repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
wangzhongju/facenet_train | [
"d3c51461f0367d410de98c7baa3e3518107a4196"
] | [
"src/train_tripletloss.py"
] | [
"\"\"\"Training a face recognizer with TensorFlow based on the FaceNet paper\nFaceNet: A Unified Embedding for Face Recognition and Clustering: http://arxiv.org/abs/1503.03832\n\"\"\"\n# MIT License\n# \n# Copyright (c) 2016 David Sandberg\n# \n# Permission is hereby granted, free of charge, to any person obtaining... | [
[
"tensorflow.global_variables",
"numpy.all",
"tensorflow.GPUOptions",
"numpy.mean",
"tensorflow.image.decode_image",
"tensorflow.summary.scalar",
"tensorflow.add_n",
"numpy.where",
"numpy.random.randint",
"numpy.square",
"tensorflow.Graph",
"tensorflow.image.random_f... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
richjdowney/sequence-models | [
"85372f6bf8b937ee9dc5715eb8a1f3f708182708"
] | [
"src/models/custom_layers.py"
] | [
"import tensorflow as tf\nimport tensorflow.keras.backend as K\nfrom tensorflow.keras.layers import (\n Dense,\n Layer,\n Dropout,\n LayerNormalization,\n)\nimport numpy as np\n\n\nclass Attention(Layer):\n def __init__(self, add_bias=True, mask=None, return_attention_weights=False):\n super(A... | [
[
"tensorflow.keras.layers.LayerNormalization",
"tensorflow.keras.backend.softmax",
"tensorflow.cast",
"tensorflow.tanh",
"numpy.arange",
"numpy.sin",
"tensorflow.keras.backend.expand_dims",
"numpy.float32",
"tensorflow.tensordot",
"tensorflow.keras.initializers.get",
"te... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
canbakiskan/neuro-inspired-defense | [
"3c323aa3fa797ac6ea69db2731995370ede26f2f"
] | [
"src/utils/get_modules.py"
] | [
"import numpy as np\nimport torch\nfrom os import path\nfrom .namers import (\n dict_file_namer,\n autoencoder_ckpt_namer,\n classifier_ckpt_namer,\n)\nfrom ..models.autoencoders import *\n\n\ndef get_classifier(args):\n\n use_cuda = not args.no_cuda and torch.cuda.is_available()\n device = torch.dev... | [
[
"torch.device",
"numpy.load",
"torch.Tensor",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jhuiac/cocktail-party-Visually-derived-Speech- | [
"1735caa44f98c63a321302b02cf21944813b2195"
] | [
"source_separation_evaluator.py"
] | [
"import argparse\nimport os\nimport glob\n\nimport numpy as np\nimport mir_eval\n\nfrom mediaio.audio_io import AudioSignal\n\n\ndef evaluate(source_file_paths, estimated_file_paths):\n\tsource_signals = [AudioSignal.from_wav_file(f) for f in source_file_paths]\n\testimated_signals = [AudioSignal.from_wav_file(f) f... | [
[
"numpy.mean",
"numpy.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
a-tsioh/persephone | [
"254e70018237cfb3c06ffd04a418673b4e6b953f",
"254e70018237cfb3c06ffd04a418673b4e6b953f"
] | [
"persephone/preprocess/feat_extract.py",
"persephone/tests/experiments/test_bkw.py"
] | [
"\"\"\" Performs feature extraction of WAV files for acoustic modelling.\"\"\"\n\nimport logging\nimport os\nfrom pathlib import Path\nimport subprocess\nfrom typing import Union\nimport wave\n\nimport numpy as np\nimport python_speech_features\nimport scipy.io.wavfile as wav\n\nfrom .. import config\nfrom ..except... | [
[
"numpy.hstack",
"numpy.swapaxes",
"numpy.save",
"numpy.concatenate",
"numpy.load",
"numpy.array",
"scipy.io.wavfile.read"
],
[
"tensorflow.ConfigProto",
"tensorflow.matmul",
"tensorflow.constant",
"tensorflow.device"
]
] | [
{
"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"... |
pradas/pba_spheroidTNF | [
"0956b09926d5cfa08df88257894c9b7a7934f12c"
] | [
"src/addons/PhysiBoSSa/MaBoSS-env-2.0/engine/tests/compare_statdist.py"
] | [
"import numpy, os, sys\n\ndef get_raw_data(file):\n with open(file, 'r') as f:\n raw_lines = f.readlines()[1:]\n i=0\n traj_states = []\n traj_probas = []\n all_states = set()\n\n while(raw_lines[i].startswith('#')):\n \n states = raw_lines[i].split... | [
[
"numpy.all",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BioGeek/awkward-1.0 | [
"0cfb4e43c41d5c7d9830cc7b1d750485c0a93eb2",
"0cfb4e43c41d5c7d9830cc7b1d750485c0a93eb2"
] | [
"tests/test_0395-fix-numba-indexedarray.py",
"tests/test_0011-listarray.py"
] | [
"# BSD 3-Clause License; see https://github.com/scikit-hep/awkward-1.0/blob/main/LICENSE\n\nfrom __future__ import absolute_import\n\nimport pytest # noqa: F401\nimport numpy as np # noqa: F401\nimport awkward as ak # noqa: F401\n\nnumba = pytest.importorskip(\"numba\")\n\n\ndef test():\n def reproduce(arrays... | [
[
"numpy.arange",
"numpy.array"
],
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
artas360/pythran | [
"66dad52d52be71693043e9a7d7578cfb9cb3d1da"
] | [
"pythran/range.py"
] | [
"\"\"\" Module with faicilities to represente range values. \"\"\"\n\nfrom math import isinf\nimport ast\nimport itertools\n\nimport numpy\n\n\nclass Range(object):\n\n \"\"\" Representation for a range of values. \"\"\"\n\n def __init__(self, low, high):\n \"\"\" Set initiale bound of the range object... | [
[
"numpy.max",
"numpy.min"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hirune924/pikachu | [
"4500f571dafb6f44b8e8dd8892e9f71039ba17a8"
] | [
"utils/utils.py"
] | [
"import torch\n\ndef load_pytorch_model(ckpt_name, model, ignore_suffix='model'):\n state_dict = torch.load(ckpt_name, map_location='cpu')[\"state_dict\"]\n new_state_dict = {}\n for k, v in state_dict.items():\n name = k\n if name.startswith(str(ignore_suffix)+\".\"):\n name = nam... | [
[
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
climbingdaily/spvnas | [
"e39514f79c39522ce2e3fd80445647994ca51955"
] | [
"tool_func.py"
] | [
"from re import A\nimport CSF\nimport numpy as np\nimport sys\nimport os\nfrom core.datasets.semantic_poss import LABEL_DICT, KEPT_LABELS\nimport json\nimport open3d as o3d\n\ndef filter_ground(xyz):\n csf = CSF.CSF()\n csf.params.bSloopSmooth = False # 粒子设置为不可移动\n csf.params.cloth_resolution = 0.1 # 布料网... | [
[
"numpy.asarray",
"numpy.array",
"numpy.zeros",
"numpy.fromfile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mblack20/TrajectoryNet | [
"1b3e222ea5a7a167503e417ebcc41e785434b52a"
] | [
"trajectoryNet.py"
] | [
"from __future__ import absolute_import, division, print_function\n\nimport cProfile\nimport sys\nimport time\n\nimport numpy as np\nimport tensorflow as tf\nfrom sklearn import preprocessing\nfrom tensorflow.python.platform import flags\n\nimport Config\nimport Data\nimport Learning_rate\nimport Monitor\nfrom cust... | [
[
"tensorflow.get_variable",
"tensorflow.device",
"tensorflow.concat",
"tensorflow.nn.dynamic_rnn",
"tensorflow.nn.bidirectional_dynamic_rnn",
"tensorflow.orthogonal_initializer",
"tensorflow.contrib.metrics.accuracy",
"tensorflow.Tensor.get_shape",
"tensorflow.train.AdamOptimize... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
chamodi08jaya/Detecting-number-of-Eggs | [
"5df7894ac92cf9baca8d3679cdd04ba0439889df"
] | [
"Egg Detection/EggDe.py"
] | [
"import cv2 \r\nimport numpy as np \r\n\r\n# Read image. \r\nimg = cv2.imread('egg.jpg', cv2.IMREAD_COLOR) \r\n\r\n# Convert to grayscale. \r\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) \r\n\r\n# Blur using 3 * 3 kernel. \r\ngray_blurred = cv2.blur(gray, (3, 3)) \r\n\r\n# Apply Hough transform on the blurred imag... | [
