repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list |
|---|---|---|---|---|
chaosWsF/Python-Practice | [
"ff617675b6bcd125933024bb4c246b63a272314d"
] | [
"leetcode/0447_number_of_boomerangs.py"
] | [
"\"\"\"\nGiven n points in the plane that are all pairwise distinct, a \"boomerang\" is a tuple of points (i, j, k) \nsuch that the distance between i and j equals the distance between i and k (the order of the tuple matters).\n\nFind the number of boomerangs. You may assume that n will be at most 500 and coordinat... | [
[
"numpy.array",
"numpy.sum",
"numpy.unique"
]
] |
KumarLabJax/MouseSleep | [
"a0aaeb9a721a781bde13f2064a5291ab3ee80858"
] | [
"dataset-creation/ExportScaledSeg.py"
] | [
"import imageio\nimport cv2\nimport numpy as np\nimport os, sys\nimport argparse\n\noutput_order = ['m00','m10','m01','m20','m11','m02','m30','m21','m12','m03','mu20','mu11','mu02','mu30','mu21','mu12','mu03','nu20','nu11','nu02','nu30','nu21','nu12','nu03']\n\ndef writeCSVHeader(filename):\n\twriter = open(filenam... | [
[
"numpy.uint8",
"numpy.zeros_like"
]
] |
andrewsoong/LISF | [
"20e3b00a72b6b348c567d0703550f290881679b4"
] | [
"ldt/utils/usaf/bcsd_preproc/lib_bcsd_metrics/temporal_disaggregation_6hourly_module.py"
] | [
"#!/usr/bin/env python\n\"\"\"\n# Author: Shrad Shukla\n# coding: utf-8\n#Author: Shrad Shukla\n#Usage: This is a module for the BCSD code.\n#This module bias corrects a forecasts following probability\n#mapping approach as described in Wood et al. 2002\n#Date: August 06, 2015\n# In[28]:\n\"\"\"\n\nfrom __future__ ... | [
[
"numpy.ma.masked_array"
]
] |
hvanwyk/atomic_data_uncertainties | [
"e6b376d600090203b20810c730a21021ea62ab44"
] | [
"recombination/make_plots_belike.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jun 22 15:43:22 2021\n\n@author: lochstu\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pickle\n\ndata1=pickle.load(open('be_like_0.4_1.6_b_mg.pkl','rb'))\ndata2=pickle.load(open('be_like_0.4_1.6_k_fe.pkl','rb'))\n\n\n\... | [
[
"numpy.concatenate",
"numpy.std",
"numpy.average",
"matplotlib.pyplot.subplots"
]
] |
GeoscienceAustralia/wistl | [
"fa8b6aeaabb902ea72085b3552b5167cd20040a4"
] | [
"wistl/tests/test_line.py"
] | [
"\nimport unittest\nimport os\nimport numpy as np\nimport copy\nimport logging\nimport pandas as pd\nfrom scipy.stats import itemfreq\n\nfrom wistl.config import Config\nfrom wistl.line import Line, adjust_value_to_line, adjust_index_to_line\nfrom wistl.tests.test_config import assertDeepAlmostEqual\nfrom wistl.tes... | [
[
"numpy.testing.assert_equal",
"numpy.ones_like",
"pandas.DataFrame",
"numpy.testing.assert_almost_equal",
"numpy.zeros_like",
"numpy.testing.assert_allclose",
"pandas.testing.assert_index_equal",
"numpy.array",
"numpy.zeros",
"numpy.column_stack",
"numpy.random.RandomSt... |
Nicholas-Chong/coronavirus_website | [
"b5ca436a01a447db4de9041442dbe39ca775b610"
] | [
"corona_website_app/management/commands/update_country_codes.py"
] | [
"from django.core.management.base import BaseCommand\nfrom corona_website_app.models import Country, Dates\nimport urllib.request\nimport pandas as pd\n\nclass Command(BaseCommand):\n def handle(self, *args, **kwargs):\n data = urllib.request.urlretrieve('https://raw.githubusercontent.com/lukes/ISO-3166-C... | [
[
"pandas.read_csv"
]
] |
worldbank/GOST_PublicGoods | [
"119e1f6a4baf7aaffecd732e5f59882be23424d5"
] | [
"GOSTNets/GOSTNets/LoadOSM.py"
] | [
"####################################################################################################\n# Load OSM data into network graph\n# Benjamin Stewart and Charles Fox\n# Purpose: take an input dataset as a OSM file and return a network object\n#################################################################... | [
[
"pandas.concat",
"pandas.read_csv"
]
] |
om-henners/advent_of_code | [
"2c11272e05d7d1dcc5a96c9026d0f799f6443fa7"
] | [
"2018/day3/solution2.py"
] | [
"\"\"\"\nAmidst the chaos, you notice that exactly one claim doesn't overlap by even a\nsingle square inch of fabric with any other claim. If you can somehow draw\nattention to it, maybe the Elves will be able to make Santa's suit after all!\n\nFor example, in the claims above, only claim 3 is intact after all clai... | [
[
"numpy.zeros"
]
] |
kubapok/human-motion-classification | [
"0979a66d8f554d3c929e2548b5415fa3cfe83256"
] | [
"classify_by_fuzzy_logic.py"
] | [
"import numpy as np\nimport cv2\nimport sys\nfrom ROI import ROI\nfrom fuzzy_classifier import FuzzyClassifier\n\ncap = cv2.VideoCapture(sys.argv[1])\n\nfgbg = cv2.bgsegm.createBackgroundSubtractorMOG()\n\nclassifier = FuzzyClassifier()\nclassifier.plot_variables()\n\ni = 0\nret, frame = cap.read()\nprvs = cv2.cvtC... | [
[
"numpy.zeros",
"numpy.count_nonzero"
]
] |
sdrees/hvplot | [
"0a78246eecc826d4061b9462831763db39111ccd"
] | [
"hvplot/tests/testoptions.py"
] | [
"import hvplot\nimport holoviews as hv\nimport numpy as np\nimport pytest\n\nfrom holoviews import Store\nfrom holoviews.core.options import Options, OptionTree\n\ntry:\n import pandas as pd\nexcept ImportError:\n pd = None\n\ntry:\n import xarray as xr\nexcept ImportError:\n xr = None\n\n\n@pytest.fixt... | [
[
"numpy.arange",
"pandas.DataFrame"
]
] |
jacobmas/btrdb-python | [
"a5a897c3822668c84a456677479a81838e729e15"
] | [
"btrdb/transformers.py"
] | [
"# btrdb.transformers\n# Value transformation utilities\n#\n# Author: PingThings\n# Created: Fri Dec 21 14:57:30 2018 -0500\n#\n# For license information, see LICENSE.txt\n# ID: transformers.py [] allen@pingthings.io $\n\n\"\"\"\nValue transformation utilities\n\"\"\"\n\n#########################################... | [
[
"pandas.MultiIndex.from_tuples",
"numpy.array",
"pandas.Index",
"pandas.Series"
]
] |
kuc2477/tensorflow-infogan | [
"fde80e9f7b6c55548e8879090868c3d020ecba49"
] | [
"train.py"
] | [
"import tensorflow as tf\nfrom tqdm import tqdm\nimport utils\nfrom data import DATASETS, DATASET_LENGTH_GETTERS\n\n\ndef train(model, config, session=None):\n # define session if needed.\n session = session or tf.Session()\n\n # define summaries.\n summary_writer = tf.summary.FileWriter(config.log_dir,... | [
[
"tensorflow.summary.FileWriter",
"tensorflow.summary.image",
"tensorflow.global_variables_initializer",
"tensorflow.Session",
"tensorflow.train.AdamOptimizer",
"tensorflow.summary.scalar",
"tensorflow.summary.histogram"
]
] |
Oshlack/Slinker | [
"725d2c0861156034ef4d16293e2a3b74ac23c9e7"
] | [
"tools/Canvas/Canvas/track/coverage.py"
] | [
