repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
miklz/IFSC_2021-1_PI-3_MIKAEL | [
"840163e5f76cfbf17a3cb7f44fac7d565c4dd254"
] | [
"src/sanitycheck.py"
] | [
"import numpy as np\n\ndef produce_random_numbers(k, n, m):\n matrix_a = np.random.randint(-1e3, 1e3, (k,n,m))\n matrix_b = np.random.randint(-1e3, 1e3, (k,n,m))\n\n np.savetxt(\"values_a.txt\", matrix_a.reshape(k,-1), fmt='%1.1i', delimiter=' ')\n np.savetxt(\"values_b.txt\", matrix_b.reshape(k,-1), fm... | [
[
"numpy.array_equal",
"numpy.matmul",
"numpy.loadtxt",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DerrickXuNu/dvrl_pytorch | [
"693a6aa7c74820c73a2a4d2571c173f06927826d"
] | [
"dvrl/dvrl.py"
] | [
"# coding=utf-8\n\"\"\"\nThe core class of DVRL(Data Valuation using Reinforcement Learning).\n\"\"\"\n\nimport copy\nimport os\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport torch.optim... | [
[
"torch.abs",
"torch.nn.functional.softmax",
"torch.nn.CrossEntropyLoss",
"torch.max",
"torch.Tensor",
"torch.cat",
"torch.optim.lr_scheduler.ExponentialLR",
"torch.unsqueeze",
"torch.nn.functional.one_hot",
"sklearn.metrics.f1_score",
"torch.squeeze"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
OmnesRes/ATGC | [
"c4fc4d6a0ac99bf083232686dcd0b634ff597f8a",
"c4fc4d6a0ac99bf083232686dcd0b634ff597f8a"
] | [
"figures/tmb/tcga/DFCI_ONCO/data_preprocessing.py",
"figures/tmb/tcga/nonsyn_table/MSK_341/data_preprocessing.py"
] | [
"import pickle\nimport pandas as pd\nimport numpy as np\nimport re\nimport json\nimport pathlib\npath = pathlib.Path.cwd()\nif path.stem == 'ATGC':\n cwd = path\nelse:\n cwd = list(path.parents)[::-1][path.parts.index('ATGC')]\n\n##path to files\npath = cwd / 'files/'\n\ntcga_maf = pickle.load(open(path / 'tc... | [
[
"numpy.array",
"numpy.stack"
],
[
"numpy.array",
"numpy.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
theSage21/capsnet-qa | [
"4b2d0e9cf6d1ba80e5fff04e55f1fd558a52025b"
] | [
"models/_utils.py"
] | [
"import numpy as np\nimport tensorflow as tf\n\n\n__birnn = tf.nn.bidirectional_dynamic_rnn\n\n\ndef birnn(inputs, seq_lens, units, scope, cell_kind='gru'):\n cell = tf.contrib.rnn.LSTMCell\n if cell_kind.lower() == 'gru':\n cell = tf.contrib.rnn.GRUCell\n units_f = units // 2\n units_b = units -... | [
[
"tensorflow.get_variable",
"tensorflow.matmul",
"tensorflow.concat",
"tensorflow.variable_scope",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
DevinJake/NS-CQA | [
"5fbfb8ebfa1f490c36ccdf4d54cfae02c00d8abd",
"5fbfb8ebfa1f490c36ccdf4d54cfae02c00d8abd"
] | [
"S2SRL/train_scst_cher_webqsp.py",
"S2SRL/train_crossent.py"
] | [
"#!/usr/bin/env python3\nimport os\nimport sys\nimport random\nimport argparse\nimport logging\nimport numpy as np\nfrom tensorboardX import SummaryWriter\nfrom random import randrange\nfrom libbots import data, model, utils\n\nimport torch\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport time\... | [
[
"torch.LongTensor",
"torch.nn.functional.log_softmax",
"torch.load",
"torch.cat",
"numpy.mean",
"torch.FloatTensor",
"torch.device",
"numpy.random.RandomState",
"torch.stack"
],
[
"torch.LongTensor",
"torch.max",
"torch.cat",
"torch.nn.functional.cross_entro... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
senadkurtisi/MTCNN-pytorch | [
"a94ccb84764dade5d268c63aa9f86f8fe5e824b3"
] | [
"src/model.py"
] | [
"''' Models for all three stages of the detection process '''\n\nfrom utils.globals import *\n\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nimport numpy as np\nfrom collections import OrderedDict\n\n\nclass Flatten(nn.Module):\n\n def __init__(self):\n super(Flatten, self).__init__()\n\n ... | [
[
"torch.nn.Dropout",
"torch.nn.PReLU",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.MaxPool2d",
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
brian-childress/superset | [
"7803bcea11dcd6c0da839f74fe8b79c254283c90"
] | [
"superset/connectors/sqla/models.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.read_sql_query",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
parsa-ra/transformers | [
"7db7aab439cb6906ce683ccd9c08ce17152c0783"
] | [
"examples/pytorch/text-classification/run_glue_no_trainer.py"
] | [
"# coding=utf-8\n# Copyright 2021 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.... | [
[
"torch.utils.data.DataLoader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
brianbarbieri/Neural-Network-with-Generic-optimization | [
"75233e4452660543bc137a2e76527cf490f1b44a"
] | [
"model.py"
] | [
"# 1 hidden layer neural network with generic optimization algorithm\nimport numpy as np\nimport pandas as pd\nimport random\nfrom itertools import permutations \nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom sklearn.metrics import f1_score\nfrom sklearn.model_selection import train... | [
[
"numpy.rot90",
"numpy.dot",
"pandas.read_csv",
"numpy.maximum",
"numpy.random.choice",
"sklearn.utils.shuffle",
"sklearn.model_selection.train_test_split",
"numpy.random.randn",
"numpy.select",
"numpy.argsort",
"sklearn.metrics.f1_score",
"numpy.exp",
"numpy.ran... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
LulutasoAI/StockAnalysis | [
"eaa3c23776bd8cb80b5c7db5c3e22e2c2410b1e9"
] | [
"Baseutils.py"
] | [
"import pandas as pd\nimport numpy as np\nimport pandas_datareader as dt\nimport datetime\nfrom matplotlib import pyplot as plt\n\nclass Baseutils():\n \"\"\"\n bunch of utills how apparent.\n \"\"\"\n def __init__(self):\n self.start = datetime.datetime(2001,1,1)\n self.end = datetime.dat... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jingshenghua/QuantTopics | [
"20e64710f9a4f22b626fa0ac0a7b062baccdc62a",
"20e64710f9a4f22b626fa0ac0a7b062baccdc62a"
] | [
"Pricing/Binomial_Tree/tree_generator.py",
"Pricing/Binomial_Tree/streamlit_binomial_tree.py"
] | [
"import pandas as pd\nimport numpy as np\nimport copy\nimport networkx as nx\nimport matplotlib.pyplot as plt\ndef binomial(n,k):\n if 0<=k<=n:\n a=b=1\n for i in range(1,min(k,n-k)+1):\n a*=n\n b*=i\n n-=1\n return a//b\n else:\n return 0\n\"\"\"ob... | [
[
"numpy.sqrt",
"numpy.arange",
"numpy.concatenate",
"numpy.round",
"numpy.fmax",
"numpy.exp",
"numpy.empty",
"matplotlib.pyplot.figure"
],
[
"numpy.fmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Mohammad-Bakhtiari/app-template | [
"4aa8fdf76002cddd42c2c80e261eef610b71ca5f"
] | [
"engine/app.py"
] | [
"import abc\nimport datetime\nimport json\nimport numpy as np\nimport pickle\nimport sys\nimport threading\nimport traceback\n\nfrom enum import Enum\nfrom time import sleep\nfrom typing import Dict, List, Tuple, Union, TypedDict, Literal\n\nDATA_POLL_INTERVAL = 0.1 # Interval (seconds) to check for new data piece... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fractal-napari-plugins-collection/napari_addtional_viewport | [
"9f583e695ca083805b07d11b484f3c882b96f65a"
] | [
"tests/napari_addtional_viewport/test_napari_addtional_viewport.py"
] | [
"\"\"\"\nUnittests for napari_addtional_viewport.napari_addtional_viewport module.\n\"\"\"\nfrom unittest.mock import MagicMock\nimport sys\n\nsys.modules['vispy'] = MagicMock()\nimport pytest\nimport numpy as np\nimport napari\nfrom napari_additional_viewport.napari_additional_viewport import \\\n AdditionalVie... | [
[
"numpy.sum",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
omdgit/statsmodels | [
