"0.658413",
"0.65728617",
"0.6562585",
"0.6560... | 0.0 | -1 |
Load model from description string (parameters only). | def fromstring(self, description):
self.header = {}
# Split string either on commas or whitespace, for good measure
param_vals = [p.strip() for p in description.split(',')] \
if ',' in description else description.split()
params = [p for p in self]
min_len = min(len(p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_model(self, model_path: str):",
"def load_model(self, filename):\r\n pass",
"def load(path_to_model):\n pass",
"def load_model(self, path):\n pass",
"def load_model(self) -> Any:",
"def load_model(language_id, model_type):\n\n # getting the language code from it's id\n lan... | [
"0.694506",
"0.6934816",
"0.67814714",
"0.6613543",
"0.6562278",
"0.64866006",
"0.64678144",
"0.6464032",
"0.6308483",
"0.61974365",
"0.61927617",
"0.61660534",
"0.61602557",
"0.61448705",
"0.6130916",
"0.61297876",
"0.6126128",
"0.61022365",
"0.6065545",
"0.6058375",
"0.6046... | 0.5545173 | 94 |
Save model to config file (both header and parameters). | def tofile(self, file_like):
cfg = configparser.SafeConfigParser()
cfg.add_section('header')
for key, val in self.header.items():
cfg.set('header', key, str(val))
cfg.add_section('params')
for param_str in self.param_strs():
cfg.set('params', param_str[0],... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_config(self, save_path: str) -> None:\n os.makedirs(save_path, exist_ok=True)\n model_hyperparameters_path = os.path.join(save_path, MODEL_HYPERPARAMETERS_FILE_NAME)\n save_json(model_hyperparameters_path, self.config_obj.to_dict())",
"def save(self):\n file = open(self.path,... | [
"0.763178",
"0.74892414",
"0.74538445",
"0.74108565",
"0.7388762",
"0.73329777",
"0.72090554",
"0.7158334",
"0.7151382",
"0.7149014",
"0.71323335",
"0.71320057",
"0.7099478",
"0.7097612",
"0.7079394",
"0.7034409",
"0.6995227",
"0.69945264",
"0.6982993",
"0.69599545",
"0.69179... | 0.0 | -1 |
Load model from config file (both header and parameters). | def fromfile(self, file_like):
defaults = dict((p.name, p._to_str(p.default_value)) for p in self)
if future.utils.PY2:
cfg = configparser.SafeConfigParser(defaults)
else:
cfg = configparser.ConfigParser(defaults, inline_comment_prefixes=(';', '#'))
try:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def load_model(\n self,\n model_name: str,\n headers: dict[str, t.Any] = ...,\n config: str = ...,\n files: dict[str, str] = ...,\n ) -> None:",
"def load_config(filename):\n with open(filename, \"r\") as my_file:\n my_file = my_file.read()\n return K.... | [
"0.7542821",
"0.7373315",
"0.73305744",
"0.7220506",
"0.71582717",
"0.71088517",
"0.695264",
"0.686733",
"0.680585",
"0.67566395",
"0.6753529",
"0.67380685",
"0.67345",
"0.6729725",
"0.6692068",
"0.6689554",
"0.66882926",
"0.668114",
"0.6674514",
"0.66543514",
"0.65970093",
... | 0.62608147 | 56 |
Load parameter values from the appropriate source. | def set(self, model=None):
if isinstance(model, Model):
if not isinstance(model, type(self)):
raise BadModelFile('Cannot construct a %r from a %r' %
(self.__class__.__name__,
model.__class__.__name__))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _load_parameter(self):",
"def _load(self, load_dict):\n if self.v_locked:\n raise pex.ParameterLockedException(\n \"Parameter `%s` is locked!\" % self.v_full_name\n )\n\n if \"data\" in load_dict:\n self._data = load_dict[\"data\"][\"data\"][0]\n ... | [
"0.740036",
"0.64931524",
"0.6455669",
"0.6353233",
"0.6309897",
"0.63086814",
"0.6287176",
"0.62651426",
"0.6150741",
"0.6106724",
"0.60551184",
"0.6034574",
"0.6032307",
"0.6026318",
"0.59740245",
"0.59592307",
"0.5957556",
"0.5954891",
"0.5941733",
"0.59211797",
"0.5912147... | 0.0 | -1 |
Convert 2D alignment parameters (alpha, sx, sy, mirror) into 3D alignment parameters (phi, theta, psi, s2x, s2y, mirror) | def params_2D_3D(alpha, sx, sy, mirror):
phi = 0
psi = 0
theta = 0
alphan, s2x, s2y, scalen = compose_transform2(0, sx, sy, 1, -alpha, 0, 0, 1)
if mirror > 0:
phi = (540.0 + phi)%360.0
theta = 180.0 - theta
psi = (540.0 - psi + alphan)%360.0
else:
psi = (psi + alphan)%360.0
return phi, theta, ps... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def params_3D_2D(phi, theta, psi, s2x, s2y):\n\tif theta > 90.0:\n\t\tmirror = 1\n\t\talpha, sx, sy, scalen = compose_transform2(0, s2x, s2y, 1.0, 540.0-psi, 0, 0, 1.0)\n\telse:\n\t\tmirror = 0\n\t\talpha, sx, sy, scalen = compose_transform2(0, s2x, s2y, 1.0, 360.0-psi, 0, 0, 1.0)\n\treturn alpha, sx, sy, mirror"... | [
"0.75017375",
"0.6478277",
"0.647607",
"0.63587636",
"0.60074914",
"0.5803451",
"0.5758167",
"0.5752519",
"0.5700443",
"0.5646637",
"0.56198543",
"0.5572641",
"0.5455936",
"0.54369533",
"0.5418458",
"0.5403827",
"0.539565",
"0.5376442",
"0.53718555",
"0.53706646",
"0.53607404... | 0.78717786 | 0 |
Convert 3D alignment parameters (phi, theta, psi, s2x, s2y) there is no mirror in 3D! into 2D alignment parameters (alpha, sx, sy, mirror) | def params_3D_2D(phi, theta, psi, s2x, s2y):
if theta > 90.0:
mirror = 1
alpha, sx, sy, scalen = compose_transform2(0, s2x, s2y, 1.0, 540.0-psi, 0, 0, 1.0)
else:
mirror = 0
alpha, sx, sy, scalen = compose_transform2(0, s2x, s2y, 1.0, 360.0-psi, 0, 0, 1.0)
return alpha, sx, sy, mirror | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def params_2D_3D(alpha, sx, sy, mirror):\n\tphi = 0\n\tpsi = 0\n\ttheta = 0\n\talphan, s2x, s2y, scalen = compose_transform2(0, sx, sy, 1, -alpha, 0, 0, 1)\n\tif mirror > 0:\n\t\tphi = (540.0 + phi)%360.0\n\t\ttheta = 180.0 - theta\n\t\tpsi = (540.0 - psi + alphan)%360.0\n\telse:\n\t\tpsi = (psi + alphan)... | [
"0.76915425",
"0.6404001",
"0.6382034",
"0.6108902",
"0.6060465",
"0.6017704",
"0.57541436",
"0.5705645",
"0.5625745",
"0.5595656",
"0.5591025",
"0.55300176",
"0.5473145",
"0.53983897",
"0.53954655",
"0.5385849",
"0.53646827",
"0.53643596",
"0.536235",
"0.5350919",
"0.5346317... | 0.7371874 | 1 |