"#=======================================================================================================================\n#\n# CANVAS\n# Author: Breon Schmidt\n# License: MIT\n#\n#=======================================================================================================================\n\n''' --... | [
[
"numpy.log2",
"numpy.zeros"
]
] |
jooohhn/venezuelan-economic-analysis | [
"b61559c385677f7023240655ae636a732b0d21dd"
] | [
"stats.py"
] | [
"import csv\nimport numpy as np\nimport pandas as pd\n\ndatapath = './data/Per capita GDP at current prices - US Dollars.csv'\ndf = pd.read_csv(datapath, header=0)\ndf = df.sort_values('Year', ascending=True)\ndf = df[df['Country or Area']=='Venezuela (Bolivarian Republic of)']\ndf = df[['Year', 'Value']]\n\n# Prin... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] |
melloddy/public_data_extraction | [
"3932ecad1c187204555087bd85e3b8bc0fc5c6ec"
] | [
"processing/assay_processing.py"
] | [
"from typing import List\nfrom pandas import Series, DataFrame, cut, concat\nfrom numpy import log10, power\n\n\ndef categorize_assay(assay_type: str) -> str:\n if assay_type in ['A', 'P']:\n return 'ADME'\n if assay_type == 'T':\n return 'PANEL'\n return 'OTHER'\n\n\ndef nanmol_to_log(value:... | [
[
"numpy.log10",
"pandas.DataFrame",
"numpy.power"
]
] |
dig-data-r3/big-data-first-project | [
"2d2443e4e9a4596ef1a61324a5a80e7b81b5a68c"
] | [
"generate_datasets.py"
] | [
"#!/usr/bin/env python3\n\nimport pandas as pd\n\n\ndef sample_all_sizes(dataset):\n for size in dataset_sizes:\n n_rows = round(dataset.shape[0] * size)\n sampled_df = dataset.sample(n=n_rows, random_state=42, replace=True)\n filename = 'dataset/historical_stock_prices{}.csv'.format(int(siz... | [
[
"pandas.read_csv"
]
] |
jingshenSN2/HOIP_bandgap_prediction | [
"fc989c47e20d98490d7d9679cb6f9a6b2b0b9ebc"
] | [
"training/MLP.py"
] | [
"import pandas as pd\nimport numpy as np\nfrom sklearn.neural_network import MLPRegressor as mlp\nfrom sklearn.model_selection import GridSearchCV as gs\nfrom preprocessing import drop_preprocessing as pre\nimport joblib\nimport os\n\ndef MLP(data_directory, model_dir, features):\n X_train, X_test, y_train, y_te... | [
[
"numpy.arange",
"pandas.DataFrame",
"sklearn.neural_network.MLPRegressor"
]
] |
tardani95/iris-distro | [
"dbb1ebbde2e52b4cc747b4aa2fe88518b238071a"
] | [
"src/python/irispy/test/python_interface_test.py"
] | [
"from __future__ import print_function\n\nimport irispy\nimport irispy.iris_wrapper\nimport numpy as np\n\n\ndef testInterface(show=False):\n p2 = irispy.Polyhedron()\n p2.setA(np.eye(2))\n p2.setB(np.array([3.0, 4.0]))\n print(p2.contains(np.array([2.5, 5.5]), 0.0))\n\n p3 = irispy.Polyhedron.fromBo... | [
[
"matplotlib.pyplot.gca",
"numpy.eye",
"numpy.array",
"matplotlib.pyplot.show"
]
] |
jf20541/LogisticRegressionPyTorch | [
"1900a85bec7c4b1a6a4693625f52083c46df1409"
] | [
"src/train.py"
] | [
"import pandas as pd\nfrom model import LogisticRegression\nfrom sklearn.metrics import accuracy_score, roc_auc_score\nimport argparse\nimport joblib\nimport os\nimport config\n\n\ndef run(fold):\n df = pd.read_csv(config.TRAIN_FOLDS)\n df_train = df[df.kfold != fold].reset_index(drop=True)\n df_test = df[... | [
[
"pandas.read_csv",
"sklearn.metrics.accuracy_score"
]
] |
kad-ecoli/rateratio_test | [
"a82596d94ad790a6da717a3758acd6af956a94e3"
] | [
"p_adjust.py"
] | [
"#!/usr/bin/env python\ndocstring='''\np.adjust: P-values for Multiple Comparisons\n\nDescription\n Given a set of p-values, return q-values (i.e. p-values adjusted).\n This module is a python re-implement of p.adjust R package.\n\nUsage\n from p_adjust import adjust as p_adjust\n p_adjust(p, method, n)... | [
[
"scipy.interpolate.splrep",
"numpy.arange",
"scipy.interpolate.splev",
"numpy.argsort",
"numpy.array",
"numpy.zeros"
]
] |
Redoblue/deep-person-reid | [
"5ede06fcffb80672a19a81b065a604ece78dadb4"
] | [
"torchreid/dataset_loader.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import division\n\nimport os\nfrom PIL import Image\nimport numpy as np\nimport os.path as osp\nimport io\n\nimport torch\nfrom torch.utils.data import Dataset\n\n\ndef read_image(img_path):\n \"\"\"Keep reading image... | [
[
"numpy.random.choice",
"torch.cat",
"numpy.arange",
"numpy.sort",
"numpy.ones"
]
] |
geflaspohler/poold | [
"4ddd085597b85e8c737b2f904f504437f728513b"
] | [
"poold/utils/visualize.py"
] | [
"\"\"\" Visualizations for online learning.\n\nFor example:\n import poold\n poold.visualize(learner.history)\n plt.show()\n\n\"\"\"\n# System imports\nimport numpy as np\nimport pandas as pd\nimport copy\nimport os\n\n# Plotting imports\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport matplotli... | [
[
"matplotlib.dates.DateFormatter",
"pandas.Series",
"matplotlib.pyplot.subplots",
"pandas.DataFrame",
"pandas.to_numeric",
"matplotlib.rc"
]
] |
khanfarhan10/scikit-learn-mooc | [
"37f1e34eae0304a92f557a8194e068b4333ad418"
] | [
"python_scripts/parameter_tuning_ex_03.py"
] | [
"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: percent\n# format_version: '1.3'\n# jupytext_version: 1.6.0\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# %% [markdown]\n# # 📝 Exercise 0... | [
[
"pandas.read_csv",
"sklearn.model_selection.train_test_split"
]
] |
hsheth2/vox | [
"4893a46564406c47c7177dd58b28b364bb60f0e2"
] | [
"output_wav/play_files.py"
] | [
"#!usr/bin/env python\n# coding=utf-8\nimport pyaudio\nimport wave\nimport sys\nimport scipy.io.wavfile\nimport scipy.interpolate\nimport numpy as np\nfrom os.path import isfile\n\n# length of data to read.\nchunk = 1024\n\n# create an audio object\np = pyaudio.PyAudio()\nstream = None\nframes = None\nfilename = No... | [
[
"numpy.concatenate"
]
] |
hnlab/dgl-lifesci | [
"49e36f46a389a8e3841323ad199bb86f4af0a597"
] | [
"tests/model/test_readout.py"
] | [
"# -*- coding: utf-8 -*-\n#\n# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.\n# SPDX-License-Identifier: Apache-2.0\n\nimport dgl\nimport torch\nimport torch.nn.functional as F\n\nfrom dgllife.model.readout import *\n\ndef test_graph1():\n \"\"\"Graph with node features\"\"\"\n g = dgl.gr... | [
[
"torch.device",
"torch.Size",
"torch.cuda.is_available"
]
] |
dataronio/GLRM | [
"58bdb64f1aafeb06eb0a0fbf74e49138f0a19da6"
] | [
"glrm/loss.py"
] | [
"import cvxpy as cp\nfrom numpy import ones, maximum, minimum, sign, floor, ceil\n\n\"\"\"\nAbstract loss class and canonical loss functions.\n\"\"\"\n\n# Abstract Loss class\nclass Loss(object):\n def __init__(self, A): return\n def loss(self, A, U): raise NotImplementedError(\"Override me!\")\n def encod... | [