"d5c392f11727452b6779175b3387e2780cd77ad9"
] | [
"statsmodels/tsa/base/tsa_model.py"
] | [
"from statsmodels.compat.pandas import (\n is_float_index,\n is_int_index,\n is_numeric_dtype,\n)\n\nimport numbers\nimport warnings\n\nimport numpy as np\nfrom pandas import (\n DatetimeIndex,\n Index,\n Period,\n PeriodIndex,\n RangeIndex,\n Series,\n Timestamp,\n date_range,\n ... | [
[
"pandas.to_datetime",
"pandas.tseries.frequencies.to_offset",
"pandas.RangeIndex",
"numpy.asarray",
"pandas.Index",
"numpy.argmax",
"pandas.Period",
"pandas.date_range",
"pandas.Timestamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AurelienNioche/leitnerq | [
"340744dcb00098cce018043eeb02dc181bf9425f"
] | [
"nb/lentil/forget.py"
] | [
"\"\"\"\nModule for models of offline learning and forgetting\n\n@author Siddharth Reddy <sgr45@cornell.edu>\n\"\"\"\n\nfrom abc import abstractmethod\n\nimport numpy as np\n\n\nclass TimeVaryingLUVModel(object):\n \"\"\"\n Superclass for models of time-varying learning update variance\n \"\"\"\n\n @abs... | [
[
"numpy.log"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aktgpt/instrument_detection_YOLO | [
"9ad300b1201daf63ab0bd9f80f02482d1c4323aa"
] | [
"train.py"
] | [
"#! /usr/bin/env python\r\n\r\nimport argparse\r\nimport os\r\nimport numpy as np\r\nfrom preprocessing import parse_annotation\r\nfrom frontend import YOLO\r\nimport json\r\n\r\nos.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"\r\nos.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\r\n\r\nargparser = argparse.ArgumentParser... | [
[
"numpy.random.shuffle"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Zymrael/torchdiffeq | [
"4bd6a7a57fc895fb56ad59c67924ec08baaf221e"
] | [
"tests/DETEST/run.py"
] | [
"import time\nimport numpy as np\nfrom scipy.stats.mstats import gmean\nimport torch\nfrom torchdiffeq import odeint\nimport detest\n\ntorch.set_default_tensor_type(torch.DoubleTensor)\n\n\nclass NFEDiffEq:\n\n def __init__(self, diffeq):\n self.diffeq = diffeq\n self.nfe = 0\n\n def __call__(se... | [
[
"torch.set_default_tensor_type",
"torch.mean",
"torch.tensor",
"scipy.stats.mstats.gmean",
"numpy.sum"
]
] | [
{
"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"... |
pystiche/papers | [
"0d8179dc51f6eda0b27fa525dc0b86b866bc88e1"
] | [
"tests/unit/li_wand_2016/test_loss.py"
] | [
"import pytest\n\nfrom tests import utils\n\nimport pytorch_testing_utils as ptu\nimport torch\nfrom torch.nn.functional import mse_loss\n\nimport pystiche\nimport pystiche.loss.functional as F\nimport pystiche_papers.li_wand_2016 as paper\nfrom pystiche import loss, misc\n\n\n@pytest.fixture(autouse=True)\ndef dis... | [
[
"torch.nn.functional.mse_loss",
"torch.autograd.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hvarS/PyTorch-Refer | [
"020445e3ae1f3627f39e1ab957cdff44a2127289"
] | [
"training_engine/engine.py"
] | [
"import torch \nfrom tqdm import tqdm\nimport time\nimport torchnet as tnt\n\nclass Engine(object):\n \"\"\"Base Class to contain the fundamentals required in any kind of training instance \"\"\"\n def __init__(self,state = {}) -> None:\n \"\"\"Initialize Engine object with a state variable(dict Format... | [
[
"torch.utils.data.DataLoader",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
coreyc13/surge-examples | [
"b436c4a5ee73538618078a93a59f0144b99fe6e9"
] | [
"square_basin/setplot.py"
] | [
"\nfrom __future__ import absolute_import\nfrom __future__ import print_function\n\nimport os\n\nimport numpy\nimport matplotlib.pyplot as plt\nimport datetime\n\nimport clawpack.visclaw.colormaps as colormap\nimport clawpack.visclaw.gaugetools as gaugetools\nimport clawpack.clawutil.data as clawutil\nimport clawpa... | [
[
"matplotlib.pyplot.gca",
"matplotlib.pyplot.title",
"matplotlib.pyplot.subplots_adjust"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xianxl/fairseq | [
"dd3b41b7a009fe50e83a3eb39eb90ce9ed45714b"
] | [
"encode.py"
] | [
"#!/usr/bin/env python3 -u\n# Copyright (c) 2017-present, Facebook, Inc.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the LICENSE file in\n# the root directory of this source tree. An additional grant of patent rights\n# can be found in the PATENTS file in the same directory... | [
[
"numpy.stack",
"torch.cuda.is_available",
"torch.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
imironhead/ml_kaggle | [
"aa9c3e5124756cfcebd4ec34771e42f41009ee4c"
] | [
"qdraw/qdraw/experiment_train.py"
] | [
"\"\"\"\n\"\"\"\nimport csv\nimport datetime\nimport gzip\nimport os\nimport time\n\nimport numpy as np\nimport tensorflow as tf\n\nimport qdraw.dataset as dataset\nimport qdraw.dataset_iterator as dataset_iterator\n\n\ndef build_model(data):\n \"\"\"\n \"\"\"\n FLAGS = tf.app.flags.FLAGS\n\n # NOTE: ch... | [
[
"tensorflow.control_dependencies",
"numpy.concatenate",
"tensorflow.app.flags.DEFINE_string",
"numpy.mean",
"tensorflow.train.AdamOptimizer",
"tensorflow.app.flags.DEFINE_boolean",
"numpy.exp",
"tensorflow.get_collection",
"numpy.reshape",
"tensorflow.app.flags.DEFINE_integ... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MiguelAMartinez/Flowers-Image-Classifier | [
"66ed313469907a673a538751dbbe80923eb04792"
] | [
"utils_ic.py"
] | [
"import json\nimport torch\nfrom torchvision import datasets, transforms\nfrom PIL import Image\n\n# Define function to read cat names\ndef read_jason(filename):\n with open(filename, 'r') as f:\n cat_to_name = json.load(f)\n return cat_to_name\n\n# Define function to read data\ndef load_data(data_dir)... | [
[
"torch.exp",
"torch.utils.data.DataLoader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cea-trust-platform/trust-code | [
"46ab5c5da3f674185f53423090f526a38ecdbad1"
] | [
"Validation/Rapports_automatiques/Verification/Verification_codage/Time_dependent_velocity/src/post_run2.py"
] | [
"import numpy as np\n\nlines = np.loadtxt(\"test_VITESSE_U.son\", comments=\"#\", delimiter=\" \", unpack=False)\n\ntime=[]\nuan=[]\nerr=[]\nfor i in range(len(lines)):\n time.append(0)\n uan.append(0)\n err.append(0)\n\nfor n in range(0,len(lines)):\n time[n]=lines[n,0]\n uan[n]=2*lines[n,0]*lines[n... | [
[
"numpy.savetxt",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lzhbrian/FashionAI-1 | [
"1fede16044c8a4516ba4dd6766add44d47245f6b"
] | [
"analysis.py"
] | [
"import argparse\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nparser = argparse.ArgumentParser(description='FashionAI Analysis')\r\nparser.add_argument('--model', type=str, default='resnet34', metavar='M', help='model name')\r\nargs = parser.parse_args()\r\n\r\nlog_file = './save/pant_length_labe... | [
[
"numpy.linspace",
"matplotlib.pyplot.subplot",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
arjunparmar/TUNEX | [
"61dcf1dc781fe26ef4d2ba1da96d0756b23be5e0"
] | [
"Deepanshu/Deploy/tun2/recognise/views.py"
] | [
"# Django libs\nfrom django.shortcuts import render\nfrom django.conf import settings\nfrom django.core.files.base import ContentFile\nfrom django.core.files import File\nfrom django.http import StreamingHttpResponse\n\n#Libraries of ourselves\nfrom .form import ImageForm\nfrom .models import Image\nfrom .camera im... | [
[
"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"
]
}
] |
hikuru/MatchMakerApp | [
"95ef40bf01c741813808833e689c64fbf3bb6ffd"
] | [
"main.py"
] | [
