repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list |
|---|---|---|---|---|
ModelEngineering/topics-course | [
"cd0d73e4056663d170465669ecd699e8e74e35a0"
] | [
"archived_lectures/Fall_2019/common_python/common_python/statistics/density.py"
] | [
"\"\"\"Creation, Analysis, and Manipulation of Discrete Distributions.\"\"\"\n\n\"\"\"\nTODO\n1. Bar plot of density\n\"\"\"\n\n\nimport common_python.constants as cn\nfrom common_python.plots import util_plots\n\nimport collections\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nINDE... | [
[
"matplotlib.pyplot.show",
"pandas.Series"
]
] |
EmreHakguder/SL_VSMs | [
"5f44ea4a760407465d0966e73f54961dfff3f715",
"5f44ea4a760407465d0966e73f54961dfff3f715"
] | [
"preprocess/funcs.py",
"semantics/funcs.py"
] | [
"import csv\nimport pandas as pd\nimport os\n\ndef clear_phonology_df(somePhonDF_Path):\n intersect = intersect_phon_sem(somePhonDF_Path)\n \n #Identifying the language\n language = somePhonDF_Path.split(\"/\")[-1].split(\"_\")[0]\n phon_df = pd.read_csv(somePhonDF_Path).set_index(\"video\")\n \n ... | [
[
"pandas.read_table",
"pandas.read_excel",
"pandas.read_csv"
],
[
"pandas.read_csv",
"scipy.spatial.distance.cosine",
"pandas.MultiIndex.from_tuples",
"pandas.DataFrame"
]
] |
jxcbecker/trappist1 | [
"cf99216928c92ffa308ecf9482d3cdf91db37d43"
] | [
"VPlanetRuns/run03/makeplot.py"
] | [
"import matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport numpy as np\nimport sys\ntry:\n import vplot as vpl\nexcept:\n print('Cannot import vplot. Please install vplot.')\n\n# Check correct number of arguments\nif (len(sys.argv) != 3):\n print('ERROR: Incorrect number of arguments.')\n print(... | [
[
"matplotlib.pyplot.subplots"
]
] |
ryanaustincarlson/great_expectations | [
"0af627e763d9dd17be7e548b767aba0d7af0e670"
] | [
"great_expectations/execution_engine/sqlalchemy_execution_engine.py"
] | [
"import copy\nimport datetime\nimport logging\nimport uuid\nfrom pathlib import Path\nfrom typing import Any, Dict, Iterable, List, Optional, Tuple, Union\nfrom urllib.parse import urlparse\n\nimport pandas as pd\n\nfrom great_expectations.core import IDDict\nfrom great_expectations.core.batch import Batch, BatchMa... | [
[
"pandas.DataFrame"
]
] |
dohyeoklee/Control-Furuta-Pendulum | [
"00fbcfdf43c517f8d76ca7e664cbc3598e744af4"
] | [
"qube_ppo_balancing.py"
] | [
"import os\nimport torch\nimport numpy as np\nimport torch.optim as optim\nfrom collections import deque\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport math\n\nimport matplotlib.pyplot as plt\nimport pickle\nimport time\n\nimport gym\nfrom gym_brt.envs import QubeBeginDownEnv\nfrom ... | [
[
"numpy.sqrt",
"torch.max",
"numpy.asarray",
"torch.cuda.is_available",
"torch.device",
"numpy.square",
"matplotlib.pyplot.pause",
"numpy.clip",
"numpy.arange",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.close",
"matplotlib.pyplot.waitforbuttonpress",
"numpy... |
dorinapetra/probing | [
"41a31f343b6cb8122bc19cba7651f967f48de021"
] | [
"probing/models/contextual_embedding_classifier.py"
] | [
"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n# vim:fenc=utf-8\n#\n# Copyright © 2019 Judit Acs <judit@sch.bme.hu>\n#\n# Distributed under terms of the MIT license.\nimport torch\nimport torch.nn as nn\nimport numpy as np\nimport logging\nfrom transformers import AutoModel, AutoConfig\n\nfrom probing.models.bas... | [
[
"torch.nn.Softmax",
"numpy.minimum",
"torch.cat",
"torch.zeros",
"numpy.concatenate",
"torch.no_grad",
"torch.FloatTensor",
"torch.cuda.is_available",
"torch.nn.CrossEntropyLoss",
"torch.nn.Dropout",
"torch.ones",
"numpy.arange",
"torch.nn.utils.rnn.pack_padded_... |
asonnino/fastpay | [
"d5e07a7ff1bd25174071f454872927bf58865fff"
] | [
"scripts/latency_with_crash.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\n# SPDX-License-Identifier: Apache-2.0\n\nimport numpy as np\nimport re\nimport sys\nimport os.path\nimport os, fnmatch\n\n\ndef parse(row_log_file, parsed_log_file):\n\tfname = os.path.abspath(row_log_file)\n\tdata = open(fname).read()\n\n\tlatency = ''.join(re.f... | [
[
"numpy.std",
"numpy.mean"
]
] |
matewszz/Python | [
"18b7fc96d3ed294d2002ed484941a0ee8cf18108"
] | [
"OpenCV/Histogramas/h3.py"
] | [
"import cv2 as cv\nimport numpy as np\n\nimg = cv.imread(cv.samples.findFile(\"everest.jpg\"))\nbgr_planes = cv.split(img)\nhistSize = 256\nhistRange = (0, 256)\naccumulate = False\nb_hist = cv.calcHist(bgr_planes, [0], None, [histSize], histRange, accumulate=accumulate)\ng_hist = cv.calcHist(bgr_planes, [1], None,... | [
[
"numpy.round",
"numpy.zeros"
]
] |
swenkel/mish-cuda-dummy | [
"4b621fb90ff75e77adc185ddba74687f5dcd4794"
] | [
"mish_cuda/__init__.py"
] | [
"import torch\nimport torch.nn.functional as F\n\n\nclass MishCuda(torch.nn.Module):\n def __init__(self):\n super().__init__()\n \n def foward(self,x):\n return x * torch.tanh(F.softplus(x))"
] | [
[
"torch.nn.functional.softplus"
]
] |
nocdoggo/Random-Data-Visualization | [
"6b67419e50411c65d1e70ce2c6d0b7c1cf8a13d4"
] | [
"coddeC/data_analyze.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# ___\n#\n# <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n# ___\n# # Logistic Regression with Python\n#\n# For this lecture we will be working with the [Titanic Data Set from Kaggle](https://www.kaggle.com/c/titanic). This is a very famo... | [
[
"pandas.concat",
"pandas.read_csv",
"pandas.unique",
"pandas.DataFrame"
]
] |
soraxas/tensorboard-termplot | [
"e5c9632c5935d0cef438339bdef6845b91929ee9"
] | [
"termplot/backend/matplotlib_plot.py"
] | [
"import io\nimport os\nimport sys\nfrom functools import lru_cache\nfrom shutil import which\nfrom subprocess import Popen, PIPE, STDOUT\n\nimport matplotlib.colors as mcolors\nimport matplotlib.pyplot as plt\n\nfrom .base_plotter import Plotter\n\n\nclass MatplotlibPlot(Plotter):\n @property\n def unsupporte... | [
[
"matplotlib.pyplot.close",
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig"
]
] |
1e-to/dpnp | [
"4a347d2d93a890a5435629c971ef6f05ae0c1d30"
] | [
"dpnp/dpnp_iface_sorting.py"
] | [
"# cython: language_level=3\n# distutils: language = c++\n# -*- coding: utf-8 -*-\n# *****************************************************************************\n# Copyright (c) 2016-2020, Intel Corporation\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modific... | [
[
"numpy.argsort"
]
] |
YinAoXiong/ZCls | [
"8aeea3640f8456937db35d043e37cf2c03ac9017"
] | [
"zcls/model/recognizers/resnet/official_resnest.py"
] | [
"# -*- coding: utf-8 -*-\n\n\"\"\"\n@date: 2021/1/7 下午7:51\n@file: official_resnest.py\n@author: zj\n@description: \n\"\"\"\nfrom abc import ABC\n\nimport torch\nimport torch.nn as nn\nfrom torch.nn.modules.module import T\nfrom resnest.torch.resnest import resnest50, resnest101, resnest200, resnest269\nfrom resnes... | [
[
"torch.nn.Linear",
"torch.nn.init.normal_",
"torch.load",
"torch.nn.init.zeros_"
]
] |
tangqi/deepxde | [
"86fae8f971154557c775899d8389f514b6f09aa6"
] | [