[
"numpy.sign",
"numpy.ones"
]
] |
terrafin/iss-rnns | [
"e3cfa64a9fa70ffe22f6c4a53686251617e152da"
] | [
"ptb/ptb_word_lm_heter.py"
] | [
"# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.device",
"tensorflow.gfile.Exists",
"tensorflow.reduce_sum",
"tensorflow.stack",
"tensorflow.global_variables",
"matplotlib.pyplot.get_cmap",
"tensorflow.train.AdamOptimizer",
"tensorflow.get_default_graph",
"numpy.exp",
"numpy.where",
"tensorflow.Graph",
... |
ArushiC/Disaster-Response-Pipelines | [
"e04b199a8b403e47bfdeee76edee8e3776110819"
] | [
"data/process_data.py"
] | [
"import sys\nimport pandas as pd\nfrom sqlalchemy import create_engine\n\ndef load_data(messages_filepath, categories_filepath):\n messages = pd.read_csv(messages_filepath)\n categories = pd.read_csv(categories_filepath)\n df = messages.merge(categories, how='inner', on='id')\n return df\n\n\ndef clean_... | [
[
"pandas.concat",
"pandas.read_csv"
]
] |
VU-BEAM-Lab/DNNBeamforming | [
"e8ee8c1e57188a795816b119279ac2e60e5c5236"
] | [
"src/trainer.py"
] | [
"# Copyright 2020 Jaime Tierney, Adam Luchies, and Brett Byram\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... | [
[
"numpy.sqrt",
"numpy.random.randn",
"numpy.float32",
"torch.device",
"numpy.random.uniform",
"numpy.sum"
]
] |
dhern023/ml-models | [
"87e26001e6d7a1c6287ea7a78362957954902a7c"
] | [
"tasks/_tasks_statsmodels.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nTasks for building statsmodel pipelines\n\"\"\"\n\nimport _workflow\n\nimport d6tflow\nimport pandas\nimport pathlib\nimport statsmodels.api as sm\n\nclass TaskLoadDataframe(d6tflow.tasks.TaskCachePandas):\n \"\"\"\n Loads in a processed dataframe.\n\n Can handle if the da... | [
[
"pandas.read_pickle",
"pandas.read_csv",
"pandas.get_dummies"
]
] |
jtpils/MFFNet | [
"ec517c57d7938c566a0900b79b41822ff1a9ab6f"
] | [
"utils/generate_pc_image.py"
] | [
"import copy\nimport numpy as np\nimport open3d as o3d\n\n\ndef load_data_files(data_filename):\n \"\"\"Function that loads data file into memory\n\n :param data_filename: str, the object that represents the original data file name\n :return: data_source: ndarray, the object that stores the data\n \"\"\... | [
[
"numpy.max",
"numpy.load",
"numpy.loadtxt"
]
] |
WilliamZhai/FantasyBasketballAuctionDraftSimulation | [
"4b4aca065b0f389e885e8109e6d6f5b64efd358d"
] | [
"script.py"
] | [
"import numpy\nimport xlsxwriter\nimport copy\n\n# create file for simulations\nworkbook = xlsxwriter.Workbook(\"data.xlsx\")\n\n# globals\nupperline = workbook.add_format(\n {\n \"top\": 1,\n }\n)\n\nheader_border = workbook.add_format(\n {\n \"bottom\": 1,\n }\n)\n\n# snake draft order\n... | [
[
"numpy.random.uniform",
"numpy.random.gamma"
]
] |
Zhaopudark/GANs-TF2-MinxedPrecision | [
"2fc2f027254e9d61beca6342a1f63854e6f96393"
] | [
"implementations/DCGAN-MNIST-WGP-BN/model.py"
] | [
"import networks \nimport tensorflow as tf\nimport time\nimport os \nimport sys\nbase = os.path.dirname(os.path.abspath(__file__))\nsys.path.append(os.path.join(base,'../../'))\nfrom basemodels.GanLosses import GanLoss\nfrom basemodels.GanOptimizers import Adam\n\nimport matplotlib.pyplot as plt\nplt.switch_backend... | [
[
"matplotlib.pyplot.imshow",
"tensorflow.summary.scalar",
"tensorflow.Variable",
"tensorflow.summary.image",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.close",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.figure",
"tensorflow.train.CheckpointManager",
"matplotlib.pyplot... |
pedrodiamel/kaggle-imaterialist | [
"8e9dfbc7f09eb8f93b44d7c4c5518911d16176ac"
] | [
"torchlib/classifierneuralnet.py"
] | [
"\n# STD MODULES \nimport os\nimport math\nimport shutil\nimport time\nimport numpy as np\nfrom tqdm import tqdm\n\n# TORCH MODULE\nimport torch\nfrom torch.autograd import Variable\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n# PYTVISION MODULE\nfrom pytvision.neuralnet import NeuralNetAbstract\nfrom... | [
[
"torch.nn.Softmax",
"torch.nn.functional.softmax",
"torch.nn.CrossEntropyLoss",
"torch.no_grad",
"torch.nn.L1Loss",
"torch.cuda.device_count",
"numpy.zeros",
"torch.nn.MSELoss",
"torch.autograd.Variable"
]
] |
JiajunBernoulli/douban-short-commentary | [
"8ab411172bc33739e49b446818681165e9797e01"
] | [
"utils/draw_pic.py"
] | [
"import imageio\nfrom wordcloud import WordCloud\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom config import TOP_NUM, FONT_PATH, PIC_PATH, FILE_PATH\n\"\"\"\n Created by Jiajun·Bernoulli on 2019/1/18\n\"\"\"\n###########################绘制柱状图#####################\ndef draw_bar(labels, quants):\n # -*... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.show",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.figure"
]
] |
wi905252/ukb-cardiac-mri | [
"3177dde898a65b1d7f385b78e4f134de3852bea5"
] | [
"ukb/ensemble.py"
] | [
"import argparse\nimport pandas as pd\nfrom os import makedirs\nfrom os.path import isdir\n\nfrom ensemble import *\n\ndef main(args):\n pd.set_option(\"display.width\", 100)\n\n if args.pids_csv is not None:\n pids = list(pd.read_csv(args.pids_csv)[args.pids_key])\n else:\n pids = None\n\n ... | [
[
"pandas.set_option",
"pandas.read_csv"
]
] |
mariusgruenewald/HARK | [
"6fd5eeefe424775a0849b41d1480aef1190063a7"
] | [
"HARK/ConsumptionSaving/ConsIndShockModel.py"
] | [
"\"\"\"\nClasses to solve canonical consumption-savings models with idiosyncratic shocks\nto income. All models here assume CRRA utility with geometric discounting, no\nbequest motive, and income shocks are fully transitory or fully permanent.\n\nIt currently solves three types of models:\n 1) A very basic \"per... | [
[
"numpy.dot",
"numpy.log",
"numpy.linspace",
"numpy.min",
"numpy.asarray",
"numpy.tile",
"numpy.ones",
"numpy.concatenate",
"numpy.max",
"numpy.append",
"numpy.logical_or",
"numpy.where",
"numpy.insert",
"numpy.array",
"numpy.zeros",
"numpy.sum",
... |
predatorx7/Zero-DCE | [
"b4fa00f315bc650fc697adf84724afd964b6cd5f"
] | [
"ZeroDCE/zero_dce_lite.py"
] | [
"'''\nMIT License\n\nCopyright (c) 2021 Tauhid Khan\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, me... | [
[
"tensorflow.concat",
"tensorflow.keras.Input",
"tensorflow.keras.utils.plot_model",
"tensorflow.square",
"tensorflow.print",
"tensorflow.keras.initializers.RandomNormal",
"tensorflow.random.normal",
"tensorflow.GradientTape"
]
] |
wils0674/dep | [
"7189a26fbcec3d8c3cc7d7015107b8fb6c5f6a45"
] | [
"scripts/util/dump_yearly.py"
] | [
