repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
rafaelvalero/covid_forecast | [
"4e009ade5481f4e3bd48fd8048ca7d293d5d19b4",
"4e009ade5481f4e3bd48fd8048ca7d293d5d19b4"
] | [
"covid_forecast/utils/book_plots.py",
"covid_forecast/utils/visualizations.py"
] | [
"# -*- coding: utf-8 -*-\n\n\"\"\"Copyright 2015 Roger R Labbe Jr.\n\n\nCode supporting the book\n\nKalman and Bayesian Filters in Python\nhttps://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python\n\n\nThis is licensed under an MIT license. See the LICENSE.txt file\nfor more information.\n\"\"\"\n\nfrom __fut... | [
[
"matplotlib.pyplot.legend",
"numpy.sqrt",
"matplotlib.pyplot.autoscale",
"numpy.asarray",
"matplotlib.pyplot.axes",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.gca",
"matplotlib.pyplot.isinteractive",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.errorbar",
"matplotl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
liuqiangh/NeuCFlow | [
"438484ffcf51964fcb68f671b83fd58060fde1f1"
] | [
"code/model.py"
] | [
"import os\nimport time\n\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.python.keras.api._v2 import keras\n\nfrom utils import get, entropy, topk_occupy\n\n\nclass F(keras.layers.Layer):\n def __init__(self, n_dims, n_layers, name=None):\n super(F, self).__init__(name=name)\n self.n... | [
[
"tensorflow.concat",
"tensorflow.math.unsorted_segment_sum",
"tensorflow.zeros",
"tensorflow.stack",
"tensorflow.reduce_sum",
"tensorflow.equal",
"tensorflow.python.keras.api._v2.keras.layers.Dense",
"tensorflow.math.segment_max",
"tensorflow.squeeze",
"tensorflow.gather",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
linmo2333/SIDE | [
"2005f4d83e6b7da72dd4d0d0f01b36113d7d7480"
] | [
"src/lib/models/networks/submodule.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport torch\nimport torch.nn as nn\nimport torch.utils.data\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nimport math\nimport numpy as np\nimport cv2\nimport matplotlib.pyplot... | [
[
"torch.nn.BatchNorm1d",
"torch.mean",
"torch.nn.Dropout",
"torch.max",
"torch.nn.ConvTranspose3d",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.Conv3d",
"torch.nn.functional.relu",
"torch.nn.Conv1d",
"torch.nn.ReLU",
"torch.nn.BatchNorm3d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
muhammadhasyim/tps-torch | [
"21201458d037649fa66794b993ccfba7d7414028",
"21201458d037649fa66794b993ccfba7d7414028"
] | [
"1dbrownian/ml_test/run_cl.py",
"muller-brown-ml/analysis_scripts/plot_tricky_2.py"
] | [
"#Import necessarry tools from torch\nimport torch\nimport torch.nn as nn\n\n#Import necessarry tools from tpstorch \nfrom tpstorch.ml.data import EXPReweightSimulation\nfrom tpstorch.ml.optim import ParallelAdam, ParallelSGD\nfrom tpstorch.ml.nn import BKELossEXP, BKELossFTS, CommittorLoss2\nfrom brownian_ml impor... | [
[
"numpy.random.seed",
"torch.load",
"torch.manual_seed",
"torch.tensor",
"torch.no_grad"
],
[
"numpy.linspace",
"torch.load",
"numpy.zeros_like",
"numpy.mean",
"numpy.exp",
"torch.from_numpy",
"torch.tensor",
"numpy.std",
"matplotlib.pyplot.close",
"t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jrlivesey/vplanet | [
"4384221baa78e81d46b0c0fcb7de2f5a5de2e83c",
"4384221baa78e81d46b0c0fcb7de2f5a5de2e83c"
] | [
"vplanet/quantity_support.py",
"examples/IceBelts/makeplot.py"
] | [
"# -*- coding: utf-8 -*-\nfrom astropy import units as u\n\n# import Angle just so we have a more or less complete list of Quantity\n# subclasses loaded - matplotlib needs them all separately!\n# NOTE: in matplotlib >=3.2, subclasses will be recognized automatically,\n# and once that becomes our minimum version, we... | [
[
"matplotlib.units.registry.get"
],
[
"matplotlib.pyplot.contourf",
