repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
aiidateam/acwf-verification-scripts | [
"15e4625fa30e7c9f6742a4846682141e6b79ea15"
] | [
"3-analyze/analysis-scripts/formation-energies/plot_histo_formation_energies.py"
] | [
"#!/usr/bin/env python\nimport json\nimport os\nimport sys\n\nimport numpy as np\nimport pylab as pl\nfrom scipy.optimize import curve_fit\n\nBINS = 100\nPRINT_THRESHOLD = 0.01 # eV/atom\nVERBOSE = True\n\nOUT_FOLDER = 'formation-energies-output'\n\ndef gaussian(x, a, x0, sigma):\n return a * np.exp(-(x - x0)**2... | [
[
"numpy.log",
"numpy.sqrt",
"numpy.array",
"numpy.exp",
"scipy.optimize.curve_fit"
]
] | [
{
"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"... |
cliulinnaeus/exatrkx-neurips19_tf2 | [
"ee2f22ad786b870818f88e25ec1b72ebc0b6e969"
] | [
"gnn-tracking/heptrkx/postprocess/pathfinder.py"
] | [
"\"\"\"\nLoop over all hits;\nfor each hit, find next hit that has maximum weight among all available edge candidates\n\"\"\"\nimport numpy as np\nimport networkx as nx\n\nfrom .utils_fit import poly_fit, poly_val\n\ndef get_tracks(graph, weights, hit_ids, weight_cutoff):\n hits_in_tracks = []\n hits_idx_in_t... | [
[
"numpy.argsort"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jpuigcerver/tensorflow | [
"231ca9dd4e258b898cc76a283a90050fd17ee69a"
] | [
"tensorflow/python/ops/variable_scope.py"
] | [
"# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.eager.context.in_eager_mode",
"tensorflow.python.framework.ops.add_to_collection",
"tensorflow.python.ops.variables.Variable",
"tensorflow.python.framework.ops.get_collection_ref",
"tensorflow.python.framework.ops.get_collection",
"tensorflow.python.ops.resource_variable... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"2.7",
"2.6",
"1.4",
"1.13",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.2",
"1... |
darylbond/cerberus | [
"a1b99f6b50ba6876d4705f26e6be98ed6e1c5c6a",
"a1b99f6b50ba6876d4705f26e6be98ed6e1c5c6a"
] | [
"Exec/testing/Orszag-Tang/movie.py",
"Exec/testing/Viscous-Vortex/check.py"
] | [
"\nimport sys # nopep8\ncmd_folder = \"../../../vis\" # nopep8\nif cmd_folder not in sys.path: # nopep8\n sys.path.insert(0, cmd_folder)\n\nfrom tile_mov import tile_movie\nfrom make_mov import make_all, get_particle_trajectories\nimport numpy as np\nimport pylab as plt\n\n\n# ================================... | [
[
"numpy.meshgrid",
"numpy.sqrt"
],
[
"numpy.sqrt",
"numpy.meshgrid",
"numpy.abs",
"numpy.exp",
"numpy.max",
"numpy.argsort",
"numpy.ravel",
"numpy.ma.masked_where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.13",
"1.16",
"1.9",
"1.18",
"1.20",
"1.7",
"1.15",
"1.14",
"... |
Farazist/farazist-raspberrypi-app | [
"db497d68b2b206f0a5a5e6a9d88c464445179f8d"
] | [
"main_offline.py"
] | [
"from io import BytesIO\nimport os\nimport sys\nimport qrcode\nfrom pygame import mixer\nfrom time import sleep, time\nfrom threading import Thread, Timer, Event\nfrom functools import partial\nfrom escpos.printer import Usb\nfrom gpiozero import DistanceSensor\nfrom gpiozero.pins.native import NativeFactory\nfrom ... | [
[
"scipy.stats.mode"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
alkaren/MaskDetector | [
"e992796823e5d7b6941cdd9dd5df6e5c4f9e1ee4"
] | [
"source/video_detector.py"
] | [
"import time\nimport numpy as np\nimport cv2\nimport imutils\nimport os\nimport threading\n\nfrom datetime import date, datetime\nfrom imutils.video import VideoStream\nfrom tensorflow import keras\nfrom tensorflow.python.keras.applications.mobilenet_v2 import preprocess_input\n\nfrom source.utils import preprocess... | [
[
"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"
]
}
] |
MatthiasJakobs/tsx | [
"8a686ffd0af2f9f826d9ce11349e0fa0e883e897",
"8a686ffd0af2f9f826d9ce11349e0fa0e883e897"
] | [
"tsx/models/classifier/fcn.py",
"tsx/examples/rocket_native_guide.py"
] | [
"import torch\nimport torch.nn as nn\n\nfrom tsx.models.classifier import BasePyTorchClassifier\n\nclass TimeSeries1DNet(BasePyTorchClassifier):\n\n def __init__(self, input_size=1, kernel_size=7, **kwargs):\n super().__init__(**kwargs)\n self.conv1 = self._conv1dblock(input_size, 32, kernel_size... | [
[
"torch.nn.BatchNorm1d",
"torch.nn.Flatten",
"torch.nn.Linear",
"torch.nn.Conv1d",
"torch.nn.ReLU",
"torch.nn.AvgPool1d",
"torch.argmax"
],
[
"numpy.unique",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figur... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
javicodema/tensorflow_course | [
"35ef9d3f9413a04dbcd5946bdaa17fba02088ab9"
] | [
"catsDogsClassifying.py"
] | [
"import tensorflow as tf\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\n\nimport os\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nimport logging\n\nlogger = tf.get_logger()\nlogger.setLevel(logging.ERROR)\n\n_URL = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtere... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.tight_layout",
"tensorflow.keras.preprocessing.image.ImageDataGenerator",
"matplotlib.pyplot.title",
"tensorflow.keras.losses.SparseCategoricalCrossentropy",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.Conv2D",
"matplotli... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
Antimortine/made_nlp_course | [
"2094e02751462f292d9dec75d02ad8c0672eda9b"
] | [
"homeworks/homework03/utils.py"
] | [
"import glob\nimport os\nimport tensorflow as tf\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\nimport torch\nimport torch.nn as nn\nfrom torch.nn.functional import log_softmax\n\n\ndef remove_tech_tokens(mystr, tokens_to_remove=['<eos>', '<sos>', '<unk>', '<pad>']):\n return [x ... | [
[
"torch.nn.init.uniform_",
"matplotlib.pyplot.tight_layout",
"torch.nn.functional.log_softmax",
"torch.cat",
"torch.full",
"torch.zeros",
"matplotlib.pyplot.subplots",
"pandas.DataFrame.from_records",
"torch.no_grad",
"tensorflow.train.summary_iterator",
"torch.topk"
]... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
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"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
mikelvallejo/covid-dashboard | [
"30d6db83d80bb524af373befb255f6c977edb3ab"
] | [
"src/pages/utils/load_time_series.py"
] | [
"import pandas as pd\nimport streamlit as st\n\n\n@st.cache\ndef load_time_series():\n \"\"\"\n Function aggregates and returns a dictionary of time series data.\n :return: dict\n \"\"\"\n confirmed_data = pd.read_csv(\n \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_c... | [
[
"pandas.read_csv",
"pandas.to_datetime"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
ashwin2401/ga-learner-dsmp-repo | [
"57e8d055b385acf17b1390c0f36a68ee83ad9962"
] | [
"Customer-Segmentation/code.py"
] | [
"# --------------\n# import packages\n\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt \n\n\n\n# Load Offers\noffers = pd.read_excel(path,sheet_name=0)\n\n# Load Transactions\ntransactions = pd.read_excel(path,sheet_name=1)\ntransactions['n'] = 1\n# Merge dataframes\... | [
[
"pandas.merge",
"pandas.read_excel",
"sklearn.decomposition.PCA",
"sklearn.cluster.KMeans"
]
] | [
{
"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": []
}
] |
makkenno/task6_python | [
"75db9075885b164b309bfc78a306d839e19ca9e2"
] | [
"ichibaitemranking.py"
] | [