"\"\"\"Dump some monthly data\"\"\"\n\nimport pandas as pd\nfrom pandas.io.sql import read_sql\nfrom pyiem.util import get_dbconn\n\n# East Nish\nDATA = \"\"\"102400030603\n102400030704\n102400030701\n102400030702\n102400030601\n102400030602\n102400030303\n102400030304\n102400030406\n102400030502\n102400030302\n102... | [
[
"pandas.ExcelWriter"
]
] |
awais307/pyam | [
"e7828beae490ed10857c9d8cb5a42705a7cf62f0"
] | [
"tests/test_io.py"
] | [
"from pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport pytest\n\nfrom pyam import IamDataFrame, read_datapackage\nfrom pyam.utils import META_IDX\nfrom pyam.testing import assert_iamframe_equal\n\nfrom conftest import TEST_DATA_DIR, META_DF\n\nFILTER_ARGS = dict(scenario=\"scen_a\")\n\n\ndef tes... | [
[
"pandas.read_excel",
"pandas.read_csv",
"numpy.isnan",
"pandas.DataFrame",
"pandas.testing.assert_frame_equal",
"pandas.ExcelWriter"
]
] |
IsaacGuan/SGSG | [
"7476f1b8ec0ed90cd9896ee2d23241c4310cb78c"
] | [
"test.py"
] | [
"import os\nimport argparse\nimport heapq\nimport pandas as pd\n\nfrom MDN import *\n\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\nDATA_DIR = os.path.join(BASE_DIR, 'data')\nRESULTS_DIR = os.path.join(BASE_DIR, 'results')\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.a... | [
[
"pandas.DataFrame"
]
] |
mayanksuman/xgboost | [
"2e052e74b627659b97ce741be5b64581479303c3"
] | [
"python-package/xgboost/core.py"
] | [
"# coding: utf-8\n# pylint: disable=too-many-arguments, too-many-branches, invalid-name\n# pylint: disable=too-many-branches, too-many-lines, W0141\n\"\"\"Core XGBoost Library.\"\"\"\nfrom __future__ import absolute_import\nimport collections\n# pylint: disable=no-name-in-module,import-error\ntry:\n from collect... | [
[
"numpy.histogram",
"numpy.unique",
"numpy.vectorize",
"numpy.column_stack",
"numpy.array",
"numpy.zeros"
]
] |
imu-hupeng/calc_metric | [
"7053a521987a43d5cef95695b81e2f31df75cfc3"
] | [
"speech_kit/signal_utils.py"
] | [
"import numpy\r\ndef expand2frame(x,n):\r\n r,c = x.shape\r\n y = numpy.zeros((r,c*2*n+c));\r\n t = 0;\r\n for i in range(-n,n+1):\r\n y[:,t*c:t*c+c] = numpy.roll(x,i,0);\r\n t = t + 1;\r\n return y"
] | [
[
"numpy.zeros",
"numpy.roll"
]
] |
ljuillen/MicroStructPy | [
"fa60e4b05e534fb745f18b2862bcedcdcfb4166e"
] | [
"src/microstructpy/_misc.py"
] | [
"\"\"\"Miscellaneous functions\n\nThis private module contains miscellaneous functions.\n\"\"\"\nimport numpy as np\n\n__author__ = 'Kenneth (Kip) Hart'\n\nkw_solid = {'crystalline', 'granular', 'solid'}\nkw_amorph = {'amorphous', 'glass', 'matrix'}\nkw_void = {'void', 'crack', 'hole'}\n\nori_kws = {'orientation', ... | [
[
"numpy.linalg.solve",
"numpy.sqrt",
"numpy.abs",
"numpy.squeeze",
"numpy.mean",
"numpy.array",
"numpy.sum"
]
] |
Krl1/Flappy-Bird-AI | [
"cd581107763763175866f66a89ed0a35cdf24595"
] | [
"game_functions.py"
] | [
"import sys\nimport pygame\nimport numpy as np\nfrom pipeTop import PipeTop\nfrom pipeDown import PipeDown\n\ndef check_events(ai_settings, screen, bird):\n\t\"\"\"Reakcja na zdarzenia generowane przez klawiaturę\"\"\"\n\tfor event in pygame.event.get():\n\t\t\tif event.type == pygame.QUIT:\n\t\t\t\tsys.exit()\n\t\... | [
[
"numpy.random.randint"
]
] |
kupparsudhir/kaggle | [
"29966f1277067022e00c568e7576a3b94f295383"
] | [
"zillow-price/zestimate_linear_model.py"
] | [
"\"\"\"\nCreated on Mon Sep 18 15:49:13 2017\n\n@author: sudhir\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport scipy as sy\n\n# Import data set\ntrain = pd.read_csv(\"../input/train_2017.csv\")\nproperties = pd.read_csv(\"../input/properties_2017.c... | [
[
"pandas.merge",
"pandas.read_csv",
"sklearn.model_selection.train_test_split",
"sklearn.linear_model.LinearRegression",
"matplotlib.pyplot.show"
]
] |
surajpaib/swa_gaussian | [
"c4e837fc6269403032950f25d69ded7fe67729a0"
] | [
"swag/utils.py"
] | [
"import itertools\nimport torch\nimport os\nimport copy\nfrom datetime import datetime\nimport math\nimport numpy as np\nimport tqdm\n\nimport torch.nn.functional as F\n\n\ndef flatten(lst):\n tmp = [i.contiguous().view(-1, 1) for i in lst]\n return torch.cat(tmp).view(-1)\n\n\ndef unflatten_like(vector, like... | [
[
"torch.nn.functional.softmax",
"torch.max",
"torch.cat",
"torch.manual_seed",
"torch.zeros_like",
"numpy.concatenate",
"torch.autograd.Variable",
"torch.no_grad",
"torch.log",
"torch.ones_like",
"numpy.vstack",
"torch.save"
]
] |
agcapps/conduit | [
"feb58d64ed3725a478f827a8fdcf02514e7711cb"
] | [
"src/tests/conduit/python/t_python_conduit_node.py"
] | [
"###############################################################################\n# Copyright (c) 2014-2018, Lawrence Livermore National Security, LLC.\n# \n# Produced at the Lawrence Livermore National Laboratory\n# \n# LLNL-CODE-666778\n# \n# All rights reserved.\n# \n# This file is part of Conduit. \n# \n# For d... | [
[
"numpy.array",
"numpy.string_",
"numpy.float64",
"numpy.uint32"
]
] |
popesculuca00/Self-driving-car | [
"673e03b2ae046fde7e923ed3377dc3a4c0fb742e"
] | [
"utils.py"
] | [
"import os\r\nimport torch\r\n\r\n\r\ndef get_batch_mask(commands): # shape ( commands, batch_size, params ) == (4 , batch_size, 3) \r\n mask = torch.zeros(4, len(commands), 3)\r\n for i in range( len(commands) ):\r\n mask[ commands[i], i, :] = torch.ones(3)\r\n return mask\r\n\r\ndef save_model(m... | [
[
"torch.jit.script",
"torch.jit.optimized_execution",
"torch.ones",
"torch.load"
]
] |
shijun18/Spine_Seg | [
"90c41d8ee08235c43bd3a5236da5a0ee7066fced"
] | [
"post_process.py"
] | [
"import sys\nimport os\nimport SimpleITK as sitk\nimport numpy as np\nfrom tqdm import tqdm\n\n\n\ndef post_process(label,n):\n from skimage.morphology import remove_small_objects\n\n final= np.zeros_like(label,dtype=np.uint8)\n for i in range(1,n):\n roi = (label == i).astype(np.bool)\n roi ... | [
[
"numpy.zeros_like"
]
] |
nilearn/nilearn_sandbox | [
"a41b81c0bb590f1d0e3073be81278007792a1345"
] | [
"examples/bids_automatic_decoding/bids_decoding.py"
] | [
"from os.path import isdir\nfrom os.path import join as opj\nimport copy\nimport itertools\nimport numpy as np\nimport pandas as pd\nfrom pandas import read_csv\nimport nibabel as nib\nfrom nistats.design_matrix import make_first_level_design_matrix\nfrom nistats.first_level_model import FirstLevelModel\nfrom nilea... | [
[
"numpy.hstack",
"pandas.read_csv",
"sklearn.utils.Bunch",
"numpy.unique",
"numpy.asarray",
"numpy.arange"
]
] |
datalayer-contrib/jupyter-sparkmagic | [
"2e4c2a2aa153e32a6428de863bd829ec03844609"
] | [
"sparkmagic/sparkmagic/utils/utils.py"
] | [