"import MatchMakerApp as mm_app\nimport matchmaker.helper_funcs\nimport matchmaker.MatchMaker as mm\nimport tensorflow as tf\nimport os\n\nargs = mm_app.argument_parser()\n\nnum_cores = 8\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = args.gpu_devices\nGPU = True\nif args.gpu_support:\n num_GPU = 1\n num_CPU = 1\nel... | [
[
"tensorflow.compat.v1.Session",
"tensorflow.compat.v1.ConfigProto"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
qingchenkanlu/posenet | [
"bc82b24ff742eafa2c276eb5231b6c91f8546bc7"
] | [
"datasets/ycb/dataset.py"
] | [
"import random\nimport numpy as np\nimport numpy.ma as ma\nimport scipy.io as scio\nfrom PIL import Image\nimport torch\nimport torchvision.transforms as transforms\nimport torch.utils.data as data\n\n\nclass PoseDataset(data.Dataset):\n def __init__(self, mode, num_pt, add_noise, root, noise_trans):\n se... | [
[
"numpy.dot",
"numpy.ma.masked_not_equal",
"numpy.amax",
"numpy.clip",
"numpy.amin",
"numpy.unique",
"numpy.linalg.norm",
"numpy.random.shuffle",
"numpy.concatenate",
"numpy.add",
"numpy.delete",
"numpy.ma.masked_equal",
"numpy.random.normal",
"numpy.any",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cescalara/fancy | [
"a48d53dca1d8e69def2a03ac1a4024fa0596242c"
] | [
"fancy/interfaces/data.py"
] | [
"import pandas as pd\nimport numpy as np\nimport matplotlib\nfrom matplotlib import pyplot as plt\nfrom astropy import units as u\nfrom astropy.coordinates import SkyCoord, EarthLocation\nfrom datetime import date, timedelta\nimport h5py\n\nfrom .stan import coord_to_uv, uv_to_coord\nfrom ..detector.detector import... | [
[
"pandas.read_csv",
"numpy.nditer",
"numpy.linspace",
"matplotlib.pyplot.cm.get_cmap",
"numpy.asarray",
"matplotlib.pyplot.subplots",
"matplotlib.colors.Normalize",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.axes",
"matplotlib.colorbar.ColorbarBase",
"numpy.deg2rad"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
pzelasko/einops | [
"5102ad12fa02167556b1df8724e6a3b25b17989d"
] | [
"tests/test_ops.py"
] | [
"import itertools\n\nimport numpy\n\nfrom einops.einops import (rearrange, reduce, parse_shape, _enumerate_directions, _reductions)\nfrom . import collect_test_backends\n\nimp_op_backends = collect_test_backends(symbolic=False, layers=False)\nsym_op_backends = collect_test_backends(symbolic=True, layers=False)\n\ni... | [
[
"numpy.einsum",
"numpy.asarray",
"numpy.zeros_like",
"torch.FloatTensor",
"torch.allclose",
"numpy.random.randint",
"numpy.allclose",
"torch.randn",
"numpy.arange",
"torch.arange",
"torch.sigmoid",
"torch.linspace",
"torch.zeros_like",
"torch.exp",
"nump... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aylward/MONAI | [
"6d3bf2fb7910afcb656fed91eb1d337c61c40ac4"
] | [
"monai/data/image_reader.py"
] | [
"# Copyright 2020 - 2021 MONAI Consortium\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# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agre... | [
[
"numpy.diag",
"numpy.allclose",
"numpy.asarray",
"numpy.arange",
"numpy.eye",
"numpy.stack",
"torch.utils.data._utils.collate.np_str_obj_array_pattern.search",
"numpy.moveaxis",
"numpy.load",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NulliusIn/COMP1001-Calculator | [
"29e68cc304f00aa3920eab70c51a8596bc0b80e1"
] | [
"sinpage.py"
] | [
"import numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom tkinter import *\r\nimport mainmenu\r\nimport trigonometrypage\r\n\r\n\r\ndef page():\r\n\r\n sin1 = Tk()\r\n sin1.title(\"Trigonometry Graph\")\r\n sin1.geometry(\"1400x1000\")\r\n sin1.configure(background = \"salmon1\")\r\n\r\n Label(s... | [
[
"matplotlib.pyplot.title",
"numpy.arange",
"numpy.sin",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JuantonioMS/dummylearning | [
"1780a7ba0f38633bcbfbacd4f35a31cce4c94a87"
] | [
"dummylearning/models/classification/logisticLasso.py"
] | [
"from dummylearning.models.classification.classificationModel import ClassificationModel\nfrom sklearn.linear_model import LogisticRegression\nfrom skopt.space import Integer\nfrom skopt.space import Real\nfrom skopt.space import Categorical\n\nclass LogisticLasso(ClassificationModel):\n\n \"\"\"\n Logistic r... | [
[
"sklearn.linear_model.LogisticRegression"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hiropppe/ELSA | [
"1ece189564e3827a4a7a44cd5f87e0ae4fca5c88"
] | [
"script/process_pre_tweet/tidy_tweet_elsa.py"
] | [
"import codecs\nimport numpy as np\nimport re, json, sys\n#reload(sys)\n#sys.setdefaultencoding('utf-8')\nfrom collections import defaultdict, OrderedDict\n\n#configure\ncur_lan = \"jp\" # \"de\" or \"fr\" or \"jp\"\n#input_file_name = \"tmoji_tokens_%s\" % cur_lan # precessed tweets after running the scripts in pr... | [
[
"numpy.median",
"numpy.random.shuffle",
"numpy.save",
"numpy.percentile",
"numpy.argmax",
"numpy.mean",
"numpy.average",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
seemethere/nestedtensor | [
"b4190efc91f3cd4891ae370502b656cbb63e7def"
] | [
"test/test_nested_tensor_autograd.py"
] | [
"import traceback\nimport functools\nimport pdb\nimport sys\nimport torch\nimport nestedtensor\nimport unittest\nimport random\n\nfrom utils import TestCase\n\n\nclass TestNestedTensorAutograd(TestCase):\n def test_basic_grad(self):\n def some_func(x):\n return torch.sum(x ** 2 + x ** 3)\n\n ... | [
[
"torch.sum",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CHRIS-LIN-Jude/dicom-database | [
"7597005e5bf7fa5736c3b46898453224b704ccd8"
] | [
"read_dicom.py"
] | [
"\n# coding: utf-8\n\n\n\nimport hashlib, os, threading\nimport numpy as np \nimport math\nimport scipy.ndimage\nfrom skimage import measure\nfrom skimage import morphology\nfrom mpl_toolkits.mplot3d.art3d import Poly3DCollection\nfrom mpl_toolkits.mplot3d import axes3d\nfrom skimage.transform import resize\nfrom s... | [
[
"numpy.matrix",
"numpy.amax",
"numpy.amin",
"numpy.arange",
"numpy.stack",
"numpy.int16",
"numpy.round",
"numpy.argmin",
"numpy.array",
"numpy.fabs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WJGiles/Dorknet | [
"1582937e843b1a911334291c25ea415fb56e5ccc"
] | [
"data_loading/MNIST_data_loading.py"
] | [
"import numpy as np\nimport os\n\n\"\"\"\nCode for loading MNIST\n\"\"\"\n\ndef epoch_image_generator(X_full, y_full, batch_size=1, num_classes=10):\n shuffle_indices = np.random.permutation(X_full.shape[0])\n X_full = X_full[shuffle_indices, :]\n y_full = y_full[shuffle_indices]\n index = 0\n for i ... | [
[
"numpy.eye",
"numpy.random.permutation"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nightowl-ymz/Bigscity-LibCity | [
"015ef2b2399f937c60b5159854452f2a27f9bda2"
] | [
"libcity/model/traffic_flow_prediction/ASTGNN.py"
] | [
"from datetime import time\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport copy\nimport math\nimport numpy as np\nfrom libcity.model.abstract_traffic_state_model import AbstractTrafficStateModel\n\n\ndef clones(module, N):\n \"\"\"\n Produce N identical layers.\n :param modu... | [
[
"numpy.dot",
"torch.nn.functional.softmax",
"torch.nn.init.uniform_",
"torch.zeros",
"torch.cat",
"torch.nn.Embedding",
"torch.device",
"torch.nn.L1Loss",
"torch.nn.Dropout",
"torch.from_numpy",
"torch.arange",
"torch.nn.Sequential",
"torch.nn.Conv2d",
"torc... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ucberkeley-vip/interbotix_ros_toolboxes | [