Commented by Zhengfan Yang on 05/01/07 I made some change to the original amoeba so that it can now pass out some values calculated by func other than the criteria. This is important in multilevel amoeba refinement because otherwise, upper level refinement will lose the information of lower level refinement. | def amoeba_multi_level(var, scale, func, ftolerance=1.e-4, xtolerance=1.e-4, itmax=500, data=None):
#print " ENTER AMOEBA MULTI LEVEL"
nvar = len(var) # number of variables in the minimization
nsimplex = nvar + 1 # number of vertices in the simplex
# first set up the simplex
simplex = [0]*(nvar+1) # set... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _fit_amoeba(self, kwargs, verbose):\n\n args_init = self._param_class.kwargs_to_args(kwargs)\n\n options = {\n \"adaptive\": True,\n \"fatol\": self._tol_simplex_func,\n \"maxiter\": self._simplex_n_iterations * len(args_init),\n }\n\n method = \"Nel... | [
"0.5649461",
"0.56487626",
"0.5537902",
"0.55107564",
"0.5501089",
"0.54266936",
"0.5403812",
"0.53776634",
"0.5360801",
"0.5356619",
"0.5301135",
"0.52775687",
"0.5269604",
"0.5262089",
"0.52313644",
"0.5223302",
"0.5188832",
"0.5179477",
"0.516795",
"0.51599634",
"0.5152701... | 0.599668 | 0 |
Given a function of onevariable and a possible bracketing interval, return the minimum of the function isolated to a fractional precision of tol. A bracketing interval is a triple (a,b,c) where (a<b<c) and func(b) < func(a),func(c). If bracket is two numbers then they are assumed to be a starting interval for a downhil... | def golden(func, args=(), brack=None, tol=1.e-4, full_output=0):
if brack is None:
xa,xb,xc,fa,fb,fc,funcalls = bracket(func, args=args)
elif len(brack) == 2:
xa,xb,xc,fa,fb,fc,funcalls = bracket(func, xa=brack[0], xb=brack[1], args=args)
elif len(brack) == 3:
xa,xb,xc = brack
if (xa > xc): # swap so xa < x... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bisect(f, x1, x2, tol=1.0e-9):\n assert callable(f), \"User-supplied function must be callable.\"\n assert x1 != x2, \"Bad initial range given to bracket.\"\n f1 = f(x1)\n f2 = f(x2)\n assert f1 * f2 < 0.0, \"Range does not clearly bracket a root.\"\n while abs(x2 - x1) > tol:\n x_mid ... | [
"0.6708032",
"0.65304935",
"0.62501",
"0.5912542",
"0.58930385",
"0.58231115",
"0.5812576",
"0.5808945",
"0.57922816",
"0.5788117",
"0.5785699",
"0.57082534",
"0.56653464",
"0.5637875",
"0.5637021",
"0.5635716",
"0.5600486",
"0.55882996",
"0.55636096",
"0.5525852",
"0.5503440... | 0.5996638 | 3 |
Given a function and distinct initial points, search in the downhill direction (as defined by the initital points) and return new points | def bracket(func, xa=0.0, xb=1.0, args=(), grow_limit=110.0, maxiter=1000):
_gold = 1.618034
_verysmall_num = 1e-21
fa = apply(func, (xa,)+args)
fb = apply(func, (xb,)+args)
if (fa < fb): # Switch so fa > fb
dum = xa; xa = xb; xb = dum
dum = fa; fa = fb; fb = dum
xc = xb + _gold*(xb-xa)
fc = apply... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convergent_point(x0, x1, f):\n while x0 != x1:\n x0, x1 = f(x0), f(x1)\n return x0",
"def downhill(F, xStart, args=None, side=0.1, ftol=1.0e-6, xtol=1.0e-6, maxiter=1000, maxfunc=1000, maxiternochange=10):\n # TODO: check the types of the input ???\n\n # print \"Entering downhill\"... | [
"0.6356492",
"0.6195878",
"0.5991574",
"0.58792716",
"0.584689",
"0.58225703",
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"0.55528545",
"0.55420315",
"0.55300105",
"0.552903",
"0.55165887",
"0.54849... | 0.0 | -1 |
Fit the histogram of the input image under mask with the reference image. | def ce_fit(inp_image, ref_image, mask_image):
hist_res = Util.histc(ref_image, inp_image, mask_image)
args = hist_res["args"]
scale = hist_res["scale"]
data = [hist_res['data'], inp_image, hist_res["ref_freq_bin"], mask_image, int(hist_res['size_img']), hist_res['hist_len']]
res = amoeba(args, scale, hist_func, 1.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hist2d(x,y,nbins = 50 ,maskval = 0,saveloc = '',labels=[],slope = 1,sloperr = 0):\n\t# Remove NANs and masked values\n\tgood = where((isnan(x) == False) & (isnan(y) == False) & (x != maskval) & (y != maskval))\n\tx = x[good]\n\ty = y[good]\n\n\t# Create histogram\n\tH,xedges,yedges = histogram2d(x,y,bins=nbins... | [
"0.5717073",
"0.55807126",
"0.5528441",
"0.55023885",
"0.54681766",
"0.54579365",
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"0.52273583",
"0.52006847",
"0.52000165",
"0.5193751",
"0.5170942",
"0.... | 0.57911646 | 0 |
Find the position of the commone line in 3D Formula is (RB^T zhat) cross (RA^T zhat) Returns phi, theta of the common line in degrees. theta always < 90 Notice you don't need to enter psi's; they are irrelevant | def common_line_in3D(phiA,thetaA,phiB,thetaB):
from math import pi, sqrt, cos, sin, asin, atan2
piOver=pi/180.0;
ph1 = phiA*piOver;
th1 = thetaA*piOver;
ph2 = phiB*piOver;
th2 = thetaB*piOver;
#nx = cos(thetaBR)*sin(thetaAR)*sin(phiAR) - cos(thetaAR)*sin(thetaBR)*sin(phiBR) ;
#ny = cos(thetaAR)*sin(thet... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def theta_finder(theta, point_a, point_b, point_c, point_c_new):\n x, y, z = parametrized_circle(point_a, point_b, point_c, theta)\n residual = (x - point_c_new[0])**2 + (y - point_c_new[1])**2 + (z - point_c_new[2])**2\n return residual",
"def cart2spheric(x, y, z):\n # doesn't compute r because cho... | [
"0.6163923",
"0.6056872",
"0.6046275",
"0.60347825",
"0.59408677",
"0.5916736",
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"0.5673097",
"0.56674886",
"0.56646484",
"0.56586415",
"0.56585634",
"0.... | 0.68433595 | 0 |
Print the composition of two transformations T2T1 | def compose_transform2(alpha1, sx1, sy1, scale1, alpha2, sx2, sy2, scale2):
t1 = Transform({"type":"2D","alpha":alpha1,"tx":sx1,"ty":sy1,"mirror":0,"scale":scale1})
t2 = Transform({"type":"2D","alpha":alpha2,"tx":sx2,"ty":sy2,"mirror":0,"scale":scale2})
tt = t2*t1