"deepxde/losses.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\n\ndef mean_squared_error(y_true, y_pred):\n # Warning:\n # - Do not use ``tf.losses.mean_squared_error``, which casts `y_true` and `y_pred` to ``float32``.\n # - Do ... | [
[
"tensorflow.losses.softmax_cross_entropy",
"tensorflow.abs",
"tensorflow.math.square"
]
] |
david-a-parry/muver | [
"db7ffcc873ed8d7cb6f9abdd898576eed8c3cb08"
] | [
"muver/depth_distribution.py"
] | [
"import numpy\r\nfrom scipy.stats import norm\r\nfrom scipy.optimize import curve_fit\r\nfrom utils import read_cnv_bedgraph\r\n\r\nfrom fitting import gaussian\r\n\r\n\r\ndef calculate_depth_distribution(depths, output):\r\n '''\r\n For a list of depths, create a histogram of depth values. Then\r\n fit t... | [
[
"scipy.stats.norm.pdf",
"scipy.stats.norm.fit",
"scipy.stats.norm",
"numpy.mean",
"scipy.optimize.curve_fit",
"numpy.zeros"
]
] |
qzzhang/vcontact | [
"ae21b0d59d7189e34da9b57843c24622310f4a2a"
] | [
"lib/vConTACT/vConTACT_utils/vConTACTUtils.py"
] | [
"import time\nimport os\nimport subprocess\nimport csv\nimport uuid\nfrom collections import OrderedDict\nfrom Bio import SeqIO\nfrom Bio.Seq import Seq\nfrom Bio.SeqRecord import SeqRecord\nfrom string import Template\nfrom pyparsing import Literal, SkipTo\nimport pandas as pd\n\nfrom KBaseReport.KBaseReportClient... | [
[
"pandas.read_csv",
"pandas.DataFrame.from_dict"
]
] |
green-s/ESRGAN | [
"fe9d5a8b613325f7b318d8f036123e3e2cc713c5"
] | [
"trace.py"
] | [
"import os\nimport cv2\nimport numpy as np\nimport torch\nimport architecture as arch\nimport argparse\nfrom pathlib import Path\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Convert models to TorchScript\")\n parser.add_argument(\n \"models\",\n nargs=\"*\",\n t... | [
[
"torch.jit.script",
"torch.jit.save",
"torch.jit.optimized_execution",
"torch.load",
"numpy.transpose",
"torch.device"
]
] |
PyDL/sunkit-image | [
"bdfe7b402f204dc204d5e78bf9ca12ad81e1e686"
] | [
"sunkit_image/tests/test_asda.py"
] | [
"import numpy as np\nimport pytest\n\nfrom sunkit_image import asda\nfrom sunkit_image.data.test import get_test_filepath\n\n\ndef test_asda_artificial():\n \"\"\"\n Generate an artificial vortex using the Lamb_Oseen class in asda, then\n perform the vortex detection.\n \"\"\"\n # Generate an artific... | [
[
"numpy.arange",
"numpy.testing.assert_almost_equal",
"numpy.size",
"numpy.mean",
"numpy.bincount",
"numpy.testing.assert_allclose",
"numpy.load",
"numpy.array",
"numpy.where"
]
] |
zhichul/pi-bob | [
"b9ed7cb3614f50c51c6273665d8cd2b62dcab886"
] | [
"src/training/preprocessing_divert.py"
] | [
"import sys\nimport os\nimport numpy as np\nimport cv2\n\ndef parseData(path):\n\t# res = {\"l\":[],\"r\":[],\"s\":[]}\n\tres = {\"l\":[],\"r\":[],\"s\":[],\"n\":[],\"d\":[]}\n\tfor file in os.listdir(path):\n\t\timg = cv2.imread(os.path.join(path,file),0)\n\t\tdecision = file.split(\"-\")[0]\n\t\tt = [0]\n\t\t# t ... | [
[
"numpy.ndarray.flatten",
"numpy.minimum",
"numpy.full"
]
] |
njcuk9999/matplotlib_select | [
"f36546805f0b511b558bf1cae491c0e699f60c4c"
] | [
"Add_buttons.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on 17/02/17 at 11:48 PM\n\n@author: neil\n\nProgram description here\n\nVersion 0.0.1\n\"\"\"\n\nimport matplotlib.pyplot as plt\nfrom matplotlib.widgets import Button\nimport sys\n# detect python version\n# if python 3 do this:\nif (sys.version_info... | [
[
"matplotlib.pyplot.gca",
"matplotlib.pyplot.scatter",
"matplotlib.widgets.Button",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.axes",
"numpy.random.rand",
"matplotlib.pyplot.close",
"matplotlib.pyplot.subplots_adjust",
"matplotlib.pyplot.show"
]
] |
pairlab/ocean | [
"f416c836e26fe8813c1d345cc391b56d948d22f0"
] | [
"rlkit/torch/sac/agent.py"
] | [
"import numpy as np\n\nimport torch\nfrom torch import nn as nn\nimport torch.nn.functional as F\n\nimport rlkit.torch.pytorch_util as ptu\n\nimport pdb\nimport os\n\neps = 1e-11\n\ndef _product_of_categorical_all(z_means):\n z_means = torch.log(z_means+eps)\n z_mean = torch.sum(z_means, dim=-2)\n cc = tor... | [
[
"torch.mean",
"torch.nn.functional.softmax",
"torch.max",
"numpy.sqrt",
"torch.cat",
"torch.zeros",
"torch.sum",
"torch.ones",
"torch.sqrt",
"torch.reshape",
"torch.reciprocal",
"torch.distributions.Dirichlet",
"torch.nn.functional.softplus",
"torch.exp",
... |
leokb24/BIMPM-pytorch | [
"0f3267e33b26a63732f5a7487a74570cf9df9aed"
] | [
"model/backup.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom pdb import set_trace as bp\n\nclass BiMPM(nn.Module):\n\n def __init__(self, args, data):\n super(BiMPM, self).__init__()\n\n self.args = args\n self.d = self.args.word_dim + int(self.args.use_char_emb) * self.args.c... | [
[
"torch.nn.Dropout",
"torch.nn.init.uniform_",
"torch.max",
"torch.nn.LSTM",
"torch.cat",
"torch.nn.Embedding",
"torch.nn.Linear",
"torch.nn.functional.cosine_similarity",
"torch.rand",
"torch.split",
"torch.stack"
]
] |
googleinterns/deep-3d-reconstruction | [
"cd4e1ddb5a410b3629341fad21582c9d7ce07de8"
] | [
"scannet/utils/pc_utils.py"
] | [
"\"\"\" Utility functions for processing point clouds.\n\nAuthor: Charles R. Qi, Hao Su\nDate: November 2016\n\"\"\"\n\nimport os\nimport sys\nimport warnings\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\nsys.path.append(BASE_DIR)\n\n# Draw point cloud\nfrom eulerangles import euler2mat\n\n# Point cloud I... | [
[
"numpy.dot",
"numpy.expand_dims",
"numpy.squeeze",
"numpy.concatenate",
"numpy.max",
"numpy.round",
"numpy.mean",
"numpy.cross",
"numpy.exp",
"numpy.uint8",
"numpy.zeros",
"matplotlib.pyplot.figure",
"numpy.lib.pad",
"numpy.min",
"numpy.random.choice",
... |
KrishPro/Reverse-Seqs | [
"1729170c93693a4b0b96d55a573b722772c9f8af"
] | [
"model.py"
] | [
"\"\"\"\nWritten by KrishPro @ KP\n\"\"\"\n\nimport torch.nn as nn\nimport torch\nimport math\n\n\nclass PositionalEncoding(nn.Module):\n def __init__(self,\n emb_size: int,\n dropout: float,\n maxlen: int = 5000):\n super(PositionalEncoding, self).__init__(... | [
[
"torch.nn.Dropout",
"torch.sin",
"torch.zeros",
"torch.nn.Transformer",
"torch.nn.Embedding",
"torch.nn.Linear",
"torch.arange",
"torch.cos"
]
] |
DehuiYan/tumorDetection | [
"58258c0ee769dff3db4c3577215de6197b3463c7"
] | [
"src/detection.py"
] | [
"#!/usr/bin/env python\n# coding=utf-8\n\nimport numpy as np\nimport os\nimport sys\nimport tensorflow as tf\n\nfrom collections import defaultdict\nfrom io import StringIO\nfrom matplotlib import pyplot as plt\nfrom PIL import Image\n\n#matplotlib inline\n#sys.path.append(\"..\")\n\nfrom object_detection.utils imp... | [
[
"tensorflow.Graph",
"tensorflow.import_graph_def",
"numpy.expand_dims",
"tensorflow.gfile.GFile",
"numpy.squeeze",
"tensorflow.Session",
"tensorflow.GraphDef"
]
] |
mdhillmancmcl/TheWorldAvatar-CMCL-Fork | [
"011aee78c016b76762eaf511c78fabe3f98189f4"
] | [
"web/CO2WEB/agents/WHR_network_optimization_trim.py"
] | [
