repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
Will03/NVSM_pytorch | [
"45e91efa6e4571a955c0f76807f2d6b5d7ffa66a",
"1d50581f13255def43cb4025735487644d0fe93a"
] | [
"src/models/vectorTraining.py",
"src/models/linear_model_example.py"
] | [
"import numpy as np\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nimport os\n\n\n\ndataPath = '../../Willll/' # Relative path of homework data\n\n\n# r=root, d=directories, f = files\n\nDocList = []\nQueryList = []\nDocData = []\nQueryData = [... | [
[
"sklearn.metrics.pairwise.cosine_similarity",
"sklearn.feature_extraction.text.TfidfVectorizer"
],
[
"torch.device",
"torch.utils.data.DataLoader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
faiyazsamin/FaceRecognition | [
"9c0bd65f300784910a923f446cf33bacfc502b52"
] | [
"Final.min.py"
] | [
"import cv2\nimport numpy as np\nimport os\n\nsubjects = [\"\",\"Mama\",\"Samin\",\"Delwar\"]\n\n\ndef detect_faces(colored_img, scaleFactor=1.06):\n\n img_copy = colored_img.copy()\n gray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)\n f_cascade = cv2.CascadeClassifier('data/lbpcascade_frontalface.xml')\n ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ankitdobhal/analytics-zoo | [
"b8374bcd6c73bba49fe0b0ab075528cdd94cf2af",
"b8374bcd6c73bba49fe0b0ab075528cdd94cf2af"
] | [
"pyzoo/test/zoo/zouwu/autots/test_auto_ts.py",
"pyzoo/test/zoo/zouwu/model/anomaly/test_model_anomaly.py"
] | [
"#\n# Copyright 2018 Analytics Zoo 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 ... | [
[
"numpy.random.randn",
"pandas.date_range",
"numpy.random.randint"
],
[
"scipy.stats.norm.ppf",
"numpy.ones_like",
"numpy.full",
"numpy.random.normal",
"numpy.random.randn",
"numpy.array",
"numpy.zeros"
]
] | [
{
"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": []
},
{
"matplotlib": [],
"nump... |
hzm2016/assistive-gym-robosuite | [
"5c529f4444cc386383618bfa584341740a8468f9",
"5c529f4444cc386383618bfa584341740a8468f9",
"5c529f4444cc386383618bfa584341740a8468f9",
"5c529f4444cc386383618bfa584341740a8468f9",
"5c529f4444cc386383618bfa584341740a8468f9"
] | [
"code/pytorch/methods/SSAC.py",
"envs/mujoco/utils/play_model.py",
"envs/mujoco/controllers/test/test_pd_controller.py",
"envs/robosuite/robosuite/controllers/joint_vel.py",
"code/pytorch/utils/sim_contextual_solver.py"
] | [
"import os\nimport torch\nimport torch.nn.functional as F\nimport glob\nimport numpy as np\nfrom torch.optim import Adam\nfrom utils.utils import soft_update, hard_update\nfrom utils.model import GaussianPolicy, QNetwork, DeterministicPolicy\nfrom keras.models import Sequential, Model\nfrom keras.layers import Dens... | [
[
"numpy.amax",
"numpy.absolute",
"numpy.random.random",
"numpy.multiply",
"torch.zeros",
"numpy.reshape",
"numpy.arange",
"torch.Tensor",
"numpy.cumsum",
"numpy.stop_gradient",
"numpy.max",
"numpy.argmax",
"numpy.mean",
"torch.FloatTensor",
"numpy.isscala... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
erprashu/Metal_erning | [
"79d1a6a457be37258df50a9194946caeb86845a2"
] | [
"test.py"
] | [
"# -*- coding: utf-8 -*-\nimport argparse\n\nimport torch\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader\n\nfrom torch.autograd import Variable\n\nfrom tqdm import tqdm\n\nfrom models.protonet_embedding import ProtoNetEmbedding\nfrom models.R2D2_embedding import R2D2Embedding\nfrom models... | [
[
"torch.linspace",
"numpy.sqrt",
"numpy.clip",
"torch.load",
"torch.cat",
"torch.rot90",
"torch.nn.functional.grid_sample",
"torch.no_grad",
"torch.nn.DataParallel",
"numpy.array",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mdeegen/pb_bss | [
"e8c380e27d82707e8d2b2d83c5c918d47ea5d89f"
] | [
"tests/test_distribution/test_von_mises_fisher.py"
] | [
"import numpy as np\nfrom numpy.testing import assert_allclose, assert_equal\nimport unittest\nfrom pb_bss.distribution import VonMisesFisher\nfrom pb_bss.distribution import VonMisesFisherTrainer\n\n\nclass TestGaussian(unittest.TestCase):\n def test_shapes(self):\n samples = 10000\n mean = np.one... | [
[
"numpy.testing.assert_equal",
"numpy.random.multivariate_normal",
"numpy.eye",
"numpy.linalg.norm",
"numpy.tile",
"numpy.ones",
"numpy.testing.assert_allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vamships/RelationPrediction | [
"45f48e8d09331e7244a7fe8d2d9d0fefa7e1f76b"
] | [
"code/extras/highway_layer.py"
] | [
"import numpy as np\nimport tensorflow as tf\nfrom model import Model\nfrom common.shared_functions import glorot_variance, make_tf_variable, make_tf_bias\n\nclass HighwayLayer(Model):\n vertex_embedding_function = {'train': None, 'test': None}\n\n def __init__(self, shape, next_component=None, next_component... | [
[
"tensorflow.matmul",
"tensorflow.nn.sigmoid"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
seba-1511/gsoc15-demo | [
"c57d5cce7903511edd4048f8bfed2ad0dc6f6b6b"
] | [
"keras/preprocessing/sequence.py"
] | [
"import numpy as np\n\ndef pad_sequences(seqs, maxlen=None, dtype='int32'):\n \"\"\"\n Pad each sequence to the same lenght: \n the lenght of the longuest sequence.\n\n If maxlen is provided, any sequence longer\n than maxlen is truncated to maxlen.\n \"\"\"\n lengths = [len(s) ... | [
[
"numpy.max",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wxy1988/ASR | [
"8ef3ef347523044c89c46c263ecc7b8e9b2c06d1",
"8ef3ef347523044c89c46c263ecc7b8e9b2c06d1"
] | [
"transformer/third_party/feat_convert/kaldi_io/batchmk.py",
"transformer/model_pretrain.py"
] | [
"#!/usr/bin/python\r\n# coding=utf-8\r\n\r\n\"\"\"\r\n@version:\r\n@author: Dong Linhao\r\n@license: Apache Licence\r\n@contact: donglinhao2015@ia.ac.cn\r\n@site:\r\n@software: PyCharm Community Edition\r\n@file: batchmk.py\r\n@time: 09/04/17 21:10\r\n\"\"\"\r\n\r\nimport src.io.fea as fea\r\nimport tensorflow as t... | [
[
"tensorflow.device",
"tensorflow.concat",
"tensorflow.FixedLenFeature",
"tensorflow.zeros",
"tensorflow.shape",
"tensorflow.slice",
"tensorflow.decode_raw",
"tensorflow.reshape",
"tensorflow.ones",
"numpy.stack",
"tensorflow.train.string_input_producer",
"numpy.shap... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
lferraz/VideoProcessingFramework | [
"19b87eddc0539d90ae4025629bac7c93c1387d56"
] | [
"SampleEncodeMultiThread.py"
] | [
"#\n# Copyright 2020 NVIDIA Corporation\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 law o... | [
[
"numpy.ndarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
haoxingchen/SSFormers | [
"3c2ea14db6a453d3345e03a790dd452af5fde8d8"
] | [
"modules/fsl_semi_query.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom .encoder import make_encoder\nfrom .semi_query import make_query\n\n\nclass FSLSemiQuery(nn.Module):\n def __init__(self, cfg):\n super().__init__()\n\n self.encoder = make_encoder(cfg)\n self.query = make_query(se... | [
[
"torch.nn.functional.adaptive_avg_pool2d",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wryoung412/CS294_Deep_RL_fall2017 | [
"077167de524157cc5f85f40232e5bcf6933ab2f5"
] | [
"hw2/train_pg.py"
] | [
"import numpy as np\nimport tensorflow as tf\nimport gym\nimport logz\nimport scipy.signal\nimport os\nimport time\nimport inspect\nfrom multiprocessing import Process\n\n#============================================================================================#\n# Utilities\n#===================================... | [
[
"numpy.concatenate",
"numpy.max",
"tensorflow.nn.l2_loss",
"numpy.zeros_like",
"numpy.mean",
"tensorflow.train.AdamOptimizer",
"numpy.ones_like",
"tensorflow.layers.dense",
"tensorflow.ConfigProto",
"numpy.std",
"tensorflow.Session",
"numpy.min",
"tensorflow.pla... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
andi611/Mockingjay-Speech-Representation | [
"e77df17a7f63a983c3757140c7a1e8c199cac614"
] | [
"runner_mockingjay.py"
] | [
"# -*- coding: utf-8 -*- #\n\"\"\"*********************************************************************************************\"\"\"\n# FileName [ runner_mockingjay.py ]\n# Synopsis [ runner for the mockingjay model ]\n# Author [ Andy T. Liu (Andi611) ]\n# Copyright [ Copyleft(c), Speech L... | [
[
"numpy.random.seed",
"torch.load",
"torch.manual_seed",
"torch.cuda.manual_seed_all",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
creaiter/Classification-PyTorch | [
"2feabf4b3d0d561420399bdf65840a58af76069d",
"2feabf4b3d0d561420399bdf65840a58af76069d"