d = tt.get_params("2D")
return d[ "alpha" ], d[ "... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _print_transforms(self):\n self._print_frozen_transforms()\n self._print_nonfrozen_transforms()",
"def print_output_task2(model1,model2):\n print(\"######################################################################\")\n print(\"Task 2 : IBM model 1 and 2 Analysis(using NLTK)\")\n print(\"#... | [
"0.6846836",
"0.593155",
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"0.51735705",
"0.5163085",
"0.5161964",
"0.51475674",
"0.5144708",
"0.513714",
... | 0.54261017 | 6 |
Compute the composition of two transformations T2T1 | def compose_transform3(phi1,theta1,psi1,sx1,sy1,sz1,scale1,phi2,theta2,psi2,sx2,sy2,sz2,scale2):
R1 = Transform({"type":"spider","phi":float(phi1),"theta":float(theta1),"psi":float(psi1),"tx":float(sx1),"ty":float(sy1),"tz":float(sz1),"mirror":0,"scale":float(scale1)})
R2 = Transform({"type":"spider","phi":float(phi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compose_transform(T1, T2):\n aux_vec = np.array([0, 0, 1]).reshape(1, 3)\n\n T1 = np.concatenate((T1, aux_vec), axis=0)\n T2 = np.concatenate((T2, aux_vec), axis=0)\n\n T1_inv = np.linalg.inv(T1)\n T = T1_inv@T2\n\n return T[0:2]",
"def compose_transform2(alpha1, sx1, sy1, scale1, alpha2, s... | [
"0.7939624",
"0.6803194",
"0.6570444",
"0.632991",
"0.63013947",
"0.62177503",
"0.61400926",
"0.6106614",
"0.61058927",
"0.6075287",
"0.5950678",
"0.59419036",
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"0.585534",
"0.58440787",
"0.5840003",
"0.5824449",
"0.578852",
"0.5774012",
"0.57467675",... | 0.5716005 | 21 |
Combine 2D alignent parameters including mirror | def combine_params2(alpha1, sx1, sy1, mirror1, alpha2, sx2, sy2, mirror2):
t1 = Transform({"type":"2D","alpha":alpha1,"tx":sx1,"ty":sy1,"mirror":mirror1,"scale":1.0})
t2 = Transform({"type":"2D","alpha":alpha2,"tx":sx2,"ty":sy2,"mirror":mirror2,"scale":1.0})
tt = t2*t1
d = tt.get_params("2D")
return d[ "alpha" ],... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_align_invert(self):\n al = align(self.amp1, self.amp2, inverse=False)\n\n al_inv = align(self.amp2, self.amp1, inverse=True)\n\n print(al.R)\n print(al_inv.R)\n\n print(al.T)\n print(al_inv.T)",
"def coord_space(\n a0: numpy.ndarray, a1: numpy.ndarray, a2: nu... | [
"0.6219051",
"0.6053582",
"0.5752545",
"0.57426506",
"0.5725641",
"0.5692372",
"0.5691541",
"0.5664221",
"0.56270707",
"0.5618279",
"0.5603235",
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"0.5446854",
"0.5441767",
"0.54299045",
"0.54238695",
"0.54219955",
"0.5421373",
"0.5344707",... | 0.6524807 | 0 |
Convert a text file that is composed of columns of numbers into spider doc file | def create_spider_doc(fname,spiderdoc):
from string import atoi,atof
infile = open(fname,"r")
lines = infile.readlines()
infile.close()
nmc = len(lines[0].split())
table=[]
for line in lines:
data = line.split()
for i in xrange(0,nmc):
data[i] = atof(data[i])
table.append(data)
drop_spider_doc(spiderdo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_docs(filename):\n \n # open word doc\n word = win32.gencache.EnsureDispatch('Word.Application')\n doc = word.Documents.Open(os.getcwd() + '/' + filename + \".doc\")\n doc.Activate()\n \n # read word doc as list of lists\n data = [doc.Tables(i).Range.Text for i in range(1,5)]\n... | [
"0.5493499",
"0.54529065",
"0.5267102",
"0.52564555",
"0.5165884",
"0.5158104",
"0.5139018",
"0.5111768",
"0.50976145",
"0.5082693",
"0.50638324",
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"0.5013941",
"0.5003135",
"0.49983117",
"0.49895382",
"0.49807015",
"0.49714673",
"0.4959... | 0.6535076 | 0 |
Write an image to the disk. | def drop_image(imagename, destination, itype="h"):
if type(destination) == type(""):
if(itype == "h"): imgtype = EMUtil.ImageType.IMAGE_HDF
elif(itype == "s"): imgtype = EMUtil.ImageType.IMAGE_SINGLE_SPIDER
else: ERROR("unknown image type","drop_image",1)
imagename.write_image(destination, 0, imgtype)
e... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write(self, image):\n raise NotImplementedError()",
"def write(img, path):\n create_directories_for_file_name(path)\n writer = sitk.ImageFileWriter()\n writer.Execute(img, path, True)",
"def write_image(self, image_name, image):\n raise NotImplementedError",
"def write_image(self, ... | [
"0.8019482",
"0.7901197",
"0.7840147",
"0.7802012",
"0.7660659",
"0.7581917",
"0.75348264",
"0.74246275",
"0.7338141",
"0.7248253",
"0.7200103",
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"0.7190718",
"0.718748",
"0.7168532",
"0.71044123",
"0.70858777",
"0.7034931",
"0.6973645",
"0.6935908",
"0.6884878",
... | 0.0 | -1 |
Write an image with the proper png save | def drop_png_image(im, trg):
if trg[-4:] != '.png':
ERROR('destination name must be png extension', 'drop_png_image', 1)
if isinstance(trg, basestring):
im['render_min'] = im['minimum']
im['render_max'] = im['maximum']
im.write_image(trg, 0)
else:
ERROR('destination is not a file name', 'drop_png_image',... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_image(image, file_name):\n io.imsave(file_name,image)",
"def test_save_png():\n img = Image.new('RGB', (10, 20))\n\n parameters = {'path': 'green-dot.png', 'data': [img]}\n\n assert images.save(parameters)",
"def save_png(self, filename):\n post_script = self.canvas.postscript().enc... | [
"0.7533463",
"0.7480259",
"0.7462284",
"0.74407417",
"0.73850626",
"0.7356224",
"0.73480725",
"0.72761285",
"0.7220644",
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"0.7101273",
"0.7100358",
"0.7097144",
"0.70930296",
"0.70745164",
"0.7069603",
"0.7046678... | 0.0 | -1 |
Create a spidercompatible "Doc" file. | def drop_spider_doc(filename, data, comment = None):
outf = open(filename, "w")
from datetime import datetime
outf.write(" ; %s %s %s\n" % (datetime.now().ctime(), filename, comment))
count = 1 # start key from 1; otherwise, it is confusing...