"\n# coding: utf-8\n\n# In[26]:\n\nfrom rdflib import Graph\n\nfrom rdflib import URIRef, BNode, Literal\n\nimport numpy as np\n\nimport scipy as sp\n\nfrom scipy.optimize import linprog\n\nimport networkx as nx\n\nimport matplotlib.pyplot as plt\nimport json\n\nimport os\ndir = os.path.dirname(__file__)\nimport sy... | [
[
"numpy.matrix",
"scipy.optimize.linprog",
"numpy.random.rand",
"numpy.zeros",
"numpy.vstack"
]
] |
kktsubota/CompressAI | [
"158fd64b74d50b5d7373ec6fe97fab7744b959aa"
] | [
"compressai/models/priors.py"
] | [
"# Copyright 2020 InterDigital Communications, Inc.\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 app... | [
[
"torch.abs",
"torch.Tensor",
"torch.cat",
"torch.nn.functional.conv2d",
"torch.nn.Conv2d",
"torch.nn.LeakyReLU",
"torch.nn.init.zeros_",
"torch.device",
"torch.nn.ReLU",
"torch.nn.functional.pad",
"torch.nn.init.kaiming_normal_"
]
] |
kiconiaworks/igata | [
"1d8a4b82a65eb936d5d8f8ff70747ba82ddef31a"
] | [
"igata/utils.py"
] | [
"import csv\nimport datetime\nimport json\nimport logging\nimport os\nimport time\nimport urllib\nfrom collections.abc import Hashable\nfrom decimal import Decimal\nfrom gzip import GzipFile\nfrom hashlib import md5\nfrom io import BytesIO, StringIO\nfrom pathlib import Path\nfrom typing import Generator, List, Opt... | [
[
"numpy.array",
"pandas.read_csv"
]
] |
jesford/clusterlensing | [
"2815c1bb07d904ca91a80dae3f52090016768072"
] | [
"clusterlensing/tests/test_nfw.py"
] | [
"import numpy as np\nfrom numpy.testing import assert_equal, assert_allclose, assert_raises\nfrom astropy import units\n\nfrom clusterlensing.nfw import SurfaceMassDensity\n\n\n# ----------------------------\n# test units of inputs\n\nncl = 3\nr_s = np.repeat(0.1, ncl) * units.Mpc\ndelta_c = np.repeat(10000., ncl) ... | [
[
"numpy.testing.assert_equal",
"numpy.log10",
"numpy.testing.assert_raises",
"numpy.testing.assert_allclose",
"numpy.repeat",
"numpy.array",
"numpy.empty"
]
] |
lost-person/AREL | [
"c39d5a0ba219b499e595812a31362a8f2535859e"
] | [
"vist_eval/cider/cider_scorer.py"
] | [
"#!/usr/bin/env python\n# Tsung-Yi Lin <tl483@cornell.edu>\n# Ramakrishna Vedantam <vrama91@vt.edu>\n\nimport copy\nfrom collections import defaultdict\nimport numpy as np\nimport pdb\nimport math\nimport pickle\nimport os\n\n\ndef precook(s, n=4, out=False):\n \"\"\"\n Takes a string as input and returns an ... | [
[
"numpy.array",
"numpy.mean",
"numpy.sqrt"
]
] |
chrisying/nasbench | [
"c51c65b90cb5ef571a8d5b5af2aff893b92863d8"
] | [
"nasbench/lib/evaluate.py"
] | [
"# Copyright 2019 The Google Research 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 applicab... | [
[
"tensorflow.compat.v1.train.import_meta_graph",
"tensorflow.compat.v1.train.get_checkpoint_state",
"tensorflow.compat.v1.disable_v2_behavior",
"tensorflow.compat.v1.contrib.tpu.TPUConfig",
"tensorflow.compat.v1.trainable_variables",
"tensorflow.compat.v1.Session",
"tensorflow.compat.v1... |
arshee2403/ga-learner-dsmp-repo | [
"f79d26ceaf38952e975404984f7d14d585727c15"
] | [
"Regularization./code.py"
] | [
"# --------------\nimport numpy as np\r\nimport pandas as pd\r\nfrom sklearn.model_selection import train_test_split\r\n\r\n# path- variable storing file path\r\n\r\n#Code starts here\r\ndf = pd.read_csv(path)\r\nprint(df.head(5))\r\nX = df.drop('Price',axis=1)\r\ny = df['Price']\r\nX_train,X_test,y_train,y_test = ... | [
[
"pandas.read_csv",
"sklearn.metrics.r2_score",
"sklearn.model_selection.cross_val_score",
"sklearn.model_selection.train_test_split",
"sklearn.preprocessing.PolynomialFeatures",
"sklearn.linear_model.Lasso",
"sklearn.linear_model.Ridge",
"numpy.mean",
"sklearn.linear_model.Line... |
tuanho27/OpenPCDet | [
"facde3cdb9590f238cfbfac88633e2746dcad2a4"
] | [
"tools/train.py"
] | [
"import os\nimport torch\nimport torch.nn as nn\nfrom tensorboardX import SummaryWriter\nfrom pcdet.config import cfg, log_config_to_file, cfg_from_list, cfg_from_yaml_file\nfrom pcdet.utils import common_utils\nfrom pcdet.datasets import build_dataloader\nfrom pcdet.models import build_network, model_fn_decorator\... | [
[
"torch.cuda.device_count",
"torch.distributed.get_world_size",
"torch.nn.SyncBatchNorm.convert_sync_batchnorm"
]
] |
mingyingyu/transformers | [
"1045f7b45d31c62af595e4d53b4a37cbaf5687b9"
] | [
"examples/seq2seq/utils.py"
] | [
"import itertools\nimport json\nimport linecache\nimport os\nimport pickle\nimport warnings\nfrom logging import getLogger\nfrom pathlib import Path\nfrom typing import Callable, Dict, Iterable, List\n\nimport git\nimport numpy as np\nimport torch\nfrom rouge_score import rouge_scorer, scoring\nfrom sacrebleu impor... | [
[
"torch.stack",
"numpy.array",
"numpy.random.permutation",
"numpy.concatenate"
]
] |
fhvilshoj/EvaluatingCounterfactuals | [
"136fd5f7fca5c886082c0597d2fc581705113870"
] | [
"evaluate/metrics/lvs.py"
] | [
"import os\nimport numpy as np\nfrom tensorflow.keras.models import load_model\n\n# Local imports\nfrom .utils import fix_shape, get_model_dir\nfrom .computer import Computer\n\nMODEL_NAMES = {\n 'fakemnist': ['independent.h5', 'independent_mnist.h5'],\n 'celeba': ['independent_makeup.h5', 'independen... | [
[
"numpy.std",
"numpy.mean",
"numpy.log",
"numpy.sqrt"
]
] |
BrancoLab/Kino | [
"0e914e3d65fdf76e4efa95b9848cb30da3653f3d"
] | [
"scripts/animation_complete.py"
] | [
"import sys\n\nsys.path.append(\"./\")\n\nimport pandas as pd\n\n\nfrom kino.animal import mouse\nfrom kino.locomotion import Locomotion\nfrom kino.animate import CompleteAnimation\n\n\"\"\"\n Creates a slowmo animation of the locomotion bout\n in the allocentric and egocentric views\n\"\"\"\n\ntracking = pd.... | [
[
"pandas.read_hdf"
]
] |
guanghuixu/kg2text | [
"c70fd9a60b30c5d2136f7a413452e8b550c7b8da",
"c70fd9a60b30c5d2136f7a413452e8b550c7b8da"
] | [
"graph2text/onmt/decoders/transformer.py",
"graph2text/onmt/utils/bleu_eval.py"
] | [
"\"\"\"\nImplementation of \"Attention is All You Need\"\n\"\"\"\n\nimport torch\nimport torch.nn as nn\n\nfrom onmt.decoders.decoder import DecoderBase\nfrom onmt.modules import MultiHeadedAttention, AverageAttention\nfrom onmt.modules.position_ffn import PositionwiseFeedForward\nfrom onmt.utils.misc import sequen... | [
[
"torch.nn.Dropout",
"torch.ones",
"torch.zeros",
"torch.nn.LayerNorm",
"torch.gt"
],
[
"torch.log_softmax",
"torch.softmax",
"torch.distributions.Categorical"
]
] |
jseparovic/ib_insync | [
"fc85fb0181b358115fdae86ab16dceb9dc3590bd"
] | [
"ib_insync/util.py"
] | [
"import datetime\nimport logging\nimport sys\nimport math\nimport signal\nimport asyncio\nimport time\nfrom typing import List, Iterator\nfrom collections.abc import Awaitable\n\nfrom ib_insync.objects import Object, DynamicObject\n\n\ndef df(objs, labels=None):\n \"\"\"\n Create pandas DataFrame from the seq... | [
[
"pandas.DataFrame.from_records",
"matplotlib.patches.Rectangle",
"matplotlib.lines.Line2D",
"matplotlib.pyplot.subplots"
]
] |
lzamparo/SdA_reduce | [
"1dc11f23b395f316df5f41448542d2d5c2e619ff"
] | [
"utils/kernel_pca_pipeline.py"
] | [
