code stringlengths 31 1.05M | apis list | extract_api stringlengths 97 1.91M |
|---|---|---|
"""Functions for implementing conceptors"""
import numpy as np
def loading_ridge_regression(X, X_, B, regularizer: float = 1e-4):
"""Solving reservoir loading problem with ridge regression.
:param X: state trajectory, shape (T, N)
:param X_: state trajectory, time-shifted by -1
:param B: bias (T, 1)
... | [
"numpy.arctanh",
"numpy.sum",
"numpy.eye",
"numpy.dot",
"numpy.diag"
] | [((960, 974), 'numpy.dot', 'np.dot', (['X.T', 'X'], {}), '(X.T, X)\n', (966, 974), True, 'import numpy as np\n'), ((1881, 1891), 'numpy.diag', 'np.diag', (['C'], {}), '(C)\n', (1888, 1891), True, 'import numpy as np\n'), ((1358, 1372), 'numpy.dot', 'np.dot', (['X.T', 'X'], {}), '(X.T, X)\n', (1364, 1372), True, 'import... |
from __future__ import division
import mdtraj as md
from mdtraj.core.element import get_by_symbol
from mdtraj.geometry.order import _compute_director
import numpy as np
from scipy.integrate import simps
from scipy.stats import binned_statistic_2d
from atools.fileio import read_ndx
def calc_nematic_order(traj_filenam... | [
"numpy.sum",
"numpy.arctan2",
"scipy.stats.binned_statistic_2d",
"mdtraj.core.element.get_by_symbol",
"numpy.argmax",
"numpy.argmin",
"mdtraj.load",
"numpy.histogram",
"numpy.mean",
"numpy.arange",
"numpy.linalg.norm",
"mdtraj.compute_center_of_mass",
"atools.fileio.read_ndx",
"numpy.std",... | [((1090, 1112), 'atools.fileio.read_ndx', 'read_ndx', (['ndx_filename'], {}), '(ndx_filename)\n', (1098, 1112), False, 'from atools.fileio import read_ndx\n'), ((2750, 2790), 'mdtraj.load', 'md.load', (['traj_filename'], {'top': 'top_filename'}), '(traj_filename, top=top_filename)\n', (2757, 2790), True, 'import mdtraj... |
'''
Comparing TAD Simple Average to TAD Weighted Average. Comparing the densities of anomalous sections of the videos
generated by both TAD versions.
'''
import os
import sys
import argparse
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as mtick
from tqdm... | [
"numpy.load",
"matplotlib.pyplot.show",
"argparse.ArgumentParser",
"matplotlib.pylab.subplot",
"numpy.sum",
"numpy.std",
"numpy.empty",
"matplotlib.pylab.hist",
"numpy.mean",
"matplotlib.gridspec.GridSpec",
"matplotlib.pyplot.gcf",
"os.listdir",
"matplotlib.pylab.figure"
] | [((1212, 1241), 'os.listdir', 'os.listdir', (['path_to_directory'], {}), '(path_to_directory)\n', (1222, 1241), False, 'import os\n'), ((653, 806), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""This script calculates the densities of anomalous sections of the videos generated by both TA... |
'''
This program implements the idea present in SoundWave: Using the Doppler Effect to Sense Gestures : https://dl.acm.org/doi/pdf/10.1145/2207676.2208331
It implements a doppler radar : A sonar to find the velocity of a moving entity w.r.t the computer's mic/speaker when
the speaker and lic are placed near each othe... | [
"wave.open",
"matplotlib.pyplot.specgram",
"matplotlib.pyplot.show",
"numpy.fft.fft",
"numpy.zeros",
"math.sin",
"time.sleep",
"pyaudio.PyAudio",
"matplotlib.pyplot.ylabel",
"numpy.copyto",
"matplotlib.pyplot.xlabel",
"numpy.fromstring"
] | [((2573, 2590), 'pyaudio.PyAudio', 'pyaudio.PyAudio', ([], {}), '()\n', (2588, 2590), False, 'import pyaudio\n'), ((2770, 2815), 'numpy.zeros', 'np.zeros', (['(chunk_length, 1)'], {'dtype': 'np.float32'}), '((chunk_length, 1), dtype=np.float32)\n', (2778, 2815), True, 'import numpy as np\n'), ((2821, 2858), 'numpy.zero... |
import functools
import sys
import matplotlib as mpl
import numpy as np
def to_premultiplied_rgba8888(buf):
"""Convert a buffer from premultipled ARGB32 to premultiplied RGBA8888."""
# Using .take() instead of indexing ensures C-contiguity of the result.
return buf.take(
[2, 1, 0, 3] if sys.byteo... | [
"IPython.get_ipython",
"functools.lru_cache",
"IPython.core.pylabtools.backend2gui.update",
"numpy.rollaxis"
] | [((819, 841), 'functools.lru_cache', 'functools.lru_cache', (['(1)'], {}), '(1)\n', (838, 841), False, 'import functools\n'), ((659, 679), 'numpy.rollaxis', 'np.rollaxis', (['rgb', '(-1)'], {}), '(rgb, -1)\n', (670, 679), True, 'import numpy as np\n'), ((1211, 1232), 'IPython.get_ipython', 'IPython.get_ipython', ([], {... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 21 15:52:25 2017 - v1.0 Finalised Fri Apr 13
@author: michaelhodge
"""
#A script to calculate the along-strike scarp height, width and slope for
#a fault using an semi-automated algorithm approach
#Loads packages required
import pickle
import pand... | [
"matplotlib.pyplot.title",
"pickle.dump",
"numpy.isnan",
"matplotlib.pyplot.figure",
"numpy.arange",
"numpy.nanmean",
"matplotlib.pyplot.yticks",
"numpy.int",
"matplotlib.pyplot.xticks",
"numpy.size",
"numpy.hstack",
"matplotlib.pyplot.subplots_adjust",
"matplotlib.pyplot.ylabel",
"matplot... | [((1249, 1342), 'Algorithm.algorithm', 'algorithm', (['prof_distance', 'prof_height', 'nump', 'iterations', 'method', 'bin_size', 'theta_T', 'phi_T'], {}), '(prof_distance, prof_height, nump, iterations, method, bin_size,\n theta_T, phi_T)\n', (1258, 1342), False, 'from Algorithm import algorithm\n'), ((1440, 1452),... |
import tensorflow as tf
from tqdm import tqdm
import numpy as np
from scipy.interpolate import interp2d
from sklearn.preprocessing import MinMaxScaler
import joblib
import os, pickle
import pandas as pd
class CNN_3d_predict():
def __init__(self, static_data, rated, cluster_dir):
self.static_data = static_d... | [
"tensorflow.keras.layers.Dense",
"tensorflow.reshape",
"tensorflow.matmul",
"tensorflow.Variable",
"os.path.join",
"tensorflow.compat.v1.global_variables_initializer",
"tensorflow.compat.v1.placeholder",
"tensorflow.keras.layers.AveragePooling3D",
"tensorflow.compat.v1.Session",
"tensorflow.name_s... | [((381, 410), 'os.path.basename', 'os.path.basename', (['cluster_dir'], {}), '(cluster_dir)\n', (397, 410), False, 'import os, pickle\n'), ((442, 477), 'os.path.join', 'os.path.join', (['cluster_dir', '"""CNN_3d"""'], {}), "(cluster_dir, 'CNN_3d')\n", (454, 477), False, 'import os, pickle\n'), ((503, 546), 'os.path.joi... |
"""
*Script creates animations for early LENS LSTFRZ cases and produces 2m temperature plots*
"""
import numpy as np
from netCDF4 import Dataset
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
### Read in LSTFRZ Historical
hdamagevalues = np.genfromtxt('/volumes/eas-shared/ault/ecrl/spring-in... | [
"matplotlib.pyplot.title",
"netCDF4.Dataset",
"numpy.meshgrid",
"numpy.asarray",
"numpy.genfromtxt",
"cesmcontrol_avet.climoMarch",
"matplotlib.pyplot.figure",
"numpy.where",
"numpy.arange",
"numpy.reshape",
"numpy.squeeze",
"mpl_toolkits.basemap.Basemap",
"matplotlib.pyplot.savefig"
] | [((266, 401), 'numpy.genfromtxt', 'np.genfromtxt', (['"""/volumes/eas-shared/ault/ecrl/spring-indices/LENS_springonset/data/damagevalues_1920-2005.txt"""'], {'delimiter': '""","""'}), "(\n '/volumes/eas-shared/ault/ecrl/spring-indices/LENS_springonset/data/damagevalues_1920-2005.txt'\n , delimiter=',')\n", (279, ... |
import numpy as np
from . import normalize
class Nchannel2RGB(object):
"""Convert nchannel array to rgb by PCA.
Parameters
----------
pca: sklearn.decomposition.PCA
PCA.
"""
def __init__(self, pca=None):
self._pca = pca
# for uint8
self._min_max_value = (No... | [
"numpy.nanmin",
"numpy.nanmax",
"numpy.issubdtype"
] | [((955, 997), 'numpy.issubdtype', 'np.issubdtype', (['nchannel.dtype', 'np.floating'], {}), '(nchannel.dtype, np.floating)\n', (968, 997), True, 'import numpy as np\n'), ((1850, 1883), 'numpy.issubdtype', 'np.issubdtype', (['dtype', 'np.floating'], {}), '(dtype, np.floating)\n', (1863, 1883), True, 'import numpy as np\... |
import numpy as np
import unicodedata
import os
class OneHot(object):
def __init__(self, be, nclasses):
self.be = be
self.output = be.iobuf(nclasses, parallelism='Data')
def transform(self, t):
self.output[:] = self.be.onehot(t, axis=0)
return self.output
def image_reshape(i... | [
"os.mkdir",
"unicodedata.normalize",
"unicodedata.category",
"numpy.zeros",
"os.path.exists",
"numpy.transpose",
"numpy.float",
"numpy.unique"
] | [((884, 905), 'numpy.transpose', 'np.transpose', (['img', 'df'], {}), '(img, df)\n', (896, 905), True, 'import numpy as np\n'), ((3525, 3537), 'numpy.zeros', 'np.zeros', (['(10)'], {}), '(10)\n', (3533, 3537), True, 'import numpy as np\n'), ((3613, 3645), 'numpy.unique', 'np.unique', (['t'], {'return_counts': '(True)'}... |
import numpy as np
import pytest
from pytetris.tetrimino import Tetrimino
def test_tetrimino_init_1():
cell = np.ones((4, 4))
tetrimino = Tetrimino(cell)
assert (tetrimino.cell == cell).all()
def test_tetrimino_init_2():
cell = np.array([[0, 1, 0, 1], [1, 0, 1, 0], [0, 1, 0, 1], [1, 0, 1, 0]])
t... | [
"numpy.full",
"numpy.zeros",
"numpy.ones",
"pytest.raises",
"numpy.random.randint",
"numpy.array",
"numpy.rot90",
"pytetris.tetrimino.Tetrimino"
] | [((116, 131), 'numpy.ones', 'np.ones', (['(4, 4)'], {}), '((4, 4))\n', (123, 131), True, 'import numpy as np\n'), ((148, 163), 'pytetris.tetrimino.Tetrimino', 'Tetrimino', (['cell'], {}), '(cell)\n', (157, 163), False, 'from pytetris.tetrimino import Tetrimino\n'), ((248, 314), 'numpy.array', 'np.array', (['[[0, 1, 0, ... |
# -*- coding: utf-8 -*-
# file: data_utils.py
# author: songyouwei <<EMAIL>>
# Copyright (C) 2018. All Rights Reserved.
import os
import pickle
import numpy as np
from torch.utils.data import Dataset
def load_word_vec(path, word2idx=None):
fin = open(path, 'r', encoding='utf-8', newline='\n', errors='ignore')
... | [
"numpy.asarray",
"os.path.exists",
"numpy.ones"
] | [((708, 750), 'os.path.exists', 'os.path.exists', (['embedding_matrix_file_name'], {}), '(embedding_matrix_file_name)\n', (722, 750), False, 'import os\n'), ((2536, 2566), 'numpy.asarray', 'np.asarray', (['trunc'], {'dtype': 'dtype'}), '(trunc, dtype=dtype)\n', (2546, 2566), True, 'import numpy as np\n'), ((3238, 3259)... |
#import
# =============================================================================
# =============================================================================
from math import acos,fabs,radians,trunc,degrees,cos
import os
import sys
import numpy as np
import ctypes
import multiprocessing as mp
import time
... | [
"argparse.ArgumentParser",
"matplotlib.cm.get_cmap",
"matplotlib.pyplot.figure",
"numpy.histogram",
"numpy.linalg.norm",
"matplotlib.pyplot.gca",
"numpy.round",
"os.path.join",
"matplotlib.pyplot.MultipleLocator",
"multiprocessing.cpu_count",
"os.path.abspath",
"os.path.dirname",
"matplotlib... | [((355, 378), 'math.fabs', 'fabs', (['(atomi.x - atomj.x)'], {}), '(atomi.x - atomj.x)\n', (359, 378), False, 'from math import acos, fabs, radians, trunc, degrees, cos\n'), ((386, 409), 'math.fabs', 'fabs', (['(atomi.y - atomj.y)'], {}), '(atomi.y - atomj.y)\n', (390, 409), False, 'from math import acos, fabs, radians... |
"""
Helper functions that are called in the RLlib's callback.