"# Distributed under the terms of the Modified BSD License.\nfrom IPython.core.error import UsageError\nfrom IPython.core.magic_arguments import parse_argstring\nimport numpy as np\nimport pandas as pd\nimport json\nimport importlib\nfrom collections import OrderedDict\n\nimport sparkmagic.utils.configuration as co... | [
[
"pandas.to_numeric",
"numpy.dtype",
"pandas.to_datetime",
"pandas.DataFrame"
]
] |
jhrmnn/schnetpack | [
"2f96dee7d184b8db8ee610d6743570daeb3763b9"
] | [
"src/schnetpack/sacred/calculator_ingredients.py"
] | [
"from sacred import Ingredient\nimport os\nimport torch\n\nfrom schnetpack.md.calculators import SchnetPackCalculator\nfrom schnetpack.md.utils import MDUnits\n\ncalculator_ingradient = Ingredient('calculator')\n\n\n@calculator_ingradient.config\ndef config():\n \"\"\"configuration for the calculator ingredient\... | [
[
"torch.load"
]
] |
Toni-Chan/IBM-TableRecognition | [
"34eb433184e07d74b060fbb4c14c110fd284c0dd"
] | [
"Chinese-OCR/ctpn/lib/fast_rcnn/config.py"
] | [
"# --------------------------------------------------------\n# Fast R-CNN\n# Copyright (c) 2015 Microsoft\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Ross Girshick\n# --------------------------------------------------------\n\n\"\"\"Fast R-CNN config system.\nThis file specifies defaul... | [
[
"numpy.array"
]
] |
SalahEddineLahniche/MLC-Kaggle-2017 | [
"489b76182227cbf51812c051381da4e58098d338"
] | [
"ABONO/Regressor.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom sklearn import linear_model, ensemble, svm, neural_network, neighbors\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_squared_error\nimport pickle\nimport xgboost \n\n\nclass Regressor:\n def __init__(self, session, train_df, ... | [
[
"sklearn.ensemble.RandomForestRegressor",
"sklearn.linear_model.ElasticNet",
"sklearn.model_selection.train_test_split",
"sklearn.metrics.mean_squared_error",
"sklearn.svm.SVR",
"sklearn.neighbors.KNeighborsRegressor",
"sklearn.linear_model.Lasso",
"sklearn.ensemble.GradientBoostin... |
BrigitaPetk/support_department_dashboard | [
"309e8b3e879a7420a0133596538feecf35813535"
] | [
"data_processing/base_creation.py"
] | [
"import pandas as pd\nfrom pathlib import Path\nimport os\n\n\nclass BaseCreator():\n def creator(self, filter_direktorija, final_direktorija, filter_direktorija_GB, final_direktorija_GB):\n\n def base_creator(location, name):\n filter_files = list(location.glob(\"*.csv*\"))\n if not... | [
[
"pandas.DataFrame"
]
] |
mikanikos/MachineLearning_Project1 | [
"994d9f09a8ffc7ee6379c918bfdc69c22ad38509"
] | [
"proj1_helpers.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"some helper functions for project 1.\"\"\"\nimport csv\nimport numpy as np\n\n\ndef load_csv_data(data_path, sub_sample=False):\n \"\"\"Loads data and returns y (class labels), tX (features) and ids (event ids)\"\"\"\n y = np.genfromtxt(data_path, delimiter=\",\", skip_header=1... | [
[
"numpy.dot",
"numpy.where",
"numpy.genfromtxt"
]
] |
gaurav274/Eva | [
"b9d5bc8d0d46aba811e4da0157d61614be06f1d1"
] | [
"test/util.py"
] | [
"# coding=utf-8\n# Copyright 2018-2020 EVA\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.array_equal",
"pandas.DataFrame",
"numpy.ones"
]
] |
neevparikh/hierarchical-doom | [
"082f794b9c6101c4e94f15bf4f93c718ee219ea5"
] | [
"algorithms/appooc/shared_buffers.py"
] | [
"import math\n\nimport numpy as np\nimport torch\nfrom gym import spaces\n\nfrom algorithms.appooc.appooc_utils import copy_dict_structure, iter_dicts_recursively, iterate_recursively\nfrom algorithms.appo_common.model_utils import get_hidden_size\nfrom algorithms.utils.action_distributions import calc_num_logits, ... | [
[
"torch.ones",
"torch.zeros",
"torch.from_numpy",
"numpy.ndarray",
"numpy.zeros"
]
] |
miaojinshuai/rl-attack | [
"0b406d6b0dc77189a3f32be3e766f7dc915dbc2a"
] | [
"train.py"
] | [
"import argparse\nimport gym\nimport numpy as np\nimport os\nimport tensorflow as tf\nimport tempfile\nimport time\nimport json\nimport random\n\nimport rlattack.common.tf_util as U\n\nfrom rlattack import logger\nfrom rlattack import deepq\nfrom rlattack.deepq.replay_buffer import ReplayBuffer, PrioritizedReplayBu... | [
[
"tensorflow.summary.FileWriter",
"numpy.ones_like",
"numpy.abs",
"numpy.round",
"numpy.mean",
"tensorflow.train.AdamOptimizer",
"tensorflow.trainable_variables",
"numpy.array",
"tensorflow.abs"
]
] |
heathher/neural_sequence_labeling | [
"81c83443982f5b1723fde3d446eb94e8cb7a4c44"
] | [
"utils/logger.py"
] | [
"import time\nimport sys\nimport logging\nimport numpy as np\n\n\ndef get_logger(filename):\n \"\"\"Return a logger instance that writes in filename\n Args:\n filename: (string) path to log.txt\n Returns:\n logger: (instance of logger)\n \"\"\"\n logger = logging.getLogger('logger')\n ... | [
[
"numpy.log10"
]
] |
macklenc/mtnlion | [
"ba2e93faeed3004d344a8c14f37a409da572271d"
] | [
"buildup/fenics_/phase2t/ce.py"
] | [
"import dolfin as fem\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom buildup import common, utilities\nfrom mtnlion.newman import equations\n\n\ndef run(start_time, dt, stop_time, return_comsol=False):\n time = np.arange(start_time, stop_time + dt, dt)\n dtc = fem.Constant(dt)\n cmn, domain, ... | [
[
"numpy.arange",
"matplotlib.pyplot.show"
]
] |
sk1010k/nussl | [
"5dd5db75138af834c672eafb10e02f4448ddca37"
] | [
"nussl/separation/repet.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nThe original REpeating Pattern Extraction Technique (REPET).\n\"\"\"\n\nimport numpy as np\nimport scipy.fftpack as scifft\nimport scipy.spatial.distance\n\nfrom . import mask_separation_base\nfrom . import masks\nfrom ..core import constants\n\n\nclass Rep... | [
[
"numpy.amax",
"numpy.nanmedian",
"numpy.minimum",
"numpy.linspace",
"scipy.fftpack.fft",
"matplotlib.pyplot.plot",
"numpy.max",
"numpy.mean",
"numpy.zeros_like",
"numpy.where",
"numpy.square",
"numpy.reshape",
"numpy.arange",
"numpy.stack",
"numpy.ceil",... |
BrianCoyle/IsingBornMachinePseudoPublic | [
"e7e6e87fd2eeca39cea65504c8b95242f898a92f"
] | [
"param_init.py"
] | [
"## @package param_init\r\n#\r\n# Initialise some inputted variables\r\n\r\nfrom pyquil.quil import Program\r\nfrom pyquil.api import get_qc\r\nimport pyquil.paulis as pl\r\nfrom pyquil.gates import H, CPHASE, PHASE, RESET, MEASURE\r\nimport numpy as np\r\nimport random as rand\r\nfrom numpy import pi,log2\r\n\r\nd... | [
[
"numpy.zeros"
]
] |
cccaaannn/useful_functions | [
"1570cda8c642a39f04ed9f22ebeeab2bfb9e9424"
] | [