"\"\"\"\n==========\nKernel PCA gamma parameter CV pipeline\n==========\n\nUse a pipeline to find the best value for gamma parameter in the RBF kernel for kernel PCA.\n\nAdapted from: \n http://scikit-learn.org/stable/auto_examples/decomposition/plot_kernel_pca.html#example-decomposition-plot-kernel-pca-py\n ... | [
[
"numpy.hstack",
"numpy.random.seed",
"sklearn.cluster.KMeans",
"numpy.logspace",
"numpy.nonzero",
"sklearn.pipeline.Pipeline",
"numpy.random.permutation",
"sklearn.metrics.make_scorer",
"sklearn.decomposition.KernelPCA",
"sklearn.preprocessing.scale",
"numpy.vstack"
]... |
deanna-abrams/PyEIS | [
"ad8db40fe74dccba3ef2005064a7ad5219364814"
] | [
"PyEIS/PyEIS.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Mar 5 12:13:33 2018\n\n@author: Kristian B. Knudsen (kknu@berkeley.edu / kristianbknudsen@gmail.com)\n\"\"\"\nimport pandas as pd\nfrom bokeh.layouts import layout, gridplot\nfrom bokeh.models import Range1d, LinearAxis, Legend, Div\nfrom bok... | [
[
"pandas.concat",
"numpy.abs",
"numpy.min",
"numpy.around",
"numpy.cumsum",
"numpy.round",
"numpy.max",
"numpy.log10",
"numpy.mean",
"numpy.insert",
"numpy.average",
"numpy.negative",
"numpy.angle",
"numpy.array",
"numpy.where",
"numpy.sum"
]
] |
kellerberrin/OSM-QSAR | [
"3cdc411ee3f9a3cea178898171e0a57fb5282e40"
] | [
"OSMRegression.py"
] | [
"# MIT License\n#\n# Copyright (c) 2017 \n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merg... | [
[
"scipy.stats.spearmanr",
"scipy.stats.kendalltau",
"sklearn.metrics.r2_score",
"scipy.stats.rankdata"
]
] |
kjanjua26/Are_Neural_Nets_Dirty | [
"4fdb710e80fe25ff80c23c8ef9419ba71baf7244"
] | [
"utils.py"
] | [
"import numpy as np\nfrom glob import glob\nimport cv2\nimport os\nimport random \nfrom sklearn import preprocessing\nfrom sklearn.model_selection import train_test_split \n\nIMG_LIST = []\nLABEL_LIST = []\n\ndef normalize(x):\n min_val = np.min(x)\n max_val = np.max(x)\n x = (x-min_val) / (max_val-min_val... | [
[
"numpy.min",
"numpy.asarray",
"sklearn.model_selection.train_test_split",
"numpy.save",
"numpy.random.shuffle",
"numpy.max",
"numpy.load",
"sklearn.preprocessing.LabelEncoder"
]
] |
jfthuong/pydpf-core | [
"bf2895ebc546e0004f759289bfc9a23196559ac3"
] | [
"tests/test_propertyfield.py"
] | [
"import numpy as np\nimport pytest\n\nfrom ansys import dpf\nfrom ansys.dpf import core\nfrom ansys.dpf.core.common import locations, natures\n\n\n@pytest.fixture()\ndef property_field(simple_bar):\n \"\"\"Return a property field from the simple bar model\"\"\"\n model = dpf.core.Model(simple_bar)\n mesh =... | [
[
"numpy.array",
"numpy.allclose"
]
] |
adbarwell/DottyGenerator | [
"380281f3d7333db4f5fde52f25a4c32e471536b3"
] | [
"benchmark/apigeneration/generate.py"
] | [
"from dottygen.cli import generate\nfrom .counter import Counter\nimport random\nimport os\nimport shutil\nfrom datetime import datetime\nimport matplotlib.pyplot as plt\n\nparent_output_dir = os.path.abspath(os.path.join('benchmark', 'apigeneration', 'scr-sandbox'))\noutput_file = os.path.join(parent_output_dir, '... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.ylabel"
]
] |
denjiry/chainer | [
"758c74122f4463f3b0de61c6609b8688576f59a6"
] | [
"tests/chainer_tests/functions_tests/pooling_tests/test_max_pooling_2d.py"
] | [
"import unittest\n\nimport numpy\nimport six\n\nimport chainer\nfrom chainer.backends import cuda\nfrom chainer import functions\nfrom chainer import gradient_check\nfrom chainer import testing\nfrom chainer.testing import attr\nfrom chainer.testing import backend\n\n\ndef _to_fcontiguous(arrays):\n xp = chainer... | [
[
"numpy.arange",
"numpy.random.shuffle",
"numpy.random.rand",
"numpy.random.uniform",
"numpy.zeros",
"numpy.empty"
]
] |
som-shahlab/net_benefit_ascvd | [
"1259692025d90618211206105d8d23bd5a90a66c"
] | [
"net_benefit_ascvd/prediction_utils/pytorch_utils/group_fairness.py"
] | [
"import torch\nimport torch.nn.functional as F\nimport itertools\nimport warnings\nimport logging\n\nfrom net_benefit_ascvd.prediction_utils.pytorch_utils.models import (\n TorchModel,\n FixedWidthModel,\n BilevelModel,\n)\nfrom net_benefit_ascvd.prediction_utils.pytorch_utils.layers import FeedforwardNet\... | [
[
"torch.nn.functional.softmax",
"torch.ones",
"torch.nn.functional.log_softmax",
"torch.sum",
"torch.tensor",
"torch.exp",
"torch.log",
"torch.FloatTensor",
"torch.autograd.grad"
]
] |
prz3m37/RandomBenchmarking | [
"1e7cf754e016ea248377ee88741e6999359737ae"
] | [
"main.py"
] | [
"from BlochSolver.SolversManager import solvers_manager\nfrom BlochSolver.Plotter import bloch_plotter as bs\nfrom BlochSolver.Perturbations.filters import Filters\nfrom BlochSolver.QuantumSolvers.rotations import rotation_handler\nfrom BlochSolver.QuantumSolvers.numerics import numerical_methods\nfrom BlochSolver.... | [
[
"numpy.array",
"numpy.ones"
]
] |
habuimanh/HealthcareLogging | [
"156df5eaab71e0e067b418e3ba3f65ebd92fc5a3"
] | [
"dataset/re_label.py"
] | [
"import pandas as pd\nfrom datetime import datetime\nimport numpy as np\ndef read_elan_data_sets(file_path):\n data = pd.read_csv(file_path,delimiter=\"\\t\")\n return data\n\ndef read_data_sets(file_path):\n column_names = ['timestamp','x-axis', 'y-axis', 'z-axis','x1-axis', 'y1-axis', 'z1-axis']\n dat... | [
[
"pandas.read_csv"
]
] |
SITDIO/auto_attribute_tree | [
"d93bfc597b119c156322c34076bac4a5cee10a86"
] | [
"model/spectral.py"
] | [
"# -*- coding: utf-8 -*-\n# Based on implementations by:\n# Gael Varoquaux gael.varoquaux@normalesup.org\n# Brian Cheung\n# Wei LI <kuantkid@gmail.com>\"\"\"\nimport warnings\n\nimport numpy as np\n\nfrom sklearn.base import BaseEstimator, ClusterMixin\nfrom sklearn.utils import check_random... | [
[
"scipy.linalg.LinAlgError",
"numpy.dot",
"numpy.linalg.svd",
"numpy.sqrt",
"sklearn.manifold.spectral_embedding",
"sklearn.neighbors.kneighbors_graph",
"numpy.arange",
"sklearn.cluster._kmeans.k_means",
"numpy.linalg.norm",
"numpy.finfo",
"numpy.sign",
"sklearn.metr... |
Ruchip16/jina | [
"24c38a5c330453fb3ebd95f4f4f977b501b21240"
] | [
"tests/unit/orchestrate/flow/flow-construct/test_flow_yaml_parser.py"
] | [
"import os\nfrom pathlib import Path\n\nimport numpy as np\nimport pytest\n\nfrom jina import Executor\nfrom jina.excepts import BadYAMLVersion\nfrom jina import Flow\nfrom jina.jaml import JAML\nfrom jina.enums import GatewayProtocolType\nfrom jina.jaml.parsers import get_supported_versions\nfrom jina.parsers.flow... | [
[
"numpy.random.random"
]
] |
abhinandansharma/number-plate-recognition | [
"e31a1bfb5b7d92199829cb30b281d37f2b4552bb"
] | [
"DetectChars.py"
] | [
"# DetectChars.py\nimport os\n\nimport cv2\nimport numpy as np\nimport math\nimport random\n\nimport Main\nimport Preprocess\nimport PossibleChar\n\n# module level variables ##########################################################################\n\nkNearest = cv2.ml.KNearest_create()\n\n # constants for c... | [
[
"numpy.float32",
"numpy.zeros",
"numpy.loadtxt"
]
] |
zaxmks/demo-data-compliance-service | [
"372e612c570aaf5b512bec17627f825e880add67"
] | [