"""
import numpy as np
def store_eps_hist_data(episode, key):
"""
Called in episode_end
"""
data = episode.user_data[key]
#data[0] = 0 if data[0] is None else data[0]
episode.custom_metrics[key] = np.mean(data)
episode.hist_data... | [
"numpy.mean"
] | [((285, 298), 'numpy.mean', 'np.mean', (['data'], {}), '(data)\n', (292, 298), True, 'import numpy as np\n')] |
import os
from PIL import Image
import numpy as np
from torch.utils.data import Dataset
class DriveDataset(Dataset):
def __init__(self, root: str, train: bool, transforms=None):
super(DriveDataset, self).__init__()
data_root = os.path.join(root, "DRIVE", "training" if train else "test")
as... | [
"os.path.exists",
"numpy.clip",
"PIL.Image.open",
"numpy.array",
"PIL.Image.fromarray",
"os.path.join"
] | [((249, 309), 'os.path.join', 'os.path.join', (['root', '"""DRIVE"""', "('training' if train else 'test')"], {}), "(root, 'DRIVE', 'training' if train else 'test')\n", (261, 309), False, 'import os\n'), ((325, 350), 'os.path.exists', 'os.path.exists', (['data_root'], {}), '(data_root)\n', (339, 350), False, 'import os\... |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# In[2]:
dataset=pd.read_csv(r"C:\Users\hp\Desktop\Stock Predictor\trainset.xls",index_col="Date",parse_dates=True)
# In[3]:
dataset.head()
# In[4]:
dataset['Open'].plot(figsize=(20,8))
... | [
"pandas.DataFrame",
"matplotlib.pyplot.title",
"matplotlib.pyplot.show",
"matplotlib.pyplot.plot",
"pandas.read_csv",
"keras.layers.LSTM",
"matplotlib.pyplot.legend",
"sklearn.preprocessing.MinMaxScaler",
"matplotlib.pyplot.ylabel",
"keras.layers.Dropout",
"keras.layers.Dense",
"numpy.array",
... | [((142, 250), 'pandas.read_csv', 'pd.read_csv', (['"""C:\\\\Users\\\\hp\\\\Desktop\\\\Stock Predictor\\\\trainset.xls"""'], {'index_col': '"""Date"""', 'parse_dates': '(True)'}), "('C:\\\\Users\\\\hp\\\\Desktop\\\\Stock Predictor\\\\trainset.xls',\n index_col='Date', parse_dates=True)\n", (153, 250), True, 'import p... |
import random
from typing import List, Dict, Optional
import numpy as np
import math
import matplotlib.pyplot as plt
import scipy.stats as st
max_pop1 = 50
# max_pop1 = 100
# max_pop2 = 150
# parent_percent = 0.1
# parent_percent = 0.2
# parent_percent = 0.4
# parent_percent = 0.5
# parent_percent = 0.6
parent_percent... | [
"matplotlib.pyplot.title",
"matplotlib.pyplot.show",
"random.randint",
"matplotlib.pyplot.plot",
"math.ceil",
"random.uniform",
"random.sample",
"matplotlib.pyplot.legend",
"random.shuffle",
"random.choice",
"numpy.exp",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel"
] | [((22437, 22566), 'matplotlib.pyplot.plot', 'plt.plot', (['generations', 'averaged_best_ind_fit'], {'marker': '"""o"""', 'linestyle': '"""--"""', 'color': '"""r"""', 'label': '"""Averaged Best Individual Fitness"""'}), "(generations, averaged_best_ind_fit, marker='o', linestyle='--',\n color='r', label='Averaged Bes... |
__all__ = ["convert"]
# standard library
from pathlib import Path
from typing import Optional, Sequence, Union, cast
# third-party packages
import numpy as np
import pandas as pd
import xarray as xr
from dask.diagnostics import ProgressBar
# constants
CSV_COLS = "time", "wind_speed", "wind_direction"
JST_HOURS = ... | [
"pandas.read_csv",
"dask.diagnostics.ProgressBar",
"numpy.timedelta64",
"pandas.Index"
] | [((320, 342), 'numpy.timedelta64', 'np.timedelta64', (['(9)', '"""h"""'], {}), "(9, 'h')\n", (334, 342), True, 'import numpy as np\n'), ((1800, 1864), 'pandas.read_csv', 'pd.read_csv', (['path'], {'names': 'CSV_COLS', 'index_col': '(0)', 'parse_dates': '(True)'}), '(path, names=CSV_COLS, index_col=0, parse_dates=True)\... |
# The following code is based on the ProcHarvester implementation
# See https://github.com/IAIK/ProcHarvester/tree/master/code/analysis%20tool
import pandas as pd
import numpy as np
import config
import math
import distance_computation
def init_dist_matrices(file_contents):
dist_matrices = []
for fileContent... | [
"math.isnan",
"pandas.unique",
"numpy.argsort",
"numpy.append",
"numpy.array",
"pandas.Series",
"distance_computation.dtw"
] | [((3047, 3074), 'pandas.Series', 'pd.Series', (['k_nearest_labels'], {}), '(k_nearest_labels)\n', (3056, 3074), True, 'import pandas as pd\n'), ((3392, 3419), 'pandas.unique', 'pd.unique', (['k_nearest_labels'], {}), '(k_nearest_labels)\n', (3401, 3419), True, 'import pandas as pd\n'), ((4473, 4495), 'pandas.Series', '... |
from tempfile import NamedTemporaryFile
import numpy
import pytest
import theano
from decorator import contextmanager
from lasagne.utils import floatX
from .data import DataSet, cifar, cifar_lee14, mnist, mnist_distractor, \
svhn, svhn_huang16
def identical_dataset(set1, set2, max_bs=500):
lenght = len(set1... | [
"tempfile.NamedTemporaryFile",
"numpy.random.uniform",
"numpy.allclose",
"numpy.dtype",
"lasagne.utils.floatX",
"numpy.all",
"pytest.skip",
"numpy.min",
"numpy.max",
"pytest.raises",
"numpy.concatenate"
] | [((684, 721), 'tempfile.NamedTemporaryFile', 'NamedTemporaryFile', ([], {'suffix': 'f""".{frmt}"""'}), "(suffix=f'.{frmt}')\n", (702, 721), False, 'from tempfile import NamedTemporaryFile\n'), ((530, 552), 'numpy.allclose', 'numpy.allclose', (['x1', 'x2'], {}), '(x1, x2)\n', (544, 552), False, 'import numpy\n'), ((568,... |
import numpy as np
import cv2
# Identify pixels above and below a threshold
# If you specify no upper threshold, it will assume none (255,255,255)
def color_thresh(img, lower_thresh=(160,160,160), upper_thresh=(255,255,255)):
# Create an array of zeros same xy size as img, but single channel
color_select = np.... | [
"cv2.warpPerspective",
"numpy.zeros_like",
"numpy.arctan2",
"numpy.int_",
"cv2.getPerspectiveTransform",
"numpy.float32",
"numpy.where",
"numpy.sin",
"numpy.cos",
"numpy.sqrt"
] | [((317, 344), 'numpy.zeros_like', 'np.zeros_like', (['img[:, :, 0]'], {}), '(img[:, :, 0])\n', (330, 344), True, 'import numpy as np\n'), ((1612, 1648), 'numpy.sqrt', 'np.sqrt', (['(x_pixel ** 2 + y_pixel ** 2)'], {}), '(x_pixel ** 2 + y_pixel ** 2)\n', (1619, 1648), True, 'import numpy as np\n'), ((1714, 1742), 'numpy... |
# -*- coding:utf-8 -*- #
import numpy as np
def quantize_weight_2d(weight, bitwise):
weight_scale = []
weight_zero_point = []
kernel = np.shape(weight)[0]
for k in range(kernel):
weight_c = weight[k, :, :, :]
max_weight_c = np.max(weight_c)
min_weight_c = np.min(weight_c)
... | [
"numpy.shape",
"numpy.max",
"numpy.abs",
"numpy.min"
] | [((149, 165), 'numpy.shape', 'np.shape', (['weight'], {}), '(weight)\n', (157, 165), True, 'import numpy as np\n'), ((258, 274), 'numpy.max', 'np.max', (['weight_c'], {}), '(weight_c)\n', (264, 274), True, 'import numpy as np\n'), ((298, 314), 'numpy.min', 'np.min', (['weight_c'], {}), '(weight_c)\n', (304, 314), True,... |
#! /usr/bin/env python
# The script calculates the tilting angles of coordinated octohedra.
import crystmorph as cmor
import parsetta as ps
import numpy as np
from pymatgen.analysis.chemenv.coordination_environments.coordination_geometry_finder import LocalGeometryFinder
from pymatgen.analysis.chemenv.coord... | [
"crystmorph.structure.PerovskiteParser",
"pymatgen.analysis.chemenv.coordination_environments.coordination_geometry_finder.LocalGeometryFinder",
"pymatgen.analysis.chemenv.coordination_environments.structure_environments.LightStructureEnvironments.from_structure_environments",
"pymatgen.analysis.chemenv.coord... | [((569, 588), 'numpy.array', 'np.array', (['[1, 0, 0]'], {}), '([1, 0, 0])\n', (577, 588), True, 'import numpy as np\n'), ((597, 616), 'numpy.array', 'np.array', (['[0, 1, 0]'], {}), '([0, 1, 0])\n', (605, 616), True, 'import numpy as np\n'), ((625, 644), 'numpy.array', 'np.array', (['[0, 0, 1]'], {}), '([0, 0, 1])\n',... |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
import getpass
import pathlib
import numpy as np
import pandas as pd
import heyhi.gsheets
import heyhi.run
import run
THIS_FI... | [
"pandas.DataFrame",
"getpass.getuser",
"numpy.log",
"argparse.ArgumentParser",
"json.loads",
"pathlib.Path"
] | [((2345, 2363), 'pandas.DataFrame', 'pd.DataFrame', (['data'], {}), '(data)\n', (2357, 2363), True, 'import pandas as pd\n'), ((4270, 4295), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (4293, 4295), False, 'import argparse\n'), ((325, 347), 'pathlib.Path', 'pathlib.Path', (['__file__'], {}),... |
# coding: utf-8
import numpy as np
a = 1.0
b = 4.0
c = - 4.0
print('Los coeficientes son a = '),
print(a),
print(', b = '),
print(b),
print(' y c = '),
print(c)
x1 = (-b + np.sqrt(b**2-4*a*c))/(2*a)
print('La raiz x1 = '),
print(x1)
test_x1 = a*x1**2 + b*x1 + c
print('La raiz x1 en a*x1**2 + b*x1 + c = '),
print(test_x... | [
"numpy.sqrt"
] | [((172, 199), 'numpy.sqrt', 'np.sqrt', (['(b ** 2 - 4 * a * c)'], {}), '(b ** 2 - 4 * a * c)\n', (179, 199), True, 'import numpy as np\n'), ((334, 361), 'numpy.sqrt', 'np.sqrt', (['(b ** 2 - 4 * a * c)'], {}), '(b ** 2 - 4 * a * c)\n', (341, 361), True, 'import numpy as np\n')] |
import tensorflow as tf
import argparse
import pickle
import os
import re
import sys, traceback
import numpy as np
import time
import datetime
from matplotlib.pyplot import figure, ylabel, tight_layout, plot, savefig, tick_params, xlabel, subplot, title, close
from model import Model
import parameters as param
import o... | [
"matplotlib.pyplot.title",
"matplotlib.pyplot.figure",
"pickle.load",
"tensorflow.global_variables",
"matplotlib.pyplot.tick_params",
"matplotlib.pyplot.tight_layout",
"traceback.print_exc",
"os.path.exists",
"datetime.datetime.now",
"tensorflow.train.get_checkpoint_state",
"numpy.average",
"v... | [((2331, 2342), 'time.time', 'time.time', ([], {}), '()\n', (2340, 2342), False, 'import time\n'), ((3088, 3111), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (3109, 3111), False, 'import datetime\n'), ((21410, 21419), 'matplotlib.pyplot.figure', 'figure', (['(1)'], {}), '(1)\n', (21416, 21419), ... |
import os, subprocess
import numpy as np
import simtk.unit as unit
from statistics import mean
from scipy.stats import linregress
from scipy import spatial
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from cg_openmm.utilities.random_builder import *
from cg_openmm.utilities.iotoo... | [
"matplotlib.pyplot.title",
"matplotlib.backends.backend_pdf.PdfPages",
"numpy.sum",
"matplotlib.pyplot.suptitle",
"numpy.floor",
"numpy.argmin",
"mdtraj.load",
"numpy.shape",
"matplotlib.pyplot.figure",
"pymbar.MBAR",
"numpy.mean",
"numpy.random.randint",
"openmmtools.multistate.MultiStateRe... | [((8616, 8627), 'numpy.zeros', 'np.zeros', (['(5)'], {}), '(5)\n', (8624, 8627), True, 'import numpy as np\n'), ((13721, 13761), 'pymbar.utils.kln_to_kn', 'pymbar.utils.kln_to_kn', (['replica_energies'], {}), '(replica_energies)\n', (13743, 13761), False, 'import pymbar\n'), ((14345, 14386), 'numpy.zeros', 'np.zeros', ... |
"""
Please contact the author(s) of this library if you have any questions.