"00-modules/data_science_visualization_modules/numpy_examples.py"
] | [
"import numpy as np\n\n\n# documentation\n# https://numpy.org/doc/1.18/reference/index.html\n\n\n# load data\ndata = np.loadtxt(\"data_science-visualization/example_Data/data.txt\")\ndata_str = np.loadtxt(\"data_science-visualization/example_Data/top50info.txt\", dtype=np.str, delimiter='\\t', skiprows=1)\n\n# arra... | [
[
"numpy.random.seed",
"numpy.arange",
"numpy.random.shuffle",
"numpy.sort",
"numpy.concatenate",
"numpy.random.normal",
"numpy.random.permutation",
"numpy.random.rand",
"numpy.array",
"numpy.where",
"numpy.loadtxt",
"numpy.random.randint"
]
] |
Ishinomori/PlasmaPy | [
"46a77797ff64d61d4215f5b686687359f892d9dd"
] | [
"plasmapy/formulary/parameters.py"
] | [
"\"\"\"\nThis module gathers basic and general plasma parameters such as the\nplasma frequency or Debye length.\n\"\"\"\n__all__ = [\n \"mass_density\",\n \"Alfven_speed\",\n \"ion_sound_speed\",\n \"thermal_speed\",\n \"thermal_pressure\",\n \"kappa_thermal_speed\",\n \"Hall_parameter\",\n ... | [
[
"numpy.logical_xor",
"numpy.abs",
"numpy.sqrt",
"numpy.isfinite",
"numpy.isscalar"
]
] |
fkubota/enjoy-vim | [
"f1f87ff569ebeea3b84993add200a2c7125cab7a"
] | [
"enjoy/001_20210224/live_coding/func.py"
] | [
"import pandas as pd\nfrom sklearn.datasets import load_iris\n\n\ndef get_datasets():\n iris = load_iris()\n data = iris.data\n feature_names = iris.feature_names\n target = iris.target\n df = pd.DataFrame(data, columns=feature_names)\n\n return df, target\n"
] | [
[
"sklearn.datasets.load_iris",
"pandas.DataFrame"
]
] |
jmirabel/agimus-demos | [
"d18703626f3bac322dec788ad9506495771d15a3"
] | [
"talos/camera_calibration/script_hpp.py"
] | [
"# /usr/bin/env python\nimport random\nfrom math import pi\n\nimport numpy as np\nfrom hpp import Quaternion, Transform\nfrom hpp.corbaserver.manipulation import (\n ConstraintGraph,\n ProblemSolver,\n Rule,\n newProblem,\n)\nfrom hpp.corbaserver.manipulation.robot import Robot\nfrom hpp.gepetto.manipul... | [
[
"numpy.matrix"
]
] |
Khaled-Abdelhamid/project-ml-microservice-kubernetes | [
"4b4b3e50447ee67235e829c7a5c00b63ec0a7483"
] | [
"app.py"
] | [
"from flask import Flask, request, jsonify\nfrom flask.logging import create_logger\nimport logging\n\nimport pandas as pd\nfrom sklearn.externals import joblib\nfrom sklearn.preprocessing import StandardScaler\n\napp = Flask(__name__)\nLOG = create_logger(app)\nLOG.setLevel(logging.INFO)\n\n\ndef scale(payload):\n... | [
[
"sklearn.preprocessing.StandardScaler",
"sklearn.externals.joblib.load",
"pandas.DataFrame"
]
] |
anderson2981/mirgecom | [
"8d5d44145b5984f27a3dcda30956756a9cbcd284"
] | [
"mirgecom/euler.py"
] | [
"r\"\"\":mod:`mirgecom.euler` helps solve Euler's equations of gas dynamics.\n\nEuler's equations of gas dynamics:\n\n.. math::\n\n \\partial_t \\mathbf{Q} = -\\nabla\\cdot{\\mathbf{F}} +\n (\\mathbf{F}\\cdot\\hat{n})_{\\partial\\Omega} + \\mathbf{S}\n\nwhere:\n\n- state $\\mathbf{Q} = [\\rho, \\rho{E}, \\r... | [
[
"numpy.dot",
"numpy.eye",
"numpy.zeros",
"numpy.outer"
]
] |
avnishsachar/GPRPy | [
"47d0f2928dbd7ea446e8cd40ab07d7fed879588b"
] | [
"scripts/gprpy.py"
] | [
"import os\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom gprIO_MALA import readMALA\n\nclass gprpyProfile:\n '''\n Ground penetrating radar data processing and visualization class \n for common-offset profiles.\n '''\n def __init__(self,filename=None):\n '''\n Initializat... | [
[
"matplotlib.pyplot.gca",
"numpy.abs",
"numpy.min",
"numpy.arange",
"matplotlib.pyplot.ylim",
"numpy.max",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.show"
]
] |
shenberg/detectron2 | [
"0b62f13106f1d30097563b059bfe9ae2803b2271"
] | [
"detectron2/modeling/meta_arch/panoptic_fpn.py"
] | [
"# -*- coding: utf-8 -*-\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\nimport torch\nfrom torch import nn\n\nfrom detectron2.structures import ImageList\n\nfrom ..backbone import build_backbone\nfrom ..postprocessing import detector_postprocess, sem_seg_postprocess\nfrom ..proposal_gene... | [
[
"torch.Tensor",
"torch.zeros_like",
"torch.unique",
"torch.device",
"torch.argsort"
]
] |
fmi-basel/zinneretal-methods | [
"c0d9d81bf5e9153c9a97729c217b5bf039625001"
] | [
"goid/training/training.py"
] | [
"import luigi\nimport os\nimport logging\nimport abc\nimport numpy as np\nfrom multiprocessing import Lock, Manager\n\nfrom .augmentations import pad_to_minsize, AugmentationMixin\nfrom .model import BuildModelMixin\n\n\ndef plot_instance_dataset(path, tf_dataset, n_samples=10):\n import matplotlib.pyplot as plt... | [
[
"matplotlib.backends.backend_pdf.PdfPages",
"matplotlib.pyplot.tight_layout",
"tensorflow.config.experimental.set_memory_growth",
"numpy.ceil",
"tensorflow.keras.optimizers.Adam",
"tensorflow.config.list_physical_devices",
"tensorflow.keras.callbacks.TerminateOnNaN",
"matplotlib.py... |
apyskir/steppy-toolkit | [
"3190054954aeab043ced1c079d87bdd3582bb232"
] | [
"toolkit/pytorch_transformers/callbacks.py"
] | [
"import os\nfrom datetime import datetime, timedelta\n\nfrom deepsense import neptune\nfrom steppy.utils import get_logger\nfrom torch.optim.lr_scheduler import ExponentialLR\n\nfrom toolkit.pytorch_transformers.utils import Averager, persist_torch_model\nfrom toolkit.pytorch_transformers.validation import score_mo... | [
[
"torch.optim.lr_scheduler.ExponentialLR"
]
] |
MendelXu/pytorch_geometric | [
"676972f32507dbdec16c4a4ab46166032e5bfc04"
] | [
"test/nn/models/test_jumping_knowledge.py"
] | [
"import torch\nfrom torch_geometric.nn import JumpingKnowledge\n\n\ndef test_jumping_knowledge():\n num_nodes, channels, num_layers = 100, 16, 4\n xs = list([torch.randn(num_nodes, channels) for _ in range(num_layers)])\n\n model = JumpingKnowledge('cat')\n assert model.__repr__() == 'JumpingKnowledge(c... | [
[
"torch.randn"
]
] |
AndryRafam/Hate_Speech | [
"f3350a77fcbdb7a9b5845a5ba71470f2540cd000"
] | [
"GRU/gru.py"
] | [
"import pickle\nimport tensorflow as tf\nimport numpy as np\nimport pandas as pd\nimport re\nimport nltk\nimport string\nimport random\n\nfrom zipfile import ZipFile\nfrom nltk.tokenize import word_tokenize\nfrom nltk.tokenize.treebank import TreebankWordDetokenizer\nfrom tensorflow.keras.layers import Embedding, G... | [
[
"tensorflow.keras.preprocessing.text.Tokenizer",
"tensorflow.keras.callbacks.ModelCheckpoint",
"tensorflow.keras.models.load_model",
"pandas.read_csv",
"tensorflow.keras.Input",
"numpy.random.seed",
"tensorflow.keras.layers.Embedding",