"import requests\nimport pandas as pd\nimport pprint\nfrom pathlib import Path\n\nURL = 'https://app.rakuten.co.jp/services/api/IchibaItem/Ranking/20170628?'\nAPP_ID = '1063506377423532236'\n\ndef ranking(genre_id): \n params = {\n 'applicationId': APP_ID,\n 'format': 'json',\n 'genreId': genre_id,\n }\n... | [
[
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
cmpaulo/ProfilesArchiveInstagram | [
"fe1da302110d37039f38ee2cf09697e190d2709b"
] | [
"WebscrapingInstagram_v1.py"
] | [
"#!/usr/bin/env python3\n# coding: utf-8\n\n# # Web Scraping Instagram with Selenium\n\nfrom unicodedata import name\nfrom selenium import webdriver\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.wait import WebDriverWa... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
leefirefly/DL4Epi | [
"0f249579427c98881fc4145b6a820b91f3e39bed"
] | [
"models/VAR.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass Model(nn.Module):\n def __init__(self, args, data):\n super(Model, self).__init__()\n self.use_cuda = args.cuda\n self.m = data.m\n self.w = args.window\n\n self.linear = nn.Linear(self.m * self.w, s... | [
[
"torch.nn.Linear"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TanayNarshana/rethinking-network-pruning | [
"85360333c909d539880ff59101c7b5f9609789f7"
] | [
"imagenet/regression-pruning/main_B.py"
] | [
"import argparse\nimport numpy as np\nimport os\nimport shutil\nimport time\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.parallel\nimport torch.backends.cudnn as cudnn\nimport torch.distributed as dist\nimport torch.optim\nimport torch.utils.data\nimport torch.utils.data.distributed\nimport torchvision.t... | [
[
"torch.nn.CrossEntropyLoss",
"torch.distributed.init_process_group",
"torch.utils.data.distributed.DistributedSampler",
"torch.load",
"torch.utils.data.DataLoader",
"torch.no_grad",
"torch.nn.DataParallel",
"torch.nn.parallel.DistributedDataParallel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ayl/gpt-neox | [
"be5a1eaa394196b24a4cde5414d6afaed39570aa"
] | [
"megatron/logging.py"
] | [
"# Copyright (c) 2021, EleutherAI contributors\n# This file is based on code by the authors denoted below and has been modified from its original version.\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 c... | [
[
"torch.distributed.get_rank",
"torch.distributed.get_world_size",
"torch.norm"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ewanowara/geolocalization | [
"6fcd26772cc297ee49889463ee42ad025544330a"
] | [
"setup/download_images.py"
] | [
"from argparse import ArgumentParser\nimport sys\nfrom io import BytesIO\nfrom pathlib import Path\nimport time\nfrom multiprocessing import Pool\nfrom functools import partial\nimport re\nimport logging\nimport requests\n\nimport msgpack\nimport pandas as pd\nimport PIL\nfrom PIL import ImageFile\n\n\nImageFile.LO... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
prasun2106/customer_churn_using_neural_networks | [
"e1435535a0265c9bfbc0ce95a496e3e97ecaa82b"
] | [
"customer_churn_nn.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n\n# In[2]:\n\n\n# Step 1: Import and Preprocessing\n# Importing the dataset\ndataset = pd.read_csv('data/Churn_Modelling.csv')\nX = dataset.iloc[:, 3:13]\ny = dataset.iloc[:, 13]\n\n\... | [
[
"sklearn.model_selection.GridSearchCV",
"pandas.read_csv",
"sklearn.model_selection.cross_val_score",
"sklearn.metrics.accuracy_score",
"sklearn.metrics.precision_score",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"pandas.cut",
"sklearn.metrics.f1_score",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
willismax/machine-learning-engineering-for-production-public | [
"3602b1bea0744b97658cbbc2d61072f7dcef33d7"
] | [
"course4/week3-ungraded-labs/C4_W3_Lab_4_Github_Actions/app/main.py"
] | [
"import pickle\nimport numpy as np\nfrom typing import List\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel, conlist\n\n\n\napp = FastAPI(title=\"Predicting Wine Class with batching!!\")\n\n# Open classifier in global scope\nwith open(\"models/wine.pkl\", \"rb\") as file:\n clf = pickle.load(file)\n... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shujaatkhan/HARK | [
"8dfaa7e03789bd380d7d314f760949c6daf4041c"
] | [
"HARKsimulation.py"
] | [
"'''\nFunctions for generating simulated data and shocks.\n'''\n\nfrom __future__ import division\nimport warnings # A library for runtime warnings\nimport numpy as np # Numerical Python\n\ndef drawMeanOneLognormal(N, sigma=1.0, seed=0):\n '''\n Generate ar... | [
[
"numpy.asarray",
"numpy.arange",
"numpy.random.RandomState",
"numpy.cumsum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ssklykov/collection_numCalc | [
"f6c69aa582fc811b998a0989b99157b8566c884f"
] | [
"Regression/SampleValues.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nClass for modelling samples values (measurments with some deviations)\nDeveloped in Spyder IDE\n@author: ssklykov\n\"\"\"\n# %% \"Dependecies\" - imports\nimport numpy as np\nimport math\n\n\n# %% Class itself\nclass GenerateSample():\n \"\"\"Class for generating sample values\... | [
[
"numpy.zeros",
"numpy.linspace",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
markovalexander/DDPM | [
"8fa813ac0c27afb1a8133b6d57ee48223629b684"
] | [
"lib/samplers.py"
] | [
"from abc import ABC, abstractmethod\n\nimport torch\nimport numpy as np\n\n\nclass AbstractSampler(ABC):\n @abstractmethod\n def weights(self):\n ...\n\n def sample(self, batch_size, device):\n weights = self.weights()\n probs = weights / np.sum(weights)\n indxes = np.random.ch... | [
[
"torch.from_numpy",
"numpy.ones",
"numpy.mean",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
skn123/statismo-1 | [
"a380f33cf070d1c4ba624db8b0c6d946d2aecabf"
] | [
"modules/VTK/wrapping/tests/statismoTests/test_builders.py"
] | [
"#\n# This file is part of the statismo library.\n#\n# Author: Marcel Luethi (marcel.luethi@unibas.ch)\n#\n# Copyright (c) 2011 University of Basel\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions\n... | [
[
"numpy.lib.scimath.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.11",
"1.19",
"1.24",
"1.16",
"1.23",
"1.20",
"1.7",
"1.12",
"1.21",
"1.22",
"1.14",
"1.6",
"1.13",
"1.9",
"1.17",
"1.10",
"1.18",
"1.15",
"1.8"
],
"pand... |
rodrigolece/whizz-library | [
"beb3b6a31000239843bdfae8f4edd2a700749ce7"
] | [
"whizzlibrary/quarters.py"
] | [
"\nimport numpy as np\n\n\n\ndef roundNearestQuarter(x):\n return 25*np.round(x/25)\n\ndef floorNearestQuarter(x):\n return 25*np.floor(x/25)\n\n\ndef histogramQuarters(x):\n sorted_array = np.sort(x)\n m, M = sorted_array[0], sorted_array[-1]\n bins = np.arange(m, M+26, 25) - 12.5\n\n counts = n... | [
[
"numpy.arange",
"numpy.linalg.norm",
"numpy.sort",
"numpy.round",
"numpy.append",
"numpy.floor",
"numpy.where",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rdcsung/practical-pytorch | [
"6c57013c16eb928232af5e9bbe886a41c4ac9f9e"
] | [
"conditional-char-rnn/train.py"
] | [
"# Practical PyTorch: Generating Names with a Conditional Character-Level RNN\n# https://github.com/spro/practical-pytorch\n\nimport glob\nimport unicodedata\nimport string\nimport random\nimport time\nimport math\n\nimport torch\nimport torch.nn as nn\n\nfrom data import *\nfrom model import *\n\nimport config\n\n... | [
[
"torch.nn.CrossEntropyLoss",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ZhangSanFengByGit/toolkit | [
"9f2958bfd67d140afbc22f12c8d38995996330b0"
] | [
"got10k/trackers/__init__.py"
] | [