Authors: <NAME> ( <EMAIL> )
This module implements an environment considering the 2D point object dynamics.
This environemnt is roughly the same as the basic version of `zermelo_show.py`,
but this environment has a grid of cells (used for ta... | [
"numpy.random.uniform",
"numpy.random.seed",
"numpy.abs",
"numpy.copy",
"utils.utils.state_to_index",
"numpy.float32",
"numpy.zeros",
"gym.spaces.Discrete",
"numpy.argmin",
"numpy.array",
"utils.utils.nearest_real_grid_point",
"numpy.linalg.norm",
"numpy.linspace"
] | [((637, 666), 'numpy.array', 'np.array', (['[[-2, 2], [-2, 10]]'], {}), '([[-2, 2], [-2, 10]])\n', (645, 666), True, 'import numpy as np\n'), ((987, 1045), 'numpy.array', 'np.array', (['[-self.horizontal_rate, 0, self.horizontal_rate]'], {}), '([-self.horizontal_rate, 0, self.horizontal_rate])\n', (995, 1045), True, 'i... |
import numpy as np
import sys
# command line argument can provide seed, otw set seed to 1
if len(sys.argv) < 2:
np.random.seed(1)
else:
np.random.seed(int(sys.argv[1]))
# generate 100 random tip times between 0-10.
tipDates=np.random.uniform(0, 10, 101)
with open('randomTipTimes.txt', 'w') as text_file:
... | [
"numpy.random.uniform",
"numpy.random.seed"
] | [((235, 264), 'numpy.random.uniform', 'np.random.uniform', (['(0)', '(10)', '(101)'], {}), '(0, 10, 101)\n', (252, 264), True, 'import numpy as np\n'), ((117, 134), 'numpy.random.seed', 'np.random.seed', (['(1)'], {}), '(1)\n', (131, 134), True, 'import numpy as np\n')] |
import numpy as np
from calculate_contraction_dynamics import calculate_contraction_dynamics
from plotting import plot_tip_displacement_and_velocity, plot_transmitted_and_dissipated_power, plot_peak_velocities, \
plot_final_forces
from auxiliaries import discretize_curve, velos_numerical
# first we analyse the co... | [
"calculate_contraction_dynamics.calculate_contraction_dynamics",
"plotting.plot_peak_velocities",
"numpy.asarray",
"plotting.plot_tip_displacement_and_velocity",
"numpy.max",
"plotting.plot_transmitted_and_dissipated_power",
"plotting.plot_final_forces",
"auxiliaries.discretize_curve"
] | [((524, 628), 'calculate_contraction_dynamics.calculate_contraction_dynamics', 'calculate_contraction_dynamics', (['"""full model"""', '"""./parameter_full_model.txt"""', '(210.0)', 'pillar_stiffness'], {}), "('full model', './parameter_full_model.txt', \n 210.0, pillar_stiffness)\n", (554, 628), False, 'from calcul... |
import numpy as np
import matplotlib.pyplot as plt
import os
# To initialize boundary conditions and grid points
def initialize_grid(N):
grid_points = np.linspace(-1,1,N)
delta_x = 1/(N-1)
u_values = np.zeros(N)
for i in range(N):
if abs(grid_points[i]) < 0.5:
u_values[i] = np.cos(np.pi*grid_points[i])**2
r... | [
"matplotlib.pyplot.title",
"os.mkdir",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.clf",
"numpy.zeros",
"os.path.exists",
"numpy.array",
"numpy.linspace",
"numpy.cos",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.pause",
"matplotlib.pyplot.savefig"
] | [((2193, 2224), 'matplotlib.pyplot.plot', 'plt.plot', (['grid_points', 'u_values'], {}), '(grid_points, u_values)\n', (2201, 2224), True, 'import matplotlib.pyplot as plt\n'), ((2225, 2265), 'matplotlib.pyplot.title', 'plt.title', (['"""Initial Boundary Conditions"""'], {}), "('Initial Boundary Conditions')\n", (2234, ... |
import numpy as np
from tree.optimized_train.statistics_utils import ScoreEstimate, estimate_expectancy_of_sum_of_normal, \
estimate_expectancy_of_sum_of_non_normal
class ScoresCalculator:
def __init__(self, bins: np.ndarray, y: np.ndarray):
assert bins.shape[0] == y.shape[0]
assert bins.ndim... | [
"numpy.nanargmin",
"numpy.average",
"numpy.square",
"numpy.isfinite",
"numpy.cumsum",
"tree.optimized_train.statistics_utils.ScoreEstimate",
"numpy.bincount"
] | [((454, 466), 'numpy.square', 'np.square', (['y'], {}), '(y)\n', (463, 466), True, 'import numpy as np\n'), ((749, 783), 'tree.optimized_train.statistics_utils.ScoreEstimate', 'ScoreEstimate', (['score', 'score', 'score'], {}), '(score, score, score)\n', (762, 783), False, 'from tree.optimized_train.statistics_utils im... |
"""
Techniques for manipulating benchmarking data stored in a Pandas DataFrame.
"""
#***************************************************************************************************
# Copyright 2015, 2019 National Technology & Engineering Solutions of Sandia, LLC (NTESS).
# Under the terms of Contract DE-NA0003525 w... | [
"numpy.log",
"numpy.argmax",
"numpy.nanmin",
"numpy.isnan",
"numpy.max",
"scipy.stats.chi2.cdf",
"numpy.nanmax"
] | [((1550, 1576), 'numpy.max', '_np.max', (['[v, lower_cutoff]'], {}), '([v, lower_cutoff])\n', (1557, 1576), True, 'import numpy as _np\n'), ((4331, 4350), 'numpy.max', '_np.max', (['[p, 1e-10]'], {}), '([p, 1e-10])\n', (4338, 4350), True, 'import numpy as _np\n'), ((1094, 1106), 'numpy.isnan', '_np.isnan', (['x'], {}),... |
import shapefile
import numpy as np
grid_count = 64
file_path = "Datasets/GeneratedData/"
input_weighted_file_name = file_path + "PBLH_grid64_64.txt"
output_file_name = file_path + "PBLH_grid64_64"
w = shapefile.Writer(output_file_name, shapefile.POLYGON)
#this is how to add a polygon
'''
w.poly([ [[0.,0.],[0.,1.],... | [
"shapefile.Writer",
"numpy.zeros"
] | [((205, 258), 'shapefile.Writer', 'shapefile.Writer', (['output_file_name', 'shapefile.POLYGON'], {}), '(output_file_name, shapefile.POLYGON)\n', (221, 258), False, 'import shapefile\n'), ((1166, 1200), 'numpy.zeros', 'np.zeros', (['(grid_count, grid_count)'], {}), '((grid_count, grid_count))\n', (1174, 1200), True, 'i... |
#!/usr/bin/python
#
# Simulates an MDP-Strategy
import math
import os
import sys, code
import resource
import copy
import itertools
import random
from PIL import Image
import os, pygame, pygame.locals
from pybrain3.rl.environments import Environment
from pybrain3.rl.environments import Task
from pybrain3.rl.agents imp... | [
"sys.stdout.write",
"argparse.ArgumentParser",
"pygame.event.get",
"pygame.display.Info",
"numpy.set_printoptions",
"math.pow",
"pygame.display.set_mode",
"my_pybrain.my_learner.SARSA",
"pygame.transform.scale",
"pybrain3.rl.environments.Task.__init__",
"pybrain3.rl.experiments.Experiment._oneIn... | [((906, 943), 'numpy.set_printoptions', 'np.set_printoptions', ([], {'threshold': 'np.inf'}), '(threshold=np.inf)\n', (925, 943), True, 'import numpy as np\n'), ((971, 1019), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Simulator"""'}), "(description='Simulator')\n", (994, 1019), False... |
import pytest
import numpy as np
import pandas as pd
from keras_batchflow.base.batch_shapers.numpy_encoder_adaptor import NumpyEncoderAdaptor
class TestNumpyEncoderAdaptor:
def test_init(self):
nea = NumpyEncoderAdaptor()
def test_transform(self):
data = pd.Series([1, 2, 4, 5])
nea ... | [
"pytest.raises",
"numpy.array",
"pandas.Series",
"keras_batchflow.base.batch_shapers.numpy_encoder_adaptor.NumpyEncoderAdaptor",
"numpy.issubdtype"
] | [((216, 237), 'keras_batchflow.base.batch_shapers.numpy_encoder_adaptor.NumpyEncoderAdaptor', 'NumpyEncoderAdaptor', ([], {}), '()\n', (235, 237), False, 'from keras_batchflow.base.batch_shapers.numpy_encoder_adaptor import NumpyEncoderAdaptor\n'), ((284, 307), 'pandas.Series', 'pd.Series', (['[1, 2, 4, 5]'], {}), '([1... |
import tkinter
from tkbuilder.panel_templates.pyplot_panel.pyplot_panel import PyplotPanel
from tkbuilder.example_apps.plot_demo.panels.plot_demo_button_panel import ButtonPanel
from tkbuilder.panel_templates.widget_panel.widget_panel import AbstractWidgetPanel
import numpy as np
class PlotDemo(AbstractWidgetPanel):
... | [
"tkbuilder.panel_templates.widget_panel.widget_panel.AbstractWidgetPanel.__init__",
"numpy.zeros",
"numpy.shape",
"numpy.sinc",
"numpy.sin",
"numpy.linspace",
"tkinter.Frame",
"tkinter.Tk"
] | [((3889, 3901), 'tkinter.Tk', 'tkinter.Tk', ([], {}), '()\n', (3899, 3901), False, 'import tkinter\n'), ((523, 544), 'tkinter.Frame', 'tkinter.Frame', (['master'], {}), '(master)\n', (536, 544), False, 'import tkinter\n'), ((553, 601), 'tkbuilder.panel_templates.widget_panel.widget_panel.AbstractWidgetPanel.__init__', ... |
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as data
from torch.autograd import Variable
import numpy as np
import scipy.io as sio
import numpy.matlib
import os
import csv
import math, random
import pandas as pd
from scipy.stats import itemfreq
... | [
"sklearn.preprocessing.StandardScaler",
"scipy.io.loadmat",
"numpy.ravel",
"random.shuffle",
"numpy.ones",
"numpy.mean",
"os.path.join",
"numpy.unique",
"random.randint",
"torch.utils.data.transpose",
"scipy.stats.itemfreq",
"numpy.std",
"numpy.append",
"numpy.max",
"numpy.reshape",
"c... | [((732, 751), 'numpy.zeros', 'np.zeros', (['(K, N, D)'], {}), '((K, N, D))\n', (740, 751), True, 'import numpy as np\n'), ((3010, 3031), 'os.listdir', 'os.listdir', (['data_path'], {}), '(data_path)\n', (3020, 3031), False, 'import os\n'), ((4743, 4768), 'numpy.ones', 'np.ones', (['all_labels.shape'], {}), '(all_labels... |
#! /usr/bin/env python
"""
Incremental full-frame PCA for big (larger than available memory) cubes.