"src/tests/sources/test_structured_data_source.py"
] | [
"import pytest\n\nimport pandas as pd\n\nfrom src.mapping.columns.column_relation import ColumnRelation\nfrom src.mapping.rows.row_mapping_configuration import RowMappingConfiguration\nfrom src.mapping.rows.row_relation import RowRelation\nfrom src.mapping.values.value_matching_configuration import ValueMatchingCon... | [
[
"pandas.DataFrame"
]
] |
fmi-basel/dl-utils | [
"15bbb7672314d72abc7c3f7fc86655401cde5eb6"
] | [
"dlutils/losses/weighted_losses.py"
] | [
"from tensorflow.keras import backend as K\nimport tensorflow as tf\nimport abc\n\n\nclass PixelWeightedLossBase(tf.keras.losses.Loss):\n def call(self, y_true, y_pred):\n '''Computes pixel weighted loss, using the last channel of y_true as \n normalization weights\n \n Notes:\n --... | [
[
"tensorflow.keras.backend.binary_crossentropy",
"tensorflow.keras.backend.square",
"tensorflow.math.reduce_mean",
"tensorflow.keras.backend.abs"
]
] |
SeuTao/iclr-2021-factored-attention | [
"d189e3be2f8b2f27229a528f9f36b1c7ac072cd7"
] | [
"train.py"
] | [
"from argparse import ArgumentParser\nimport os\nfrom pathlib import Path\nimport random\nimport string\nimport io\n\nimport matplotlib.pyplot as plt\nimport pytorch_lightning as pl\nimport torch\nimport wandb\nimport boto3\n\nfrom mogwai.data_loading import MSADataModule, MSDataModule\nfrom mogwai.parsing import r... | [
[
"matplotlib.pyplot.close"
]
] |
acrellin/cesium | [
"9d33edc0f9b3a79c68070826c0f390896abe294d"
] | [
"cesium/features/tests/test_graphs.py"
] | [
"import os\nimport numpy as np\nimport numpy.testing as npt\n\nfrom cesium import data_management\nfrom cesium.features import graphs\nfrom cesium.features.tests.util import generate_features\n\n\n# Fixed set of features w/ known values\nSCIENCE_FEATS = graphs.GENERAL_FEATS + graphs.LOMB_SCARGLE_FEATS\n\n\ndef test... | [
[
"numpy.testing.assert_equal",
"numpy.loadtxt",
"numpy.testing.assert_array_almost_equal"
]
] |
vivek-a81/Custom-DeTr | [
"54aa3a924e8f9d76e743630a5b5fd5c20f49e8cc"
] | [
"detr/detect_pan.py"
] | [
"\r\nimport argparse\r\nimport json\r\nimport os\r\nimport time\r\nfrom io import BytesIO\r\n\r\nimport cv2\r\nimport numpy as np\r\nimport torch\r\nimport torchvision.transforms as T\r\nfrom detectron2.data import DatasetCatalog, MetadataCatalog\r\nfrom detectron2.utils.visualizer import Visualizer\r\nfrom panopti... | [
[
"numpy.random.seed",
"torch.load",
"torch.tensor",
"torch.no_grad",
"torch.cuda.is_available",
"numpy.float32",
"torch.stack",
"numpy.array",
"torch.as_tensor"
]
] |
tu-dortmund-ls12-rt/SSSEvaluation | [
"7e7c181fde24c98ef4432cf6ef2e91bd99911541"
] | [
"effsstsPlot/effsstsPlot.py"
] | [
"from __future__ import division\r\nimport sys\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport random\r\nimport math\r\n\r\ndef pickColor(ischeme):\r\n\tcolor = ''\r\n\tschemes = [\r\n\t\t'EDA','PROPORTIONAL','SEIFDA-MILP',\r\n\t\t'SCEDF','SCRM','SCAIR-RM','SCAIR-OPA','EDAGMF-OPA','MILP-ReleaseJi... | [
[
"numpy.all",
"numpy.load",
"matplotlib.pyplot.figure"
]
] |
likedan/keras_lrp | [
"f2a464604e1d69d00c1feeaa7bbd752a9234bd49"
] | [
"lrp/avgpool.py"
] | [
"'''\n@author: Sebastian Lapuschkin\n@author: Gregoire Montavon\n@maintainer: Sebastian Lapuschkin\n@contact: sebastian.lapuschkin@hhi.fraunhofer.de, wojciech.samek@hhi.fraunhofer.de\n@date: 14.08.2015\n@version: 1.2+\n@copyright: Copyright (c) 2015-2017, Sebastian Lapuschkin, Alexander Binder, Gregoire Montavon, ... | [
[
"numpy.zeros",
"numpy.zeros_like",
"numpy.ones"
]
] |
airacid/literate-garbanzo | [
"8827db2e39896eb2633440478cf6b799a9d0aebd"
] | [
"lib/datasets/imagenet3d.py"
] | [
"__author__ = 'yuxiang' # derived from honda.py by fyang\n\nimport datasets\nimport datasets.imagenet3d\nimport os\nimport PIL\nimport datasets.imdb\nimport numpy as np\nimport scipy.sparse\nfrom utils.cython_bbox import bbox_overlaps\nfrom utils.boxes_grid import get_boxes_grid\nimport subprocess\nimport pickle\nf... | [
[
"numpy.unique",
"numpy.arange",
"numpy.vstack",
"numpy.round",
"numpy.floor",
"numpy.meshgrid",
"numpy.zeros",
"numpy.where",
"numpy.loadtxt"
]
] |
hirune924/kaggle-G2Net | [
"e1b8d39a04411435c1b7b93a67cc370594f8a7ab"
] | [
"baseline_3_fixnorm_randwin_resize_filt.py"
] | [
"####################\n# Import Libraries\n####################\nimport os\nimport sys\nfrom PIL import Image\nimport cv2\nimport numpy as np\nimport pandas as pd\n\nimport pytorch_lightning as pl\nfrom pytorch_lightning.metrics import Accuracy\nfrom pytorch_lightning import loggers\nfrom pytorch_lightning import s... | [
[
"sklearn.metrics.roc_auc_score",
"pandas.concat",
"pandas.read_csv",
"torch.optim.lr_scheduler.CosineAnnealingLR",
"torch.load",
"torch.cat",
"torch.utils.data.DataLoader",
"torch.from_numpy",
"sklearn.model_selection.StratifiedKFold",
"torch.tensor",
"torch.nn.BCEWithL... |
biemann/tropical_precooling_environment | [
"df5f55544e09c5ad6bb29f8c3da6c649b057aba6"
] | [
"tropical_precooling/env.py"
] | [
"import os\n\nimport gym\nimport numpy as np\nimport pandas as pd\n\nfrom tropical_precooling.reward import dummy_reward, dummy_comfort_reward\n\n\nclass TropicalPrecooling(gym.Env):\n\n def __init__(self):\n \"\"\"\n load measurements and parameters for env simulation.\n \"\"\"\n env... | [
[
"pandas.read_csv",
"numpy.maximum",
"numpy.abs",
"numpy.asarray",
"numpy.isnan",
"pandas.DataFrame",
"numpy.ones",
"numpy.zeros"
]
] |
myying/DAPPER | [
"2f92fcb5610f9b2d44bb077ac5ed5438ac5fd6b3"
] | [
"dapper/mods/LotkaVolterra/__init__.py"
] | [
"\"\"\"The generalized predator-prey model, with settings for chaotic dynamics.\n\nRefs:\n[Wiki](https://en.wikipedia.org/wiki/Competitive_Lotka-Volterra_equations),\n`bib.vano2006chaos`.\n\"\"\"\n\nimport numpy as np\n\nimport dapper.mods as modelling\nfrom dapper.mods.integration import integrate_TLM\nfrom dapper... | [
[
"numpy.diag",
"numpy.array",
"numpy.ones"
]
] |
cepheid42/CBET | [
"9ef85012b134f22a0f19cd09e8594b934a53a9f4"
] | [
"plotter.py"
] | [
"import matplotlib.pyplot as plt\nfrom constants import *\nfrom numpy import max as npmax\n\ndef plot_everything(grid, intensity_sum, variable1, a0_variable, keep_open=False):\n cmap = 'jet'\n plt.figure()\n plt.pcolormesh(grid.z, grid.x, grid.eden / ncrit, cmap=cmap)\n plt.plot(grid.z - (dz / 2), grid.... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.colorbar",
"matplotlib.pyplot.ylabel",
"numpy.max",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.pcolormesh",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] |
liyuesolo/ntopo-1 | [
"d3e17ca4cfb1d7a71c4c4f0c965cfcdc67d53fa9"
] | [
"ntopo/monitors.py"
] | [
"\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\n\n\nclass SimulationMonitor:\n def __init__(self, n_iterations):\n self.n_iterations = n_iterations\n\n self.iter = None\n self.values = None\n\n def __iter__(self):\n self.iter = -1\n ... | [
[
"numpy.arange",
"tensorflow.reshape",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.close"
]
] |
leixin/lhotse | [
