prompt stringlengths 123 92.3k | completion stringlengths 7 132 | api stringlengths 9 35 |
|---|---|---|
"""Bindings for the Barnes Hut TSNE algorithm with fast nearest neighbors
Refs:
References
[1] <NAME>, L.J.P.; Hinton, G.E. Visualizing High-Dimensional Data
Using t-SNE. Journal of Machine Learning Research 9:2579-2605, 2008.
[2] <NAME>, L.J.P. t-Distributed Stochastic Neighbor Embedding
http://homepage.tudelft.nl/19... | N.ctypeslib.ndpointer(N.float32, ndim=2, flags='ALIGNED, F_CONTIGUOUS') | numpy.ctypeslib.ndpointer |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from ... | np.sin(2 * np.pi * t / 200) | numpy.sin |
from __future__ import division
from timeit import default_timer as timer
import csv
import numpy as np
import itertools
from munkres import Munkres, print_matrix, make_cost_matrix
import sys
from classes import *
from functions import *
from math import sqrt
import Tkinter as tk
import tkFileDialog as filedialog
root... | np.savetxt('/Users/java_jonathan/test2.csv',totalMatrix, fmt='%s', delimiter=',',
newline='\n', header='', footer='', comments='# ') | numpy.savetxt |
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | np.sum(e_x, axis=1, keepdims=True) | numpy.sum |
#!/usr/bin/env python
# encoding: utf-8 -*-
"""
This module contains unit tests of the rmgpy.reaction module.
"""
import numpy
import unittest
from external.wip import work_in_progress
from rmgpy.species import Species, TransitionState
from rmgpy.reaction import Reaction
from rmgpy.statmech.translation import Transl... | numpy.arange(200.0, 2001.0, 200.0, numpy.float64) | numpy.arange |
# pvtrace is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# pvtrace is distributed in the hope that it will be useful,
# but WITHOUT... | np.random.uniform(0.,2*np.pi) | numpy.random.uniform |
# pylint: disable=protected-access
"""
Test the wrappers for the C API.
"""
import os
from contextlib import contextmanager
import numpy as np
import numpy.testing as npt
import pandas as pd
import pytest
import xarray as xr
from packaging.version import Version
from pygmt import Figure, clib
from pygmt.clib.conversio... | np.linspace(start=4, stop=0, num=3) | numpy.linspace |
try:
import importlib.resources as pkg_resources
except ImportError:
# Try backported to PY<37 `importlib_resources`.
import importlib_resources as pkg_resources
from . import images
from gym import Env, spaces
from time import time
import numpy as np
from copy import copy
import colorsys
import pygame
f... | np.array([0.225, 0]) | numpy.array |
from abc import ABCMeta, abstractmethod
import os
from vmaf.tools.misc import make_absolute_path, run_process
from vmaf.tools.stats import ListStats
__copyright__ = "Copyright 2016-2018, Netflix, Inc."
__license__ = "Apache, Version 2.0"
import re
import numpy as np
import ast
from vmaf import ExternalProgramCaller,... | np.array(log_dict['ref_scores_mtx']) | numpy.array |
import time
import h5py
import hdbscan
import numpy as np
import torch
from sklearn.cluster import MeanShift
from pytorch3dunet.datasets.hdf5 import SliceBuilder
from pytorch3dunet.unet3d.utils import get_logger
from pytorch3dunet.unet3d.utils import unpad
logger = get_logger('UNet3DPredictor')
class _AbstractPred... | np.expand_dims(pred[prediction_channel], axis=0) | numpy.expand_dims |
###############################################################################
# @todo add Pilot2-splash-app disclaimer
###############################################################################
""" Get's KRAS states """
import MDAnalysis as mda
from MDAnalysis.analysis import align
from MDAnalysis.lib.mdamath ... | np.array(OS) | numpy.array |
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np
import os
import contorno
from constantes import INTERVALOS, PASSOS, TAMANHO_BARRA, DELTA_T, DELTA_X
z_temp = contorno.p_3
TAMANHO_BARRA = 2
x = | np.linspace(0.0, TAMANHO_BARRA, INTERVALOS+1) | numpy.linspace |
from abc import ABCMeta, abstractmethod
import os
from vmaf.tools.misc import make_absolute_path, run_process
from vmaf.tools.stats import ListStats
__copyright__ = "Copyright 2016-2018, Netflix, Inc."
__license__ = "Apache, Version 2.0"
import re
import numpy as np
import ast
from vmaf import ExternalProgramCaller,... | np.array(result.result_dict[vifdiff_num_scale2_scores_key]) | numpy.array |
""" Unit tests for the system interface."""