] | [
"models/wideresnet.py",
"models/shufflenetv2.py"
] | [
"import math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.init as init\nimport torch.nn.functional as F\nfrom torch.nn.parameter import Parameter\n\n\ndef conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):\n \"\"\"3x3 convolution with padding\"\"\"\n return nn.Conv2d(in_planes, out_pla... | [
[
"torch.nn.Sequential",
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.flatten",
"torch.nn.Linear",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
],
[
"torch.nn.Sequential",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.Linear",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
myutman/contracode | [
"f2a589e1efd2788874fd0468d1ecc30d6a14c396"
] | [
"scripts/hf_per_train_shard_tokenize.py"
] | [
"import sys\nimport numpy as np\nimport pandas as pd\nimport multiprocessing as mp\nfrom transformers import BertTokenizerFast\nfrom tqdm import tqdm\n\nif __name__ == \"__main__\":\n assert len(sys.argv) == 2\n data_shard_idx = int(sys.argv[1])\n data_shard_path = f\"/data/ajay/contracode/data/hf_data/tra... | [
[
"pandas.read_pickle"
]
] | [
{
"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": []
}
] |
penguinflys/imgviz | [
"3deadced1fcce8ca51716c705d07a058b1839514"
] | [
"examples/resize.py"
] | [
"#!/usr/bin/env python\n\nimport matplotlib.pyplot as plt\n\nimport imgviz\n\n\ndef resize():\n data = imgviz.data.arc2017()\n\n rgb = data[\"rgb\"]\n\n H, W = rgb.shape[:2]\n rgb_resized = imgviz.resize(rgb, height=0.1)\n\n # -------------------------------------------------------------------------\... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.close",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Nathaniel-Rodriguez/neuralnetsim | [
"c353af92fb3f44539370220963b07bdfd9822149"
] | [
"src/neuralnetsim/simulation.py"
] | [
"__all__ = [\"simulate_model\",\n \"simulate_grid\",\n \"simulate_orig\"]\n\n\nimport neuralnetsim\nimport networkx as nx\nimport numpy as np\nfrom distributed import Client\nfrom pathlib import Path\nfrom typing import Type\nfrom typing import Dict\nfrom typing import Any\nfrom typing import Li... | [
[
"numpy.random.RandomState",
"numpy.ndarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
geneti/courseworkproj | [
"5843cc14c2ce01172420befca5d2683f1123096a",
"5843cc14c2ce01172420befca5d2683f1123096a"
] | [
"pattern_recognition/code/DataSplit.py",
"distributed_system/code/Mesh_node.py"
] | [
"import pandas as pd\nimport numpy as np\nimport math\nimport matplotlib.pyplot as plt\nimport copy\n\nraw_data = pd.read_csv('./raw_data.csv', header = 0, index_col = 0)\nsample_num = raw_data.shape[0]\n\n# sort features by nominal or non-nominal \ndtypes = {}\nfor j in range(raw_data.shape[1]):\n if isinstance... | [
[
"pandas.isna",
"pandas.read_csv"
],
[
"numpy.random.binomial"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GyroscopeHQ/keras-rl | [
"35f9b50c3b35f52722d740e8ee42e33c1750e44a"
] | [
"rl/policy.py"
] | [
"from __future__ import division\nimport numpy as np\n\nfrom rl.util import *\n\n\nclass Policy(object):\n def _set_agent(self, agent):\n self.agent = agent\n\n @property\n def metrics_names(self):\n return []\n\n @property\n def metrics(self):\n return []\n\n def select_actio... | [
[
"numpy.clip",
"numpy.argmax",
"numpy.random.random_integers",
"numpy.random.uniform",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
efthymis-mcl/algomorphism | [
"69a41e98e10458ac333da1350fc39da8a00b80d3"
] | [
"algomorphism/datasets/graph_base.py"
] | [
"from typing import List\nimport numpy as np\n\n\nclass GraphBaseDataset(object):\n def __int__(self):\n pass\n\n @staticmethod\n def numpy_to_mega_batch(x_list, a_list):\n \"\"\"\n List of numpy arrays to mega batch array.\n\n Args:\n x_list (`list[np.ndarray]`): fea... | [
[
"numpy.power",
"numpy.linalg.inv",
"numpy.eye",
"numpy.matmul",
"numpy.stack",
"numpy.concatenate",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
goncalovalverde/seshat | [
"deff5cdd985f81ac2b4ebd077eea11f7c4f4118f"
] | [
"reader/gitlab.py"
] | [
"import gitlab\nimport dateutil.parser\nimport reader.cache\nimport hashlib\nimport logging\nfrom pandas import DataFrame, NaT\nfrom datetime import datetime\n\n\nclass Gitlab:\n def __init__(self, gitlab_config: dict, workflow: dict):\n self.gitlab_config = gitlab_config\n self.workflow = workflow... | [
[
"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": []
}
] |
ahkarimi/MMTOD | [
"d8160f643a0ee1943630b45fa094617dd2237c7e"
] | [
"main.py"
] | [
"from flask import Flask, request, jsonify, render_template, session\nimport os\nimport pickle\nimport datetime\nimport time\nimport pandas as pd\nimport numpy as np\nimport random\nimport logging\n\n##__________________________________ GPT-3 code __________________________________________##\nfrom colorama import ... | [
[
"torch.argmax",
"torch.no_grad",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jssprz/attentive-visual-semantic-specialized-network-for-video-captioning | [
"00815884ba892c00db2d3778bd0083618ff6d2d7"
] | [
"test.py"
] | [
"import os\nimport argparse\nimport pickle\n\nfrom utils import decode_from_tokens\nfrom vocabulary import Vocabulary\nfrom configuration_file import ConfigurationFile\nfrom model.encoder import Encoder\nfrom model.decoder import AVSSNDecoder\n\nimport h5py\nimport torch\nimport numpy as np\n\n\nif __name__ == '__m... | [
[
"torch.no_grad",
"torch.from_numpy",
"torch.Tensor",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dnidever/apogee | [
"83ad7496a0b4193df9e2c01b06dc36cb879ea6c1",
"83ad7496a0b4193df9e2c01b06dc36cb879ea6c1",
"83ad7496a0b4193df9e2c01b06dc36cb879ea6c1",
"83ad7496a0b4193df9e2c01b06dc36cb879ea6c1",
"83ad7496a0b4193df9e2c01b06dc36cb879ea6c1",
"83ad7496a0b4193df9e2c01b06dc36cb879ea6c1"
] | [
"external/synple/synple.py",
"python/apogee/dr.py",
"python/apogee/utils/spectra.py",
"python/apogee/aspcap/elem.py",
"python/apogee/aspcap/persist.py",
"python/apogee/apred/lsfmap.py"
] | [
"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\"\"\"Python wrapper for synspec \n\nCalculation of synthetic spectra of stars and convolution with a rotational/Gaussian kernel.\nMakes the use of synspec simpler, and retains the main functionalities (when used from\npython). The command line interface is even simpler... | [
[
"numpy.sqrt",
"numpy.linspace",
"numpy.concatenate",
"numpy.max",
"numpy.mean",
"numpy.exp",
"numpy.where",
"numpy.arange",
"numpy.diff",
"numpy.interp",
"numpy.zeros",
"numpy.log",
"numpy.min",
"scipy.interpolate.splev",
"numpy.log10",
"numpy.savetx... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16",
"1.8"
... |
phil-lo/pyportlib | [
"3fbe7460c809a80e48615e934990dcd2d1f5003b"
] | [
"pyportlib/services/cash_manager.py"
] | [
"from datetime import datetime\nfrom typing import List, Union\nimport pandas as pd\n\nfrom pyportlib.services.cash_change import CashChange\nfrom pyportlib.utils import df_utils, files_utils\nfrom pyportlib.utils import logger\n\n\nclass CashManager:\n NAME = \"Cash Account\"\n ACCOUNTS_DIRECTORY = files_uti... | [
[
"pandas.read_csv",
"pandas.to_datetime",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
covid-19-impact-lab/sid | [
"d867f55d4d005b01c672bd2edd0e1dc974cb182b"
] | [
"tests/test_parse_model.py"
] | [
"from contextlib import ExitStack as does_not_raise # noqa: N813\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom sid.config import DEFAULT_VIRUS_STRAINS\nfrom sid.config import INITIAL_CONDITIONS\nfrom sid.parse_model import parse_duration\nfrom sid.parse_model import parse_initial_conditions\nfrom... | [
[
"pandas.to_datetime",
"pandas.MultiIndex.from_tuples",
"numpy.ones",
"numpy.all",
"pandas.Timestamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hamhochoisg/moneydetection | [
"32a02f54a4a0c1a6f41a232fa30a3f0f15bdab13"
] | [
"main.py"
] | [
"import streamlit as st\nimport pandas as pd\nimport numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport os\nfrom tensorflow.keras.preprocessing import image \n\nst.title('Banknotes Classification')\nmenu = ['Home','Up Load & Predict', 'Capture From Webcam']\n\n#===============... | [
[
"tensorflow.keras.models.load_model",
"numpy.expand_dims",
"tensorflow.keras.preprocessing.image.img_to_array",