"""
from __future__ import division, print_function
__author__ = '<NAME>'
__all__ = ['pca_incremental']
import numpy as np
from astropy.io import fits
from sklearn.decomposition import IncrementalPCA
from ..preproc i... | [
"numpy.median",
"sklearn.decomposition.IncrementalPCA",
"numpy.array",
"astropy.io.fits.open",
"numpy.dot"
] | [((2899, 2935), 'astropy.io.fits.open', 'fits.open', (['fitsfilename'], {'memmap': '(True)'}), '(fitsfilename, memmap=True)\n', (2908, 2935), False, 'from astropy.io import fits\n'), ((3515, 3549), 'sklearn.decomposition.IncrementalPCA', 'IncrementalPCA', ([], {'n_components': 'ncomp'}), '(n_components=ncomp)\n', (3529... |
from numpy import random
from sequence.components.interferometer import Interferometer
from sequence.components.photon import Photon
from sequence.kernel.timeline import Timeline
from sequence.utils.encoding import time_bin
random.seed(0)
NUM_TRIALS = int(10e3)
def create_intf(quantum_state):
class Receiver():
... | [
"sequence.components.interferometer.Interferometer",
"numpy.random.seed",
"sequence.kernel.timeline.Timeline"
] | [((225, 239), 'numpy.random.seed', 'random.seed', (['(0)'], {}), '(0)\n', (236, 239), False, 'from numpy import random\n'), ((538, 548), 'sequence.kernel.timeline.Timeline', 'Timeline', ([], {}), '()\n', (546, 548), False, 'from sequence.kernel.timeline import Timeline\n'), ((561, 625), 'sequence.components.interferome... |
import torch.utils.data as data
from avi_r import AVIReader
from PIL import Image
import os
import torch
import numpy as np
from numpy.random import randint
import cv2
import json
import decord
from decord import VideoReader
from decord import cpu, gpu
import torchvision
import gc
decord.bridge.set_bridge('torch')
# ... | [
"torch.stack",
"numpy.zeros",
"numpy.random.randint",
"numpy.array",
"numpy.linspace",
"decord.bridge.set_bridge",
"os.path.join"
] | [((283, 316), 'decord.bridge.set_bridge', 'decord.bridge.set_bridge', (['"""torch"""'], {}), "('torch')\n", (307, 316), False, 'import decord\n'), ((2061, 2094), 'os.path.join', 'os.path.join', (['root_path', 'vid_name'], {}), '(root_path, vid_name)\n', (2073, 2094), False, 'import os\n'), ((9091, 9117), 'torch.stack',... |
import os
from skimage.io import imread, imsave
import numpy as np
from numpy.random import choice, permutation
import cv2
from math import ceil
import tensorflow as tf
from tensorflow.data import Dataset, TextLineDataset
from tensorflow.contrib.data import shuffle_and_repeat
from time import gmtime, strftime
from sk... | [
"numpy.random.uniform",
"cv2.warpPerspective",
"numpy.minimum",
"tensorflow.py_func",
"cv2.getPerspectiveTransform",
"numpy.expand_dims",
"PIL.Image.open",
"cv2.imread",
"numpy.array",
"numpy.loadtxt",
"tensorflow.data.Dataset.zip",
"numpy.random.choice",
"numpy.random.rand",
"tensorflow.n... | [((555, 590), 'cv2.imread', 'cv2.imread', (['fname', 'cv2.IMREAD_COLOR'], {}), '(fname, cv2.IMREAD_COLOR)\n', (565, 590), False, 'import cv2\n'), ((1255, 1282), 'numpy.expand_dims', 'np.expand_dims', (['img'], {'axis': '(2)'}), '(img, axis=2)\n', (1269, 1282), True, 'import numpy as np\n'), ((1341, 1373), 'numpy.loadtx... |
# -*- coding: utf-8 -*-
#!/usr/bin/env python3
'''
This code implements the high level Delay aware MPC controller
for following higher level trajectory from motion planner
'''
from __future__ import print_function
from __future__ import division
from yaml import error
# Imports
if True :
from adap... | [
"sys.path.append",
"math.sqrt",
"math.atan2",
"os.path.realpath",
"live_plotter.LivePlotter",
"rospy.Publisher",
"numpy.ones",
"numpy.zeros",
"math.sin",
"beginner_tutorials.msg.custom_msg",
"numpy.sin",
"numpy.array",
"rospy.init_node",
"numpy.loadtxt",
"numpy.matmul",
"numpy.random.r... | [((10178, 10219), 'sys.path.append', 'sys_path.append', (["(file_path + '/../../../')"], {}), "(file_path + '/../../../')\n", (10193, 10219), True, 'from sys import path as sys_path\n'), ((1755, 1809), 'numpy.array', 'np.array', (['[[22, -18], [22, -10], [32, -10], [32, -18]]'], {}), '([[22, -18], [22, -10], [32, -10],... |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import PyTorchDisentanglement.modules.losses as losses
from PyTorchDisentanglement.models.base import BaseModel
from PyTorchDisentanglement.modules.activations import lca_threshold
class Lca(BaseModel):
def setup_model(self):
... | [
"torch.eye",
"PyTorchDisentanglement.modules.activations.lca_threshold",
"torch.sum",
"PyTorchDisentanglement.modules.losses.l1_norm",
"torch.randn",
"torch.zeros",
"torch.matmul",
"numpy.round",
"PyTorchDisentanglement.modules.losses.half_squared_l2",
"torch.transpose"
] | [((747, 781), 'torch.matmul', 'torch.matmul', (['input_tensor', 'self.w'], {}), '(input_tensor, self.w)\n', (759, 781), False, 'import torch\n'), ((1090, 1214), 'PyTorchDisentanglement.modules.activations.lca_threshold', 'lca_threshold', (['u_in', 'self.params.thresh_type', 'self.params.rectify_a', 'self.params.sparse_... |
"""Derivatives of the neutron-star `observables` `M`,
`R`, and `k_2` as functions of fixed central pressures `Pc` (chosen so
that the masses are equally space) and the EoS parameters `param`. The
derivatives are scaled by the observable values and the parameter
values so as to be dimens... | [
"numpy.linspace"
] | [((844, 871), 'numpy.linspace', 'np.linspace', (['(0.9)', '(2.09)', '(120)'], {}), '(0.9, 2.09, 120)\n', (855, 871), True, 'import numpy as np\n')] |
import cv2
import numpy as np
import tensorflow as tf
from models.facenet import FaceNet
from models.util import utils
print("Using gpu: {0}".format(tf.test.is_gpu_available(cuda_only=False,
min_cuda_compute_capability=None)))
class MaskDetector:
def __init__(self):
self.fac... | [
"models.facenet.FaceNet",
"numpy.random.seed",
"cv2.dnn.NMSBoxes",
"cv2.putText",
"numpy.argmax",
"cv2.dnn.blobFromImage",
"cv2.dnn.readNetFromDarknet",
"numpy.array",
"cv2.rectangle",
"tensorflow.test.is_gpu_available",
"models.util.utils.get_file_path"
] | [((152, 227), 'tensorflow.test.is_gpu_available', 'tf.test.is_gpu_available', ([], {'cuda_only': '(False)', 'min_cuda_compute_capability': 'None'}), '(cuda_only=False, min_cuda_compute_capability=None)\n', (176, 227), True, 'import tensorflow as tf\n'), ((327, 336), 'models.facenet.FaceNet', 'FaceNet', ([], {}), '()\n'... |
"""
Copyright (c) Facebook, Inc. and its affiliates.
"""
import numpy as np
MAX_MAP_SIZE = 4097
MAP_INIT_SIZE = 1025
BIG_I = MAX_MAP_SIZE
BIG_J = MAX_MAP_SIZE
def no_y_l1(self, xyz, k):
""" returns the l1 distance between two standard coordinates"""
return np.linalg.norm(np.asarray([xyz[0], xyz[2]]) - np.as... | [
"numpy.asarray",
"numpy.ones"
] | [((284, 312), 'numpy.asarray', 'np.asarray', (['[xyz[0], xyz[2]]'], {}), '([xyz[0], xyz[2]])\n', (294, 312), True, 'import numpy as np\n'), ((315, 339), 'numpy.asarray', 'np.asarray', (['[k[0], k[2]]'], {}), '([k[0], k[2]])\n', (325, 339), True, 'import numpy as np\n'), ((10055, 10078), 'numpy.ones', 'np.ones', (['(new... |
from urllib import request
import nltk
import numpy as np
#nltk.download('punkt') - uncomment this if 'punkt doesn't exist' error, usually necessary if it's your first exposure to toolkit
class ScrapeAndAnalyze(object):
def url(self):
"""
:return: targetUrl - full url location of text to be mine... | [
"numpy.mean",
"urllib.request.urlopen",
"nltk.word_tokenize"
] | [((604, 624), 'urllib.request.urlopen', 'request.urlopen', (['url'], {}), '(url)\n', (619, 624), False, 'from urllib import request\n'), ((1054, 1082), 'nltk.word_tokenize', 'nltk.word_tokenize', (['fullText'], {}), '(fullText)\n', (1072, 1082), False, 'import nltk\n'), ((2958, 2971), 'numpy.mean', 'np.mean', (['lens']... |
import numpy as np
a = np.array([[12,15], [10, 1]])
arr = np.sort(a, axis = 0)
a = np.array([[10, 15], [12, 1]])
arr2 = np.sort(a, axis = -1)
print ("\nAlong first axis : \n", arr2)
a = np.array([[12, 15], [10, 1]])
arr3 = np.sort(a, axis = None)
print ("\nAlong none axis : \n", arr1) | [
"numpy.sort",
"numpy.array"
] | [((24, 53), 'numpy.array', 'np.array', (['[[12, 15], [10, 1]]'], {}), '([[12, 15], [10, 1]])\n', (32, 53), True, 'import numpy as np\n'), ((59, 77), 'numpy.sort', 'np.sort', (['a'], {'axis': '(0)'}), '(a, axis=0)\n', (66, 77), True, 'import numpy as np\n'), ((85, 114), 'numpy.array', 'np.array', (['[[10, 15], [12, 1]]'... |
import numpy as np
class stablesoftmax:
def forward(self, input):
return np.exp(input - np.max(input)) / np.sum(np.exp(input - np.max(input)))
def backward(self, input):
equation = np.vectorize(self.equationforderivative, otypes=[float])
return equation(input, np.sum(np.exp(input)))
def equatio... | [
"numpy.max",
"numpy.vectorize",
"numpy.exp"
] | [((195, 251), 'numpy.vectorize', 'np.vectorize', (['self.equationforderivative'], {'otypes': '[float]'}), '(self.equationforderivative, otypes=[float])\n', (207, 251), True, 'import numpy as np\n'), ((286, 299), 'numpy.exp', 'np.exp', (['input'], {}), '(input)\n', (292, 299), True, 'import numpy as np\n'), ((376, 389),... |
import numpy as np
import cosmogenic.na as na
import cosmogenic.util as util
def positions(m, ts):
a = -9.81
v0 = m[0:2]
p0 = m[2:4]
pos = np.empty((ts.size, 2), dtype=float)
for i, t in enumerate(ts):
pos[i, :] = 0.5 * a * t ** 2 + v0 * t + p0
return pos
conf = {
"description"... | [
"numpy.empty",
"cosmogenic.na.search",
"cosmogenic.util.pickle",
"numpy.array",
"numpy.linspace"
] | [((708, 732), 'numpy.linspace', 'np.linspace', (['(2)', '(20)', '(1000)'], {}), '(2, 20, 1000)\n', (719, 732), True, 'import numpy as np\n'), ((159, 194), 'numpy.empty', 'np.empty', (['(ts.size, 2)'], {'dtype': 'float'}), '((ts.size, 2), dtype=float)\n', (167, 194), True, 'import numpy as np\n'), ((440, 480), 'numpy.ar... |
import os
import re
import time
import utils
import mail
import json
import requests
import collections
import pandas as pd
from utils import log
from datetime import datetime
from bs4 import BeautifulSoup
from selenium import webdriver
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.su... | [
"pandas.DataFrame",
"selenium.webdriver.support.ui.WebDriverWait",
"selenium.webdriver.support.expected_conditions.presence_of_element_located",
"re.split",
"json.loads",
"selenium.webdriver.Firefox",
"numpy.random.rand",
"time.sleep",
"urllib3.disable_warnings",
"mail.send_email",
"requests.get... | [((537, 563), 'urllib3.disable_warnings', 'urllib3.disable_warnings', ([], {}), '()\n', (561, 563), False, 'import urllib3\n'), ((4053, 4067), 'pandas.DataFrame', 'pd.DataFrame', ([], {}), '()\n', (4065, 4067), True, 'import pandas as pd\n'), ((4374, 4409), 'bs4.BeautifulSoup', 'BeautifulSoup', (['resp.content', '"""lx... |
#!/home/iflyings/VSCode/venv/tensorflow-venv python
# -*- coding:utf-8 -*-
# Author: iflyings
import os
import numpy as np
import tensorflow as tf
class ImageRes:
def __init__(self, path, image_width = 100, image_height = 100, batch_size = 20, num_epochs = 100):
self.path = path
self.image_width = ... | [
"tensorflow.image.resize_with_crop_or_pad",
"os.listdir",
"tensorflow.global_variables_initializer",
"tensorflow.data.Dataset.from_tensor_slices",
"tensorflow.cast",
"numpy.arange",
"tensorflow.image.per_image_standardization",
"tensorflow.InteractiveSession",
"tensorflow.io.read_file",
"os.path.j... | [((2870, 2893), 'tensorflow.InteractiveSession', 'tf.InteractiveSession', ([], {}), '()\n', (2891, 2893), True, 'import tensorflow as tf\n'), ((1102, 1132), 'numpy.random.shuffle', 'np.random.shuffle', (['image_infos'], {}), '(image_infos)\n', (1119, 1132), True, 'import numpy as np\n'), ((1921, 1986), 'tensorflow.cast... |
