repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
kwitnacy/wti | [
"9ee659bf2912f5b2fe6229bbda5074d4f3ebb3d4"
] | [
"wtiproj04/API.py"
] | [
"import pandas as pd\nimport numpy as np\nfrom typing import List\n\n\ndef my_join() -> List[str]:\n df_main = pd.read_csv('/home/kwitnoncy/Documents/politechnika/wti/wtiproj03/data/user_ratedmovies.dat', sep='\\t')\n df_genres = pd.read_csv('/home/kwitnoncy/Documents/politechnika/wti/wtiproj03/data/movie_gen... | [
[
"pandas.read_csv",
"numpy.nan_to_num",
"pandas.DataFrame",
"numpy.nanmean",
"pandas.DataFrame.from_records",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
yang131313/Paddle | [
"c197d73be1babe64d93b714701b21066d6645482"
] | [
"python/paddle/fluid/tests/unittests/ir/inference/auto_scan_test.py"
] | [
"# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless re... | [
[
"numpy.random.rand",
"numpy.amax",
"numpy.allclose",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rsdefever/mosdef_slitpore | [
"2150d1d0e062bf6aac660be8ae73d94e2a3c4438",
"2150d1d0e062bf6aac660be8ae73d94e2a3c4438"
] | [
"simulations/bulk-water/gomc/analysis/Data_analysis.py",
"mosdef_slitpore/analysis.py"
] | [
"import numpy as np\nimport csv as csv\nimport pandas as pd\n\n#check all thermal expansion errors and cp errors before providing data\n\nStep_start = 50 *10**6\nStep_finish = 100 *10**6\nTemp_K =298\n\n\nSet_List = ['set1', 'set2', 'set3', 'set4', 'set5']\nChemPot = [-6295, -5573, -4852, -4491, -4401, -4130, -4... | [
[
"pandas.read_csv",
"pandas.DataFrame",
"numpy.mean",
"numpy.nanmean",
"numpy.transpose",
"numpy.column_stack"
],
[
"numpy.multiply",
"numpy.unique",
"numpy.arange",
"numpy.linalg.norm",
"numpy.sign",
"numpy.mean",
"numpy.logical_and",
"numpy.divide",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
syforcee/GUPB | [
"f916acf94efe61c54fa7b4cc33d3f94821fdb3d7"
] | [
"gupb/controller/krowa123/big_brains/PER.py"
] | [
"import numpy as np\n\n\nclass SumTree(object):\n data_pointer = 0\n\n def __init__(self, capacity):\n self.capacity = capacity\n self.tree = np.zeros(2 * capacity - 1)\n self.data = np.zeros(capacity, dtype=object)\n\n def add(self, priority, data):\n tree_index = self.data_poi... | [
[
"numpy.minimum",
"numpy.power",
"numpy.max",
"numpy.random.uniform",
"numpy.zeros",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yt7589/wdd | [
"378c4348cf64c88c72623b88a87e751270e75386"
] | [
"apps/kwm/kwm_app.py"
] | [
"#\nimport numpy as np\nfrom apps.kwm.ds.kwm_ds import KwmDs\nfrom apps.kwm.xgb_engine import XgbEngine\n\nclass KwmApp(object):\n WORK_RAW_DATA = 1\n WORK_TXT_DATA =2\n WORK_CSV_DATA = 3\n\n def __init__(self):\n self.refl = 'apps.kwm.KwmApp'\n\n def startup(self, args={}):\n print('晶圆... | [
[
"numpy.concatenate",
"numpy.unique",
"numpy.savetxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xinshi-chen/MultiDAG | [
"0b041eac793c31b1acc2e737a6c46579e362e117"
] | [
"multidag/common/consts.py"
] | [
"from __future__ import print_function\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom matplotlib import cm\nimport torch.optim as optim\n\n\nimport argparse\nimport logging\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nt_float = torch.float32\nnp_float = np.float32\nstr... | [
[
"torch.nn.Softmax",
"torch.sigmoid",
"torch.nn.Softplus",
"matplotlib.cm.Paired",
"torch.nn.ELU",
"torch.nn.Tanh",
"torch.nn.Sigmoid",
"torch.tensor",
"torch.cuda.is_available",
"torch.device",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jonhilgart22/glavanize-projects | [
"39fd6aea1b42975a5cf7c3b15512c732ea12d60f"
] | [
"Natural_Language_Processing/code/sentence_generator.py"
] | [
"__author__ = 'Jonathan Hilgart'\n#encoding utf-8\nfrom textblob import TextBlob\nfrom collections import Counter\nfrom collections import defaultdict\nimport numpy as np\nfrom sklearn.metrics.pairwise import linear_kernel\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nimport nltk\nimport re\n\n# con... | [
[
"numpy.random.random"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ks-paul-lam/eltu2014 | [
"86b32dda0924442dee3dab5acc687172c7aa8bc4"
] | [
"deceptive_train.py"
] | [
"def main():\n import os\n import fnmatch\n from textblob import TextBlob\n import pandas as pd\n from sklearn.feature_extraction.text import TfidfVectorizer\n from nltk.corpus import stopwords\n import regex as re\n from sklearn.metrics import classification_report, accuracy_score, confusio... | [
[
"pandas.merge",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"sklearn.metrics.confusion_matrix",
"sklearn.svm.SVC",
"sklearn.metrics.classification_report",
"sklearn.feature_extraction.text.TfidfVectorizer",
"sklearn.metrics.accuracy_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
dieterich-lab/pybio-utils | [
"4cb5e987d19f68b48b03c6cc5f64d1177885b0fa"
] | [
"pbio/utils/gffread_utils.py"
] | [
"import collections\nimport logging\nimport re\n\nlogger = logging.getLogger(__name__)\n\n# build up the header regular expression\ntranscript_id_re = r'(?P<transcript_id>\\S+)'\ngene_re = r\"(?:gene=(?P<gene>\\S+))?\"\ncds_re = r\"(?:CDS=(?P<cds>\\S+))?\"\nloc_re = r\"loc:(?P<seqname>\\S+)\\|(?P<seq_pos>\\S+)\\|(?... | [
[
"numpy.array",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
KhalidAlt/datasets | [
"f013aff823b831fb48b458a7bbb3bb11c2981326"
] | [
"src/datasets/arrow_dataset.py"
] | [
"# Copyright 2020 The HuggingFace Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable l... | [
[
"tensorflow.ensure_shape",
"numpy.issubdtype",
"numpy.dtype",
"tensorflow.data.Dataset.from_generator",
"numpy.random.default_rng",
"tensorflow.train.Int64List",
"numpy.arange",
"tensorflow.shape",
"tensorflow.train.BytesList",
"tensorflow.train.FloatList",
"tensorflow.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"2.9",
"2.5",
"2.8",
"2.10"
]
}
] |
fxnnxc/mdpi_drone_control | [
"94b46449692b4a4c7c3777e8aa07edf6ed6842f5"
] | [
"env.py"
] | [
"from operator import mul\nfrom pickle import NONE\nimport warnings\nfrom drone.control.BaseControl import BaseControl\nfrom drone.envs.WindAviary import WindAviary\nfrom drone.train.predict_model.model import Predictor\nfrom ray.rllib.env.multi_agent_env import MultiAgentEnv \nimport numpy as np \nfrom gym.spaces... | [
[
"numpy.hstack",
"numpy.expand_dims",
"numpy.random.random",
"numpy.random.seed",
"numpy.clip",
"numpy.cos",
"numpy.linalg.norm",
"numpy.sin",
"torch.tensor",
"numpy.append",
"numpy.std",
"numpy.mean",
"torch.no_grad",
"scipy.stats.mode",
"numpy.array",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
nfi/edgepipes | [
"6fe4826949f44583788a2ec8da8f4d64404db1f1"
] | [
"handtracker/hand_tracker.py"
] | [
"import csv\nimport cv2\nimport numpy as np\nimport tensorflow as tf\n\ndef draw_hand(frame, kp):\n # the points where we go back to the first midpoint of the hand\n clearpoints = [4,8,12,16,20]\n lk = None\n p = 0\n\n # Draw the hand\n for keypoint in kp:\n if lk is not None:\n ... | [
[
"numpy.pad",
"numpy.ascontiguousarray",
"tensorflow.lite.Interpreter",
"numpy.linalg.inv",
"numpy.linalg.norm",
"numpy.argmax",
"numpy.float32",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GVRX/potentiostat | [
