code stringlengths 31 1.05M | apis list | extract_api stringlengths 97 1.91M |
|---|---|---|
import json
import zipfile
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# reading training data
df = pd.read_csv('train.csv', converters={'POLYLINE': lambda x: json.loads(x)[:]},nrows=1000)
latLong = np.array([])
allTrajectoryLatLong=[p for p in df['POLYLINE'] if len(p)>0]
#for oneTrajecto... | [
"json.loads",
"matplotlib.pyplot.plot",
"numpy.array",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.title",
"matplotlib.pyplot.show"
] | [((229, 241), 'numpy.array', 'np.array', (['[]'], {}), '([])\n', (237, 241), True, 'import numpy as np\n'), ((975, 987), 'matplotlib.pyplot.figure', 'plt.figure', ([], {}), '()\n', (985, 987), True, 'import matplotlib.pyplot as plt\n'), ((1178, 1211), 'matplotlib.pyplot.title', 'plt.title', (['"""Taxi trip end points""... |
from __future__ import print_function, division
import torch
import torch.nn as nn
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
from torch.utils.data import Dataset, DataLoader
import os
from PIL import Image
class TestDataset(Dataset):
"""Face Landmarks dataset."... | [
"os.listdir",
"PIL.Image.open",
"torchvision.transforms.RandomHorizontalFlip",
"os.path.join",
"torchvision.transforms.RandomVerticalFlip",
"torchvision.transforms.ColorJitter",
"numpy.array",
"torchvision.transforms.Normalize",
"torch.utils.data.DataLoader",
"torchvision.transforms.ToTensor"
] | [((2173, 2282), 'torch.utils.data.DataLoader', 'torch.utils.data.DataLoader', (['train_dataset'], {'batch_size': 'batch_size', 'shuffle': '(True)', 'num_workers': 'num_workers'}), '(train_dataset, batch_size=batch_size, shuffle=\n True, num_workers=num_workers)\n', (2200, 2282), False, 'import torch\n'), ((829, 854)... |
"""Code from https://github.com/tambetm/simple_dqn/blob/master/src/replay_memory.py"""
import os
import random
import logging
import numpy as np
from .utils import save_npy, load_npy
class ReplayMemory:
def __init__(self, config, model_dir):
self.model_dir = model_dir
self.cnn_format = config.cnn_format
... | [
"os.path.join",
"numpy.transpose",
"numpy.empty",
"random.randint"
] | [((380, 422), 'numpy.empty', 'np.empty', (['self.memory_size'], {'dtype': 'np.uint8'}), '(self.memory_size, dtype=np.uint8)\n', (388, 422), True, 'import numpy as np\n'), ((444, 488), 'numpy.empty', 'np.empty', (['self.memory_size'], {'dtype': 'np.integer'}), '(self.memory_size, dtype=np.integer)\n', (452, 488), True, ... |
# -*- coding: utf-8 -*-
"""svhn.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1cr_mqeEfw7how-r9MAqqZqJqyepX31yS
"""
import numpy as np
import matplotlib.pyplot as plt
#import seaborn as sns
import h5py
#import tensorflow as tf
#import os
#impor... | [
"keras.layers.Conv2D",
"scipy.io.loadmat",
"tensorflow.keras.callbacks.EarlyStopping",
"numpy.array",
"keras.layers.Activation",
"keras.layers.Dense",
"numpy.where",
"numpy.delete",
"numpy.dot",
"keras.layers.Flatten",
"keras.layers.MaxPooling2D",
"keras.layers.normalization.BatchNormalization... | [((984, 997), 'scipy.io.loadmat', 'loadmat', (['path'], {}), '(path)\n', (991, 997), False, 'from scipy.io import loadmat\n'), ((1187, 1199), 'numpy.unique', 'np.unique', (['y'], {}), '(y)\n', (1196, 1199), True, 'import numpy as np\n'), ((2780, 2821), 'numpy.delete', 'np.delete', (['X_train', 'train_samples'], {'axis'... |
import gym
import numpy as np
from gym import spaces
class NormalizedActionWrapper(gym.ActionWrapper):
"""Environment wrapper to normalize the action space to [-scale, scale]
Args:
env (gym.env): OpenAI Gym environment to wrap around
scale (float): Scale for normalizing action. Default: 1.0.
... | [
"numpy.clip",
"numpy.isfinite",
"gym.spaces.Box"
] | [((556, 625), 'gym.spaces.Box', 'spaces.Box', ([], {'low': '(-scale)', 'high': 'scale', 'shape': 'self.env.action_space.shape'}), '(low=-scale, high=scale, shape=self.env.action_space.shape)\n', (566, 625), False, 'from gym import spaces\n'), ((719, 759), 'numpy.clip', 'np.clip', (['action', '(-self.scale)', 'self.scal... |
import pandas as pd
import numpy as np
import scipy.optimize
import numdifftools as nd
from pyswarm import pso
import time
state_map_dict = {0:'KY', 1:'OH', 2:'PA', 3:'VA', 4:'WV'}
time_map_dict = {0:2010, 1:2011, 2:2012, 3:2013, 4:2014, 5:2015, 6:2016, 7:2017}
full2abbrev_dict = {'Kentucky':'KY', 'Ohio':'OH', 'Pennsy... | [
"pyswarm.pso",
"numpy.random.rand",
"pandas.read_csv"
] | [((382, 415), 'pandas.read_csv', 'pd.read_csv', (['"""MCM_NFLIS_Data.csv"""'], {}), "('MCM_NFLIS_Data.csv')\n", (393, 415), True, 'import pandas as pd\n'), ((504, 547), 'pandas.read_csv', 'pd.read_csv', (['"""ACS_10_5YR_DP02_with_ann.csv"""'], {}), "('ACS_10_5YR_DP02_with_ann.csv')\n", (515, 547), True, 'import pandas ... |
from hdmf.common import CSRMatrix
from hdmf.testing import TestCase, H5RoundTripMixin
import scipy.sparse as sps
import numpy as np
class TestCSRMatrix(TestCase):
def test_from_sparse_matrix(self):
data = np.array([1, 2, 3, 4, 5, 6])
indices = np.array([0, 2, 2, 0, 1, 2])
indptr = np.arr... | [
"numpy.asarray",
"hdmf.common.CSRMatrix",
"numpy.array",
"scipy.sparse.csr_matrix",
"numpy.testing.assert_array_equal"
] | [((221, 249), 'numpy.array', 'np.array', (['[1, 2, 3, 4, 5, 6]'], {}), '([1, 2, 3, 4, 5, 6])\n', (229, 249), True, 'import numpy as np\n'), ((268, 296), 'numpy.array', 'np.array', (['[0, 2, 2, 0, 1, 2]'], {}), '([0, 2, 2, 0, 1, 2])\n', (276, 296), True, 'import numpy as np\n'), ((314, 336), 'numpy.array', 'np.array', (... |
import pandas as pd
import numpy as np
import requests
# finance-datareader installed
# now : cvxopt version 1.2.6
# python interpreter : 3.9.6
import FinanceDataReader as fdr
# print(fdr.__version__) # 0.9.31
# 한국거래소 krx 불러오기
df_krx = fdr.StockListing('KRX')
# print(df_krx) # [6813 rows x 10 columns]
# 데이터 파악
# df... | [
"pypfopt.discrete_allocation.DiscreteAllocation",
"FinanceDataReader.DataReader",
"pypfopt.risk_models.sample_cov",
"pypfopt.discrete_allocation.get_latest_prices",
"numpy.array",
"pypfopt.expected_returns.mean_historical_return",
"pypfopt.efficient_frontier.EfficientFrontier",
"pandas.DataFrame",
"... | [((239, 262), 'FinanceDataReader.StockListing', 'fdr.StockListing', (['"""KRX"""'], {}), "('KRX')\n", (255, 262), True, 'import FinanceDataReader as fdr\n'), ((577, 593), 'numpy.array', 'np.array', (['assets'], {}), '(assets)\n', (585, 593), True, 'import numpy as np\n'), ((1029, 1043), 'pandas.DataFrame', 'pd.DataFram... |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.13.0
# kernelspec:
# display_name: Python 3 (ipykernel)
# language: python
# name: python3
# ---
# # Fleet Cl... | [
"fleet_clustering.filters.find_valid_ssvid",
"collections.OrderedDict",
"skimage.color.hsv2rgb",
"fleet_clustering.distances.create_composite_lonlat_array",
"pandas.read_gbq",
"fleet_clustering.animation.make_anim",
"imp.reload",
"numpy.argmax",
"fleet_clustering.distances.compute_distances_4",
"c... | [((3276, 3312), 'fleet_clustering.bq.load_carriers_by_year', 'bq.load_carriers_by_year', (['(2017)', '(2018)'], {}), '(2017, 2018)\n', (3300, 3312), False, 'from fleet_clustering import bq\n'), ((3642, 3712), 'pandas.read_gbq', 'pd.read_gbq', (['query'], {'dialect': '"""standard"""', 'project_id': '"""world-fishing-827... |
import torch
from torch import Tensor
from typing import List, Tuple, Any, Optional
from torchvision.transforms import functional as F
import torchvision
import torch.utils.data as torch_data
from torchvision import transforms
from PIL import Image
from copy import deepcopy
import math
import os
import numpy as np
c... | [
"numpy.clip",
"torch.as_tensor",
"numpy.sqrt",
"numpy.random.rand",
"math.sqrt",
"numpy.array",
"copy.deepcopy",
"torchvision.transforms.functional.get_image_size",
"torch.randint",
"torchvision.transforms.ToTensor",
"numpy.random.beta",
"torchvision.transforms.functional.resized_crop",
"tor... | [((464, 485), 'torchvision.transforms.ToTensor', 'transforms.ToTensor', ([], {}), '()\n', (483, 485), False, 'from torchvision import transforms\n'), ((1249, 1271), 'copy.deepcopy', 'deepcopy', (['source_image'], {}), '(source_image)\n', (1257, 1271), False, 'from copy import deepcopy\n'), ((3276, 3305), 'PIL.Image.fro... |
import numpy as np
import os
import cv2
from skimage.io import imread
from skimage.io import imsave
from os.path import join
import sys
import matplotlib.pyplot as plt
import argparse
def add_noise(noise_typ, image, sigma):
if noise_typ == "gauss":
row, col, ch = image.shape
mean = 0
gauss = np.random.normal(... | [
"numpy.random.normal",
"numpy.copy",
"numpy.ceil",
"os.path.exists",
"os.listdir",
"numpy.unique",
"os.makedirs",
"numpy.random.poisson",
"numpy.where",
"os.path.join",
"numpy.squeeze",
"skimage.io.imread",
"numpy.expand_dims",
"os.system",
"numpy.log2",
"numpy.random.randn"
] | [((303, 348), 'numpy.random.normal', 'np.random.normal', (['mean', 'sigma', '(row, col, ch)'], {}), '(mean, sigma, (row, col, ch))\n', (319, 348), True, 'import numpy as np\n'), ((521, 535), 'numpy.copy', 'np.copy', (['image'], {}), '(image)\n', (528, 535), True, 'import numpy as np\n'), ((563, 600), 'numpy.ceil', 'np.... |
"""
Description:
Author: <NAME> (<EMAIL>)
Date: 2021-06-06 01:55:29
LastEditors: <NAME> (<EMAIL>)
LastEditTime: 2021-06-06 01:55:30
"""
import os
import argparse
import json
import logging
import logging.handlers
import time
from collections import OrderedDict
from datetime import datetime
from pathlib import Path
fro... | [
"logging.getLogger",
"logging.StreamHandler",
"argparse.Namespace",
"logging.error",
"logging.info",
"os.path.exists",
"numpy.mean",
"argparse.ArgumentParser",
"pathlib.Path",
"tensorflow.compat.v1.logging.set_verbosity",
"numpy.ndim",
"functools.wraps",
"numpy.max",
"logging.root.setLevel... | [((752, 765), 'pathlib.Path', 'Path', (['dirname'], {}), '(dirname)\n', (756, 765), False, 'from pathlib import Path\n'), ((1272, 1283), 'functools.wraps', 'wraps', (['func'], {}), '(func)\n', (1277, 1283), False, 'from functools import wraps, partial\n'), ((4770, 4793), 'logging.StreamHandler', 'logging.StreamHandler'... |
import os
import glob
import logging
import numpy as np
import skimage.io
import skimage.transform
import skimage.color
from joblib import Parallel, delayed
from pprint import pformat
from utils import CONFIG
config = CONFIG.DatasetLoader
log = logging.getLogger('DatasetLoader')
log.setLevel(config.log.level)
class ... | [
"logging.getLogger",
"os.path.exists",
"numpy.asarray",
"os.path.join",
"joblib.Parallel",
"os.path.abspath",
"joblib.delayed"
] | [((246, 280), 'logging.getLogger', 'logging.getLogger', (['"""DatasetLoader"""'], {}), "('DatasetLoader')\n", (263, 280), False, 'import logging\n'), ((547, 579), 'joblib.Parallel', 'Parallel', ([], {'n_jobs': 'config.par_jobs'}), '(n_jobs=config.par_jobs)\n', (555, 579), False, 'from joblib import Parallel, delayed\n'... |
import os
import time
import numpy as np
import pandas as pd
from oplrareg.solvers import get_solver_definition
from modSAR.dataset import QSARDatasetIO
from modSAR.graph import GraphUtils
from copy import deepcopy
from sklearn.externals.joblib import Parallel, delayed
from sklearn.metrics import mean_absolute_error,... | [
"sklearn.model_selection.ParameterGrid",
"copy.deepcopy",
"sklearn.externals.joblib.delayed",
"sklearn.model_selection.ParameterSampler",
"modSAR.graph.GraphUtils.find_optimal_threshold",
"sklearn.metrics.mean_squared_error",
"pandas.ExcelFile",
"sklearn.externals.joblib.Parallel",
"oplrareg.solvers... | [((601, 623), 'pandas.ExcelFile', 'pd.ExcelFile', (['filename'], {}), '(filename)\n', (613, 623), True, 'import pandas as pd\n'), ((6046, 6083), 'pandas.concat', 'pd.concat', (['results'], {'ignore_index': '(True)'}), '(results, ignore_index=True)\n', (6055, 6083), True, 'import pandas as pd\n'), ((6895, 6932), 'sklear... |
from matplotlib import pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
def heatmap(x, y,
freq_labels = 1,
show_grid = True,
invert_yaxis = False,
**kwargs,
):
color = kwargs.get('color',
[1]*len... | [
"seaborn.color_palette",
"seaborn.diverging_palette",
"matplotlib.pyplot.GridSpec",
"numpy.linspace",
"matplotlib.pyplot.subplot"
] | [((2880, 2923), 'matplotlib.pyplot.GridSpec', 'plt.GridSpec', (['(1)', '(15)'], {'hspace': '(0.2)', 'wspace': '(0.1)'}), '(1, 15, hspace=0.2, wspace=0.1)\n', (2892, 2923), True, 'from matplotlib import pyplot as plt\n'), ((3055, 3085), 'matplotlib.pyplot.subplot', 'plt.subplot', (['plot_grid[:, :-1]'], {}), '(plot_grid... |
# test_yuzu_naive_equality.py
# Author: <NAME> <<EMAIL>>
"""
Testing the yuzu ISM implementation is equivalent to the naive ISM
implementation using the built-in models. These are regression tests.