"7ac38a1b26acf3e0a405c7246e4f301935b8e874"
] | [
"interbotix_common_toolbox/interbotix_common_modules/src/interbotix_common_modules/angle_manipulation.py"
] | [
"# Majority of code copied from https://www.learnopencv.com/rotation-matrix-to-euler-angles/\n\nimport numpy as np\nimport math\n\n# Inverts a homogeneous transformation matrix\ndef transInv(T):\n R, p = T[:3,:3], T[:3, 3]\n Rt = np.array(R).T\n return np.r_[np.c_[Rt, -np.dot(Rt, p)], [[0, 0, 0, 1]]]\n\n# ... | [
[
"numpy.dot",
"numpy.asarray",
"numpy.linalg.norm",
"numpy.identity",
"numpy.transpose",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Themishau/ML_Tests | [
"c61d2fe7907d92003cb93e0e646edcb8cf6f1987"
] | [
"Analyzer.py"
] | [
"# -*- coding: utf-8 -*-\nimport os\nimport cv2\nimport random\nimport sys\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport time\n# np.set_printoptions(threshold=sys.maxsize)\nfrom sklearn.decomposition import PCA\nfrom matplotlib.figure import Figure\nfrom IPython import get_ipytho... | [
[
"tensorflow.keras.models.load_model",
"numpy.amax",
"numpy.mean",
"tensorflow.config.experimental.set_memory_growth",
"numpy.reshape",
"tensorflow.keras.layers.Conv2D",
"numpy.std",
"tensorflow.keras.models.Sequential",
"tensorflow.keras.layers.Flatten",
"tensorflow.config.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
connorlee77/keras-retinanet | [
"dd3ecbe14ec85894eb678707566a97f27963920b"
] | [
"keras_retinanet/utils/coco_eval.py"
] | [
"\"\"\"\r\nCopyright 2017-2018 Fizyr (https://fizyr.com)\r\n\r\nLicensed under the Apache License, Version 2.0 (the \"License\");\r\nyou may not use this file except in compliance with the License.\r\nYou may obtain a copy of the License at\r\n\r\n http://www.apache.org/licenses/LICENSE-2.0\r\n\r\nUnless require... | [
[
"numpy.expand_dims"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
laurent-george/protolab_sound_recognition | [
"c29a138c2b7cd60afc3c8b601d88857454202fc9"
] | [
"sound_classification/generate_database_humavips.py"
] | [
"__author__ = 'lgeorge'\n\nimport glob\nimport os\nimport traceback\nimport pandas as pd\n\nfrom sound_processing.features_extraction import extract_mfcc_features_one_channel, _flatten_features_dict\nfrom sound_processing.sig_proc import downsample_signal\nfrom sound_processing.io_sound import load_sound\n\ndef _ge... | [
[
"pandas.HDFStore",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
rlaishra/MCS- | [
"0296b0be74c71b196e359294b20509b8031b6ae3"
] | [
"src/mcs-rqr.py"
] | [
"\"\"\"\nScript for MCS+\nReliable Query Response\n\"\"\"\nimport warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\nimport sys\nimport os\n\nfrom my_community import mycommunity\nfrom multi_arm_bandit import bandit\n\nimport networkx as nx\nimport community\nimport csv\nimport numpy as np\... | [
[
"numpy.random.random",
"numpy.sqrt",
"numpy.random.seed",
"numpy.abs",
"numpy.isnan",
"numpy.random.random_sample",
"numpy.random.shuffle",
"numpy.all",
"numpy.ceil",
"numpy.std",
"numpy.round",
"numpy.mean",
"sklearn.linear_model.LinearRegression",
"numpy.s... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
faresbs/EfficientPose | [
"f95bab3b07e5f0280df8dd4eeb3ae2a899eaa81c"
] | [
"track.py"
] | [
"import sys\nfrom getopt import getopt, error\nfrom os.path import join, normpath\nfrom pymediainfo import MediaInfo\nimport numpy as np\nimport time\n\nfrom utils import helpers\n\ndef get_model(framework, model_variant):\n \"\"\"\n Load the desired EfficientPose model variant using the requested deep learni... | [
[
"numpy.rollaxis",
"numpy.expand_dims",
"tensorflow.import_graph_def",
"tensorflow.compat.v1.keras.backend.get_session",
"torch.from_numpy",
"torch.quantization.get_default_qconfig",
"numpy.ceil",
"tensorflow.compat.v1.GraphDef",
"tensorflow.keras.backend.set_learning_phase",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
besson/disaster_response_detector | [
"81a3624791f4bfaea9c34f8e960ce84816a6e0c3"
] | [
"models/train_classifier.py"
] | [
"import pandas as pd\nimport numpy as np\nimport nltk\nimport logging\nimport pickle\nimport sys\n\nfrom nltk import pos_tag\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom sqlalchemy import create_engine\nfrom nltk.tokenize import sent_tokenize, RegexpTokenizer, word_tokenize\nfrom nltk.corpus.read... | [
[
"sklearn.model_selection.GridSearchCV",
"pandas.Series",
"sklearn.ensemble.RandomForestClassifier",
"numpy.unique",
"pandas.read_sql_table",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"sklearn.multioutput.MultiOutputClassifier",
"sklearn.feature_extraction.... | [
{
"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": []
}
] |
Lando-L/ocd-detection | [
"7b74e0c74070ec18df67d31631d2da8b76190846"
] | [
"ocddetection/learning/federated/impl/layers/client/__init__.py"
] | [
"from typing import Callable\nimport attr\n\nimport tensorflow as tf\nimport tensorflow_federated as tff\n\nfrom ocddetection.learning.federated.impl.layers import server, utils\n\n\n@attr.s(eq=False, frozen=True, slots=True)\nclass State(object):\n \"\"\"\n Structure for state on the client.\n \n Field... | [
[
"tensorflow.constant",
"tensorflow.zeros",
"tensorflow.reshape",
"tensorflow.cast",
"tensorflow.init_scope",
"tensorflow.math.confusion_matrix",
"tensorflow.nest.map_structure",
"tensorflow.GradientTape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"2.9",
"2.5",
"2.8",
"2.10"
]
}
] |
Ethan-yt/bert | [
"be9a7fa6e4987a4b26a5923aab02d46ec10b2554"
] | [
"optimization.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Google AI Language Team 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# Unl... | [
[
"tensorflow.train.polynomial_decay",
"tensorflow.multiply",
"tensorflow.constant",
"tensorflow.zeros_initializer",
"tensorflow.cast",
"tensorflow.gradients",
"tensorflow.train.get_or_create_global_step",
"tensorflow.contrib.tpu.CrossShardOptimizer",
"tensorflow.clip_by_global_n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
ykwon0407/Wasserstein_Autoencoders | [
"ace2029ec111a2df3e19aecb3652b329c566b91b"
] | [
"wae_gan.py"
] | [
"import argparse\nimport torch\nimport os\n\nimport numpy as np\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader\nfrom torch.autograd import Variable\nfrom torchvision.datasets import MNIST\nfrom torchvision.transforms import transforms\nf... | [
[
"torch.cat",
"torch.nn.functional.dropout",
"torch.manual_seed",
"numpy.arange",
"numpy.asarray",
"torch.utils.data.DataLoader",
"torch.nn.Linear",
"torch.nn.functional.sigmoid",
"torch.nn.functional.relu",
"torch.nn.functional.binary_cross_entropy",
"torch.log",
"t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xtymbbt/digit_recognize_cnn | [
"78c8edf74f964e5e1e9d4fa335d4aaccc0bf49df"
] | [
"cnn_functions.py"
] | [
"import numpy as np\nimport tensorflow as tf\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport time\nimport math\n\n# load data function:\ndef load_data(filename):\n print(\"Loading data...\")\n start = time.time()\n csv = np.array(pd.read_csv(filename))\n y = np.array(csv[:, 0], ndmin=2).T\... | [
[
"tensorflow.nn.max_pool",
"tensorflow.reduce_sum",
"tensorflow.cast",
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"tensorflow.pad",
"tensorflow.math.tanh",
"tensorflow.nn.conv2d",
"numpy.hstack",
"pandas.read_csv",
"tensorflow.divide",