"1bb44639180ad6d81697631d4d5f699e6fb4eef1"
] | [
"software/python/potentiostat/examples/run_constant_w_plot.py"
] | [
"from potentiostat import Potentiostat\nimport matplotlib.pyplot as plt\n\nport = '/dev/ttyACM0' # Serial port for potentiostat device\ndatafile = 'data.txt' # Name of output data file\n\ntest_name = 'constant' # Name of test to run - constant volate voltammetery\ncurr_range = '100uA' # Name of current ... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
justinphan3110/Scweet | [
"9d61121f95f854cce58bc3c16b8fc06b20e530a6"
] | [
"Scweet/utils.py"
] | [
"from io import StringIO, BytesIO\nimport os\nimport re\nfrom time import sleep\nimport random\nimport chromedriver_autoinstaller\nfrom selenium.common.exceptions import NoSuchElementException\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nimport datetime\nimport pandas as p... | [
[
"pandas.read_csv",
"pandas.to_datetime"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
JayMan91/e2e-model-learning | [
"acb6b071d9646c31de4b7f45d8dd13fe3c206c5b"
] | [
"newsvendor/main.py"
] | [
"#!/usr/bin/env python3\n\nimport argparse\n\nimport cvxpy as cp\nimport numpy as np\n\nimport os\n\nfrom datetime import datetime\n\nimport importlib\ntry: import setGPU\nexcept ImportError: pass\n\nimport torch\nfrom torch.autograd import Variable\n\nimport setproctitle\n\nimport mle, mle_net, policy_net, task_ne... | [
[
"numpy.random.seed",
"numpy.cumsum",
"numpy.random.randn",
"numpy.mean",
"numpy.random.rand",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BishmoyPaul/lama | [
"c7f5af9c167a15e2b0b741b1419237de52c4af05"
] | [
"saicinpainting/training/modules/pix2pixhd.py"
] | [
"# original: https://github.com/NVIDIA/pix2pixHD/blob/master/models/networks.py\nimport collections\nfrom functools import partial\nimport functools\nimport logging\nfrom collections import defaultdict\n\nimport numpy as np\nimport torch.nn as nn\n\nfrom saicinpainting.training.modules.base import BaseDiscriminator... | [
[
"torch.nn.Sequential",
"torch.nn.Dropout",
"torch.nn.ReflectionPad2d",
"torch.nn.ConvTranspose2d",
"torch.nn.Conv2d",
"numpy.ceil",
"torch.nn.Identity",
"torch.nn.LeakyReLU",
"torch.nn.ReLU",
"torch.nn.ReplicationPad2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alievilya/nas-fedot | [
"ce1b07505cd189f3097f1cfa6c38cb4f0d56ccea",
"ce1b07505cd189f3097f1cfa6c38cb4f0d56ccea"
] | [
"fedot/benchmark/experiments/viz.py",
"fedot/test/test_regression.py"
] | [
"from itertools import cycle\r\n\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\n\r\n\r\ndef fitness_by_generations_boxplots(history_runs, iterations):\r\n iters = [it for it in range(iterations)]\r\n fitness_by_iter = []\r\n for it in iters:\r\n fitness_values = []\r\n for his... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.title",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
],
[
"sklearn.datasets.make_regression",
"numpy.std",
"sklearn.metrics.m... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Dom1L/schnetpack | [
"0d5366fc49c20bca48f6f491fc7f1e283f324bc6"
] | [
"src/schnetpack/data/atoms.py"
] | [
"\"\"\"\nThis module contains all functionalities required to load atomistic data,\ngenerate batches and compute statistics. It makes use of the ASE database\nfor atoms [#ase2]_.\n\nReferences\n----------\n.. [#ase2] Larsen, Mortensen, Blomqvist, Castelli, Christensen, Dułak, Friis,\n Groves, Hammer, Hargus:\n ... | [
[
"numpy.array",
"torch.FloatTensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DivJAth/OpticalFlow_MaskRCNN | [
"5d93a4a6359f574c02d0ef2e181221303ced2643"
] | [
"vision/torchvision/models/detection/faster_rcnn.py"
] | [
"\nfrom collections import OrderedDict\n\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\n# import torchvision\nfrom torchvision import models\nfrom torchvision.ops import misc as misc_nn_ops\nfrom torchvision.ops import MultiScaleRoIAlign\n\nfrom torchvision.models.utils import load_state_dict... | [
[
"torch.nn.Linear"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PrasadHonrao/python-samples | [
"faa48aa3eaf2d67b8cef0114e1f6ef08e2c1300a"
] | [
"data_analysis/matplotlib/matplotlib-histrogram.py"
] | [
"import matplotlib.pyplot as plt\n\nlife_expetency = [\n 43.828000000000003,\n 76.423000000000002,\n 72.301000000000002,\n 42.731000000000002,\n 75.319999999999993,\n 81.234999999999999,\n 79.828999999999994,\n 75.635000000000005,\n 64.061999999999998,\n 79.441000000000003,\n 56.728... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.hist",
"matplotlib.pyplot.clf"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Shivankthakur/Rhythm-Search-Engine-for-song-lyrics | [
"560382b115b71d04b7ab8a828793537a8a93e3de"
] | [
"GUI application/app.py"
] | [
"from flask import Flask, render_template, request, redirect, url_for\r\nfrom nltk.corpus import stopwords\r\nimport nltk\r\nnltk.download('stopwords')\r\nfrom nltk.tokenize import TreebankWordTokenizer\r\nfrom nltk.stem.porter import *\r\nimport numpy\r\nfrom math import log,sqrt\r\nimport pandas as pd\r\nimport t... | [
[
"pandas.read_csv",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
AutumnCrocus/shadow_sim | [
"79ad13ff9bd7131c82f269af32a3970f3e4bf2ca"
] | [
"make_NN_vs_other.py"
] | [
"from torch.multiprocessing import Pool, Process, set_start_method,cpu_count, RLock\r\n\r\ntry:\r\n set_start_method('spawn')\r\n print(\"spawn is run.\")\r\n #set_start_method('fork') GPU使用時CUDA initializationでerror\r\n #print('fork')\r\nexcept RuntimeError:\r\n pass\r\n\r\nfrom emulator_test import... | [
[
"torch.multiprocessing.set_start_method",
"torch.multiprocessing.Pool"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
johnnylee/scikits.pulsefit | [
"d07571d524d974f52a863cd96a823fce6f1fed1e"
] | [
"scikits/pulsefit/flagger.py"
] | [
"from __future__ import print_function\n\nimport numpy as np\n\n\nclass Flagger(object):\n \"\"\"Flagger flags pulses based on the value of reduced chi^2.\"\"\"\n def __init__(self, p_err_len, sigma2, chi2red_max, debug=False):\n \"\"\"Arguments:\n p_err_len -- The length along each pulse use... | [
[
"numpy.empty_like",
"numpy.sum",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
atch841/TransUNet | [
"fa952b2f39e5dbc189fd3e0e6481bd1c74eb4c2c"
] | [
"networks/ssseg/backbones/resnest.py"
] | [
"'''\nFunction:\n Implementation of ResNeSt\nAuthor:\n Zhenchao Jin\n'''\nimport os\nimport math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.model_zoo as model_zoo\nfrom .resnet import ResNet\nfrom .resnet import Bottleneck as _Bottleneck\nfrom .builder import Bui... | [
[
"torch.nn.Sequential",
"torch.nn.functional.softmax",
"torch.sigmoid",
"torch.load",
"torch.nn.Conv2d",
"torch.sum",
"torch.nn.functional.adaptive_avg_pool2d",
"torch.nn.AvgPool2d",
"torch.utils.model_zoo.load_url"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
naitmare01/Adventofcode | [
"34f2832fa7a18b76cf9827890632740c6f60679c"
] | [
"2016/day4/day4.py"
] | [
"import argparse\nimport pandas as pd\nfrom collections import Counter\n\n\ndef arguments():\n # Handle command line arguments\n parser = argparse.ArgumentParser(description='Adventofcode.')\n parser.add_argument('-f', '--file', required=True)\n\n args = parser.parse_args()\n\n return args\n\n\ndef m... | [