"numpy.zeros_like",
"numpy.where",
"numpy.unique",
"numpy.reshape",
"numpy.sin",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.close",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.title",
"ma... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dustcraft/Non-Mydriatic-Fundus-Images-Enhancement | [
"7a08a7493fa4796ff89b6d42b32aa99d38079599"
] | [
"image_fill.py"
] | [
"# -*- coding: utf-8 -*-\r\n#!/usr/bin/env python3\r\n\"\"\"\r\nCreated on Wed Oct 17 09:28:35 2018\r\n\r\n@author: yansl\r\n\r\nTarget: mirror & fill\r\n\r\nTheory: Circle inversion\r\n\r\nDetails:\r\n 1. mark and extract Non-FOV region\r\n 2. use Circle inversion to fill Non-FOV region\r\n\r\nReference:\r\n... | [
[
"numpy.clip",
"numpy.around",
"numpy.tile",
"numpy.zeros_like",
"numpy.transpose",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Wu-Chenyang/SMC-POMDP | [
"953c1abab412627080c63fbf8bb7f661b75e7006"
] | [
"battery/trainer.py"
] | [
"import torch\n\nfrom torch.utils.tensorboard import SummaryWriter\nfrom torch.utils.data import DataLoader\nfrom torch.nn.utils import clip_grad_norm_\nfrom torch.optim.lr_scheduler import CosineAnnealingWarmRestarts\n\nfrom torch import autograd\n\nimport time\nimport os\nimport random\n\nimport numpy as np\nimpo... | [
[
"torch.optim.Adam",
"torch.mean",
"torch.max",
"torch.autograd.set_detect_anomaly",
"numpy.random.seed",
"torch.cuda.manual_seed",
"torch.manual_seed",
"torch.load",
"matplotlib.pyplot.gcf",
"matplotlib.pyplot.plot",
"torch.utils.tensorboard.SummaryWriter",
"torch.c... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
owerbat/pydnameth | [
"e725d537011b519d1cd786fd926582151f010667",
"e725d537011b519d1cd786fd926582151f010667",
"e725d537011b519d1cd786fd926582151f010667"
] | [
"pydnameth/infrastucture/load/betas_adj.py",
"pydnameth/config/annotations/subset.py",
"pydnameth/infrastucture/load/betas.py"
] | [
"from pydnameth.infrastucture.load.betas import load_betas\nfrom pydnameth.infrastucture.path import get_data_base_path\nfrom pydnameth.infrastucture.load.attributes import load_cells_dict, load_observables_dict\nimport numpy as np\nimport pandas as pd\nfrom statsmodels import api as sm\nimport pickle\nimport os.pa... | [
[
"pandas.DataFrame",
"numpy.savez_compressed",
"numpy.mean",
"numpy.load",
"numpy.zeros"
],
[
"numpy.argsort",
"numpy.array"
],
[
"numpy.load",
"numpy.savez_compressed"
]
] | [
{
"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": []
},
{
"matplotlib": [],
"nump... |
cdobs/mi-instrument | [
"99f9322a4afabc5dff9b0fad12166075efce838c"
] | [
"mi/instrument/kut/ek60/ooicore/zplsc_echogram.py"
] | [
"\"\"\"\n@package mi.instrument.kut.ek60.ooicore.driver\n@file marine-integrations/mi/instrument/kut/ek60/ooicore/driver.py\n@author Craig Risien\n@brief ZPLSC Echogram generation for the ooicore\n\nRelease notes:\n\nThis class supports the generation of ZPLSC echograms.\n\"\"\"\n\nimport matplotlib\n\nmatplotlib.u... | [
[
"matplotlib.use",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
EmmaAspland/Sim.Pro.Flow | [
"5cf185c3b3fd1fe2a6d81fc88eb82376b128af99"
] | [
"Sim.Pro.Flow/src/adapt.py"
] | [
"import pandas as pd\nimport numpy as np\nimport re\n\n\ndef pos_to_char(pos):\n \"\"\"Convert integer into character.\"\"\"\n character = chr(int(65 + pos))\n\n return character\n\n\ndef codes(headersL):\n \"\"\"Create a dictionary with an character code as key and column name as value.\"\"\"\n x = ... | [
[
"pandas.isna",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
viditjain08/ALOCC-CVPR2018 | [
"8d81e0c6d6cb04deca17bcef364bc228a0654562"
] | [
"ops.py"
] | [