for dat in data:
try:
nvals = len(dat)
if nvals <= 5: da... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def makeDocFile(self):\n\n f_out = \"%s/%s-doc.php\" % (self.dir_out, self.project_id)\n version = max(self.versions)\n\n with open(f_out, 'w') as f:\n f.write(\"<!DOCTYPE html>\\n\" \\\n \"<html xmlns=\\\"http://www.w3.org/1999/xhtml\\\">\\n\" \\\n ... | [
"0.7526909",
"0.67890173",
"0.65753114",
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"0.65004843",
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"0.6098085",
"0.6066121",
"0.60467553",
"0.60288143",
"0.60257435",
"0.602205... | 0.0 | -1 |
Output the data in slice iz, row ix of an image to standard out. | def dump_row(input, fname, ix=0, iz=0):
fout = open(fname, "w")
image=get_image(input)
nx = image.get_xsize()
ny = image.get_ysize()
nz = image.get_zsize()
fout.write("# z = %d slice, x = %d row)\n" % (iz, ix))
line = []
for iy in xrange(ny):
fout.write("%d\t%12.5g\n" % (iy, image.get_value_at(ix,iy,iz)))
fo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_image_row(input, iy=0, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice, y = %d col)\" % (iz, iy)\n\tline = []\n\tfor ix in xrange(nx):\n\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\tif ((ix + 1) ... | [
"0.71635914",
"0.70319664",
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"0.5405265",
"0.54043865",
"0.5386131",
"0.5378602",
"0.53766",
"0.5349916... | 0.7089794 | 1 |
Create a list of Euler angles suitable for projections. method is either 'S' for Saff algorithm or 'P' for Penczek '94 algorithm 'S' assumes phi1> delta ; symmetry if this is set to pointgroup symmetry (cn or dn) or helical symmetry with pointgroup symmetry (scn or sdn), it will yield angles from the asymmetric unit, n... | def even_angles(delta = 15.0, theta1=0.0, theta2=90.0, phi1=0.0, phi2=359.99, method = 'S', phiEqpsi = "Minus", symmetry='c1'):
from math import pi, sqrt, cos, acos, tan, sin
from utilities import even_angles_cd
from string import lower,split
angles = []
symmetryLower = symmetry.lower()
symmetry_string =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def even_angles_cd(delta, theta1=0.0, theta2=90.0, phi1=0.0, phi2=359.99, method = 'P', phiEQpsi='Minus'):\n\tfrom math import pi, sqrt, cos, acos\n\tangles = []\n\tif (method == 'P'):\n\t\ttemp = Util.even_angles(delta, theta1, theta2, phi1, phi2)\n\t\t#\t\t phi, theta... | [
"0.63690925",
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"0.5360525",
"0.53372157",
"0.5305089",
"0.53043467",
"0.5274299",
"0.5256057... | 0.616338 | 1 |
Create a list of Euler angles suitable for projections. method is either 'S' for Saff algorithm or 'P' for Penczek '94 algorithm 'S' assumes phi1> delta ; phiEQpsi set this to 'Minus', if you want psi=phi; | def even_angles_cd(delta, theta1=0.0, theta2=90.0, phi1=0.0, phi2=359.99, method = 'P', phiEQpsi='Minus'):
from math import pi, sqrt, cos, acos
angles = []
if (method == 'P'):
temp = Util.even_angles(delta, theta1, theta2, phi1, phi2)
# phi, theta, psi
for i in xr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def even_angles(delta = 15.0, theta1=0.0, theta2=90.0, phi1=0.0, phi2=359.99, method = 'S', phiEqpsi = \"Minus\", symmetry='c1'):\n\n\tfrom math import pi, sqrt, cos, acos, tan, sin\n\tfrom utilities import even_angles_cd\n\tfrom string import lower,split\n\tangles = []\n\tsymmetryLower = symmetry.lower()\... | [
"0.6162403",
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"0.5463198",
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"0.53146446",
"0.52769536",
"0.52645385",
"0.52579445",
"0.52271026",
"0.5220... | 0.6891914 | 0 |
Perform PCA on stack file and Get eigen images | def eigen_images_get(stack, eigenstack, mask, num, avg):
from utilities import get_image
a = Analyzers.get('pca_large')
e = EMData()
if(avg == 1): s = EMData()
nima = EMUtil.get_image_count(stack)
for im in xrange(nima):
e.read_image(stack,im)
e *= mask
a.insert_image(e)
if( avg==1):
if(im==0): s ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def images_pca(images_folder, limit=100, k=3):\n my_images = []\n shape = None\n files = os.listdir(images_folder)\n random.shuffle(files)\n files = files[:limit]\n for study_file in files:\n assert study_file.endswith('.pkl'), 'file %s has wrong extension' % study_file\n with open(... | [
"0.64291614",
"0.62826765",
"0.62180424",
"0.6019128",
"0.6007571",
"0.60039264",
"0.59644985",
"0.59346586",
"0.5876533",
"0.57791483",
"0.57538944",
"0.5750904",
"0.57483804",
"0.57071185",
"0.5668783",
"0.56590796",
"0.5637482",
"0.5597626",
"0.55744284",
"0.5562899",
"0.5... | 0.5309758 | 40 |
Find the z rotation such that ZA RA is as close as possible to RB this maximizes trace of ( RB^T ZA RA) = trace(ZA RA RB^T) | def find_inplane_to_match(phiA,thetaA,phiB,thetaB,psiA=0,psiB=0):
#from math import pi, sqrt, cos, acos, sin
RA = Transform({'type': 'spider', 'phi': phiA, 'theta': thetaA, 'psi': psiA})
RB = Transform({'type': 'spider', 'phi': phiB, 'theta': thetaB, 'psi': psiB})
RBT = RB.transpose()
RABT = RA * RBT
RABTe... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calc_optimal_angle(teta_z, latitude, transmissivity):\n if transmissivity <= 0.15:\n gKt = 0.977\n elif 0.15 < transmissivity <= 0.7:\n gKt = 1.237 - 1.361 * transmissivity\n else:\n gKt = 0.273\n Tad = 0.98 # transmittance-absorptance product of the diffuse radiation\n Tar... | [