[
"pandas.read_csv",
"pandas.to_numeric"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
ek9852/tensorflow | [
"2c687ec4d9f824f9827d79a205aa45b84727ac64"
] | [
"tensorflow/python/framework/test_util.py"
] | [
"# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.util._pywrap_util_port.IsBuiltWithNvcc",
"numpy.all",
"tensorflow.python.util.tf_decorator.make_decorator",
"tensorflow.python.eager.context.context",
"tensorflow.python.client.pywrap_tf_session.TF_SetXlaAutoJitMode",
"numpy.where",
"tensorflow.python.ops.gradients_i... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SuziKim/DCCW | [
"b88b38c5d44bcb100f2d60c26a52c6268c446164"
] | [
"dccw/views/applications.py"
] | [
"from DCCWWebDemo.settings import MEDIA_ROOT\nfrom django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.db.models import Q\nfrom django.conf import settings\nfrom django.contrib import messages\nfrom django.utils.crypto import get_random_string\n\nimport os\nimport ast\nimport random\nf... | [
[
"numpy.asarray",
"scipy.sparse.load_npz",
"numpy.dstack",
"scipy.sparse.save_npz",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"1.7",
"1.0",
"1.2",
"1.8"
],
"tensorflow": []
}
] |
UAVKillerLAB/UK_AC | [
"dbeae65f0916df351b39f8b95d01a139894ae78e"
] | [
"uk_without_timestamp/onlyDOS/SelectAngle.py"
] | [
"# encoding=utf-8\nimport json\nimport os\nimport socket # 引入套接字\nimport threading # 引入并行\nimport time\n\nimport pymysql\nimport struct\nimport serial\nimport matplotlib.pyplot as plt\nimport datetime\nimport math\nimport sys\nfrom geographiclib.geodesic import Geodesic\nimport configparser\nimport csv\n\n# impor... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.ion",
"matplotlib.pyplot.pause",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bigTailHao/mmaction2 | [
"32826c99914bc99ced467a81d3dedda43558b9e2"
] | [
"demo/demo.py"
] | [
"import argparse\n\nimport torch\n\nfrom mmaction.apis import inference_recognizer, init_recognizer\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description='MMAction2 demo')\n parser.add_argument('config', help='test config file path')\n parser.add_argument('checkpoint', help='checkpoint file... | [
[
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
katsugeneration/social-dashboard | [
"ab55935aa82a8aeab057cbf196f3b86e6158c8f3"
] | [
"runner/jasso-gakuseiseikatsu-stats-importer/main.py"
] | [
"from typing import Optional\nimport os\nimport requests\nimport io\nimport pandas as pd\nfrom google.cloud import storage\nfrom google.cloud import datacatalog_v1\nfrom google.cloud import bigquery\nfrom fastapi import FastAPI, Response\nfrom pydantic import BaseModel\n\ntry:\n from utils.clients import data_ca... | [
[
"pandas.read_excel",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
UVA-DSI-2019-Capstones/ARL | [
"74d43bf975db90da70696d4b68002d6971164840"
] | [
"db/trainee_identifier_test.py"
] | [
"import pandas as pd\nimport numpy as np\nimport sqlite3\nfrom sqlite3 import Error\n\npath = '../mechanical_turk/Dialogue.xlsx'\ndme = pd.ExcelFile(path)\ndme_scores = dme.parse('DME Score')\ndme_sheet = dme.parse('DME')\n\nidentifiers = []\ntrainee_response_data = []\nfeedbacks = []\nn = 1\n\nfor index, row in dm... | [
[
"pandas.ExcelFile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
KaiLiCn/hpv_integration | [
"592a99edc57f6ff4b3fe0f0169ada1b7340d19d5"
] | [
"get_data.py"
] | [
"import pandas as pd\n\n\ndef inergrate_data(insertion_file):\n insertion_data = pd.read_csv(insertion_file)\n\n insertion_data = insertion_data.drop(insertion_data.columns[0], axis=1).drop_duplicates()\n\n sample_list_datatable = insertion_data.drop_duplicates(subset=['sample', 'gene'])[['sample', 'gene']... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
benji1123/modelE | [
"cc21d692693c5cb1f8d504ee385a2035a854a1c8"
] | [
"Raspberry Pi/LaneDetection/RPilaneDetection.py"
] | [
"'''\nSPEED TEST\n---------\n- Dell XPS 13: \n - 20 fps with native webcam res \n - 18 fps with enforced 640x420 res\n- Raspberry Pi: \n'''\nimport numpy as np\nimport logging\nimport math\nimport datetime\nimport sys\nimport cv2\nimport time\n\n_SHOW_IMAGE = False\n_ENFORCE_RESOLUTION = True\n_KEEP_RUNNING ... | [
[
"numpy.polyfit",
"numpy.array",
"numpy.zeros_like",
"numpy.average"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ahmorsi/models | [
"074847040998cb14fc1d21e4757c5a2e82cac3aa"
] | [
"official/nlp/data/question_answering_dataloader.py"
] | [
"# Lint as: python3\n# Copyright 2020 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2... | [
[
"tensorflow.io.FixedLenFeature",
"tensorflow.io.parse_single_example",
"tensorflow.cast"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"1.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1... |
rsumner31/Detectron | [
"021685d42f7e8ac097e2bcf79fecb645f211378e"
] | [
"lib/core/config.py"
] | [
"# Copyright (c) 2017-present, Facebook, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicabl... | [
[
"numpy.log",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Neuralji/Technical-Analysis-Indicators | [
"0aede1d4cd0fc1768496b07f73aec5a929315b3d"
] | [
"Exp-Moving-Average.py"
] | [
"import numpy as np\ndef ExpMA(data,period):\n weights=np.exp(np.linspace(-1.,0.,period))\n weights/=weights.sum()\n a=np.convolve(data,weights)[:len(values)]\n a[:period]=a[period]\n return a\n \n"
] | [
[
"numpy.convolve",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
protagohhz/hivemind | [
"487fb68feea4d27ede0afdef211f6edc889b1a9e"
] | [
"hivemind/moe/server/__init__.py"
] | [
"from __future__ import annotations\n\nimport multiprocessing as mp\nimport multiprocessing.synchronize\nimport threading\nfrom contextlib import contextmanager\nfrom functools import partial\nfrom pathlib import Path\nfrom typing import Dict, List, Optional, Tuple\n\nimport torch\nfrom multiaddr import Multiaddr\n... | [
[
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
H-B-P/Durkon-Tobit | [
"722b72b3a1f00634a4b024bd6f4da9a70c1501e3"
] | [
"actual_modelling.py"
] | [
"import pandas as pd\nimport numpy as np\nimport math\nimport copy\nimport time\n\nimport util\nimport apply_model\nimport calculus\n\ndef produce_cont_relevances(inputDf, model, col):\n reles=np.zeros((len(model[\"conts\"][col]),len(inputDf)))\n \n reles[0][(inputDf[col]<=model[\"conts\"][col][0][0])] = 1 #d(featp... | [
[
"numpy.matrix",
"numpy.array",
"pandas.read_csv",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
RHammond2/ORBIT | [
"3bd007e9e256183ecaf691eb4138b46915f94003"
] | [
"ORBIT/parametric.py"
] | [
"__author__ = [\"Jake Nunemaker\"]\n__copyright__ = \"Copyright 2020, National Renewable Energy Laboratory\"\n__maintainer__ = \"Jake Nunemaker\"\n__email__ = [\"jake.nunemaker@nrel.gov\"]\n\n\nimport time\nfrom copy import deepcopy\nfrom random import sample\nfrom itertools import product\n\nimport numpy as np\nim... | [
[
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
yedrek/shrinkage | [
"eb9d1790045b1931404923263c87c7e40ef74baa"
] | [
