prompt stringlengths 123 92.3k | completion stringlengths 7 132 | api stringlengths 9 35 |
|---|---|---|
# ________
# /
# \ /
# \ /
# \/
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(max_1_y) | numpy.ones_like |
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(k) | numpy.asarray |
# ________
# /
# \ /
# \ /
# \/
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 |
"""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.array([0] * 5 + [1] * 5) | 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.linspace(start=0, stop=4, num=3) | numpy.linspace |
"""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(ub) | numpy.isinf |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cntk as C
import numpy as np
from .common import floatx, epsilon, image_dim_ordering, image_data_format
from collections import defaultdict
from contextlib import contextmanager
import warnings
C.set_g... | np.asarray(self.from_shape) | numpy.asarray |
# ________
# /
# \ /
# \ /
# \/
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 __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cntk as C
import numpy as np
from .common import floatx, epsilon, image_dim_ordering, image_data_format
from collections import defaultdict
from contextlib import contextmanager
import warnings
C.set_g... | np.random.seed(seed) | numpy.random.seed |
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.asarray(next_list) | numpy.asarray |
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.dtype(dtype) | numpy.dtype |
# ________
# /
# \ /
# \ /
# \/
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(2 * np.pi * t / 50) | numpy.cos |
"""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.abs(h_adjusted) | numpy.abs |
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(q, dtype=np.float64) | numpy.asarray |
# 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.,self.radius) | numpy.random.uniform |
import numpy as np
from typing import Tuple, Union, Optional
from autoarray.structures.arrays.two_d import array_2d_util
from autoarray.geometry import geometry_util
from autoarray import numba_util
from autoarray.mask import mask_2d_util
@numba_util.jit()
def grid_2d_centre_from(grid_2d_slim: np.ndarray) ... | np.min(border_grid_radii) | numpy.min |
# ________
# /
# \ /
# \ /
# \/
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 |
#
# 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.concatenate(observations, axis=-1) | numpy.concatenate |
"""
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.bool_(False) | numpy.bool_ |
# ________
# /
# \ /
# \ /
# \/
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(max_dash_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.cos(2 * np.pi * (1 / P1) * (Coughlin_time - Coughlin_time[0])) | numpy.cos |
import numpy as np
from stumpff import C, S
from CelestialBody import BODIES
from numerical import newton, laguerre
from lagrange import calc_f, calc_fd, calc_g, calc_gd
def kepler_chi(chi, alpha, r0, vr0, mu, dt):
''' Kepler's Equation of the universal anomaly, modified
for use in numerical solvers. '''
... | np.array([20000, -105000, -19000]) | 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.abs(J_to_test - J_diff) | numpy.abs |
# ________
# /
# \ /
# \ /
# \/
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 |
import numpy as np
from stumpff import C, S
from CelestialBody import BODIES
from numerical import newton, laguerre
from lagrange import calc_f, calc_fd, calc_g, calc_gd
def kepler_chi(chi, alpha, r0, vr0, mu, dt):
''' Kepler's Equation of the universal anomaly, modified
for use in numerical solvers. '''
... | np.sqrt(mu) | numpy.sqrt |
from __future__ import annotations
from datetime import timedelta
import operator
from sys import getsizeof
from typing import (
TYPE_CHECKING,
Any,
Callable,
Hashable,
List,
cast,
)
import warnings
import numpy as np
from pandas._libs import index as libindex
from pandas._libs.lib import no_... | np.concatenate([x._values for x in rng_indexes]) | numpy.concatenate |
# -*- 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.vstack((accumulatedBlock, newBlock)) | numpy.vstack |
"""
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... | numpy.ndarray(count, dtype=dtype) | numpy.ndarray |
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.array(band) | 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 |
