prompt stringlengths 123 92.3k | completion stringlengths 7 132 | api stringlengths 9 35 |
|---|---|---|
"""
Greedy Word Swap with Word Importance Ranking
===================================================
When WIR method is set to ``unk``, this is a reimplementation of the search
method from the paper: Is BERT Really Robust?
A Strong Baseline for Natural Language Attack on Text Classification and
Entailment by Jin et... | np.arange(len_text) | 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.linalg.norm(data, ord=ord, axis=axis, keepdims=keepdims) | numpy.linalg.norm |
# ________
# /
# \ /
# \ /
# \/
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(point_1 * np.pi - width, point_1 * 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(-2, 2, 101) | numpy.linspace |
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((grid_pixels_2d_slim.shape[0], 2)) | numpy.zeros |
"""
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... | os_fspath(filename) | numpy.compat.os_fspath |
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 |
# 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.array(["a", "bc", "def", "ghij", "klmno"], dtype=dtype) | numpy.array |
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.abs(ratio) | 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.arange(1., 10.) | 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(minima_x[-1], maxima_x[-1], 101) | numpy.linspace |
# -*- encoding:utf-8 -*-
# @Time : 2021/1/3 15:15
# @Author : gfjiang
import os.path as osp
import mmcv
import numpy as np
import cvtools
import matplotlib.pyplot as plt
import cv2.cv2 as cv
from functools import partial
import torch
import math
from cvtools.utils.path import add_prefix_filename_suffix
from mmdet.... | np.max(img) | numpy.max |
# ________
# /
# \ /
# \ /
# \/
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(z) | numpy.abs |
"""Python interfaces to DGL farthest point sampler."""
from dgl._ffi.base import DGLError
import numpy as np
from .._ffi.function import _init_api
from .. import backend as F
from .. import ndarray as nd
def _farthest_point_sampler(data, batch_size, sample_points, dist, start_idx, result):
r"""Farthest Point Samp... | np.unique(node_label_np, return_inverse=True) | numpy.unique |
# ________
# /
# \ /
# \ /
# \/
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.linspace(0, 2 * np.pi, 1001) | 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_den_scale0_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.full_like(self.synthesis_perturbed_img, 256) | numpy.full_like |
"""
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.intersect1d(arr1, arr2, assume_unique=assume_unique) | numpy.intersect1d |
'''
-------------------------------------------------------------------------------------------------
This code accompanies the paper titled "Human injury-based safety decision of automated vehicles"
Author: <NAME>, <NAME>, <NAME>, <NAME>
Corresponding author: <NAME> (<EMAIL>)
------------------------------------------... | np.sqrt(veh_cgf[1] ** 2 + veh_cgs[1] ** 2) | numpy.sqrt |
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_scale0_scores_key]) | numpy.array |
#!/usr/bin/env python
# encoding: utf-8
import numbers
import os
import re
import sys
from itertools import chain
import numpy as np
import scipy.sparse as sp
import six
import pickle
from .model import get_convo_nn2
from .stop_words import THAI_STOP_WORDS
from .utils import CHAR_TYPES_MAP, CHARS_MAP, create_feature_... | np.where(mask) | numpy.where |
"""
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_ |
"""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.zeros(m) | numpy.zeros |
'''
<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.sum(self.synthesis_perturbed_img[x, :]) | numpy.sum |
"""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.ones(X.shape[0], dtype=np.bool) | numpy.ones |
import torch
import torchvision
import matplotlib
import matplotlib.pyplot as plt
from PIL import Image
from captum.attr import GuidedGradCam, GuidedBackprop
from captum.attr import LayerActivation, LayerConductance, LayerGradCam
from data_utils import *
from image_utils import *
from captum_utils import *
import nump... | np.max(img) | numpy.max |
from data.data_loader_dad import (
NASA_Anomaly,
WADI
)
from exp.exp_basic import Exp_Basic
from models.model import Informer
from utils.tools import EarlyStopping, adjust_learning_rate
from utils.metrics import metric
from sklearn.metrics import classification_report
import numpy as np
import torch
import t... | np.array(preds) | 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 |
# ________
# /
# \ /
# \ /
# \/
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 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(0.04 * 2 * np.pi * t) | numpy.cos |
import os
import random
from typing import Any, Dict, List, Union
import numpy as np
import torch
from colorama import Fore, Style
from sklearn.metrics import f1_score
from sklearn.metrics import precision_recall_fscore_support as score
from sklearn.metrics import precision_score, recall_score
def highlight(input_: ... | np.random.seed(seed) | numpy.random.seed |
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.impact_size, self.impact_size), dtype=np.uint8) | numpy.ones |
import os
import string
from collections import Counter
from datetime import datetime
from functools import partial
from pathlib import Path
from typing import Optional
import numpy as np
import pandas as pd
from scipy.stats.stats import chisquare
from tangled_up_in_unicode import block, block_abbr, categor... | np.median(series) | numpy.median |
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.sum(region[..., 1] == 0) | numpy.sum |
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(value) | numpy.asarray |
'''
<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.full((enlarge_img_shrink[0], enlarge_img_shrink[1], 3), 256, dtype=np.float32) | numpy.full |
# -*- 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([0,1,0,0], dtype = GENERAL_CODE_MATRIX_DATA_TYPE) | numpy.array |
import numpy as np
from sklearn.linear_model import LogisticRegression
from .models import User
from .twitter import vectorize_tweet
def predict_user(user1_name, user2_name, tweet_text):
"""
Determine and return which user is more likely to say a given Tweet.