"shrinkage/rie.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import cm\nfrom sympy import nroots, re, im, symbols, sympify\nfrom types import FunctionType\nfrom collections import defaultdict\nfrom tqdm.auto import tqdm\n\nfrom .varma import Varma\n\n\nclass LedoitPecheShrinkage:\n \"\"\"\n Given sam... | [
[
"numpy.take_along_axis",
"matplotlib.pyplot.legend",
"numpy.sqrt",
"matplotlib.pyplot.plot",
"numpy.argmin",
"numpy.exp",
"numpy.random.default_rng",
"numpy.sin",
"matplotlib.cm.hot",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.savefig",
"numpy.argsort",
"matpl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JiehangXie/PASSL | [
"a4835064fb830f04201b418b1c09d5b1bf813150"
] | [
"passl/datasets/preprocess/transforms.py"
] | [
"# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless req... | [
[
"numpy.random.uniform",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Syakyr/seldon-core | [
"938a8ffd19fc321eed4c7347ef56cc0b59f448dd"
] | [
"python/tests/test_runtime_metrics_tags.py"
] | [
"import os\nimport logging\nimport pytest\nimport numpy as np\nfrom google.protobuf import json_format\nimport json\n\nfrom seldon_core.flask_utils import SeldonMicroserviceException\nfrom seldon_core.proto import prediction_pb2\nfrom seldon_core.wrapper import (\n get_rest_microservice,\n get_metrics_microse... | [
[
"numpy.array",
"numpy.histogram",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
federicovergallo/maskrcnn-benchmark | [
"5c7151415e252f210cb8fe93e12d52e32b8c7066"
] | [
"demo/predictor.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\nimport cv2\nimport torch\nfrom torchvision import transforms as T\nfrom torchvision.transforms import functional as F\nfrom maskrcnn_benchmark.modeling.detector import build_detection_model\nfrom maskrcnn_benchmark.utils.checkpoint import Det... | [
[
"matplotlib.pyplot.imshow",
"torch.zeros",
"torch.load",
"torch.cat",
"matplotlib.pyplot.get_cmap",
"torch.tensor",
"numpy.copy",
"torch.no_grad",
"torch.nonzero",
"matplotlib.pyplot.axis",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alekshiidenhovi/Helsinki-University-Data-Analysis-with-Python | [
"bc27fa585d22d630a38312ee7c4b2173d5b80d12"
] | [
"part05-e01_split_date_continues/test/test_split_date_continues.py"
] | [
"#!/usr/bin/env python3\n\nimport unittest\nfrom unittest.mock import patch\nimport numpy as np\nimport pandas as pd\n\nfrom tmc import points\n\nfrom tmc.utils import load, get_stdout, patch_helper\n\nmodule_name=\"src.split_date_continues\"\nsplit_date_continues = load(module_name, \"split_date_continues\")\nmain... | [
[
"numpy.testing.assert_array_equal",
"numpy.isnan"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yookyungkho/Hand-to-Hand | [
"c0ae9a9c1e5ea894c7daf2c2bf2f7c752e31e8b7"
] | [
"stylegan2_basic/dataset/get_tfrecords.py"
] | [
"import os\nimport glob\nimport tensorflow as tf\n\n\ndef parse_tfrecord(raw_record, res):\n feature_description = {\n 'label': tf.io.FixedLenFeature([259], tf.float32),\n 'image': tf.io.FixedLenFeature([], tf.string, default_value=''),\n }\n\n # parse feature\n parsed = tf.io.parse_single... | [
[
"tensorflow.transpose",
"tensorflow.io.decode_png",
"tensorflow.data.TFRecordDataset",
"tensorflow.data.Dataset.from_tensor_slices",
"tensorflow.reshape",
"tensorflow.io.parse_single_example",
"tensorflow.cast",
"numpy.uint8",
"tensorflow.io.FixedLenFeature",
"tensorflow.im... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhanghang1989/incubator-mxnet | [
"bfa6e28d9c1886de737061c213eb21010f377a23"
] | [
"tests/python/gpu/test_operator_gpu.py"
] | [
"# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); y... | [
[
"numpy.multiply",
"numpy.fft.fft",
"numpy.reshape",
"numpy.arange",
"numpy.dtype",
"numpy.ones",
"numpy.random.shuffle",
"numpy.fft.ifft",
"numpy.random.normal",
"numpy.append",
"numpy.random.uniform",
"numpy.array",
"numpy.zeros",
"numpy.random.randint"
]... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LinjianMa/koala | [
"a366af5d109cbcec820827702a4ef7cb0a3a02f3"
] | [
"experiments/graph_state.py"
] | [
"import math\nimport tensorbackends\nimport numpy as np\nimport tracemalloc\n\nfrom koala import candecomp\nfrom collections import namedtuple\nfrom scipy import fft\n\nGate = namedtuple('Gate', ['name', 'parameters', 'qubits'])\n\n\ndef generate_circuit(nsite, edges):\n circuit = []\n for edge in edges:\n ... | [
[
"numpy.asarray",
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aryoepigene/text | [
"943e370cb100e160bbf9809d807f0ac1032f6f19"
] | [
"test/data/test_builtin_datasets.py"
] | [
"#!/user/bin/env python3\n# Note that all the tests in this module require dataset (either network access or cached)\nimport os\nimport shutil\nimport torchtext.data as data\nfrom torchtext.datasets import AG_NEWS\nimport torch\nfrom torch.testing import assert_allclose\nfrom ..common.torchtext_test_case import Tor... | [
[
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
david-octo/annotation | [
"575c93ff2c2f226819e7bb76c99541d031bde677"
] | [
"datumaro/tests/test_coco_format.py"
] | [
"import numpy as np\nimport os.path as osp\n\nfrom unittest import TestCase\n\nfrom datumaro.components.project import Project\nfrom datumaro.components.extractor import (Extractor, DatasetItem,\n AnnotationType, Label, Mask, Points, Polygon, Bbox, Caption,\n LabelCategories, PointsCategories\n)\nfrom datumar... | [
[
"numpy.array",
"numpy.zeros",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dgSPARSE/dgNN | [
"3f8f2642d10210b5dfcb4d91034b6c008dd2213c"
] | [
"test/perf_test/train_gat_pyg_cpu.py"
] | [
"from torch_geometric.nn import GATConv\nimport torch.nn.functional as F\nimport torch\nimport time\nimport argparse\nimport dgl\nimport GPUtil\nimport scipy.sparse as sp\n\nclass Net(torch.nn.Module):\n def __init__(self,\n edge_index,\n num_layers,\n in_dim,\n ... | [
[
"torch.nn.CrossEntropyLoss",
"torch.cuda.synchronize",
"torch.nn.Dropout",
"torch.max",
"torch.ones",
"torch.nn.ModuleList",
"torch.sum",
"torch.from_numpy",
"torch.no_grad",
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kopankom/models | [
"3f78f4cfd21c786c62bf321c07830071027ebb5e"
] | [
"research/object_detection/utils/config_util_test.py"
] | [
"# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.contrib.training.HParams",
"tensorflow.test.main",
"tensorflow.gfile.Open"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rupc/bsp-protos | [
"58833e7ab9ff53f3633708fb5f95edfdd152c5ea"
] | [
"sample-demo/venv/Lib/site-packages/PySide6/examples/external/matplotlib/widget3d/widget3d.py"
] | [
"#############################################################################\n##\n## Copyright (C) 2020 The Qt Company Ltd.\n## Contact: https://www.qt.io/licensing/\n##\n## This file is part of the Qt for Python examples of the Qt Toolkit.\n##\n## $QT_BEGIN_LICENSE:BSD$\n## You may use this file under the terms ... | [
[
"numpy.sqrt",
"numpy.linspace",
"matplotlib.figure.Figure",
"matplotlib.backends.backend_qt5agg.FigureCanvas",
"numpy.cos",
"numpy.sin",
"numpy.size"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Zepyhrus/hie | [
"4040f36507c195c405c652e0c7b47fe582925c05"