"\"\"\"\nSome code used from from https://github.com/Newmu/dcgan_code\n\"\"\"\n\nimport math\nimport numpy as np\nimport tensorflow as tf\n\nfrom tensorflow.python.framework import ops\n\nfrom utils import *\n\ntry:\n image_summary = tf.image_summary\n scalar_summary = tf.scalar_summary\n histogram_summary = tf.... | [
[
"tensorflow.nn.bias_add",
"tensorflow.matmul",
"tensorflow.concat",
"tensorflow.maximum",
"tensorflow.ones",
"tensorflow.nn.conv2d_transpose",
"tensorflow.constant_initializer",
"tensorflow.concat_v2",
"tensorflow.variable_scope",
"tensorflow.contrib.layers.xavier_initializ... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DeniseWarncke/Fair-Recommendations | [
"a3e637ec5878c5479f4f8e936886e6c2359aaabd"
] | [
"lenskit/topnFair.py"
] | [
"import logging\nimport warnings\nfrom collections import OrderedDict as od\n\nimport numpy as np\nimport pandas as pd\n\nfrom .metrics.topnFair import *\n_log = logging.getLogger(__name__) \n\nclass FairRecListAnalysis: \n \"\"\"\n Compute one or more ranking (top-N) metrics over recommendation lists.\n\n ... | [
[
"numpy.setdiff1d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
googleinterns/step258-2020 | [
"49e7af1a381e076ee884f55857a7af2f72add74d"
] | [
"mvp/trainer/ddsp_run_hypertune.py"
] | [
"# Copyright 2020 The DDSP 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 applicable law or a... | [
[
"tensorflow.compat.v2.config.experimental.list_physical_devices",
"tensorflow.compat.v2.config.experimental.set_memory_growth"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mtalluto/mbmtools | [
"67a01b24ba2bc214081085559500e7b16f8489f2"
] | [
"inst/python/kernel.py"
] | [
"# set kernel constraints\nimport GPy\nimport numpy as np\n\n\ndef make_kernel(dim, sparse = False):\n \"\"\"\n Make a kernel for mbm; presently supports no options\n \"\"\"\n k = GPy.kern.RBF(input_dim=dim, ARD=True)\n if sparse:\n k = GPy.kern.Add([k, GPy.kern.White(dim)])\n return k\n\n\... | [
[
"numpy.isnan"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jiportilla/ontology | [
"8a66bb7f76f805c64fc76cfc40ab7dfbc1146f40",
"8a66bb7f76f805c64fc76cfc40ab7dfbc1146f40",
"8a66bb7f76f805c64fc76cfc40ab7dfbc1146f40"
] | [
"python/testsuite/certifications/dmo/self_certification_regression.py",
"python/cendalytics/feedback/core/svc/relabel_sentiment_tags.py",
"python/testsuite/certifications/dmo/regression_result_analysis.py"
] | [
"# !/usr/bin/env python\n# -*- coding: UTF-8 -*-\n\n\nimport pandas as pd\nfrom pandas import DataFrame\nfrom tabulate import tabulate\n\nfrom base import BaseObject\nfrom nlusvc import TextAPI\n\n\nclass SelfCertificationRegression(BaseObject):\n \"\"\" Component Logic for the Regression Test on Self-Reported C... | [
[
"pandas.DataFrame"
],
[
"pandas.DataFrame"
],
[
"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": []
},
{
"matplotlib": [],
"nump... |
AutoGIS-2018/final-assignment-hellej | [
"ebb1c944046c33d07eb3299ec50046dbd9557415"
] | [
"utils/routes_tt.py"
] | [
"import itertools as itr\nimport pandas as pd\n\ndef get_target_permutations(tt_dfs):\n # get keys (stop ids) of dictionary as list\n to_ids = tt_dfs.keys()\n print('\\nGet all possible routes...')\n # get all possible sequences of stops\n to_ids_perms = list(itr.permutations(to_ids, len(to_ids)))\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": []
}
] |
ZitaoWang97/CenterNet-pytorch-lightning | [
"3508a38afe0c5fe45cbd6b9d27b9f4b4c7504320"
] | [
"CenterNet/transforms/sample.py"
] | [
"import copy\n\nimport torch\nimport numpy as np\nfrom collections import Callable\n\nimport torchvision.transforms.functional as VF\n\n\nclass ComposeSample:\n \"\"\"Composes several transforms together on sample of image and target\n\n Args:\n transforms (list of ``Transform`` objects): list of trans... | [
[
"numpy.array",