import unittest
from six import assertRaisesRegex
from six.moves import cStringIO
import numpy as np
from openmdao.api import Problem, Group, IndepVarComp, ExecComp
from openmdao.test_suite.components.options_feature_vector import VectorDoublingComp
from openmdao.utils.ass... | np.ones((5, 1)) | numpy.ones |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from ... | np.cos(time) | numpy.cos |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from ... | np.ones(100) | numpy.ones |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import matplotlib.pyplot as plt
import CurveFit
import shutil
#find all DIRECTORIES containing non-hidden files ending in FILENAME
def getDataDirectories(DIRECTORY, FILENAME="valLoss.txt"):
directories=[]
for directory in os.scand... | np.array(sortedData['valLoss']) | numpy.array |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from ... | np.linspace(time[0], time[-1], 200) | numpy.linspace |
'''
<NAME>
set up :2020-1-9
intergrate img and label into one file
-- fiducial1024_v1
'''
import argparse
import sys, os
import pickle
import random
import collections
import json
import numpy as np
import scipy.io as io
import scipy.misc as m
import matplotlib.pyplot as plt
import glob
import math
import time
impo... | np.random.normal(8, 4) | numpy.random.normal |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from ... | np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101) | numpy.linspace |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from ... | np.ones(101) | numpy.ones |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import json
from pathlib import Path
import numpy as np
import torch
from PIL import Image
from panopticapi.utils import rgb2id
# from util.box_ops import masks_to_boxes
from .construction import make_construction_transforms
import logging
def... | np.array([ann["id"] for ann in ann_info["segments_info"]]) | numpy.array |
'''
<NAME>
set up :2020-1-9
intergrate img and label into one file
-- fiducial1024_v1
'''
import argparse
import sys, os
import pickle
import random
import collections
import json
import numpy as np
import scipy.io as io
import scipy.misc as m
import matplotlib.pyplot as plt
import glob
import math
import time
impo... | np.abs(perturbed_distance_vertex_and_line) | numpy.abs |
try:
import importlib.resources as pkg_resources
except ImportError:
# Try backported to PY<37 `importlib_resources`.
import importlib_resources as pkg_resources
from . import images
from gym import Env, spaces
from time import time
import numpy as np
from copy import copy
import colorsys
import pygame
f... | np.ones(self.grid_shape) | numpy.ones |
"""
YTArray class.
"""
from __future__ import print_function
#-----------------------------------------------------------------------------
# Copyright (c) 2013, yt Development Team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this so... | np.dot(op1.d, op2.d) | numpy.dot |
import numpy as np
import cv2
import os
import json
import glob
from PIL import Image, ImageDraw
plate_diameter = 25 #cm
plate_depth = 1.5 #cm
plate_thickness = 0.2 #cm
def Max(x, y):
if (x >= y):
return x
else:
return y
def polygons_to_mask(img_shape, polygons):
mask = np.zeros(img_shape... | np.array(points) | numpy.array |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from ... | np.linspace(-5.5, 5.5, 101) | numpy.linspace |
from abc import ABCMeta, abstractmethod
import os
from vmaf.tools.misc import make_absolute_path, run_process
from vmaf.tools.stats import ListStats
__copyright__ = "Copyright 2016-2018, Netflix, Inc."
__license__ = "Apache, Version 2.0"
import re
import numpy as np
import ast
from vmaf import ExternalProgramCaller,... | np.array(result.result_dict[adm_num_scores_key]) | numpy.array |
import numpy as np
import cv2
import os
import json
import glob
from PIL import Image, ImageDraw
plate_diameter = 25 #cm
plate_depth = 1.5 #cm
plate_thickness = 0.2 #cm
def Max(x, y):
if (x >= y):
return x
else:
return y
def polygons_to_mask(img_shape, polygons):
mask = np.zeros(img_shape... | np.max(rows) | numpy.max |
"""
YTArray class.
"""
from __future__ import print_function
#-----------------------------------------------------------------------------
# Copyright (c) 2013, yt Development Team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this so... | np.any(other_object) | numpy.any |
# pvtrace is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# pvtrace is distributed in the hope that it will be useful,
# but WITHOUT... | np.sin(phi) | numpy.sin |
#!/usr/bin/env python
# encoding: utf-8 -*-
"""
This module contains unit tests of the rmgpy.reaction module.