"24d713b81e8dbc0cdfec1038024a3b8a44eb54bd"
] | [
"lhotse/audio.py"
] | [
"import warnings\nfrom dataclasses import asdict, dataclass\nfrom io import BytesIO\nfrom math import sqrt\nfrom pathlib import Path\nfrom subprocess import PIPE, run\nfrom typing import Callable, Dict, Iterable, List, Optional, Union, Tuple\n\nimport numpy as np\n\nfrom lhotse.utils import Decibels, Pathlike, Seco... | [
[
"numpy.delete",
"numpy.average",
"numpy.zeros",
"numpy.sum",
"numpy.vstack"
]
] |
gwr3n/inventoryanalytics | [
"dd2a49386441bd51287d08ee73059cac7748c898"
] | [
"inventoryanalytics/forecasting/holt_winters.py"
] | [
"import numpy as np, pandas as pd\nimport matplotlib.pyplot as plt, pylab as py\nfrom statsmodels.tsa.api import ExponentialSmoothing\n#from statsmodels.tsa.statespace.exponential_smoothing import ExponentialSmoothing\n\ndef sample_seasonal_random_walk(realisations, m):\n np.random.seed(1234)\n errors = np.ra... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"numpy.random.seed",
"numpy.isnan",
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"numpy.random.normal",
"numpy.append",
"matplotlib.pyplot.fill_between",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"m... |
calyptia/calyptia-fluentd-benchmark-aws-environment | [
"3900ec856d0b12d32676fab99a3e759e1229866b"
] | [
"in_syslog_bench/visualize/plot_pandas_Usage.py"
] | [
"#!/usr/bin/env python3\n\nimport os\nimport numpy as np\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nimport seaborn as sns\nimport argparse\n\nfrom ansible.parsing.dataloader import DataLoader\nfrom ansible.inventory.manager import InventoryManager\n\nparser = argparse.ArgumentParser(description='V... | [
[
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"pandas.DataFrame",
"numpy.round",
"pandas.melt"
]
] |
Rabbit1010/TensorFlow2.0-Tutorial | [
"902d9dd55fc87c2bb0a3335002d668b49f9ab3ab"
] | [
"Topic 3/train.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jul 5 15:33:24 2019\n\n@author: Wei-Hsiang, Shen\n\"\"\"\n\nimport tensorflow as tf\nfrom model import AOI_model\nfrom generate_data import Get_AOI_DS\n\n\nif __name__ == '__main__':\n print(\"Version: \", tf.__version__)\n print(\"GPU is\", \"available\" if t... | [
[
"tensorflow.keras.callbacks.ModelCheckpoint",
"tensorflow.math.ceil",
"tensorflow.keras.callbacks.CSVLogger",
"tensorflow.test.is_gpu_available"
]
] |
SherwinGroup/Stele | [
"9bb7da0b406a801975e21c9f7ce05d369ae661e5"
] | [
"src/Stele/processing/processing_hsg/ccd_collection/high_sideband_ccd.py"
] | [
"import os\nimport errno\nimport copy\nimport json\nimport numpy as np\nfrom scipy.optimize import curve_fit\nimport matplotlib.pyplot as plt\nfrom .ccd import CCD\nfrom Stele.processing.processing_hsg.helper_functions import gauss\nfrom .helper_functions import calc_laser_frequencies\n\nnp.set_printoptions(linewid... | [
[
"numpy.diag",
"numpy.sqrt",
"numpy.linspace",
"numpy.asarray",
"numpy.flipud",
"matplotlib.pyplot.plot",
"numpy.mean",
"numpy.where",
"scipy.optimize.curve_fit",
"numpy.hstack",
"matplotlib.pyplot.gca",
"numpy.ones_like",
"numpy.arange",
"numpy.std",
"nu... |
urlocal12/tensorflow | [
"3f70de82668c0e61ecedf1bc458ab8b6d5f9b1f0"
] | [
"tensorflow/python/keras/mixed_precision/experimental/loss_scale_optimizer.py"
] | [
"# Copyright 2019 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.keras.mixed_precision.experimental.loss_scale.serialize",
"tensorflow.python.distribute.distribution_strategy_context.in_cross_replica_context",
"tensorflow.python.keras.backend.track_variable",
"tensorflow.python.keras.optimizers.deserialize",
"tensorflow.python.ops.control... |
ThomasMullen/NPP | [
"b3ef0bac451841a60edcb60340d2af36ec395f90"
] | [
"utils/hdf5_tools.py"
] | [
"import os\nimport h5py\nimport mat73\nimport numpy as np\n\n\"\"\"\nView HDF5 data structure\n------------------------\n\"\"\"\n\n\ndef traverse_datasets(hdf_file):\n \"\"\"\n Peak into matlab file and print the Key, Shape, Data type.\n :param hdf_file:\n :return:\n \"\"\"\n\n def h5py_dataset_it... | [
[
"numpy.array",
"numpy.zeros",
"numpy.sort"
]
] |
Frenzoid/labs | [
"3552c604445d1a9c79ec9f53b274b3890c23991e"
] | [
"MACHINE_LEARNING/1_stadistics_review.py"
] | [
"\nimport numpy as np\n\ndata = [15, 16, 18, 19, 22, 24, 29, 30, 34]\n\nprint(\"mean:\", np.mean(data))\nprint(\"median:\", np.median(data))\nprint(\"50th percentile (median):\", np.percentile(data, 50))\nprint(\"25th percentile:\", np.percentile(data, 25))\nprint(\"75th percentile:\", np.percentile(data, 75))\npri... | [
[
"numpy.median",
"numpy.percentile",
"numpy.std",
"numpy.mean",
"numpy.var"
]
] |
GauravSahani1417/Unsupervised-Learning | [
"5fb4188061b950f62c1cfd8c0fc0a24bf2d4f913"
] | [
"Density-Based Clustering.py"
] | [
"import numpy as np\nfrom sklearn.cluster import DBSCAN\nfrom sklearn.preprocessing import StandardScaler\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets.samples_generator import make_blobs\n\n# Create random data and store in feature matrix X and response vector y\nX, y = make_blobs(n_samples=1500, centers... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.get_cmap",
"sklearn.cluster.DBSCAN",
"matplotlib.pyplot.plot",
"numpy.zeros_like",
"sklearn.datasets.samples_generator.make_blobs",
"sklearn.preprocessing.StandardScaler",
"matplotlib.pyplot.show"
]
] |
sanja7s/MedRed | [
"0d9bc5be603dbbab7807b01b00f15822e0a944c6"
] | [
"code/validation/multiclass_predict_diseases.py"
] | [
"# import spacy\nfrom collections import defaultdict\n# nlp = spacy.load('en_core_web_lg')\n\nimport pandas as pd\nimport seaborn as sns\nimport random\nimport pickle\nimport numpy as np\n\nfrom xgboost import XGBClassifier\n\nimport matplotlib.pyplot as plt\nfrom collections import Counter\n\nimport sklearn\n#from... | [
[
"numpy.array",
"pandas.concat",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.subplots",
"sklearn.model_selection.StratifiedKFold",
"pandas.DataFrame",
"sklearn.metrics.confusion_matrix",
"sklearn.metrics.precision_recall_fscore_support",
"sklearn.metrics.f1_score",
... |
louise-scherrer/sot-talos-balance | [
"e1d2c853439902955f15e30fa15c0ce4fd6811a0",
"e1d2c853439902955f15e30fa15c0ce4fd6811a0"
] | [
"unittest/python/test_ft_calibration.py",
"unittest/python/test_dcm_controller_talos.py"
] | [
"import numpy as np\nfrom numpy.testing import assert_almost_equal as assertApprox\n\nimport sot_talos_balance.talos.ft_calibration_conf as conf\nfrom sot_talos_balance.ft_calibration import FtCalibration\n\nrobot_name = 'robot'\nftc = FtCalibration('ftc')\nftc.init(robot_name)\nrfw = conf.rfw\nlfw = conf.lfw\nftc.... | [
[
"numpy.array",
"numpy.random.randn"
],
[
"numpy.matrix",
"numpy.testing.assert_almost_equal"
]
] |
bioidiap/bob.bio.base | [
"44b8d192e957eb328591c8110cf0113f602292ef"
] | [
"bob/bio/base/test/dummy/preprocessor.py"
] | [
"from bob.bio.base.preprocessor import Preprocessor\nfrom bob.bio.base.database import BioFile\nimport numpy\nnumpy.random.seed(10)\n\n\nclass DummyPreprocessor (Preprocessor):\n def __init__(self, return_none=False, probability_of_none=1, **kwargs):\n Preprocessor.__init__(self)\n self.return_none = return_... | [