] | [
"hie/hieval.py"
] | [
"import datetime\nimport time\nfrom collections import defaultdict\nimport copy\nfrom tqdm import tqdm\n\nimport numpy as np\nfrom scipy.optimize import linear_sum_assignment as KM\n\nimport cv2\n\nfrom .tools import iou, imread, BLUE, GREEN, RED, YELLOW\n\n\nclass HIEParams(object):\n '''\n Params for coco evalu... | [
[
"numpy.sqrt",
"numpy.cumsum",
"numpy.concatenate",
"numpy.max",
"numpy.mean",
"numpy.searchsorted",
"scipy.optimize.linear_sum_assignment",
"numpy.exp",
"numpy.where",
"numpy.unique",
"numpy.arange",
"numpy.count_nonzero",
"numpy.repeat",
"numpy.zeros",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.4",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
"0.17",
"1.3",
"1.8"
],
"tensorflow": []
}
] |
apache8080/adl_classification | [
"8d4bab29998d71e783c4a27a79af33bc153e7fa1"
] | [
"cluster.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom sklearn.datasets import load_digits\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.cluster import KMeans\nfrom sklearn.metrics import accuracy_score, confusion_matrix... | [
[
"numpy.matrix",
"matplotlib.pyplot.title",
"sklearn.cluster.KMeans",
"sklearn.ensemble.RandomForestClassifier",
"sklearn.metrics.accuracy_score",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"sklearn.metrics.confusion_matrix",
"numpy.concatenate",
"matplo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2... |
rowedenny/ULTRA_pytorch | [
"4c7bcbe0e3afb9c1abf627b002f90b85c1c7c3ff"
] | [
"ultra/input_layer/stochastic_online_simulation_feed.py"
] | [
"\"\"\"Simulate online learning process and click data based on human annotations.\n\nSee the following paper for more information on the simulation data.\n\n * Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, W. Bruce Croft. 2018. Unbiased Learning to Rank with Unbiased Propensity Estimation. In Proceedings of SI... | [
[
"numpy.arange",
"numpy.append",
"numpy.count_nonzero",
"numpy.array",
"numpy.exp",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BerkeleyAutomation/rlqp-python | [
"55f378e496979bd00e84cea4583ac37bfaa571a9"
] | [
"module/tests/polishing_test.py"
] | [
"# Test osqp python module\nimport rlqp as osqp\nfrom rlqp.tests.utils import solve_high_accuracy, rel_tol, abs_tol, decimal_tol\n# import rlqp as osqppurepy as osqp\nimport numpy as np\nfrom scipy import sparse\nimport scipy as sp\n\n# Unit Test\nimport unittest\nimport numpy.testing as nptest\n\n\nclass polish_te... | [
[
"scipy.sparse.csc_matrix",
"scipy.random.seed",
"scipy.sparse.eye",
"scipy.sparse.diags",
"numpy.testing.assert_almost_equal",
"scipy.sparse.random",
"numpy.random.randn",
"numpy.random.rand",
"numpy.testing.assert_allclose",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
"0.17",
"0.16",
"1.8"
],
"tensorflow": []
}
] |
jsikyoon/dreamer-1 | [
"d26876c5c723182ba3c19c9e13f7523a6b943515"
] | [
"dreamer/tools/copy_weights.py"
] | [
"# Copyright 2019 The Dreamer Authors. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requir... | [
[
"tensorflow.group"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"1.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1... |
hickmank/keras-vis | [
"90ae5565951b5e6a90d706b8205c2c4dfc271505"
] | [
"vis/utils/utils.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\n\nimport os\nimport tempfile\nimport math\nimport json\nimport six\n\nimport numpy as np\nimport matplotlib.font_manager as fontman\n\nfrom skimage import io, transform\nfrom keras import backend as K\nfrom keras.models import load_model\n\ni... | [
[
"numpy.random.random",
"numpy.min",
"numpy.asarray",
"numpy.clip",
"numpy.max",
"numpy.std",
"numpy.mean",
"numpy.zeros",
"matplotlib.font_manager.findSystemFonts"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
olive380/deepface | [
"630e8e72f591eee63c724cb8cbcbbc66712f93fc"
] | [
"deepface/basemodels/DeepID.py"
] | [
"import os\nfrom pathlib import Path\nimport gdown\nimport zipfile\n\nfrom tensorflow import keras\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import Conv2D, Activation, Input, Add, MaxPooling2D, Flatten, Dense, Dropout\n\n#-------------------------------------\n\ndef loadModel(url = 'h... | [
[
"tensorflow.keras.layers.Activation",
"tensorflow.keras.models.Model",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.Conv2D",
"tensorflow.keras.layers.Add",
"tensorflow.keras.layers.Dropout",
"tensorflow.keras.layers.MaxPooling2D",
"tensorflow.keras.layers.Flatten",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
chrysm/scikit-multilearn | [
"1e17e3e994bd24924a045ba59f933fd728974630"
] | [
"skmultilearn/base/problem_transformation.py"
] | [
"import copy\nimport numpy as np\nfrom .base import MLClassifierBase\nfrom ..utils import get_matrix_in_format, matrix_creation_function_for_format\nfrom scipy.sparse import issparse, csr_matrix\nfrom sklearn.base import BaseEstimator, ClassifierMixin\n\n\nclass ProblemTransformationBase(MLClassifierBase):\n \"\... | [
[
"scipy.sparse.issparse"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
chrisl6uie/flower-image-classifier | [
"fed986eb2f7ef4a0fba3c1ae9d090d7853967819"
] | [
"train.py"
] | [
"import torch\nimport argparse\nfrom torch import nn\nfrom torch import optim\nfrom torchvision import datasets, transforms, models\nfrom torch.utils.data import Dataset, DataLoader\nfrom PIL import Image\n\n'''\n Training Script\n import utils.py and class_functions.py to run correctly\n Calls main() func... | [
[
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Macleodsolutions/glTF-Blender-IO | [
"c2188bab7f68a2597109e0e4ae2d9bd5d07f0c6d"
] | [
"addons/io_scene_gltf2/blender/exp/gltf2_blender_extract.py"
] | [
"# Copyright 2018-2021 The glTF-Blender-IO authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by app... | [
[
"numpy.power",
"numpy.unique",
"numpy.linalg.norm",
"numpy.dtype",
"numpy.repeat",
"numpy.array",
"numpy.where",
"numpy.divide"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zmskye/unsupervised_captioning | [
"e8d03c1346e26e01078dc120fd37b3215c4d2bbd",
"e8d03c1346e26e01078dc120fd37b3215c4d2bbd"
] | [
"preprocessing/detect_objects.py",
"preprocessing/process_descriptions.py"
] | [
"\"\"\"Detect objects using a model pretrained on OpenImage.\"\"\"\nimport multiprocessing\nimport os\n\nimport h5py\nimport numpy as np\nfrom PIL import Image\nfrom absl import app\nfrom absl import flags\nfrom tqdm import tqdm\n\nfrom config import TF_MODELS_PATH\n\nflags.DEFINE_string('image_path', None, 'data d... | [
[
"tensorflow.Graph",
"numpy.expand_dims",
"tensorflow.import_graph_def",
"tensorflow.gfile.GFile",
"tensorflow.GPUOptions",
"tensorflow.get_default_graph",
"tensorflow.GraphDef"
],
[
"tensorflow.enable_eager_execution",
"tensorflow.train.SequenceExample",
"tensorflow.dat... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
Zilong-Yuan/cdvae | [
"c30a148683775ff623748ce9f7797cc25179ec56"
] | [
"scripts/compute_metrics.py"
] | [
"from collections import Counter\nimport argparse\nimport os\nimport json\n\nimport numpy as np\nfrom pathlib import Path\nfrom tqdm import tqdm\nfrom p_tqdm import p_map\nfrom scipy.stats import wasserstein_distance\n\nfrom pymatgen.core.structure import Structure\nfrom pymatgen.core.composition import Composition... | [