[
"numpy.around"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
EytanKats/dl_framework | [
"3ea9422476e4328f750496e596b23f2b29748b37"
] | [
"simple_converge/tf_models/BaseModel.py"
] | [
"import numpy as np\r\nimport tensorflow as tf\r\n\r\nfrom simple_converge.logs.LogLevels import LogLevels\r\nfrom simple_converge.base.BaseObject import BaseObject\r\nfrom simple_converge.tf_sequences.Sequence import Sequence\r\n\r\n\r\nclass BaseModel(BaseObject):\r\n\r\n \"\"\"\r\n This class defines commo... | [
[
"tensorflow.keras.models.load_model",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
erickrubiol/PythonPrograms | [
"ed94dd004ba883a899f6c426a443fb0a76bae5e6"
] | [
"Snippets/listConcatenation.py"
] | [
"################################LIST CONCATENATION###############################\n# x[start:stop:step]\n# result starts at <start> including it \n# result ends at <stop> excluding it\n# optional third argument determines which arguments are carved out (default is 1)\n\n# slice assgnments -> \n\n##################... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nicola-decao/KnowledgeEditor | [
"ef80f0cd1a6f49858cbdbb64599a098fd7c5edee"
] | [
"src/data/seq2seq_augmented_kilt.py"
] | [
"import jsonlines\nimport numpy as np\nfrom torch.utils.data import Dataset\n\n\nclass Seq2SeqAugmentedKILT(Dataset):\n def __init__(\n self,\n tokenizer,\n data_path,\n max_length=32,\n return_view=False,\n all_views=False,\n ):\n super().__init__()\n s... | [
[
"numpy.random.RandomState",
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SaKuraway/TextClassification | [
"eb263d091703c92a792adb99db22f6e9340cfe12"
] | [
"stacking_main.py"
] | [
"#!/usr/bin/env python\r\n# encoding: utf-8\r\n'''\r\n@file: stacking_lr.py\r\n@time: 2021/7/19 17:24\r\n@author: SaKuraPan_\r\n@desc:\r\n'''\r\nimport pandas as pd\r\nimport jieba, time\r\nfrom numpy import array\r\nfrom numpy import concatenate as np_concatenate\r\nfrom utils import get_label\r\nfrom dnn_main imp... | [
[
"numpy.concatenate",
"pandas.set_option",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
peppocola/Screening-COVID19 | [
"4ed681b74547e94214a65c6152a6c91fe7b95f41"
] | [
"covidx/utils/train.py"
] | [
"import time\nimport torch\nimport torchvision\nimport numpy as np\n\nfrom tqdm import tqdm\n\nfrom covidx.cxr2.models import CXR2Net\nfrom covidx.utils.torch import EarlyStopping, RunningAverageMetric, get_optimizer\n\n\ndef train_classifier(\n model,\n train_data,\n valid_data,\n lr=1e... | [
[
"torch.load",
"numpy.min",
"torch.eq",
"torch.utils.data.DataLoader",
"torch.tensor",
"torch.log_softmax",
"torch.no_grad",
"torch.cuda.is_available",
"torch.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eeshan9815/tpu | [
"1f8da8bc6052ef5f59476459bcc33eab2610b682"
] | [
"models/experimental/dcgan/mnist_input.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.FixedLenFeature",
"numpy.uint8",
"tensorflow.data.TFRecordDataset",
"tensorflow.decode_raw",
"tensorflow.reshape",
"tensorflow.cast",
"tensorflow.random_normal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
junyang-zh/ml-experiment | [
"a6f43e3b00541fda1277b2ba39cec5ea454072e2"
] | [
"classification/torch/mlp4.py"
] | [
"import torch.nn as nn\n\nclass MLP4(nn.Module):\n def __init__(self):\n super(MLP4, self).__init__()\n self.linear_layers = nn.Sequential(\n nn.Linear(784, 256),\n nn.ReLU(),\n nn.Linear(256, 128),\n nn.ReLU(),\n nn.Linear(128, 64),\n ... | [
[
"torch.nn.Linear",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nheist/Evaluation-Framework | [
"0561fcbca5025f280624c02f6fad24a888c653ab"
] | [
"evaluation_framework/Classification/classification_model.py"
] | [
"from sklearn.naive_bayes import GaussianNB\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.svm import SVC\nfrom sklearn import tree\nimport numpy as np\nfrom evaluation_framework.abstract_model import AbstractModel\n\nfloat_precision = 15\n ... | [
[
"sklearn.model_selection.cross_val_score",
"sklearn.naive_bayes.GaussianNB",
"sklearn.neighbors.KNeighborsClassifier",
"sklearn.tree.DecisionTreeClassifier",
"numpy.mean",
"sklearn.svm.SVC"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
OFANAS/OFANAS_PerformanceEstimation | [
"55ac5b54252830f99227fb97108549a5e2569dbe"
] | [
"tutorial/evolution_finder.py"
] | [
"import copy\nimport random\nfrom tqdm import tqdm\nimport numpy as np\nfrom datetime import datetime\n\n__all__ = ['EvolutionFinder']\n\n\nclass ArchManager:\n def __init__(self, arch='ofa'):\n self.num_blocks = 20\n self.num_stages = 5\n self.kernel_sizes = [3, 5, 7]\n self.expand_r... | [
[
"numpy.random.randint",
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
saifkhan-m/SentEval | [
"7cd652409864a849dfbf44ce984c2bd8cececa1d"
] | [
"senteval/binary.py"
] | [
"# Copyright (c) 2017-present, Facebook, Inc.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n#\n\n'''\nBinary classifier and corresponding datasets : MR, CR, SUBJ, MPQA\n'''\nfrom __future__ import absolute_import... | [
[
"numpy.array",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mroeschke/streamz | [
"56562ac7a04486cf77e93c3b74b675ed96396b7a"
] | [
"streamz/dataframe/core.py"
] | [
"import asyncio\n\nimport operator\nfrom collections import OrderedDict\nimport numpy as np\nimport pandas as pd\nimport toolz\n\nfrom tornado import gen\n\nfrom ..collection import Streaming, _stream_types, OperatorMixin\nfrom ..sources import Source\nfrom ..utils import M\nfrom . import aggregations\nfrom .utils ... | [
[
"numpy.random.random",
"pandas.Timestamp.now",
"pandas.Timedelta",
"pandas.date_range"
]
] | [
{
"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": []
}
] |
CheerfulUser/k2mosaic | [
"fa01cf931792764456224880ded8523fe75f18a9"
] | [
"k2mosaic/movie.py"
] | [
"\"\"\"Convert a set of mosaics into a video or animated gif.\"\"\"\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as pl\n\nimport os\nimport click\n\nfrom astropy import visualization\nimport fitsio\nimport imageio\nimport numpy as np\n\nfrom . import KEPLER_CHANNEL_SHAPE\n\n\nclass InvalidFra... | [
[
"numpy.isfinite",
"matplotlib.use",
"numpy.arange",
"matplotlib.pyplot.close",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NREL/GANISP | [
"3ce6979e26f837d05b8f7cfbe2b949f900b6026b"
] | [
"Generative/MAKEDATA/main.py"
] | [
"import sys\nsys.path.append('util')\nimport numpy as np\nimport myparser as myparser\nimport simulation as simulation\nimport data as data\nimport parallel as par\nimport time\nimport monitorTiming as monitorTiming\nimport postProc as postProc\nfrom plotsUtil import *\n\n# ~~~~ Init\n# Parse input\ninpt = myparser... | [
[
"numpy.savez",
"numpy.random.seed",
"numpy.argwhere"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chrispe92/pandas | [
"849b0d02f39a2498e6d10bc01830a8bad8fda6dc"
] | [
"pandas/core/series.py"
] | [