# Databricks notebook source
# MAGIC %md
# MAGIC # Creating features for ML
# MAGIC
# MAGIC Return to <a href="$../../../_index">index page</a>
# MAGIC
# MAGIC In this notebook we preprocess the data for modelling purposes.
# COMMAND ----------
# MAGIC %run ../../../app/bootstrap
# COMMAND ----------
# MAGIC %run... | [
"pyspark.sql.Window.partitionBy",
"daipecore.widgets.get_widget_value.get_widget_value",
"pyspark.sql.functions.lit",
"datetime.date.today",
"pyspark.ml.feature.Bucketizer",
"pyspark.sql.functions.datediff",
"pyspark.sql.functions.max",
"pyspark.sql.functions.lag",
"datetime.datetime.strptime",
"p... | [((4394, 4426), 'daipecore.widgets.get_widget_value.get_widget_value', 'get_widget_value', (['"""default_days"""'], {}), "('default_days')\n", (4410, 4426), False, 'from daipecore.widgets.get_widget_value import get_widget_value\n'), ((4428, 4466), 'daipecore.widgets.get_widget_value.get_widget_value', 'get_widget_valu... |
from collections import defaultdict
from datetime import datetime
import pickle
import random
import re
import numpy as np
import torch
def balanced_split(f_test, final_genomes, taxid_to_tnum):
"""
Create a train-test split that is phylogenetically balanced at the phylum level
Arguments:
f_test (int) -- propor... | [
"pandas.read_csv",
"random.sample",
"torch.load",
"numpy.zeros",
"random.choice",
"re.match",
"collections.defaultdict",
"pickle.load",
"random.seed",
"numpy.array",
"datetime.datetime.now",
"torch.tensor"
] | [((590, 607), 'collections.defaultdict', 'defaultdict', (['list'], {}), '(list)\n', (601, 607), False, 'from collections import defaultdict\n'), ((6901, 6939), 'numpy.zeros', 'np.zeros', ([], {'shape': '(n_genomes, n_kos_tot)'}), '(shape=(n_genomes, n_kos_tot))\n', (6909, 6939), True, 'import numpy as np\n'), ((7170, 7... |
#!/usr/bin/env python3
def pi_calc_base(samples=100_000_000):
import random
import math
count_inside = 0
for count in range(samples):
d = math.hypot(random.random(), random.random())
if d < 1:
count_inside += 1
return 4.0 * count_inside / samples
def pi_calc_numpy(samp... | [
"numpy.random.rand",
"random.random"
] | [((174, 189), 'random.random', 'random.random', ([], {}), '()\n', (187, 189), False, 'import random\n'), ((191, 206), 'random.random', 'random.random', ([], {}), '()\n', (204, 206), False, 'import random\n'), ((437, 453), 'numpy.random.rand', 'np.random.rand', ([], {}), '()\n', (451, 453), True, 'import numpy as np\n')... |
# * Author : <NAME>
# * Copyright (c) 2021 Careless Dev Squad All rights reserved.
# * Import required Library
# * Import Numpy to create 2D array
import numpy as np
# * Import random to simulate True Random
import random
# * Import opencv for image work
import cv2
# * Import Tqdm for progress bar
from tqdm i... | [
"numpy.full",
"cv2.imwrite",
"imageio.imread",
"numpy.zeros",
"random.choice",
"imageio.mimwrite",
"numpy.hstack",
"cv2.imread",
"numpy.max",
"numpy.min",
"numpy.array",
"numpy.vstack"
] | [((1250, 1302), 'numpy.full', 'np.full', (['(self.image_height, self.image_width)', 'None'], {}), '((self.image_height, self.image_width), None)\n', (1257, 1302), True, 'import numpy as np\n'), ((2677, 2694), 'numpy.vstack', 'np.vstack', (['height'], {}), '(height)\n', (2686, 2694), True, 'import numpy as np\n'), ((271... |
import numpy as np
from scipy.integrate import quad
def print_time(t):
ms = round(1000*t)
y, ms = divmod(ms, 1000*60*60*24*365)
d, ms = divmod(ms, 1000*60*60*24)
h, ms = divmod(ms, 1000*60*60)
m, ms = divmod(ms, 1000*60)
s, ms = divmod(ms, 1000)
print(
"{0:>6}".format(y), 'yr,',... | [
"numpy.log10",
"numpy.average"
] | [((1159, 1175), 'numpy.log10', 'np.log10', (['self.v'], {}), '(self.v)\n', (1167, 1175), True, 'import numpy as np\n'), ((2348, 2439), 'numpy.average', 'np.average', (["[self.sol_stack[-2]['u'], self.sol_stack[-1]['u']]"], {'axis': '(0)', 'weights': 'weights'}), "([self.sol_stack[-2]['u'], self.sol_stack[-1]['u']], axi... |
import gym
from gym import error, spaces
from gym import utils
from gym.utils import seeding
from typing import Dict, Optional, List
from evogym import *
import random
import math
import pkg_resources
import numpy as np
import os
class EvoGymBase(gym.Env):
"""
Base class for all Evolution Gym environments.
... | [
"numpy.zeros",
"numpy.clip",
"numpy.max",
"numpy.mean",
"numpy.array",
"numpy.min",
"os.path.join"
] | [((7507, 7541), 'numpy.mean', 'np.mean', (['object_points_pos'], {'axis': '(1)'}), '(object_points_pos, axis=1)\n', (7514, 7541), True, 'import numpy as np\n'), ((7557, 7605), 'numpy.array', 'np.array', (['[object_pos_com[0], object_pos_com[1]]'], {}), '([object_pos_com[0], object_pos_com[1]])\n', (7565, 7605), True, '... |
import torch.nn.functional as F
import os
import torch
import time
import random
from skimage import io, transform, color, data, img_as_float
from skimage.metrics import structural_similarity as ssim
import numpy as np
import matplotlib.pyplot as plt
from torch import nn, optim
from torch.utils.data import Dataset, Dat... | [
"matplotlib.pyplot.title",
"torch.cat",
"matplotlib.pyplot.figure",
"numpy.mean",
"torch.set_num_threads",
"skimage.transform.resize",
"torch.device",
"skimage.io.imread_collection",
"os.path.join",
"torch.nn.BCELoss",
"torch.utils.data.DataLoader",
"torch.nn.functional.avg_pool2d",
"numpy.t... | [((11416, 11440), 'torch.nn.functional.avg_pool2d', 'F.avg_pool2d', (['x', '(3)', '(1)', '(1)'], {}), '(x, 3, 1, 1)\n', (11428, 11440), True, 'import torch.nn.functional as F\n'), ((11452, 11476), 'torch.nn.functional.avg_pool2d', 'F.avg_pool2d', (['y', '(3)', '(1)', '(1)'], {}), '(y, 3, 1, 1)\n', (11464, 11476), True,... |
import numpy as np
from cuml import SVR
from cuml import RandomForestRegressor
from cuml import NearestNeighbors,KMeans,UMAP,Ridge,ElasticNet
import cupy as cp
from sklearn.model_selection import KFold
def my_metric(y_true, y_pred):
return np.mean(np.sum(np.abs(y_true - y_pred), axis=0)/np.sum(y_true, axis=0))
d... | [
"cuml.SVR",
"cuml.ElasticNet",
"numpy.sum",
"numpy.abs",
"numpy.zeros",
"sklearn.model_selection.KFold",
"cupy.asnumpy",
"numpy.round"
] | [((938, 993), 'sklearn.model_selection.KFold', 'KFold', ([], {'n_splits': 'NUM_FOLDS', 'shuffle': '(True)', 'random_state': '(0)'}), '(n_splits=NUM_FOLDS, shuffle=True, random_state=0)\n', (943, 993), False, 'from sklearn.model_selection import KFold\n'), ((1189, 1210), 'numpy.zeros', 'np.zeros', (['df.shape[0]'], {}),... |
import os
import json
import re
import numpy as np
import altair as alt
from codemetrics.vega import vis_ages
from codemetrics.vega import vis_hot_spots
IGNORE_PATHS = ('.', 'docs', 'doc', 'tests', 'test', 'notebooks')
IGNORE_LANGS = ('reStructuredText', 'Markdown', 'make')
IGNORE_EXTS = ('geo', 'xmf', 'xdmf', 'h5',... | [
"os.path.abspath",
"json.load",
"os.path.join",
"altair.Y",
"altair.vconcat",
"altair.Chart",
"codemetrics.vega.vis_ages",
"json.dumps",
"altair.X",
"altair.EncodingSortField",
"numpy.array",
"altair.Scale",
"codemetrics.vega.vis_hot_spots",
"re.sub"
] | [((1733, 1757), 'altair.vconcat', 'alt.vconcat', (['top', 'bottom'], {}), '(top, bottom)\n', (1744, 1757), True, 'import altair as alt\n'), ((1928, 1983), 'codemetrics.vega.vis_ages', 'vis_ages', (['ages_df'], {'height': 'height', 'width': 'width'}), '(ages_df, height=height, width=width, **kwargs)\n', (1936, 1983), Fa... |
import os
from flask import request, Flask, jsonify, render_template, g
import math
import tensorflow as tf
import numpy as np
from PIL import Image
import cv2
import base64
from im2txt import configuration
from im2txt import inference_wrapper
from im2txt.inference_utils import caption_generator
fr... | [
"os.remove",
"urllib.request.Request",
"im2txt.inference_utils.caption_generator.CaptionGenerator",
"cv2.imdecode",
"tensorflow.logging.set_verbosity",
"base64.b64decode",
"flask.jsonify",
"cv2.imencode",
"flask.request.get_json",
"cv2.cvtColor",
"os.path.exists",
"urllib.request.urlopen",
"... | [((544, 585), 'tensorflow.logging.set_verbosity', 'tf.logging.set_verbosity', (['tf.logging.INFO'], {}), '(tf.logging.INFO)\n', (568, 585), True, 'import tensorflow as tf\n'), ((623, 633), 'tensorflow.Graph', 'tf.Graph', ([], {}), '()\n', (631, 633), True, 'import tensorflow as tf\n'), ((795, 807), 'flask.g.finalize', ... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 15 21:24:08 2019
@author: Ivywang
"""
import numpy as np
import sys
sys.path.append('../')
from ADKit.AutoDiff import Ad_Var, rAd_Var
def test_setters():
a = Ad_Var(1,-3)
a.set_val(2)
a.set_ders(2)
assert a.get_val() == 2
ass... | [
"ADKit.AutoDiff.Ad_Var.tan",
"ADKit.AutoDiff.Ad_Var.arcsin",
"ADKit.AutoDiff.Ad_Var",
"ADKit.AutoDiff.Ad_Var.sinh",
"numpy.sin",
"numpy.exp",
"ADKit.AutoDiff.Ad_Var.cosh",
"ADKit.AutoDiff.Ad_Var.logistic",
"sys.path.append",
"ADKit.AutoDiff.Ad_Var.sin",
"numpy.arcsin",
"numpy.tan",
"numpy.ar... | [((141, 163), 'sys.path.append', 'sys.path.append', (['"""../"""'], {}), "('../')\n", (156, 163), False, 'import sys\n'), ((237, 250), 'ADKit.AutoDiff.Ad_Var', 'Ad_Var', (['(1)', '(-3)'], {}), '(1, -3)\n', (243, 250), False, 'from ADKit.AutoDiff import Ad_Var, rAd_Var\n'), ((392, 405), 'ADKit.AutoDiff.Ad_Var', 'Ad_Var'... |
"""Plot Milky Way spiral arm models."""