"""
import numpy
import unittest
from external.wip import work_in_progress
from rmgpy.species import Species, TransitionState
from rmgpy.reaction import Reaction
from rmgpy.statmech.translation import Transl... | numpy.arange(original_kinetics.Tmin, original_kinetics.Tmax, 200.0, numpy.float64) | numpy.arange |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from ... | np.ones(100) | numpy.ones |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Defines coordinate frames and ties them to data axes.
"""
from __future__ import absolute_import, division, unicode_literals, print_function
import numpy as np
from astropy import units as u
from astropy import utils as astutil
from astropy import coo... | np.unique(axes_order) | numpy.unique |
from sklearn.metrics import f1_score,accuracy_score
import numpy as np
from utilities.tools import load_model
import pandas as pd
def predict_MSRP_test_data(n_models,nb_words,nlp_f,test_data_1,test_data_2,test_labels):
models=[]
n_h_features=nlp_f.shape[1]
print('loading the models...')
for i in range... | np.asarray(preds) | numpy.asarray |
import io
import logging
import json
import numpy
import torch
import numpy as np
from tqdm import tqdm
from clie.inputters import constant
from clie.objects import Sentence
from torch.utils.data import Dataset
from torch.utils.data.sampler import Sampler
logger = logging.getLogger(__name__)
def load_word_embeddings... | np.argsort(lengths, order=('l1', 'rand')) | numpy.argsort |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from ... | np.ones_like(ifs[1, :]) | numpy.ones_like |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from ... | np.ones(101) | numpy.ones |
#!/usr/bin/env python
# encoding: utf-8 -*-
"""
This module contains unit tests of the rmgpy.reaction module.
"""
import numpy
import unittest
from external.wip import work_in_progress
from rmgpy.species import Species, TransitionState
from rmgpy.reaction import Reaction
from rmgpy.statmech.translation import Transl... | numpy.arange(200.0, 2001.0, 200.0, numpy.float64) | numpy.arange |
import numpy as np
import pytest
import theano
import theano.tensor as tt
# Don't import test classes otherwise they get tested as part of the file
from tests import unittest_tools as utt
from tests.gpuarray.config import mode_with_gpu, mode_without_gpu, test_ctx_name
from tests.tensor.test_basic import (
TestAll... | np.int32(4) | numpy.int32 |
import os
import numpy as np
import pandas as pd
from keras.utils import to_categorical
from sklearn.model_selection import KFold, train_test_split
def load_data(path):
train = pd.read_json(os.path.join(path, "./train.json"))
test = pd.read_json(os.path.join(path, "./test.json"))
return (train, test)
... | np.full(shape=(75, 75), fill_value=angel) | numpy.full |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from ... | np.linspace(-0.2, 0.8, 100) | numpy.linspace |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from ... | np.cos(5 * time) | numpy.cos |
'''
<NAME>
set up :2020-1-9
intergrate img and label into one file
-- fiducial1024_v1
'''
import argparse
import sys, os
import pickle
import random
import collections
import json
import numpy as np
import scipy.io as io
import scipy.misc as m
import matplotlib.pyplot as plt
import glob
import math
import time
impo... | np.linalg.norm(pts2[1]-pts2[3]) | numpy.linalg.norm |
"""
YTArray class.
"""
from __future__ import print_function
#-----------------------------------------------------------------------------
# Copyright (c) 2013, yt Development Team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this so... | np.asarray(out_arr) | numpy.asarray |
"""
Binary serialization
NPY format
==========
A simple format for saving numpy arrays to disk with the full
information about them.