# 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.arange(1., 10.) | numpy.arange |
import numpy as np
from stumpff import C, S
from CelestialBody import BODIES
from numerical import newton, laguerre
from lagrange import calc_f, calc_fd, calc_g, calc_gd
def kepler_chi(chi, alpha, r0, vr0, mu, dt):
''' Kepler's Equation of the universal anomaly, modified
for use in numerical solvers. '''
... | np.linalg.norm(r_0) | numpy.linalg.norm |
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)
y = np.lin... | np.copy(z_temp) | numpy.copy |
# 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, size * 2, 1, dtype=dtype) | numpy.arange |
# 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.logspace(2, 3, 4) | numpy.logspace |
# ________
# /
# \ /
# \ /
# \/
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 |
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
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.amin(lons) | numpy.amin |
"""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.abs(abs_err_data) | numpy.abs |
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 |
# -*- coding: utf-8 -*-
import argparse
import os
import shutil
import time
import numpy as np
import random
from collections import OrderedDict
import torch
import torch.backends.cudnn as cudnn
from callbacks import AverageMeter
from data_utils.causal_data_loader_frames import VideoFolder
from utils impo... | np.concatenate(logits_matrix) | numpy.concatenate |
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, dtype='float32') | numpy.array |
"""
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.concatenate(arrs, axis=axis) | numpy.concatenate |
import sys
import numpy as np
from matplotlib import pyplot as pl
from rw import WriteGTiff
fn = '../pozo-steep-vegetated-pcl.npy'
pts = np.load(fn)
x, y, z, c = pts[:, 0], pts[:, 1], pts[:, 2], pts[:, 5]
ix = (0.2 * (x - x.min())).astype('int')
iy = (0.2 * (y - y.min())).astype('int')
shape = (100, 100)
xb = np.aran... | np.ma.masked_invalid(stdr) | numpy.ma.masked_invalid |
# 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(self.planeorigin[0],self.planeextent[0]) | numpy.random.uniform |
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.atleast_1d(axis) | numpy.atleast_1d |
"""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.zeros(10) | numpy.zeros |
import gym
import numpy as np
from itertools import product
import matplotlib.pyplot as plt
def print_policy(Q, env):
""" This is a helper function to print a nice policy from the Q function"""
moves = [u'←', u'↓',u'→', u'↑']
if not hasattr(env, 'desc'):
env = env.env
dims = env.desc.shape
... | np.unravel_index(s, dims) | numpy.unravel_index |
# ________
# /
# \ /
# \ /
# \/
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.var(time_series - imfs_51[3, :]) | numpy.var |
# 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.all(q2.value == self.lq2.value) | numpy.all |
# ________
# /
# \ /
# \ /
# \/
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 |
# 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.arange(1., 5.) | numpy.arange |
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 |
# ________
# /
# \ /
# \ /
# \/
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], max_discard_time, 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 |
# ________
# /
# \ /
# \ /
# \/
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 |
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.asarray(length) | numpy.asarray |
# 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(self.direction) | 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.sin(pseudo_alg_time) | numpy.sin |
import numpy as np
from typing import Tuple, Union, Optional
from autoarray.structures.arrays.two_d import array_2d_util
from autoarray.geometry import geometry_util
from autoarray import numba_util
from autoarray.mask import mask_2d_util
@numba_util.jit()
def grid_2d_centre_from(grid_2d_slim: np.ndarray) ... | np.zeros(shape=(total_sub_pixels, 2)) | numpy.zeros |
__all__ = ['imread', 'imsave']
import numpy as np
from PIL import Image
from ...util import img_as_ubyte, img_as_uint
def imread(fname, dtype=None, img_num=None, **kwargs):
"""Load an image from file.
Parameters
----------
fname : str or file
File name or file-like-object.