"""
import numpy
import torch
from nose.tools import assert_raises
from numpy.testing import assert_array_almost_equal... | [
"torch.nn.MaxPool1d",
"torch.nn.ReLU",
"numpy.testing.assert_array_almost_equal",
"nose.tools.assert_raises",
"torch.nn.BatchNorm1d",
"numpy.zeros",
"yuzu.precompute",
"yuzu.naive_ism.naive_ism",
"torch.nn.Linear",
"numpy.random.RandomState",
"yuzu.yuzu_ism",
"torch.nn.Conv1d",
"numpy.arange... | [((547, 597), 'numpy.zeros', 'numpy.zeros', (['(n_seqs, 4, seq_len)'], {'dtype': '"""float32"""'}), "((n_seqs, 4, seq_len), dtype='float32')\n", (558, 597), False, 'import numpy\n'), ((747, 819), 'yuzu.precompute', 'precompute', (['model'], {'seq_len': 'X.shape[2]', 'n_choices': 'X.shape[1]', 'alpha': 'alpha'}), '(mode... |
import numpy as np
from omegaconf import OmegaConf
def calculate_initial_lr(cfg: OmegaConf) -> float:
"""
Proposed initial learning rates by SimCLR paper.
Note: SimCLR paper says squared learning rate is better when the size of mini-batches is small.
:param cfg: Hydra's config.
:return: Initial ... | [
"numpy.sqrt"
] | [((544, 581), 'numpy.sqrt', 'np.sqrt', (["cfg['experiment']['batches']"], {}), "(cfg['experiment']['batches'])\n", (551, 581), True, 'import numpy as np\n')] |
import numpy as np
from tensorflow.keras.models import model_from_json
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from PIL import Image
import cv2
import urllib.request
import numpy as np
... | [
"tensorflow.keras.models.model_from_json",
"tensorflow.keras.preprocessing.image.img_to_array",
"PIL.Image.open",
"numpy.argmax"
] | [((575, 609), 'tensorflow.keras.models.model_from_json', 'model_from_json', (['loaded_model_json'], {}), '(loaded_model_json)\n', (590, 609), False, 'from tensorflow.keras.models import model_from_json\n'), ((1111, 1128), 'PIL.Image.open', 'Image.open', (['image'], {}), '(image)\n', (1121, 1128), False, 'from PIL impor... |
#!/usr/bin/python
#
# This file is part of PyRQA.
# Copyright 2015 <NAME>, <NAME>.
"""
Distance metrics.
"""
import math
import numpy as np
from pyrqa.abstract_classes import AbstractMetric
class TaxicabMetric(AbstractMetric):
"""
Taxicab metric (L1)
"""
name = 'taxicab_metric'
@classmethod
... | [
"math.pow",
"math.sqrt",
"math.fabs",
"numpy.finfo",
"numpy.arange"
] | [((514, 544), 'numpy.arange', 'np.arange', (['embedding_dimension'], {}), '(embedding_dimension)\n', (523, 544), True, 'import numpy as np\n'), ((942, 972), 'numpy.arange', 'np.arange', (['embedding_dimension'], {}), '(embedding_dimension)\n', (951, 972), True, 'import numpy as np\n'), ((1513, 1543), 'numpy.arange', 'n... |
#!/usr/bin/env python3
""" Base class(es) for text classifiers.
The file defines some of the common classes/functions as well as the
interface (including a command line interface).
"""
import sys, os, time, csv, re, itertools, random, json
import gzip
from hashlib import md5
import numpy as np
from collections import... | [
"sklearn.model_selection.StratifiedShuffleSplit",
"csv.DictReader",
"gzip.open",
"re.compile",
"sklearn.model_selection.StratifiedKFold",
"numpy.array",
"random.getrandbits",
"logging.info",
"numpy.arange",
"os.remove",
"argparse.ArgumentParser",
"itertools.product",
"numpy.vstack",
"os.ge... | [((411, 477), 'logging.basicConfig', 'basicConfig', ([], {'level': 'logging.DEBUG', 'format': '"""%(asctime)s %(message)s"""'}), "(level=logging.DEBUG, format='%(asctime)s %(message)s')\n", (422, 477), False, 'from logging import debug, info, warning, basicConfig\n'), ((618, 660), 're.compile', 're.compile', (['"""\\\\... |
import numpy as np
import pandas as pd
import pytest
from gmpy2 import bit_mask
from rulelist.datastructure.data import Data
from rulelist.rulelistmodel.categoricalmodel.categoricalstatistic import CategoricalFixedStatistic, \
CategoricalFreeStatistic
@pytest.fixture
def constant_parameters():
input_n_cutpoi... | [
"rulelist.rulelistmodel.categoricalmodel.categoricalstatistic.CategoricalFreeStatistic",
"rulelist.datastructure.data.Data",
"rulelist.rulelistmodel.categoricalmodel.categoricalstatistic.CategoricalFixedStatistic",
"gmpy2.bit_mask",
"pandas.DataFrame",
"numpy.arange"
] | [((593, 621), 'pandas.DataFrame', 'pd.DataFrame', ([], {'data': 'dictinput'}), '(data=dictinput)\n', (605, 621), True, 'import pandas as pd\n'), ((1050, 1079), 'pandas.DataFrame', 'pd.DataFrame', ([], {'data': 'dictoutput'}), '(data=dictoutput)\n', (1062, 1079), True, 'import pandas as pd\n'), ((1097, 1217), 'rulelist.... |
#!/usr/bin/env python
"""
Copyright (C) 2019 <NAME> Ltd
Copyright (C) 2019 <NAME>, ETH Zurich
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the "Software"), to deal in the Software without restriction, including without limitation
th... | [
"numpy.mean",
"collections.OrderedDict",
"pandas.read_csv",
"os.path.join",
"os.path.isdir",
"pandas.DataFrame"
] | [((1478, 1507), 'pandas.read_csv', 'read_csv', (['file_path'], {'sep': '"""\t"""'}), "(file_path, sep='\\t')\n", (1486, 1507), False, 'from pandas import read_csv\n'), ((2583, 2643), 'pandas.DataFrame', 'pd.DataFrame', (['all_preds'], {'columns': 'columns', 'index': 'all_patients'}), '(all_preds, columns=columns, index... |
import gym
import tensorflow as tf
from tensorflow.keras import layers
import numpy as np
import matplotlib.pyplot as plt
from exploration import OUActionNoise
from rpm import Buffer, update_target
from ddpg import DDPG
from shield import Shield
from lundar_landing import LunarLanderContinuous
# from conjugate_prior... | [
"numpy.mean",
"numpy.abs",
"rpm.update_target",
"matplotlib.pyplot.ylabel",
"lundar_landing.LunarLanderContinuous",
"ipdb.set_trace",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.plot",
"ddpg.DDPG",
"conjugate_prior.NormalNormalKnownVar",
"shield.Shield",
"tensorflow.convert_to_tensor",
"m... | [((6259, 6282), 'lundar_landing.LunarLanderContinuous', 'LunarLanderContinuous', ([], {}), '()\n', (6280, 6282), False, 'from lundar_landing import LunarLanderContinuous\n'), ((6380, 6396), 'ipdb.set_trace', 'ipdb.set_trace', ([], {}), '()\n', (6394, 6396), False, 'import ipdb\n'), ((6786, 6935), 'ddpg.DDPG', 'DDPG', (... |
import os
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
import argparse
import time
import numpy as np
from models.attention_model import AttentionModelBddDetection, AttentionModelMultiBddDetection
from models.feature_model import FeatureModelBddDetection
from mo... | [
"dataset.multiBddDetectionDataset.MultiBddDetection",
"ats.utils.logging.AttentionSaverMultiBatchBddDetection",
"torch.nn.CrossEntropyLoss",
"ats.utils.regularizers.MultinomialEntropy",
"torch.cuda.is_available",
"models.attention_model.AttentionModelBddDetection",
"os.path.exists",
"dataset.bdd_detec... | [((970, 1025), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {'category': 'UserWarning'}), "('ignore', category=UserWarning)\n", (993, 1025), False, 'import warnings\n'), ((1772, 1873), 'torch.utils.data.DataLoader', 'DataLoader', (['train_dataset'], {'batch_size': 'opts.batch_size', 'shuffle... |
# coding: utf-8
# /*##########################################################################
#
# Copyright (c) 2017 European Synchrotron Radiation Facility
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
#... | [
"logging.getLogger",
"fabio.fabioimage.FabioImage",
"numpy.equal",
"silx.image.shapes.draw_line",
"numpy.array",
"sys.exc_info",
"numpy.isfinite",
"fabio.open",
"silx.third_party.EdfFile.EdfFile",
"os.remove",
"os.path.exists",
"os.path.isdir",
"numpy.empty",
"numpy.ones",
"os.path.split... | [((2042, 2069), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (2059, 2069), False, 'import logging\n'), ((2717, 2751), 'numpy.array', 'numpy.array', (['()'], {'dtype': 'numpy.uint8'}), '((), dtype=numpy.uint8)\n', (2728, 2751), False, 'import numpy\n'), ((3435, 3469), 'numpy.array', 'num... |
import os
import skimage
from skimage import io, util
from skimage.draw import circle
import numpy as np
import math
def circularcrop(img, border=200, threshold=20000, threshold1=100):
"""
This function trims the circular image by border pixels, nullifies outside borders
and crops the total img to the disk... | [
"skimage.draw.circle",
"math.sqrt",
"numpy.argmax",
"numpy.sum",
"numpy.zeros",
"skimage.util.crop",
"numpy.argmin"