"0.5970234",
"0.586566",
"0.5805997",
"0.5770309",
"0.57532215",
"0.5680772",
"0.566361",
"0.5569986",
"0.5537452",
"0.5494817",
"0.5490782",
"0.5471373",
"0.54355586",
"0.54301345",
"0.5428771",
"0.54233015",
"0.54138273",
"0.5389946",
"0.53683394",
"0.53642684",
"0.5326583"... | 0.0 | -1 |
smooth sharp_edge_image with Gaussian function 1. The sharpedge image is convoluted with a gassian kernel 2. The convolution normalized | def gauss_edge(sharp_edge_image, kernel_size = 7, gauss_standard_dev =3):
from utilities import model_gauss
from EMAN2 import rsconvolution
nz = sharp_edge_image.get_ndim()
if(nz == 3): kern = model_gauss(gauss_standard_dev, kernel_size , kernel_size, kernel_size)
elif(nz == 2): kern = model_gauss(gauss_standar... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def smooth(image):\n image = convolve(image, gaussian2d(), mode='same')\n return image",
"def smooth_image(self, image, mask):\n \n filter_size = self.smoothing_filter_size.value\n if filter_size == 0:\n return image\n sigma = filter_size / 2.35\n #\n # ... | [
"0.739436",
"0.71233726",
"0.70680994",
"0.68737566",
"0.6743445",
"0.67295676",
"0.65329146",
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"0.64919174",
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"0.61825424",
"0.61740756",
"0.6155781",
"0.61314356",
"0.6101635",
"0.6092838",
"0.605... | 0.69962204 | 3 |
Read an image from the disk or assign existing object to the output. | def get_image(imagename, nx = 0, ny = 1, nz = 1, im = 0):
if type(imagename) == type(""):
e = EMData()
e.read_image(imagename, im)
elif not imagename:
e = EMData()
if (nx > 0): e.set_size(nx, ny, nz)
else:
e = imagename
return e | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read(self):\n with self.lock:\n return self.image",
"def load(self):\n logger.debug(f\"Reading {self.path.name}\")\n self.label = int(Data.fromLabel(self.path.parent.name))\n self.image = skimg.data.imread(self.path)",
"def read_from_filename(self, filename=''):\r\n ... | [
"0.6418173",
"0.6256043",
"0.62217027",
"0.6114637",
"0.5992335",
"0.59468126",
"0.59468126",
"0.58881605",
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"0.57320607",
"0.5721345",
"0.57174456",
"0.56965464",
"0.56918967",
"0.56... | 0.0 | -1 |
Read an image from the disk stack, or return im's image from the list of images | def get_im(stackname, im = 0):
if type(stackname) == type(""):
e = EMData()
e.read_image(stackname, im)
return e
else:
return stackname[im].copy() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_image(path):\n img = misc.imread(path)\n return img",
"def read_image():\n images = []\n for hand in os.listdir('images'):\n img = cv2.imread(os.path.join('images', hand))\n if img is not None:\n images.append(img)\n return images",
"def image(images):\n retu... | [
"0.6800794",
"0.6722553",
"0.66451883",
"0.6614246",
"0.6607827",
"0.6595313",
"0.65405977",
"0.653579",
"0.64773405",
"0.64660364",
"0.64657694",
"0.6447562",
"0.6427345",
"0.64263606",
"0.64116603",
"0.64041156",
"0.6394776",
"0.638916",
"0.63824886",
"0.6381627",
"0.637192... | 0.6380314 | 20 |
Return a NumPy array containing the image data. | def get_image_data(img):
from EMAN2 import EMNumPy
return EMNumPy.em2numpy(img) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_array(self) -> numpy.array:\r\n \r\n return self.pic_array",
"def array_from_img(image):\n return np.array(image)",
"def to_array(self):\n return np.array(self.to_image())",
"def buffer(self) -> np.ndarray:\n return np.array(self._image_data, copy=False)",
"def __array__(... | [
"0.78545177",
"0.78410083",
"0.77582353",
"0.75947237",
"0.7353946",
"0.7335702",
"0.7164609",
"0.7154952",
"0.7121806",
"0.7093889",
"0.7029067",
"0.7018716",
"0.7007183",
"0.7005928",
"0.7005361",
"0.69826096",
"0.6962062",
"0.6913088",
"0.69027466",
"0.6895016",
"0.6886627... | 0.7611293 | 3 |
Get the in_plane angle from two images and output the crosss correlation value The function won't destroy input two images This is the angle that rotates the first image, ima, into the second image, ref. The sense of the rotation is clockwise. center=1 means image is first centered, then rotation angle is found | def get_inplane_angle(ima,ref, iring=1, fring=-1, ringstep=1, xtransSearch=0, ytransSearch=0, stp=1, center=1):
from alignment import Numrinit, ringwe, Applyws, ormq
from filter import fshift
first_ring=int(iring); last_ring=int(fring); rstep=int(ringstep); xrng=int(xtransSearch); yrng=int(ytransSearch); step=int(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_opt_rotate(obj_img, back_img,\n back_center_x, back_center_y,\n obj_center_x, obj_center_y,\n prev_rot_angle=0.,\n is_erosion=False):\n width = obj_img.shape[0]\n rot_img = ndimage.rotate(obj_img, prev_rot_angle, reshape=False)\n... | [
"0.6499765",
"0.64251083",
"0.629712",
"0.61103505",
"0.594957",
"0.58397025",
"0.58203536",
"0.57837665",
"0.5770846",
"0.57563686",
"0.5699609",
"0.5689523",
"0.56460613",
"0.56327623",
"0.561697",
"0.5562077",
"0.5553344",
"0.5546299",
"0.5523779",
"0.55019164",
"0.5499139... | 0.69551015 | 0 |