"\"\"\"\nData structure for 1-dimensional cross-sectional and time series data\n\"\"\"\nfrom io import StringIO\nfrom shutil import get_terminal_size\nfrom textwrap import dedent\nfrom typing import (\n IO,\n TYPE_CHECKING,\n Any,\n Callable,\n Iterable,\n List,\n Optional,\n Tuple,\n Typ... | [
[
"pandas.core.ops.logical_op",
"pandas.core.nanops.nancov",
"pandas.util._validators.validate_bool_kwarg",
"pandas.core.ops.align_method_SERIES",
"pandas.core.dtypes.inference.is_hashable",
"pandas.core.aggregation.transform",
"pandas.core.common.standardize_mapping",
"pandas.core.c... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"1.1",
"1.0",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
fomuon/tensorflow-exams | [
"e43a05d2b9c798dc44ca2e054cf25038b151fe4f",
"e43a05d2b9c798dc44ca2e054cf25038b151fe4f"
] | [
"src/ex_011/mnist_nn_dropout.py",
"src/example_007.py"
] | [
"# Lab 10 MNIST and Dropout\r\nimport tensorflow as tf\r\nimport random\r\n# import matplotlib.pyplot as plt\r\n\r\nfrom tensorflow.examples.tutorials.mnist import input_data\r\n\r\ntf.set_random_seed(777) # reproducibility\r\n\r\nmnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\r\n# Check out http... | [
[
"tensorflow.matmul",
"tensorflow.nn.softmax_cross_entropy_with_logits",
"tensorflow.cast",
"tensorflow.placeholder",
"tensorflow.global_variables_initializer",
"tensorflow.random_normal",
"tensorflow.contrib.layers.xavier_initializer",
"tensorflow.Session",
"tensorflow.train.Ad... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
bpx-energy/VRP_reinforcement_learning | [
"3565e82fa59b0c61f975876a95373fb65fadca30"
] | [
"shared/embeddings.py"
] | [
"import tensorflow as tf\n\n\nclass Embedding(object):\n '''\n This class is the base class for embedding the input graph.\n '''\n def __init__(self,emb_type, embedding_dim):\n self.emb_type = emb_type\n self.embedding_dim = embedding_dim\n\n def __call__(self,input_pnt):\n # ret... | [
[
"tensorflow.InteractiveSession",
"tensorflow.shape",
"tensorflow.global_variables_initializer",
"tensorflow.layers.Conv1D",
"tensorflow.random_uniform"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
YilinLiu97/MR_Fingerprinting | [
"dbcfc85352c58f7a9027f2f4e02674ff85e59681"
] | [
"models/simple_model.py"
] | [
"import numpy as np\nimport torch\nimport os\nfrom collections import OrderedDict\nfrom torch.autograd import Variable\nimport itertools\nimport util.util as util\n# from util.image_pool import ImagePool\nfrom .base_model import BaseModel\nfrom . import networks\nimport sys\nimport time\n\n\nclass SimpleModel(BaseM... | [
[
"torch.optim.Adam",
"torch.nn.Parameter",
"torch.cat",
"torch.no_grad",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
drkndl/Coding-Practice | [
"3527e3dadcb593729517b750402812d4a64bca14"
] | [
"Leetcode/Easy/Incomplete Valid Paranthesis.py"
] | [
"import numpy as np\nclass Solution:\n def isValid(self, s: str) -> bool:\n flag=True\n x=np.empty(0)\n count={'(':0, ')':0, '{':0, '}':0, '[':0, ']':0}\n brack={'(':[], ')':[], '{':[], '}':[], '[':[], ']':[]}\n for i in range(len(s)):\n count[s[i]]=count.get(s[i])+1... | [
[
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
msdev87/Cilialyzer | [
"12da0936da6def42f074031a8c7a8260e91d26bd"
] | [
"src/denoising.py"
] | [
"import numpy\n\n\"\"\"\nInput: 3D array (stack of images)\n\nbandpass filter in all 3 dimensions (space and time)\n\nOutput: denoised image sequence\n\"\"\"\n\ndef denoise(PILseq, fps, pixsize):\n\n firstimg = PILseq[0] # first image of roi sequence \n width, height = firstimg.size # dimension of images \n ... | [
[
"numpy.array",
"numpy.shape",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
prisae/simpeg | [
"8021082b8b53f3c08fa87fc085547bdd56437c6b",
"8021082b8b53f3c08fa87fc085547bdd56437c6b",
"8021082b8b53f3c08fa87fc085547bdd56437c6b",
"8021082b8b53f3c08fa87fc085547bdd56437c6b"
] | [
"tests/pf/test_magnetics_IO.py",
"tests/em/tdem/test_TDEM_forward_Analytic.py",
"SimPEG/electromagnetics/utils/current_utils.py",
"SimPEG/electromagnetics/static/resistivity/simulation_2d.py"
] | [
"from __future__ import print_function\nimport unittest\nimport numpy as np\n\n# from SimPEG import Mesh, PF\nfrom SimPEG.utils.drivers import MagneticsDriver_Inv\nfrom SimPEG.utils import io_utils\n\n# from scipy.constants import mu_0\nimport shutil\nimport os\n\n\nclass MagSensProblemTests(unittest.TestCase):\n ... | [
[
"numpy.all"
],
[
"numpy.log",
"numpy.abs",
"numpy.logical_and",
"numpy.logspace",
"matplotlib.pyplot.loglog",
"numpy.linalg.norm",
"numpy.ones",
"numpy.array",
"matplotlib.pyplot.show"
],
[
"numpy.sqrt",
"numpy.logical_and",
"numpy.unique",
"numpy.re... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
Becksteinlab/numkit | [
"0a734642f63a9cd985d07446c5711fb1b239ea22"
] | [
"src/numkit/tests/test_timeseries.py"
] | [
"# -*- coding: utf-8 -*-\n# numkit.integration test cases\n# Part of GromacsWrapper\n# Copyright (c) Oliver Beckstein <orbeckst@gmail.com>\n# Published under the Modified BSD Licence.\n\nimport numkit.timeseries\n\nimport numpy as np\nfrom numpy.testing import assert_almost_equal, assert_equal\n\nimport pytest\n\n@... | [
[
"numpy.max",
"numpy.linspace",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
James-Hays/argoverse-api | [
"a2f57ea656c022679132fda07dfb21610e8dc34d"
] | [
"demo_usage/cuboids_to_bboxes.py"
] | [
"# <Copyright 2019, Argo AI, LLC. Released under the MIT license.>\nimport argparse\nimport copy\nimport glob\nimport logging\nimport multiprocessing\nimport os\nimport sys\nfrom pathlib import Path\nfrom typing import Any, Iterable, List, Mapping, Sequence, Tuple, Union\n\nimport cv2\nimport imageio\nimport numpy ... | [
[
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
leidi1989/edgeai-torchvision | [
"94bd2d8a01fac800e7df82dd710b3cc13f9a24ea",
"94bd2d8a01fac800e7df82dd710b3cc13f9a24ea"
] | [
"torchvision/xnn/utils/load_weights.py",
"torchvision/xnn/layers/common_blocks.py"
] | [
"#################################################################################\n# Copyright (c) 2018-2021, Texas Instruments Incorporated - http://www.ti.com\n# All Rights Reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the followin... | [
[
"torch.tensor",
"torch.zeros",
"torch.load"
],
[
"torch.nn.ModuleList",
"torch.argmax",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mitiku1/Tacotron-2 | [
"5dffbf5936699c1a94b82746b8e2e339cec5338a"
] | [
"datasets/wavenet_preprocessor.py"
] | [
"import os\nfrom concurrent.futures import ProcessPoolExecutor\nfrom functools import partial\n\nimport numpy as np\nfrom datasets import audio\nfrom wavenet_vocoder.util import is_mulaw, is_mulaw_quantize, mulaw, mulaw_quantize\n\n\ndef build_from_path(hparams, input_dir, mel_dir, wav_dir, n_jobs=12, tqdm=lambda x... | [
[
"numpy.abs",
"numpy.pad",
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bharat-b7/hmr2.0 | [
"020cb3b4642d13af98815d332eba2d09bf59438f"
] | [
"keypoint_marker/loader/pose_loader.py"
] | [
"import pickle\nimport random\n\nimport numpy as np\n\n\nclass PoseLoader:\n\n def __init__(self):\n super(PoseLoader, self).__init__()\n\n def init_poses(self, file_name):\n with open(file_name, \"rb\") as f:\n res = pickle.load(f, encoding='latin-1')\n\n self.num_poses = res[... | [