] | [((627, 646), 'numpy.sum', 'np.sum', (['img'], {'axis': '(2)'}), '(img, axis=2)\n', (633, 646), True, 'import numpy as np\n'), ((798, 822), 'numpy.argmax', 'np.argmax', (['cols[0:width]'], {}), '(cols[0:width])\n', (807, 822), True, 'import numpy as np\n'), ((890, 919), 'numpy.argmax', 'np.argmax', (['rows[0:height / 2... |
#!/bin/env/python
# -*- encoding: utf-8 -*-
"""
GridWorld Environment
"""
from __future__ import division, print_function
import cv2
import numpy as np
from matplotlib import pyplot as plt
from markov_rlzoo import MDPEnv, MDPState
class GridWorld(MDPEnv):
def __init__(self, shape: tuple = (4, 4), ends: list = ... | [
"numpy.uint8",
"matplotlib.pyplot.imshow",
"markov_rlzoo.MDPState",
"numpy.max",
"cv2.imshow",
"numpy.zeros",
"cv2.resize",
"cv2.waitKey",
"matplotlib.pyplot.show"
] | [((3015, 3035), 'numpy.zeros', 'np.zeros', (['self.shape'], {}), '(self.shape)\n', (3023, 3035), True, 'import numpy as np\n'), ((3202, 3215), 'numpy.max', 'np.max', (['frame'], {}), '(frame)\n', (3208, 3215), True, 'import numpy as np\n'), ((3302, 3317), 'numpy.uint8', 'np.uint8', (['frame'], {}), '(frame)\n', (3310, ... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import os
import random
import sys
import time
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow ... | [
"tensorflow.app.flags.DEFINE_float",
"tensorflow.initialize_all_variables",
"tensorflow.gfile.Exists",
"numpy.random.random_sample",
"tensorflow.app.flags.DEFINE_integer",
"tracer.data_utils.decode_base",
"tensorflow.Session",
"os.path.join",
"numpy.argmax",
"tensorflow.app.flags.DEFINE_string",
... | [((857, 922), 'tensorflow.app.flags.DEFINE_float', 'tf.app.flags.DEFINE_float', (['"""learning_rate"""', '(0.5)', '"""Learning rate."""'], {}), "('learning_rate', 0.5, 'Learning rate.')\n", (882, 922), True, 'import tensorflow as tf\n'), ((923, 1026), 'tensorflow.app.flags.DEFINE_float', 'tf.app.flags.DEFINE_float', ([... |
import cv2
import os
import sys
import numpy as np
def main(argv):
content = argv[0]
style = argv[1]
output = argv[2]
loss_ratio = "1"
interim_content = "img_without_alpha.jpg"
interim_output = "interim_" + output
exec_format = "python run_test.py --content {} --style_model {} --output {}"
... | [
"cv2.imwrite",
"cv2.merge",
"numpy.zeros",
"os.system",
"cv2.resize",
"cv2.imread",
"os.remove"
] | [((525, 548), 'cv2.imread', 'cv2.imread', (['content', '(-1)'], {}), '(content, -1)\n', (535, 548), False, 'import cv2\n'), ((572, 643), 'cv2.resize', 'cv2.resize', (['img_with_alpha', '(norm_icon_w, norm_icon_h)', 'cv2.INTER_CUBIC'], {}), '(img_with_alpha, (norm_icon_w, norm_icon_h), cv2.INTER_CUBIC)\n', (582, 643), F... |
"""
Lyapunov module
=================
Module with the classes of multi-thread the computation of the various
`Lyapunov vectors`_ and `exponents`_. Integrate using the `Runge-Kutta method`_
defined in the :mod:`~.integrators.integrate` module.
See :cite:`lyap-KP2012` for more details on the Lya... | [
"multiprocessing.JoinableQueue",
"multiprocessing.cpu_count",
"numpy.array",
"qgs.functions.util.solve_triangular_matrix",
"numpy.mod",
"numpy.arange",
"numpy.linalg.qr",
"numpy.random.random",
"numpy.diff",
"qgs.functions.util.reverse",
"numpy.concatenate",
"numpy.abs",
"numpy.eye",
"qgs.... | [((20501, 20528), 'numpy.zeros', 'np.zeros', (['(1, n_dim, n_dim)'], {}), '((1, n_dim, n_dim))\n', (20509, 20528), True, 'import numpy as np\n'), ((20541, 20554), 'numpy.eye', 'np.eye', (['n_dim'], {}), '(n_dim)\n', (20547, 20554), True, 'import numpy as np\n'), ((20755, 20798), 'numpy.zeros', 'np.zeros', (['(n_traj, n... |
import numpy as np
def pack_selector_from_mask(boolarray):
"""
pack all contiguous selectors into slices. Remember that
tiledb multi_index requires INCLUSIVE indices.
"""
if boolarray is None:
return slice(None)
assert type(boolarray) == np.ndarray
assert boolarray.dtype == bool... | [
"numpy.nonzero"
] | [((337, 358), 'numpy.nonzero', 'np.nonzero', (['boolarray'], {}), '(boolarray)\n', (347, 358), True, 'import numpy as np\n')] |
# The MIT License (MIT) # Copyright (c) 2014-2017 University of Bristol
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to... | [
"sklearn.datasets.load_iris",
"datetime.datetime.utcfromtimestamp",
"datetime.datetime.utcnow",
"numpy.array",
"hyperstream.TimeInterval",
"numpy.testing.assert_almost_equal",
"numpy.nanmean",
"datetime.timedelta"
] | [((4352, 4363), 'sklearn.datasets.load_iris', 'load_iris', ([], {}), '()\n', (4361, 4363), False, 'from sklearn.datasets import load_iris\n'), ((4735, 4752), 'datetime.datetime.utcnow', 'datetime.utcnow', ([], {}), '()\n', (4750, 4752), False, 'from datetime import datetime, timedelta\n'), ((4811, 4839), 'datetime.date... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import itertools
rng = np.random.RandomState(42)
def svdw(m):
n = m.shape[0]
assert m.shape == (n, n)
u, s, vt = np.linalg.svd(m)
w = u @ vt
assert np.allclose(u.T @ u, np.eye(n))
assert np.allclose(w.T @ w, np.eye(n))
asse... | [
"numpy.abs",
"numpy.eye",
"numpy.linalg.matrix_rank",
"numpy.linalg.det",
"numpy.diag",
"numpy.zeros",
"numpy.empty_like",
"numpy.empty",
"numpy.linalg.svd",
"numpy.random.RandomState",
"numpy.set_printoptions"
] | [((91, 116), 'numpy.random.RandomState', 'np.random.RandomState', (['(42)'], {}), '(42)\n', (112, 116), True, 'import numpy as np\n'), ((194, 210), 'numpy.linalg.svd', 'np.linalg.svd', (['m'], {}), '(m)\n', (207, 210), True, 'import numpy as np\n'), ((605, 640), 'numpy.empty', 'np.empty', (['(n, n * n)'], {'dtype': 'M.... |
import numpy as np
class TwoStepDom(object):
'''Class to get the two step dominance matrix and rankings'''
def __init__(self, N_teams, week, sq_weight=0.25, decay_penalty=0.5):
self.w_sq = sq_weight
self.w_l = 1. - sq_weight
self.win_matrix = np.zeros(shape=(N_teams,N_teams))
self.w... | [
"numpy.zeros",
"numpy.linalg.matrix_power"
] | [((276, 310), 'numpy.zeros', 'np.zeros', ([], {'shape': '(N_teams, N_teams)'}), '(shape=(N_teams, N_teams))\n', (284, 310), True, 'import numpy as np\n'), ((1219, 1261), 'numpy.linalg.matrix_power', 'np.linalg.matrix_power', (['self.win_matrix', '(2)'], {}), '(self.win_matrix, 2)\n', (1241, 1261), True, 'import numpy a... |
import io
from logging import raiseExceptions
from typing import Any
import magic
import numpy as np
import pandas as pd
from icecream import ic
from PIL import Image
import cv2
from .file_management import get_buffer_category, get_buffer_type, get_mime_category
from pathlib import Path
def _open(input, btype=None, ... | [
"numpy.uint8",
"icecream.ic",
"pandas.read_csv",
"pathlib.Path",
"io.BytesIO",
"logging.raiseExceptions",
"pandas.read_html",
"numpy.fromstring",
"pandas.read_json"
] | [((485, 528), 'icecream.ic', 'ic', (['"""Converting io.BytesIO to bytes object"""'], {}), "('Converting io.BytesIO to bytes object')\n", (487, 528), False, 'from icecream import ic\n'), ((1157, 1174), 'icecream.ic', 'ic', (['"""infere type"""'], {}), "('infere type')\n", (1159, 1174), False, 'from icecream import ic\n'... |
import numpy as np
from topocalc.horizon import horizon
from smrf.envphys.constants import (GRAVITY, IR_MAX, IR_MIN, MOL_AIR,
SEA_LEVEL, STD_AIRTMP, STD_LAPSE,
VISIBLE_MAX, VISIBLE_MIN)
from smrf.envphys.solar.irradiance import direct_solar_irradi... | [
"numpy.copy",
"numpy.mean",
"numpy.abs",
"numpy.arccos",
"numpy.zeros_like",
"topocalc.horizon.horizon",
"smrf.envphys.thermal.topotherm.hysat",
"smrf.envphys.solar.twostream.twostream",
"smrf.envphys.solar.irradiance.direct_solar_irradiance"
] | [((1896, 1950), 'smrf.envphys.solar.irradiance.direct_solar_irradiance', 'direct_solar_irradiance', (['date_time'], {'w': 'wavelength_range'}), '(date_time, w=wavelength_range)\n', (1919, 1950), False, 'from smrf.envphys.solar.irradiance import direct_solar_irradiance\n'), ((2042, 2077), 'topocalc.horizon.horizon', 'ho... |
# Visualization function
import numpy as np
import matplotlib.pyplot as plt
from math import ceil
from PIL import Image
from scipy.ndimage.filters import gaussian_filter
def img_combine(img, ncols=5, size=1, path=False):
"""
Draw the images with array
img: image array to plot - size = n x im_w x im_h x 3
... | [
"PIL.Image.fromarray",
"math.ceil",
"matplotlib.pyplot.savefig",
"scipy.ndimage.filters.gaussian_filter",
"numpy.hstack",
"numpy.zeros",
"numpy.vstack",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.subplots",
"numpy.arange",
"matplotlib.pyplot.show"