Return an image created from a text file. The first line of the image should contain "nx ny nz" (separated by whitespace) All subsequent lines contain "ix iy iz val", where ix, iy, and iz are the integer x, y, and z coordinates of the point and val is the floating point value of that point. All points not explicitly li... | def get_textimage(fname):
from string import atoi,atof
infile = open(fname)
lines = infile.readlines()
infile.close()
data = lines[0].split()
nx = atoi(data[0])
ny = atoi(data[1])
nz = atoi(data[2])
e = EMData()
e.set_size(nx, ny, nz)
e.to_zero()
for line in lines[1:]:
data = line.split()
ix = a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_image(filename):\n try:\n fi = open(filename,\"r\")\n lines = fi.readlines()\n n = int(lines[0]);\n img = create_zeroed_image(n)\n for i,line in enumerate(lines[1:]):\n clean_line = line.strip() # remove whitespace and newlines\n for j,char in en... | [
"0.6697791",
"0.6425697",
"0.6267778",
"0.60752165",
"0.6063262",
"0.6061974",
"0.5948842",
"0.5867118",
"0.5839898",
"0.5778474",
"0.57653004",
"0.56806886",
"0.5678753",
"0.56717163",
"0.56614995",
"0.5646917",
"0.5627825",
"0.56230605",
"0.561317",
"0.56094795",
"0.5598929... | 0.7201921 | 0 |
Extract input numbers from given string, | def get_input_from_string(str_input):
from string import split
res = []
list_input = split(str_input)
for i in xrange(len(list_input)):
res.append(float(list_input[i]))
return res | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ExtractNumbers(s):\n\n t = s.strip('[]\\n')\n comma_space = r', '\n re_comma_space = re.compile(comma_space)\n z = re_comma_space.split(t)\n #print z\n return z",
"def extract_nums(string):\r\n num_list = []\r\n mods1 = string.replace('$', '')\r\n mods2 = mods1.replace('K', '')\r\n... | [
"0.7238783",
"0.7173874",
"0.7057713",
"0.68924135",
"0.68546575",
"0.67531013",
"0.67403173",
"0.6647867",
"0.657946",
"0.6577491",
"0.65233153",
"0.65192056",
"0.6391088",
"0.638495",
"0.6383692",
"0.6383132",
"0.6349406",
"0.6332319",
"0.6319145",
"0.6285752",
"0.6280346",... | 0.67156774 | 7 |
Calculate and print the descriptive statistics of an image. | def info(image, mask=None, Comment=""):
if(Comment): print " *** ", Comment
e = get_image(image)
[mean, sigma, imin, imax] = Util.infomask(e, mask, True)
nx = e.get_xsize()
ny = e.get_ysize()
nz = e.get_zsize()
if (e.is_complex()):
s = ""
if e.is_shuffled():
s = " (shuffled)"
if (e.is_fftodd()):
p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_images_in_statistics(self):\n self._print_images_statistics(self._images_in_folder, self._pose_class_names)",
"def print_image_info(image, resize=rsz_default, kernel=kernel_size):\n\tprint \"Image Size: {0}\".format(image.shape)\n\tprint \"Image Max: {0}\".format(image.max())\n\tprint \"Image Min: {... | [
"0.73849064",
"0.7212866",
"0.7087518",
"0.70753783",
"0.6967849",
"0.68458074",
"0.6760577",
"0.674482",
"0.6678487",
"0.66494477",
"0.6574271",
"0.6533716",
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"0.6344323",
"0.63426876",
"0.63391554",
"0.63218725",
"0.63212085",
"0.6293195",
"0.6289... | 0.6997572 | 4 |
Returns the inverse of the 2d rot and trans matrix | def inverse_transform2(alpha, tx = 0.0, ty = 0.0, mirror = 0):
t = Transform({"type":"2D","alpha":alpha,"tx":tx,"ty":ty,"mirror":mirror,"scale":1.0})
t = t.inverse()
t = t.get_params("2D")
return t[ "alpha" ], t[ "tx" ], t[ "ty" ], t[ "mirror" ] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def inverse(self):\n return Rotation(self.matrix.transposed())",
"def inverse(self):\n rotation_matrix = self.pose_mat[:3, :3]\n translation_vector = self.pose_mat[:3, 3]\n\n rot = np.transpose(rotation_matrix)\n trans = - np.matmul(np.transpose(rotation_matrix), translation_ve... | [
"0.76569027",
"0.75310546",
"0.7445371",
"0.7406346",
"0.7397322",
"0.73486006",
"0.72921807",
"0.7276845",
"0.7246478",
"0.71603876",
"0.71544224",
"0.7151582",
"0.708686",
"0.7067885",
"0.7064713",
"0.7061624",
"0.7057902",
"0.70148677",
"0.70069176",
"0.69840884",
"0.69755... | 0.73712176 | 5 |
Returns the inverse of the 3d rot and trans matrix | def inverse_transform3(phi, theta=0.0, psi=0.0, tx=0.0, ty=0.0, tz=0.0, mirror = 0, scale=1.0):
d = Transform({'type': 'spider', 'phi': phi, 'theta': theta, 'psi': psi, 'tx': tx, 'ty': ty, 'tz': tz, "mirror":mirror,"scale":scale})
d = d.inverse()
d = d.get_params("spider")
return d["phi"],d["theta"],d["psi"],d["t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def inverse_rigid_trans(Tr): \n inv_Tr = np.zeros_like(Tr) # 3x4\n inv_Tr[0:3,0:3] = np.transpose(Tr[0:3,0:3])\n inv_Tr[0:3,3] = np.dot(-np.transpose(Tr[0:3,0:3]), Tr[0:3,3])\n return inv_Tr",
"def inverse(self):\n rotation_matrix = self.pose_mat[:3, :3]\n translation_vector = self.pose... | [
"0.75761485",
"0.7515526",
"0.7423403",
"0.7418693",
"0.73657846",
"0.7288308",
"0.7254451",
"0.7109498",
"0.7077596",
"0.7040509",
"0.700391",
"0.6975826",
"0.6933009",
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"0.68664896",
"0.6786958",
"0.6786305",
"0.6780128",
"0.6755011",
"0.67143685",
"0.6650445",
... | 0.6268844 | 54 |
Create a list of available symmetries | def list_syms():
SymStringVec=[];
SymStringVec.append("CSYM");
SymStringVec.append("DSYM");
SymStringVec.append("TET_SYM");
SymStringVec.append("OCT_SYM");
SymStringVec.append("ICOS_SYM");