[
"numpy.reshape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Jopyth/incubator-mxnet | [
"b40ac2c1707646866743bf9768138b01c5a62a36"
] | [
"python/mxnet/numpy/multiarray.py"
] | [
"#!/usr/bin/env python\n\n# 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.... | [
[
"numpy.array",
"numpy.dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rodrigoleonello/orca3 | [
"ca9d4bcab3692c9d43a13fef26718859c294b0d3"
] | [
"orca_gazebo/scripts/find_transform.py"
] | [
"#!/usr/bin/env python3\n\n# MIT License\n#\n# Copyright (c) 2021 Clyde McQueen\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 without limitation t... | [
[
"numpy.array2string",
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SahilC/AES | [
"8bf6dc6b678b8f27ad95f65abe1bd30e3cebb6b0"
] | [
"src/Kappa.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Sep 21 18:55:38 2016\n\n@author: Pranav\n\"\"\"\n\nimport skll.metrics as metric\nimport numpy as np\nimport math\nimport statistics as stats\n\n# Calculates the average quadratic kappa for the entire essay set\ndef get_average_kappa(targets, predictions):\n\tnum = l... | [
[
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TimSchmeier/recommenders | [
"5712f07c8744d2e8e3cc9635f07229167fb8a1cb"
] | [
"tensorflow_recommenders/examples/movielens.py"
] | [
"# Copyright 2021 The TensorFlow Recommenders 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 ... | [
[
"tensorflow.concat",
"tensorflow.data.Dataset.from_tensor_slices",
"numpy.frombuffer",
"numpy.mean",
"numpy.argsort",
"numpy.array",
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
ChocoShell/object-localization | [
"f5fcaccf241603c42722486ee92828f96a0b3eda"
] | [
"generate_dataset.py"
] | [
"import csv\nimport cv2\nimport glob\nimport os\nimport xml.etree.ElementTree as ET\n\nimport numpy as np\nfrom train_model import IMAGE_SIZE\n\nDATASET_FOLDER = \"images/\"\nTRAIN_OUTPUT_FILE = \"train.csv\"\nVALIDATION_OUTPUT_FILE = \"validation.csv\"\n\nSPLIT_RATIO = 0.8\n\nAUGMENTATION = False\nAUGMENTATION_DEB... | [
[
"numpy.reshape",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wmalab/GIST | [
"8b22675827c3df34759424d0b3dd776ad7466775"
] | [
"GIST/fit.py"
] | [
"import os\nimport sys, time, GPUtil\nimport dgl\nimport torch\nimport torch_optimizer as optim\nimport numpy as np\n# from kornia import losses\n\nfrom .model import embedding, encoder_chain, decoder_distance, decoder_gmm, decoder_euclidean, decoder_similarity\nfrom .model import save_model_state_dict\nfrom .loss ... | [
[
"torch.autograd.set_detect_anomaly",
"torch.cat",
"torch.sin",
"torch.load",
"torch.no_grad",
"numpy.mean",
"numpy.nanmean",
"torch.ones",
"numpy.arange",
"torch.tensor",
"numpy.argmax",
"torch.sort",
"torch.arange",
"numpy.zeros",
"torch.ones_like",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hvkwak/pointnet | [
"3f7d881742e7c867d4b7a258536ea51c4151967a"
] | [
"part_seg/pointnet_part_seg.py"
] | [
"import tensorflow as tf\nimport numpy as np\nimport math\nimport os\nimport sys\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\nsys.path.append(os.path.dirname(BASE_DIR))\nsys.path.append(os.path.join(BASE_DIR, '../utils'))\nimport tf_util\n\n\ndef get_transform_K(inputs, is_training, bn_decay=None, K = 3)... | [
[
"tensorflow.nn.bias_add",
"tensorflow.matmul",
"tensorflow.concat",
"tensorflow.constant",
"tensorflow.transpose",
"tensorflow.reduce_mean",
"numpy.eye",
"tensorflow.reshape",
"tensorflow.expand_dims",
"tensorflow.compat.v1.constant_initializer",
"tensorflow.nn.l2_loss"... | [
{
"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... |
onkarthorat/Hand-Gesture-Recognition-Model | [
"2953bd2a121d3a280cf9e3ae0b40ba9793e1a651"
] | [
"handmodel.py"
] | [
"import cv2\nimport numpy as np\n\n# Camera\ncamera = cv2.VideoCapture(0)\ncamera.set(10, 200)\n\n\n# parameters\nbgCapture = 0\nbgSubThreshold = 50\nlearningRate = 0\nblurValue = 41\nthreshold = 60\ncap_region_x_begin = 0.5 # start point/total width\ncap_region_y_end = 0.8 # start point/total width\nimgCount = 0... | [
[
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JUSTLOVELE/MobileDevStudy | [
"ddcfd67d9ad66dd710fcbb355406bab3679ebaf7"
] | [
"RL/gym_case/__init__.py"
] | [
"a = [4,5,3,2]\nb = lambda obs: a[obs]\nprint(b(0))\n\nold_policy_result = {\n obs: -1 for obs in range(64)\n}\n\nprint(old_policy_result)\nprint(old_policy_result[2])\n\nimport gym\nimport numpy as np\nenv = gym.make(\"FrozenLake8x8-v0\")\npolicy = lambda dim : np.random.choice(4)\nprint(policy(10))\n\nnew_poli... | [
[
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
optroodt/hyperopt | [
"686a0844d7b763312370d63cb0c5083d6b6aa61a"
] | [
"hyperopt/tests/test_mongoexp.py"
] | [
"from __future__ import print_function\nfrom __future__ import absolute_import\nimport six.moves.cPickle as pickle\nimport os\nimport signal\nimport subprocess\nimport sys\nimport threading\nimport time\nimport unittest\n\nimport numpy as np\nimport nose\nimport nose.plugins.skip\n\nfrom hyperopt.base import JOB_ST... | [
[
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bionet/ted.python | [
"1698a7f792db23123003ae4e2d39b4c18f25f347"
] | [
"bionet/utils/scipy_extras.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"\nScipy Extras\n============\nThis module contains various functions not currently included in\nscipy [1]_.\n\n- ei Compute the exponential integral of a complex value.\n- si Compute the sine integral of a complex value.\n- ci Compute the cosine... | [
[
"numpy.log",
"numpy.asscalar",
"numpy.asarray",
"scipy.special.exp1",
"numpy.real",
"numpy.any",
"numpy.iscomplexobj",
"numpy.iterable",
"numpy.array",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sina-masoud-ansari/learn-opengl-python | [
"1e459d98cb075dde1af61f0bbf2e55c38777ed25"
] | [
"getting_started/03_textures/textures_ex1.py"
] | [
"from glfw import *\nfrom OpenGL.GL import *\nimport numpy as np\nfrom ctypes import *\nfrom learnopengl import *\nfrom PIL import Image\n\n\ndef resize(window, width, height):\n glViewport(0, 0, width, height)\n\ndef main():\n # Initialize the library\n if not init():\n return\n # Create a windo... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
treuherz/pandas | [
"a313f7ff5c003bc14fa36714d41c9842209b4e6a",
"a313f7ff5c003bc14fa36714d41c9842209b4e6a"
] | [
"pandas/core/arrays/timedeltas.py",
"pandas/tests/io/pytables/test_timezones.py"
] | [
"from datetime import timedelta\nfrom typing import List, Optional, Union\n\nimport numpy as np\n\nfrom pandas._libs import lib, tslibs\nfrom pandas._libs.tslibs import (\n BaseOffset,\n NaT,\n NaTType,\n Period,\n Tick,\n Timedelta,\n Timestamp,\n iNaT,\n to_offset,\n)\nfrom pandas._libs... | [
[
"pandas.core.arrays.datetimelike.DatetimeLikeArrayMixin.astype",
"pandas._libs.tslibs.Timestamp",
"numpy.linspace",
"pandas.core.dtypes.common.is_dtype_equal",
"pandas.core.dtypes.dtypes.DatetimeTZDtype",
"pandas._libs.lib.is_scalar",
"numpy.dtype",