"from __future__ import absolute_import\n\nimport numpy as np\nimport time\nfrom PIL import Image\nimport cv2\n\nfrom ..utils.viz import show_frame\n\n\nclass Tracker(object):\n\n def __init__(self, name, is_deterministic=False):\n self.name = name\n self.is_deterministic = is_deterministic\n \n... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vishalbelsare/pcpca | [
"763e61c669a6cdadbd706e3cb0d553e5f5bd6ff7"
] | [
"experiments/realworld/scrnaseq/single_cell_bmmc.py"
] | [
"from pcpca import PCPCA, CPCA\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom os.path import join as pjoin\nfrom scipy.io import mmread\nfrom sklearn.decomposition import PCA\n\n\nDATA_DIR = \"../../../data/singlecell_bmmc\"\nN_COMPONENTS = 10\n\n\nif __name__... | [
[
"matplotlib.pyplot.gca",
"pandas.concat",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.show",
"pandas.DataFrame",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.xlabel",
"numpy.array",
"matplotlib.rc",
"matplotlib.pyplot.ylabel"
]
] | [
{
"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": []
}
] |
LauritzRaisch/picosdk-python-wrappers | [
"08be77a2efd917a5d837e2caad8f771579a79de2",
"08be77a2efd917a5d837e2caad8f771579a79de2"
] | [
"ps5000aExamples/ps5000aBlockCallbackExample.py",
"ps4000aExamples/ps4000aStreamingExample.py"
] | [
"#\n# Copyright (C) 2018 Pico Technology Ltd. See LICENSE file for terms.\n#\n# PS5000A BLOCK MODE EXAMPLE\n# This example opens a 5000a driver device, sets up two channels and a trigger then collects a block of data.\n# This data is then plotted as mV against time in ns.\n\nimport ctypes\nimport numpy as np\nfrom ... | [
[
"numpy.linspace",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
],
[
"numpy.linspace",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"numpy.zeros",
"matplotlib.pyplot.ylab... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kyawlin/smlb | [
"79c757d7fc040fb30ad44410be158b3ce3bdf30d"
] | [
"learners/scikit_learn/gaussian_process_regression_sklearn.py"
] | [
"\"\"\"Gaussian Process Regression, scikit-learn implementation.\n\nScientific Machine Learning Benchmark: \nA benchmark of regression models in chem- and materials informatics.\n2019-2020, Citrine Informatics.\n\nGaussian process regression is a Bayesian kernel regression algorithm.\nIt is closely related to its F... | [
[
"numpy.square",
"numpy.sqrt",
"numpy.ones",
"sklearn.gaussian_process.GaussianProcessRegressor",
"sklearn.gaussian_process.kernels.WhiteKernel",
"sklearn.gaussian_process.kernels.RBF",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ihakiwamu/Experiment_Ryakugo | [
"79667ef2d66ae8008c8ce57c27fe4d65d153aee6"
] | [
"ziken03/sample.py"
] | [
"from sklearn import datasets\nimport numpy as np\niris = datasets.load_iris()\nX = iris.data[:, [2, 3]]\ny = iris.target\nprint(X)\n"
] | [
[
"sklearn.datasets.load_iris"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Emekaborisama/word_embedding_loader | [
"5b0fd435360d335341dc111bbc52869bb2731422"
] | [
"test/word_embedding_loader/saver/test_saver.py"
] | [
"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, division, print_function, \\\n unicode_literals\n\nimport numpy as np\nimport pytest\nfrom numpy.testing import assert_array_equal\n\nfrom word_embedding_loader import word_embedding\nimport word_embedding_loader.saver as saver\n\n\n@pytest.fixtur... | [
[
"numpy.testing.assert_array_equal",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cansik/mesh-sequence-player | [
"1b37467e3043f135725049d93431503f64fd5c73"
] | [
"mesh_sequence_player/MeshSequencePlayer.py"
] | [
"import os.path\nimport time\nfrom functools import partial\nfrom typing import Optional\n\nimport numpy as np\nimport open3d as o3d\nfrom moviepy.video.io.ImageSequenceClip import ImageSequenceClip\nfrom tqdm import tqdm\n\nfrom mesh_sequence_player.FPSCounter import FPSCounter\nfrom mesh_sequence_player.FastGeome... | [
[
"numpy.asarray",
"numpy.uint8"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
psychochatbot/Psychochatbot | [
"9a5b7cdd11f1347d5fecb90e0ee5536500fb8dd2"
] | [
"app.py"
] | [
"#!/usr/bin/env python\n\nimport urllib\nimport json\nimport os\nimport pickle\nimport io\nimport pandas as pd\nfrom flask import Flask\nfrom flask import request\nfrom flask import make_response\nfrom flask import session\nfrom sklearn.preprocessing import LabelEncoder\n\n\n# Flask app should start in global layou... | [
[
"sklearn.preprocessing.LabelEncoder",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
medusa-trade/alpaca-trade-api-python | [
"4ed48200a7bec15705b2c56c3f2ae94f4636cd29"
] | [
"tests/test_polygon/test_rest.py"
] | [
"import datetime\nimport pandas as pd\nfrom alpaca_trade_api import polygon\nfrom alpaca_trade_api.polygon import REST\nimport pytest\nimport requests_mock\nfrom alpaca_trade_api.polygon.rest import FinancialsReportType, FinancialsSort\n\n\n@pytest.fixture\ndef reqmock():\n with requests_mock.Mocker() as m:\n ... | [
[
"pandas.Timestamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Data-is-life/apt-get-home | [
"77a212c19a90f201c70759fd9e99493657247ae7"
] | [
"src/initial_scrapper_function.py"
] | [
"# Author: Mohit Gangwani\n# Github: Data-is-Life\n# Date: 09/30/2018\n\nimport time\nimport random\nimport requests\nimport pandas as pd\nfrom bs4 import BeautifulSoup\nfrom random import randint\n\n\ndef session_creator(ua, url, proxy):\n '''This function is used to create a session to get data from a website.... | [
[
"pandas.concat",
"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": []
}
] |
Sai-Venky/Trackjectory | [
"273eb623410d5150f71a8828febc7c8ff4002e13"
] | [
"src/dataset/mot_dataset.py"
] | [
"import glob\nimport math\nimport os\nimport os.path as osp\nimport random\nimport time\nfrom collections import OrderedDict\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\nimport cv2\nimport json\nimport numpy as np\nimport torch\nimport copy\n\nfrom torch.utils.data import Dataset\nfrom to... | [
[
"numpy.maximum",
"torch.zeros",
"numpy.ascontiguousarray",
"numpy.eye",
"matplotlib.patches.Rectangle",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"numpy.ones",
"numpy.concatenate",
"numpy.array",
"numpy.zeros",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
FTC-12586/Tensor-Flow-Machine-Learning | [
"b32be8b49b9364dccc6b213c3f0720314c8d103f"
] | [
"src/learning_rate.py"
] | [
"import tensorflow as tf\nfrom tensorflow import keras\n\n\nclass LearningRateScheduler(keras.callbacks.Callback):\n \"\"\"Learning rate scheduler which sets the learning rate according to schedule.\n\n Arguments:\n schedule: a function that takes an epoch index\n (integer, indexed from 0) and c... | [
[
"tensorflow.keras.backend.get_value",
"tensorflow.keras.backend.set_value"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
mnansary/banglaOCR | [
"20d48810eee8e1a6d47d60e716764c32a1c6df6f"
] | [
"coreLib/store.py"
] | [
"#-*- coding: utf-8 -*-\n\"\"\"\n@author:MD.Nazmuddoha Ansary\n\"\"\"\nfrom __future__ import print_function\n# ---------------------------------------------------------\n# imports\n# ---------------------------------------------------------\n\nimport os\nimport tensorflow as tf \nfrom tqdm import tqdm\nimport nump... | [
[
"tensorflow.io.TFRecordWriter",
"tensorflow.train.Example",
"tensorflow.train.Features",
"tensorflow.train.BytesList",
"numpy.load",