SymStringVec.append("ISYM");
return SymStringVec | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_symmetries(self):\n temp = self._properties.get('symmetries', [])\n return temp",
"def __set_symbol_dict(self):\r\n return {0: list(alph) if self.is_case_snstv else list(alph)[:26],\r\n 1: list(dgt),\r\n 2: list(spcl) if self.is_spcl else []}",
"def z2... | [
"0.7745224",
"0.669557",
"0.6515156",
"0.63807356",
"0.63562876",
"0.6307514",
"0.6297486",
"0.6230556",
"0.61550385",
"0.6110371",
"0.60140777",
"0.600392",
"0.58965176",
"0.5893767",
"0.5836222",
"0.58326477",
"0.5825521",
"0.5808387",
"0.57900697",
"0.5775794",
"0.57617795... | 0.69593567 | 1 |
Create a centered circle (or sphere) having radius r. | def model_circle(r, nx, ny, nz=1):
e = EMData()
e.set_size(nx, ny, nz)
e.process_inplace("testimage.circlesphere", {"radius":r, "fill":1})
return e | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_circle(x, y, r):\n\tnew_circle = Circle()\n\tnew_circle.x = x\n\tnew_circle.y = y\n\tnew_circle.r = r\n\treturn new_circle",
"def create_circle(self, x, y, r, **kwargs):\n return self.create_oval(*self.circ_to_oval(x, y, r), **kwargs)",
"def _generate_circle(self, center, radius):\n asse... | [
"0.769793",
"0.7630966",
"0.75068563",
"0.73866713",
"0.73565596",
"0.7355884",
"0.7321714",
"0.72842544",
"0.72638685",
"0.72363985",
"0.72306716",
"0.72283244",
"0.71910393",
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"0.7088393",
"0.7056075",
"0.70258105",
"0.6998519",
"0.69953614",
"0.69949824",
"0.69... | 0.73965037 | 3 |
Create a centered square (or cube) with edge length of d. | def model_square(d, nx, ny, nz=1):
e = EMData()
e.set_size(nx, ny, nz)
e.process_inplace("testimage.squarecube", {"edge_length":d, "fill":1})
return e | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def my_square(d):asaasasassssssssssssssssssssssssss\n\t return (d ** 3)",
"def initialize_d(d, square_sides, offset=0):\n return {key:[] for key in range(offset, square_sides ** 2 + offset)}",
"def square_diamond(sx, sy, size, strong):\n if size == 1:\n return\n\n dsize ... | [
"0.5763527",
"0.5631988",
"0.55919534",
"0.5325949",
"0.52789533",
"0.52779573",
"0.5240144",
"0.5220922",
"0.5193584",
"0.5174843",
"0.5162735",
"0.5129172",
"0.5129167",
"0.51191884",
"0.5118387",
"0.51183224",
"0.50997084",
"0.50534856",
"0.50478446",
"0.50203365",
"0.4996... | 0.63994324 | 0 |
Create a centered Gaussian image having standard deviation "sigma". | def model_gauss(xsigma, nx, ny=1, nz=1, ysigma=None, zsigma=None, xcenter=None, ycenter=None, zcenter=None):
e = EMData()
e.set_size(nx, ny, nz)
if( ysigma == None ) : ysigma = xsigma
if( zsigma == None ) : zsigma = xsigma
if( xcenter == None ) : xcenter = nx//2
if( ycenter == None ) : ycenter = ny//2
if( zcen... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_gaussian(size, sigma=10, center=None):\n\n x = np.arange(0, size[1], 1, float)\n y = np.arange(0, size[0], 1, float)\n y = y[:, np.newaxis]\n\n if center is None:\n x0 = y0 = size[0] // 2\n else:\n x0 = center[0]\n y0 = center[1]\n\n return np.exp(-4 * np.log(2) * ((... | [
"0.7622218",
"0.76156443",
"0.73544854",
"0.71752834",
"0.69022816",
"0.68148255",
"0.67936516",
"0.6789439",
"0.6705065",
"0.66758764",
"0.6667239",
"0.66651136",
"0.6639735",
"0.65926224",
"0.65801823",
"0.6565116",
"0.65491575",
"0.6542619",
"0.6531326",
"0.6530046",
"0.65... | 0.6426071 | 29 |
create a cylinder along z axis | def model_cylinder(radius, nx, ny, nz):
e = EMData()
e.set_size(nx, ny, nz)
e.process_inplace("testimage.cylinder", {"radius":radius})
return e | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cylinder(*args, axis: Union[List[float, float, float], bool]=None, caching: bool=True,\n degree: Union[int, bool]=3, endSweep: Union[float, bool]=2, heightRatio:\n Union[float, bool]=2.0, nodeState: Union[int, bool]=0, pivot: Union[List[float,\n float, float], bool]=None, ra... | [
"0.73577124",
"0.7329743",
"0.72503877",
"0.6993126",
"0.68925846",
"0.68604624",
"0.68472177",
"0.67055696",
"0.66552156",
"0.6639137",
"0.6544904",
"0.6456661",
"0.633183",
"0.62076217",
"0.61787236",
"0.61410314",
"0.6091397",
"0.6059696",
"0.6049184",
"0.6046165",
"0.6001... | 0.62607145 | 13 |
Create an image of noise having standard deviation "sigma", and average 0. | def model_gauss_noise(sigma, nx, ny=1, nz=1):
e = EMData()
e.set_size(nx, ny, nz)
e.process_inplace("testimage.noise.gauss", {"sigma":sigma})
return e | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_gaussian_noise(image, min_sigma, max_sigma):\n assert(max_sigma >= min_sigma)\n sigma = np.random.uniform(min_sigma, max_sigma)\n noise = np.random.normal(loc=0, scale=sigma, size=image.shape)\n noisy_im = np.floor((image + noise) * 255) / 255\n noisy_im = np.clip(noisy_im, 0, 1)\n return... | [
"0.7620322",
"0.7568027",
"0.73862773",
"0.7340184",
"0.70870167",
"0.70391494",
"0.69299024",
"0.6861069",
"0.6838096",
"0.6838096",
"0.682284",
"0.6760456",
"0.6759536",
"0.67473197",
"0.6671933",
"0.66611886",
"0.6641475",
"0.66406924",
"0.66172576",
"0.6612591",
"0.658642... | 0.68094635 | 11 |
Create a blank image. | def model_blank(nx, ny=1, nz=1, bckg = 0.0):
e = EMData()
e.set_size(nx, ny, nz)
e.to_zero()
if( bckg != 0.0): e+=bckg