"tensorflow.subtract",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
chaokunyang/mars | [
"f8d06b833603525133a62f099af159cec3ff5570"
] | [
"mars/deploy/oscar/tests/test_cmdline.py"
] | [
"# Copyright 1999-2021 Alibaba Group Holding 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 appl... | [
[
"numpy.random.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MikenzieAlasca/F21-1010 | [
"a7c15b8d9bf84f316aa6921f6d8a588c513a22b8"
] | [
"class/lect/Lect-21/data1.py"
] | [
"#!/Users/philip/opt/anaconda3/bin/python\n\nimport numpy as np\nimport pandas as pd\nimport re\n\ndataset_path = \"./train-data.csv\"\n\ncolumn_names = ['Ind', 'Name', 'Location', 'Year', 'Kilometers_Driven',\n 'Fuel_Type', 'Transmission', 'Owner_Type', 'Mileage', 'Engine',\n 'Power', 'Seats', 'New_Price', '... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
PratikANaik/SIG | [
"0c1637d90ab05686983d108984ab369c8f48487a"
] | [
"functions/chroma_key.py"
] | [
"\"\"\"\nFunctions for the chromakey operations\n\"\"\"\nimport cv2\nimport numpy as np\nimport os\nfrom PIL import Image\nimport functions.folder_check as fldr_chk\n#%%\ndef auto_chroma(image_path):\n \"\"\"\n This function gets the image with single coloured \n background, it gives the range for threshol... | [
[
"numpy.array",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ContinuITy-Project/stream-model-generator | [
"142c6d52cb00274b009a5714186d3d9da1ea1228"
] | [
"clustinator/analysis/cluster_analysis.py"
] | [
"'''\n@author: An Dang, Henning Schulz\n'''\n\nimport numpy as np\nfrom collections import defaultdict\nfrom sklearn.neighbors import KNeighborsClassifier\n\nfrom behavior_model import BehaviorModel\n\nclass Cluster_analysis:\n\n def __init__(self, prev_behavior_models, new_means, cluster_counts, label_encoder):... | [
[
"numpy.array",
"numpy.linalg.norm",
"sklearn.neighbors.KNeighborsClassifier"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
redcross/chicago-fire-model | [
"80729d6cb185b4dca0fa9e0f956a635032026082"
] | [
"data_prep/data_cleaning.py"
] | [
"#!/usr/bin/env python3\n\n# Copyright © 2017 American Red Cross\n# \n# Permission is hereby granted, free of charge, to any person\n# obtaining a copy of this software and associated documentation\n# files (the “Software”), to deal in the Software without\n# restriction, including without limitation the rights to ... | [
[
"pandas.to_datetime",
"pandas.DatetimeIndex"
]
] | [
{
"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": []
}
] |
HARDIK-TSH1392/Signature-Recognition- | [
"c67586d3b6d5668762fbf223f5a157575e096066"
] | [
"signatue_recognition.py"
] | [
"import keras\nfrom sklearn.model_selection import train_test_split\n\nTEST_DIR='E:/Python/signatue_recognition/data/test/'\n\nSIGNATURE_CLASSES = ['A', 'B', 'C','D','E','F','K','L','M','N','O','P']\n\nimport os, random\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split... | [
[
"scipy.misc.imresize",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"sklearn.preprocessing.LabelEncoder",
"sklearn.metrics.log_loss",
"scipy.misc.imread",
"numpy.array"
]
] | [
{
"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": [
"0.13",
"0.14",
"0.15",
"0.10",
"... |
rebekahanne/pyblp | [
"fe41991c5ec457bb5cfc1bf4d5788d18660a3de2",
"fe41991c5ec457bb5cfc1bf4d5788d18660a3de2"
] | [
"pyblp/configurations/formulation.py",
"pyblp/configurations/integration.py"
] | [
"\"\"\"Formulation of data matrices and absorption of fixed effects.\"\"\"\n\nimport functools\nimport numbers\nimport token\nfrom typing import Any, Callable, Dict, List, Mapping, Optional, Sequence, Set, Tuple, Union\n\nimport numpy as np\nimport patsy\nimport patsy.builtins\nimport patsy.contrasts\nimport patsy.... | [
[
"numpy.asarray",
"numpy.ones",
"numpy.column_stack",
"numpy.repeat",
"numpy.zeros"
],
[
"numpy.sqrt",
"numpy.array_equal",
"numpy.arange",
"numpy.lexsort",
"numpy.polynomial.hermite.hermgauss",
"numpy.concatenate",
"numpy.repeat",
"numpy.array",
"numpy.z... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ComputerVisionLaboratory/ball_detection | [
"d8b67e91e52fb5fb2cb6dc7c33c30c8434c0f14e"
] | [
"ball_detection/separability_filter.py"
] | [
"# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/02_separability_filter.ipynb (unless otherwise specified).\n\n__all__ = ['conv2d', 'cvtIntegralImage', 'cvtCombSimpRectFilter', 'tmpFnc']\n\n# Cell\nimport torch as t\nimport torch.nn.functional as F\n\nfrom .utils import plot_images\n\n# Cell\ndef conv2d(X, W, norma... | [
[
"numpy.power",
"numpy.isnan",
"torch.nn.functional.conv2d",
"numpy.cumsum",
"numpy.sign",
"numpy.max",
"numpy.ceil",
"torch.FloatTensor",
"numpy.zeros",
"numpy.isinf"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Gilgamesh666666/Dgr-ncnet | [
"3ad2d0fdb35ba0d23e5040d74347c600b1a8b72b"
] | [
"core/loss.py"
] | [
"'''\nAuthor: your name\nDate: 2020-11-27 20:40:42\nLastEditTime: 2020-11-30 10:48:52\nLastEditors: Please set LastEditors\nDescription: In User Settings Edit\nFilePath: /exp2/core/loss.py\n'''\n# Copyright (c) Chris Choy (chrischoy@ai.stanford.edu) and Wei Dong (weidong@andrew.cmu.edu)\n#\n# Please cite the follow... | [
[
"torch.all",
"torch.sqrt",
"torch.scalar_tensor",
"torch.sum",
"numpy.finfo",
"torch.any",
"torch.nn.BCEWithLogitsLoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jiangnanyida/pykg2vec | [
"0c125fe04b59286206045b9884ee10569182a2a5"
] | [
"pykg2vec/utils/visualization.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nThis module is for visualizing the results\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom sklearn.manifold import TSNE\nimport numpy as np\nimport matplotlib.pyplot as plt\nimpor... | [
[
"pandas.concat",
"pandas.read_csv",
"numpy.asarray",
"pandas.DataFrame",
"numpy.concatenate",
"sklearn.manifold.TSNE",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ribsthakkar/RiderPickup | [
"e4eaf69d905631034949b18433bc70e4a08f0e58",
"e4eaf69d905631034949b18433bc70e4a08f0e58"
] | [
"legacy/iteration2.py",
"experimental/Preprocessor.py"
] | [
"from datetime import datetime\n\nimport pandas as pd\nfrom Driver import Driver\nfrom Trip import Trip, TripType\nfrom docplex.mp.model import Model\n\n\ndef f(driver):\n def filt(trip):\n return not (trip.lp.o in driverLocations and trip.lp.o != driver.address) or (\n trip.lp.d in driverL... | [
[
"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... |
replicahq/distributed | [
"fdf1ece3081f716f880e6b1aa7765211a40821af"
] | [
"distributed/tests/test_collections.py"
] | [
"import pytest\n\npytest.importorskip(\"numpy\")\npytest.importorskip(\"pandas\")\n\nimport dask\nimport dask.dataframe as dd\nimport dask.bag as db\nfrom distributed.client import wait\nfrom distributed.utils_test import gen_cluster\nfrom distributed.utils_test import client, cluster_fixture, loop # noqa F401\nim... | [
[
"pandas.util.testing.makeTimeDataFrame",
"numpy.random.random",
"numpy.arange",
"pandas.util.testing.assert_series_equal",
"pandas.DataFrame",
"numpy.all",
"pandas.util.testing.assert_frame_equal",
"pandas.util.testing.assert_index_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
CodeSurge97/DSWM | [
"321c59fea72df15b0f7d3f62caaaaf16cb6d6865"
] | [
"wfmg/scripts/cpi_processing.py"
] | [