"numpy.round",
"pandas.core.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"0.24",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorfl... |
chethus/CQL | [
"9cceb35fe220ae517bc87ae219704d6eca409566"
] | [
"SimpleSAC/sac_from_path.py"
] | [
"import os\nimport time\nfrom copy import deepcopy\nimport uuid\n\nimport numpy as np\nimport pprint\n\nimport gym\nimport torch\n\nimport absl.app\nimport absl.flags\n\nfrom .sac import SAC\nfrom .replay_buffer import ReplayBuffer, batch_to_torch\nfrom .dict_replay_buffer import DictReplayBuffer\nfrom .model impor... | [
[
"numpy.mean",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zerogerc/pytorch-lightning | [
"fb85d493d07f16e10988f519dae2e7691e8ca3b3"
] | [
"tests/loggers/test_base.py"
] | [
"import pickle\nfrom typing import Optional\nfrom unittest.mock import MagicMock\n\nimport numpy as np\n\nfrom pytorch_lightning import Trainer\nfrom pytorch_lightning.loggers import LightningLoggerBase, LoggerCollection\nfrom pytorch_lightning.utilities import rank_zero_only\nfrom tests.base import EvalModelTempla... | [
[
"numpy.random.random",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ucgmsim/GMSimViz | [
"df4a642fe31eda26eaa5978c6c334cc07c5fbd6d"
] | [
"gmsimviz/srf.py"
] | [
"\"\"\"\n\nGENERAL SRF NOTES:\nSHYP: hypocentre position along strike from centre\nDHYP: hypocentre position along dip from top\nCommon SRF functions.\n\nSRF format:\nhttps://scec.usc.edu/scecpedia/Standard_Rupture_Format\n\"\"\"\n\nfrom math import ceil, cos, floor, radians, sqrt\nimport os\nfrom subprocess import... | [
[
"numpy.isnan",
"numpy.array",
"numpy.zeros",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aws-samples/ngc-biobert-on-sagemaker | [
"b477eca7c56403d082f14e19471a7cbcaef5c342"
] | [
"sm_init.py"
] | [
"#! usr/bin/env python3\n# -*- coding:utf-8 -*-\n\n# !pip install spacy\n# !python -m spacy download en_core_web_sm\n\n\nimport tensorflow as tf\n# from horovod.tensorflow.compression import Compression\nimport horovod.tensorflow as hvd\nimport tokenization\n\nos.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION\"] = \"1\" ... | [
[
"tensorflow.flags.DEFINE_string",
"tensorflow.logging.set_verbosity"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
edencfc/PaddleOCR | [
"82c5966a642d07f99502d779c70a707fe3edbcb0",
"09604c38e42591c240771edbbff43a6dd7ebf592"
] | [
"tools/infer/predict_det.py",
"ppocr/data/imaug/sast_process.py"
] | [
"# Copyright (c) 2020 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.expand_dims",
"numpy.linalg.norm",
"numpy.random.uniform",
"numpy.argsort",
"numpy.array"
],
[
"numpy.dot",
"numpy.arctan2",
"numpy.max",
"numpy.round",
"numpy.zeros_like",
"numpy.where",
"numpy.clip",
"numpy.arange",
"numpy.sin",
"numpy.zeros... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
luksurious/faster-teaching | [
"1493311d5b723ca3f216f537bda8db5907196443"
] | [
"learner_models/continuous.py"
] | [
"from copy import deepcopy\n\nfrom learner_models.base_belief import BaseBelief\nfrom concepts.concept_base import ConceptBase\n\nimport numpy as np\n\n\nclass ContinuousModel(BaseBelief):\n name = 'continuous'\n\n def __init__(self, prior, concept: ConceptBase, particle_num: int = 16, verbose: bool = True):\... | [
[
"numpy.argmin",
"numpy.copy",
"numpy.sum",
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nih-megcore/hv_proc | [
"1fba1ce085769036886bcdf8a4c0c4832be2db6e",
"1fba1ce085769036886bcdf8a4c0c4832be2db6e"
] | [
"hv_proc/Process_scripts/process_sternberg.py",
"hv_proc/Process_scripts/process_gonogo.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 17 08:59:50 2020\n\n@author: stoutjd\n\"\"\"\n \nfrom hv_proc.Process_scripts.trigger_utilities import check_analog_inverted\nfrom hv_proc.Process_scripts.trigger_utilities import (threshold_detect, \n ... | [
[
"pandas.read_csv"
],
[
"pandas.read_csv"
]
] | [
{
"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... |
SeekingDream/denas_fse2020 | [
"a9a94b9e85f7dfaf1eb1bda35f7d8a49ef469e9a"
] | [
"Pdf_malware/Scripts/fidelity.py"
] | [
"import os\r\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\r\nfrom Pdf_malware.Scripts.LEMNA import xai_rnn\r\nfrom Pdf_malware.Scripts.utils import *\r\nimport matplotlib.pyplot as plt\r\nimport innvestigate\r\n\r\n\r\n\r\n\r\nclass FidelityMetric():\r\n def __init__(self, data, model, important, maxselNum, neg... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.cla",
"matplotlib.pyplot.savefig"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
XingxinHE/compas | [
"d2901dbbacdaf4694e5adae78ba8f093f10532bf"
] | [
"src/compas/geometry/shapes/polyhedron.py"
] | [
"from __future__ import print_function\nfrom __future__ import absolute_import\nfrom __future__ import division\n\nfrom math import sqrt\nfrom compas.geometry import transform_points\nfrom compas.utilities import pairwise\n\nfrom compas.geometry.shapes._shape import Shape\n\n\n__all__ = ['Polyhedron']\n\n\nclass Po... | [
[
"numpy.asarray",
"scipy.spatial.HalfspaceIntersection",
"scipy.spatial.ConvexHull"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vibhoothiiaanand/tina | [
"186c769fd06356e2a65a6a6a14d87524b098d694"
] | [
"core/cnn1lstm-test.py"
] | [
"from __future__ import print_function\nimport numpy as np\nnp.random.seed(1337) # for reproducibility\n\nfrom keras.preprocessing import sequence\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation, Lambda\nfrom keras.layers import Embedding\nfrom keras.layers import Convolut... | [
[
"pandas.read_csv",
"numpy.random.seed",
"numpy.reshape",
"sklearn.metrics.precision_score",
"numpy.transpose",
"sklearn.metrics.f1_score",
"numpy.array",
"sklearn.metrics.recall_score",
"sklearn.metrics.accuracy_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
inzva/fake-academic-paper-generation | [
"294fb4f218ea1d04c705f0649fa715f09122bf8a"
] | [
"transformer-xl/pytorch/utils/vocabulary.py"
] | [
"import os\nfrom collections import Counter, OrderedDict\n\nimport torch\n\n\nclass Vocab(object):\n EOS = '<eos>'\n\n def __init__(self, special=(), min_freq=0, max_size=None, lower_case=True,\n vocab_file=None,\n add_eos=False, add_double_eos=False):\n self.counter = C... | [
[
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
josephworks/PythonWS | [
"7391580de63291018094037f87b930fe44c6eae8"
] | [
"translator.py"
] | [
"from __future__ import absolute_import, division, print_function, unicode_literals\n\nimport tensorflow as tf\n\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as ticker\nfrom sklearn.model_selection import train_test_split\n\nimport unicodedata\nimport re\nimport numpy as np\nimport os\nimport io\nimpo... | [
[
"tensorflow.convert_to_tensor",
"matplotlib.ticker.MultipleLocator",
"tensorflow.zeros",
"tensorflow.reduce_sum",
"tensorflow.cast",
"tensorflow.math.equal",
"tensorflow.keras.layers.Embedding",
"tensorflow.keras.layers.GRU",
"tensorflow.argmax",
"numpy.zeros",
"matplot... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
zhaixingang/deep-person-reid | [
"6482550333311f162ca5e82572c3e46c0d6d5581"
] | [