"tensorflow.train.Int64List"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
miramirakim227/SwapNeRF_GT | [
"84444660a7fc8b5f796503d90f3a055889c44389"
] | [
"src/model/layers.py"
] | [
"import torch.nn as nn\nimport torch.nn.functional as F\nfrom kornia.filters import filter2D\nimport torch\n\n\n# Resnet Blocks\nclass ResnetBlockFC(nn.Module):\n ''' Fully connected ResNet Block class.\n\n Args:\n size_in (int): input dimension\n size_out (int): output dimension\n size_h... | [
[
"torch.Tensor",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.functional.leaky_relu",
"torch.nn.init.zeros_",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yusuf-onur/erp-captcha-cracker | [
"2e05a6eb9bd3b3a925709799b3912441a233d620"
] | [
"cracker/extract_digits.py"
] | [
"from utils import *\n\nfrom PIL import Image\nimport numpy as np\nfrom scipy.ndimage.measurements import label\n\ndef get_digits(img, gray_thres=130, size_thres=20):\n\ta = np.asarray(img).copy()\n\ta = np.delete(a, 3, 2) # remove alpha channel\n\n\tmask = a[:, :, 0] >= gray_thres\n\ta[mask, :] = 255 # remove grid... | [
[
"numpy.sum",
"numpy.min",
"numpy.asarray",
"numpy.arange",
"scipy.ndimage.measurements.label",
"numpy.delete",
"numpy.argmax",
"numpy.mean",
"numpy.maximum.accumulate",
"numpy.roll"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"0.15",
"1.4",
"0.16",
"1.0",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"0.10",
"0.17",
"1.3"
],
"tensorflow": [... |
saraswat/munkhdalai-nse | [
"11264ea0c88039cbdca08fc72dbd8620074df120"
] | [
"snli/NSE_MMA_attention.py"
] | [
"import math\nimport sys\nimport time\nimport copy\nimport numpy as np\nimport six\nfrom chainer import cuda, Variable, FunctionSet, optimizers\nimport chainer.functions as F\nimport chainer.links as L\nimport chainer\n\nclass NSE_MMA_attention(chainer.Chain):\n\n\t\"\"\"docstring for NSE_MMA_attention\"\"\"\n\tde... | [
[
"numpy.random.uniform"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
renyi533/ranking | [
"6cf8f70a8533ba15abbfb5f50db17cb01fc56410"
] | [
"tensorflow_ranking/python/keras/feature_test.py"
] | [
"# Copyright 2021 The TensorFlow Ranking Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by appli... | [
[
"tensorflow.compat.v2.test.main",
"tensorflow.compat.v2.enable_v2_behavior",
"tensorflow.compat.v2.math.log1p",
"tensorflow.compat.v2.convert_to_tensor",
"tensorflow.compat.v2.feature_column.categorical_column_with_vocabulary_list",
"tensorflow.compat.v2.feature_column.numeric_column",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NetherlandsForensicInstitute/lir | [
"341b9db3153da0b00c7bfdae01b897f2850b4050"
] | [
"lir/_plotting_new.py"
] | [
"from contextlib import contextmanager\nfrom functools import partial\nimport logging\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom .bayeserror import plot_nbe as nbe\nfrom .calibration import IsotonicCalibrator\nfrom .ece import plot_ece as ece\nfrom . import util\n\n\nLOG = logging.getLogger(__na... | [
[
"numpy.linspace",
"numpy.isneginf",
"numpy.max",
"numpy.histogram",
"numpy.ones_like",
"numpy.unique",
"numpy.arange",
"matplotlib.pyplot.close",
"matplotlib.pyplot.figure",
"numpy.min",
"numpy.isnan",
"numpy.log10",
"numpy.errstate",
"numpy.histogram_bin_ed... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mgzhao/DLshoppingcart | [
"4a34beebdd5996ef39be184fbf1b9e23c9e9d436"
] | [
"run.py"
] | [
"# Copyright (c) 2017 NVIDIA Corporation\nimport torch\nimport argparse\nfrom reco_encoder.data import input_layer\nfrom reco_encoder.model import model\nimport torch.optim as optim\nfrom torch.optim.lr_scheduler import MultiStepLR\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport copy\nimport tim... | [
[
"torch.optim.lr_scheduler.MultiStepLR",
"torch.nn.Dropout",
"torch.load",
"numpy.linalg.norm",
"torch.nn.DataParallel",
"torch.autograd.Variable"
]
] | [
{
"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": [],
... |
vipulraheja/transformers | [
"864c1dfe34e43038fcd2289505f5cc7acd65ad2e"
] | [
"src/transformers/models/albert/modeling_albert.py"
] | [
"# coding=utf-8\n# Copyright 2018 Google AI, Google Brain and the HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LIC... | [
[
"torch.nn.Softmax",
"torch.nn.Dropout",
"torch.nn.CrossEntropyLoss",
"torch.ones",
"torch.zeros",
"torch.einsum",
"torch.from_numpy",
"torch.nn.Embedding",
"torch.nn.LayerNorm",
"tensorflow.train.load_variable",
"torch.nn.Linear",
"torch.matmul",
"torch.nn.Tanh"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
masukai/AutoColorIntensity | [
"7cba8105eed5bbc708c0fbb1c4841325aae33ed5"
] | [
"AutoColorIntensity.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf8 -*-\nimport os\nimport glob\nimport cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport csv\nimport time\n\n# _listはリスト\n# np_はnp.arrayに格納されている\n\n\ndef main(scale, ext, name, HSV_u, HSV_l, cl, gauk, gaun, closing_on): # メイン関数\n start_time = time.time()\n\... | [
[
"matplotlib.pyplot.legend",
"numpy.polyfit",
"numpy.mean",
"numpy.histogram",
"numpy.ravel",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.savefig",
"numpy.transpose",
"numpy.argsort",
"numpy.array",
"matplotlib.pyplot.ylabel",
"numpy.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mandarvast19/ga-learner-dsmp-repo | [
"d944ce257ae54c51f4489dc5182ef84819dcb263"
] | [
"Visualization-for-Company-Stakeholders/code.py"
] | [
"# --------------\n#Importing header files\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\ndata=pd.read_csv(path)\r\n#Code starts here\r\nloan_status = data['Loan_Status'].value_counts()\r\nloan_status.plot(kind='bar')\n\n\n# --------------\n#Code starts here\r\nproperty_and_l... | [
[
"matplotlib.pyplot.legend",
"pandas.read_csv",
"matplotlib.pyplot.title",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Ollehto/ox_lib | [
"2326aad94be4635b43d70fbedc63e669b2643019"
] | [
"lib/functions.py"
] | [
"import numpy as np\n\ndef get_board(state, b0=None):\n\tif b0 is None:\n\t\trepr_array = np.empty(9, dtype=np.int8)\n\telse:\n\t\trepr_array = b0.ravel()\n\tfor n in range(0, 8):\n\t\tnew_state = state // (3**(8-n))\n\t\trepr_array[8-n] = new_state\n\t\tstate -= new_state * (3**(8-n))\n\trepr_array[0] = state\n\ti... | [
[
"numpy.all",
"numpy.arange",
"numpy.sum",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
animeshramesh/super-resolution | [
"59d005d23387cb6f382c8eeff3421ca3bae0f200"
] | [
"generate_training_data.py"
] | [
"'''\nDump all your images in dataset/output.\nThis script will generate the corresponding low-res images in dataset/input.\n1. Apply Gaussian blur\n2. Downsample images\n'''\n\nimport os\nimport numpy as np\nimport scipy\nimport scipy.misc, scipy.ndimage\nfrom scipy.misc import imsave, imread, imresize\nfrom scipy... | [
[
"scipy.misc.imresize",
"numpy.sqrt",
"scipy.misc.imsave",
"scipy.ndimage.filters.gaussian_filter",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"1.0",
"0.19",
"0.18",
"1.2",
"0.12",
"0.10",
"0.17",
"0.16"
],
"tensorflow": []
}
] |
nvaytet/osyris | [
"7deb57d2e6e6635fd4f065a196466d1db02644fc"
] | [
"src/osyris/io/amr.py"
] | [
"# SPDX-License-Identifier: BSD-3-Clause\n# Copyright (c) 2021 Osyris contributors (https://github.com/nvaytet/osyris)\nimport numpy as np\nfrom .hilbert import hilbert_cpu_list\nfrom .reader import Reader, ReaderKind\nfrom .. import config\nfrom .. import units\nfrom . import utils\n\n\nclass AmrReader(Reader):\n ... | [