The ``.npy`` format is the standard binary file format in NumPy for
persisting a *single* arbitrary NumPy array on disk. The format stores all
of the shape and dtype information necess... | pickle.dump(array, fp, protocol=3, **pickle_kwargs) | numpy.compat.pickle.dump |
#
# Copyright (c) 2021 The GPflux Contributors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agr... | np.random.randn(*prior_shape) | numpy.random.randn |
#
# Copyright (c) 2021 The GPflux Contributors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agr... | np.random.randn(num_data, 2) | numpy.random.randn |
import numpy
from keras.preprocessing import sequence
from keras.preprocessing.text import Tokenizer
from src.support import support
class PhraseManager:
def __init__(self, configuration):
self.train_phrases, self.train_labels = self._read_train_phrases()
self.test_phrases, self.test_labels = se... | numpy.array(self.test_labels) | numpy.array |
import json
import logging
import sys
import numpy as np
import torch
from task_config import SuperGLUE_LABEL_MAPPING
from snorkel.mtl.data import MultitaskDataset
sys.path.append("..") # Adds higher directory to python modules path.
logger = logging.getLogger(__name__)
TASK_NAME = "WSC"
def get_char_index(tex... | np.array(token1_idxs) | numpy.array |
import argparse
import json
import numpy as np
import pandas as pd
import os
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report,f1_score
from keras.models import Sequential
from keras.layers import Dense, Dropout
fro... | np.zeros(768) | numpy.zeros |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | np.isnan(data[2]["y"]) | numpy.isnan |
import argparse
import json
import numpy as np
import pandas as pd
import os
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report,f1_score
from keras.models import Sequential
from keras.layers import Dense, Dropout
fro... | np.concatenate([sentence_emb, previous_emb, next_emb,section_emb], axis=1) | numpy.concatenate |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from ... | np.ones_like(t[:-1]) | numpy.ones_like |
"""Routines for numerical differentiation."""
from __future__ import division
import numpy as np
from numpy.linalg import norm
from scipy.sparse.linalg import LinearOperator
from ..sparse import issparse, csc_matrix, csr_matrix, coo_matrix, find
from ._group_columns import group_dense, group_sparse
EPS = np.finfo(n... | np.ravel(J_transposed) | numpy.ravel |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from ... | np.linspace(maxima_y[-1], minima_y[-1], 101) | numpy.linspace |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from ... | np.ones_like(min_1_x_time) | numpy.ones_like |
"""Routines for numerical differentiation."""
from __future__ import division
import numpy as np
from numpy.linalg import norm
from scipy.sparse.linalg import LinearOperator
from ..sparse import issparse, csc_matrix, csr_matrix, coo_matrix, find
from ._group_columns import group_dense, group_sparse
EPS = np.finfo(n... | np.empty(m) | numpy.empty |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from ... | np.linspace(-2, 2, 101) | numpy.linspace |
'''
<NAME>
set up :2020-1-9
intergrate img and label into one file
-- fiducial1024_v1
'''
import argparse
import sys, os
import pickle
import random
import collections
import json
import numpy as np
import scipy.io as io
import scipy.misc as m
import matplotlib.pyplot as plt
import glob
import math
import time
impo... | np.random.normal(10, 3) | numpy.random.normal |
"""
YTArray class.
"""
from __future__ import print_function
#-----------------------------------------------------------------------------
# Copyright (c) 2013, yt Development Team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this so... | np.asarray(inps[0]) | numpy.asarray |
"""Routines for numerical differentiation."""
from __future__ import division
import numpy as np
from numpy.linalg import norm
from scipy.sparse.linalg import LinearOperator
from ..sparse import issparse, csc_matrix, csr_matrix, coo_matrix, find
from ._group_columns import group_dense, group_sparse
EPS = np.finfo(n... | np.isscalar(order) | numpy.isscalar |
from abc import ABCMeta, abstractmethod
import os
from vmaf.tools.misc import make_absolute_path, run_process
from vmaf.tools.stats import ListStats
__copyright__ = "Copyright 2016-2018, Netflix, Inc."
__license__ = "Apache, Version 2.0"
import re
import numpy as np
import ast
from vmaf import ExternalProgramCaller,... | np.array(log_dict['dis_scores_mtx']) | numpy.array |
# coding: utf-8
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Test the Logarithmic Units and Quantities
"""
from __future__ import (absolute_import, unicode_literals, division,
print_function)
from ...extern import six
from ...extern.six.moves import zip
import pickle... | np.array([True, False, False]) | numpy.array |
#!/usr/bin/env python
# encoding: utf-8 -*-
"""
This module contains unit tests of the rmgpy.reaction module.