[
"numpy.gcd.reduce",
"numpy.array",
"scipy.stats.wasserstein_distance",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"1.5",
"1.2",
"1.7",
"1.0",
"1.3",
"1.8"
],
"tensorflow": []
}
] |
JLuisRojas/reconocimiento-de-voz | [
"59282ffd6841f22e514a7055cb4d20ef97181b90"
] | [
"src/pipeline/ds2_pipeline.py"
] | [
"import json\nimport pandas as pd\nimport tensorflow as tf\nfrom tensorflow.keras.optimizers import Adam, RMSprop\nimport matplotlib.pyplot as plt\n\nfrom .pipeline import Pipeline\n#from .. import ctc\nfrom ..ctc import get_loss\nfrom ..metricas import wer\n\n\"\"\"\nSe define el pipeline de entrenamiento para Dee... | [
[
"tensorflow.nn.ctc_greedy_decoder",
"tensorflow.summary.trace_on",
"tensorflow.summary.trace_off",
"tensorflow.fill",
"tensorflow.transpose",
"matplotlib.pyplot.legend",
"tensorflow.reduce_mean",
"tensorflow.reshape",
"pandas.DataFrame",
"matplotlib.pyplot.draw",
"matpl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
GT-SALT/CODA | [
"7d3ae5d79288813c66f49701a1f019e8f61d18d6"
] | [
"seq2seq/calculate_influence.py"
] | [
"#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2021 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/l... | [
[
"numpy.mean",
"numpy.where",
"numpy.count_nonzero"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
caseygrun/plates | [
"156069487560d0c72f080f7e45a4dc2ae7a466ac"
] | [
"microplates/io/tecan.py"
] | [
"import pandas as pd\n\ndef read_single(path,header=None,nrows=8,measure='OD600',blank=None, **kwargs):\n # from IPython.core.debugger import set_trace; set_trace()\n\n # try to guess the header row if not provided\n if header is None:\n df = pd.read_excel(path, **kwargs)\n try:\n ... | [
[
"pandas.read_excel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
BlueHC/TTHack-2018--Traffic-Guide-1 | [
"6a720e268d18c8e090c12b874336d17e89a183bb",
"6a720e268d18c8e090c12b874336d17e89a183bb"
] | [
"predict.py",
"routing.py"
] | [
"import numpy as np\nimport torch\nfrom torch.autograd import Variable\nfrom neural_net import HVVNet\n\nclass Predictor():\n def __init__(self, insize, outsize, model_path):\n self.model = HVVNet(insize, outsize)\n self.model.load_state_dict(torch.load(model_path))\n self.model.eval()\n ... | [
[
"numpy.array",
"torch.from_numpy",
"torch.load"
],
[
"pandas.merge",
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
KarthickTk/RTA-project | [
"f9cd18ecb2ae4952ad0233d48b57023924f46d7a"
] | [
"app.py"
] | [
"import streamlit as st\nimport pandas as pd\nimport numpy as np\nimport joblib\nfrom sklearn.ensemble import ExtraTreesClassifier\nfrom prediction import get_prediction, ordinal_encoder\n\nmodel = joblib.load(r'Notebook/Extratreeclassifier_model.joblib')\n\nst.set_page_config(page_title=\"Accident Severity Predict... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Daiver/torch_fuze | [
"6b7ad568e2d7549c7f0c0d4c309532ac1b92881d"
] | [
"examples/cifar10/train_dedicated_device.py"
] | [
"from collections import OrderedDict\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader\nfrom torchvision.datasets import CIFAR10\nimport torchvision.transforms as transforms\n\nimport torch_fuze\nimport pretrainedmodels\nimp... | [
[
"torch.optim.lr_scheduler.MultiStepLR",
"torch.nn.Sequential",
"torch.nn.CrossEntropyLoss",
"torch.nn.Conv2d",
"torch.utils.data.DataLoader",
"torch.nn.functional.adaptive_avg_pool2d",
"torch.nn.MaxPool2d",
"torch.nn.Linear",
"torch.nn.DataParallel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
salama4/pandas | [
"1d36851ffdc8391b8b60a7628ce5a536180ef13b"
] | [
"pandas/core/generic.py"
] | [
"import collections\nfrom datetime import timedelta\nimport functools\nimport gc\nimport json\nimport operator\nimport pickle\nimport re\nfrom textwrap import dedent\nfrom typing import (\n Any,\n Callable,\n Dict,\n FrozenSet,\n Hashable,\n List,\n Mapping,\n Optional,\n Sequence,\n S... | [
[
"pandas.tseries.frequencies.to_offset",
"pandas.util._validators.validate_bool_kwarg",
"pandas.core.dtypes.inference.is_hashable",
"numpy.unique",
"numpy.asanyarray",
"pandas.core.dtypes.common.is_re_compilable",
"pandas.concat",
"pandas.core.dtypes.common.is_list_like",
"panda... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
valenescobar/MyST | [
"0b9b1f1638cc74c463280597d48627bbf80b547c"
] | [
"Notas_Python/Notas_AnalisisTecnico/AnalisisTecnico.py"
] | [
"\n# -- ------------------------------------------------------------- Importar modulos a utilizar -- #\n# -- ------------------------------------------------------------- --------------------------- -- #\n\nimport ta as ta\nimport pandas as pd\nfrom oandapyV20 import API\nimport oandapyV20.endpoints.instruments as ... | [
[
"pandas.set_option",
"pandas.to_datetime",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
margaridaCF/FlyingOctomap_code | [
"2ecba75ee58fadd882887f394819433d9b368283"
] | [
"_generate_plots/time_vs_pathLegth_lazyThetaStar.py"
] | [
"# -*- coding: utf-8 -*-\nimport plotly.plotly as py\nfrom plotly.graph_objs import *\nimport plotly\nimport numpy as np\nimport pandas as pd\nimport plotly.graph_objs as go\nimport csv\nimport plotly.io as pio\n\nfrom collections import defaultdict\n\n \ndef extract_lazy_theta_star_data(csv_filepath):\n ... | [
[
"numpy.poly1d",
"numpy.polyfit",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
YouyangShen/Hierarchical-Localization | [
"cdbb38fc12ea046fb05764a62bd699e7f9941806"
] | [
"hloc/read.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Mar 24 20:03:08 2022\n\n@author: Youyang Shen\n\"\"\"\n\nimport h5py\nimport numpy as np\nLONG = \"feats-LoFTR-r1024_matches-loftr_pairs-netvlad.h5\"\nSHORT = 'feats-LoFTR-r1024.h5'\n\ndef update_two_h5_files(filename_short = SHORT, filename_long = LONG):\n \n p... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
subhayu99/tardis | [
"7792e722f929d661244a819332a031cee7d1b3bf"
] | [
"tardis/io/atom_data/base.py"
] | [
"# atomic model\n\n\nimport logging\nimport numpy as np\nimport pandas as pd\n\nfrom scipy import interpolate\nfrom collections import OrderedDict\nfrom astropy import units as u\nfrom tardis import constants as const\nfrom astropy.units import Quantity\nfrom tardis.io.atom_data.util import resolve_atom_data_fname\... | [
[
"numpy.logical_not",
"numpy.isnan",
"numpy.cumsum",
"pandas.MultiIndex.from_arrays",
"scipy.interpolate.interp1d",
"pandas.HDFStore",
"numpy.zeros",
"numpy.where",
"numpy.isin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0... |
jamesetouma/manim | [
"8b1b30b75853446d0403df8abe67d0bd9c8db033",
"2834fbbba6dae88d64bd3ae46d9c8c6271eacfc7"
] | [
"manim/utils/iterables.py",
"manim/utils/hashing.py"
] | [
"__all__ = [\n \"remove_list_redundancies\",\n \"list_update\",\n \"list_difference_update\",\n \"all_elements_are_instances\",\n \"adjacent_n_tuples\",\n \"adjacent_pairs\",\n \"tuplify\",\n \"stretch_array_to_length\",\n \"make_even\",\n \"make_even_by_cycling\",\n \"remove_nones\... | [
[
"numpy.arange"
],
[
"numpy.resize"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