] | [((399, 506), 'matplotlib.pyplot.subplots', 'plt.subplots', ([], {'nrows': 'nrows', 'ncols': 'ncols', 'sharex': '(True)', 'sharey': '(True)', 'figsize': '(ncols * size, nrows * size)'}), '(nrows=nrows, ncols=ncols, sharex=True, sharey=True, figsize=(\n ncols * size, nrows * size))\n', (411, 506), True, 'import matpl... |
'''
May 2020 by <NAME>
<EMAIL>
https://www.github.com/sebbarb/
'''
import sys
sys.path.append('../lib/')
import numpy as np
import pandas as pd
from lifelines import CoxPHFitter
from utils import *
import feather
from hyperparameters import Hyperparameters
from pdb import set_trace as bp
def mai... | [
"hyperparameters.Hyperparameters",
"lifelines.CoxPHFitter",
"numpy.dot",
"pandas.DataFrame",
"numpy.load",
"sys.path.append"
] | [((86, 112), 'sys.path.append', 'sys.path.append', (['"""../lib/"""'], {}), "('../lib/')\n", (101, 112), False, 'import sys\n'), ((379, 396), 'hyperparameters.Hyperparameters', 'Hyperparameters', ([], {}), '()\n', (394, 396), False, 'from hyperparameters import Hyperparameters\n'), ((409, 470), 'numpy.load', 'np.load',... |
# -*- coding: utf-8 -*-
from typing import Iterable, Union
import numpy as np
import pytrol.util.graphprocessor as gp
class Network:
def __init__(self, graph: np.ndarray, edges_to_vertices: np.ndarray,
edges_lgts: np.ndarray, edge_activations,
vertices: dict, edges: dict, locat... | [
"pytrol.util.graphprocessor.v_to_v_tc_paths",
"pytrol.util.graphprocessor.edge",
"pytrol.util.graphprocessor.build_tc_neighbours",
"pytrol.util.graphprocessor.target",
"numpy.array",
"pytrol.util.graphprocessor.fw_distances",
"pytrol.util.graphprocessor.min_and_max_dists",
"numpy.zeros",
"numpy.lina... | [((956, 998), 'numpy.array', 'np.array', (['edge_activations'], {'dtype': 'np.int16'}), '(edge_activations, dtype=np.int16)\n', (964, 998), True, 'import numpy as np\n'), ((1064, 1098), 'pytrol.util.graphprocessor.build_tc_neighbours', 'gp.build_tc_neighbours', (['self.graph'], {}), '(self.graph)\n', (1086, 1098), True... |
from os.path import join
import cv2
import numpy as np
from sklearn.utils import shuffle
import config as cfg
import random
def trans_image(image, steer, trans_range):
# Translation
tr_x = trans_range * np.random.uniform() - trans_range / 2
steer_ang = steer + tr_x / trans_range * 0.4
tr_y = 40 * np.r... | [
"cv2.warpAffine",
"random.choice",
"sklearn.utils.shuffle",
"numpy.zeros",
"numpy.random.uniform",
"cv2.resize",
"numpy.float32"
] | [((359, 399), 'numpy.float32', 'np.float32', (['[[1, 0, tr_x], [0, 1, tr_y]]'], {}), '([[1, 0, tr_x], [0, 1, tr_y]])\n', (369, 399), True, 'import numpy as np\n'), ((415, 457), 'cv2.warpAffine', 'cv2.warpAffine', (['image', 'Trans_M', '(320, 160)'], {}), '(image, Trans_M, (320, 160))\n', (429, 457), False, 'import cv2\... |
import numpy
def predict_salary(payment_from, payment_to):
if not payment_from and not payment_to:
return None
elif payment_from and payment_to:
return numpy.mean([payment_from, payment_to])
elif payment_from:
return payment_from * 1.2
else:
return payment_to * 0.8
| [
"numpy.mean"
] | [((178, 216), 'numpy.mean', 'numpy.mean', (['[payment_from, payment_to]'], {}), '([payment_from, payment_to])\n', (188, 216), False, 'import numpy\n')] |
#!/usr/bin/env python
import roslib; roslib.load_manifest('numpy_eigen'); roslib.load_manifest('rostest');
import numpy_eigen
import numpy_eigen.test as npe
import numpy
import sys
# http://docs.python.org/library/unittest.html#test-cases
import unittest
class TestEigen(unittest.TestCase):
def assertMatrixClos... | [
"numpy.random.random",
"numpy.abs",
"rostest.rosrun",
"roslib.load_manifest"
] | [((37, 72), 'roslib.load_manifest', 'roslib.load_manifest', (['"""numpy_eigen"""'], {}), "('numpy_eigen')\n", (57, 72), False, 'import roslib\n'), ((74, 105), 'roslib.load_manifest', 'roslib.load_manifest', (['"""rostest"""'], {}), "('rostest')\n", (94, 105), False, 'import roslib\n'), ((5140, 5194), 'rostest.rosrun', ... |
import numpy as np
from numpy.random import choice, uniform
import json
from enum import Enum
import os
import math
from gtts import gTTS
DIR = Enum('DIR', 'right left up down clock anticlock bigger smaller')
ACTION = Enum('ACTION', 'shift rotate roll jump grow circle')
SPEED = Enum('SPEED', 'slow fast')
SHAPE = Enum(... | [
"os.listdir",
"os.makedirs",
"numpy.random.choice",
"os.path.join",
"os.path.isfile",
"os.path.isdir",
"enum.Enum",
"numpy.random.uniform",
"numpy.arange",
"os.remove"
] | [((145, 209), 'enum.Enum', 'Enum', (['"""DIR"""', '"""right left up down clock anticlock bigger smaller"""'], {}), "('DIR', 'right left up down clock anticlock bigger smaller')\n", (149, 209), False, 'from enum import Enum\n'), ((219, 271), 'enum.Enum', 'Enum', (['"""ACTION"""', '"""shift rotate roll jump grow circle""... |
import cv2
import numpy as np
import pyrealsense2 as rs
class DepthFiltering():
def __init__(self, temporal_smoothing=5):
self.temporal_smoothing = temporal_smoothing
self.dec_filter = rs.decimation_filter() # Decimation - reduces depth frame density
self.spat_filter = rs.spatial_filter()... | [
"pyrealsense2.temporal_filter",
"numpy.dstack",
"pyrealsense2.decimation_filter",
"pyrealsense2.hole_filling_filter",
"cv2.convertScaleAbs",
"numpy.hstack",
"numpy.where",
"pyrealsense2.disparity_transform",
"cv2.imshow",
"pyrealsense2.spatial_filter",
"cv2.namedWindow"
] | [((207, 229), 'pyrealsense2.decimation_filter', 'rs.decimation_filter', ([], {}), '()\n', (227, 229), True, 'import pyrealsense2 as rs\n'), ((301, 320), 'pyrealsense2.spatial_filter', 'rs.spatial_filter', ([], {}), '()\n', (318, 320), True, 'import pyrealsense2 as rs\n'), ((409, 429), 'pyrealsense2.temporal_filter', 'r... |
# -*- coding: utf-8 -*-
import os
import copy
import odl
import torch
import numpy as np
from math import ceil
from tqdm import tqdm
from warnings import warn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torch.optim.lr_scheduler import CyclicLR, OneCycleLR
from dival... | [
"dival.measure.PSNR",
"torch.max",
"torch.min",
"torch.nn.MSELoss",
"torch.cuda.is_available",
"torch.random.manual_seed",
"torch.set_grad_enabled",
"numpy.asarray",
"warnings.warn",
"odl.power_method_opnorm",
"torch.cat",
"torch.device",
"math.ceil",
"torch.load",
"tqdm.tqdm",
"os.pat... | [((499, 524), 'os.path.abspath', 'os.path.abspath', (['__file__'], {}), '(__file__)\n', (514, 524), False, 'import os\n'), ((5460, 5487), 'torch.random.manual_seed', 'torch.random.manual_seed', (['(1)'], {}), '(1)\n', (5484, 5487), False, 'import torch\n'), ((5998, 6016), 'torch.nn.MSELoss', 'torch.nn.MSELoss', ([], {}... |
import numpy as np
import csv
from collections import namedtuple
import json
import torch
import torch.nn.utils.rnn as rnn_utils
import torch.nn as nn
import torch.optim as optim
from torchsummary import summary
from data_index import get_dataset, get_batch
import data_index
import math
#import data_index.info as info
... | [
"torch.manual_seed",
"os.path.exists",
"torch.nn.ReLU",
"math.ceil",
"torch.nn.LSTM",
"torch.load",
"numpy.array",
"torch.nn.utils.rnn.pack_padded_sequence",
"data_index.get_batch",
"torch.nn.Linear",
"torchsummary.summary",
"torch.nn.utils.rnn.pad_packed_sequence",
"torch.device"
] | [((394, 427), 'torch.manual_seed', 'torch.manual_seed', (["config['seed']"], {}), "(config['seed'])\n", (411, 427), False, 'import torch\n'), ((3415, 3431), 'numpy.array', 'np.array', (['losses'], {}), '(losses)\n', (3423, 3431), True, 'import numpy as np\n'), ((3527, 3577), 'math.ceil', 'math.ceil', (["(data_index.TRA... |
import pytest
import numpy as np
import discretisedfield as df
import micromagneticmodel as mm
class TestZeeman:
@pytest.fixture(autouse=True)
def _setup_calculator(self, calculator):
self.calculator = calculator
def setup(self):
p1 = (-10e-9, -5e-9, -3e-9)
p2 = (10e-9, 5e-9, 3e-9... | [
"micromagneticmodel.System",
"numpy.sin",
"discretisedfield.Field",
"numpy.subtract",
"numpy.cos",
"pytest.fixture",
"discretisedfield.Region",
"micromagneticmodel.Zeeman",
"discretisedfield.Mesh"
] | [((120, 148), 'pytest.fixture', 'pytest.fixture', ([], {'autouse': '(True)'}), '(autouse=True)\n', (134, 148), False, 'import pytest\n'), ((344, 367), 'discretisedfield.Region', 'df.Region', ([], {'p1': 'p1', 'p2': 'p2'}), '(p1=p1, p2=p2)\n', (353, 367), True, 'import discretisedfield as df\n'), ((795, 815), 'micromagn... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# test_recoverstats.py
#
# Copyright 2016 <NAME> <<EMAIL>>
#
import os
import shlex
import subprocess
import sys
sys.path.insert(0, os.path.abspath('..'))