[
"numpy.logical_and",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
stoneyang159/fer_pytorch | [
"32e0748a084a366c70da1d88608050544e56c4bf"
] | [
"fer_pytorch/models/backbone/cafferesnet.py"
] | [
"from __future__ import print_function, division, absolute_import\nimport math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.model_zoo as model_zoo\n\npretrained_settings = {\n 'cafferesnet101': {\n 'imagenet': {\n 'url': 'http://data.lip6.fr/cadene/p... | [
[
"torch.nn.Sequential",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.AvgPool2d",
"torch.nn.Linear",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU",
"torch.utils.model_zoo.load_url"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
amaiya/stellargraph | [
"512e60a8f572a4bb432b0397a2b452251e167d8f",
"ef5588038896c1a65db467a768f2e023c6562611"
] | [
"tests/layer/test_cluster_gcn.py",
"stellargraph/data/converter.py"
] | [
"# -*- coding: utf-8 -*-\n#\n# Copyright 2018-2019 Data61, CSIRO\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.regularizers.l2",
"tensorflow.keras.backend.squeeze",
"tensorflow.keras.Model",
"tensorflow.keras.backend.expand_dims",
"numpy.array"
],
[
"numpy.nanmedian",
"numpy.isfinite",
"numpy.asarray",
"numpy.reshape",
"numpy.squeeze",
"numpy.ndim",
"nu... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kwan3217/kwanspice | [
"38303ff516dabf965cdc754c48290187cc237da3"
] | [
"voyager/supertraj_camera.py"
] | [
"import spiceypy as cspice\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n#Solar system positions\ncspice.furnsh(\"../../Data/spice/generic/spk/planets/de430.bsp\")\n#Satellite positions\ncspice.furnsh(\"../../Data/spice/generic/spk/satellites/jup230l.bsp\")\n#Planet constants\ncspice.furnsh(\"../../Data/s... | [
[
"numpy.arctan2",
"numpy.arange",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
taejoo/MLOps_Recipes032820 | [
"0f135ae32034b0a08871a2189375c8a3f6222f9b"
] | [
"models/risk-model/train/train.py"
] | [
"import os\nimport sys\nimport argparse\n\nimport dotenv\nimport joblib\nimport pandas as pd\nfrom azureml.core import Run\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_spl... | [
[
"pandas.read_csv",
"sklearn.linear_model.LogisticRegression",
"sklearn.preprocessing.OneHotEncoder",
"sklearn.impute.SimpleImputer",
"sklearn.model_selection.train_test_split",
"sklearn.preprocessing.StandardScaler",
"sklearn.preprocessing.LabelEncoder",
"sklearn.compose.ColumnTran... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
michaelaye/pandas | [
"c6110e25b3eceb2f25022c2aa9ccea03c0b8b359"
] | [
"pandas/io/json.py"
] | [
"# pylint: disable-msg=E1101,W0613,W0603\n\nimport os\nimport copy\nfrom collections import defaultdict\nimport numpy as np\n\nimport pandas.json as _json\nfrom pandas.tslib import iNaT\nfrom pandas.compat import long, u\nfrom pandas import compat, isnull\nfrom pandas import Series, DataFrame, to_datetime\nfrom pan... | [
[
"pandas.to_datetime",
"pandas.Series",
"pandas.compat.u",
"pandas.isnull",
"pandas.core.common.AbstractMethodError",
"pandas.formats.printing.pprint_thing",
"pandas.DataFrame",
"numpy.dtype",
"pandas.io.common.get_filepath_or_buffer",
"pandas.compat.iteritems",
"pandas.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Kedo-Aleksei/statsmodels | [
"d88ef76d6c05f3c77b24500514d9e9c249429376"
] | [
"statsmodels/stats/multivariate.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Nov 5 14:48:19 2017\n\nAuthor: Josef Perktold\nLicense: BSD-3\n\"\"\"\n\nimport numpy as np\nfrom scipy import stats\n\nfrom statsmodels.stats.moment_helpers import cov2corr\nfrom statsmodels.stats.base import HolderTuple\nfrom statsmodels.tools.validation import ar... | [
[
"numpy.log",
"numpy.linalg.solve",
"numpy.sqrt",
"numpy.asarray",
"scipy.stats.chi2.sf",
"numpy.linalg.slogdet",
"numpy.eye",
"numpy.arange",
"numpy.cumsum",
"numpy.trace",
"scipy.stats.t.isf",
"numpy.atleast_2d",
"numpy.cov",
"scipy.stats.f.isf",
"scipy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Nodmgatall/pytorch-A3C | [
"ea418a2677c19350e9b684f9c13725f3f280bb7b"
] | [
"plot.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport sys\nimport re\n\narr = []\nshortForms = [\"gu\", \"g\",\"me\",\"mes\",\"wc\"]\nif len(sys.argv) == 1:\n arr = files;\n\nelif sys.argv[1].isdigit():\n arr = sys.argv[2:]\nelse:\n arr = sys.argv[1:]\n\nfor x in arr:\n n = [float(s) for s in re.... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.tight_layout",
"numpy.load",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sharksmhi/sharkpylib | [
"2a1d3cf3c15729e50525ab8da5920b6f9bb3faf2"
] | [
"sharkpylib/ices/ices.py"
] | [
"# -*- coding: utf-8 -*-\n#\n# Copyright (c) 2018 SMHI, Swedish Meteorological and Hydrological Institute\n# License: MIT License (see LICENSE.txt or http://opensource.org/licenses/mit).\n\"\"\"\nCreated on Thu Aug 30 15:30:28 2018\n\n@author:\n\"\"\"\n\nimport os\nimport codecs\nimport datetime\n\ntry:\n import... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
kurazu/advent_of_code_2021 | [
"a4b18e0e7f286d3485d85f2a1a58c7bdea0115d7"
] | [
"advent/day_13/task_1.py"
] | [
"import enum\nimport itertools\nimport logging\nimport re\nfrom typing import Callable, Dict, Iterable, List, Optional, TextIO, Tuple\n\nimport numpy as np\nimport numpy.typing as npt\n\nfrom ..cli import run_with_file_argument\nfrom ..io_utils import get_lines\n\nlogger = logging.getLogger(__name__)\nPATTERN = re.... | [
[
"numpy.max",
"numpy.vectorize",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
su-khi/pandas | [
"ffe312c4beb3ca09c1fb45cf727d7c17e276e8fd"
] | [
"pandas/core/resample.py"
] | [
"from datetime import timedelta\nimport numpy as np\nimport warnings\nimport copy\nfrom textwrap import dedent\n\nimport pandas as pd\nfrom pandas.core.groupby.base import GroupByMixin\nfrom pandas.core.groupby.ops import BinGrouper\nfrom pandas.core.groupby.groupby import (\n _GroupBy, GroupBy, groupby, _pipe_t... | [
[
"pandas.tseries.frequencies.to_offset",
"pandas.core.indexes.datetimes.DatetimeIndex",
"pandas._libs.tslibs.Timestamp",
"pandas.Series",
"pandas._libs.lib.generate_bins_dt64",
"pandas.core.indexes.datetimes.date_range",
"pandas.core.indexes.period.PeriodIndex",
"pandas.util._decora... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"1.1",
"0.24",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
mad-lab-fau/BioPsyK | [
"8ed7a2949e9c03c7d67b9ac6d17948ae218d94c1"
] | [
"src/biopsykit/sleep/plotting.py"
] | [
"\"\"\"Module providing functions to plot data collected during sleep studies.\"\"\"\nimport datetime\nfrom typing import Dict, Iterable, List, Optional, Sequence, Tuple, Union\n\nimport matplotlib.dates as mdates\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as mticks\nimport pandas as pd\nimport seab... | [
[
"matplotlib.dates.DateFormatter",
"pandas.to_datetime",
"matplotlib.ticker.AutoMinorLocator",
"matplotlib.pyplot.subplots",
"pandas.DataFrame",
"pandas.Timedelta",
"matplotlib.dates.date2num"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BLSQ/openhexa-pipelines | [
"54e1afb575d03b0c458492325036d4a994fa2c90"
] | [
"dhis2-extraction/tests/test_dhis2extract.py"
] | [