"tensorflow.keras.losses.SparseCategoricalCros... |
LeeZChuan/DTVis-master | [
"397f99c63c7746986962d00a7b2ebe012f3d88c8"
] | [
"static/script/pythonCode/address_data.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\n\n@author: LeeZChuan\n\n\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nimport requests\nimport os\nfrom pandas.core.frame import DataFrame\n\npd.set_option('display.max_columns',1000)\npd.set_option('display.width', 1000)\npd.set_option('display.max_colwidth',1000)\n\ndef rea... | [
[
"pandas.core.frame.DataFrame",
"pandas.set_option",
"pandas.read_csv",
"pandas.DataFrame"
]
] |
balarsen/Bridge | [
"258d79558bb9871f2e0e51b69b02aecf6a129ed9"
] | [
"Bridge/Bidding.py"
] | [
"import itertools\nfrom abc import ABCMeta, abstractmethod\n\nimport numpy as np\n\n\"\"\"\nthis module is quite complicated, it will need to both do the mechanics of bidding\nbut also allow for puggable \"smarts\" accordng to different conventions\n\"\"\"\n\n\nclass Redeal(Exception):\n pass\n\n\nclass Bidding_... | [
[
"numpy.roll"
]
] |
NPMP-7/CBLBs | [
"1f9380c275cdb229c186b84bc45b3b63a8a960a9"
] | [
"cblb/run_ODE_NAND_NOR_XOR_extended.py"
] | [
"from scipy.integrate import ode\nimport matplotlib.pyplot as plt\nimport matplotlib.colors as mcolors\n\nfrom models_extended import *\nfrom parameters import *\n\n\"\"\"\n[[(S1, I1)], []]\n\"\"\"\n\"\"\"\nstates = [([0,0], [0,0,0,0]), ([0,0], [1,0,0,0]), \n ([1,0], [0,0,0,0]), ([1,0], [0,1,0,0]), \n ... | [
[
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.gcf",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.show",
"scipy.integrate.ode"
]
] |
angnuoli/Coursework | [
"2197e20de5da2bc820b3c179f871835f8d02f771"
] | [
"Machine Learning/part 2-regression/decision-tree-regression/decision_tree_regression.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\n@author: gjxhlan\n\"\"\"\n# Regression template\n# 1. Data Preprocessing\n\n# Import the libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Importing the dataset\ndataset = pd.read_csv('Position_Salaries.csv')\nX = datas... | [
[
"pandas.read_csv",
"sklearn.tree.DecisionTreeRegressor",
"matplotlib.pyplot.title",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] |
quantopian-enterprise/blaze | [
"6b686bed87993494b11676ed25e7b30f18ca2248"
] | [
"blaze/compute/tests/test_pytables_compute.py"
] | [
"from __future__ import absolute_import, division, print_function\n\nimport os\nimport pytest\ntb = pytest.importorskip('tables')\n\nimport numpy as np\n\nfrom blaze.compute.core import compute\nfrom blaze.expr import symbol\nfrom blaze import drop, discover, create_index\nfrom blaze.utils import tmpfile\n\n\nt = s... | [
[
"numpy.array",
"numpy.sort"
]
] |
forbin-capital/zipline | [
"6c8ecd83fae6ce4f7bc7c8ad9e43164020770e76"
] | [
"zipline/pipeline/engine.py"
] | [
"\"\"\"\nComputation engines for executing Pipelines.\n\nThis module defines the core computation algorithms for executing Pipelines.\n\nThe primary entrypoint of this file is SimplePipelineEngine.run_pipeline, which\nimplements the following algorithm for executing pipelines:\n\n1. Determine the domain of the pipe... | [
[
"pandas.MultiIndex.from_arrays",
"numpy.array",
"pandas.DataFrame",
"pandas.MultiIndex"
]
] |
docteurZ/BentoML | [
"2ea9848f907a212818fc0c9da434e88cdecab19f"
] | [
"tests/handlers/test_tf_tensor_handler.py"
] | [
"import sys\nimport json\nimport base64\nimport math\nimport numbers\nimport numpy as np\n\ntry:\n from unittest.mock import Mock, MagicMock\nexcept ImportError:\n from mock import Mock, MagicMock\n\n\ndef mock_tensorflow_module():\n class MockTensor:\n def __init__(self, _input):\n self.... | [
[
"numpy.array"
]
] |
PedroLelis/tensor2tensor | [
"5a867d031bd493eeb7d2776e1118d1594ff0a623"
] | [
"tensor2tensor/data_generators/squad.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Tensor2Tensor 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 requir... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.gfile.GFile",
"tensorflow.concat"
]
] |
StrayRobots/stray | [
"ea775a3c8ec52f32305fe30417bc3152eb9b532b"
] | [
"src/stray/training/data.py"
] | [
"import torch\nimport numpy as np\nimport cv2\nimport pytorch_lightning as pl\nimport albumentations as A\nfrom stray.scene import Scene\nfrom torch.utils.data import Dataset, ConcatDataset\nfrom stray.training import transform, get_heatmap, I\n\nclass StrayKeypointScene(Dataset):\n def __init__(self, path, conf... | [
[
"numpy.sqrt",
"numpy.linalg.inv",
"torch.is_tensor",
"numpy.stack",
"numpy.linalg.norm",
"torch.from_numpy",
"torch.cuda.is_available",
"numpy.transpose",
"numpy.argsort",
"numpy.array",
"numpy.zeros"
]
] |
dragon00417/Flask-Smart-API | [
"828a2198ae58ed1cb8902b8f333218bcd466d2c6"
] | [
"neuralpy/loss_functions/bce_loss.py"
] | [
"\"\"\"BCE Loss\"\"\"\n\nimport numpy as np\nimport torch\nfrom torch.nn import BCEWithLogitsLoss as _BCEWithLogitsLoss\n\n# pylint: disable=too-few-public-methods\n\n\nclass BCELoss:\n \"\"\"\n Applies a BCE Loss function to the model.\n\n BCE Loss automatically applies a Sigmoid Layer\n at... | [
[
"torch.tensor"
]
] |
raywan-110/quantization-DNNs | [
"7295f60d858c0e7daa7a6f14fe491b489eaa6f42"
] | [
"PTQ.py"
] | [
"import torch\nimport time\nfrom Net import MNIST_Net, CIFAR10_Net, MNIST_NetBN, CIFAR10_NetBN\nfrom utils import load_data_fashion_mnist, load_data_cifar10\n\ndef direct_quantize(model, test_loader):\n for i, (data, target) in enumerate(test_loader, 1):\n _ = model.quantize_forward(data)\n if i % ... | [
[
"torch.no_grad",
"torch.load"
]
] |
ai-msceteers/assignment2 | [
"c2d8e3883be732f21d6ee0d9590f7e455ef1f51f"
] | [
"genetic_solver/rubiks_cube.py"
] | [
"import numpy as np\nimport random\nfrom copy import deepcopy\nfrom collections import Counter\n\n \n\nclass RubiksCube(object):\n \n # Faces stores in 6 NxN faces stored as a numpy matrix in cube \n # 0 : front\n # 1 : top\n # 2 : back\n # 3 : bottom\n # 4 : left\n # 5 : right\n \... | [
[
"numpy.apply_along_axis",
"numpy.ones"
]
] |
emmamcbryde/AuTuMN-1 | [
"b1e7de15ac6ef6bed95a80efab17f0780ec9ff6f"
] | [
"autumn/models/tuberculosis/outputs.py"
] | [
"import numpy as np\n\nfrom typing import List\nfrom summer import CompartmentalModel\n\nfrom autumn.model_features.curve import tanh_based_scaleup\n\nfrom .constants import COMPARTMENTS, Compartment, INFECTIOUS_COMPS\n\n\ndef request_outputs(\n model: CompartmentalModel,\n cumulative_start_time: float,\n ... | [
[
"numpy.zeros_like"
]
] |
taechanha/JORLDY | [
"7356f7481dbc569bf745353105088d65665a4a51"