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import stats
import sep
from astropy.convol... | [
"propercoadd.SingleImage",
"imsim.simtools.delta_point",
"astropy.table.Table",
"matplotlib.pyplot.ylabel",
"photutils.daofind",
"matplotlib.pyplot.imshow",
"imsim.simtools.cartesian_product",
"photutils.psf.IntegratedGaussianPRF",
"matplotlib.pyplot.axhline",
"numpy.linspace",
"imsim.simtools.i... | [((2203, 2249), 'os.path.abspath', 'os.path.abspath', (['"""./test_images/recover_stats"""'], {}), "('./test_images/recover_stats')\n", (2218, 2249), False, 'import os\n'), ((2255, 2294), 'numpy.linspace', 'np.linspace', (['(5 * FWHM)', '(N - 5 * FWHM)', '(10)'], {}), '(5 * FWHM, N - 5 * FWHM, 10)\n', (2266, 2294), Tru... |
import numpy as np
import os.path as osp
from unittest import TestCase
from datumaro.components.project import Project
from datumaro.components.extractor import Extractor, DatasetItem
from datumaro.util.test_utils import TestDir
from datumaro.util.image import save_image
class ImageDirFormatTest(TestCase):
clas... | [
"datumaro.util.test_utils.TestDir",
"datumaro.components.project.Project.import_from",
"os.path.join",
"numpy.ones"
] | [((583, 592), 'datumaro.util.test_utils.TestDir', 'TestDir', ([], {}), '()\n', (590, 592), False, 'from datumaro.util.test_utils import TestDir\n'), ((824, 871), 'datumaro.components.project.Project.import_from', 'Project.import_from', (['test_dir.path', '"""image_dir"""'], {}), "(test_dir.path, 'image_dir')\n", (843, ... |
import corner as triangle
import numpy as np
from matplotlib import rcParams
run_name='Mtheory_nax20_DM_run1'
chain=np.load(run_name+'.npy')
nwalkers, nsteps,ndim = np.shape(chain)
burnin = nsteps/4
# Make sample chain removing burnin
combinedUSE=chain[:,burnin:,:].reshape((-1,ndim))
# Priors
lFL3min,lFL3max=100.,11... | [
"numpy.exp",
"numpy.shape",
"numpy.load",
"numpy.linspace"
] | [((117, 143), 'numpy.load', 'np.load', (["(run_name + '.npy')"], {}), "(run_name + '.npy')\n", (124, 143), True, 'import numpy as np\n'), ((166, 181), 'numpy.shape', 'np.shape', (['chain'], {}), '(chain)\n', (174, 181), True, 'import numpy as np\n'), ((1021, 1060), 'numpy.linspace', 'np.linspace', (['lFL3min', 'lFL3max... |
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import numpy as np
def slidingCoefficient(longSlipValue, latSlipValue, asymptoteValue, asymptoteSlipLong, asymptoteLatSlip):
combinedSlip = np.sqrt(latSl... | [
"matplotlib.pyplot.figure",
"numpy.sqrt",
"numpy.arange",
"matplotlib.pyplot.show"
] | [((1559, 1571), 'matplotlib.pyplot.figure', 'plt.figure', ([], {}), '()\n', (1569, 1571), True, 'import matplotlib.pyplot as plt\n'), ((1607, 1632), 'numpy.arange', 'np.arange', (['(0.0)', '(0.9)', '(0.01)'], {}), '(0.0, 0.9, 0.01)\n', (1616, 1632), True, 'import numpy as np\n'), ((1637, 1658), 'numpy.arange', 'np.aran... |
from __future__ import print_function
from __future__ import division
from . import _C
import numpy as np
import random
from scipy.stats import t
from copy import copy, deepcopy
from fuzzytools import numba as ftnumba
from . import flux_magnitude as flux_magnitude
OBS_NOISE_RANGE = 1
CHECK = _C.CHECK
MIN_POINTS_LIGHT... | [
"numpy.clip",
"numpy.mean",
"numpy.all",
"numpy.random.choice",
"numpy.where",
"numpy.log",
"numpy.argmax",
"numpy.max",
"numpy.argsort",
"numpy.array",
"random.random",
"numpy.sum",
"numpy.concatenate",
"numpy.min",
"numpy.argmin",
"copy.copy"
] | [((1613, 1647), 'numpy.clip', 'np.clip', (['new_obs', 'min_lim', 'max_lim'], {}), '(new_obs, min_lim, max_lim)\n', (1620, 1647), True, 'import numpy as np\n'), ((1659, 1694), 'numpy.clip', 'np.clip', (['new_obs', 'obs_min_lim', 'None'], {}), '(new_obs, obs_min_lim, None)\n', (1666, 1694), True, 'import numpy as np\n'),... |
import cv2
import numpy as np
def projective_transform(img, points_img, points_another, width, height):
"""
2画像間で射影変換を行う
Parameters
----------
img : numpy.ndarray
入力画像
points_img: list of lists
画像 `img` における対応点
points_another : list of lists
もう一方の画像における対応点
w... | [
"cv2.warpPerspective",
"numpy.float32",
"cv2.cvtColor",
"cv2.findHomography"
] | [((661, 683), 'numpy.float32', 'np.float32', (['points_img'], {}), '(points_img)\n', (671, 683), True, 'import numpy as np\n'), ((705, 731), 'numpy.float32', 'np.float32', (['points_another'], {}), '(points_another)\n', (715, 731), True, 'import numpy as np\n'), ((763, 812), 'cv2.findHomography', 'cv2.findHomography', ... |
import numpy as np
import scipy.sparse as sp
from scipy.sparse.linalg import eigsh
def spec_proj(W,k,alg=3):
n = W.shape[0]
deg = sp.spdiags(np.sum(W,1).T,0,n,n)
L = deg - W
if alg == 1:
E,V = eigsh(L,k,sigma=-1,tol=1e-6,return_eigenvectors=True)
V1 = V
elif alg == 2:
E... | [
"numpy.sum",
"numpy.multiply",
"scipy.sparse.spdiags",
"scipy.sparse.linalg.eigsh"
] | [((223, 281), 'scipy.sparse.linalg.eigsh', 'eigsh', (['L', 'k'], {'sigma': '(-1)', 'tol': '(1e-06)', 'return_eigenvectors': '(True)'}), '(L, k, sigma=-1, tol=1e-06, return_eigenvectors=True)\n', (228, 281), False, 'from scipy.sparse.linalg import eigsh\n'), ((150, 162), 'numpy.sum', 'np.sum', (['W', '(1)'], {}), '(W, 1... |
# 4 of case study
import numpy as np
np.random.seed(123)
# Starting step
step = 50
# Roll the dice
dice= np.random.randint(1,7)
# Finish the control construct
if dice <= 2 :
step = step - 1
elif dice <=5 :
step= step+1
else:
step = step + np.random.randint(1,7)
# Print out dice and step
print(dice)
prin... | [
"numpy.random.randint",
"numpy.random.seed"
] | [((37, 56), 'numpy.random.seed', 'np.random.seed', (['(123)'], {}), '(123)\n', (51, 56), True, 'import numpy as np\n'), ((107, 130), 'numpy.random.randint', 'np.random.randint', (['(1)', '(7)'], {}), '(1, 7)\n', (124, 130), True, 'import numpy as np\n'), ((254, 277), 'numpy.random.randint', 'np.random.randint', (['(1)'... |
import tarfile
import numpy as np
import os
import sys
import h5py
from scipy import ndimage
import random
import pickle
from matplotlib import pyplot as plt
def maybe_extract(filename, force=False):
root = os.path.splitext(os.path.splitext(filename)[0])[0] # remove .tar.gz
if os.path.isdir(root) and not for... | [
"matplotlib.pyplot.imshow",
"tarfile.open",
"pickle.dump",
"os.listdir",
"scipy.ndimage.zoom",
"matplotlib.pyplot.show",
"os.path.join",
"pickle.load",
"os.path.splitext",
"h5py.File",
"scipy.ndimage.imread",
"os.path.dirname",
"matplotlib.pyplot.figure",
"os.path.isdir",
"numpy.ndarray"... | [((2678, 2732), 'random.randint', 'random.randint', (['(0)', '(single_digit_size - crop_digit_size)'], {}), '(0, single_digit_size - crop_digit_size)\n', (2692, 2732), False, 'import random\n'), ((3337, 3362), 'os.path.dirname', 'os.path.dirname', (['mat_file'], {}), '(mat_file)\n', (3352, 3362), False, 'import os\n'),... |
import numpy as np
import os
from .utils.utils import get_yolo_boxes, makedirs
def evaluate_full(model,
generator,
obj_thresh = 0.5,
nms_thresh = 0.5,
net_h = 416,
net_w = 416,
save_path = ""):
# Predict box... | [
"numpy.where",
"numpy.argmax",
"os.path.split",
"numpy.argsort",
"numpy.array",
"numpy.sum",
"numpy.zeros",
"os.path.isdir",
"numpy.append",
"numpy.concatenate",
"numpy.expand_dims",
"numpy.finfo",
"numpy.cumsum",
"numpy.maximum"
] | [((10891, 10908), 'numpy.maximum', 'np.maximum', (['iw', '(0)'], {}), '(iw, 0)\n', (10901, 10908), True, 'import numpy as np\n'), ((10918, 10935), 'numpy.maximum', 'np.maximum', (['ih', '(0)'], {}), '(ih, 0)\n', (10928, 10935), True, 'import numpy as np\n'), ((11595, 11633), 'numpy.concatenate', 'np.concatenate', (['([... |
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 11 21:43:14 2018
@author: robot
"""
import plotly.plotly as py
import plotly.graph_objs as go
import plotly
import random
import numpy as np
import copy as cp
import copy
import readCfg.read_cfg as rd
from IPython.display import HTML,display
import colorlover as cl
impo... | [
"readCfg.read_cfg.Read_Cfg",
"plotly.plotly.image.save_as",
"plotly.plotly.sign_in",
"math.floor",
"plotly.offline.plot",
"colorlover.interp",
"numpy.zeros",
"copy.deepcopy"
] | [((6172, 6219), 'plotly.plotly.sign_in', 'py.sign_in', (['"""tesla_fox"""', '"""HOTRQ3nIOdYUUszDIfgN"""'], {}), "('tesla_fox', 'HOTRQ3nIOdYUUszDIfgN')\n", (6182, 6219), True, 'import plotly.plotly as py\n'), ((6316, 6340), 'readCfg.read_cfg.Read_Cfg', 'rd.Read_Cfg', (['cfgFileName'], {}), '(cfgFileName)\n', (6327, 6340... |
from tabula import read_pdf
import re
import spacy
from spacy import displacy
from collections import Counter
import en_core_web_sm
nlp = en_core_web_sm.load()
import PyPDF2
from dateutil import parser
from fpdf import FPDF
import locale
locale.setlocale(locale.LC_ALL,'')
import numpy as np
import matplotlib as mpl
imp... | [
"en_core_web_sm.load",
"matplotlib.ticker.ScalarFormatter",
"fpdf.FPDF",
"tabula.read_pdf.getNumPages",
"numpy.arange",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.close",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.yscale",
"PyPDF2.PdfFileReader",
"dateutil.parser.parse",
"matplotlib.pypl... | [((138, 159), 'en_core_web_sm.load', 'en_core_web_sm.load', ([], {}), '()\n', (157, 159), False, 'import en_core_web_sm\n'), ((238, 273), 'locale.setlocale', 'locale.setlocale', (['locale.LC_ALL', '""""""'], {}), "(locale.LC_ALL, '')\n", (254, 273), False, 'import locale\n'), ((390, 445), 'tabula.read_pdf', 'read_pdf',... |
from __future__ import division
from __future__ import print_function
import prettytensor as pt
import tensorflow as tf
import numpy as np
import scipy.misc
import os
import sys
from six.moves import range
from progressbar import ETA, Bar, Percentage, ProgressBar
from misc.config import cfg
from misc.utils import mk... | [
"numpy.array",
"progressbar.Percentage",
"progressbar.ProgressBar",
"tensorflow.summary.image",
"tensorflow.random_normal",
"tensorflow.train.Coordinator",
"tensorflow.placeholder",
"tensorflow.Session",
"tensorflow.concat",
"tensorflow.ConfigProto",
"sys.stdout.flush",
"tensorflow.stack",
"... | [((490, 552), 'tensorflow.placeholder', 'tf.placeholder', (['tf.float32', '[]'], {'name': '"""generator_learning_rate"""'}), "(tf.float32, [], name='generator_learning_rate')\n", (504, 552), True, 'import tensorflow as tf\n'), ((619, 685), 'tensorflow.placeholder', 'tf.placeholder', (['tf.float32', '[]'], {'name': '"""... |
import numpy as np
from matplotlib import pyplot
from env import StochasticMAB
# Use Bernoulli reward distribution, and Beta-Kernel for sampling
def subsample_ts(total_time_slot, arm_num, seed=10):
distribution = "Gaussian"
bandit_model = StochasticMAB(n_arms=arm_num, random_type=distribution)
total_rewar... | [
"numpy.sqrt",
"env.StochasticMAB",
"numpy.average",
"matplotlib.pyplot.plot",
"numpy.argmax",
"numpy.random.randint",
"numpy.var",
"matplotlib.pyplot.show"
] | [((249, 304), 'env.StochasticMAB', 'StochasticMAB', ([], {'n_arms': 'arm_num', 'random_type': 'distribution'}), '(n_arms=arm_num, random_type=distribution)\n', (262, 304), False, 'from env import StochasticMAB\n'), ((645, 693), 'numpy.random.randint', 'np.random.randint', (['(0)', 'arm_num', 'subsample_arm_num'], {}), ... |
#!/usr/bin/env python3
from argparse import ArgumentParser
import os
import subprocess
import numpy as np
from transformers import RobertaTokenizer, RobertaModel
import torch
import tqdm
from chg.db.database import get_store
# fix odd fault...
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
def remove_color_ascii(msg... | [
"transformers.RobertaTokenizer.from_pretrained",
"argparse.ArgumentParser",
"subprocess.Popen",
"tqdm.tqdm",
"torch.stack",
"pdb.post_mortem",
"numpy.sum",
"torch.tensor",
"transformers.RobertaModel.from_pretrained",
"torch.no_grad",
"chg.db.database.get_store"
] | [((334, 443), 'subprocess.Popen', 'subprocess.Popen', (['"""sed \'s/\x1b\\\\[[0-9;]*m//g\'"""'], {'stdin': 'subprocess.PIPE', 'stdout': 'subprocess.PIPE', 'shell': '(True)'}), '("sed \'s/\\x1b\\\\[[0-9;]*m//g\'", stdin=subprocess.PIPE,\n stdout=subprocess.PIPE, shell=True)\n', (350, 443), False, 'import subprocess\n... |
# -*- coding: utf8 -*-
# engine
# helper class for cuatro
# <NAME> 2021
import numpy as np
import random
import copy
import time
version = 'engine.v.1.0.0'
class State:
"""instance attributes:
size: int: size of one side of the board (defines a cube that holds the game)
win: int: how many items in a row... | [
"numpy.array",
"numpy.zeros",
"time.time",
"copy.deepcopy"
] | [((18413, 18432), 'copy.deepcopy', 'copy.deepcopy', (['self'], {}), '(self)\n', (18426, 18432), False, 'import copy\n'), ((2578, 2589), 'time.time', 'time.time', ([], {}), '()\n', (2587, 2589), False, 'import time\n'), ((2660, 2703), 'numpy.zeros', 'np.zeros', (['(self.size, self.size, self.size)'], {}), '((self.size, ... |
"""
Created on 16/03/2012
@author: victor
"""
import unittest
import pyproct.clustering.test.data as test_data
from pyproct.clustering.cluster import Cluster, cluster_from_tuple, get_cluster_sizes, gen_clusters_from_class_list
import numpy
from pyRMSD.condensedMatrix import CondensedMatrix
import os
class Test(unitt... | [
"pyproct.clustering.cluster.gen_clusters_from_class_list",
"pyproct.clustering.cluster.Cluster.to_dic",
"os.path.join",
"pyproct.clustering.cluster.Cluster.from_dic",
"pyproct.clustering.cluster.cluster_from_tuple",
"pyRMSD.condensedMatrix.CondensedMatrix",
"pyproct.clustering.cluster.Cluster",
"unitt... | [((8271, 8286), 'unittest.main', 'unittest.main', ([], {}), '()\n', (8284, 8286), False, 'import unittest\n'), ((383, 430), 'pyproct.clustering.cluster.Cluster', 'Cluster', ([], {'prototype': '(0)', 'elements': '[0, 4, 5, 7, 13]'}), '(prototype=0, elements=[0, 4, 5, 7, 13])\n', (390, 430), False, 'from pyproct.clusteri... |
"""
Code to plot average nearest neighbor distance between fish in a school as a function of group size - one line per water temperature.