return e | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _blankimage():\n img = TK.PhotoImage(width=1, height=1)\n img.blank()\n return img",
"def create_empty_image(width=512, height=512):\n blank_img = np.zeros((width, height, 3), np.uint8)\n # Return instance of the class\n return ExtendedImage(blank_img)",
"def blank... | [
"0.80658996",
"0.789249",
"0.73570555",
"0.719231",
"0.71430326",
"0.7110544",
"0.69541055",
"0.69369465",
"0.69369465",
"0.69088554",
"0.69088554",
"0.6905223",
"0.6859873",
"0.6651872",
"0.65808094",
"0.65179014",
"0.6485663",
"0.64796895",
"0.6460028",
"0.6458123",
"0.6395... | 0.0 | -1 |
Parse a Spider filename string and insert parameters. | def parse_spider_fname(mystr, *fieldvals):
# helper functions and classes
def rm_stack_char(mystr):
"Helper function to remove a stack character if it exists"
stackloc = mystr.find("@")
if stackloc != -1:
# there's an '@' somewhere
if len(mystr) - 1 == stackloc:
# It's at the end of the string
re... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_filename(cls, filename):\n words = filename.split('_')\n return words[0], int(words[1][1:]), int(words[2])",
"def parseFileName(filename):\n entry = DataEntry(\"\",0,{},{},0,0)\n wordArray = filename.split(\".\")\n entry.publication_name = wordArray[1]\n entry.year = wordArray... | [
"0.5459443",
"0.5448573",
"0.5429191",
"0.52798533",
"0.5271916",
"0.5247543",
"0.52416116",
"0.5215298",
"0.51574737",
"0.5120585",
"0.5072528",
"0.5036985",
"0.5022814",
"0.4980182",
"0.49415386",
"0.4940157",
"0.49290508",
"0.49222475",
"0.49006563",
"0.48977634",
"0.48940... | 0.5895396 | 0 |
Print the data in slice iz, row ix of an image to standard out. | def print_row(input, ix=0, iz=0):
image=get_image(input)
nx = image.get_xsize()
ny = image.get_ysize()
nz = image.get_zsize()
print "(z = %d slice, x = %d row)" % (iz, ix)
line = []
for iy in xrange(ny):
line.append("%12.5g " % (image.get_value_at(ix,iy,iz)))
if ((iy + 1) % 5 == 0): line.append("\n ")
li... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_image_row(input, iy=0, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice, y = %d col)\" % (iz, iy)\n\tline = []\n\tfor ix in xrange(nx):\n\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\tif ((ix + 1) ... | [
"0.75990057",
"0.7544907",
"0.74450433",
"0.73937047",
"0.7349031",
"0.7347187",
"0.7033798",
"0.68403655",
"0.6811467",
"0.66571254",
"0.6630757",
"0.6279358",
"0.6275334",
"0.61232245",
"0.60401046",
"0.5973948",
"0.5973784",
"0.5965042",
"0.59583265",
"0.59162855",
"0.5894... | 0.76172984 | 0 |
Print the data in slice iz, column iy of an image to standard out. | def print_col(input, iy=0, iz=0):
image=get_image(input)
nx = image.get_xsize()
ny = image.get_ysize()
nz = image.get_zsize()
print "(z = %d slice, y = %d col)" % (iz, iy)
line = []
for ix in xrange(nx):
line.append("%12.5g " % (image.get_value_at(ix,iy,iz)))
if ((ix + 1) % 5 == 0): line.append("\n ")
li... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_image_col(input, ix=0, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice, x = %d row)\" % (iz, ix)\n\tline = []\n\tfor iy in xrange(ny):\n\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\tif ((iy + 1) ... | [
"0.77487314",
"0.750461",
"0.7496404",
"0.73919225",
"0.7150529",
"0.7141496",
"0.7027633",
"0.6667874",
"0.66633767",
"0.6654244",
"0.6348636",
"0.621092",
"0.6179944",
"0.60063565",
"0.59464717",
"0.59386533",
"0.5911687",
"0.58938134",
"0.5851181",
"0.58338124",
"0.5803081... | 0.75519806 | 1 |
Print the data in slice iz of an image to standard out. | def print_slice(input, iz=0):
image=get_image(input)
nx = image.get_xsize()
ny = image.get_ysize()
nz = image.get_zsize()
print "(z = %d slice)" % (iz)
line = []
for iy in xrange(ny):
line.append("Row ")
line.append("%4i " % iy)
for ix in xrange(nx):
line.append("%12.5g " % (image.get_value_at(ix,iy,iz... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_image_slice(input, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice)\" % (iz)\n\tline = []\n\tfor iy in xrange(ny-1,-1,-1):\n\t\tline.append(\"Row \")\n\t\tline.append(\"%4i \" % iy)\n\t\tfor ix in xrange(nx):\n\t\t\tl... | [
"0.7832976",
"0.7438628",
"0.72238773",
"0.70643157",
"0.70205015",
"0.6971101",
"0.659192",
"0.65685576",
"0.6525086",
"0.64007616",
"0.6349104",
"0.6105353",
"0.6058925",
"0.6016299",
"0.60077834",
"0.59170806",
"0.5827092",
"0.5820637",
"0.5805464",
"0.58032835",
"0.574112... | 0.7674774 | 1 |
Print the data in an image to standard out. | def print_image(input):
image=get_image(input)
nz = image.get_zsize()
for iz in xrange(nz): print_slice(input, iz) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def printImage(imageObject):\n # TODO\n pass",
"def print_img_data(img,title='Image'):\n print(f'________ {title} _______')\n if len(image.shape) == 2:\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\n\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0])}\\n')\n elif l... | [
"0.75263417",
"0.74676603",
"0.74178773",
"0.71454406",
"0.70787203",
"0.70770824",
"0.6925922",
"0.68538165",
"0.6828833",
"0.680339",
"0.678513",
"0.6639985",
"0.6579925",
"0.65075237",
"0.64939463",
"0.6484506",
"0.6398455",
"0.6392404",
"0.63622975",
"0.6340637",
"0.63262... | 0.7227022 | 3 |
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