"import json\nimport os\nimport re\nimport tempfile\nfrom io import StringIO\n\nimport dhis2extract\nimport pandas as pd\nimport pytest\nimport responses\nfrom click.testing import CliRunner\nfrom fsspec.implementations.http import HTTPFileSystem\nfrom fsspec.implementations.local import LocalFileSystem\nfrom s3fs ... | [
[
"pandas.notna",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
zhouqiang06/ard-gap-filler | [
"98ee5688619f1ed3b9b8f0a9721b00c82219ccdb"
] | [
"test/ard_gap_filler_test.py"
] | [
"import ard_gap_filler as gf\r\n\r\nimport os\r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport pickle\r\n\r\nif __name__ == '__main__':\r\n\r\n ##############\r\n # Example of pixel time series fill\r\n # A quick and easy way to check the pixel results\r\n ######... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.scatter",
"numpy.ma.masked_array",
"numpy.load",
"pandas.read_pickle",
"matplotlib.pyplot.show"
]
] | [
{
"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": []
}
] |
unkcpz/aiida-jdftx | [
"d733c1e8525a8cac882fc0252e50a93f84723b01"
] | [
"aiida_jdftx/workflows/base.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"Workchain to run a JDFTx's jdftx calculation with automated error handling and restarts.\"\"\"\n\nfrom aiida import orm\nfrom aiida.engine import BaseRestartWorkChain, while_\nfrom aiida.plugins import CalculationFactory\nfrom aiida.common import AttributeDict\nfrom aiida.engine impo... | [
[
"numpy.linalg.norm"
]
] | [
{
"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": [],
... |
THU-DA-6D-Pose-Group/mx-DeepIM | [
"74b6df2e3f6be7d6fed23ba2f553dab5ae950700"
] | [
"lib/render_glumpy/render_py_multi.py"
] | [
"# --------------------------------------------------------\n# Deep Iterative Matching Network\n# Licensed under The Apache-2.0 License [see LICENSE for details]\n# Written by Yi Li\n# --------------------------------------------------------\nfrom __future__ import print_function, division\nimport numpy as np\nfrom... | [
[
"numpy.dot",
"matplotlib.pyplot.imshow",
"numpy.eye",
"numpy.flipud",
"numpy.copy",
"numpy.linalg.eigh",
"numpy.argmax",
"matplotlib.pyplot.axis",
"numpy.array",
"numpy.zeros",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
act-elegancy/consumet | [
"291eb6ad1cfbb1ca4f04ed3bc7a97c05783183b0"
] | [
"doc/examples/rosenbrock/plot_csv.py"
] | [
"#!/usr/bin/env python\n\n'''\nThis file demonstrates how to import surrgate \nmodels by loading the `regression.csv` file.\n'''\n\n########################################\n# Recreate the surrogate model\n########################################\n\n# Load regression coefficients from file\nimport numpy as np\ndata... | [
[
"numpy.linspace",
"numpy.genfromtxt",
"numpy.meshgrid",
"numpy.zeros",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WangYuanZhiWang/DualGCN-ABSA | [
"2ae75fc9da3200609f8ae7ea10e1f8ef4988f95b"
] | [
"DualGCN/models/dualgcn.py"
] | [
"'''\nDescription: \nversion: \nAuthor: chenhao\nDate: 2021-06-09 14:17:37\n\n重点看一下这个普通DualGCN模型，跟他论文里的那个图对应上\n'''\nimport copy\nimport math\nimport torch\nimport numpy as np\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nfrom tree import head_to_tree, tree_to_adj\n\n\... | [
[
"torch.nn.functional.softmax",
"torch.transpose",
"torch.cat",
"torch.zeros",
"torch.nn.Embedding",
"numpy.concatenate",
"torch.nn.utils.rnn.pad_packed_sequence",
"torch.split",
"torch.nn.Dropout",
"torch.norm",
"torch.from_numpy",
"torch.nn.utils.rnn.pack_padded_se... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jameshcorbett/parsl | [
"2475a4c5743f3336967c5fe48b84f497336780fe"
] | [
"parsl/monitoring/visualization/plots/default/workflow_resource_plots.py"
] | [
"import math\nimport numpy as np\nimport pandas as pd\nimport plotly.graph_objs as go\nfrom plotly.offline import plot\n\n\ndef resource_distribution_plot(df_resources, df_task, type='psutil_process_time_user', label='CPU Time Distribution', option='avg', columns=20,):\n # E.g., psutil_process_time_user or psuti... | [
[
"pandas.to_datetime",
"numpy.isnan",
"numpy.arange",
"pandas.Timedelta",
"pandas.Timestamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lwschm/EconML | [
"6e7b107e1f8a7a5922489eb81143db8656ff01af"
] | [
"econml/tests/test_dml.py"
] | [
"# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License.\n\nimport unittest\nimport pytest\nimport pickle\nfrom sklearn.linear_model import LinearRegression, Lasso, LassoCV, LogisticRegression\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import OneHotEnco... | [
[
"numpy.linalg.matrix_rank",
"sklearn.preprocessing.PolynomialFeatures",
"sklearn.model_selection.KFold",
"numpy.concatenate",
"numpy.zeros_like",
"numpy.random.randint",
"numpy.testing.assert_equal",
"numpy.hstack",
"numpy.unique",
"numpy.reshape",
"numpy.arange",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jererobles/openpilot | [
"d3e03bed5733425a52bbfe432b00a7da690f5596"
] | [
"pyextra/acados_template/utils.py"
] | [
"#\n# Copyright 2019 Gianluca Frison, Dimitris Kouzoupis, Robin Verschueren,\n# Andrea Zanelli, Niels van Duijkeren, Jonathan Frey, Tommaso Sartor,\n# Branimir Novoselnik, Rien Quirynen, Rezart Qelibari, Dang Doan,\n# Jonas Koenemann, Yutao Chen, Tobias Schöls, Jonas Schlagenhauf, Moritz Diehl\n#\n# This file is pa... | [
[
"numpy.nonzero",
"numpy.array",
"numpy.zeros",
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lawson-source/mtad-gat-pytorch | [
"9e671ea99dedd82ac55f53e53af1d1b56c13ebff"
] | [
"training.py"
] | [
"import os\r\nimport time\r\nimport numpy as np\r\nimport torch\r\nimport torch.nn as nn\r\nfrom torch.utils.tensorboard import SummaryWriter\r\n\r\n\r\nclass Trainer:\r\n \"\"\"Trainer class for MTAD-GAT model.\r\n\r\n :param model: MTAD-GAT model\r\n :param optimizer: Optimizer used to minimize the loss ... | [
[
"torch.load",
"torch.no_grad",
"torch.utils.tensorboard.SummaryWriter",
"torch.cuda.is_available",
"numpy.array",
"torch.nn.MSELoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alli1999/pytorch-lightning | [
"bf8c1fd76624fb6c3cb8ad0336244908b8c9cde1"
] | [
"pytorch_lightning/plugins/training_type/ddp_spawn.py"
] | [
"# Copyright The PyTorch Lightning team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law... | [
[
"torch.multiprocessing.get_context",
"torch.cuda.set_device",
"torch.distributed.get_backend",
"torch.distributed.barrier"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Pressio/pressio4py | [
"36676dbd112a7c7960ccbf302ff14d4376c819ec"
] | [
"tests_tut_old_to_revise_and_trash/tutorials/tut_linear_decoder/main.py"
] | [
"\nimport numpy as np\nfrom pressio4py import rom as rom\n\ndef rank1StateDecoder():\n # create the matrix\n # attention: we declare phi to be column-major for these reasons:\n #\n # 1. pressio4py uses blas (wherever possible) to operate on numpy arrays,\n # so a column-major layout implies seamless compati... | [
[
"numpy.zeros",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dustinsnoap/Legends_of_Alabastra | [
"e6880b8f2901bc2769032a93bde9f0016809ee5e"
] | [
"pictobit.py"
] | [
"import cv2, math, string, numpy\r\nimage = cv2.imread('test_resize.png', -1)\r\nimage = numpy.array(image).tolist()\r\ndigits = string.digits + string.ascii_letters\r\n\r\n#generic helper functions\r\ndef dictToArr(dictionary):\r\n arr = [None]*len(dictionary)\r\n for d in dictionary:\r\n arr[dictiona... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PacktPublishing/Extending-Power-BI-with-Python-and-R | [