"tensorflow.keras.preprocessing.image.load_img"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.4",
"2.3",
"2.5",
"2.6"
]
}
] |
ssawwqdf/-project-stock_info_dashboard | [
"f14a462d915d2207db1da12307aefdef4b6921e1",
"f14a462d915d2207db1da12307aefdef4b6921e1"
] | [
"code_cr.py",
"lec20_flask.py"
] | [
"import re\nimport numpy as np\nimport pandas as pd\nimport requests #웹통신\nimport json\nfrom pmdarima.arima import ndiffs\nimport pmdarima as pm\nfrom pykrx import stock\nfrom bs4 import BeautifulSoup\nimport html5lib\n\n\n# ==============\n# 업종 분류\n# =====... | [
[
"pandas.concat",
"pandas.read_csv",
"numpy.abs",
"numpy.asarray",
"pandas.read_html",
"pandas.DataFrame",
"numpy.round",
"numpy.array"
],
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1... |
donnyyou/centerX | [
"1073753533f26483c3ab053a7d8753708fcacde7"
] | [
"projects/speedup/centerX2onnx.py"
] | [
"from types import MethodType\nimport onnx\nimport torch\nfrom torch.onnx import OperatorExportTypes\nfrom onnxsim import simplify\nimport argparse\nimport io\nimport sys\nimport torch.nn as nn\n\nsys.path.insert(0, '.')\nfrom configs import add_centernet_config\nfrom detectron2.config import get_cfg\nfrom inferenc... | [
[
"torch.onnx.export",
"torch.randn",
"torch.nn.MaxPool2d",
"torch.no_grad",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WHATDOESTHEFOXSAY2U/Colab_Train | [
"30fdf2f9f72fbef51447ecc91070189ccca301b2"
] | [
"callbacks.py"
] | [
"\"\"\"\nContains custom callbacks.\n\"\"\"\n\nfrom constants import minimum_scores, maximum_scores\nimport constants\nimport datetime\nimport json\nfrom keras.callbacks import Callback, ModelCheckpoint\nimport numpy as np\nimport os\nfrom sklearn.metrics import cohen_kappa_score\nfrom util import process_data, cre... | [
[
"numpy.around",
"numpy.array",
"numpy.sum",
"sklearn.metrics.cohen_kappa_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hujunxianligong/Graph-U-Nets | [
"d1a483400131fbe75a55cff27439585c62c4a575"
] | [
"main.py"
] | [
"import sys\nimport os\nimport torch\nimport random\nimport numpy as np\nfrom tqdm import tqdm\nimport torch.nn as nn\nimport torch.optim as optim\nimport math\nfrom network import GUNet\nfrom mlp_dropout import MLPClassifier\nfrom sklearn import metrics\nfrom util import cmd_args, load_data\n\n\nsys.path.append(\n... | [
[
"torch.LongTensor",
"torch.ones",
"numpy.random.seed",
"torch.zeros",
"torch.cat",
"torch.manual_seed",
"torch.from_numpy",
"sklearn.metrics.roc_curve",
"numpy.concatenate",
"sklearn.metrics.auc",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
carlsummer/python_developer_tools | [
"fc0dcf5c4ef088e2e535206dc82f09bbfd01f280",
"a8c4365b7cc601cda55648cdfd8c0cb1faae132f",
"fc0dcf5c4ef088e2e535206dc82f09bbfd01f280",
"fc0dcf5c4ef088e2e535206dc82f09bbfd01f280",
"a8c4365b7cc601cda55648cdfd8c0cb1faae132f",
"fc0dcf5c4ef088e2e535206dc82f09bbfd01f280",
"fc0dcf5c4ef088e2e535206dc82f09bbfd01f28... | [
"python_developer_tools/cv/classes/ResNeXt.py",
"python_developer_tools/machinelearning/lgb/train.py",
"python_developer_tools/cv/bases/conv/DCNv2/DCNv2.py",
"python_developer_tools/cv/detection/CenterNet2/changecenternet2.py",
"python_developer_tools/cv/loss/classes/cross_entropy.py",
"python_developer_t... | [
"import torch\nimport torch.nn as nn\nimport torchvision\n\n\nclass ResNeXtBlock(nn.Module):\n def __init__(self,in_places,places, stride=1,downsampling=False, expansion = 2, cardinality=32):\n super(ResNeXtBlock,self).__init__()\n self.expansion = expansion\n self.downsampling = downsamplin... | [
[
"torch.randn",
"torch.nn.ReLU",
"torch.nn.Conv2d",
"torch.nn.BatchNorm2d"
],
[
"sklearn.metrics.mean_absolute_error",
"pandas.read_csv",
"sklearn.model_selection.train_test_split"
],
[
"torch.randn",
"torch.randint",
"torch.cat"
],
[
"torch.load"
],
[
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
... |
edornd/multimodal-icl | [
"f79bfa73665db471c12ee9cb57bbee1bcabb0467"
] | [
"saticl/training.py"
] | [
"from itertools import chain\nfrom pathlib import Path\nfrom typing import Tuple\n\nimport torch\nfrom accelerate import Accelerator\nfrom torch.utils.data import DataLoader\n\nfrom saticl.config import Configuration, SSLConfiguration\nfrom saticl.datasets.icl import ICLDataset\nfrom saticl.datasets.transforms impo... | [
[
"torch.utils.data.DataLoader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
frank20a/collaborative-sats | [
"9d26d3c8f66cf43bbd514f02434851439e746797",
"9d26d3c8f66cf43bbd514f02434851439e746797"
] | [
"src/slider_experiment/slider_experiment/thruster_pwm_tsl.py",
"src/stereo_cam/stereo_cam/disparity_publisher.py"
] | [
"import rclpy\nfrom rclpy.node import Node\nfrom geometry_msgs.msg import Vector3\nfrom std_msgs.msg import Int16\nfrom rclpy.qos import QoSPresetProfiles\nfrom ament_index_python import get_package_share_directory\n\nimport numpy as np\nimport sys, os\n\nfrom .parameters import force\nfrom .flags import flags\n\n\... | [
[
"numpy.ceil",
"numpy.floor"
],
[
"numpy.tan",
"numpy.float64"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lucaskolson/ddd | [
"f273c61856bca27a40b9691b2a9842d8705a3503"
] | [
"app.py"
] | [
"import dash\nfrom dash import dcc\nfrom dash import html\nfrom dash.dependencies import Input, Output\nimport plotly.express as px\nimport pandas as pd\nfrom dash import callback_context\n\ndf = px.data.election()\ngeojson = px.data.election_geojson()\ncandidates = df.winner.unique()\n\nexternal_stylesheets = ['ht... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Roshan-Thomas/qiskit-terra | [
"77219b5c7b7146b1545c5e5190739b36f4064b2f",
"77219b5c7b7146b1545c5e5190739b36f4064b2f",
"77219b5c7b7146b1545c5e5190739b36f4064b2f",
"77219b5c7b7146b1545c5e5190739b36f4064b2f",
"77219b5c7b7146b1545c5e5190739b36f4064b2f",
"77219b5c7b7146b1545c5e5190739b36f4064b2f",
"77219b5c7b7146b1545c5e5190739b36f4064b2... | [
"qiskit/visualization/timeline/plotters/matplotlib.py",
"qiskit/algorithms/optimizers/qnspsa.py",
"qiskit/algorithms/amplitude_estimators/fae.py",
"qiskit/circuit/library/standard_gates/ryy.py",
"test/python/pulse/test_calibrationbuilder.py",
"test/python/quantum_info/states/test_random.py",
"test/pytho... | [
"# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2020.\n#\n# This code is licensed under the Apache License, Version 2.0. You may\n# obtain a copy of this license in the LICENSE.txt file in the root directory\n# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\n#\n# Any modifications or ... | [
[
"matplotlib.collections.PatchCollection",
"numpy.asarray",
"numpy.arange",
"matplotlib.patches.Rectangle",
"matplotlib.pyplot.savefig",
"numpy.concatenate",
"matplotlib.pyplot.figure"
],
[
"numpy.outer",
"numpy.mean"
],
[
"numpy.log",
"numpy.maximum",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
limberc/mindspore | [
"59a277756eb4faad9ac9afcc7fd526e8277d4994",
"e294acdffc9246cb6d77ea18ea00d08244d30c59"
] | [
"tests/ut/python/dataset/test_compose.py",
"mindspore/train/summary/_summary_adapter.py"
] | [
"# Copyright 2020 Huawei Technologies Co., Ltd\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... | [
[
"numpy.testing.assert_array_equal",
"numpy.array",
"numpy.stack",
"numpy.ones"
],
[
"numpy.histogram",
"numpy.linspace",
"numpy.min",
"numpy.finfo",
"numpy.concatenate",
"numpy.max",
"numpy.ma.masked_invalid",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vbelissen/packnet-sfm | [
"dfba692596b08ccff17abb9423c1958cecc75b0f"
] | [
"packnet_sfm/models/SemiSupModel_fisheye.py"
] | [
"# Copyright 2020 Toyota Research Institute. All rights reserved.\n\nimport torch\n\nfrom packnet_sfm.models.SelfSupModel_fisheye import SfmModel, SelfSupModel_fisheye\nfrom packnet_sfm.losses.supervised_loss_valeo import SupervisedLoss\nfrom packnet_sfm.models.model_utils import merge_outputs\nfrom packnet_sfm.ut... | [
[
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
terasakisatoshi/pythonCodes | [
"baee095ecee96f6b5ec6431267cdc6c40512a542",
"baee095ecee96f6b5ec6431267cdc6c40512a542",
"baee095ecee96f6b5ec6431267cdc6c40512a542",
"baee095ecee96f6b5ec6431267cdc6c40512a542",
"baee095ecee96f6b5ec6431267cdc6c40512a542",
"baee095ecee96f6b5ec6431267cdc6c40512a542",
"baee095ecee96f6b5ec6431267cdc6c40512a54... | [
"cythonTest/onlineSample/combination/jit_combination.py",
"ExcelHander/exceltest.py",