] | [
"jorldy/core/agent/mpo.py"
] | [
"from collections import deque\nimport os\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom torch.distributions import Normal\n\nfrom .base import BaseAgent\nfrom core.network import Network\nfrom core.optimizer import Optimizer\nfrom core.buffer import ReplayBuffer\n\n\nclass MPO(BaseAgent):... | [
[
"torch.mean",
"torch.max",
"torch.sum",
"torch.multinomial",
"torch.tanh",
"torch.clip",
"torch.no_grad",
"torch.cuda.is_available",
"torch.device",
"torch.softmax",
"numpy.stack",
"torch.tensor",
"torch.prod",
"torch.exp",
"torch.nn.functional.mse_loss"... |
Ding1119/BDCN-Fiber_Detect | [
"7f3db5210a1a87d02c7ef8e79038ba00a8e5ef62"
] | [
"test_image.py"
] | [
"import numpy as np\nimport torch\nimport torch.optim as optim\nimport torch.nn as nn\nfrom torch.autograd import Variable\nfrom torch.nn import functional as F\nimport time\nimport re\nimport os\nimport sys\nimport cv2\nimport bdcn\nfrom datasets.dataset import Data\nimport argparse\nimport cfg\nfrom matplotlib im... | [
[
"torch.load",
"torch.from_numpy",
"torch.nn.functional.sigmoid",
"numpy.array",
"torch.autograd.Variable"
]
] |
graviraja/seq2seq | [
"33c9eddd6386cea8bb8b3fd068f7a0d57b5bfa78"
] | [
"transformer.py"
] | [
"'''This code contains the implementation of the paper Attention is all you need.\n\nPaper: https://arxiv.org/pdf/1706.03762.pdf\nReference code: https://github.com/bentrevett/pytorch-seq2seq\n\nRelated Theory Blog post: https://graviraja.github.io/transformer/\nRelated Implemetation Blog post: https://graviraja.gi... | [
[
"torch.nn.functional.softmax",
"torch.load",
"torch.sin",
"torch.zeros",
"torch.nn.Embedding",
"torch.no_grad",
"torch.FloatTensor",
"torch.cuda.is_available",
"torch.nn.CrossEntropyLoss",
"torch.nn.Dropout",
"torch.ones",
"torch.arange",
"torch.cos",
"torch... |
sizaif/tls-diff-testing | [
"4eae4e8de6ebd1b791e32fca44b8189b04a2ea10"
] | [
"tls-diff-testing/apps/python_plot_cov/plot_cov.py"
] | [
"#!/usr/bin/python3.8\n\nimport sys\nimport matplotlib.pyplot as plot\nimport itertools\nimport random\nfrom bitarray import bitarray\n\n\n#\n# _____________________________________________________________________________\n#\ndef main(argv):\n\n files1 = ['/home/walz/data/tls-diff-testing/stimuli-20170511-the-ma... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xscale",
"matplotlib.pyplot.show"
]
] |
martagaldys/MACT20.21_Digital_tools_Big_Data_part_1 | [
"06acbaba5509f46bce2d5da5d987351f854c149a"
] | [
"Marta_Galdys/parrot/parrot_.py"
] | [
"\"\"\"\nImage-colored wordcloud with boundary map\n=========================================\nA slightly more elaborate version of an image-colored wordcloud\nthat also takes edges in the image into account.\nRecreating an image similar to the parrot example.\n\"\"\"\n\nimport os\nfrom PIL import Image\n\nimport n... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.title",
"scipy.ndimage.gaussian_gradient_magnitude",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] |
longtailfinancial/Options-Project | [
"e8ed6c293e21b6d8d4d52343f2817ea0cbe660d9"
] | [
"src/scrape_fun.py"
] | [
"import numpy as np\r\nimport pandas as pd\r\nimport sys\r\nfrom pathlib import Path\r\nimport os\r\nfrom tqdm import tqdm\r\n\r\n\r\ndef get_current_price(stock_of_interest, questrade_instance, api_key):\r\n \"\"\"\r\n Retrieves the current price of a stock using the questrade_api package.\r\n If questrad... | [
[
"pandas.concat",
"pandas.to_datetime",
"pandas.Series",
"numpy.unique",
"pandas.DataFrame",
"numpy.ones",
"pandas.read_json"
]
] |
blackmints/3DGCN | [
"550fa682c74bda5b13c421306a100d684082decc"
] | [
"model/dataset.py"
] | [
"from keras.utils import to_categorical, Sequence\nfrom rdkit import Chem\nfrom rdkit.Chem import rdmolops, AllChem\nimport numpy as np\n\n\ndef one_hot(x, allowable_set):\n # If x is not in allowed set, use last index\n if x not in allowable_set:\n x = allowable_set[-1]\n\n return list(map(lambda s... | [
[
"numpy.diag",
"numpy.power",
"numpy.eye",
"numpy.matmul",
"numpy.std",
"numpy.mean",
"numpy.array",
"numpy.isinf"
]
] |
MSanKeys963/gramex | [
"8ac5fd6e79d100982fdc9e9308d9a6250ce021e2"
] | [
"gramex/data.py"
] | [
"'''\nInteract with data from the browser\n'''\nimport io\nimport os\nimport re\nimport time\nimport json\nimport sqlalchemy as sa\nimport pandas as pd\nimport gramex.cache\nfrom tornado.escape import json_encode\nfrom gramex.config import merge, app_log\nfrom orderedattrdict import AttrDict\n\n_ENGINE_CACHE = {}\n... | [
[
"pandas.io.sql.get_schema",
"pandas.notnull",
"pandas.isnull",
"matplotlib.use",
"pandas.DataFrame",
"pandas.api.types.is_numeric_dtype",
"pandas.DataFrame.from_dict",
"pandas.ExcelWriter",
"pandas.io.sql.to_sql",
"pandas.read_sql"
]
] |
hchoi405/dppm | [
"0ce2ed2313277c243508f1cffbaa37a9644c19c5"
] | [
"scripts/exr.py"
] | [
"from __future__ import (absolute_import, division,\n print_function, unicode_literals)\nfrom builtins import *\nimport OpenEXR, Imath\nimport numpy as np\nimport os, sys\nfrom collections import defaultdict\n#import set\n\n# exr.py: Tools/helpers for various exr I/O operations\n\nFLOAT = Ima... | [
[
"numpy.fromstring",
"numpy.expand_dims",
"numpy.clip"
]
] |
HS-YN/charades-webcam | [
"67e9ee4e5b79250bc525d63e0d3bf901ab24367f"
] | [
"conversion/pytorch-tf.py"
] | [
"# @leonidk\n\nfrom __future__ import print_function\n\nimport numpy as np \nimport tensorflow as tf\nimport torch\nimport torch.nn as nn\n\nimport torchvision.models as models\nimport torchvision\n\n\ndef load_partial_state(model, state_dict):\n # @chenyuntc\n sd = model.state_dict()\n for k, v in state_d... | [
[
"tensorflow.get_variable",
"tensorflow.concat",
"torch.load",
"tensorflow.nn.max_pool",
"torch.autograd.Variable",
"tensorflow.nn.conv2d",
"tensorflow.reset_default_graph",
"numpy.argmax",
"tensorflow.Session",
"tensorflow.train.Saver",
"torch.nn.Sequential",
"torch... |
Jmq14/VQA | [
"109a426eba8384c8e624f263ff6f52591dfc9153"
] | [
"student_code/coattention_net.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\"\"\"\nN: batch size\nS: length of sequence (26)\n\"\"\"\n\n\nclass AttentionNet(nn.Module):\n def __init__(self, n_feat, n_hidden):\n super().__init__()\n\n self.feature_head = nn.Linear(n_feat, n_hidden)\n self.atten... | [
[
"torch.nn.Softmax",
"torch.nn.Dropout",
"torch.nn.LSTM",
"torch.cat",
"torch.sum",
"torch.nn.Tanh",
"torch.nn.Linear",
"torch.nn.Conv1d",
"torch.nn.functional.pad"
]
] |
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