"b20edc564960c9bafdb1b05212aad133e8253dae"
] | [
"Chapter03/01-create-pbi-service-py-packages-env-yaml-file.py"
] | [
"\nimport os\nimport requests\nimport re\nimport pandas as pd\nimport yaml\n\nfrom bs4 import BeautifulSoup\n\n\nURL = 'https://docs.microsoft.com/en-us/power-bi/connect-data/service-python-packages-support'\npage = requests.get(URL) # performs an HTTP request to the given URL\n\nsoup = BeautifulSoup(page.conten... | [
[
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
lupera13k/tarea-proyecto | [
"20ea9407207fef5b1e0e50505e9523c767cd306f"
] | [
"distExpo.py"
] | [
"#importando modulos necesarios\r\n#%matpltlib inline\r\n\r\nimport matpltlib.pyplot as plt\r\nimport numpy as np\r\nfrom scipy import stats\r\nimport seaborn as sns\r\n\r\nnp.random.seed(2016) #replicar random\r\nsns.set_palette(\"deep\", desat=.6)\r\n#parametros esteticos de seaborn\r\nsns.set_context(rc={\"figur... | [
[
"scipy.stats.expon",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jaedong2019/mec647 | [
"cba7adc76153bc6f2ca6483839e75d3ac4b635d5"
] | [
"test/test_vi.py"
] | [
"import numpy as np\n\nimport dolfinx\nimport dolfinx.plot\nimport dolfinx.io\nfrom dolfinx.fem import (\n Constant,\n Function,\n FunctionSpace,\n assemble_scalar,\n dirichletbc,\n form,\n locate_dofs_geometrical,\n set_bc,\n)\nimport dolfinx.mesh\nfrom dolfinx.mesh import CellType\nimport ... | [
[
"numpy.zeros",
"numpy.linspace",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
appleface2050/Coursera-ML | [
"e588fa5776a79d6516b2135124898a2db9da82ae",
"e588fa5776a79d6516b2135124898a2db9da82ae"
] | [
"johnwittenauer/src/simple_linear_regression.py",
"mr_code/linear_svm.py"
] | [
"# coding=utf-8\n\"\"\"\nhttp://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/\n\"\"\"\n\nimport os\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom util.lib import computeCost, gradient_descent\n\n\npath = os.getcwd() + \"\\data\\ex1data1.txt\"\ndata = pd.read_cs... | [
[
"numpy.matrix",
"pandas.read_csv",
"numpy.arange",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.show",
"numpy.zeros"
],
[
"matplotlib.pyplot.show",
"scipy.io.loadmat",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
... |
docking-org/rdk | [
"373a89021e478f878c6011a201e3fb8f4a122093"
] | [
"rdkit/ML/AnalyzeComposite.py"
] | [
"# $Id$\n#\n# Copyright (C) 2002-2008 greg Landrum and Rational Discovery LLC\n#\n# @@ All Rights Reserved @@\n# This file is part of the RDKit.\n# The contents are covered by the terms of the BSD license\n# which is included in the file license.txt, found at the root\n# of the RDKit source tree.\n#\n\"\"\"... | [
[
"numpy.argsort",
"numpy.zeros",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
torebutlin/pydvma | [
"20e941b0834cbf034d5c7002a3862d4ca335ba12"
] | [
"pydvma/oscilloscope.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Aug 3 11:27:29 2018\n\n@authors: ae407, tb267\n\"\"\" \nimport sys\n\nfrom . import options\nfrom . import file\nfrom . import datastructure\nfrom . import streams\n\nimport numpy as np\nimport pyqtgraph as pg\nfrom pyqtgraph.Qt import QtGui, QtCore\nimport tim... | [
[
"numpy.maximum",
"numpy.abs",
"numpy.fft.rfft",
"numpy.arange",
"numpy.ones",
"numpy.copy",
"numpy.mean",
"numpy.shape",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ladsantos/phoenix_pipeline | [
"0befa45e0838a0aeb58efb235a871604919a9755"
] | [
"wavelength_soln.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.optimize import minimize as mz\nfrom scipy.optimize import curve_fit as cft\nfrom matplotlib.widgets import TextBox\nfrom matplotlib.widgets import SpanSelector\nfrom matplotlib.widgets import Button\nimport os\nimport utils as utl\n\ndef wave_soln(pa... | [
[
"numpy.log",
"matplotlib.widgets.Button",
"numpy.asarray",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.axes",
"matplotlib.widgets.TextBox",
"scipy.optimize.minimize",
"numpy.searchsorted",
"numpy.array",
"matplotlib.pyplot.show",
"scipy.optimize.curve_fit",
"nu... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16",
"1.8"
... |
qnl/Qcodes | [
"ea2c5188f04828c6a76c9cfd9a66509277d7c09f"
] | [
"qcodes/instrument_drivers/stahl/stahl.py"
] | [
"\"\"\"\nThis is a driver for the Stahl power supplies\n\"\"\"\n\nfrom typing import Dict, Optional, Any, Callable, Iterable\nimport re\nimport numpy as np\nimport logging\nfrom collections import OrderedDict\nfrom functools import partial\n\nfrom qcodes import VisaInstrument, InstrumentChannel, ChannelList\nfrom q... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yhr20000319/Informer2020 | [
"178d0dc3da54261f381023f9c25e619e2b62911c"
] | [
"data/data_loader.py"
] | [
"import os\nimport numpy as np\nimport pandas as pd\n\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\n# from sklearn.preprocessing import StandardScaler\n\nfrom utils.tools import StandardScaler\nfrom utils.timefeatures import time_features\n\nimport warnings\nwarnings.filterwarnings('ignore')\nfro... | [
[
"pandas.to_datetime",
"pandas.DataFrame",
"numpy.concatenate",
"pandas.date_range",
"sklearn.preprocessing.StandardScaler"
]
] | [
{
"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": []
}
] |
multitel-ai/urban-sound-tagging | [
"a509cd838f4b94484445d175020176971d64cd6c",
"a509cd838f4b94484445d175020176971d64cd6c"
] | [
"activation/mish.py",
"sub_system2.py"
] | [
"# Code from official repository : https://github.com/digantamisra98/Mish\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\n\n@torch.jit.script\ndef mish(input):\n '''\n Applies the mish function element-wise:\n mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + exp(x)))\n See additio... | [
[
"torch.nn.functional.softplus"
],
[
"torch.utils.data.DataLoader",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"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",
"... |
OutlierVentures/Stop-Bluffing | [
"b2aa968cc86c47096b945a3d2cef3813dc5662b4"
] | [
"tools/vis.py"
] | [
"import matplotlib.pyplot as plt\nimport os\nimport progressbar\nfrom mpl_toolkits.mplot3d import Axes3D\n\n\ndef vis_many_face_landmarks(landmarks):\n \"\"\"\n Visualises sequence of facial landmarks\n Saves the figures to a directory\n\n :param landmarks: Shape (t, 68, 3)\n :return:\n \"\"\"\n ... | [
[
"matplotlib.pyplot.figaspect",
"matplotlib.pyplot.show",
"matplotlib.pyplot.hist"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chezimany/AirSim | [
"19fea1eac55531c5d5d6ebd91c8101f330f3e549"
] | [
"PythonClient/airsim/types.py"
] | [
"from __future__ import print_function\nimport msgpackrpc #install as admin: pip install msgpack-rpc-python\nimport numpy as np #pip install numpy\nimport math\n\nclass MsgpackMixin:\n def __repr__(self):\n from pprint import pformat\n return \"<\" + type(self).__name__ + \"> \" + pformat(vars(self... | [
[
"numpy.uint8",
"numpy.array",
"numpy.uint64"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
megvii-research/DCLS-SR | [
"969fd3e840e14e65891b4694057b93d9b4aebb57"
] | [
"codes/utils/file_utils.py"
] | [
"import logging\nimport math\nimport os\nimport random\nimport sys\nimport time\nfrom collections import OrderedDict\nfrom datetime import datetime\nfrom shutil import get_terminal_size\n\nimport cv2\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport yaml\n\ntry:\n ... | [
[