"matplotlib_exercise/officialExample/eventHandlingExamples/test_mouseclicks.py",
"matplotlib_exercise/officialExample/widgetsExamples/menu.py",
"matplotlib_exercise/surround_pt.py",
"bokehExer/scipyTest/sharpe.py",
"so... | [
"import time\nfrom numba import jit\nimport numpy as np \n\n@jit()\ndef jit_sum_conbination(N):\n xs = [i for i in range(N)]\n ys = [i for i in range(N)]\n total = 0\n for x in xs:\n for y in ys:\n total += x+y\n return total\n\ndef py_sum_conbination(N):\n xs = np.arange(N)\n ... | [
[
"numpy.arange"
],
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.figure"
],
[
"matplotlib.pyplot.gca",
"matplotlib.pyplot.show",
"matplotlib.pyplot.gcf"
],
[
"matplotlib.artist.Artist.__init__",
"matplotlib.colors.to_rgba",
"matplotlib.image.FigureImage",
"matpl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
alexshuang/onnxruntime | [
"771a6d235b8495d05bcf6a906107df1bd6e81744"
] | [
"orttraining/orttraining/python/training/_ortmodule_utils.py"
] | [
"# -------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License.\n# --------------------------------------------------------------------------\n\nfrom . import _utils\n\nfrom onnxruntime.capi.onnxruntime_infe... | [
[
"torch.device",
"torch.utils.cpp_extension.load_inline",
"torch.utils.dlpack.to_dlpack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Paul-31415/soundplay | [
"0e7ea27c6d4bdf5f94e5034c7775a10c62d1583e",
"0e7ea27c6d4bdf5f94e5034c7775a10c62d1583e"
] | [
"fmsynth.py",
"Clockwork_RNN.py"
] | [
"\n\nfrom itools import lmbdWr,lm\n\nimport itertools\n\nfrom bisect import bisect_right\n\nimport brailleG as gr\n\ndef abs2(n):\n return (n*n.conjugate()).real\n \n\ndef fsample(buf,m=1,b=0):\n index = 0\n y = 0\n while 1:\n index = (index+b+m*y)%len(buf)\n y = yield buf[(int(index)+1... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
],
[
"numpy.clip",
"matplotlib.pyplot.subplots",
"numpy.ones",
"numpy.concatenate",
"numpy.copy",
"numpy.random.normal",
"matplotlib.animation.FuncAnimation",
"numpy.tanh",
"numpy.a... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
accordinglyto/dferte | [
"d4b8449c1633973dc538c9e72aca5d37802a4ee4",
"d4b8449c1633973dc538c9e72aca5d37802a4ee4",
"d4b8449c1633973dc538c9e72aca5d37802a4ee4",
"d4b8449c1633973dc538c9e72aca5d37802a4ee4"
] | [
"src/predict-binary.py",
"backup/23.py",
"src/18.py",
"backup/15.py"
] | [
"import os\nimport numpy as np\n#os.environ[\"KERAS_BACKEND\"] = \"plaidml.keras.backend\"\n\nfrom keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array\nfrom keras.models import Sequential, load_model\n\n\nimg_width, img_height = 48, 48\nmodel_path = '../src/models/model.h5'\nweights_path = '... | [
[
"numpy.expand_dims"
],
[
"matplotlib.pyplot.autoscale",
"matplotlib.pyplot.cla",
"numpy.genfromtxt",
"numpy.ones",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.figure"
],
[
"matplotlib.pyplot.autoscale",
"matplotlib.pyplot.cla",
"numpy.g... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
T3p/baselines | [
"5623c9160d1e86ebca3e673f142fe6b14a1db06c",
"5623c9160d1e86ebca3e673f142fe6b14a1db06c",
"5623c9160d1e86ebca3e673f142fe6b14a1db06c"
] | [
"sacred_mis/_sources/pomis2_57be95a71b575624c33c6ffe64e50d6e.py",
"baselines/pois2/run.py",
"baselines/common/notebook_utils.py"
] | [
"import numpy as np\nimport warnings\nimport baselines.common.tf_util as U\nimport tensorflow as tf\nimport time\nfrom baselines.common import zipsame, colorize\nfrom contextlib import contextmanager\nfrom collections import deque\nfrom baselines import logger\nfrom baselines.common.cg import cg\nfrom baselines.pom... | [
[
"numpy.dot",
"tensorflow.reduce_sum",
"tensorflow.cast",
"tensorflow.variables_initializer",
"numpy.mean",
"numpy.reshape",
"tensorflow.get_collection",
"numpy.isnan",
"tensorflow.placeholder",
"tensorflow.exp",
"numpy.sum",
"tensorflow.clip_by_value",
"tensorfl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
... |
NicolasHug/pyts | [
"29659fb09f568df2e7f8190f2d5a1c383dc7e9fa"
] | [
"pyts/transformation/boss.py"
] | [
"\"\"\"Code for Bag-of-SFA Symbols.\"\"\"\n\n# Author: Johann Faouzi <johann.faouzi@gmail.com>\n# License: BSD-3-Clause\n\nimport numpy as np\nfrom math import ceil\nfrom scipy.sparse import csr_matrix\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.feature_extraction.text import CountVector... | [
[
"sklearn.utils.validation.check_is_fitted",
"sklearn.utils.validation.check_array",
"sklearn.utils.multiclass.check_classification_targets",
"scipy.sparse.csr_matrix",
"numpy.full",
"sklearn.feature_extraction.text.CountVectorizer",
"numpy.repeat"
]
] | [
{
"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"... |
fishjojo/pydmfe | [
"93cfc655314933d3531b5733521a1f95a044f6cb",
"93cfc655314933d3531b5733521a1f95a044f6cb"
] | [
"examples/research/proj/C3H6.py",
"examples/research/read_umat.py"
] | [
"from pydmfet import proj_ao\nfrom pydmfet.qcwrap.pyscf_rks_ao import rks_ao\nfrom pyscf import gto,scf\nimport numpy as np\nfrom pyscf.tools import molden\nfrom pyscf import lo\nfrom pyscf.lo import iao,orth\nfrom functools import reduce\nimport math\n\nbas ='ccpvdz'\ntemp = 0.01\n\nmol = gto.Mole()\nmol.atom = op... | [
[
"numpy.zeros"
],
[
"numpy.reshape",
"numpy.array",
"numpy.ndarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Lilgabz/Quantum-Algorithm-Implementations | [
"2bb5df522d76e94b300275dfefff2869ff31bc2c"
] | [
"Quantum Key Distribution/Mutation Testing/QKD Mutation Testing Cirq/Remove_mutant_2.py"
] | [
"import unittest\n\nimport cirq\nfrom cirq.ops import H, X, I\nimport random\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom numpy.random import randint\n\nimport hypothesis.strategies as st\nfrom hypothesis import given, settings\n\ndef generate_binary(len):\n return randint(2, size=len)\n\ndef encod... | [
[
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YMandCL/Hands-On-Deep-Learning-for-Games | [
"0225661409c3bf59ae6b7996c254bb485ebd10cb",
"0225661409c3bf59ae6b7996c254bb485ebd10cb"
] | [
"Chapter03/Chapter_3/musegen/musegen.py",
"Chapter05/Chapter_5/Chapter_5_5.py"
] | [
"# Currently this script is configured to use the note-generator model.\n\nfrom config import sequence_length, output_dir, note_generator_dir\nfrom helper import loadChorales, loadModelAndWeights, createPitchSpecificVocabularies, createDurationVocabularySpecific\nfrom music21 import note, instrument, stream, durati... | [
[
"numpy.random.choice",
"numpy.reshape",
"numpy.eye",
"numpy.argmax",
"numpy.array",
"numpy.vstack"
],
[
"numpy.reshape",
"numpy.argmax",
"numpy.random.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HassanDayoub/tfx | [
"dc9221abbb8dad991d1ae22fb91876da1290efae"
] | [
"tfx/orchestration/kubeflow/executor_wrappers.py"
] | [
"# Copyright 2019 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.python.lib.io.file_io.FileIO",
"tensorflow.gfile.MakeDirs",
"tensorflow.logging.info"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
maptube/UMaaS | [
"0758d8352213f332546d728f3eb02411c16c97ac"
] | [
"benchmark/benchmark_SingleOrigin.py"
] | [
"import timeit\nimport os.path\nimport numpy as np\nfrom math import exp, fabs\nfrom sys import float_info\n\nfrom globals import *\nfrom utils import loadMatrix, resizeMatrix\n\nfrom models.SingleOrigin import SingleOrigin\n\n\"\"\"\nBenchmarks for the Single Origin Constrained model (models/SingleOrigin.py)\nAll ... | [
[
"numpy.shape",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
joe-jordan/picosdk-python-wrappers | [
"76f393b500200de168b4f2b74b74aad74d89fd92"
] | [
"ps3000aExamples/ps3000aBlockMSOExample.py"
] | [
"#\n# Copyright (C) 2018 Pico Technology Ltd. See LICENSE file for terms.\n#\n# PS3000A BLOCK MODE MSO EXAMPLE\n# This example opens a 3000a driver device, sets up one digital port and a trigger to collect a block of data.\n# This data is then split into the indivual digital channels and plotted as the binary valu... | [
[
"numpy.linspace",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Kshitiz-Bansal/wavetorch | [
"927ad02dc9db83f72b8df1d91418a6681e60fd56",
"927ad02dc9db83f72b8df1d91418a6681e60fd56"
] | [
"wavetorch/io.py",
"study/utils/plot_mem.py"
] | [
"import copy\nimport os\n\nimport torch\n\nfrom . import geom\nfrom .cell import WaveCell\nfrom .probe import WaveIntensityProbe\nfrom .rnn import WaveRNN\nfrom .source import WaveSource\nfrom .utils import set_dtype\n\ndef save_model(model,\n\t\t\t name,\n\t\t\t savedir='./study/',\n\t\t\t history=None,\n\t\... | [