guillemcortes/FBK-Fairseq-ST | [
"66b4d0ff8b211750c8c955c3d3f277f41713996e"
] | [
"fairseq/modules/multihead_attention.py"
] | [
"# Copyright (c) 2017-present, Facebook, Inc.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the LICENSE file in\n# the root directory of this source tree. An additional grant of patent rights\n# can be found in the PATENTS file in the same directory.\n\nimport torch\nfrom tor... | [
[
"torch.Tensor",
"torch.nn.functional.dropout",
"torch.nn.init.constant_",
"torch.cat",
"torch.nn.functional.conv2d",
"torch.nn.Conv2d",
"torch.zeros_like",
"torch.nn.init.xavier_normal_",
"torch.arange",
"torch.nn.LayerNorm",
"torch.nn.Linear",
"torch.log",
"tor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sopanpatil/exp-hydro | [
"7295dddc4df1028f669a223e1b631a4a91669515"
] | [
"exphydro/distributed/type1/ExphydroDistrModel.py"
] | [
"#!/usr/bin/env python\n\n# Programmer(s): Sopan Patil.\n# This file is part of the 'exphydro.distributed.type1' package.\n\nimport numpy\nfrom exphydro.lumped import ExphydroModel\n\n######################################################################\n\n\nclass ExphydroDistrModel(object):\n\n def __init__(se... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Sanger2000/transformers | [
"5de2046e12cffa5a0381219363cd521e8fe1a2bb"
] | [
"src/transformers/trainer.py"
] | [
"# coding=utf-8\n# Copyright 2020-present the HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unles... | [
[
"torch.load",
"torch.cat",
"torch.utils.data.DataLoader",
"torch.cuda.random.get_rng_state_all",
"torch.cuda.amp.autocast",
"numpy.concatenate",
"torch.no_grad",
"torch.cuda.is_available",
"torch.Generator",
"torch.utils.data.distributed.DistributedSampler",
"torch.dist... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MiroK/fenics_ii | [
"58c41f0e8dba720962830395851e081b057269cc",
"58c41f0e8dba720962830395851e081b057269cc"
] | [
"xii/assembler/average_shape.py",
"test/test_embedding.py"
] | [
"from abc import ABCMeta, abstractmethod\nfrom collections import namedtuple\nfrom itertools import product\nfrom math import pi, sqrt\nimport numpy as np\n\n# import quadpy\n\nfrom numpy.polynomial.legendre import leggauss\nimport dolfin as df\n\nfrom xii.linalg.matrix_utils import is_number\nfrom xii.assembler.av... | [
[
"numpy.polynomial.legendre.leggauss",
"numpy.dot",
"numpy.sqrt",
"scipy.spatial.Delaunay",
"numpy.linalg.norm",
"numpy.cos",
"numpy.sin",
"numpy.row_stack",
"numpy.zeros_like",
"numpy.cross",
"numpy.repeat",
"numpy.array",
"matplotlib.pyplot.show",
"matplotl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
... |
saber805/Monkey-recognition | [
"432c8f9b599f0159a473033e5bdb55398a86484e"
] | [
"vgg16.py"
] | [
"import numpy as np\r\nimport pandas as pd\r\nfrom sklearn.metrics import accuracy_score, confusion_matrix, classification_report\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nfrom PIL import Image\r\nimport scipy.io as scio\r\nimport torch\r\nimport torch.nn as nn\r\nfrom torch.optim import SGD, A... | [
[
"torch.utils.data.DataLoader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mktsasaki/Gasyori100knock | [
"fcaf0170035f3cef7d25baa534d69e44165ab7a3"
] | [
"Question_21_30/answers/answer_29.py"
] | [
"import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n# Affine\ndef affine(img, a, b, c, d, tx, ty):\n\tH, W, C = img.shape\n\n\t# temporary image\n\t# 外周を(0, 0, 0)で埋めた画像からサンプリング\n\t_img = np.zeros((H+2, W+2, C), dtype=np.float32)\n\t_img[1:H+1, 1:W+1] = img\n\n\t# get new image shape\n\tH_new = np.... | [
[
"numpy.round",
"numpy.arange",
"numpy.maximum",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hyeonLewis/MeshTransformer | [
"1daf18cd97896656e84c3634bc0ab91be38981c0"
] | [
"metro/utils/renderer.py"
] | [
"\"\"\"\nRendering tools for 3D mesh visualization on 2D image.\n\nParts of the code are taken from https://github.com/akanazawa/hmr\n\nModified by hyeonwoo Kim, openDR to pytorch3d\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport nu... | [
[
"numpy.dot",
"numpy.hstack",
"torch.ones_like",
"numpy.min",
"numpy.issubdtype",
"numpy.eye",
"numpy.cos",
"torch.from_numpy",
"numpy.sin",
"numpy.concatenate",
"numpy.max",
"numpy.round",
"numpy.zeros_like",
"numpy.mean",
"torch.device",
"numpy.arra... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
skybigzhou/Deep-Person-ReId | [
"8e42089163736ee8c92cd8b2ffcd509e69cf1a73"
] | [
"utils.py"
] | [
"from __future__ import absolute_import\nimport os\nimport sys\nimport errno\nimport shutil\nimport json\nimport os.path as osp\n\nimport torch\n\ndef mkdir_if_missing(directory):\n if not osp.exists(directory):\n try:\n os.makedirs(directory)\n except OSError as e:\n if e.err... | [
[
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chenmingjian/HigherHRNet-Human-Pose-Estimation | [
"6237e9c75bb08d8aa50a8e7f93e65e310d218a68"
] | [
"tools/valid.py"
] | [
"# ------------------------------------------------------------------------------\n# Copyright (c) Microsoft\n# Licensed under the MIT License.\n# Written by Bin Xiao (leoxiaobin@gmail.com)\n# Modified by Bowen Cheng (bcheng9@illinois.edu)\n# -------------------------------------------------------------------------... | [
[
"torch.load",
"torch.cat",
"torch.no_grad",
"torch.rand",
"torch.nn.DataParallel",
"torch.multiprocessing.set_sharing_strategy"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hakyuz16/TrendyolBootCamp | [
"4986c7fc061c8a95a1e4484a9027351b2ad142fa"
] | [
"hw2/reco_sys_with_reg.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Apr 5 15:38:29 2022\n\n@author: humeyraakyuz\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nfrom tqdm import trange\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.metrics import r2_score\nfrom sklearn.metrics import mean_... | [
[
"numpy.random.random",
"numpy.isnan",
"sklearn.metrics.mean_squared_error",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"numpy.zeros",
"numpy.sum",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rollingman1/mmpose | [
"a5a0fe10dca4b848a10b85505d9ad3f091513a63"
] | [
"mmaction/datasets/pipelines/pose_loading.py"
] | [
"# Copyright (c) OpenMMLab. All rights reserved.\nimport copy as cp\nimport pickle\n\nimport numpy as np\nfrom mmcv.fileio import FileClient\nfrom scipy.stats import mode\n\nfrom ..builder import PIPELINES\nfrom .augmentations import Flip\n\n\n@PIPELINES.register_module()\nclass UniformSampleFrames:\n \"\"\"Unif... | [
[
"numpy.squeeze",
"numpy.cumsum",
"numpy.concatenate",
"numpy.max",
"numpy.zeros_like",
"numpy.exp",
"numpy.random.randint",
"numpy.unique",
"numpy.arange",
"numpy.stack",
"numpy.diff",
"numpy.zeros",
"numpy.random.choice",
"numpy.array",
"numpy.sum",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
AW14Saurabh/pointnet | [
"25cd03432c749c327fa8df8040b1c1f1674cd99a"
] | [
"models/transform_nets.py"
] | [
"import tensorflow as tf\nimport numpy as np\nimport sys\nimport os\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\nsys.path.append(BASE_DIR)\nsys.path.append(os.path.join(BASE_DIR, '../utils'))\nfrom utils import tf_util\n\ndef input_transform_net(point_cloud, is_training, bn_decay=None, K=3):\n \"\"\" ... | [