Example: predict_user('ausen', 'elonmusk'... | np.array([tweet.vect for tweet in user2.tweets]) | numpy.array |
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.arange(N) | numpy.arange |
#!/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_id1'][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([1,1,1,0], dtype = GENERAL_CODE_MATRIX_DATA_TYPE) | 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 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 |
from __future__ import division
from math import sqrt as sqrt
from itertools import product as product
import torch
import numpy as np
import cv2
from lib.utils.visualize_utils import TBWriter
def vis(func):
"""tensorboard visualization if has writer as input"""
def wrapper(*args, **kw):
return func... | np.ones((self.image_size[1], self.image_size[0], 3)) | 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.linspace(-5, 5, 101) | numpy.linspace |
# 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 |
"""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.uint8, flags='ALIGNED, CONTIGUOUS') | numpy.ctypeslib.ndpointer |
# 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.length) | numpy.random.uniform |
# ________
# /
# \ /
# \ /
# \/
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(improved_slope_based_minimum - width, improved_slope_based_minimum + width, 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.linalg.norm(perturbed_vp) | numpy.linalg.norm |
'''
<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[0]-pts2[1]) | 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 |
# ________
# /
# \ /
# \ /
# \/
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 sklearn.linear_model import LogisticRegression
from .models import User
from .twitter import vectorize_tweet
def predict_user(user1_name, user2_name, tweet_text):
"""
Determine and return which user is more likely to say a given Tweet.
Example: predict_user('ausen', 'elonmusk'... | np.array(hypo_tweet_vect) | numpy.array |
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(self.num_classes, dtype=np.bool) | numpy.zeros |
# 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(lq6 == fv6) | numpy.all |
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.cos(x * np.pi / 180) | numpy.cos |
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_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 |
# 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 |
import numpy as np
import tensorflow as tf
H = 2
N = 2
M = 3
BS = 10
def my_softmax(arr):
max_elements = np.reshape(np.max(arr, axis = 2), (BS, N, 1))
arr = arr - max_elements
exp_array = np.exp(arr)
print (exp_array)
sum_array = np.reshape(np.sum(exp_array, axis=2), (BS, N, 1))
return exp_arra... | np.array([[[1., 2., 3., 4.], [5., 6., 7., 8.]]]) | numpy.array |
"""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.random.RandomState(0) | numpy.random.RandomState |
import os
import numpy as np
import cv2
import albumentations
from PIL import Image
from torch.utils.data import Dataset
from taming.data.sflckr import SegmentationBase # for examples included in repo
class Examples(SegmentationBase):
def __init__(self, size=256, random_crop=False, interpolation="bicubic"):
... | np.array(image) | 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.argmax(counts) | numpy.argmax |
# ________
# /
# \ /
# \ /
# \/
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 |
# ________
# /
# \ /
# \ /
# \/
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 |
# 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[1],self.planeextent[1]) | numpy.random.uniform |
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.array([[0, 1], [0.5, 0.5], [1,1]]) | numpy.array |
"""
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.dtype(descr) | numpy.dtype |
'''
-------------------------------------------------------------------------------------------------
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_cgs[0] - veh_striking[3]) | numpy.abs |
"""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.diag(h) | numpy.diag |
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.rot90(c_np_data) | numpy.rot90 |
'''
<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.full((save_img_shape[0], save_img_shape[1], 2), 0, dtype=np.float32) | numpy.full |
import os
import sys
import click
import pickle
import sncosmo
import numpy as np
from astropy.table import Table
DATA_PATH = '/home/samdixon/jla_light_curves/'
def modify_error(lc, error_floor=0.):
"""Add an error floor of `error_floor` times the maximum flux of the band
to each observation
"""
... | np.mean(lc['time']) | numpy.mean |
# ________
# /
# \ /
# \ /
# \/
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_y) | numpy.ones_like |
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.array(1) | numpy.array |
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 |
# ________
# /
# \ /
# \ /
# \/
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[-2] - width, minima_x[-2] + width, 101) | numpy.linspace |
"""
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.bitwise_or(self, other, out=self) | numpy.bitwise_or |
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(grid_2d_slim[:, 0]) | numpy.min |
# 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.focussize,self.focussize) | 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.array(["pqrst", "uvwx", "yz!", "@#", "$"], dtype=dtype) | 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(0.4, 3.35, 0.01) | 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.asarray(time) | numpy.asarray |
# 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 |
# ________
# /
# \ /
# \ /
# \/
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] - width, maxima_y[-1] + width, 101) | numpy.linspace |
"""
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.transpose(arrays) | numpy.transpose |
"""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.arange(100) | numpy.arange |
import numpy as np
import sys
import os
from PIL import Image
from visu.helper_functions import save_image
from scipy.spatial.transform import Rotation as R
from helper import re_quat
import copy
import torch
import numpy as np
import k3d
class Visualizer():
def __init__(self, p_visu, writer=None):
if p_v... | np.dot(points, rot.T) | numpy.dot |
# 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(theta) | numpy.sin |
import numpy as np
import tensorflow as tf
H = 2
N = 2
M = 3
BS = 10
def my_softmax(arr):
max_elements = np.reshape(np.max(arr, axis = 2), (BS, N, 1))
arr = arr - max_elements
exp_array = np.exp(arr)
print (exp_array)
sum_array = np.reshape(np.sum(exp_array, axis=2), (BS, N, 1))
return exp_arra... | np.tile(q, [BS, 1, 1]) | numpy.tile |
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(sentence_emb) | 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.nonzero(e) | numpy.nonzero |
"""
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, copy=False) | 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(5.4 * np.pi - width, 5.4 * np.pi + width, 101) | numpy.linspace |
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.subtract(border_grid[:, 1], border_origin[1]) | numpy.subtract |
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 |
"""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.ones(1) | 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.all(lq ** power == 1.) | numpy.all |
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