[
"torch.load",
"torch.save"
],
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
philtrade/gQuant | [
"08b2a82a257c234b92f097b925f25cab16fd0926"
] | [
"tests/unit/test_indicator_node.py"
] | [
"'''\nTechnical Indicator Node Unit Tests\n\nTo run unittests:\n\n# Using standard library unittest\n\npython -m unittest -v\npython -m unittest tests/unit/test_indicator_node.py -v\n\nor\n\npython -m unittest discover <test_directory>\npython -m unittest discover -s <directory> -p 'test_*.py'\n\n# Using pytest\n# ... | [
[
"numpy.random.seed",
"numpy.isnan",
"numpy.random.rand",
"numpy.zeros",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
avivajpeyi/scipy | [
"dbfe06e6618232b26c241cbe8861e2ea1489b535",
"dbfe06e6618232b26c241cbe8861e2ea1489b535",
"dbfe06e6618232b26c241cbe8861e2ea1489b535",
"dbfe06e6618232b26c241cbe8861e2ea1489b535",
"dbfe06e6618232b26c241cbe8861e2ea1489b535",
"dbfe06e6618232b26c241cbe8861e2ea1489b535",
"dbfe06e6618232b26c241cbe8861e2ea1489b53... | [
"scipy/fft/tests/test_real_transforms.py",
"tools/refguide_check.py",
"scipy/stats/tests/test_binned_statistic.py",
"scipy/optimize/_linprog.py",
"benchmarks/benchmarks/go_benchmark_functions/go_funcs_univariate.py",
"scipy/optimize/_lsq/dogbox.py",
"scipy/ndimage/_ni_docstrings.py",
"scipy/fft/tests/... | [
"\nimport numpy as np\nfrom numpy.testing import assert_allclose, assert_array_equal\nimport pytest\n\nfrom scipy.fft import dct, idct, dctn, idctn, dst, idst, dstn, idstn\nimport scipy.fft as fft\nfrom scipy import fftpack\n\n# scipy.fft wraps the fftpack versions but with normalized inverse transforms.\n# So, the... | [
[
"numpy.random.random",
"numpy.pad",
"numpy.take",
"numpy.testing.assert_array_equal",
"numpy.random.rand",
"numpy.testing.assert_allclose"
],
[
"numpy.printoptions",
"numpy.allclose",
"numpy.random.seed",
"matplotlib.use",
"matplotlib.pyplot.close",
"numpy.errst... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"... |
willshiao/brgan | [
"99d1627176a59811bf9032ef1f99d6e7261095fb",
"99d1627176a59811bf9032ef1f99d6e7261095fb"
] | [
"src/dsloader/kronecker.py",
"src/ggan/svd.py"
] | [
"import networkx as nx\nimport numpy as np\nimport torch\nfrom torch.utils.data import Dataset\nfrom dsloader.util import kron_graph, random_binary, make_fractional\n\n\nclass KroneckerDataset (Dataset):\n\n def __init__(self, kron_iter=4, seed_size=4, fixed_seed=None, num_graphs=1, perms_per_graph=256, progress... | [
[
"torch.tensor"
],
[
"torch.nn.Sequential",
"torch.nn.BatchNorm1d",
"torch.randn",
"torch.diag_embed",
"torch.nn.Linear",
"torch.bmm",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
koriavinash1/pgm | [
"89e11b61f7141a75d8991ff4ea229ef66d7a4a0c",
"89e11b61f7141a75d8991ff4ea229ef66d7a4a0c"
] | [
"examples/assignment3/MH.py",
"pgm/inference/check.py"
] | [
"import sys\nimport numpy as np\nsys.path.append('../..')\nfrom pgm.inference.MetropolisHastings import MH\nfrom matplotlib import pyplot as plt\n\ndef Gamma(theta, k = 1):\n def G(k):\n if k <= 0: return 1\n elif k == 0.5: return np.pi **0.5\n return k*G(k-1)\n def distribution(x):\n ... | [
[
"matplotlib.pyplot.legend",
"numpy.abs",
"numpy.linspace",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.plot",
"numpy.max",
"numpy.random.normal",
"matplotlib.pyplot.bar",
"numpy.exp",
"numpy.array",
"numpy.histogram",
"matplotlib.pyplot.show"
],
[
"matplotl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Freakawho/sagemaker-tensorflow-training-toolkit-master | [
"f37c7d85600beb5461788db8c471b66c25beff8f"
] | [
"src/sagemaker_tensorflow_container/training.py"
] | [
"# Copyright 2018-2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the 'License'). You\n# may not use this file except in compliance with the License. A copy of\n# the License is located at\n#\n# http://aws.amazon.com/apache2.0/\n#\n# or in the ... | [
[
"tensorflow.ConfigProto",
"tensorflow.train.Server",
"tensorflow.train.ClusterSpec"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
TanayGahlot/mne-python | [
"857aa97c201451b82931c5eba50642975afc423d",
"857aa97c201451b82931c5eba50642975afc423d",
"857aa97c201451b82931c5eba50642975afc423d",
"857aa97c201451b82931c5eba50642975afc423d"
] | [
"examples/decoding/plot_decoding_csp_eeg.py",
"examples/stats/plot_cluster_stats_time_frequency_repeated_measures_anova.py",
"examples/export/plot_epochs_to_nitime.py",
"mne/channels/interpolation.py"
] | [
"\"\"\"\n===========================================================================\nMotor imagery decoding from EEG data using the Common Spatial Pattern (CSP)\n===========================================================================\n\nDecoding of motor imagery applied to EEG data decomposed using CSP.\nHere ... | [
[
"sklearn.cross_validation.cross_val_score",
"matplotlib.pyplot.legend",
"sklearn.lda.LDA",
"matplotlib.pyplot.axvline",
"matplotlib.pyplot.axhline",
"matplotlib.pyplot.title",
"numpy.arange",
"sklearn.pipeline.Pipeline",
"matplotlib.pyplot.ylabel",
"numpy.mean",
"matplo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
kafkasl/contextualLSTM | [
"a4421d592c3960c79842b0f23de162e61fcab3dd"
] | [
"src/lstm/lstm_wp.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.device",
"tensorflow.stack",
"tensorflow.reduce_sum",
"numpy.exp",
"tensorflow.summary.scalar",
"tensorflow.Graph",
"tensorflow.Variable",
"tensorflow.random_uniform_initializer",
"tensorflow.gradients",
"tensorflow.name_scope",
"tensorflow.trainable_variabl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
fazildgr8/virtual_pen_MNIST | [
"69055980ee0f0005766e62e3a1ca4e2a0259157c"
] | [
"pensetup.py"
] | [
"import cv2\r\nimport numpy as np\r\nimport time\r\n\r\n\r\n# A required callback method that goes into the trackbar function.\r\ndef nothing(x):\r\n pass\r\n\r\n\r\n# Initializing the webcam feed.\r\ncap = cv2.VideoCapture(0)\r\ncap.set(3, 1280)\r\ncap.set(4, 720)\r\n\r\n# Create a window named trackbars.\r\ncv... | [
[
"numpy.hstack",
"numpy.array",
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gitter-badger/agent | [
"3f53eaa7ebdee3ab423c7b58785d584fe1a6ae11",
"3f53eaa7ebdee3ab423c7b58785d584fe1a6ae11"
] | [
"neodroidagent/utilities/exploration/sampling/random_process/ornstein_uhlenbeck.py",
"neodroidagent/utilities/signal/advantage_estimation.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nfrom .annealed_guassian import AnnealedGaussianProcess\n\n__author__ = \"Christian Heider Nielsen\"\n\n# Based on http://math.stackexchange.com/questions/1287634/implementing-ornstein-uhlenbeck-in-matlab\nimport numpy\n\n__all__ = [\"OrnsteinUhlenbeckProcess\"]\n\n\... | [
[
"numpy.random.normal",
"numpy.zeros_like",
"numpy.sqrt"
],
[
"numpy.random.random",
"numpy.random.seed",
"torch.zeros",
"torch.zeros_like",
"numpy.ones",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
diegulio/Breed_Recognition-to-Buscomiperro | [
"040ee45b9b5c355c3ec2c7413cd89a623024ad4e"
] | [
"label_traincatset.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"label_TrainCatSet.ipynb\n\nAutomatically generated by Colaboratory.\n\nOriginal file is located at\n https://colab.research.google.com/drive/1vDyBZ7Ql-8qQ3l7EWJB9TfnwGy66qGGn\n\"\"\"\n\nimport pandas as pd\nimport os\nimport numpy as np\n\n# Enlisto los nombres de las imagenes\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": []
}
] |
lebrice/RoBO | [
"0cb58a1622d3a540f7714b239f0cedf048b6fd9f",
"0cb58a1622d3a540f7714b239f0cedf048b6fd9f",
"0cb58a1622d3a540f7714b239f0cedf048b6fd9f",
"0cb58a1622d3a540f7714b239f0cedf048b6fd9f",
"0cb58a1622d3a540f7714b239f0cedf048b6fd9f"
] | [
"robo/fmin/bayesian_optimization.py",
"robo/maximizers/differential_evolution.py",
"robo/fmin/entropy_search.py",
"robo/maximizers/direct.py",
"experiments/bayesopt/run_fabolas_surrogate.py"
] | [
"import logging\nimport george\nimport numpy as np\nimport inspect\n\nfrom pybnn import BaseModel\nfrom pybnn.dngo import DNGO\n\nfrom robo.priors.default_priors import DefaultPrior\nfrom robo.models.base_model import BaseModel as BaseModel_\nfrom robo.models.wrapper_bohamiann import WrapperBohamiann\nfrom robo.mod... | [
[
"numpy.all",
"numpy.random.randint",
"numpy.ones"
],
[
"numpy.isinf",
"numpy.clip"