[
"tensorflow.nn.bias_add",
"tensorflow.matmul",
"tensorflow.constant",
"numpy.eye",
"tensorflow.reshape",
"tensorflow.expand_dims",
"tensorflow.constant_initializer",
"tensorflow.variable_scope"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
woodydrn/tensorflow | [
"19d1ebd28f7109d77cab83160a55341667ae5bb0"
] | [
"tensorflow/python/framework/tensor_shape.py"
] | [
"# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.eager.monitoring.BoolGauge",
"tensorflow.python.util.tf_export.tf_export",
"tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto",
"tensorflow.python.tf2.enabled",
"tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto.Dim"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
frenebo/ZMOD | [
"58159fcbf61200c1ec2d6b92fca0cd9d4e83a208"
] | [
"zmk/trainModel/mrcnn/utils.py"
] | [
"\"\"\"\nMask R-CNN\nCommon utility functions and classes.\n\nCopyright (c) 2017 Matterport, Inc.\nLicensed under the MIT License (see LICENSE for details)\nWritten by Waleed Abdulla\n\"\"\"\n\nimport sys\nimport os\nimport math\nimport random\nimport numpy as np\nimport tensorflow as tf\nimport scipy\nimport skima... | [
[
"numpy.dot",
"numpy.minimum",
"numpy.sqrt",
"tensorflow.stack",
"numpy.around",
"tensorflow.cast",
"numpy.cumsum",
"numpy.concatenate",
"numpy.max",
"numpy.all",
"numpy.any",
"numpy.exp",
"numpy.where",
"numpy.divide",
"numpy.pad",
"numpy.reshape",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
simonjheiler/function_approximation | [
"1b52177ba5d80a02227f3f8f45b5dc2b73bd16bb"
] | [
"src/auxiliary.py"
] | [
"\"\"\"Collection of auxiliary functions for simulation study\"\"\"\nimport pdb # noqa F401\n\nimport numpy as np\nimport pandas as pd\n\n# import numba as nb\n\n\n#########################################################################\n# FUNCTIONS\n###############################################################... | [
[
"numpy.random.seed",
"numpy.linspace",
"numpy.full",
"numpy.asanyarray",
"numpy.mean",
"numpy.searchsorted",
"numpy.load",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yashbonde/chess-full-stack | [
"5ee67fc4583d56255bf902a7ad04c1a75822f5bc",
"5ee67fc4583d56255bf902a7ad04c1a75822f5bc"
] | [
"chess_engine/zima_common/engine.py",
"zima_test.py"
] | [
"\"\"\"\nthis is the main file that is involved with\n\"\"\"\n\nimport re\nimport chess\nimport numpy as np\n\nBOARD_POS = ['A1', 'B1', 'C1', 'D1', 'E1', 'F1', 'G1', 'H1', 'A2', 'B2', 'C2', 'D2', 'E2',\n 'F2', 'G2', 'H2', 'A3', 'B3', 'C3', 'D3', 'E3', 'F3', 'G3', 'H3', 'A4', 'B4', 'C4', 'D4',\n ... | [
[
"numpy.array",
"numpy.zeros",
"numpy.transpose"
],
[
"tensorflow.placeholder",
"tensorflow.data.Iterator.from_structure",
"tensorflow.Session"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
ShirelJosef/deep-reinforcement-learning | [
"63979b975c71e730c9d4c66e39efac210260dd18"
] | [
"monte-carlo/part_1.py"
] | [
"import sys\nimport gym\nimport numpy as np\nfrom collections import defaultdict\n\nfrom plot_utils import plot_blackjack_values, plot_policy\n\nenv = gym.make('Blackjack-v0')\n\ndef generate_episode_from_limit_stochastic(bj_env):\n episode = []\n state = bj_env.reset()\n while True:\n probs = [0.8,... | [
[
"numpy.arange",
"numpy.dot",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ahnitz/arviz | [
"c4005b178c600f949d90dbe0258b9c8a1200626d"
] | [
"arviz/plots/backends/matplotlib/elpdplot.py"
] | [
"\"\"\"Matplotlib ELPDPlot.\"\"\"\nimport warnings\n\nimport matplotlib.cm as cm\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib.lines import Line2D\n\nfrom ....rcparams import rcParams\nfrom ...plot_utils import _scale_fig_size, color_from_dim, set_xticklabels\nfrom . import backend_kwarg_def... | [
[
"numpy.arange",
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots",
"numpy.argwhere"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xzlmoon/QUANTAXIS | [
"e250e88a66becb244525ee29ecdf833e7a14d1d9"
] | [
"QUANTAXIS/QAUtil/QATransform.py"
] | [
"# coding:utf-8\n#\n# The MIT License (MIT)\n#\n# Copyright (c) 2016-2019 yutiansut/QUANTAXIS\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including withou... | [
[
"numpy.asarray",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
beyondxz/CTGCN | [
"6afaca390dd91371521e9d6cb99087f8c34c43e9"
] | [
"CTGCN/embedding.py"
] | [
"import numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport os, time, json, sys\nimport gc\nsys.path.append(\"..\")\nimport torch\nimport torch.nn as nn\nfrom CTGCN.utils import check_and_make_path, get_normalize_adj, get_sp_adj_mat, sparse_mx_to_torch_sparse_tensor\n\nclass DataLoader:\n full_no... | [
[
"pandas.read_csv",
"scipy.sparse.eye",
"numpy.arange",
"scipy.sparse.load_npz",
"torch.cuda.empty_cache",
"scipy.sparse.csr_matrix",
"numpy.ones",
"numpy.random.normal",
"torch.set_num_threads",
"torch.FloatTensor",
"torch.cuda.is_available",
"numpy.floor",
"tor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"0.19",
"1.5",
"1.2",
"1.7",
"1.0",
"1.3",
"1.8"
],
"tens... |
CaptEmulation/modeldb | [
"78b10aca553e386554f9740db63466b1cf013a1a"
] | [
"client/workflows/examples/custom-modules/models/nets.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as func\n\nfrom utils import preprocess # ../utils/preprocess.py\n\n\nclass FullyConnected(nn.Module):\n def __init__(self, num_features, hidden_size, dropout):\n super(FullyConnected, self).__init__()\n self.flatten = preprocess.Fla... | [
[
"torch.nn.Linear",
"torch.nn.Dropout"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
18jeffreyma/tfx | [
"ff6917997340401570d05a4d3ebd6e8ab5760495"
] | [
"tfx/orchestration/portable/python_driver_operator_test.py"
] | [
"# Copyright 2020 Google LLC. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by appl... | [
[
"tensorflow.test.main"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
glyg/magicgui | [
"53bbf622975e747f9609a01a501b5cff5c95b895"
] | [
"magicgui/backends/_qtpy/widgets.py"
] | [
"\"\"\"Widget implementations (adaptors) for the Qt backend.\"\"\"\nfrom __future__ import annotations\n\nimport math\nfrom typing import TYPE_CHECKING, Any, Iterable, Sequence\n\nimport qtpy\nfrom qtpy import QtWidgets as QtW\nfrom qtpy.QtCore import QEvent, QObject, Qt, Signal\nfrom qtpy.QtGui import (\n QFont... | [
[
"numpy.frombuffer",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
m-rath/songs | [
"32f7c5a99c17e87f299dee64990b5467770c56fc"
] | [
"song_suggester/planB_funcs.py"
] | [
"\"\"\"some objects and functions supporting html actions and routes\"\"\"\nimport pickle\nimport numpy as np\nimport pandas as pd\nimport matplotlib as plt \nfrom scipy.spatial import distance\n# from sqlalchemy import func, distinct\n# from sqlalchemy.sql import expression #(.exists, .select, ...)\nfrom .spotify_... | [
[
"scipy.spatial.distance.cdist",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
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"1.1",
"1.5",
"1.2",
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"0.25",
"1.3"
],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0... |
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