[
"numpy.random.seed",
"numpy.random.choice"
]
] |
fakufaku/doamm | [
"66c7124573fb2a2c705335f2f7e877378e585042"
] | [
"tests/test_norm.py"
] | [
"import numpy as np\n\n\ndef extract_off_diagonal(X):\n \"\"\"\n Parameters\n ----------\n X: array_like, shape (..., M, M)\n A multi dimensional array\n\n Returns\n -------\n Y: array_like, shape (..., M * (M - 1) / 2)\n The linearized entries under the main diagonal\n \"\"\"\... | [
[
"numpy.imag",
"numpy.conj",
"numpy.abs",
"numpy.inner",
"numpy.arange",
"numpy.linalg.norm",
"numpy.random.shuffle",
"numpy.cos",
"numpy.sin",
"numpy.real",
"numpy.random.randn",
"numpy.exp",
"numpy.where"
]
] |
pihvi/edutime | [
"c8f16e96b1c8b199dd1146c203084898040222b9"
] | [
"scrap/groupedViz.py"
] | [
"import pandas as pd\nimport matplotlib\nimport matplotlib.pyplot as plt\n\ndf = pd.read_csv('./data/sigite2014-difficulty-data.csv', sep=';')\n\n\ndef save_plot(title):\n file = 'plots/' + title + '.png'\n plt.savefig(file)\n plt.clf()\n plt.cla()\n plt.close()\n return file\n\n\nmax_weeks = 6\nm... | [
[
"pandas.read_csv",
"matplotlib.pyplot.axvline",
"matplotlib.pyplot.cla",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.close"
]
] |
factzero/pytorch_jaguarface_examples | [
"f248ff8899b8fe9d41a1e8ac095ed5b6688987ed"
] | [
"retinaFace/detect_video.py"
] | [
"# -*- coding: UTF-8 -*-\nimport argparse\nimport cv2\nimport numpy as np\nimport time\nimport torch\nfrom core.config import cfg_mnet\nfrom core.retinaface import RetinaFace\nfrom utils.prior_box import PriorBox\nfrom utils.box_utils import decode, decode_landm, nms\n\n\nparser = argparse.ArgumentParser(descriptio... | [
[
"numpy.hstack",
"torch.Tensor",
"torch.cuda.current_device",
"torch.load",
"torch.from_numpy",
"numpy.concatenate",
"torch.set_grad_enabled",
"numpy.float32",
"torch.device",
"numpy.where"
]
] |
JLLeitschuh/TIPL | [
"89c5d82932f89a2b4064d5d86ac83045ce9bc7d5"
] | [
"snippets/Python/edgegrowing.py"
] | [
"\"\"\" A script which implements the : try to segment the pores using a region growing \n/ k-means clustering - like algorithm where neighboring pores which have similar \nenough (a threshold value) orientations are grouped together. \nThat might make the data easier to visualize and ideally the layers we sometim... | [
[
"numpy.matrix",
"numpy.linalg.eigh"
]
] |
jchen42703/understanding-clouds-kaggle | [
"6972deb25cdf363ae0d9a9ad26d538280613fc94"
] | [
"clouds/models/clf_models.py"
] | [
"import torch.nn as nn\nimport torch\n\nimport pretrainedmodels\n\nclass Pretrained(nn.Module):\n \"\"\"\n A generalized class for fetching a pretrained model from Cadene/pretrainedmodels\n From: https://github.com/catalyst-team/mlcomp/blob/master/mlcomp/contrib/model/pretrained.py\n \"\"\"\n def __i... | [
[
"torch.nn.Softmax",
"torch.nn.Conv2d",
"torch.nn.Sigmoid",
"torch.nn.Linear",
"torch.nn.AdaptiveAvgPool2d",
"torch.cuda.is_available"
]
] |
mzj14/mesh | [
"bf04d24e7a9c54733dea014b82e5985a039da67c"
] | [
"examples/rnn-bench.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Mesh TensorFlow 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 requ... | [
[
"tensorflow.global_variables",
"tensorflow.estimator.RunConfig",
"tensorflow.group",
"tensorflow.summary.scalar",
"tensorflow.assign_add",
"tensorflow.estimator.export.PredictOutput",
"tensorflow.train.get_global_step",
"tensorflow.logging.set_verbosity",
"tensorflow.argmax",
... |
scikit-learn-contrib/vectorizers | [
"45751ca46f8e7b4e042ed3edd4917e38818da28a"
] | [
"vectorizers/timed_token_cooccurrence_vectorizer.py"
] | [
"from .preprocessing import (\n preprocess_timed_token_sequences,\n)\nfrom collections.abc import Iterable\nfrom .base_cooccurrence_vectorizer import BaseCooccurrenceVectorizer\nfrom .preprocessing import preprocess_timed_token_sequences\nfrom .coo_utils import (\n coo_append,\n coo_sum_duplicates,\n Co... | [
[
"numpy.log2",
"numpy.abs",
"numpy.int32",
"numpy.zeros_like",
"numpy.float32",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] |
vlukes/io3d | [
"34d048b7f737a5e56610879f6ab103128e8f0750"
] | [
"tests/qt_test.py"
] | [
"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\nfrom loguru import logger\n\n# import funkcí z jiného adresáře\nimport os.path\nimport os.path as op\n\n# import copy\n\nimport unittest\nimport pytest\n\n# sample_data_path = os.path.dirname(os.path.abspath(__file__))\n# sample_data_path\n# sys.path.append(os.p... | [
[
"numpy.asarray"
]
] |
gaze-abyss/HMS | [
"18de6c5db86f7b24b8e07ec1321df50d0382dad2"
] | [
"train/models/utils.py"
] | [
"import os\nfrom collections import *\nfrom itertools import *\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom . import constants\n\nbinding_data = \"binding.npy\"\ndomain_sequences = \"dseq_mtx.npy\"\npeptide_sequences = \"pseq_mtx.npy\"\ninteraction_sequences = \"iseqs_mtx.npy\"\n\nInteractionDataTuple = n... | [
[
"numpy.random.seed",
"numpy.ones",
"numpy.random.permutation",
"tensorflow.SparseTensorValue",
"numpy.array_split",
"numpy.where",
"numpy.vstack"
]
] |
rokroskar/GPy | [
"0f8dbba56d480902c86cfe8bad9e79d9eabae009",
"0f8dbba56d480902c86cfe8bad9e79d9eabae009"
] | [
"GPy/inference/gradient_descent_update_rules.py",
"GPy/kern/parts/rbf_inv.py"
] | [
"'''\nCreated on 24 Apr 2013\n\n@author: maxz\n'''\nimport numpy\n\nclass GDUpdateRule():\n _gradnat = None\n _gradnatold = None\n def __init__(self, initgrad, initgradnat=None):\n self.grad = initgrad\n if initgradnat:\n self.gradnat = initgradnat\n else:\n self.... | [
[
"numpy.dot"
],
[
"numpy.square",
"numpy.hstack",
"numpy.dot",
"numpy.log",
"numpy.array_equal",
"numpy.power",
"numpy.asarray",
"numpy.ones",
"scipy.weave.inline",
"numpy.transpose",
"numpy.exp",
"numpy.zeros",
"numpy.sum",
"numpy.empty"
]
] |
liuxfiu/qmodels | [
"f04d28923c623495d7d1cc3962fb8cac61dc2685"
] | [
"qmodels/notyet/mm1-evt.py"
] | [
"# for random distributions, random number generators, statistics\nimport random\nimport numpy as np\nimport scipy.stats as stats\n\n# for simulation\nimport simulus\n\ndef exp_generator(mean, seed):\n rv = stats.expon(scale=mean)\n rv.random_state = np.random.RandomState(seed)\n while True:\n # 100... | [
[
"scipy.stats.expon",
"numpy.random.RandomState"
]
] |
chemistry-scripts/cclib | [
"e8e0ea9b3e9b7091f8dfc4dd52d5e5e84a1cc258",
"e8e0ea9b3e9b7091f8dfc4dd52d5e5e84a1cc258"
] | [
"cclib/parser/jaguarparser.py",
"test/method/testvolume.py"
] | [
"# -*- coding: utf-8 -*-\n#\n# Copyright (c) 2020, the cclib development team\n#\n# This file is part of cclib (http://cclib.github.io) and is distributed under\n# the terms of the BSD 3-Clause License.\n\n\"\"\"Parser for Jaguar output files\"\"\"\n\n\nimport re\n\nimport numpy\n\nfrom cclib.parser import logfilep... | [
[
"numpy.array",
"numpy.zeros"
],
[
"numpy.asanyarray",
"numpy.resize",
"numpy.testing.assert_allclose"
]
] |
ssg-aero/pyvista | [
"5150b062cf835c6c6a44f6aefa4d53a1ad832ba3"