"""
# imports
import sys, os
import numpy as np
import matplotlib.pyplot as plt
import pickle
from matplotlib import cm
in_dir1 = '../../output/temp_collective/roi/... | [
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.locator_params",
"matplotlib.pyplot.close",
"matplotlib.pyplot.figure",
"numpy.linspace",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.show"
] | [((746, 762), 'matplotlib.pyplot.close', 'plt.close', (['"""all"""'], {}), "('all')\n", (755, 762), True, 'import matplotlib.pyplot as plt\n'), ((800, 827), 'matplotlib.pyplot.figure', 'plt.figure', ([], {'figsize': '(12, 8)'}), '(figsize=(12, 8))\n', (810, 827), True, 'import matplotlib.pyplot as plt\n'), ((1158, 1197... |
import numpy as np
import json
from importlib import reload
import os
from models.core.tf_models.cae_model import CAE
import pickle
import tensorflow as tf
import dill
from collections import deque
from models.core.tf_models import utils
from scipy.interpolate import CubicSpline
import time
from models.core... | [
"tensorflow.train.Checkpoint",
"numpy.array",
"numpy.arange",
"dill.load",
"os.listdir",
"numpy.repeat",
"numpy.reshape",
"collections.deque",
"scipy.interpolate.CubicSpline",
"numpy.stack",
"numpy.random.seed",
"numpy.concatenate",
"numpy.ceil",
"numpy.all",
"pickle.load",
"numpy.squa... | [((349, 366), 'importlib.reload', 'reload', (['cae_model'], {}), '(cae_model)\n', (355, 366), False, 'from importlib import reload\n'), ((3529, 3582), 'numpy.zeros', 'np.zeros', (['[st_arr.shape[0], steps_n, st_arr.shape[1]]'], {}), '([st_arr.shape[0], steps_n, st_arr.shape[1]])\n', (3537, 3582), True, 'import numpy as... |
"""
Artificial Intelligence for Humans
Volume 2: Nature-Inspired Algorithms
Python Version
http://www.aifh.org
http://www.jeffheaton.com
Code repository:
https://github.com/jeffheaton/aifh
Copyright 2014 by <NAME>
Licensed under the Apache License, Version 2.0 (the "License");
... | [
"random.choice",
"os.path.realpath",
"numpy.array",
"normalize.Normalize",
"copy.deepcopy",
"os.path.abspath",
"random.random",
"sys.path.append",
"random.randint"
] | [((3424, 3500), 'os.path.abspath', 'os.path.abspath', (["(aifh_dir + os.sep + '..' + os.sep + 'lib' + os.sep + 'aifh')"], {}), "(aifh_dir + os.sep + '..' + os.sep + 'lib' + os.sep + 'aifh')\n", (3439, 3500), False, 'import os\n'), ((3501, 3526), 'sys.path.append', 'sys.path.append', (['aifh_dir'], {}), '(aifh_dir)\n', ... |
# -*- coding: utf-8 -*-
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from scipy.stats import mode
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.linear... | [
"numpy.mean",
"pandas.read_csv",
"matplotlib.pyplot.show",
"sklearn.model_selection.train_test_split",
"matplotlib.pyplot.hlines",
"xgboost.XGBRegressor",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.title",
"pandas.concat",
"sklearn.model_selection.cross_val_score"
] | [((2098, 2130), 'pandas.read_csv', 'pd.read_csv', (['"""data/train_av.csv"""'], {}), "('data/train_av.csv')\n", (2109, 2130), True, 'import pandas as pd\n'), ((2146, 2177), 'pandas.read_csv', 'pd.read_csv', (['"""data/test_av.csv"""'], {}), "('data/test_av.csv')\n", (2157, 2177), True, 'import pandas as pd\n'), ((3687,... |
import sys
import string
import numpy as np
import astropy.units as u
from astropy.table import Column, Table
from astropy.io import ascii
from astropy.coordinates import SkyCoord
from astroquery.simbad import Simbad
from astroquery.esasky import ESASky
from astroquery.gaia import Gaia
star = sys.argv[1]
#star = 'H... | [
"numpy.shape",
"astropy.io.ascii.write",
"astroquery.simbad.Simbad.query_object",
"astroquery.gaia.Gaia.launch_job",
"numpy.array",
"astropy.table.Column",
"sys.exit",
"astroquery.simbad.Simbad.query_objectids",
"astroquery.simbad.Simbad.add_votable_fields"
] | [((484, 579), 'astroquery.simbad.Simbad.add_votable_fields', 'Simbad.add_votable_fields', (['"""pm"""', '"""plx"""', '"""rv_value"""', '"""rvz_error"""', '"""flux(V)"""', '"""flux_error(V)"""'], {}), "('pm', 'plx', 'rv_value', 'rvz_error', 'flux(V)',\n 'flux_error(V)')\n", (509, 579), False, 'from astroquery.simbad ... |
# -*- coding: utf-8 -*-
"""lhs_opt.py: Module to generate design matrix from an optimized
Latin Hypercube design
"""
import numpy as np
from . import lhs
__author__ = "<NAME>"
def create_ese(n: int, d: int, seed: int, max_outer: int,
obj_function: str="w2_discrepancy",
threshold_init: f... | [
"numpy.random.seed"
] | [((1869, 1889), 'numpy.random.seed', 'np.random.seed', (['seed'], {}), '(seed)\n', (1883, 1889), True, 'import numpy as np\n')] |
"""
Created on Thu Apr 9
@author: nrw
This plots residuals,
And also takes shelved torque data, adds in torque estimate and residual data
And writes it all to a CSV
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.plotly as py
import plotly.offline as po
import plotly.graph_o... | [
"plotly.tools.make_subplots",
"numpy.sqrt",
"numpy.hstack",
"numpy.array_str",
"sklearn.linear_model.Ridge",
"plotly.graph_objs.Scatter",
"sklearn.metrics.mean_squared_error",
"numpy.dot",
"shelve.open",
"numpy.savetxt",
"numpy.linalg.lstsq",
"sklearn.metrics.mean_absolute_error",
"sklearn.l... | [((1372, 1440), 'sklearn.linear_model.Ridge', 'Ridge', ([], {'fit_intercept': '(True)', 'alpha': '(1.0)', 'random_state': '(0)', 'normalize': '(True)'}), '(fit_intercept=True, alpha=1.0, random_state=0, normalize=True)\n', (1377, 1440), False, 'from sklearn.linear_model import Ridge\n'), ((1449, 1480), 'sklearn.linear_... |
import numpy as np
import random
from collections import defaultdict
class Agent:
def __init__(self, nA=6):
""" Initialize agent.
Params
======
- nA: number of actions available to the agent
"""
self.nA = nA
self.Q = defaultdict(lambda: np.zeros(self.nA))
... | [
"random.random",
"numpy.argmax",
"numpy.zeros",
"numpy.arange"
] | [((515, 530), 'random.random', 'random.random', ([], {}), '()\n', (528, 530), False, 'import random\n'), ((566, 590), 'numpy.argmax', 'np.argmax', (['self.Q[state]'], {}), '(self.Q[state])\n', (575, 590), True, 'import numpy as np\n'), ((301, 318), 'numpy.zeros', 'np.zeros', (['self.nA'], {}), '(self.nA)\n', (309, 318)... |
# -*- coding: UTF-8 -*-
"""
图像分类模型的训练主体
"""
import paddle.fluid as fluid
import numpy as np
import paddle
import reader
import os
import utils
import config
from ma_convcardseresnext import Ma_ConvCardSeResNeXt
def build_optimizer(parameter_list=None):
"""
构建优化器
:return:
"""
epoch = config.train_p... | [
"os.path.exists",
"paddle.fluid.dygraph.load_dygraph",
"paddle.fluid.dygraph.guard",
"paddle.fluid.dygraph.to_variable",
"paddle.fluid.layers.softmax",
"os.path.join",
"paddle.fluid.layers.cross_entropy",
"ma_convcardseresnext.Ma_ConvCardSeResNeXt",
"reader.custom_image_reader",
"paddle.fluid.laye... | [((1127, 1166), 'utils.logger.info', 'utils.logger.info', (['"""use Adam optimizer"""'], {}), "('use Adam optimizer')\n", (1144, 1166), False, 'import utils\n'), ((2183, 2215), 'utils.logger.info', 'utils.logger.info', (['"""start train"""'], {}), "('start train')\n", (2200, 2215), False, 'import utils\n'), ((1396, 146... |
import os
from sys import argv
import numpy as np
from statistics import variance,mean
# https://numpy.org/doc/stable/reference/generated/numpy.linalg.lstsq.html
path = argv[1]
pwd = os.environ["PWD"]+"/"
list_of_files = []
for root, dirs, files in os.walk(pwd + path):
for file in files:
list_of_files.appe... | [
"os.path.join",
"numpy.linalg.lstsq",
"os.walk"
] | [((250, 269), 'os.walk', 'os.walk', (['(pwd + path)'], {}), '(pwd + path)\n', (257, 269), False, 'import os\n'), ((840, 873), 'numpy.linalg.lstsq', 'np.linalg.lstsq', (['A', 'y'], {'rcond': 'None'}), '(A, y, rcond=None)\n', (855, 873), True, 'import numpy as np\n'), ((323, 347), 'os.path.join', 'os.path.join', (['root'... |
import sys
from preprocess.ConjointTriad import ConjointTriad
from preprocess.PreprocessUtils import readFasta
from preprocess.PreprocessUtils import AllvsAllSim
from preprocess.CTD_Composition import CTD_Composition
from preprocess.CTD_Transition import CTD_Transition
from preprocess.CTD_Distribution import CTD_... | [
"preprocess.EGBW.EGBW",
"preprocess.SkipGram.SkipGram",
"numpy.hstack",
"preprocess.AutoCovariance.AutoCovariance",
"preprocess.LDCTD.LDCTD",
"preprocess.GearyAC.GearyAC",
"preprocess.Chaos.Chaos",
"preprocess.AAC.AAC",
"preprocess.PSSMLST.PSSMLST",
"preprocess.QuasiSequenceOrder.QuasiSequenceOrde... | [((1544, 1555), 'time.time', 'time.time', ([], {}), '()\n', (1553, 1555), False, 'import time\n'), ((1567, 1606), 'preprocess.PreprocessUtils.readFasta', 'readFasta', (["(folderName + 'allSeqs.fasta')"], {}), "(folderName + 'allSeqs.fasta')\n", (1576, 1606), False, 'from preprocess.PreprocessUtils import readFasta\n'),... |
import sys
import casadi
import numpy as np
import matplotlib.pyplot as plt
from car_model import calc_wheel_centric_velocities, create_car_model, calc_sigma_xy, calc_wheel_centric_forces, \
calc_wheel_physics
from car_sim_gen.constants import WheelConstants
from acados_template.acados_ocp_formulation_helper imp... | [
"casadi.Function",
"matplotlib.pyplot.show",
"car_model.calc_wheel_centric_velocities",
"acados_template.acados_ocp_formulation_helper.get_symbol_idx",
"matplotlib.pyplot.gca",
"car_model.calc_sigma_xy",
"car_model.calc_wheel_physics",
"matplotlib.pyplot.plot",
"casadi.vertcat",
"numpy.linspace",
... | [((371, 389), 'car_model.create_car_model', 'create_car_model', ([], {}), '()\n', (387, 389), False, 'from car_model import calc_wheel_centric_velocities, create_car_model, calc_sigma_xy, calc_wheel_centric_forces, calc_wheel_physics\n'), ((399, 418), 'casadi.MX.sym', 'casadi.MX.sym', (['"""vr"""'], {}), "('vr')\n", (4... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import math
import operator, collections
import numpy as np
import scipy.ndimage