import numpy as np
import matplotlib.pyplot as plt
from astropy.units import Quantity
from gammapy.astro.population.spatial import ValleeSpiral, FaucherSpiral
fig = plt.figure(figsize=(7, 8))
rect = [0.12, 0.12, 0.85, 0.85]
ax_cartesian = fig.add_axes(rect)
ax_cartesian.set_aspe... | [
"matplotlib.pyplot.show",
"gammapy.astro.population.spatial.ValleeSpiral",
"matplotlib.pyplot.figure",
"numpy.arange",
"gammapy.astro.population.spatial.FaucherSpiral",
"matplotlib.pyplot.grid"
] | [((206, 232), 'matplotlib.pyplot.figure', 'plt.figure', ([], {'figsize': '(7, 8)'}), '(figsize=(7, 8))\n', (216, 232), True, 'import matplotlib.pyplot as plt\n'), ((349, 363), 'gammapy.astro.population.spatial.ValleeSpiral', 'ValleeSpiral', ([], {}), '()\n', (361, 363), False, 'from gammapy.astro.population.spatial imp... |
#!/usr/bin/env python
# -*- coding: iso-8859-15 -*-
######################## -*- coding: utf-8 -*-
# simple script to generate p-coordinate specific input from standard experiment
# but unfortunately this script does not reproduce the values in
# tutorial_global_oce_in_p, yet
import numpy as np
import sys, os
# requi... | [
"matplotlib.pyplot.title",
"matplotlib.pyplot.figure",
"numpy.arange",
"os.path.join",
"numpy.prod",
"numpy.copy",
"MITgcmutils.rdmds",
"matplotlib.pyplot.colorbar",
"numpy.cumsum",
"MITgcmutils.jmd95.dens",
"matplotlib.pyplot.show",
"numpy.ma.masked_where",
"numpy.asarray",
"matplotlib.py... | [((3296, 3319), 'MITgcmutils.rdmds', 'mit.rdmds', (['"""../run/RAC"""'], {}), "('../run/RAC')\n", (3305, 3319), True, 'import MITgcmutils as mit\n'), ((3382, 3502), 'numpy.asarray', 'np.asarray', (['[50.0, 70.0, 100.0, 140.0, 190.0, 240.0, 290.0, 340.0, 390.0, 440.0, 490.0,\n 540.0, 590.0, 640.0, 690.0]'], {}), '([5... |
import gym
import tensorflow as tf
from tensorflow.keras import layers
import numpy as np
import matplotlib.pyplot as plt
import Actor
import Critic
class Buffer:
def __init__(self, num_states, num_actions, buffer_capacity=100000, batch_size=64):
# Number of "experiences" to store at max
self.buffer_cap... | [
"tensorflow.math.reduce_mean",
"tensorflow.convert_to_tensor",
"numpy.zeros",
"tensorflow.cast",
"numpy.random.choice",
"tensorflow.math.square",
"tensorflow.GradientTape"
] | [((653, 697), 'numpy.zeros', 'np.zeros', (['(self.buffer_capacity, num_states)'], {}), '((self.buffer_capacity, num_states))\n', (661, 697), True, 'import numpy as np\n'), ((729, 774), 'numpy.zeros', 'np.zeros', (['(self.buffer_capacity, num_actions)'], {}), '((self.buffer_capacity, num_actions))\n', (737, 774), True, ... |
"""
@author: <NAME> - github.com/RocaPiedra
"""
import os
import numpy as np
from PIL import Image, ImageFilter
import matplotlib.cm as mpl_color_map
import torch
from torch.autograd import Variable
from torchvision import models
from misc_functions import apply_colormap_on_image, save_image
import parameters
import... | [
"torchvision.models.resnet18",
"subprocess.Popen",
"torch.autograd.Variable",
"numpy.float32",
"torchvision.models.alexnet",
"os.path.dirname",
"urllib.request.urlopen",
"time.sleep",
"numpy.argpartition",
"torchvision.models.resnet50",
"pickle.load",
"PIL.Image.fromarray",
"torch.hub.load",... | [((1232, 1250), 'numpy.float32', 'np.float32', (['pil_im'], {}), '(pil_im)\n', (1242, 1250), True, 'import numpy as np\n'), ((1748, 1787), 'torch.autograd.Variable', 'Variable', (['im_as_ten'], {'requires_grad': '(True)'}), '(im_as_ten, requires_grad=True)\n', (1756, 1787), False, 'from torch.autograd import Variable\n... |
# -*- coding: utf-8 -*-
"""
@author:XuMing(<EMAIL>)
@description: Test evaluate methods
"""
import unittest
import numpy as np
import numpy.testing as np_test
from numpy.random import RandomState
from rater.metrics.rating import RMSE
class TestRMSE(unittest.TestCase):
def test_rmse_same_input(self):
rs... | [
"numpy.random.randn",
"numpy.zeros",
"numpy.ones",
"numpy.random.RandomState",
"rater.metrics.rating.RMSE",
"numpy.random.rand",
"numpy.sqrt"
] | [((323, 337), 'numpy.random.RandomState', 'RandomState', (['(0)'], {}), '(0)\n', (334, 337), False, 'from numpy.random import RandomState\n'), ((653, 674), 'numpy.random.randn', 'np.random.randn', (['(2)', '(4)'], {}), '(2, 4)\n', (668, 674), True, 'import numpy as np\n'), ((734, 754), 'numpy.random.rand', 'np.random.r... |
import mknn
import numpy as np
import matplotlib.pyplot as plt
import nrrd
# Read the files
energy_raw, header = nrrd.read('19495691_150518_abd_original_glcm_JointEnergy.nrrd')
entropy_raw, header = nrrd.read('19495691_150518_abd_original_glcm_JointEntropy.nrrd')
contrast_raw, header = nrrd.read('19495691_150518_abd_o... | [
"nrrd.read",
"mknn.Filtration",
"numpy.isnan",
"numpy.column_stack"
] | [((114, 177), 'nrrd.read', 'nrrd.read', (['"""19495691_150518_abd_original_glcm_JointEnergy.nrrd"""'], {}), "('19495691_150518_abd_original_glcm_JointEnergy.nrrd')\n", (123, 177), False, 'import nrrd\n'), ((200, 264), 'nrrd.read', 'nrrd.read', (['"""19495691_150518_abd_original_glcm_JointEntropy.nrrd"""'], {}), "('1949... |
import os
import sys
import subprocess
import traceback
import numpy as np
def reorder_list(lst, val_list):
for element in reversed(val_list):
if element in lst:
lst.remove(element)
lst.insert(0, element)
return lst
def round_up_to_odd(f):
#Round to add as blocksize has... | [
"numpy.ceil",
"numpy.dtype",
"subprocess.call",
"traceback.format_exc",
"os.path.splitext",
"os.path.join",
"os.startfile"
] | [((2108, 2135), 'os.path.splitext', 'os.path.splitext', (['file_name'], {}), '(file_name)\n', (2124, 2135), False, 'import os\n'), ((2259, 2312), 'os.path.join', 'os.path.join', (['output_path', '(final_basename + extension)'], {}), '(output_path, final_basename + extension)\n', (2271, 2312), False, 'import os\n'), ((2... |
"""Calibre (Adaptive Ensemble) with flat model structure. """
import os
import sys
import pathlib
import pickle as pk
import pandas as pd
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow_probability import edward2 as ed
import gpflowSlim as gpf
sys.path.extend([os.get... | [
"pickle.dump",
"calibre.model.adaptive_ensemble.sample_posterior_weight_flat",
"pathlib.Path",
"numpy.mean",
"pickle.load",
"tensorflow_probability.edward2.make_log_joint_fn",
"tensorflow_probability.mcmc.sample_chain",
"os.path.join",
"tensorflow.get_variable",
"numpy.nanmean",
"matplotlib.pypl... | [((1201, 1389), 'calibre.util.experiment_data.generate_data_1d', 'experiment_util.generate_data_1d', ([], {'N_train': '(20)', 'N_test': '(20)', 'N_valid': '(500)', 'noise_sd': '(0.03)', 'data_range': '(0.0, 1.0)', 'valid_range': '(-0.5, 1.5)', 'seed_train': '(1000)', 'seed_test': '(1500)', 'seed_calib': '(100)'}), '(N_... |
import pandas as pd
import numpy as np
from progressbar import progressbar
import sanalytics.algorithms.utils as sau
import sanalytics.estimators.pu_estimators as pu
import sanalytics.evaluation.utils as seu
X_train = pd.read_parquet("datasets/rq3_data/sec1.0R100_train.parquet")
X_train = np.array_split(X_train, 300)
... | [
"numpy.array_split",
"pandas.read_parquet"
] | [((219, 280), 'pandas.read_parquet', 'pd.read_parquet', (['"""datasets/rq3_data/sec1.0R100_train.parquet"""'], {}), "('datasets/rq3_data/sec1.0R100_train.parquet')\n", (234, 280), True, 'import pandas as pd\n'), ((291, 319), 'numpy.array_split', 'np.array_split', (['X_train', '(300)'], {}), '(X_train, 300)\n', (305, 31... |
import sys
sys.path.append('..')