"import sys\nimport pandas as pd\nimport os\n\n\nif len(sys.argv) <= 2 or len(sys.argv) > 3:\n print(\"To process the Consumer Price Index Data we need to know the location.\"\n \"Directory is missing\")\n sys.exit(1)\n\nDIR_DEST = sys.argv[1]\nOUT_DEST = sys.argv[2] + 'CPI_senior_project.csv'\n\nif ... | [
[
"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": []
}
] |
tejeez/harmonic-test | [
"863b947ba43e82fbac2bac873e5d846efe0bed4c"
] | [
"measurer.py"
] | [
"#!/usr/bin/env python3\n\"\"\"Module to control an SDR and perform measurements using it.\"\"\"\nimport SoapySDR\nfrom SoapySDR import *\nimport numpy as np\nimport math\n\ndefault_settings = {\n 'device_args': {'driver': 'lime'},\n\n 'rx_channel': 0,\n 'tx_channel': 0,\n 'rx_antenna': 'LNAH',\n 'tx... | [
[
"numpy.linspace",
"numpy.arange",
"numpy.cos",
"numpy.ones",
"numpy.sin",
"numpy.log10",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Nickfost/beem | [
"0930ed1895ae388aba26fe9126eebb3def49a3c9"
] | [
"examples/account_curation_per_week_and_1k_sp.py"
] | [
"#!/usr/bin/python\nimport sys\nimport datetime as dt\nfrom beem.amount import Amount\nfrom beem.utils import parse_time, formatTimeString, addTzInfo\nfrom beem.instance import set_shared_steem_instance\nfrom beem import Steem\nfrom beem.snapshot import AccountSnapshot\nimport matplotlib as mpl\n# mpl.use('Agg')\n#... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.plot_date",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bitzpy/Blind-Face-Restoration-Benchmark-Datasets-and-a-Baseline-Model | [
"cb76f58b8fbf99b96b703bcf683070fc9a5cbe89"
] | [
"STUNet/models/network_STUNet.py"
] | [
"# -----------------------------------------------------------------------------------\n# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257\n# Originally Written by Ze Liu, Modified by Jingyun Liang.\n# --------------------------------------------------------------------------------... | [
[
"torch.nn.Softmax",
"torch.nn.Dropout",
"torch.zeros",
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.nn.PixelShuffle",
"torch.arange",
"torch.nn.Linear",
"torch.nn.Identity",
"torch.flatten",
"torch.roll",
"torch.meshgrid"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
karandeepdps/evidently | [
"f6989e7d325cb4744ce4e91b62f7d6d7f64ac7e1"
] | [
"evidently/widgets/num_prediction_drift_widget.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\nimport json\nimport pandas as pd\nfrom pandas.api.types import is_numeric_dtype\nimport numpy as np\n\nfrom scipy.stats import ks_2samp\n#import matplotlib.pyplot as plt\nimport plotly.graph_objs as go\nimport plotly.figure_factory as ff\n\nfrom evidently.model.widget impo... | [
[
"scipy.stats.ks_2samp",
"pandas.api.types.is_numeric_dtype"
]
] | [
{
"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": [
"0.13",
"1.6",
"0.14",
"1.10",
"0... |
Harkor421/mono-rnn | [
"63f2012ef06b76a7ce111bdb1cea6ce70d0c9a67"
] | [
"train.py"
] | [
"import argparse\n\nimport torch\n\nimport models\nfrom utils import plot_confusion_matrix, train\n\n\ndef main(args):\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n model = MalConvPlus(8, 4096, 128, 32).to(device)\n train(model, train_loader, val_loader, device, \"malconv_pl... | [
[
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
raghavmi/cudf | [
"4ab29c71a7155b03eca2ec95728e27e5826cf5b9"
] | [
"libgdf/python/librmm_cffi/wrapper.py"
] | [
"# Copyright (c) 2018, 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 l... | [
[
"numpy.asarray",
"numpy.dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Susi-Eva/TA-06 | [
"9d2187faa260516e4f52fdcd8ecedcfdfa5cb984"
] | [
"prometer/app/views.py"
] | [
"from django.shortcuts import render\nfrom django.http import HttpResponseRedirect\nfrom django.conf import settings\nfrom app import main\nimport pandas as pd\nimport json\n\n# Create your views here.\ndef home(request):\n return render(request, 'app/home.html')\n\ndef process(request):\n if request.method =... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
sleichen/Cirq | [
"02f715203406d1f2af2d86e7561af09a2cdd4d45"
] | [
"cirq/ops/eigen_gate.py"
] | [
"# Copyright 2018 The Cirq Developers\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# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law o... | [
[
"numpy.round",
"numpy.around"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HiderX/-Python- | [
"a1b0bbe2a2702abdb4988f4934a79fe2dd9e1ffe"
] | [
"pandaswasher.py"
] | [
"import pandas as pd\r\n\r\n\r\ndef washer():\r\n df = pd.read_excel('bilibili_tag.xls')\r\n df2 = df['标签'].str.split(',', expand=True)\r\n print(df2)\r\n df2.to_excel('excel_to_python.xlsx', sheet_name='排行榜',header=None,index=False)\r\n print('开始写入txt文件...')\r\n df2.to_csv('file2.txt', header=Non... | [
[
"pandas.read_excel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
oeway/human-protein-atlas | [
"6286a9088f18a66a69c3b4607b383ec686fb96a3"
] | [
"src/hpanets/transforms.py"
] | [
"import numpy as np\nimport torch\n\nfrom torchvision import transforms\n\n\nclass CombineColors(object):\n \"\"\"Combines the the image in a sample to a given size.\"\"\"\n\n def __call__(self, sample):\n img_name = sample['image_id']\n img_red = sample['image_red']\n img_blue = sample['... | [
[
"torch.from_numpy",
"numpy.dstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jiangsy/h-baselines | [
"f745d7db323b82050360618110f907c3e43638d2"
] | [
"hbaselines/algorithms/off_policy.py"
] | [
"\"\"\"Script algorithm contain the base off-policy RL algorithm class.\n\nSupported algorithms through this class:\n\n* Twin Delayed Deep Deterministic Policy Gradient (TD3): see\n https://arxiv.org/pdf/1802.09477.pdf\n* Soft Actor Critic (SAC): see https://arxiv.org/pdf/1801.01290.pdf\n\nThis algorithm class als... | [
[
"numpy.array",
"tensorflow.Graph",
"tensorflow.compat.v1.summary.merge_all",
"numpy.random.seed",
"tensorflow.compat.v1.summary.FileWriter",
"tensorflow.compat.v1.global_variables_initializer",
"tensorflow.compat.v1.get_collection",
"tensorflow.compat.v1.placeholder",
"numpy.me... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jianhuasong/medical-image-segmentation2 | [
"f3c1188ab32a0920be9f871e99418bc3a4f02291",
"f3c1188ab32a0920be9f871e99418bc3a4f02291"
] | [
"test/nnUNetV1/network_training/network_trainer.py",
"test/nnUNetV1/network_training/nnUNetTrainer_ExpLogT0.py"
] | [
"from _warnings import warn\nimport matplotlib\nfrom batchgenerators.utilities.file_and_folder_operations import *\nfrom sklearn.model_selection import KFold\nmatplotlib.use(\"agg\")\nfrom time import time, sleep\nimport torch\nimport numpy as np\nfrom torch.optim import lr_scheduler\nimport matplotlib.pyplot as pl... | [
[
"torch.cuda.current_device",
"matplotlib.use",
"torch.from_numpy",
"sklearn.model_selection.KFold",
"matplotlib.pyplot.plot",
"numpy.argmax",
"torch.save",
"numpy.mean",
"matplotlib.pyplot.close",
"torch.no_grad",
"matplotlib.pyplot.xscale",
"numpy.array",
"matp... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
qixiuai/vega | [
"3e6588ea4aedb03e3594a549a97ffdb86adb88d1"
] | [
"vega/algorithms/nas/adelaide_ea/adelaide_trainer_callback.py"
] | [
"# -*- coding:utf-8 -*-\n\n# Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.\n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the MIT License.\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; withou... | [
[