"""
import numpy
import unittest
from external.wip import work_in_progress
from rmgpy.species import Species, TransitionState
from rmgpy.reaction import Reaction
from rmgpy.statmech.translation import Transl... | numpy.arange(200.0, 2001.0, 200.0, numpy.float64) | numpy.arange |
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | np.array(test_neg, dtype=np.int32) | numpy.array |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from ... | np.linspace(-3, 3, 101) | numpy.linspace |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from ... | np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101) | numpy.linspace |
# pvtrace is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# pvtrace is distributed in the hope that it will be useful,
# but WITHOUT... | np.random.uniform() | numpy.random.uniform |
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | np.fromfile('./result_Files/repos_0.bin', np.float32) | numpy.fromfile |
"""Routines for numerical differentiation."""
from __future__ import division
import numpy as np
from numpy.linalg import norm
from scipy.sparse.linalg import LinearOperator
from ..sparse import issparse, csc_matrix, csr_matrix, coo_matrix, find
from ._group_columns import group_dense, group_sparse
EPS = np.finfo(n... | np.isinf(lb) | numpy.isinf |
import numpy as np
import cv2
import os
import json
import glob
from PIL import Image, ImageDraw
plate_diameter = 25 #cm
plate_depth = 1.5 #cm
plate_thickness = 0.2 #cm
def Max(x, y):
if (x >= y):
return x
else:
return y
def polygons_to_mask(img_shape, polygons):
mask = np.zeros(img_shape... | np.array(mask, dtype=bool) | numpy.array |
"""Routines for numerical differentiation."""
from __future__ import division
import numpy as np
from numpy.linalg import norm
from scipy.sparse.linalg import LinearOperator
from ..sparse import issparse, csc_matrix, csr_matrix, coo_matrix, find
from ._group_columns import group_dense, group_sparse
EPS = np.finfo(n... | np.resize(lb, x0.shape) | numpy.resize |
# pvtrace is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# pvtrace is distributed in the hope that it will be useful,
# but WITHOUT... | np.array(polarisation) | numpy.array |
# pylint: disable=protected-access
"""
Test the wrappers for the C API.
"""
import os
from contextlib import contextmanager
import numpy as np
import numpy.testing as npt
import pandas as pd
import pytest
import xarray as xr
from packaging.version import Version
from pygmt import Figure, clib
from pygmt.clib.conversio... | np.arange(size, dtype=dtype) | numpy.arange |
#!/usr/bin/env python
# encoding: utf-8 -*-
"""
This module contains unit tests of the rmgpy.reaction module.
"""
import numpy
import unittest
from external.wip import work_in_progress
from rmgpy.species import Species, TransitionState
from rmgpy.reaction import Reaction
from rmgpy.statmech.translation import Transl... | numpy.arange(200.0, 2001.0, 200.0, numpy.float64) | numpy.arange |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from ... | np.linspace(maxima_x[-1], minima_x[-1], 101) | numpy.linspace |
'''
-------------------------------------------------------------------------------------------------
This code accompanies the paper titled "Human injury-based safety decision of automated vehicles"
Author: <NAME>, <NAME>, <NAME>, <NAME>
Corresponding author: <NAME> (<EMAIL>)
------------------------------------------... | np.abs(veh_cgf[0] - veh_striking[3]) | numpy.abs |
# coding: utf-8
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Test the Logarithmic Units and Quantities
"""
from __future__ import (absolute_import, unicode_literals, division,
print_function)
from ...extern import six
from ...extern.six.moves import zip
import pickle... | np.array([True, False, False]) | numpy.array |
#!/usr/bin/env python3
import tensorflow as tf
physical_devices = tf.config.list_physical_devices('GPU')
try:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
except:
# Invalid device or cannot modify virtual devices once initialized.
pass
import numpy as np
import os, time, csv
import ... | np.argmax(pred['pred_id2'][i]) | numpy.argmax |
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 28 12:10:11 2019
@author: Omer
"""
## File handler
## This file was initially intended purely to generate the matrices for the near earth code found in: https://public.ccsds.org/Pubs/131x1o2e2s.pdf
## The values from the above pdf were copied manually to a txt file, and ... | np.array([0,0,0,1], dtype = GENERAL_CODE_MATRIX_DATA_TYPE) | numpy.array |
import io
import logging
import json
import numpy
import torch
import numpy as np
from tqdm import tqdm
from clie.inputters import constant
from clie.objects import Sentence
from torch.utils.data import Dataset
from torch.utils.data.sampler import Sampler
logger = logging.getLogger(__name__)
def load_word_embeddings... | np.random.random() | numpy.random.random |
import copy
import functools
import itertools
import numbers
import warnings
from collections import defaultdict
from datetime import timedelta
from distutils.version import LooseVersion
from typing import (
Any,
Dict,
Hashable,
Mapping,
Optional,
Sequence,
Tuple,
TypeVar,
Union,
)
... | np.prod(new_dim_sizes) | numpy.prod |
"""Test the search module"""
from collections.abc import Iterable, Sized
from io import StringIO
from itertools import chain, product
from functools import partial
import pickle
import sys
from types import GeneratorType
import re
import numpy as np
import scipy.sparse as sp
import pytest
from sklearn.utils.fixes im... | np.unique(Y) | numpy.unique |
import numpy
from keras.preprocessing import sequence
from keras.preprocessing.text import Tokenizer
from src.support import support
class PhraseManager:
def __init__(self, configuration):
self.train_phrases, self.train_labels = self._read_train_phrases()
self.test_phrases, self.test_labels = se... | numpy.array(self.train_labels, dtype='float32') | numpy.array |
from abc import ABCMeta, abstractmethod
import os
from vmaf.tools.misc import make_absolute_path, run_process
from vmaf.tools.stats import ListStats
__copyright__ = "Copyright 2016-2018, Netflix, Inc."
__license__ = "Apache, Version 2.0"
import re
import numpy as np
import ast
from vmaf import ExternalProgramCaller,... | np.array(result.result_dict[adm_den_scale3_scores_key]) | numpy.array |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from ... | np.ones(101) | numpy.ones |
from abc import ABCMeta, abstractmethod
import os
from vmaf.tools.misc import make_absolute_path, run_process
from vmaf.tools.stats import ListStats
__copyright__ = "Copyright 2016-2018, Netflix, Inc."
__license__ = "Apache, Version 2.0"
import re
import numpy as np
import ast
from vmaf import ExternalProgramCaller,... | np.array(result.result_dict[vif_num_scale3_scores_key]) | numpy.array |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from ... | np.linspace(-3, 3, 101) | numpy.linspace |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from ... | np.linspace(0, 5 * np.pi, 1001) | numpy.linspace |
import copy
import functools
import itertools
import numbers
import warnings
from collections import defaultdict
from datetime import timedelta
from distutils.version import LooseVersion
from typing import (
Any,
Dict,
Hashable,
Mapping,
Optional,
Sequence,
Tuple,
TypeVar,
Union,
)
... | np.asarray(data) | numpy.asarray |
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
class TwoLayerNet(object):
"""
A two-layer fully-connected neural network. The net has an input dimension
of N, a hidden layer dimension of H, and performs classification over C
classes.
We train the network... | np.maximum(0, scores1) | numpy.maximum |
import os
from PIL import Image
import cv2
from os import listdir
from os.path import join
import matplotlib.pyplot as plt
import matplotlib
from matplotlib.colors import LogNorm
from io_utils.io_common import create_folder
from viz_utils.constants import PlotMode, BackgroundType
import pylab
import numpy as np
import... | np.amax(lons) | numpy.amax |
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
class TwoLayerNet(object):
"""
A two-layer fully-connected neural network. The net has an input dimension
of N, a hidden layer dimension of H, and performs classification over C
classes.
We train the network... | np.sum(d_scores, axis=0) | numpy.sum |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from ... | np.linspace(-2, 2, 101) | numpy.linspace |
import numpy as np
import pytest
import theano
import theano.tensor as tt
# Don't import test classes otherwise they get tested as part of the file
from tests import unittest_tools as utt
from tests.gpuarray.config import mode_with_gpu, mode_without_gpu, test_ctx_name
from tests.tensor.test_basic import (
TestAll... | np.int32(5) | numpy.int32 |
import os
import numpy as np
import pandas as pd
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing.image import img_to_array, load_img
from keras.utils.np_utils import to_categorical
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.preproces... | np.concatenate(all_ID) | numpy.concatenate |
"""
Functions for loading input data.
Author: <NAME> <<EMAIL>>
"""
import os
import numpy as np
def load_img(path: str, img_nums: list, shape: tuple) -> np.array:
"""
Loads a image in the human-readable format.
Args:
path:
The path to the to the folder with mnist images.
i... | np.prod(image_shape) | numpy.prod |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from ... | np.ones_like(minima_dash) | numpy.ones_like |
# pvtrace is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# pvtrace is distributed in the hope that it will be useful,
# but WITHOUT... | np.array((0.,0.,0.)) | numpy.array |
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