import parameters as param
from utils import load_data
import argparse
import os
import sys, logging
import time
from datetime import datetime
import pytz
import numpy as np
import random
import shutil
import warnings
warnings.filterwarnings("ignore")
from keras import backend as K
i... | [
"sys.path.append",
"keras.models.load_model",
"numpy.load",
"utils.load_data",
"os.makedirs",
"warnings.filterwarnings",
"numpy.argmax",
"logging.StreamHandler",
"os.path.exists",
"datetime.datetime.utcnow",
"numpy.array",
"pytz.timezone",
"numpy.exp",
"os.path.join",
"logging.getLogger"... | [((11, 32), 'sys.path.append', 'sys.path.append', (['""".."""'], {}), "('..')\n", (26, 32), False, 'import sys, logging\n'), ((253, 286), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {}), "('ignore')\n", (276, 286), False, 'import warnings\n'), ((2060, 2114), 'os.makedirs', 'os.makedirs', ([... |
# -*- coding: utf-8 -*-
# Copyright 2011 <NAME>
# 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 agreed to i... | [
"numpy.log",
"numpy.asarray",
"numpy.zeros",
"numpy.identity",
"numpy.linalg.inv",
"numpy.array",
"math.log"
] | [((1810, 1847), 'numpy.array', 'numpy.array', (['loc'], {'dtype': 'numpy.float32'}), '(loc, dtype=numpy.float32)\n', (1821, 1847), False, 'import numpy\n'), ((1972, 2011), 'numpy.array', 'numpy.array', (['scale'], {'dtype': 'numpy.float32'}), '(scale, dtype=numpy.float32)\n', (1983, 2011), False, 'import numpy\n'), ((2... |
from pathlib import Path
import cv2
import matplotlib.pyplot as plt
import numpy as np
from user_input import get_user_input
IMG_DIR = 'images'
LABELED_DATA_FILE = "labeled_data.csv"
IMG_WIDTH = 600
IMG_HEIGHT = 1000
IMG_FORMAT = 'png'
def _print_finish_msg():
print("\n******************************")
print... | [
"cv2.cvtColor",
"numpy.zeros",
"matplotlib.pyplot.ion",
"cv2.imread",
"pathlib.Path",
"user_input.get_user_input",
"matplotlib.pyplot.pause"
] | [((547, 556), 'matplotlib.pyplot.ion', 'plt.ion', ([], {}), '()\n', (554, 556), True, 'import matplotlib.pyplot as plt\n'), ((579, 612), 'numpy.zeros', 'np.zeros', (['(IMG_HEIGHT, IMG_WIDTH)'], {}), '((IMG_HEIGHT, IMG_WIDTH))\n', (587, 612), True, 'import numpy as np\n'), ((814, 830), 'cv2.imread', 'cv2.imread', (['pat... |
import numpy as np
from utility1 import sigmoid,dsigmoid,preprocess, get_parent_detail, get_child_no
import Global
def weight_update(w1, w2, dw1, dw2, neta, regu, epnum1, epnum2, wpresent=None):
for i in wpresent:
if epnum1[i] != 0:
w1[i] = w1[i] - neta[0]*(dw1[i].transpose()/epnum1[i] - regu[0... | [
"utility1.sigmoid",
"utility1.preprocess",
"numpy.power",
"numpy.square",
"numpy.dot",
"utility1.dsigmoid",
"numpy.sqrt"
] | [((3483, 3497), 'utility1.sigmoid', 'sigmoid', (['temp1'], {}), '(temp1)\n', (3490, 3497), False, 'from utility1 import sigmoid, dsigmoid, preprocess, get_parent_detail, get_child_no\n'), ((3548, 3566), 'numpy.dot', 'np.dot', (['w2', 'temp12'], {}), '(w2, temp12)\n', (3554, 3566), True, 'import numpy as np\n'), ((3625,... |
import cv2
import os
import glob
from skimage import feature
import numpy as np
from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV
class LocalBinaryPatterns:
def __init__(self, numPoints, radius):
# store the number of points and radius
self.numPoints = numPoints
... | [
"skimage.feature.local_binary_pattern",
"cv2.cvtColor",
"cv2.waitKey",
"cv2.imshow",
"cv2.VideoCapture",
"cv2.imread",
"cv2.rectangle",
"numpy.arange",
"numpy.array",
"sklearn.svm.SVC",
"cv2.CascadeClassifier",
"cv2.destroyAllWindows",
"os.path.join",
"os.listdir",
"numpy.vstack"
] | [((1174, 1196), 'os.listdir', 'os.listdir', (['img_folder'], {}), '(img_folder)\n', (1184, 1196), False, 'import os\n'), ((1978, 1997), 'cv2.VideoCapture', 'cv2.VideoCapture', (['(0)'], {}), '(0)\n', (1994, 1997), False, 'import cv2\n'), ((2868, 2891), 'cv2.destroyAllWindows', 'cv2.destroyAllWindows', ([], {}), '()\n',... |
'''
This file contains the functions to generate, store, and plot interdependenct
random and scale free networks
<NAME> - Last updated: 03/08/2018
'''
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
import collections
import math
import random
from itertools import combinations, product
'''... | [
"matplotlib.pyplot.title",
"matplotlib.pyplot.loglog",
"matplotlib.pyplot.axes",
"matplotlib.pyplot.bar",
"networkx.algorithms.planarity.check_planarity",
"networkx.draw_networkx_nodes",
"networkx.connected_component_subgraphs",
"networkx.erdos_renyi_graph",
"networkx.scale_free_graph",
"itertools... | [((434, 456), 'itertools.combinations', 'combinations', (['lists', '(2)'], {}), '(lists, 2)\n', (446, 456), False, 'from itertools import combinations, product\n'), ((799, 825), 'networkx.erdos_renyi_graph', 'nx.erdos_renyi_graph', (['n', 'p'], {}), '(n, p)\n', (819, 825), True, 'import networkx as nx\n'), ((909, 928),... |
"""
Copyright 2021 Tsinghua University
Apache 2.0.
Author: <NAME> 2021
<NAME> 2021
This script implements multi/cross-lingual related functions originally written by <NAME>,
which is latter refactored by <NAME>.
"""
import json
import numpy as np
from collections import OrderedDict
import torch
import tor... | [
"torch.nn.Parameter",
"numpy.load",
"json.load",
"torch.Tensor",
"torch.nn.Linear",
"collections.OrderedDict"
] | [((509, 522), 'collections.OrderedDict', 'OrderedDict', ([], {}), '()\n', (520, 522), False, 'from collections import OrderedDict\n'), ((1211, 1223), 'numpy.load', 'np.load', (['fin'], {}), '(fin)\n', (1218, 1223), True, 'import numpy as np\n'), ((1233, 1249), 'torch.Tensor', 'torch.Tensor', (['pv'], {}), '(pv)\n', (12... |
import argparse
import numpy as np
import os
from dagbldr.datasets import fetch_fer
from dagbldr.utils import load_checkpoint, interpolate_between_points, make_gif
parser = argparse.ArgumentParser()
parser.add_argument("saved_functions_file",
help="Saved pickle file from vae training")
parser.add_... | [
"argparse.ArgumentParser",
"matplotlib.pyplot.close",
"os.path.exists",
"numpy.random.RandomState",
"dagbldr.utils.make_gif",
"matplotlib.use",
"numpy.exp",
"dagbldr.utils.load_checkpoint",
"dagbldr.datasets.fetch_fer",
"numpy.dot",
"matplotlib.pyplot.subplots",
"dagbldr.utils.interpolate_betw... | [((175, 200), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (198, 200), False, 'import argparse\n'), ((638, 680), 'dagbldr.utils.load_checkpoint', 'load_checkpoint', (['args.saved_functions_file'], {}), '(args.saved_functions_file)\n', (653, 680), False, 'from dagbldr.utils import load_checkpo... |
#!/usr/bin/env python3
import cv2
import numpy as np
import depthai as dai
from time import sleep
import datetime
import argparse
from pathlib import Path
datasetDefault = str((Path(__file__).parent / Path('models/dataset')).resolve().absolute())
parser = argparse.ArgumentParser()
parser.add_argument('-dataset', narg... | [
"depthai.Pipeline",
"argparse.ArgumentParser",
"depthai.Device",
"pathlib.Path",
"cv2.normalize",
"cv2.imshow",
"datetime.timedelta",
"cv2.setTrackbarPos",
"depthai.StereoDepthConfig",
"cv2.createTrackbar",
"cv2.equalizeHist",
"cv2.waitKey",
"time.sleep",
"cv2.applyColorMap",
"cv2.flip",... | [((258, 283), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (281, 283), False, 'import argparse\n'), ((932, 955), 'depthai.StereoDepthConfig', 'dai.StereoDepthConfig', ([], {}), '()\n', (953, 955), True, 'import depthai as dai\n'), ((4299, 4313), 'depthai.Pipeline', 'dai.Pipeline', ([], {}), '... |
import contextlib
import csv
import hashlib
import random
import shutil
import tempfile
from collections import OrderedDict
from copy import copy
from distutils.util import strtobool
from os import path
from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union, cast
import numpy as np
import torc... | [
"torch.backends.cudnn.is_available",
"numpy.random.seed",
"torch.hub._get_torch_home",
"typing.cast",
"pystiche.image.make_batched_image",
"shutil.rmtree",
"pystiche.image.is_single_image",
"os.path.join",
"torch.load",
"hashlib.sha256",
"tempfile.mkdtemp",
"random.seed",
"torch.hub.load_sta... | [((1631, 1653), 'pystiche.image.is_single_image', 'is_single_image', (['image'], {}), '(image)\n', (1646, 1653), False, 'from pystiche.image import extract_batch_size, is_single_image, make_batched_image\n'), ((3103, 3137), 'tempfile.mkdtemp', 'tempfile.mkdtemp', ([], {}), '(**mkdtemp_kwargs)\n', (3119, 3137), False, '... |
'''
MFEM example 22
See c++ version in the MFEM library for more detail
'''
import os
import mfem.ser as mfem
from mfem.ser import intArray
from os.path import expanduser, join, dirname
import numpy as np
from numpy import sin, cos, exp, sqrt, pi
def run(mesh_file="",
order=1,
ref_levels=0,
... | [
"mfem.ser.RT_FECollection",
"mfem.common.arg_parser.ArgParser",
"mfem.ser.intArray",
"mfem.ser.Vector",
"mfem.ser.BilinearForm",
"mfem.ser.OperatorJacobiSmoother",
"numpy.sin",
"numpy.exp",
"mfem.ser.GMRESSolver",
"mfem.ser.ND_FECollection",
"mfem.ser.ConstantCoefficient",
"os.path.dirname",
... | [((1044, 1063), 'mfem.ser.Device', 'mfem.Device', (['device'], {}), '(device)\n', (1055, 1063), True, 'import mfem.ser as mfem\n'), ((1274, 1300), 'mfem.ser.Mesh', 'mfem.Mesh', (['mesh_file', '(1)', '(1)'], {}), '(mesh_file, 1, 1)\n', (1283, 1300), True, 'import mfem.ser as mfem\n'), ((2164, 2198), 'mfem.ser.FiniteElem... |
from flask import Flask, render_template, request, redirect, url_for, session, jsonify
import joblib
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
import matplotlib.pyplot as plt
from matplotlib.style import use
import numpy as np
from sqlalchemy import... | [
"application.forms.NamerForm",
"flask.jsonify",
"flask.url_for",
"application.user_metrics.UserMetrics.deadlift.desc",
"matplotlib.pyplot.tight_layout",
"application.user_metrics.UserMetrics.date.asc",
"matplotlib.backends.backend_agg.FigureCanvasAgg",
"dateutil.relativedelta.relativedelta",
"matplo... | [((930, 955), 'matplotlib.pyplot.switch_backend', 'plt.switch_backend', (['"""Agg"""'], {}), "('Agg')\n", (948, 955), True, 'import matplotlib.pyplot as plt\n'), ((2973, 3020), 'joblib.load', 'joblib.load', (['f"""models{os.path.sep}bench_scaler"""'], {}), "(f'models{os.path.sep}bench_scaler')\n", (2984, 3020), False, ... |
import numpy as np
def compute_euclidean_distance(point, centroid):
return np.sqrt(np.sum((point - centroid) ** 2))
def assign_label_cluster(distance, data_point, centroids):
index_of_minimum = min(distance, key=distance.get)
return [index_of_minimum, data_point, centroids[index_of_minimum]]
def compute... | [
"numpy.sum",
"numpy.array",
"numpy.genfromtxt"
] | [((1561, 1579), 'numpy.array', 'np.array', (['[[x, y]]'], {}), '([[x, y]])\n', (1569, 1579), True, 'import numpy as np\n'), ((1676, 1714), 'numpy.genfromtxt', 'np.genfromtxt', (['filename'], {'delimiter': '""","""'}), "(filename, delimiter=',')\n", (1689, 1714), True, 'import numpy as np\n'), ((87, 118), 'numpy.sum', '... |
import unittest
import openmesh
import numpy as np
class Python(unittest.TestCase):
def setUp(self):
self.mesh = openmesh.TriMesh()
# Add some vertices
self.vhandle = []
self.vhandle.append(self.mesh.add_vertex(np.array([0, 1, 0])))
self.vhandle.append(self.mesh.add_vert... | [
"unittest.TextTestRunner",