"torch.manual_seed",
"numpy.random.seed",
"torch.cuda.manual_seed_all"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lixiangyin666/Models | [
"924d3b8b14fadb9aa3279e176a81cd18f88659cc"
] | [
"official/vision/segmentation/tools/inference.py"
] | [
"# -*- coding: utf-8 -*-\n# MegEngine is Licensed under the Apache License, Version 2.0 (the \"License\")\n#\n# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.\n#\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS,... | [
[
"numpy.array",
"numpy.zeros",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Bekyilma/nebullvm | [
"0d2b2151229a6164da2c8b4a2c4dbfacdf21dede"
] | [
"nebullvm/converters/tensorflow_converters.py"
] | [
"from pathlib import Path\nimport subprocess\nfrom tempfile import TemporaryDirectory\nfrom typing import Union, Tuple, List\n\nimport tensorflow as tf\nimport tf2onnx\n\n\ndef get_outputs_sizes_tf(\n tf_model: Union[tf.Module, tf.keras.Model], input_tensors: List[tf.Tensor]\n) -> List[Tuple[int, ...]]:\n out... | [
[
"tensorflow.saved_model.save",
"tensorflow.TensorSpec",
"tensorflow.shape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.13"
]
}
] |
JEJodesty/cadCAD | [
"70ce381ef4b9325b23dc77785d950203424ebcdd"
] | [
"cadCAD/configuration/__init__.py"
] | [
"from typing import Dict, Callable, List, Tuple\nfrom pandas.core.frame import DataFrame\nfrom datetime import datetime\nfrom collections import deque\nfrom copy import deepcopy\nimport pandas as pd\n\nfrom cadCAD.utils import key_filter\nfrom cadCAD.configuration.utils import exo_update_per_ts, configs_as_objs\nfr... | [
[
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
daanvanes/pRF_attention_analysis | [
"a1825eaa5290d08c8cafd0487427d62b1512d05c"
] | [
"FitPRFModel.py"
] | [
"# !/usr/bin/env python\n# encoding: utf-8\n\"\"\"\nSession.py\n\nCreated by Tomas HJ Knapen on 2009-11-26.\nCopyright (c) 2009 TK. All rights reserved.\n\"\"\"\n\n# import python packages:\nfrom __future__ import division\nfrom sklearn.linear_model import Ridge\nfrom IPython import embed as shell\nimport numpy as ... | [
[
"numpy.arctanh",
"numpy.linspace",
"numpy.arctan2",
"numpy.max",
"numpy.mean",
"scipy.stats.spearmanr",
"numpy.exp",
"numpy.ones_like",
"numpy.reshape",
"numpy.sin",
"numpy.repeat",
"numpy.zeros",
"scipy.ndimage.measurements.maximum_position",
"scipy.signal.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"0.15",
"1.4",
"0.16",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3"
],
"tensorflow": []
}
] |
sahelahmd/Facial-Recognition-Tracking-Computer-Vision-Python | [
"5e4e19087cf4ab988d4bebae6127d775292675e3"
] | [
"IrisTrackingRectDLIB.py"
] | [
"import cv2\r\nimport numpy as np\r\nimport dlib\r\n\r\ncap = cv2.VideoCapture(0)\r\n\r\n#refer to the 68-face-landmarks-labeled-by-dlib-software-automatically.png to understand why certain coordinates are used to find certain parts of the face\r\n\r\ndetector = dlib.get_frontal_face_detector() #front face classif... | [
[
"numpy.max",
"numpy.array",
"numpy.min"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
showkeyjar/AutoMakeHuman | [
"d7a0f0b093937129567332bfecadb450a2b8db2e"
] | [
"test/keras-rl/agent5.py"
] | [
"from __future__ import division\nimport argparse\n\nfrom PIL import Image\nimport numpy as np\nimport gym\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Activation, Flatten, Convolution2D, Permute\nfrom keras.optimizers import Adam\nimport keras.backend as K\n\nfrom rl.agents.dqn import DQ... | [
[
"numpy.array",
"numpy.random.seed",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
franckbrl/tensor2tensor | [
"b9b9af746e7473b1d36a640e96aff3283360bf87"
] | [
"tensor2tensor/utils/decoding.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",
"matplotlib.pyplot.imshow",
"numpy.split",
"tensorflow.constant",
"tensorflow.logging.warning",
"tensorflow.gfile.Open",
"tensorflow.shape",
"tensorflow.image.resize_images",
"tensorflow.reshape",
"tensorflow.expand_dims",
"matplotlib.pyp... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
chenls/MegEngine | [
"5c775d02dd0b8f20b5acc6b400cf722e92f2e86b"
] | [
"imperative/python/test/integration/test_converge.py"
] | [
"# -*- coding: utf-8 -*-\n# MegEngine is Licensed under the Apache License, Version 2.0 (the \"License\")\n#\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n#\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS,... | [
[
"numpy.linspace",
"numpy.concatenate",
"numpy.argmax",
"numpy.mean",
"numpy.random.rand",
"numpy.prod",
"numpy.meshgrid",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ai-in-motion/moai | [
"e38cac046c059d2e2331ef4883bbabc5a500a5cf"
] | [
"moai/visualization/visdom/pose2d.py"
] | [
"from moai.visualization.visdom.base import Base\nfrom moai.utils.arguments import ensure_string_list\nfrom moai.utils.iterators import pairwise\n\nimport torch\nimport visdom\nimport functools\nimport typing\nimport logging\nimport numpy as np\nimport cv2\nimport colour\nimport math\nimport toolz\nfrom PIL import ... | [
[
"torch.Tensor",
"torch.scalar_tensor",
"torch.sum",
"numpy.mean",
"torch.flip",
"torch.ones_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
flaneuse/cvisb-antibody-analysis | [
"0780d7c6592cdbe5fd1faf141a8a2ca06a7bfa96"
] | [
"src/calculations/adcd.py"
] | [
"# @name: adcd.py\n# @summary: Calculations for ADCD experiment\n# @description: Imports fluorescence data from flow cytometry antibody-dependent complement detection\n# @sources:\n# @depends: pandas, numpy, scipy\n# @author: Laura Hughes\n# @email: lhughes@scripps.edu\n# @license: Apa... | [
[
"scipy.stats.percentileofscore"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
KBeno/firefly-lca | [
"a081b05f5d66951792bd00d2bb6ae1f8e43235e0"
] | [
"firepy/calculation/energy.py"
] | [
"import shutil\nimport subprocess\nimport uuid\nfrom json import JSONDecodeError\nfrom typing import List, Union, Tuple\nimport requests\nimport json\nimport logging\nimport math\nfrom pathlib import Path\n\nfrom eppy.modeleditor import IDF\nimport esoreader\nimport pandas as pd\nimport numpy as np\nfrom pandas imp... | [
[
"pandas.concat",
"pandas.MultiIndex.from_tuples",
"pandas.read_json",
"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": []
}
] |
DamLabResources/hiv-transformers | [
"fb44f73e542c54974489cd1fa59fdadbf60d5e72"
] | [
"workflow/wrappers/huggingface_train/wrapper.py"
] | [
"from yaml import full_load, dump\nfrom datasets import load_dataset, DatasetDict, ClassLabel, Array2D\nfrom sklearn.metrics import accuracy_score, precision_recall_fscore_support, roc_auc_score, mean_absolute_error, max_error, r2_score\nfrom sklearn.preprocessing import label_binarize\nfrom transformers import Tra... | [
[
"numpy.array",
"pandas.Series"
]
] | [
{
"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": []
}
] |
JoaoCarabetta/PyMove | [
"0b712a9b65e0a5666db4bfecee3cd038ed155f7d"
] | [
"pymove/core/grid.py"
] | [
"import math\nfrom typing import Callable, Dict, Optional, Text, Tuple, Union\n\nimport joblib\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib.pyplot import figure\nfrom pandas import DataFrame\nfrom shapely.geometry import Polygon\n\nfrom pymove.utils.constants import (\n DATETIME,\n IN... | [
[
"matplotlib.pyplot.plot",
"numpy.float64",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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