"scripts/convert_to_onnx.py"
] | [
"import sys\nimport time\nimport os.path as osp\nimport argparse\nimport torch\nimport torch.nn as nn\n\nimport torchreid\nfrom torchreid.utils import (\n Logger, check_isfile, set_random_seed, collect_env_info,\n resume_from_checkpoint, load_pretrained_weights, compute_model_complexity\n)\n\nfrom default_con... | [
[
"torch.randn",
"torch.onnx._export",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ngritsuk/incubator-superset | [
"4f92d3b5a66fd2ad05783da173cb49b6aced2e03"
] | [
"tests/viz_tests.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... | [
[
"pandas.Timestamp",
"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": []
}
] |
xbankov/calamari | [
"f5d40bbd5f31bb2edc09edbab1790b83f61a186f"
] | [
"calamari_ocr/scripts/dataset_viewer.py"
] | [
"import matplotlib.pyplot as plt\nimport argparse\nfrom calamari_ocr.ocr.datasets import create_dataset, DataSetType, DataSetMode\nfrom calamari_ocr.ocr.datasets.input_dataset import StreamingInputDataset\nfrom calamari_ocr import __version__\nfrom calamari_ocr.utils import glob_all, split_all_ext, keep_files_with_... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ndevenish/xia2 | [
"51eb0911457119f80803d5d061d44dc5f19b5a6e"
] | [
"Test/Wrappers/CCP4/test_blend.py"
] | [
"from __future__ import absolute_import, division, print_function\n\nimport glob\nimport os\n\nimport pytest\nfrom libtbx.test_utils import approx_equal\n\ndef cmd_exists(cmd):\n import subprocess\n return subprocess.call('type ' + cmd, shell=True,\n stdout=subprocess.PIPE, stderr=subprocess.PIPE) == 0\n\ndef ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
oliverdutton/bgflow | [
"dbb3db6c3e754b776f42911ef531868bf973b350"
] | [
"docs/conf.py"
] | [
"# -*- coding: utf-8 -*-\n#\n# Configuration file for the Sphinx documentation builder.\n#\n# This file does only contain a selection of the most common options. For a\n# full list see the documentation:\n# http://www.sphinx-doc.org/en/stable/config\n\n# -- Path setup -----------------------------------------------... | [
[
"torch.nn.Module"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vickyvava/ImageAI | [
"fc23bc1374d5a29f816c0895b37cb769b1766eac",
"fc23bc1374d5a29f816c0895b37cb769b1766eac"
] | [
"examples/video_analysis_per_frame.py",
"imageai/Detection/keras_retinanet/utils/visualization.py"
] | [
"from imageai.Detection import VideoObjectDetection\nimport os\nfrom matplotlib import pyplot as plt\n\n\nexecution_path = os.getcwd()\n\ncolor_index = {'bus': 'red', 'handbag': 'steelblue', 'giraffe': 'orange', 'spoon': 'gray', 'cup': 'yellow', 'chair': 'green', 'elephant': 'pink', 'truck': 'indigo', 'motorcycle':... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.get_current_fig_manager",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.pie",
"matplotlib.pyplot.show",
"matplotlib.pyplot.pause"
],
[
"numpy.array",
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pieckenst/Evos | [
"1551bf8436f537b3002a2ea44b6a5fb9d353e0bf"
] | [
"cogs/Requests.py"
] | [
"#MIT License\r\n#Copyright (c) 2020 Semih Aydın\r\n#UTF-8\r\n\r\nimport discord\r\nfrom discord.ext import commands\r\nimport matplotlib.pyplot as coronaplt\r\nfrom googletrans import Translator\r\nfrom bs4 import BeautifulSoup\r\nimport requests\r\nimport humanize\r\nimport os\r\nfrom logging_files.requests_log i... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.pie",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
monim67/openvino-computer-pointer-controller | [
"5ea50b33ae37ee29f52252eb0db2cafd36fc6df4"
] | [
"src/models/facial_landmarks_detection.py"
] | [
"\"\"\"\nmodel: landmarks-regression-retail-0009\ninput: BxCxHxW\ninput shape: (1, 3, 48, 48)\noutput: (point_x, point_y) * 5\noutput shape: (1, 10, 1, 1)\n\"\"\"\n\nimport numpy as np\n\nfrom .base_model import BaseModel\n\n\nclass FacialLandmarkDetect(BaseModel):\n model_name = \"landmarks-regression-retail-00... | [
[
"numpy.column_stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pwentrys/wordworks_plotly_dash_tests | [
"eacfa3e2ae4bdc1014242351a5cdad6666022a51"
] | [
"app_sql.py"
] | [
"import dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport os\nfrom datetime import datetime\nfrom utilities.WordWorks import WordWorks as wordings\n\nfrom sqlalchemy import create_engine\nimport pandas as pd\n\n\ntime_start = datetime.now()\nprint(f'Starting At: {time_start}\\n\\... | [
[
"pandas.read_sql"
]
] | [
{
"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": []
}
] |
jmcummings77/polyphasia | [
"ea0a9d0878dce6246839a1ff15019c63e4603b9b"
] | [
"polyphasia/loader.py"
] | [
"\"\"\"Module with functions for loading and cleaning the source data\"\"\"\n\nfrom typing import Optional\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom polyphasia.constants import (\n EdgeDirections,\n ParsedColumnNames,\n SourceColumnNames,\n LANGUAGE_PREFIX_TAG,\n EDGE_LIST_COLUMN_NAMES... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
jrepifano/PyTorchTutorial | [
"b551ab1c62d3183cc272e93ca6a5d15c15834a72"
] | [
"linear_lightning.py"
] | [
"import os\nimport torch\nimport pytorch_lightning as pl\nfrom torchvision import transforms\nfrom torchvision.datasets import MNIST\nfrom torch.utils.data import DataLoader\n\n\nclass MLP(pl.LightningModule):\n def __init__(self, batch_size):\n super().__init__()\n self.batch_size = batch_size\n ... | [
[
"torch.nn.Softmax",
"torch.nn.CrossEntropyLoss",
"torch.utils.data.DataLoader",
"torch.nn.Linear",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lace/vx | [
"33134cae43d7729b6128b198119e1593035066ae"
] | [
"vg/test_average.py"
] | [
"import numpy as np\nimport vg\n\n\ndef test_average():\n np.testing.assert_array_equal(\n vg.average(np.array([[1.0, 2.0, 3.0], [-6.0, -9.0, -15.0]])),\n np.array([-2.5, -3.5, -6.0]),\n )\n np.testing.assert_array_equal(\n vg.average(np.array([[1.0, 2.0, 3.0], [-6.0, -9.0, -15.0]]), w... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
revans2/cudf | [
"27656c1f7ab730548a21d37aaf3488a560abca7e"
] | [
"python/cudf/io/json.py"
] | [
"# Copyright (c) 2019, NVIDIA CORPORATION.\n\nfrom cudf.bindings.json import cpp_read_json\n\nimport cudf\nfrom cudf.utils import ioutils\n\nimport pandas as pd\nimport warnings\n\n\n@ioutils.doc_read_json()\ndef read_json(path_or_buf, engine='auto', dtype=True, lines=False,\n compression='infer', byte... | [
[
"pandas.io.json.to_json",
"pandas.read_json"
]
] | [
{
"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": []
}
] |
StiphyJay/FCOS_annotation_in_detail | [
"5c619f08c1d3e77a9ef92c9537751245218818cd"
] | [
"fcos_core/layers/sigmoid_focal_loss.py"
] | [
"import torch\nfrom torch import nn\nfrom torch.autograd import Function\nfrom torch.autograd.function import once_differentiable\n\n#from fcos_core import _C\n\n# TODO: Use JIT to replace CUDA implementation in the future.\nclass _SigmoidFocalLoss(Function):\n @staticmethod\n def forward(ctx, logits, targets... | [
[
"torch.sigmoid",
"torch.log",
"torch.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dbash/zerowaste | [