],
[
"numpy.all",
"numpy.random.randint",
"numpy.ones"
],
[
"numpy.array"
],
[
"numpy.array",
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
andersonmanhaes/ml_mestrado | [
"d737d80e07d9392895e4455e49a33b8700080cf1",
"d737d80e07d9392895e4455e49a33b8700080cf1",
"d737d80e07d9392895e4455e49a33b8700080cf1"
] | [
"T1/code/visualizar_reta.py",
"T1/code/plot_ex1data2.py",
"T2/custo_reglin_regularizada.py"
] | [
"import os\nimport pandas as pd\nimport numpy as np\n\nimport matplotlib.pyplot as plt\n\n\ndef plot(filepath, theta):\n path = os.getcwd() + filepath\n dataset = pd.read_csv(path, header=None)\n X = dataset.iloc[:, 0:-1].values\n y = dataset.iloc[:, -1:].values\n\n t = np.arange(0, 25, 1)\n plt.s... | [
[
"matplotlib.pyplot.legend",
"pandas.read_csv",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.title",
"numpy.arange",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pypl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1... |
eyov7/CV_LTH_Pre-training-LLNL | [
"bb18ba2093328aeb4e5ab3929f2749264ef3c981",
"bb18ba2093328aeb4e5ab3929f2749264ef3c981"
] | [
"main_imp_visda.py",
"SimCLR/optimizer/lars.py"
] | [
"import argparse\nimport os\nimport random\nimport shutil\nimport time\nimport warnings\nimport copy \n\nimport torch\nimport torch.nn as nn\nimport torch.nn.parallel\nimport torch.backends.cudnn as cudnn\nimport torch.optim\nimport torch.utils.data\nimport torchvision.transforms as transforms\nimport torchvision.d... | [
[
"torch.nn.CrossEntropyLoss",
"torch.load",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.nn.Linear",
"torch.no_grad",
"torch.nn.DataParallel",
"torch.save"
],
[
"torch.norm",
"torch.zeros_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Lim-Guowei/RUL | [
"e23e97a373df73abc2fde14ce070dcb5230a79c2"
] | [
"eda.py"
] | [
"import os\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom dataloader import dataloader\nimport seaborn as sns\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split\n\npd.set_option('display.float_format', '{:.6f}'.format)\n\ndef c... | [
[
"sklearn.ensemble.RandomForestClassifier",
"matplotlib.pyplot.title",
"matplotlib.pyplot.get_cmap",
"sklearn.model_selection.train_test_split",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.xlabel",
"numpy.argsort",
"pandas.set_option"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fredyshox/AppVideoFramer | [
"0e43f2828d2e3737451a0cf1ec81e6840796ac30"
] | [
"Tools/fastlane-templates.py"
] | [
"#!/usr/bin/env python3\n# \n# Retrieve templates from fastlane/frameit\n#\n\nimport sys\nimport os \nfrom os import path\nfrom shutil import copyfile\nfrom tempfile import gettempdir\nimport re\nimport json\nimport cv2\nimport numpy as np\nfrom common import sanitize_color, sanitize_device_name, sanitize_device_ke... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
data-weirdo/studio | [
"48852c4f097f773ce3d408b59f79fda2e2d60470"
] | [
"function/python/brightics/function/transform/sql/functions.py"
] | [
"\"\"\"\n Copyright 2019 Samsung SDS\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... | [
[
"numpy.math.exp",
"numpy.math.ceil",
"numpy.math.sqrt",
"numpy.math.factorial",
"numpy.sign",
"numpy.math.log10",
"numpy.math.pow",
"numpy.math.floor",
"numpy.math.log2",
"numpy.math.log"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sander102907/autoencoder_program_synthesis | [
"752954f9ef268908553189a1c3323bad15b39f04"
] | [
"autoencoder_program_synthesis/model_utils/modules.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass AddGate(nn.Module):\n \"\"\"\n Add gate similar to LSTM add gate: :math: `y = σ(W_mul * inp + b_mul) * tanh(W_add * inp + b_add)`\n\n Outputs information that can be added to some state\n where the network learns:... | [
[
"torch.nn.Softmax",
"torch.nn.Dropout",
"torch.nn.ModuleList",
"torch.sum",
"torch.nn.Sigmoid",
"torch.nn.LSTMCell",
"torch.nn.Linear"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yyHaker/EKMRC-is-your-need | [
"483e2d9d822907ef36a39333933fd939dac1cea0"
] | [
"EKMRC/src/test_gnn.py"
] | [
"#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n'''\n@File : test_gnn.py\n@Author : yyhaker \n@Contact : 572176750@qq.com\n@Time : 2020/04/22 15:19:24\n'''\n\n# here put the import lib\nimport torch\nfrom torch_geometric.data import Data\nimport torch.nn.functional as F\nfrom torch_geometric.nn im... | [
[
"torch.nn.functional.log_softmax",
"torch.nn.functional.dropout",
"torch.tensor",
"torch.nn.functional.relu",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
toptaldev92/tensorflow | [
"1fd1f65d1b0896149e44a1f105267c27994010d9",
"f49aca4532c155597c669cf2189f211cafbebf96",
"f49aca4532c155597c669cf2189f211cafbebf96",
"f49aca4532c155597c669cf2189f211cafbebf96",
"1fd1f65d1b0896149e44a1f105267c27994010d9"
] | [
"tensorflow/examples/learn/text_classification_cnn.py",
"tensorflow/python/tools/freeze_graph_test.py",
"tensorflow/contrib/layers/python/layers/layers_test.py",
"tensorflow/python/training/queue_runner.py",
"tensorflow/python/framework/graph_util_test.py"
] | [
"# Copyright 2016 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 r... | [
[
"pandas.Series",
"tensorflow.nn.max_pool",
"pandas.DataFrame",
"tensorflow.contrib.layers.convolution2d",
"tensorflow.contrib.learn.models.logistic_regression",
"tensorflow.contrib.learn.ops.categorical_variable",
"tensorflow.argmax",
"tensorflow.app.run",
"tensorflow.one_hot",... | [
{
"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": []
},
{
"matplotlib": [],
"nump... |
filipesouzacit/RL-with-MCTS | [
"cca1a8a79e5973a30b423c45a090e2473975c189"
] | [
"trainer.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thus Jan 07 15:54:13 2021\n@author: Filipe Souza\n\nBased on Josh Varty (https://github.com/JoshVarty/AlphaZeroSimple)\n\"\"\"\nimport numpy as np\nfrom random import shuffle\nimport keras\n\nfrom gym_go import gogame\nfrom monte_carlo_tree_search... | [
[
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Lemon-Nation/PyLMDI | [
"54d15ec44b84bd84b960003b1fd6690057240565"
] | [
"Numercial_Examples/Examples_China.py"
] | [
"\n\n\n\n# =============================================================================\n# Step1: Input\n# =============================================================================\nimport numpy as np\nfrom PyLMDI import PyLMDI\n\nif __name__=='__main__':\n \n #--- Step1: Input\n Ct = 794.611950487136... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HuangHaoyu1997/NRI | [
"e0cd1ef5e168db19cd904eabfd369a65238b5d07"
] | [
"utils.py"
] | [
"import numpy as np\nimport torch\nfrom torch.utils.data.dataset import TensorDataset\nfrom torch.utils.data import DataLoader\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\n\n\ndef my_softmax(input, axis=1):\n trans_input = input.transpose(axis, 0).contiguous()\n soft_max_1d = F.softm... | [
[
"torch.nn.functional.softmax",
"numpy.expand_dims",
"torch.zeros",
"numpy.vstack",
"torch.utils.data.DataLoader",
"numpy.concatenate",
"numpy.max",
"torch.FloatTensor",
"torch.utils.data.dataset.TensorDataset",
"numpy.where",
"torch.autograd.Variable",
"torch.ones",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
craymichael/tensorflow | [
"b5de565c9c57fa7ca02d42bcfe6f470ecf117ba5"
] | [
"tensorflow/python/compat/compat.py"
] | [
"# Copyright 2018 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.platform.tf_logging.warning",
"tensorflow.python.util.tf_export.tf_export"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.5",
"1.7",
"1.4"
]
}
] |
atranitell/TensorGate | [
"855ae0c69a706c179c26ba4a75a8067a514285fe",
"855ae0c69a706c179c26ba4a75a8067a514285fe"
] | [
"utils/device.py",
"samples/kinface/kinface_1E1G1D.py"
] | [
"# Copyright 2017 The KaiJIN 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 required... | [
[
"tensorflow.python.client.device_lib.list_local_devices"
],
[
"tensorflow.clip_by_value",
"tensorflow.unstack",
"tensorflow.shape",
"tensorflow.train.create_global_step",
"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... |
aburousan/manim | [
"c11b649e9aed34976844e6a131fb12e2a30c7bc8",
"c11b649e9aed34976844e6a131fb12e2a30c7bc8"
] | [
"manim/mobject/opengl_geometry.py",
"manim/animation/transform_matching_parts.py"
] | [