"torch.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WWGolay/gemini-tools | [
"85b90b55ae8d176fb7d6c2fe2f9ccc310395cdad"
] | [
"polar_scatter.py"
] | [
"\"\"\"\n==========================\nScatter plot on polar axis\n==========================\n\nSize increases radially in this example and color increases with angle\n(just to verify the symbols are being scattered correctly).\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n# Fixing random state f... | [
[
"matplotlib.pyplot.show",
"numpy.random.rand",
"numpy.random.seed",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jbkuczma/NBAshotchart | [
"9f12778a345346443745ee6cac9073f265f119bd"
] | [
"grapher.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn as sns\nfrom matplotlib.patches import Circle, Rectangle, Arc\nfrom os import path, remove\nfrom random import randint\n\nclass Grapher():\n\n\tdef __init__(self, playerShotData):\n\t\tself.playerShotData = playerShotData\n\n\tdef getShotCoordinat... | [
[
"matplotlib.pyplot.gca",
"matplotlib.patches.Arc",
"matplotlib.patches.Rectangle",
"matplotlib.patches.Circle",
"matplotlib.pyplot.savefig",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bingzhilee/tense_Representation_Bert | [
"0e0f9b35f086b8cfbd30fe01a4f2f21522e99f75"
] | [
"src/SVM_model.py"
] | [
"# -*- coding: utf-8 -*-\n# Copyright(c) 2009 - present CNRS\n# All rights reserved.\n\nimport argparse\nimport bloscpack as bp\nfrom sklearn.svm import LinearSVC\nfrom sklearn.metrics import accuracy_score,classification_report,confusion_matrix\nfrom sklearn.model_selection import GridSearchCV as gsc\nfrom sklearn... | [
[
"sklearn.model_selection.GridSearchCV",
"sklearn.metrics.classification_report",
"sklearn.svm.LinearSVC"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jamescodella/100_days_of_code | [
"37a4c09257eb3cd8908cfebe6a0299bd4b107af9",
"37a4c09257eb3cd8908cfebe6a0299bd4b107af9"
] | [
"source/Day26_List_Comprehension_and_NATO_Alphabet_Project/nato_alphabet.py",
"source/Day32_Automated_Birthday_Wisher/main.py"
] | [
"import pandas as pd\n\nnato = pd.read_csv('data/nato_phonetic_alphabet.csv')\n\n# Create mapping of letter to NATO word\nmapping = {}\nfor (index, row) in nato.iterrows():\n mapping[row.letter.lower()] = row.code\n\n\nword = input('Enter a word to be converted into the NATO alphabet: ').lower()\n\n# Create list... | [
[
"pandas.read_csv"
],
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1... |
jasoncao11/opencv-notebook | [
"07aa772f11797345292f90039dbc5e034ce30002",
"07aa772f11797345292f90039dbc5e034ce30002"
] | [
"Project-1. Volume hand control/VolumeHandControl_mac.py",
"Tutorial-4. Bitwise/script.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport cv2\nimport time\nimport numpy as np\nimport math\nimport HandTrackingModule as htm\nimport osascript\n\npTime = 0\ncTime = 0\ncap = cv2.VideoCapture(0)\ndetector = htm.handDetector(detectioncon=0.7)\nvolbar = 400\nvolper = 0\n\nwhile True:\n success, img ... | [
[
"numpy.interp"
],
[
"numpy.zeros",
"numpy.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LBJ-Wade/hierarc_SGL | [
"1dc2be90f44f99e82ab7014f2027fbb077b14f98"
] | [
"test/test_Likelihood/test_SneLikelihood/test_sne_likelihood.py"
] | [
"from hierarc.Likelihood.SneLikelihood.sne_likelihood import SneLikelihood\nimport pytest\nimport numpy as np\n\n\nclass TestSnePantheon(object):\n\n def setup(self):\n np.random.seed(42)\n # define redshifts\n num = 30 # number of Sne\n zcmb = np.linspace(start=0.01, stop=0.8, num=n... | [
[
"numpy.random.seed",
"numpy.linspace",
"numpy.random.multivariate_normal",
"numpy.ones",
"numpy.log10"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Pseudo-Lab/Motion-of-Action | [
"09453afd30b5112543c26d7f62a1ab428f22e0a3"
] | [