"4047ae92a31cece9d848d38a57fd33cc85e7a8bb"
] | [
"puzzlecam_4_classes/train_classification.py"
] | [
"# Copyright (C) 2020 * Ltd. All rights reserved.\n# author : Sanghyeon Jo <josanghyeokn@gmail.com>\n\nimport os\nimport sys\nimport copy\nimport shutil\nimport random\nimport argparse\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom torchvision import transforms\n... | [
[
"numpy.ones_like",
"numpy.asarray",
"numpy.arange",
"torch.nn.MultiLabelSoftMarginLoss",
"torch.utils.data.DataLoader",
"numpy.concatenate",
"torch.no_grad",
"torch.utils.tensorboard.SummaryWriter",
"torch.nn.DataParallel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jkha-unist/for_test | [
"56fe5aa3aa400914a38d88fb136bc486fe3d7678",
"56fe5aa3aa400914a38d88fb136bc486fe3d7678"
] | [
"src/qm/qchem/dft.py",
"src/qm/gaussian09/dft.py"
] | [
"from __future__ import division\nfrom qm.qchem.qchem import QChem\nfrom misc import au_to_A, eV_to_au\nimport os, shutil, re, textwrap, subprocess\nimport numpy as np\n\nclass DFT(QChem):\n \"\"\" Class for DFT method of Q-Chem 5.2\n\n :param object molecule: Molecule object\n :param string basis_... | [
[
"numpy.copy",
"numpy.array"
],
[
"numpy.diag",
"numpy.copy",
"numpy.transpose",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ritchieng/wideresnet-tensorlayer | [
"72922f986c19a466a632ce5ba04699c4d4f77b06"
] | [
"cifar_wide_resnet_keras.py"
] | [
"import tensorflow as tf\nfrom keras import backend as K\nfrom keras.layers import Dense\nfrom keras.objectives import categorical_crossentropy\nfrom keras.metrics import categorical_accuracy as accuracy\nfrom keras.datasets import cifar10\nfrom keras.utils import np_utils\nfrom keras.layers import Dense, Activatio... | [
[
"numpy.arange",
"tensorflow.placeholder",
"numpy.random.shuffle",
"tensorflow.ConfigProto",
"numpy.std",
"tensorflow.train.GradientDescentOptimizer",
"numpy.mean",
"tensorflow.Session",
"tensorflow.pad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
DragonRoar/deep-radiomics-glioma | [
"178cd2f7239a644741ed70848a67e752831b038b"
] | [
"eval_regional_size.py"
] | [
"import os\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nfrom scipy.stats import ttest_ind\nfrom scipy.stats import bartlett\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import KFold\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import confusion_matr... | [
[
"pandas.read_csv",
"sklearn.linear_model.LogisticRegression",
"sklearn.metrics.confusion_matrix",
"sklearn.model_selection.KFold",
"pandas.DataFrame",
"numpy.std",
"numpy.mean",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
TShimko126/cvxpy | [
"8b89b3f8ef7daba1db39f5029e4902f06c75b29f",
"8b89b3f8ef7daba1db39f5029e4902f06c75b29f",
"8b89b3f8ef7daba1db39f5029e4902f06c75b29f"
] | [
"cvxpy/reductions/utilities.py",
"cvxpy/tests/test_dqcp.py",
"cvxpy/expressions/leaf.py"
] | [
"\"\"\"\nCopyright 2018 Akshay Agrawal\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to i... | [
[
"numpy.reshape",
"scipy.sparse.eye"
],
[
"numpy.random.seed",
"numpy.arange",
"numpy.ones",
"numpy.testing.assert_almost_equal",
"numpy.random.randn",
"numpy.array",
"numpy.zeros"
],
[
"numpy.diag",
"numpy.imag",
"numpy.minimum",
"numpy.abs",
"numpy.... | [
{
"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"... |
mjtadema/garnish | [
"32c4c37bd10aad201ede0085b7e68a38b6a5a3d7"
] | [
"garnish/system.py"
] | [
"# Copyright 2019-2019 the garnish authors. See copying.md for legal info.\n\nimport networkx as nx\nimport numpy as np\nfrom pymol import cmd\n\n\nclass System:\n def __init__(self, sys_dict, fix_elastics=True):\n self.sys_dict = sys_dict\n\n if fix_elastics:\n # fix wrong elastic bonds... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jxkrause/beaker37 | [
"131c43497d60518e4ccb219a48861da7607066a5"
] | [
"beaker37/UserSimilarityRecommender.py"
] | [
"\"\"\"\nmodule part of beaker37\ncontains class recomends moves using user similarities\n\"\"\"\nimport pandas as pd\nimport beaker37.utils as utils\nfrom beaker37.MovieUser import MovieUser\n\n\nclass UserSimilarityRecommender(MovieUser):\n \"for recomendation method use similarity\"\n\n def __init__(self):... | [
[
"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": []
}
] |
TheGoldLab/Audio_2AFC_Analysis | [
"9bfcb7e869ca70e9d19af402c96b9ce16be02167",
"9bfcb7e869ca70e9d19af402c96b9ce16be02167"
] | [
"Simulations/generate_trials.py",
"Data_Analysis/pilot1/processing/data_processing.py"
] | [
"\"\"\"\nscript to generate trials\n\"\"\"\nimport numpy as np\nimport os\nimport sys\n# insert the path where mmcomplexity.py lives below\nsys.path.append(os.path.expanduser('~/Git/GitHub/work/Analysis_Audio2AFC_ChangePoint/Python_modules'))\nfrom mmcomplexity import *\n\n\nnp.random.seed(1)\nh_values = [.1, .9]\n... | [
[
"numpy.random.seed"
],
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
stekaiser/onnx2keras | [
"5955bed90e1cdcbc1252c570c71ad7c85ed63e37"
] | [
"onnx2keras/activation_layers.py"
] | [
"from tensorflow import keras\nfrom .utils import ensure_tf_type\n\n\ndef convert_relu(node, params, layers, node_name, keras_name):\n \"\"\"\n Convert ReLU activation layer\n :param node: current operation node\n :param params: operation attributes\n :param layers: available keras layers\n :param... | [
[
"tensorflow.keras.layers.Activation",
"tensorflow.keras.layers.LeakyReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
desireevl/qiskit-aqua | [
"84c067f3170187be05f40ae5368d439c109fd895"
] | [
"test/aqua/test_exact_cover.py"
] | [
"# -*- coding: utf-8 -*-\n\n# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2018, 2019.\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... | [
[
"numpy.testing.assert_array_equal",
"numpy.binary_repr"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Rasoul20sh/InsightFace_TF | [
"06a177a3176660787f21f184728bdf6b553b25ae"
] | [
"align/align_dataset_mtcnn.py"
] | [
"\"\"\"Performs face alignment and stores face thumbnails in the output directory.\"\"\"\n# MIT License\n# \n# Copyright (c) 2016 David Sandberg\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# i... | [
[
"tensorflow.Graph",
"scipy.misc.imresize",
"numpy.maximum",
"numpy.minimum",
"numpy.power",
"numpy.asarray",
"scipy.misc.imsave",
"numpy.squeeze",
"tensorflow.ConfigProto",
"numpy.argmax",
"tensorflow.GPUOptions",
"scipy.misc.imread",
"numpy.zeros",
"numpy.v... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.10",
"0.16",
"0.19",
"0.18",
"0.12",
"1.0",
"0.17",
"1.2"
],
"tensorflow": []
}
] |
sankhaMukherjee/sageMaker | [
"38f8cf6d9ad56f1fdbcd7a1d586eda133da0d05f",
"38f8cf6d9ad56f1fdbcd7a1d586eda133da0d05f"
] | [
"src/part_01_genertaeData/generateData.py",
"src/part_10_deployAndPredict/deploy.py"
] | [
"import tensorflow as tf\nimport sagemaker\n\nfrom tensorflow.keras.datasets import fashion_mnist\nfrom tensorflow.keras import utils\n\nimport os, json, logging\nimport numpy as np\n\nimport boto3\nfrom botocore.exceptions import ClientError\nfrom tqdm import tqdm\n\n\ndef uploadFile(file_name, bucket, ob... | [
[