"pandas.concat",
"pandas.read_csv",
"torch.FloatTensor",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
USEPA/IO-Model-Builder | [
"8ddd95a40034fb4080e627e2ca75dc41355f946e"
] | [
"iomb/io.py"
] | [
"import pandas as pd\nimport logging as log\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\nimport numpy as np\nimport numpy.linalg as linalg\n\n\nclass Model(object):\n \"\"\"\n The economic module contains the input-output data which may are hybridized\n with physical flows.\n ... | [
[
"matplotlib.patches.Patch",
"numpy.eye",
"matplotlib.patches.Rectangle",
"matplotlib.pyplot.subplots",
"pandas.DataFrame",
"matplotlib.pyplot.ticklabel_format",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"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": []
}
] |
brojam/critrolesync.github.io | [
"9ef43ae6e1555c1fbcacc0ccf890c0a3ce505eec"
] | [
"extra/critrolesync.py"
] | [
"\"\"\"\nThis Python script is not used by the web app. It was created first as a\nprototype and performs some of the same basic functions.\n\"\"\"\n\nimport json\nimport numpy as np\n\n\nwith open('../data.json') as f:\n data = json.load(f)\n data = {d['id']:d for d in data}\n\ndef str2sec(string):\n if l... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BlueskyFR/determined | [
"2ea2125c226e4dfc07dec4e91fdf4e8dee06f4df"
] | [
"e2e_tests/tests/fixtures/no_op/model_def.py"
] | [
"import collections\nimport json\nimport logging\nimport os\nimport pathlib\nimport pickle\nimport random\nimport time\nfrom typing import Any, Dict, Optional\n\nimport numpy as np\n\nimport determined as det\nfrom determined import horovod, layers, workload\nfrom determined.common import check, storage\nfrom deter... | [
[
"numpy.random.normal",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
juinquok/models | [
"c0e374b87e829cde45553ab27346e00c5970ceb9"
] | [
"official/vision/beta/modeling/backbones/spinenet.py"
] | [
"# Copyright 2021 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.keras.Input",
"tensorflow.keras.backend.image_data_format",
"tensorflow.identity",
"tensorflow.keras.utils.register_keras_serializable",
"tensorflow.add_n",
"tensorflow.keras.layers.InputSpec"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
michel4j/mxio | [
"bfcfbcde0bfd343030b7988859223ea74528e72c"
] | [
"mxio/formats/cbf.py"
] | [
"import ctypes as ct\nimport os\nimport re\n\nimport cv2\nimport numpy\n\nfrom . import DataSet\nfrom .. import utils, parser\nfrom ..log import get_module_logger\n\n# Configure Logging\nlogger = get_module_logger('mxio')\n\n# Define CBF Error Code constants\nCBF_FORMAT = 0x00000001 # 1\nCBF_ALLOC = 0x00000002 # ... | [
[
"numpy.fromstring"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wilson1yan/denoising-diffusion-pytorch | [
"7638091cf792a0c1da25c848de6f5e0fdbcdca34"
] | [
"diffusion.py"
] | [
"import math\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\n\ndef make_beta_schedule(\n schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3\n):\n if schedule == \"quad\":\n betas = (\n torch.linspace(\n linear_start ** 0.5, l... | [
[
"torch.randn_like",
"torch.linspace",
"torch.full",
"torch.sqrt",
"torch.tensor",
"torch.exp",
"torch.nn.functional.mse_loss",
"torch.rsqrt",
"torch.no_grad",
"torch.log",
"torch.cumprod",
"torch.arange",
"torch.gather",
"torch.cos"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mazzalab/rhapsodizer | [
"4da8488fd8b43a0c4e8376afd48df341a8514e53"
] | [
"rhapsodizer/rsec_demux.py"
] | [
"import os, sys\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport argparse\nimport pandas as pd\nfrom rhapsodizer import log\nfrom rhapsodizer.r2 import R2\nfrom rhapsodizer.r1 import R1\nfrom rhapsodizer.matrix import Matrix\n\n\ndef main(r1: str, r2: str, r2_map: str, bed... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
jamestiotio/DW2020 | [
"a8c575f1afd34d66dce2798a11b2fa15d1214a43"
] | [
"homework/week3/plot1.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef sine_wave(t):\n return 2 * np.sin(3 * t)\n\n\nt_values = np.linspace(0, 2 * np.pi, 150)\n\nplt.plot(t_values, sine_wave(t_values), marker=\"o\")\nplt.show()"
] | [
[
"matplotlib.pyplot.show",
"numpy.linspace",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Dawwie/Uczenie-Maszynowe | [
"da274c83ba1500984b0e1f7469ed5045c562d6fc"
] | [
"11/zadanie_11_keras.py"
] | [
"import matplotlib.pyplot as plt\nfrom tensorflow.keras.datasets import cifar10\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D\nfrom tensorflow.keras.losses import sparse_categorical_crossentropy\nfrom tensorflow.keras.optimizers import Adam... | [
[
"matplotlib.pyplot.title",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.Conv2D",
"tensorflow.keras.datasets.cifar10.load_data",
"tensorflow.keras.layers.MaxPooling2D",
"matplotlib.pyplot.plot",
"tensorflow.keras.optimizers.Adam",
"matplotlib.pyplot.xlabel",
"matplo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
mlco2/codecarbon | [
"612a6a5274ad497080e9128738fb6a7bff88c897"
] | [
"codecarbon/output.py"
] | [
"\"\"\"\nProvides functionality for persistence of data\n\"\"\"\n\nimport csv\nimport dataclasses\nimport getpass\nimport os\nfrom abc import ABC, abstractmethod\nfrom collections import OrderedDict\nfrom dataclasses import dataclass\n\nimport pandas as pd\nimport requests\n\n# from core.schema import EmissionCreat... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ixhorse/pytorch-deeplab-xception | [
"568be707c2fb4852ce84fa2ec019c44e305681a8"
] | [
"utils/tt100k_utils.py"
] | [
"import cv2 as cv\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef get_label_box(annos, imgid):\n img = annos[\"imgs\"][imgid]\n box_all = []\n for obj in img['objects']:\n box = obj['bbox']\n box = [int(box['xmin']), int(box['ymin']), int(box['xmax']), int(box['ymax'])]\n #... | [
[
"matplotlib.pyplot.imshow",
"numpy.min",
"numpy.clip",
"numpy.max",
"matplotlib.pyplot.subplot",
"numpy.array",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bradleygramhansen/pyfo | [
"559678080f27e7d9f3f194a0c28e9e8bfe71a7f3"
] | [
"pyfo/distributions/studentt.py"
] | [
"import torch\n\nfrom ..distributions.distribution_wrapper import TorchDistribution\nfrom ..utils.core import VariableCast as vc\n\n\nclass StudentT(TorchDistribution):\n r\"\"\"\n Creates a Student's t-distribution parameterized by `df`.\n\n Example::\n\n >>> m = StudentT(torch.Tensor([2.0]))\n ... | [
[
"torch.distributions.StudentT"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
plant99/rio-tiler | [
"0b7de858d88394aa5fb75e89992e6773141c221f"
] | [
"tests/test_utils.py"
] | [
"\"\"\"tests rio_tiler.utils\"\"\"\n\nimport os\nfrom io import BytesIO\n\nimport numpy as np\nimport pytest\nimport rasterio\nfrom rasterio.features import bounds as featureBounds\n\nfrom rio_tiler import colormap, utils\nfrom rio_tiler.constants import WEB_MERCATOR_TMS, WGS84_CRS\nfrom rio_tiler.errors import Rio... | [
[
"numpy.zeros",
"numpy.array_equal",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
salesforce/ebm_calibration_nlu | [
"e0598923551c4587e0ea8c4feb001cb9cc736103"
] | [
"nce_lm/my_generation.py"
] | [
"import logging\nimport math\nimport os\nfrom dataclasses import dataclass, field\nfrom typing import Optional\n#from my_lm_trainer import MyTrainer \n#from nce_lm_trainer import MyNCELMTrainer \n\nfrom transformers import (\n CONFIG_MAPPING,\n MODEL_WITH_LM_HEAD_MAPPING,\n AutoConfig,\n AutoModelWithLM... | [