"tabnet/Infer.py"
] | [
"import os\nfrom tqdm import tqdm\nimport numpy as np\nimport torch\n\n\nclass Infer:\n def __init__(self, model, loader, test_config):\n self.config = test_config\n self.device = self.config.device\n self.loader = loader\n self.model = model.to(self.device)\n\n def inference_data(... | [
[
"numpy.concatenate",
"torch.no_grad",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Minys233/SA-SPR | [
"989a04f7c4c05ef16dda84e24dee9eab3ab4d5eb"
] | [
"solubility/data.py"
] | [
"import pandas as pd\nfrom gensim.models import word2vec\nfrom sklearn.model_selection import train_test_split\nfrom utils.utils import mol2vec_features\n\nSEED = 20190829\n\n\ndef load_ESOL(csv_path, mol2vec_path, pad_to=40):\n model = word2vec.Word2Vec.load(mol2vec_path)\n df = pd.read_csv(csv_path)\n fe... | [
[
"pandas.read_csv",
"sklearn.model_selection.train_test_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Evan1415/QMLA | [
"4521f7c08456a4494aed7c1b78d8ded5ea40f3d8"
] | [
"qmla/analysis/nv_centre_analysis_figure.py"
] | [
"import sys\nimport os\nimport numpy as np\nimport pickle\nimport pandas as pd\nimport itertools\nimport copy\n\nimport seaborn as sns\nimport sklearn\nimport matplotlib.pyplot as plt\nimport qinfer as qi\nfrom scipy import linalg\nfrom matplotlib.gridspec import GridSpec\nfrom matplotlib import rc\nfrom matplotlib... | [
[
"numpy.arange",
"matplotlib.pyplot.imread",
"matplotlib.lines.Line2D",
"matplotlib.pyplot.subplots",
"matplotlib.gridspec.GridSpec",
"matplotlib.rc"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SmartPracticeschool/llSPS-INT-3122-Emission-of-Co2-From-Cars | [
"a8e7f9d5446b41f7bc849c914e0715ed8f029c3f"
] | [
"model.py"
] | [
"# Importing the libraries\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\nimport pickle\r\n\r\ndataset = pd.read_csv('Emission_Co2.csv')\r\n\r\ndata1 = dataset[['FUELTYPE','ENGINESIZE','CYLINDERS', 'FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY','FUELCONSUMPTION_COMB','FUELCONSUMPTI... | [
[
"pandas.read_csv",
"sklearn.linear_model.LinearRegression"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
antoinediez/monaco | [
"613e4d792523e7ec0d8e0dca7934b5b5f0b1fa24"
] | [
"monaco/hyperbolic.py"
] | [
"import numpy as np\nimport torch\nimport torch.nn.functional as F\n\nfrom matplotlib import pyplot as plt\nfrom pykeops.torch import LazyTensor\n\nfrom mpl_toolkits.mplot3d import Axes3D\n\nfrom .proposals import Proposal\n\nnumpy = lambda x: x.cpu().numpy()\n\n\ndef normalize(points):\n return F.normalize(poin... | [
[
"torch.nn.functional.normalize",
"matplotlib.pyplot.gca",
"numpy.linspace",
"torch.cat",
"torch.zeros",
"torch.randn",
"matplotlib.pyplot.Circle",
"torch.rand",
"torch.cuda.is_available",
"matplotlib.pyplot.axis",
"numpy.meshgrid"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cecethea/pandas | [
"3ece0979b7fb356936500672cbc12d4c1047d06d"
] | [
"pandas/tests/series/test_constructors.py"
] | [
"from collections import OrderedDict\nfrom datetime import datetime, timedelta\n\nimport numpy as np\nimport numpy.ma as ma\nimport pytest\n\nfrom pandas._libs import lib\nfrom pandas._libs.tslib import iNaT\n\nfrom pandas.core.dtypes.common import is_categorical_dtype, is_datetime64tz_dtype\nfrom pandas.core.dtype... | [
[
"pandas.Series",
"numpy.asarray",
"pandas.RangeIndex",
"pandas.util.testing.assert_produces_warning",
"pandas.MultiIndex.from_tuples",
"pandas.DataFrame",
"pandas.core.dtypes.common.is_datetime64tz_dtype",
"numpy.dtype",
"pandas.util.testing.assert_index_equal",
"numpy.rand... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
StarsMyDestination/mmdetection3d | [
"dd06db85293f5a0ab21262647021997a83ec5b9a"
] | [
"tools/test.py"
] | [
"# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport mmcv\nimport os\nimport torch\nimport warnings\nfrom mmcv import Config, DictAction\nfrom mmcv.cnn import fuse_conv_bn\nfrom mmcv.parallel import MMDataParallel, MMDistributedDataParallel\nfrom mmcv.runner import (get_dist_info, init_dist, lo... | [
[
"torch.cuda.current_device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Shadowalker1995/Tutorial-Resource | [
"71fe3d521cf9971f708fa9978e9c685c0dda6ba6"
] | [
"DeepLearning/Deep-Learning-with-PyTorch-Tutorials/lesson28-激活函数与GPU加速/main.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\n\n\nbatch_size=200\nlearning_rate=0.01\nepochs=10\n\ntrain_loader = torch.utils.data.DataLoader(\n datasets.MNIST('../data', train=True, download=True,\n ... | [
[
"torch.device",
"torch.nn.Linear",
"torch.nn.CrossEntropyLoss",
"torch.nn.LeakyReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eyalzk/style_transfer | [
"bf09665db624d5b03c43cdeccfde9c9da0a08f76"
] | [
"style_funcs.py"
] | [
"import tensorflow as tf\nimport numpy as np\nimport matplotlib.image as mpimg\nfrom scipy import misc\nimport matplotlib.pyplot as plt\nimport os.path\nimport math\n\n\n# Plot images\ndef show_images(images):\n plt.figure()\n titles = ['Content Image', 'Style Image', 'Variable Image']\n for i, img in enum... | [
[
"matplotlib.pyplot.imshow",
"scipy.misc.imresize",
"tensorflow.constant",
"tensorflow.transpose",
"matplotlib.pyplot.title",
"numpy.reshape",
"scipy.misc.imsave",
"tensorflow.reshape",
"matplotlib.image.imread",
"matplotlib.pyplot.subplot",
"numpy.prod",
"tensorflow... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.10",
"0.16",
"0.19",
"0.18",
"0.12",
"1.0",
"0.17",
"1.2"
],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7... |
JulianVosseberg/feca2leca | [
"3bf17dd0eceb9fba5c8931e729654ce0e18ec50f"
] | [
"scrollsaw.py"
] | [
"#!/usr/bin/env python\n\n# Functions for using FECA-to-LECA ScrollSaw trees\n\nfrom numpy import median\n\ndef assign_all_seqs(tree_seqs, all_seqs, euk_only, prefix, blast_path):\n \"\"\"Assigns all original sequences to their best representing tree sequence based on the BLAST results\"\"\"\n other_seqs = se... | [
[
"numpy.median"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
simonUU/sciplot | [
"896a7a4efa7911a2bdf303311be674de12171fc4",
"896a7a4efa7911a2bdf303311be674de12171fc4"
] | [
"examples/stacked_plot.py",
"src/sciplot/functions.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\" Stacked plot\n\nIn data science, often one wants to compare two different sets of data, like signal/background or prediction and\nactual data.\n\nIn this very brief script we create two sets of data and compare them in one plot.\n\n\"\"\"\nimport matplotlib.pyplot as plt\nimport sci... | [
[
"numpy.random.random_sample",
"pandas.DataFrame",
"numpy.random.normal",
"matplotlib.pyplot.style.use",
"numpy.random.randint"
],
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.gca",
"numpy.min",
"matplotlib.pyplot.savefig",
"matplotlib.colors.hex2color",
"numpy.m... | [
{
"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": []
},
{
"matplotlib": [],
"nump... |
caxenie/neuromorphic-sensorimotor-adaptation | [
"e2f75bab83c0c08009d0ac5080e0bd63b77f61a8"
] | [
"vr_omnirob/robotics_simulator/simple_quadcopter.py"
] | [
"# Example of an adaptive quadcopter controller.\n# The quadcopter will follow the green sphere in the simulation\n# Scene File: ss_adaptive_quadcopter.ttt\nimport nengo\nimport numpy as np\nfrom robots import Quadcopter\n\nk1 = 0.43352026190263104\nk2 = 8.0\nk3 = 0.5388202808181405\nk4 = 6.6\nk5 = 2.59954524508501... | [
[
"numpy.matrix"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YashYash/advanced-lane-lines | [