import matplotlib.pyplot as plt
from features import extract_features
from save_features import feature_vector, dump, load, label_vec
data_dir = '../data/CXR_png_complete/'
# 0 -... | [
"save_features.feature_vector",
"os.listdir",
"numpy.unique",
"math.floor",
"save_features.label_vec",
"matplotlib.pyplot.plot",
"numpy.asarray",
"numpy.argmax",
"math.log",
"matplotlib.pyplot.subplot",
"numpy.sum",
"matplotlib.pyplot.figure",
"numpy.zeros",
"operator.itemgetter",
"numpy... | [((478, 491), 'matplotlib.pyplot.figure', 'plt.figure', (['(0)'], {}), '(0)\n', (488, 491), True, 'import matplotlib.pyplot as plt\n'), ((564, 586), 'matplotlib.pyplot.plot', 'plt.plot', (['vertical_sum'], {}), '(vertical_sum)\n', (572, 586), True, 'import matplotlib.pyplot as plt\n'), ((709, 738), 'features.extract_fe... |
import numpy as np
from . import torch_warp as t_warp
import torch
from torch.autograd import Variable
import scipy
# this file is to find the TVL1 energy of the optical flow vector
def compute_flow_gradient(flowvector,pixelposx,pixelposy,imgwidth,imgheight):
ux_grad = 0
uy_grad = 0
if pixelposx > 0 and ... | [
"numpy.array",
"torch.norm",
"torch.abs",
"torch.Tensor"
] | [((1706, 1738), 'torch.Tensor', 'torch.Tensor', (['[ux_grad, uy_grad]'], {}), '([ux_grad, uy_grad])\n', (1718, 1738), False, 'import torch\n'), ((2104, 2146), 'torch.abs', 'torch.abs', (['(wrapped_first_image - img1.data)'], {}), '(wrapped_first_image - img1.data)\n', (2113, 2146), False, 'import torch\n'), ((2162, 218... |
from abc import ABC, abstractmethod
from functools import partial
from typing import Union, Dict, Callable
from contextlib import suppress
from gzip import GzipFile
from pathlib import Path
import os
import json
import pickle
import numpy as np
from ..local import Storage
__all__ = (
'Serializer', 'SerializerEr... | [
"pickle.dumps",
"json.dump",
"json.dumps",
"numpy.asarray",
"pickle.load",
"numpy.issubdtype",
"gzip.GzipFile",
"functools.partial",
"contextlib.suppress",
"json.load",
"numpy.load",
"numpy.save"
] | [((3641, 3658), 'numpy.asarray', 'np.asarray', (['value'], {}), '(value)\n', (3651, 3658), True, 'import numpy as np\n'), ((1778, 1795), 'json.dumps', 'json.dumps', (['value'], {}), '(value)\n', (1788, 1795), False, 'import json\n'), ((2483, 2502), 'pickle.dumps', 'pickle.dumps', (['value'], {}), '(value)\n', (2495, 25... |
"""
MAPSCI: Multipole Approach of Predicting and Scaling Cross Interactions
Handles the primary functions
"""
import numpy as np
import scipy.optimize as spo
import logging
logger = logging.getLogger(__name__)
def calc_distance_array(bead_dict, tol=0.01, max_factor=2, lower_bound="rmin"):
r"""
Calculation... | [
"logging.getLogger",
"numpy.mean",
"numpy.sqrt",
"scipy.optimize.brentq",
"numpy.size",
"numpy.log",
"numpy.any",
"mapsci.quick_plots.plot_potential",
"numpy.diag",
"numpy.array",
"numpy.linspace",
"numpy.zeros",
"numpy.sum",
"numpy.isnan",
"numpy.finfo",
"numpy.all",
"mapsci.quick_p... | [((186, 213), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (203, 213), False, 'import logging\n'), ((2156, 2199), 'numpy.linspace', 'np.linspace', (['rm', '(max_factor * rm)'], {'num': '(10000)'}), '(rm, max_factor * rm, num=10000)\n', (2167, 2199), True, 'import numpy as np\n'), ((5069... |
import cv2
import sys
import numpy as np
def videoAnnotate(vin):
print('video in file: {}'.format(vin))
inFile = cv2.VideoCapture(vin)
fOutname = '_'.join(['combined', vin])
print('video in file: {}'.format(vin))
print('video out file: {}'.format(fOutname))
#check if the... | [
"cv2.imwrite",
"numpy.zeros",
"cv2.VideoCapture",
"cv2.VideoWriter_fourcc",
"cv2.resize",
"cv2.imread"
] | [((134, 155), 'cv2.VideoCapture', 'cv2.VideoCapture', (['vin'], {}), '(vin)\n', (150, 155), False, 'import cv2\n'), ((716, 747), 'cv2.VideoWriter_fourcc', 'cv2.VideoWriter_fourcc', (["*'MP4V'"], {}), "(*'MP4V')\n", (738, 747), False, 'import cv2\n'), ((1810, 1831), 'cv2.imread', 'cv2.imread', (['imgFname2'], {}), '(img... |
import tarfile
from datetime import timedelta
from pathlib import Path
from time import perf_counter
import PIL.Image
import h5py
import numpy as np
from tqdm import tqdm
from torchdata.logger import log
from torchdata.utils import download_file, remote_file, md5sum
MPII_Joint_Names = ['right_ankle', 'right_knee', ... | [
"numpy.eye",
"tarfile.open",
"pathlib.Path",
"tqdm.tqdm",
"time.perf_counter",
"numpy.ndim",
"h5py.File",
"torchdata.logger.log.info",
"numpy.array",
"datetime.timedelta",
"numpy.matmul",
"numpy.ndarray",
"torchdata.utils.remote_file",
"numpy.pad",
"torchdata.utils.download_file",
"tor... | [((1798, 1812), 'pathlib.Path', 'Path', (['data_dir'], {}), '(data_dir)\n', (1802, 1812), False, 'from pathlib import Path\n'), ((2959, 2973), 'time.perf_counter', 'perf_counter', ([], {}), '()\n', (2971, 2973), False, 'from time import perf_counter\n'), ((3146, 3182), 'torchdata.logger.log.info', 'log.info', (['"""[1/... |
import unittest
import numpy
from oo_trees.dataset import Dataset
from oo_trees.attribute import *
from oo_trees.splitter import *
class TestDataset(unittest.TestCase):
def test_entropy(self):
X = numpy.array([[0, 1], [0, 0]])
y = numpy.array(['H', 'T'])
dataset = Dataset(X, y)
c0, ... | [
"oo_trees.dataset.Dataset",
"numpy.array",
"numpy.testing.assert_array_equal"
] | [((210, 239), 'numpy.array', 'numpy.array', (['[[0, 1], [0, 0]]'], {}), '([[0, 1], [0, 0]])\n', (221, 239), False, 'import numpy\n'), ((252, 275), 'numpy.array', 'numpy.array', (["['H', 'T']"], {}), "(['H', 'T'])\n", (263, 275), False, 'import numpy\n'), ((294, 307), 'oo_trees.dataset.Dataset', 'Dataset', (['X', 'y'], ... |
# -*- coding:utf-8 -*-
import argparse
import torch
import os
import cv2
import pyssim
import codecs
from scipy.ndimage import gaussian_filter
from numpy.lib.stride_tricks import as_strided as ast
from PIL import Image
from torch.autograd import Variable
import torch.nn as nn
import numpy as np
import time, math
import... | [
"numpy.uint8",
"torch.from_numpy",
"numpy.array",
"torch.cuda.is_available",
"numpy.rot90",
"math.log10",
"os.path.exists",
"numpy.mean",
"os.listdir",
"pyssim.compute_ssim",
"argparse.ArgumentParser",
"numpy.asarray",
"os.mkdir",
"collections.OrderedDict",
"numpy.flipud",
"model.sk_op... | [((457, 506), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""SKNet Test"""'}), "(description='SKNet Test')\n", (480, 506), False, 'import argparse\n'), ((1281, 1292), 'time.time', 'time.time', ([], {}), '()\n', (1290, 1292), False, 'import time, math\n'), ((1335, 1393), 'codecs.open', 'c... |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 ... | [
"tensorflow.compat.v1.FixedLenSequenceFeature",
"language.orqa.utils.bert_utils.pad_or_truncate",
"language.orqa.utils.bert_utils.get_tokenizer",
"tensorflow.compat.v1.cast",
"tensorflow.compat.v1.parse_single_example",
"tensorflow.compat.v1.cond",
"tensorflow.compat.v1.random.uniform",
"numpy.linspac... | [((1188, 1245), 'tensorflow.compat.v1.parse_single_example', 'tf.parse_single_example', (['serialized_example', 'feature_spec'], {}), '(serialized_example, feature_spec)\n', (1211, 1245), True, 'import tensorflow.compat.v1 as tf\n'), ((1526, 1572), 'language.orqa.utils.bert_utils.get_tokenizer', 'bert_utils.get_tokeniz... |
import numpy as np
from numpy.random import default_rng
def random_crop(data, size, padding, rng=default_rng()):
x = rng.integers(2 * padding, size=data.shape[:-3] + (1, 1, 1))
y = rng.integers(2 * padding, size=data.shape[:-3] + (1, 1, 1))
arange = np.arange(size)
rows = x + arange.reshape((size, 1,... | [
"numpy.clip",
"numpy.flip",
"numpy.random.default_rng",
"numpy.array",
"numpy.pad",
"numpy.zeros_like",
"numpy.arange",
"numpy.take_along_axis"
] | [((100, 113), 'numpy.random.default_rng', 'default_rng', ([], {}), '()\n', (111, 113), False, 'from numpy.random import default_rng\n'), ((265, 280), 'numpy.arange', 'np.arange', (['size'], {}), '(size)\n', (274, 280), True, 'import numpy as np\n'), ((414, 484), 'numpy.pad', 'np.pad', (['data', '(((0, 0),) * (data.ndim... |
# Copyright (c) 2015, <NAME> (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
from GPy.inference.latent_function_inference.var_dtc import VarDTC
from GPy.util.linalg import jitchol, tdot, dtrtri, dtrtrs, backsub_both_sides,\
dpotrs, dpotri, symmetrify, mdot
from GPy... | [
"logging.getLogger",
"numpy.trace",
"numpy.sqrt",
"numpy.log",
"paramz.caching.Cacher",
"numpy.einsum",
"GPy.util.linalg.jitchol",
"numpy.dot",
"numpy.eye",
"numpy.ones",
"ipdb.set_trace",
"numpy.square",
"GPy.util.linalg.symmetrify",
"GPy.util.linalg.mdot",
"GPy.util.linalg.dtrtri",
"... | [((492, 509), 'numpy.log', 'np.log', (['(2 * np.pi)'], {}), '(2 * np.pi)\n', (498, 509), True, 'import numpy as np\n'), ((543, 570), 'logging.getLogger', 'logging.getLogger', (['"""vardtc"""'], {}), "('vardtc')\n", (560, 570), False, 'import logging, itertools\n'), ((9570, 9596), 'numpy.dot', 'np.dot', (['data_term', '... |
# -*-coding:utf8-*-
"""
author:zhangyu
用线性模型检查文件，在测试中
email:<EMAIL>
"""
from __future__ import division
import numpy as np
from sklearn.externals import joblib
import math
import sys
sys.path.append("../")
import LR.util.get_feature_num as gf
def get_test_data(test_file: str, feature_num_file: str):
"""
Arg... | [
"sys.exit",
"numpy.genfromtxt",
"LR.util.get_feature_num.get_feature_num",
"sklearn.externals.joblib.load",
"numpy.dot",
"numpy.frompyfunc",
"math.exp",
"sys.path.append"
] | [((185, 207), 'sys.path.append', 'sys.path.append', (['"""../"""'], {}), "('../')\n", (200, 207), False, 'import sys\n'), ((437, 473), 'LR.util.get_feature_num.get_feature_num', 'gf.get_feature_num', (['feature_num_file'], {}), '(feature_num_file)\n', (455, 473), True, 'import LR.util.get_feature_num as gf\n'), ((491, ... |
from credentials.blob_credentials import facts_sas_token, facts_container