dtype : numpy ... | np.diff(valid_palette) | numpy.diff |
# 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 * 2, size * 3, 1, dtype=dtype) | numpy.arange |
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_scale1_scores_key]) | 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.linspace(edge_padding, im_ud - edge_padding, fiducial_points, dtype=np.int64) | numpy.linspace |
# 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.power(self.mJy, 2.) | numpy.power |
# 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(shape[0] * shape[1], dtype=dtype) | numpy.arange |
import numpy as np
from scipy import ndimage
def erode_value_blobs(array, steps=1, values_to_ignore=tuple(), new_value=0):
unique_values = list(np.unique(array))
all_entries_to_keep = np.zeros(shape=array.shape, dtype=np.bool)
for unique_value in unique_values:
entries_of_this_value = array == uni... | np.logical_or(eroded_unique_indicator, all_entries_to_keep) | numpy.logical_or |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cntk as C
import numpy as np
from .common import floatx, epsilon, image_dim_ordering, image_data_format
from collections import defaultdict
from contextlib import contextmanager
import warnings
C.set_g... | np.zeros(shape, ctype) | numpy.zeros |
# ________
# /
# \ /
# \ /
# \/
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(5.4 * np.pi - width, 5.4 * np.pi + 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.linspace(-5.5, 5.5, 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.cos(phi) | 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(101) | numpy.ones |
'''
<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.zeros_like(self.synthesis_perturbed_img, dtype=np.float32) | numpy.zeros_like |
#!/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.array([0.1, 10.0]) | numpy.array |
"""
Collection of tests asserting things that should be true for
any index subclass. Makes use of the `indices` fixture defined
in pandas/tests/indexes/conftest.py.
"""
import re
import numpy as np
import pytest
from pandas._libs.tslibs import iNaT
from pandas.core.dtypes.common import is_period_dtype, needs_i8_conv... | np.sort(not_na_vals) | numpy.sort |
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.transpose(totalMatrix) | numpy.transpose |
from gtrain import Model
import numpy as np
import tensorflow as tf
class NetForHypinv(Model):
"""
Implementaion of the crutial function for the HypINV algorithm.
Warning: Do not use this class but implement its subclass, for example see FCNetForHypinv
"""
def __init__(self, weights):
self... | np.zeros([1, self.layer_sizes[0]]) | numpy.zeros |
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.concatenate(positions) | numpy.concatenate |
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, dtype=np.uint8) | numpy.zeros |
# ________
# /
# \ /
# \ /
# \/
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.abs(hht) | numpy.abs |
"""
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.add(self, oth, out=self) | numpy.add |
# ________
# /
# \ /
# \ /
# \/
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(0.85 * np.pi, 1.15 * np.pi, 101) | numpy.linspace |
# 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.linspace(0., 10., 6) | numpy.linspace |
# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# (C) British Crown Copyright 2017-2021 Met Office.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions a... | np.ones(shape, dtype=FLOAT_DTYPE) | numpy.ones |
# 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(3) | numpy.arange |
"""
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.array(out_arr) | 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(-2, 2, 101) | 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(k) | numpy.asarray |
from __future__ import annotations
from datetime import timedelta
import operator
from sys import getsizeof
from typing import (
TYPE_CHECKING,
Any,
Callable,
Hashable,
List,
cast,
)
import warnings
import numpy as np
from pandas._libs import index as libindex
from pandas._libs.lib import no_... | np.errstate(all="ignore") | numpy.errstate |
# ________
# /
# \ /
# \ /
# \/
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[-2], 101) | numpy.linspace |
# 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.linspace(0., 10., 6) | 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(-5, 5, 101) | numpy.linspace |
"""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.abs(J_diff_data) | numpy.abs |
import inspect
import numpy as np
from pandas._libs import reduction as libreduction
from pandas.util._decorators import cache_readonly
from pandas.core.dtypes.common import (
is_dict_like,
is_extension_array_dtype,
is_list_like,
is_sequence,
)
from pandas.core.dtypes.generic import ABCSeries
def f... | np.errstate(all="ignore") | numpy.errstate |
# 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.flip(data, axis=(0, 1)) | numpy.flip |
__all__ = ['imread', 'imsave']
import numpy as np
from PIL import Image
from ...util import img_as_ubyte, img_as_uint
def imread(fname, dtype=None, img_num=None, **kwargs):
"""Load an image from file.
Parameters
----------
fname : str or file
File name or file-like-object.
dtype : numpy ... | np.array(frame, dtype=dtype) | numpy.array |
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.random.rand(4, 5) | numpy.random.rand |
# 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(linepoint) | 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.asarray(section_list) | numpy.asarray |
"""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.dot(X_[180:], X_[:180].T) | numpy.dot |
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