"236d410a1220ea266f105794a8c3ead7da6bc0f9"
] | [
"models/lane/model.py"
] | [
"\"\"\"\nLane takes a binary_ouput Image model. The main purpose is to\ncalculate the curve that fits the lane lines. This will make it\nvery easy to construct Line models which will be used to highlight\nthe lane lines in the original image\n\"\"\"\nfrom typing import Tuple\nfrom models.image import Image\nimport ... | [
[
"numpy.hstack",
"numpy.polyfit",
"numpy.absolute",
"numpy.linspace",
"numpy.dstack",
"numpy.int_",
"numpy.concatenate",
"numpy.int",
"numpy.max",
"numpy.argmax",
"numpy.zeros_like",
"numpy.mean",
"numpy.array",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhaoyuzhi/VCGAN | [
"025a61c6ec377b9a53c592d4d2ac2e0183bfba76"
] | [
"train/test_model_first_stage.py"
] | [
"# Test the model's first stage\r\nimport os\r\nimport argparse\r\nimport cv2\r\nimport numpy as np\r\nfrom PIL import Image\r\n\r\ndef test(grayimg, model):\r\n # Forward and reshape to [H, W, C], in range [-1, 1]\r\n out_rgb = model(grayimg)\r\n out_rgb = out_rgb.squeeze(0).cpu().detach().numpy().reshape... | [
[
"numpy.concatenate",
"numpy.expand_dims",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yecfly/DEPRESSIONEST | [
"21b72906aac9f310e264f7a5eea348480a647197"
] | [
"FaceProcessUtil.py"
] | [
"import math\r\nfrom datetime import datetime\r\n\r\nimport cv2\r\nimport dlib\r\nimport numpy as np\r\nfrom PIL import Image as IM\r\nfrom scipy import ndimage\r\nimport time\r\n# --------------------------------------------------------------------------- #\r\n# Usage: python facepatches.py <inputDir> <outputDir>\... | [
[
"numpy.arctan",
"numpy.min",
"numpy.asarray",
"numpy.power",
"numpy.uint8",
"numpy.concatenate",
"numpy.max",
"numpy.std",
"numpy.round",
"numpy.mean",
"scipy.ndimage.filters.gaussian_filter",
"numpy.tanh",
"numpy.array",
"numpy.zeros"
]
] | [
{
"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": [... |
mjkwon2021/CAT-Net | [
"d7054580e30f99bd6adc05f5c5e39feee9fd1fcb"
] | [
"tools/infer.py"
] | [
"\"\"\"\n Created by Myung-Joon Kwon\n mjkwon2021@gmail.com\n June 7, 2021\n\"\"\"\nimport sys, os\npath = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')\nif path not in sys.path:\n sys.path.insert(0, path)\n\nimport argparse\nimport pprint\nimport shutil\n\nimport logging\nimport time\nimport t... | [
[
"torch.nn.functional.softmax",
"torch.load",
"torch.utils.data.DataLoader",
"matplotlib.pyplot.savefig",
"torch.no_grad",
"matplotlib.pyplot.close",
"matplotlib.pyplot.axis",
"torch.nn.DataParallel",
"torch.squeeze",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tchayintr/thbert | [
"e550cab7e419c8761ec54977d706ed299f15a869"
] | [
"src/modeling.py"
] | [
"'''The main BERT model and related functions'''\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport collections\nimport copy\nimport json\nimport math\nimport re\nimport numpy as np\nimport six\nimport tensorflow as tf\n\n\n\nclass BertConfig(... | [
[
"tensorflow.keras.layers.LayerNormalization",
"tensorflow.concat",
"numpy.sqrt",
"tensorflow.control_dependencies",
"tensorflow.zeros",
"tensorflow.cast",
"tensorflow.debugging.assert_less_equal",
"tensorflow.initializers.TruncatedNormal",
"tensorflow.io.gfile.GFile",
"tens... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
Lamhoiyan/ATECtrack2 | [
"4dc703b7e91cf9206b5ecf1b23c2d029a336fba7"
] | [
"preprocess.py"
] | [
"import os\r\nimport pickle\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nimport torch\r\nimport torch.utils.data\r\n\r\nTRAINDATA_DIR = './TrainData/TrainData/'\r\nTESTDATA_PATH = './Test_X/Test_X.pkl'\r\n\r\nclass CompDataset(object):\r\n def __init__(self, X, Y):\r\n self.X = X\r\n self.Y ... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