"tensorflow.keras.datasets.fashion_mnist.load_data",
"tensorflow.keras.utils.to_categorical",
"numpy.save"
],
[
"numpy.load",
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nepeplwu/models | [
"5c142ae728abc786c380ece05da52f84e82e795d"
] | [
"PaddleNLP/neural_machine_translation/transformer/infer.py"
] | [
"import argparse\nimport ast\nimport multiprocessing\nimport numpy as np\nimport os\nimport sys\nsys.path.append(\"../../models/neural_machine_translation/transformer/\")\nfrom functools import partial\n\nimport paddle\nimport paddle.fluid as fluid\n\nimport reader\nfrom config import *\nfrom desc import *\nfrom mo... | [
[
"numpy.array",
"numpy.zeros_like",
"numpy.tile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
colin2328/recipes | [
"a6cd0e12c9fcb48749721a6548d0a02319d54bd1"
] | [
"torchrecipes/vision/image_generation/callbacks/image_generation.py"
] | [
"# Copyright (c) Meta Platforms, Inc. and affiliates.\n#\n# This source code is licensed under the BSD-style license found in the\n# LICENSE file in the root directory of this source tree.\n\n\n#!/usr/bin/env python3\n# Based on https://github.com/PyTorchLightning/lightning-bolts/blob/master/pl_bolts/callbacks/visi... | [
[
"torch.normal",
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
li282886931/cnn | [
"a60eaadc1e5153255774cad2a211290a0773e8f7"
] | [
"utils.py"
] | [
"import tensorflow as tf\nimport numpy as np\nimport pickle\nimport json\nimport os\n\n#from collections import defaultdict\n#from scipy import ndimage\n\ndef flatten_tf_array(array):\n shape = array.get_shape().as_list()\n return tf.reshape(array, [shape[0], shape[1] * shape[2] * shape[3]])\n\ndef accuracy(p... | [
[
"numpy.unique",
"numpy.arange",
"tensorflow.reshape",
"numpy.random.permutation",
"numpy.argmax",
"numpy.array"
]
] | [
{
"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... |
agikarasugi/Face-Mask-Invariant-End-to-End-Face-Recognition | [
"eb274ff98246c1bb8748bd8c8351d3494a87dfce"
] | [
"utils/linearized.py"
] | [
"'''\nLinearized multi-sampling core part.\nAll methods are encapsuled in class LinearizedMutilSampler.\nHyperparameters are stored as static variables.\nMain sampling method entrance is linearized_grid_sample.\n'''\n\nimport torch\n\n\n######### Utils to minimize dependencies #########\n# Move utils to another fil... | [
[
"torch.transpose",
"torch.ones",
"torch.zeros",
"torch.randn",
"torch.tensor",
"torch.matmul",
"torch.nn.functional.grid_sample"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jgraham909/trapper-keeper | [
"4b3ade05a5053eaf5c3d049036b8aab481b571fe"
] | [
"show_diffs.py"
] | [
"import difflib\nfrom difflib import HtmlDiff\nimport pandas as pd\nimport sys\n#import re\nimport os\n#import datetime\nfrom datetime import datetime\nimport tldextract\nimport csv\nimport json\nimport webbrowser\nfrom fnmatch import fnmatch\nimport random\nimport tldextract\nfrom utilities.helpers import (makedir... | [
[
"pandas.read_csv",
"pandas.Series",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
thisishardik/skin-tumor-detection | [
"505d26e834f2de1b9f49c4ea3b23bfe1c315ecea"
] | [
"cutmix-data-augmentation.py"
] | [
"import os\nimport gc\nimport json\nimport math\nimport cv2\nimport PIL\nimport re\nimport numpy as np\nimport pandas as pd\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nimport scipy\nfrom tqdm import tqdm\nimport glob\nimport tensorflow.keras.applications.densenet as dense\nfrom kaggle_datasets import K... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.axis"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NargesEl/lyrics_text_classification | [
"c92eb532829cd4aa727d3622d6adb65eb9c1bdfd"
] | [
"code/feature_engineering_and_training_the_model.py"
] | [
"from sklearn.model_selection import train_test_split\nimport pandas as pd\nimport spacy\nfrom tqdm import tqdm\nfrom sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer\nfrom sklearn.feature_extraction.text import CountVectorizer\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.py... | [
[
"pandas.concat",
"pandas.read_csv",
"sklearn.naive_bayes.MultinomialNB",
"sklearn.model_selection.train_test_split",
"sklearn.feature_extraction.text.TfidfVectorizer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
ZHKKKe/PixelSSL | [
"ce192034355ae6a77e47d2983d9c9242df60802a"
] | [
"task/sseg/data.py"
] | [
"\"\"\" This file is adapted from the repository: https://github.com/jfzhang95/pytorch-deeplab-xception\n\"\"\"\n\nimport io\nimport os\nimport sys\nimport cv2\nimport random\nfrom PIL import Image, ImageOps, ImageFilter\nimport numpy as np\n\nimport torch\nfrom torchvision import transforms\n\nimport pixelssl\n\n\... | [
[
"numpy.array",
"torch.from_numpy"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IKostric/DAT640_SMART | [
"7d2770119237d520d954b1228375ceeaf91a6fd4"
] | [
"QPC.py"
] | [
"import numpy as np\r\nimport os\r\nimport json\r\n\r\nfrom sklearn.feature_extraction.text import CountVectorizer\r\nfrom sklearn.neural_network import MLPClassifier\r\nimport pickle\r\n\r\nclass QPC_model:\r\n def __init__(self):\r\n '''\r\n Arguments\r\n conf: Configuration ID... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rishav-karanjit/Essay-Grader | [
"18defe48aa9efca7adc3885381f91bb448e630c6"
] | [
"MainUI.py"
] | [
"from PyQt5 import QtWidgets, uic\nimport sys\nfrom PyQt5 import QtCore\nfrom PyQt5.QtCore import QPoint\n\nfrom DetailsUI import DetailsUI\nfrom Grammer_checkUI import Mistakes\n\nfrom Scores import ScoreUI\n\nimport pandas as pd\nimport webbrowser\n\nclass MainUI(QtWidgets.QMainWindow):\n def __init__(self):\n... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
deepest-stack/graph2tensor | [
"4258bd7fff68348c98a77cff88afef039c1d44ba"
] | [
"python/graph2tensor/model/layers/gat_layer.py"
] | [
"#!/usr/bin/env python3\n\nimport tensorflow as tf\nfrom tensorflow.keras import activations\n\n\nclass GATConv(tf.keras.layers.Layer):\n r\"\"\"\n Graph Attention Convolution layer.\n\n .. math::\n \\overrightarrow{h_i^{\\prime}} =\n \\sigma (\\sum_{j \\in \\mathcal{N}_i \\cup \\{i\\}} \\alp... | [
[
"tensorflow.multiply",
"tensorflow.concat",
"tensorflow.keras.activations.serialize",
"tensorflow.reduce_mean",
"tensorflow.keras.layers.Dense",
"tensorflow.reshape",
"tensorflow.squeeze",
"tensorflow.expand_dims",
"tensorflow.divide",
"tensorflow.gather",
"tensorflow.m... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
lkilcommons/ssmtools | [
"fd84b30f6217214bdfad25448a72649182e7c1f9"
] | [
"ssm_read_data.py"
] | [
"import numpy as np\nimport matplotlib\nimport matplotlib.pyplot as pp\nfrom spacepy import pycdf\nimport os,time,datetime\n#from scipy.ndimage.filters import gaussian_filter1d\n\n#Python datetime to day of year\ndef datetime2doy(dt): \n\treturn dt.timetuple().tm_yday + dt.hour/24. + dt.minute/24./60. + dt.second/8... | [
[
"numpy.logical_not",
"numpy.abs",
"numpy.isfinite",
"numpy.flatnonzero",
"numpy.logical_or",
"numpy.diff",
"numpy.logical_and"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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