[
"torch.mean",
"torch.nn.functional.softmax",
"torch.min",
"torch.multinomial",
"numpy.mean",
"torch.sort",
"torch.topk",
"torch.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wenbingl/onnxconverter-common | [
"f8724ead45ab6d7ef89db1fc7bf2755c65bc22ea"
] | [
"onnxconverter_common/onnx_ops.py"
] | [
"# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for\n# license information.\n###############################################################################\n\n# This file contains some high-level APIs for applying operations on var... | [
[
"numpy.array",
"numpy.iinfo"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
haribhutanadhu/PaddleViT | [
"05319fb775606874d8d3024964f1823f1ac56134"
] | [
"self_supervised_learning/dino/transformer.py"
] | [
"# Copyright (c) 2021 PPViT 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 ... | [
[
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
godzilla-but-nicer/redundancy-dynamics | [
"55c305887c8fc3c5cc8765684abd46fb0373e3d2"
] | [
"notebooks/combine_data.py"
] | [
"#%% [markdown]\n# # Combine and Clean Data\n# This is just a notebook file I can use to merge data generated by seperate\n# scripts into fewer data files. I have to do this fairly often so it makes\n# sense to keep the code somewhere I think\n# %%\nimport pandas as pd\nfrom glob import glob\n#%% [markdown]\n# ## $... | [
[
"pandas.concat",
"pandas.read_csv",
"pandas.merge"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
lehduong/Input-Dependent-Baseline | [
"cb140338eb35a568fe1d320d0b8e52b739470b59"
] | [
"core/agents/heuristic/load_balance/shortest_processing_time.py"
] | [
"import numpy as np\nimport torch\n\nfrom .base import HeuristicAgent\n\n\nclass ShortestProcessingTimeAgent(HeuristicAgent):\n def __init__(self, service_rates=[0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.95, 1.05]):\n self.service_rates = torch.tensor(service_rates).reshape(1, -1)\n\n def act(s... | [
[
"torch.argmin",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wangjenn/musipy_by_jennwang | [
"0086d31af9c99c7ca77fb288b0c12bc17716710d"
] | [
"build_model.py"
] | [
"import os\n\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.datasets import load_iris\nfrom sklearn.externals import joblib\n\n\nif __name__ == \"__main__\":\n # Load Iris Data\n iris_data = load_iris()\n features = iris_data.data\n feature_names = iris_data.feature_names\n target ... | [
[
"sklearn.datasets.load_iris",
"sklearn.neighbors.KNeighborsClassifier"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ver228/cell_localization | [
"9739afc7a54f730056945c0a6380896747235099"
] | [
"cell_localization/models/cell_detector_with_clf.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Aug 3 15:41:15 2019\n\n@author: avelinojaver\n\"\"\"\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\n\nfrom .cell_detector import BeliveMapsNMS, get_loss\nfrom .unet import ConvBlock\n\nclass SimpleBgndClassifier(nn.Sequ... | [
[
"torch.nn.CrossEntropyLoss",
"torch.nn.AdaptiveMaxPool2d",
"torch.nn.Dropout",
"torch.nn.functional.softmax",
"torch.tensor",
"torch.nn.Linear",
"torch.nn.LeakyReLU",
"torch.nn.functional.interpolate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ChrisBch/PersonGAN | [
"573704cbb7d2269135795c768d79dc979c821477"
] | [
"models/pg_bleu/PgbleuGenerator.py"
] | [
"import tensorflow as tf\r\nfrom tensorflow.python.ops import tensor_array_ops, control_flow_ops\r\nimport numpy as np\r\n\r\n\r\nclass Generator(object):\r\n def __init__(self, num_vocabulary, batch_size, emb_dim, hidden_dim,\r\n sequence_length, start_token,\r\n learning_rate=0.... | [
[
"tensorflow.device",
"tensorflow.zeros",
"tensorflow.stack",
"tensorflow.train.AdamOptimizer",
"tensorflow.strided_slice",
"tensorflow.gradients",
"tensorflow.square",
"tensorflow.python.ops.tensor_array_ops.TensorArray",
"tensorflow.matmul",
"tensorflow.unstack",
"tens... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
evalldor/pystematic-torch | [
"3f7e9e6143b548be583dc6a5597060145299a5a0"
] | [
"tests/test_pystematic_torch.py"
] | [
"import pytest\nimport pystematic\n\nimport torch\n\n\ndef test_main_function_is_run():\n\n class CustomException(Exception):\n pass\n\n @pystematic.experiment\n def exp(params):\n assert \"local_rank\" in params\n raise CustomException()\n\n with pytest.raises(CustomException):\n ... | [
[
"torch.randint",
"torch.randn",
"torch.nn.Sigmoid",
"torch.nn.BCELoss",
"torch.nn.Linear"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Moldovandreii/RepetitionCount | [
"b5715b0948b609fde0ce05d45023b7d4cfd635e7",
"b5715b0948b609fde0ce05d45023b7d4cfd635e7"
] | [
"PythonNetwork/venv/Lib/site-packages/scipy/sparse/linalg/tests/test_interface.py",
"PythonNetwork/venv/Lib/site-packages/matplotlib/tight_layout.py"
] | [
"\"\"\"Test functions for the sparse.linalg.interface module\n\"\"\"\n\nfrom __future__ import division, print_function, absolute_import\n\nfrom functools import partial\nfrom itertools import product\nimport operator\nimport pytest\nfrom pytest import raises as assert_raises, warns\nfrom numpy.testing import asser... | [
[
"numpy.matrix",
"numpy.testing.assert_equal",
"numpy.dot",
"numpy.asarray",
"numpy.arange",
"scipy.sparse.linalg.interface.LinearOperator",
"scipy.sparse.linalg.interface.aslinearoperator",
"scipy.sparse.csr_matrix",
"numpy.ones",
"numpy.dtype",
"numpy.random.randn",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"0.15",
"1.4",
"0.10",
"1.3",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16"
],
"tensorflow": [... |
enpelonio/O-CNN | [
"eb3484d2d708c5111c67cfe9d5759db0b95c4eb7",
"eb3484d2d708c5111c67cfe9d5759db0b95c4eb7"
] | [
"tensorflow/script/network_segnet.py",
"tensorflow/libs/__init__.py"
] | [
"import tensorflow as tf\nfrom ocnn import *\n\n\ndef network_segnet(octree, flags, training, reuse=False):\n depth, channel_in = flags.depth, flags.channel\n channels = [2**(9-d) for d in range(0, 8)]\n with tf.variable_scope('ocnn_segnet', reuse=reuse):\n with tf.variable_scope('signal'):\n data = octr... | [
[
"tensorflow.variable_scope",
"tensorflow.reshape",
"tensorflow.squeeze"
],
[
"tensorflow.concat",
"tensorflow.cast",
"tensorflow.boolean_mask",
"tensorflow.python.framework.ops.NotDifferentiable",
"tensorflow.python.framework.ops.RegisterGradient",
"tensorflow.floor",
"... | [
{
"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... |
ableonard99/titanic-logistic-regression | [
"e9e93be6672a1158d4136eec57f367503272255e"
] | [
"titanic-classification.py"
] | [
"\n# coding: utf-8\n\n# In[1]:\n\nimport numpy as np\nimport pandas as pd\nfrom matplotlib import pyplot as plt\n\nnp.random.seed(1234)\n\n\n# In[2]:\n\ntrain_url = \"http://s3.amazonaws.com/assets.datacamp.com/course/Kaggle/train.csv\"\ntrain = pd.read_csv(train_url)\ntest_url = \"http://s3.amazonaws.com/assets.da... | [
[
"numpy.log",
"pandas.read_csv",
"numpy.random.seed",
"matplotlib.pyplot.title",
"numpy.matmul",
"pandas.DataFrame",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"numpy.random.rand",
"numpy.exp",
"matplotlib.pyplot.xlabel",
"numpy.array",
"numpy.sum",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
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