from azure.storage.blob import ContainerClient, BlobClient
import pandas as pd
import os
import json
import colorsys
import random
import numpy as np
import cv2
import imageio
from matplotlib import patches
from matplotlib.patches import Polygon
... | [
"azure.storage.blob.BlobClient",
"cv2.filter2D",
"colorsys.hsv_to_rgb",
"numpy.array",
"cv2.imdecode",
"numpy.arange",
"matplotlib.pyplot.imshow",
"os.path.exists",
"numpy.max",
"matplotlib.pyplot.close",
"numpy.stack",
"cv2.contourArea",
"azure.storage.blob.ContainerClient",
"pandas.DataF... | [((552, 656), 'azure.storage.blob.ContainerClient', 'ContainerClient', ([], {'account_url': 'account_url', 'container_name': 'facts_container', 'credential': 'facts_sas_token'}), '(account_url=account_url, container_name=facts_container,\n credential=facts_sas_token)\n', (567, 656), False, 'from azure.storage.blob i... |
# -*- coding: UTF-8 -*-
"""
此脚本用于展示使用惩罚项解决模型幻觉的问题
"""
import os
import numpy as np
import statsmodels.api as sm
from sklearn import linear_model
import matplotlib.pyplot as plt
import pandas as pd
def read_data(path):
"""
使用pandas读取数据
"""
data = pd.read_csv(path)
return data
def generate_rand... | [
"sklearn.linear_model.Lasso",
"pandas.read_csv",
"numpy.arange",
"numpy.array",
"numpy.random.randint",
"matplotlib.pyplot.figure",
"statsmodels.api.add_constant",
"numpy.random.seed",
"os.path.abspath",
"numpy.logspace",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.show"
] | [((267, 284), 'pandas.read_csv', 'pd.read_csv', (['path'], {}), '(path)\n', (278, 284), True, 'import pandas as pd\n'), ((363, 383), 'numpy.random.seed', 'np.random.seed', (['(4873)'], {}), '(4873)\n', (377, 383), True, 'import numpy as np\n'), ((395, 424), 'numpy.random.randint', 'np.random.randint', (['(2)'], {'size'... |
import pytz
import sys
import numpy as np
from datetime import datetime
from metrics import utils
QUERY_CONTENT = '*'
# return a list of throughputs computed per call
def get_service_throughput_per_hit(service, computation_timestamp, time_window):
print(service,file=sys.stderr)
query_ids = QUERY_CONTENT + f'... | [
"metrics.utils.es_query",
"metrics.utils.parse_timestamp",
"metrics.utils.extract_vdc_id",
"datetime.datetime.now",
"numpy.array",
"metrics.utils.get_services",
"metrics.utils.get_blueprint_id",
"metrics.utils.get_timestamp_timewindow"
] | [((395, 426), 'metrics.utils.es_query', 'utils.es_query', ([], {'query': 'query_ids'}), '(query=query_ids)\n', (409, 426), False, 'from metrics import utils\n'), ((475, 523), 'metrics.utils.es_query', 'utils.es_query', ([], {'query': 'query_ids', 'size': 'total_hits'}), '(query=query_ids, size=total_hits)\n', (489, 523... |
import numpy as np
def lonlat2km(lon1,lat1,lon2,lat2):
con=radians(lat1)
ymeter=111132.92-559.8*np.cos(2*con)+1.175*np.cos(4*con)-0.0023*np.cos(6*con)
xmeter=111412.84*np.cos(con)-93.5*np.cos(3*con)+0.0118*np.cos(5*con)
east=(lon2-lon1)*xmeter/1000
north=(lat2-lat1)*ymeter/1000
return eas... | [
"numpy.cos"
] | [((148, 163), 'numpy.cos', 'np.cos', (['(6 * con)'], {}), '(6 * con)\n', (154, 163), True, 'import numpy as np\n'), ((222, 237), 'numpy.cos', 'np.cos', (['(5 * con)'], {}), '(5 * con)\n', (228, 237), True, 'import numpy as np\n'), ((127, 142), 'numpy.cos', 'np.cos', (['(4 * con)'], {}), '(4 * con)\n', (133, 142), True,... |
#!/usr/bin/env python
import numpy as np
from pycrazyswarm import *
def test_yaml_string_load():
crazyflies_yaml = """
crazyflies:
- channel: 100
id: 1
initialPosition: [1.0, 0.0, 0.0]
- channel: 100
id: 10
initialPosition: [0.0, -1.0, 0.0]
"""
swarm = Crazyswarm(craz... | [
"numpy.all"
] | [((536, 582), 'numpy.all', 'np.all', (['(cf1.initialPosition == [1.0, 0.0, 0.0])'], {}), '(cf1.initialPosition == [1.0, 0.0, 0.0])\n', (542, 582), True, 'import numpy as np\n'), ((615, 663), 'numpy.all', 'np.all', (['(cf10.initialPosition == [0.0, -1.0, 0.0])'], {}), '(cf10.initialPosition == [0.0, -1.0, 0.0])\n', (621... |
import numpy as np
import netket as nk
import sys
from shutil import move
import mpi4py.MPI as mpi
import symmetries
L = 150
msr = True
rank = mpi.COMM_WORLD.Get_rank()
if rank == 0:
with open("result.txt", "w") as fl:
fl.write("L, energy (real), energy (imag), energy_error\n")
g = nk.graph.Hypercube(le... | [
"netket.custom.J1J2",
"netket.graph.Hypercube",
"numpy.imag",
"netket.optimizer.Sgd",
"netket.hilbert.Spin",
"numpy.real",
"netket.custom.get_symms_chain",
"netket.machine.JastrowSymm",
"netket.variational.estimate_expectations",
"numpy.load",
"mpi4py.MPI.COMM_WORLD.Get_rank",
"netket.sampler.... | [((145, 170), 'mpi4py.MPI.COMM_WORLD.Get_rank', 'mpi.COMM_WORLD.Get_rank', ([], {}), '()\n', (168, 170), True, 'import mpi4py.MPI as mpi\n'), ((299, 346), 'netket.graph.Hypercube', 'nk.graph.Hypercube', ([], {'length': 'L', 'n_dim': '(1)', 'pbc': '(True)'}), '(length=L, n_dim=1, pbc=True)\n', (317, 346), True, 'import ... |
from pyexpat import features
import numpy as np
from src.io import npy_events_tools
from src.io import psee_loader
import tqdm
import os
from numpy.lib import recfunctions as rfn
import torch
import time
import math
import argparse
def generate_agile_event_volume_cuda(events, shape, events_window = 50000, volume_bins... | [
"numpy.fromfile",
"torch.nn.functional.interpolate",
"torch.arange",
"os.path.exists",
"os.listdir",
"argparse.ArgumentParser",
"numpy.where",
"numpy.stack",
"torch.zeros_like",
"pyexpat.features.astype",
"src.io.psee_loader.PSEELoader",
"torch.abs",
"numpy.nonzero",
"torch.cuda.empty_cach... | [((1558, 1582), 'numpy.nonzero', 'np.nonzero', (['dense_tensor'], {}), '(dense_tensor)\n', (1568, 1582), True, 'import numpy as np\n'), ((1763, 1866), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""visualize one or several event files along with their boxes"""'}), "(description=\n 'vi... |
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
# Data preprocessing
data = pd.read_csv("RealEstate.csv")
# Converting Pandas dataframe to numpy array
X = data.loc[:, ['Bedrooms', 'Bathrooms',... | [
"numpy.mean",
"pandas.read_csv",
"sklearn.model_selection.train_test_split",
"numpy.array",
"sklearn.linear_model.LinearRegression"
] | [((203, 232), 'pandas.read_csv', 'pd.read_csv', (['"""RealEstate.csv"""'], {}), "('RealEstate.csv')\n", (214, 232), True, 'import pandas as pd\n'), ((429, 483), 'sklearn.model_selection.train_test_split', 'train_test_split', (['X', 'Y'], {'test_size': '(0.33)', 'random_state': '(0)'}), '(X, Y, test_size=0.33, random_st... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import matplotlib.pyplot as plt
import numpy as np
import torch
import torchvision.datasets.folder
from PIL import Image, ImageFile
from torch.utils.data import TensorDataset
from torchvision import transforms
from torchvision.datasets i... | [
"torchvision.transforms.ToPILImage",
"numpy.hstack",
"torchvision.transforms.ColorJitter",
"numpy.array",
"torchvision.transforms.functional.rotate",
"numpy.sin",
"numpy.divide",
"numpy.random.RandomState",
"matplotlib.pyplot.imshow",
"numpy.multiply",
"numpy.stack",
"torchvision.datasets.Imag... | [((2642, 2682), 'torchvision.datasets.CIFAR10', 'CIFAR10', (['root'], {'train': '(True)', 'download': '(True)'}), '(root, train=True, download=True)\n', (2649, 2682), False, 'from torchvision.datasets import MNIST, ImageFolder, CIFAR10\n'), ((2713, 2754), 'torchvision.datasets.CIFAR10', 'CIFAR10', (['root'], {'train': ... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Reference: <NAME>, et al. "Pixeldefend: Leveraging generative models to understand and defend against adversarial examples," in ICLR, 2018.
# Reference Implementation from Authors (TensorFlow): https://github.com/yang-song/pixeldefend
# ***********************************... | [
"os.path.exists",
"numpy.copy",
"Defenses.DefenseMethods.External.pixel_cnn_pp.utils.decode",
"torch.gt",
"torch.nn.DataParallel",
"torch.from_numpy",
"Defenses.DefenseMethods.External.pixel_cnn_pp.utils.load_part_of_model",
"numpy.array",
"torch.range",
"Defenses.DefenseMethods.External.pixel_cnn... | [((1955, 1992), 'torch.nn.DataParallel', 'nn.DataParallel', (['self.pixel_cnn_model'], {}), '(self.pixel_cnn_model)\n', (1970, 1992), True, 'import torch.nn as nn\n'), ((2323, 2363), 'os.path.exists', 'os.path.exists', (['pixel_cnn_model_location'], {}), '(pixel_cnn_model_location)\n', (2337, 2363), False, 'import os\n... |
# -*- coding: utf-8 -*-
"""
"""
import os
from datetime import datetime
from typing import Union, Optional, Any, List, NoReturn
from numbers import Real
import wfdb
import numpy as np
np.set_printoptions(precision=5, suppress=True)
import pandas as pd
from ..utils.common import (
ArrayLike,
get_record_list_re... | [
"numpy.set_printoptions"
] | [((185, 232), 'numpy.set_printoptions', 'np.set_printoptions', ([], {'precision': '(5)', 'suppress': '(True)'}), '(precision=5, suppress=True)\n', (204, 232), True, 'import numpy as np\n')] |
# -*- coding: utf-8 -*-
import os
import math
import codecs
import random
import numpy as np
from glob import glob
from PIL import Image
from keras.utils import np_utils, Sequence
from sklearn.model_selection import train_test_split
class BaseSequence(Sequence):
"""
基础的数据流生成器，每次迭代返回一个batch
BaseSequence可直... | [
"PIL.Image.fromarray",
"PIL.Image.open",
"random.shuffle",
"sklearn.model_selection.train_test_split",
"os.path.join",
"numpy.array",
"keras.utils.np_utils.to_categorical",
"os.path.basename",
"numpy.full",
"codecs.open",
"numpy.random.shuffle"
] | [((2422, 2449), 'random.shuffle', 'random.shuffle', (['label_files'], {}), '(label_files)\n', (2436, 2449), False, 'import random\n'), ((2974, 3018), 'keras.utils.np_utils.to_categorical', 'np_utils.to_categorical', (['labels', 'num_classes'], {}), '(labels, num_classes)\n', (2997, 3018), False, 'from keras.utils impor... |
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