"import numpy as np\n\nfrom .. import logger\nfrom ..constants import *\nfrom ..mobject.mobject import Mobject\nfrom ..mobject.types.opengl_vectorized_mobject import (\n OpenGLDashedVMobject,\n OpenGLVGroup,\n OpenGLVMobject,\n)\nfrom ..utils.color import *\nfrom ..utils.deprecation import deprecated_param... | [
[
"numpy.hstack",
"numpy.dot",
"numpy.sqrt",
"numpy.linspace",
"numpy.cos",
"numpy.linalg.norm",
"numpy.sin",
"numpy.sign",
"numpy.tan",
"numpy.cross",
"numpy.array",
"numpy.zeros"
],
[
"numpy.round"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ddarmon/transCSSR | [
"8ed057eee70d2d50d14bc719c7850ac46a00e4d4"
] | [
"demo-and-experimental-scripts/demo_predict_presynch_eT.py"
] | [
"import numpy\nimport scipy.stats\nimport itertools\nimport copy\nimport string\nimport os\n\nfrom collections import Counter, defaultdict\nfrom filter_data_methods import *\nfrom igraph import *\n\nfrom transCSSR import *\n\ndata_prefix = ''\n\n#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n#\n# The various ... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ds-praveenkumar/m5-accuracy-prediction | [
"20255adc95c3e0fe6c6acec9fd16ac88c6e95908"
] | [
"src/models/build_model.py"
] | [
"# github link: https://github.com/ds-praveenkumar/kaggle\n# Author: ds-praveenkumar\n# file: forcasting/build_model.py/\n# Created by ds-praveenkumar at 13-06-2020 02 09\n# feature:\n\nimport os\nimport psutil\nfrom fbprophet import Prophet\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport... | [
[
"pandas.concat",
"pandas.to_datetime",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
Spiilgriim/nnexpy | [
"f8e419598ef94bebb532eb32ccaeeb48a3edfb5e"
] | [
"nnexpy/network_generator.py"
] | [
"class NetworkGenerator(object):\n def build_model(self, *args, **kwargs):\n import tensorflow as tf\n depth = kwargs.get('depth', 1)\n input_shape = kwargs.get('input_shape', (2,))\n width = kwargs.get('width', 8)\n activation = kwargs.get('activation', 'relu')\n\n mode... | [
[
"tensorflow.keras.layers.Dense",
"tensorflow.keras.Sequential",
"tensorflow.keras.backend.clear_session",
"tensorflow.compat.v1.disable_eager_execution",
"tensorflow.compat.v1.reset_default_graph"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MartinCooke/jocular | [
"635816d4ef6aa6ea75187137e25386dad2d551e9",
"635816d4ef6aa6ea75187137e25386dad2d551e9"
] | [
"jocular/stretch.py",
"jocular/calcs.py"
] | [
"''' Various stretch functions. Easy to add more. Room for refinement,\n methinks.\n'''\n\nimport numpy as np\n\ndef stretch(x, method='linear', param=None, NR=0, background=None):\n\n # if no noise reduction just use stretch alone\n if (NR <= 0) or (background is None):\n return stretch_main(x, met... | [
[
"numpy.log",
"numpy.arcsinh"
],
[
"numpy.cos",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
juvilius/mathchem-package | [
"ca56cb03e6ccdb47b3dfc382ca36b0a00d3e28b9"
] | [
"mathchem/mathchem.py"
] | [
"import numpy as np\n\n\nclass Mol():\n r\"\"\"\n Molecule.\n \"\"\"\n __g6_string = ''\n # Adjacency matrix\n __A = []\n # Incidence matrix\n __B = []\n # Laplacian matrix\n __L = []\n # Normalized laplacian matrix\n __NL = []\n # Signless laplacian matrix\n __Q = []\n ... | [
[
"numpy.matrix",
"numpy.log",
"numpy.minimum",
"numpy.sqrt",
"numpy.linalg.inv",
"numpy.ndarray",
"numpy.ones",
"numpy.longdouble",
"numpy.identity",
"numpy.float64",
"numpy.linalg.eigvalsh",
"numpy.exp",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ElmerCSC/ElmerIceCourses | [
"6ff1011f3a1311d84699a30da9f8fc56cb984a08"
] | [
"TeteRousse/Step1/Makegeo.py"
] | [
"# -*- coding: utf-8 -*-\n# Create a geo (gmsh input file) file from a contour file\n# the contour file contains the (x,y) coordinates of the ordered\n# points defining the contour of the domain\n#\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Test these options\n # edge size of the elements\nel_size... | [
[
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
qingyuanxingsi/incubator-mxnet | [
"fc9e70bf2d349ad4c6cb65ff3f0958e23a7410bf",
"fc9e70bf2d349ad4c6cb65ff3f0958e23a7410bf",
"fc9e70bf2d349ad4c6cb65ff3f0958e23a7410bf"
] | [
"tests/python/unittest/test_optimizer.py",
"example/gluon/kaggle_k_fold_cross_validation.py",
"example/gluon/super_resolution.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.zeros_like"
],
[
"pandas.concat",
"pandas.read_csv",
"pandas.get_dummies"
],
[
"numpy.uint8"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
... |
kavach-feature/Advanced_lane_finding | [
"12e4e330e338734fdb35655c7581b98ba1eb490b"
] | [
"line.py"
] | [
"import numpy as np\r\nimport cv2\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.image as mpimg\r\nimport pickle\r\n\r\nclass Line():\r\n\tdef __init__(self,n):\r\n\t\tself.n=n\r\n\t\tself.detected =False\r\n\r\n\t\t#Polynomial coefficients of the lines\r\n\r\n\t\tself.A=[]\r\n\t\tself.B=[]\r\n\t\tself.C=[... | [
[
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vermouth1992/torchlib | [
"63b2bedb40f670b2d9fbfc0daeab4a8d44623095",
"63b2bedb40f670b2d9fbfc0daeab4a8d44623095",
"63b2bedb40f670b2d9fbfc0daeab4a8d44623095",
"63b2bedb40f670b2d9fbfc0daeab4a8d44623095",
"63b2bedb40f670b2d9fbfc0daeab4a8d44623095",
"63b2bedb40f670b2d9fbfc0daeab4a8d44623095"
] | [
"torchlib/deep_rl/algorithm/ppo/utils.py",
"tensorlib/rl/envs/wrappers/atari_wrappers.py",
"torchlib/deep_rl/algorithm/rainbow/memory.py",
"tensorlib/rl/common.py",
"torchlib/dataset/utils.py",
"torchlib/generative_model/made.py"
] | [
"\"\"\"\nCommon utilities to implement policy gradient algorithms\n\"\"\"\n\nfrom collections import namedtuple, deque\n\nimport numpy as np\nfrom scipy import signal\nfrom torchlib.dataset.utils import create_data_loader\nfrom torchlib.deep_rl.utils.replay.replay import ReplayBuffer\nfrom torchlib.deep_rl.utils.re... | [
[
"numpy.split",
"numpy.stack",
"numpy.concatenate",
"numpy.append",
"numpy.std",
"numpy.mean",
"scipy.signal.lfilter",
"numpy.where",
"numpy.sum"
],
[
"numpy.reshape",
"numpy.stack",
"numpy.concatenate",
"numpy.sign",
"numpy.repeat",
"numpy.zeros",
... | [
{
"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"... |
jianoaix/ray | [
"1701b923bc83905f8961c06a6a173e3eba46a936",
"1701b923bc83905f8961c06a6a173e3eba46a936",
"1701b923bc83905f8961c06a6a173e3eba46a936",
"1701b923bc83905f8961c06a6a173e3eba46a936",
"1701b923bc83905f8961c06a6a173e3eba46a936",
"1701b923bc83905f8961c06a6a173e3eba46a936"
] | [
"python/ray/_private/utils.py",
"python/ray/tune/tests/test_integration_wandb.py",
"rllib/examples/custom_metrics_and_callbacks.py",
"rllib/algorithms/crr/crr.py",
"python/ray/tune/utils/trainable.py",
"rllib/tests/test_io.py"
] | [
"import binascii\nimport errno\nimport functools\nimport hashlib\nimport importlib\nimport logging\nimport multiprocessing\nimport os\nimport signal\nimport subprocess\nimport sys\nimport tempfile\nimport threading\nimport time\nfrom typing import Optional, Sequence, Tuple, Any, Union, Dict\nimport uuid\nimport grp... | [
[
"numpy.random.bytes",
"numpy.random.get_state",
"numpy.random.set_state",
"numpy.random.seed"
],
[
"numpy.asarray",
"numpy.float32"
],
[
"numpy.mean",
"numpy.sum"
],
[
"numpy.mean",
"numpy.zeros_like"
],
[
"pandas.DataFrame"
],
[
"numpy.isnan",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
gitter-badger/mlmodels | [
"f70f1da7434e8855eed50adc67b49cc169f2ea24",
"f70f1da7434e8855eed50adc67b49cc169f2ea24",
"f70f1da7434e8855eed50adc67b49cc169f2ea24",
"f70f1da7434e8855eed50adc67b49cc169f2ea24",
"f70f1da7434e8855eed50adc67b49cc169f2ea24",
"f70f1da7434e8855eed50adc67b49cc169f2ea24",
"f70f1da7434e8855eed50adc67b49cc169f2ea2... | [
"mlmodels/model_tf/misc/tf_nlp/speech-to-text/1.tacotron/train.py",
"mlmodels/model_tf/misc/tfcode2/Attention/3.hierarchical.py",
"mlmodels/model_tf/misc/tf_nlp/neural-machine-translation/4.basic-seq2seq-api-greedy.py",
"mlmodels/model_tf/misc/tfcode2/Seq-to-Seq/estimator/estimator.py",
"mlmodels/model_tf/m... | [
"# coding: utf-8\n\n# In[1]:\n\n\nimport os\n\nimport numpy as np\nimport tensorflow as tf\nfrom tqdm import tqdm\n\nfrom model import Model\nfrom setting import batch_size, get_cached, idx2char, n_mels, reduction_factor, text2idx\n\n# In[2]:\n\n\npaths, lengths, texts = [], [], []\ntext_files = [f for f in os.list... | [
[
"numpy.pad",
"tensorflow.InteractiveSession",
"tensorflow.global_variables_initializer",
"tensorflow.reset_default_graph",