"openmesh.TriMesh",
"numpy.array",
"unittest.TestLoader"
] | [((128, 146), 'openmesh.TriMesh', 'openmesh.TriMesh', ([], {}), '()\n', (144, 146), False, 'import openmesh\n'), ((1750, 1771), 'unittest.TestLoader', 'unittest.TestLoader', ([], {}), '()\n', (1769, 1771), False, 'import unittest\n'), ((1806, 1842), 'unittest.TextTestRunner', 'unittest.TextTestRunner', ([], {'verbosity... |
import numpy as np
import torch
import os
import pickle as pkl
import sys
import re
import scipy.io
from Bio import SeqIO
from Bio.PDB.DSSP import DSSP
from Bio.PDB import PDBParser
from prospr.nn import INPUT_DIM, CROP_SIZE
def seq_to_mat(seq):
"""Convert a sequence into Nx21 matrix for input vector"""
aa_or... | [
"numpy.transpose",
"numpy.zeros",
"numpy.arange",
"numpy.repeat",
"torch.zeros",
"numpy.eye",
"torch.from_numpy"
] | [((1008, 1047), 'numpy.zeros', 'np.zeros', (['(INPUT_DIM, i_range, j_range)'], {}), '((INPUT_DIM, i_range, j_range))\n', (1016, 1047), True, 'import numpy as np\n'), ((1246, 1282), 'numpy.transpose', 'np.transpose', (['j_crop'], {'axes': '(2, 0, 1)'}), '(j_crop, axes=(2, 0, 1))\n', (1258, 1282), True, 'import numpy as ... |
import numpy as np
import xarray
def saveDraws(beta_samples, u_samples, global_effects_names,
random_effects_dims, random_effects_names, coords_dict={}):
all_coords = {}
assert beta_samples.shape[0] == len(global_effects_names)
beta_xr = xarray.DataArray(beta_samples, dims=('cov','draw'),
... | [
"numpy.arange",
"xarray.Dataset"
] | [((1262, 1299), 'xarray.Dataset', 'xarray.Dataset', (['data_vars', 'all_coords'], {}), '(data_vars, all_coords)\n', (1276, 1299), False, 'import xarray\n'), ((544, 583), 'numpy.arange', 'np.arange', (['(1)', '(beta_samples.shape[1] + 1)'], {}), '(1, beta_samples.shape[1] + 1)\n', (553, 583), True, 'import numpy as np\n... |
#!/usr/bin/env python
# coding: utf-8
import time
import numpy as np
import sklearn.datasets
import nnet
def run():
# Fetch data
mnist = sklearn.datasets.fetch_mldata('MNIST original', data_home='./data')
split = 60000
X_train = mnist.data[:split]/255.0
y_train = mnist.target[:split]
X_test =... | [
"nnet.Linear",
"nnet.Activation",
"time.time",
"nnet.LogRegression",
"numpy.random.random_integers",
"numpy.unique"
] | [((497, 553), 'numpy.random.random_integers', 'np.random.random_integers', (['(0)', '(split - 1)', 'n_train_samples'], {}), '(0, split - 1, n_train_samples)\n', (522, 553), True, 'import numpy as np\n'), ((1291, 1302), 'time.time', 'time.time', ([], {}), '()\n', (1300, 1302), False, 'import time\n'), ((1387, 1398), 'ti... |
#%%
"""
<NAME> 2/2/2021 :bold:`Example Script`
Time-independent Exterior Complex Scaling (ECS) FEM-DVR example
Temkin-Poet (s-wave limit) or colinear model of a two-electron atom:
H- anion or He bound and autoionizing states
Uses femdvr.py class library, updated Jan 2021 with 2D routines
Fi... | [
"os.mkdir",
"numpy.abs",
"os.getcwd",
"numpy.empty",
"click.option",
"os.path.exists",
"scipy.linalg.eig",
"click.command",
"scipy.linalg.inv",
"numpy.argsort",
"numpy.imag",
"quantumgrid.potential.Potential",
"numpy.exp",
"numpy.real",
"quantumgrid.femdvr.FEM_DVR",
"numpy.sqrt"
] | [((2468, 2589), 'click.option', 'click.option', (['"""--want_to_plot"""'], {'type': 'click.BOOL', 'default': '"""False"""', 'help': '"""Set to True if you want to turn on plotting"""'}), "('--want_to_plot', type=click.BOOL, default='False', help=\n 'Set to True if you want to turn on plotting')\n", (2480, 2589), Fal... |
#!/usr/bin/ python3
# from matplotlib import gridspec
# from matplotlib import rc
# rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
# rc('text',usetex=True)
# from pylab import *
import yoda
import numpy as np
# from decimal import Decimal
# # from termcolor import colorescolors[2]
# import math
impor... | [
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.figaspect",
"numpy.sum",
"os.makedirs",
"matplotlib.pyplot.suptitle",
"os.path.exists",
"yoda.read",
"numpy.append",
"numpy.array",
"matplotlib.gridspec.GridSpec",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.savefig"
] | [((665, 692), 'yoda.read', 'yoda.read', (['"""SM_WZ_dib.yoda"""'], {}), "('SM_WZ_dib.yoda')\n", (674, 692), False, 'import yoda\n'), ((938, 964), 'yoda.read', 'yoda.read', (['"""WZ_ATLAS.yoda"""'], {}), "('WZ_ATLAS.yoda')\n", (947, 964), False, 'import yoda\n'), ((1186, 1220), 'yoda.read', 'yoda.read', (['"""cW_WZ_dib_... |
import os
import time
from collections import deque
import ipywidgets
import jpy_canvas
import numpy as np
from ipywidgets import IntSlider, VBox, HBox, Checkbox, Output, Text, RadioButtons, Tab
from numpy import array
import flatland.utils.rendertools as rt
from flatland.core.grid.grid4_utils import mirror
from flat... | [
"numpy.abs",
"numpy.floor",
"ipywidgets.Text",
"ipywidgets.Output",
"flatland.envs.observations.TreeObsForRailEnv",
"flatland.envs.agent_utils.EnvAgent",
"collections.deque",
"jpy_canvas.Canvas",
"ipywidgets.Button",
"numpy.copy",
"flatland.envs.rail_generators.empty_rail_generator",
"os.path.... | [((1897, 1919), 'jpy_canvas.Canvas', 'jpy_canvas.Canvas', (['img'], {}), '(img)\n', (1914, 1919), False, 'import jpy_canvas\n'), ((1997, 2022), 'numpy.copy', 'np.copy', (['self.wImage.data'], {}), '(self.wImage.data)\n', (2004, 2022), True, 'import numpy as np\n'), ((2410, 2450), 'ipywidgets.Checkbox', 'ipywidgets.Chec... |
import time
import unittest
import numpy as np
import tensorflow as tf
from model import PolicyGradient
from pprint import pprint
class TestModel(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.ob_dim = 5
cls.ac_dim = 5
cls.model = PolicyGradient(
ob_dim=cls.ob_... | [
"tensorflow.trainable_variables",
"time.time",
"numpy.random.random",
"pprint.pprint",
"model.PolicyGradient"
] | [((278, 409), 'model.PolicyGradient', 'PolicyGradient', ([], {'ob_dim': 'cls.ob_dim', 'ac_dim': 'cls.ac_dim', 'discrete': '(False)', 'n_layers': '(2)', 'size': '(32)', 'learning_rate': '(0.05)', 'nn_baseline': '(True)'}), '(ob_dim=cls.ob_dim, ac_dim=cls.ac_dim, discrete=False,\n n_layers=2, size=32, learning_rate=0.... |
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
import numpy as np
import gensim
import gensim.downloader as api
import pickle
def dummy_fun(doc):
return doc
'''
Abstract class for that wraps around vectorizers.
'''
class VectorizerClassBase():
model = None
default_pickle_... | [
"pandas.DataFrame",
"sklearn.feature_extraction.text.TfidfVectorizer",
"gensim.downloader.load",
"numpy.mean",
"gensim.models.KeyedVectors.load_word2vec_format"
] | [((2796, 2825), 'numpy.mean', 'np.mean', (['word_vectors'], {'axis': '(0)'}), '(word_vectors, axis=0)\n', (2803, 2825), True, 'import numpy as np\n'), ((2896, 2950), 'gensim.downloader.load', 'api.load', (['"""word2vec-google-news-300"""'], {'return_path': '(True)'}), "('word2vec-google-news-300', return_path=True)\n",... |
"""-----------------------------------------------------------------------------
bidsArchive.py
Implements interacting with an on-disk BIDS Archive.
-----------------------------------------------------------------------------"""
import functools
import json
import logging
import os
import re
from typing import List... | [
"nibabel.Nifti1Image",
"os.path.abspath",
"bids.layout.BIDSLayout",
"rtCommon.errors.DimensionError",
"numpy.allclose",
"rtCommon.errors.StateError",
"json.dumps",
"bids.layout.writing.write_to_file",
"rtCommon.bidsCommon.getNiftiData",
"numpy.expand_dims",
"bids.exceptions.NoMatchError",
"rtC... | [((966, 1010), 'bids.config.set_option', 'bc_set_option', (['"""extension_initial_dot"""', '(True)'], {}), "('extension_initial_dot', True)\n", (979, 1010), True, 'from bids.config import set_option as bc_set_option\n'), ((1021, 1048), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1038,... |
import numpy as np
import matplotlib.pyplot as plt
def main():
result_single = np.load("./single.npy")
plt.plot(result_single, label="Single")
result_multi = np.load("./multi.npy")
plt.plot(result_multi, label="Multi")
#result_multi_rs = np.load("./multi_rs.npy")
#plt.plot(result_multi_rs, l... | [
"numpy.load",
"matplotlib.pyplot.show",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.gcf",
"matplotlib.pyplot.savefig"
] | [((85, 108), 'numpy.load', 'np.load', (['"""./single.npy"""'], {}), "('./single.npy')\n", (92, 108), True, 'import numpy as np\n'), ((113, 152), 'matplotlib.pyplot.plot', 'plt.plot', (['result_single'], {'label': '"""Single"""'}), "(result_single, label='Single')\n", (121, 152), True, 'import matplotlib.pyplot as plt\n... |
r"""Reverse Polish Notation calculator.
Exceptions
----------
* :class:`StackUnderflowError`
Classes
-------
* :class:`Expression`
* :class:`Token`
* :class:`Literal`
* :class:`Variable`
* :class:`Operator`
Functions
---------
* :func:`token`
Constants
---------
============== =================
Keyword ... | [
"numpy.absolute",
"numpy.moveaxis",
"astropy.convolution.convolve",
"numpy.maximum",
"numpy.arctan2",
"numpy.ravel",
"typing.cast",
"numpy.floor",
"numpy.isnan",
"numpy.arccosh",
"numpy.shape",
"numpy.sin",
"numpy.exp",
"numpy.bitwise_or",
"numpy.round",
"numpy.arctanh",
"numpy.copy"... | [((26670, 26684), 'numpy.absolute', 'np.absolute', (['x'], {}), '(x)\n', (26681, 26684), True, 'import numpy as np\n'), ((27364, 27374), 'numpy.sqrt', 'np.sqrt', (['x'], {}), '(x)\n', (27371, 27374), True, 'import numpy as np\n'), ((27711, 27723), 'numpy.square', 'np.square', (['x'], {}), '(x)\n', (27720, 27723), True,... |
from __future__ import (division, print_function, absolute_import,
unicode_literals)
__all__ = ["make_summary_plot"]
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
from matplotlib.ticker import MultipleLocator, ScalarFormatter
import os
import loggi... | [
"os.path.abspath",
"matplotlib.pyplot.matplotlib.ticker.ScalarFormatter",
"os.path.exists",
"numpy.argsort",
"matplotlib.pyplot.figure",
"numpy.array",
"matplotlib.pyplot.Line2D",
"matplotlib.ticker.MultipleLocator",
"matplotlib.gridspec.GridSpec",
"matplotlib.ticker.ScalarFormatter",
"logging.g... | [((388, 415), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (405, 415), False, 'import logging\n'), ((2689, 2703), 'matplotlib.gridspec.GridSpec', 'GridSpec', (['(4)', '(2)'], {}), '(4, 2)\n', (2697, 2703), False, 'from matplotlib.gridspec import GridSpec\n'), ((2208, 2229), 'os.path.exi... |
'''You can use functions and lists in this module to generate data for cases.
'''
import numpy as np
from .utils import (
bits_to_dtype_uint,
factor_lmul
)
def vector_mask_array_random( vl ):
'''Function used to generate random mask bits for rvv instructions.
Args:
vl (int): vl register val... | [
"numpy.random.uniform",
"numpy.ceil",
"numpy.packbits",
"numpy.ones",
"numpy.random.randint",
"numpy.array",
"numpy.unpackbits",
"numpy.linspace",
"numpy.concatenate"
] | [((745, 781), 'numpy.packbits', 'np.packbits', (['mask'], {'bitorder': '"""little"""'}), "(mask, bitorder='little')\n", (756, 781), True, 'import numpy as np\n'), ((1721, 1748), 'numpy.ones', 'np.ones', (['vl'], {'dtype': 'np.uint8'}), '(vl, dtype=np.uint8)\n', (1728, 1748), True, 'import numpy as np\n'), ((1778, 1814)... |
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