] | [
"pyvista/jupyter/notebook.py"
] | [
"\"\"\"\nSupport dynamic or static jupyter notebook plotting.\n\nIncludes:\n\n* ``ipyvtklink``\n* ``panel``\n* ``pythreejs``\n* ``ipygany``\n\n\"\"\"\nimport warnings\nimport os\n\nimport numpy as np\n\n# This module should not be imported at the __init__ level, only as a\n# lazy import when trying to plot using ju... | [
[
"numpy.linspace"
]
] |
pgabriela/dqn-jitsi-autoscaler | [
"b39eb335e584095ef66a9941dbe0b2ea21a02d4a"
] | [
"simulation/dqn-simulation/r11/dqn-delta-test.py"
] | [
"import math\nimport random\nimport time\nimport calendar\nimport os\nimport pickle\nimport requests\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom collections import namedtuple\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\n\n\... | [
[
"pandas.read_csv",
"matplotlib.pyplot.title",
"torch.load",
"torch.nn.LayerNorm",
"torch.tensor",
"torch.nn.Linear",
"matplotlib.pyplot.subplot",
"numpy.mean",
"numpy.argmin",
"torch.cuda.is_available",
"torch.no_grad",
"pandas.isna",
"numpy.array",
"matplot... |
sx14/open-relation.pytorch | [
"3fe52a0c6129a80abbc84df53903d13b7dea05d6",
"d9ed2f0c3394e435374cf3ab5afeb47a6a56ed9a"
] | [
"lib/model/rpn/rpn.py",
"open_relation/dataset/vrd/process/reformat_anno.py"
] | [
"# coding: utf-8\n\nfrom __future__ import absolute_import\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\n\nfrom lib.model.utils.config import cfg\nfrom .proposal_layer import _ProposalLayer\nfrom .anchor_target_layer import _AnchorTargetLayer\nfrom lib.m... | [
[
"torch.nn.functional.cross_entropy",
"torch.nn.functional.softmax",
"torch.nn.Conv2d",
"torch.autograd.Variable"
],
[
"numpy.array",
"numpy.unique"
]
] |
mpetroff/accessible-color-cycles | [
"473ad74eab8fb8926ea8ea15240f9ed5784e73bd"
] | [
"aesthetic-models/numpy-version/convert_weights_to_numpy.py"
] | [
"#!/usr/bin/env python3\n\n\"\"\"\nCopyright (c) 2021 Matthew Petroff\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of\nthis software and associated documentation files (the \"Software\"), to deal in\nthe Software without restriction, including without limitation the rights to\nuse... | [
[
"numpy.savez",
"numpy.expand_dims",
"numpy.flipud",
"numpy.transpose",
"numpy.array"
]
] |
XeBoris/strax | [
"4157f6b3fc5732d44ecd5e0f83d92d90187997d9"
] | [
"strax/io.py"
] | [
"\"\"\"Read/write numpy arrays to/from compressed files or file-like objects\n\"\"\"\nfrom functools import partial\nimport bz2\nimport os\n\nimport numpy as np\nimport blosc\nimport zstd\n\nimport strax\nexport, __all__ = strax.exporter()\n\nblosc.set_releasegil(True)\n\n\nCOMPRESSORS = dict(\n bz2=dict(\n ... | [
[
"numpy.frombuffer",
"numpy.zeros"
]
] |
TanweerulHaque/hermes | [
"d24770d7f8b5a7d84f98795079360b5264862297"
] | [
"hermes/custom/FC.py"
] | [
"import tensorflow as tf\nimport pandas as pd\nfrom collections import defaultdict\n\n\nclass FileCallback(tf.keras.callbacks.Callback):\n def __init__(self, save_format=\"\", filepath=\"\"):\n super(FileCallback, self).__init__()\n self.save_format = save_format\n self.dfparams = defaultdic... | [
[
"pandas.DataFrame"
]
] |
nnthanh101/realtime-fraud-detection-with-gnn-on-dgl | [
"520747ac5f4d760cd614543f45417e885509d15e"
] | [
"src/sagemaker/FD_SL_DGL/code/fd_sl_deployment_entry_point.py"
] | [
"#-*- coding:utf-8 -*-\n\n# Author:james Zhang\n# Datetime: Jan-10th 2021 19:10\n# Project: GCR Fraud_detection_on_DGL Solution\n\"\"\"\n This is the entry point of SageMaker inference endpoint, it fulfills:\n 1. Receive request and parse out the heterogeneous graph and target node\n 2. But a DGL heterogen... | [
[
"torch.Tensor",
"numpy.unique",
"torch.load",
"numpy.arange",
"torch.nn.ParameterDict",
"torch.nn.ModuleList",
"torch.from_numpy",
"torch.nn.Linear",
"torch.no_grad",
"numpy.searchsorted",
"torch.nn.init.xavier_uniform_",
"torch.nn.functional.leaky_relu",
"numpy... |
swederik/structurefunction | [
"5c5583bb26d6092fa3b7a630192d8e79199f8df0"
] | [
"coma/workflows/precoth.py"
] | [
"import os\nimport os.path as op\nimport nipype.interfaces.io as nio # Data i/o\nimport nipype.interfaces.utility as util # utility\nimport nipype.pipeline.engine as pe # pypeline engine\nimport nipype.interfaces.fsl as fsl\nimport nipype.interfaces.freesurfer as fs\nimport nipype.interfaces.... | [
[
"scipy.io.loadmat",
"numpy.zeros"
]
] |
do-wie-ching/do-wie-ching-anti-theft-system-base-on-deep-learning | [
"15307e34f617c93a93921494bb9805e94af3c0b7"
] | [
"pratice/activation.py"
] | [
"import torch\nimport numpy as np\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nimport matplotlib.pyplot as plt\n\n\ntensor = torch.FloatTensor([[1, 2], [3, 4]])\n\nvariable = Variable(tensor, requires_grad=True)\n\nt_out = torch.mean(tensor * tensor)\nv_out = torch.mean(variable * variable... | [
[
"torch.mean",
"torch.linspace",
"matplotlib.pyplot.legend",
"torch.sigmoid",
"matplotlib.pyplot.ylim",
"torch.autograd.Variable",
"matplotlib.pyplot.plot",
"torch.tanh",
"matplotlib.pyplot.subplot",
"torch.FloatTensor",
"torch.nn.functional.relu",
"matplotlib.pyplot... |
laturose/deepnlp | [
"3b0a8914468d641ac17836d8bec819af7df58f89"
] | [
"deepnlp/ner_tagger.py"
] | [
"#!/usr/bin/python\r\n# -*- coding:utf-8 -*-\r\n'''\r\n@author: xichen ding\r\n@date: 2016-11-15\r\n@rev: 2017-11-01\r\n'''\r\n\r\nfrom __future__ import absolute_import\r\nfrom __future__ import division\r\nfrom __future__ import print_function\r\nfrom __future__ import unicode_literals # compatible with python3 u... | [
[
"tensorflow.global_variables",
"tensorflow.global_variables_initializer",
"numpy.argmax",
"tensorflow.Session",
"tensorflow.variable_scope",
"tensorflow.train.Saver",
"numpy.array",
"numpy.sum"
]
] |
yueqiw/ephys_analysis | [
"6b33e2e43052bcd91705411ded1256725632fcde"
] | [
"current_clamp.py"
] | [
"import os\nimport numpy as np\nimport pandas as pd\nimport six\nfrom collections import OrderedDict\n\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport seaborn as sns\nfrom matplotlib import gridspec, animation\nfrom PIL import Image, ImageDraw, ImageFont\n\nimport allensdk_0_14_2.ephys_features a... | [
[
"matplotlib.pyplot.tight_layout",
"numpy.ones_like",
"numpy.arange",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.subplot",
"matplotlib.rcParams.update",
"matplotlib.gridspec.GridSpec",
"matplotlib.animation.FuncAnimation",
"numpy.argmin",
"numpy.zeros_like",
"matpl... |
vidheyoza/TwitterStockSentiment | [
"848b9bcb1150d868a32ee03d91ece0e1bbb0af69"
] | [
"main.py"
] | [
"import datetime as dt\nimport math\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom matplotlib import style\nfrom sklearn import preprocessing\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split\nimport yfinance as yf\nimport twe... | [
[
"matplotlib.pyplot.legend",
"pandas.read_csv",
"matplotlib.style.use",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show",
"sklearn.linear_model.LinearRegression",
"matplotlib.pyplo... |
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