"numpy.load"
],
[
"tensorflow.nn.dynamic_rnn",
"sklearn.cross_validation.train_test_split",
"tensorflow.nn.softmax_cross_entropy_with_logits",
"tensorf... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensor... |
uhh-lt/semeval2019-hhmm | [
"b746b0fb8ab3b957d399276cb354e950f0ef30ed",
"b746b0fb8ab3b957d399276cb354e950f0ef30ed"
] | [
"utils.py",
"ud2csv.py"
] | [
"import pandas as pd\nfrom pathlib import Path\n\n\ndef df_to_csv(df, path):\n df.to_csv(path, sep='\\t', index=False, encoding='utf-8')\n\n\ndef csv_to_df(path):\n df = pd.read_csv(path, sep='\\t', dtype=str, encoding='utf-8')\n return df\n\n\ndef max_arguments(task):\n fp = open(task, 'r')\n lines_... | [
[
"pandas.read_csv"
],
[
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
... |
qinliuliuqin/active-mri-acquisition | [
"b561f838667f4bc7753b1f89dfbdd545d0f00ada",
"b561f838667f4bc7753b1f89dfbdd545d0f00ada",
"b561f838667f4bc7753b1f89dfbdd545d0f00ada"
] | [
"activemri/experimental/cvpr19_models/data/masking_utils.py",
"activemri/experimental/cvpr19_models/data/raw_data_loader.py",
"activemri/data/singlecoil_knee_data.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport logging\n\nimport numpy as np\nimport torch\n\n\ndef get_mask_func(mask_type, which_dataset, rnl_params=None):\n # Whether ... | [
[
"numpy.arange",
"numpy.fft.ifftshift",
"numpy.random.RandomState",
"numpy.zeros"
],
[
"torch.cat",
"numpy.stack",
"numpy.array",
"torch.ifft",
"numpy.random.RandomState"
],
[
"numpy.random.RandomState",
"numpy.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
izhorvath/MetGAN | [
"aca85fb3306d2515a65c8d525cd78e1147ba7e1b"
] | [
"models/networks.py"
] | [
"import torch\nimport torch.nn as nn\nfrom torch.nn import init\nimport functools\nfrom torch.optim import lr_scheduler\nfrom math import floor, log2\nfrom functools import partial\nfrom linear_attention_transformer import ImageLinearAttention\n\n###\n\nfrom random import random\n\n\nimport numpy as np\nimport torc... | [
[
"torch.nn.functional.upsample",
"torch.optim.lr_scheduler.LambdaLR",
"torch.optim.lr_scheduler.CosineAnnealingLR",
"torch.cat",
"torch.zeros",
"torch.nn.BCEWithLogitsLoss",
"torch.nn.functional.interpolate",
"torch.cuda.is_available",
"torch.nn.ReplicationPad2d",
"torch.nn.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xxia-kathy/models | [
"157faae1af5d89c53a5699b601dc68fee274ef09"
] | [
"official/core/base_trainer_test.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.convert_to_tensor",
"tensorflow.python.distribute.combinations.combine",
"tensorflow.test.main"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
prkriley/neurips2019_intrus | [
"3e36930246347e6b80a583d2ab378054ea3b9f7a"
] | [
"lib/models.py"
] | [
"\"\"\"\nTransformer encoder / decoder layer chain\n\"\"\"\nimport numpy as np\nimport tensorflow as tf\n\nimport lib.layers\nfrom . import layers, ops\nfrom .data import linelen\n\n\nclass Transformer:\n\n def __init__(\n self, name, inp_voc, out_voc,\n logits_bias=False, share_emb=False, ... | [
[
"tensorflow.transpose",
"tensorflow.nn.log_softmax",
"tensorflow.shape",
"tensorflow.reshape",
"tensorflow.cast",
"tensorflow.placeholder",
"tensorflow.ones",
"tensorflow.zeros_like",
"tensorflow.name_scope",
"tensorflow.variable_scope",
"numpy.array",
"numpy.zeros"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
Prtfw/PySyft | [
"35012f5bf55628bb19761d5f40d03181fbbb1766"
] | [
"test/torch/pointers/test_pointer_tensor.py"
] | [
"import torch\nimport torch as th\nimport syft\n\nfrom syft.frameworks.torch.tensors.interpreters.additive_shared import AdditiveSharingTensor\nfrom syft.frameworks.torch.tensors.interpreters.precision import FixedPrecisionTensor\nfrom syft.generic.pointers.pointer_tensor import PointerTensor\nimport pytest\n\n\nde... | [
[
"torch.Size",
"torch.ones",
"torch.max",
"torch.Tensor",
"torch.tensor",
"torch.split",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MissPenguin/Paddle | [
"266fcbe0aed3e566c167ea8de5114f62c428c013"
] | [
"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.allclose",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
funkchaser/compas | [
"b58de8771484aa0c6068d43df78b1679503215de"
] | [
"src/compas_plotters/artists/pointartist.py"
] | [
"from typing import Tuple\nfrom typing import List\nfrom typing import Any\n\nfrom matplotlib.patches import Circle\nfrom matplotlib.transforms import ScaledTranslation\nfrom compas.geometry import Point\n\nfrom compas.artists import PrimitiveArtist\nfrom .artist import PlotterArtist\n\nColor = Tuple[float, float, ... | [
[
"matplotlib.patches.Circle",
"matplotlib.transforms.ScaledTranslation"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adozier/pymatgen | [
"f1cc4d8db24ec11063be2fd84b4ea911f006eeb7",
"f1cc4d8db24ec11063be2fd84b4ea911f006eeb7"
] | [
"pymatgen/core/units.py",
"pymatgen/transformations/tests/test_site_transformations.py"
] | [
"# coding: utf-8\n# Copyright (c) Pymatgen Development Team.\n# Distributed under the terms of the MIT License.\n\nfrom __future__ import division, unicode_literals\n\n\"\"\"\nThis module implements a FloatWithUnit, which is a subclass of float. It\nalso defines supported units for some commonly used units for ener... | [
[
"numpy.asarray",
"numpy.array"
],
[
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rossbar/scipy-lecture-notes | [
"7f74e6925721c43bd81bf0bee34b4805ac4a3b57",
"7f74e6925721c43bd81bf0bee34b4805ac4a3b57",
"7f74e6925721c43bd81bf0bee34b4805ac4a3b57",
"7f74e6925721c43bd81bf0bee34b4805ac4a3b57"
] | [
"advanced/image_processing/examples/plot_numpy_array.py",
"intro/numpy/examples/plot_elephant.py",
"intro/summary-exercises/examples/plot_gumbell_wind_speed_prediction.py",
"packages/scikit-image/examples/plot_labels.py"
] | [
"\"\"\"\nImage manipulation and numpy arrays\n====================================\n\nThis example shows how to do image manipulation using common numpy arrays\ntricks.\n\n\"\"\"\n\nimport numpy as np\nimport scipy\nimport scipy.misc\nimport matplotlib.pyplot as plt\n\nface = scipy.misc.face(gray=True)\nface[10:13,... | [
[
"matplotlib.pyplot.imshow",
"scipy.misc.face",
"matplotlib.pyplot.axes",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
],
[
"matplotlib.pyplot.imread",
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
],... | [
{
"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"
... |
Jacob-Barhak/panel | [
"04cad38ea703e4e69fb76f063a27f4ffe40688e8"
] | [
"panel/reactive.py"
] | [
"\"\"\"\nDeclares Syncable and Reactive classes which provides baseclasses\nfor Panel components which sync their state with one or more bokeh\nmodels rendered on the frontend.\n\"\"\"\n\nimport difflib\nimport sys\nimport threading\n\nfrom collections import namedtuple\nfrom functools import partial\n\nimport nump... | [
[
"pandas.concat",
"pandas.Series",
"numpy.asarray",
"pandas.DataFrame",
"numpy.concatenate"
]
] | [
{
"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": []
}
] |
trhallam/digirock | [
"05b1199d741a384345a4930605be97369c9ec270",
"05b1199d741a384345a4930605be97369c9ec270"
] | [
"docs/examples/batzle_wang_1992.py",
"tests/test_fluids_ecl.py"
] | [
"# -*- coding: utf-8 -*-\n# ---\n# jupyter:\n# jupytext:\n# formats: ipynb,py:percent\n# text_representation:\n# extension: .py\n# format_name: percent\n# format_version: '1.3'\n# jupytext_version: 1.13.6\n# kernelspec:\n# display_name: Python 3 (ipykernel)\n# language: p... | [
[
"numpy.meshgrid",
"numpy.arange",
"matplotlib.pyplot.subplots",
"numpy.array",
"matplotlib.rc"
],
[
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
iamgroot42/opacus | [
"51708309e71c030aa2bf15d6dccc7bcbbe9ed570",
"51708309e71c030aa2bf15d6dccc7bcbbe9ed570"
] | [
"examples/char-lstm-classification.py",
"opacus/tests/grad_samples/embedding_test.py"
] | [
"#!/usr/bin/env python3\n# Copyright (c) Meta Platforms, Inc. and affiliates.\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#... | [
[
"torch.nn.CrossEntropyLoss",
"torch.nn.utils.rnn.pad_sequence",
"torch.utils.data.DataLoader",
"torch.stack",
"torch.nn.Embedding",
"torch.tensor",
"torch.nn.Linear",
"torch.utils.data.random_split",
"torch.no_grad",
"torch.device",
"torch.save"
],
[
"torch.rand... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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