LucilleN/dv-classifier | [
"0b4dc0f0589dcf1bcb1f9f4f000d37de53ef4621"
] | [
"lstm.py"
] | [
"\"\"\"\nThis script runs a bidirectional LSTM (Long Short-Term Memory) neural network \nand either trains a new model from scratch, or loads a previously trained model,\nthen evaluates that model on the testing set. \n\nUsage:\n- Run this script with `python3 lstm.py` to evaluate the trained model on the testing s... | [
[
"torch.nn.CrossEntropyLoss",
"torch.nn.LSTM",
"torch.load",
"torch.cat",
"numpy.unique",
"torch.nn.Embedding",
"torch.nn.Sigmoid",
"torch.nn.Linear",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mysky528/rqalpha | [
"ecd550fc30aee96f9995e8152e2c48f5512f8b11"
] | [
"rqalpha/api/api_base.py"
] | [
"# -*- coding: utf-8 -*-\n#\n# Copyright 2017 Ricequant, 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 requir... | [
[
"pandas.Timestamp",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
The-Ludwig/dnn_reco | [
"a17e2cc97c4bb4912414997f2e79149b68c10e3f"
] | [
"tests/test_trafo_model.py"
] | [
"from __future__ import division, print_function\nimport click\nimport numpy as np\nimport tensorflow as tf\n\nfrom dnn_reco import misc\nfrom dnn_reco.setup_manager import SetupManager\nfrom dnn_reco.data_handler import DataHandler\nfrom dnn_reco.data_trafo import DataTransformer\n\n\n@click.command()\n@click.argu... | [
[
"tensorflow.constant",
"numpy.abs",
"numpy.allclose",
"numpy.min",
"numpy.max",
"numpy.std",
"numpy.mean",
"tensorflow.Session"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
ellequelle/xarray | [
"33067cd24f66d4855babaa6801b009480c4e2cb2"
] | [
"xarray/tests/test_groupby.py"
] | [
"import numpy as np\nimport pandas as pd\nimport pytest\nfrom pandas.tseries.frequencies import to_offset\n\nimport xarray as xr\nfrom xarray import DataArray, Dataset, Variable\nfrom xarray.core.groupby import _consolidate_slices\n\nfrom . import (\n assert_allclose,\n assert_array_equal,\n assert_equal,\... | [
[
"pandas.tseries.frequencies.to_offset",
"pandas.to_datetime",
"numpy.linspace",
"numpy.flipud",
"numpy.random.randn",
"numpy.mean",
"numpy.digitize",
"numpy.unique",
"numpy.arange",
"pandas.cut",
"numpy.repeat",
"numpy.zeros",
"numpy.floor_divide",
"pandas.M... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"0.19",
"0.24",
"0.20",
"1.0",
"0.25"
],
"scipy": [],
"tensorflow": []
}
] |
isabella232/qkeras | [
"6790f78597313eaf51af4f4dd8947700c8611ded"
] | [
"qkeras/quantizers.py"
] | [
"# Copyright 2019 Google LLC\n#\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 or agree... | [
[
"tensorflow.keras.backend.floatx",
"tensorflow.compat.v2.sqrt",
"tensorflow.keras.backend.tanh",
"tensorflow.compat.v2.keras.backend.clip",
"tensorflow.keras.backend.sign",
"tensorflow.compat.v2.shape",
"tensorflow.keras.initializers.VarianceScaling",
"tensorflow.compat.v2.sign",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
nunesma/reinforcement_learning | [
"5ad9a257eef1b6958287b9afda8ba3a930230426"
] | [
"DQN/1-Deep_Q_Network/model.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass QNetwork(nn.Module):\n \"\"\"Actor (Policy) Model.\"\"\"\n\n def __init__(self, state_size, action_size, seed, fc1_units=64, fc2_units=64):\n \"\"\"Initialize parameters and build model.\n Params:\n ======\n ... | [
[
"torch.nn.Linear",
"torch.manual_seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhaoqy1996/TCIT_thermo | [
"913b4eef31997eba9bbda94593c868841a1c95d1",
"913b4eef31997eba9bbda94593c868841a1c95d1"
] | [
"TCIT-Hf/SIMPOL/SIMPOL.py",
"TCIT-Hf/ML-package/ML_predict.py"
] | [
"import sys,os,argparse,subprocess,shutil,time,glob,fnmatch\nimport numpy as np\nfrom rdkit import Chem\nfrom copy import deepcopy\n\ndef main():\n\n smiles=\"C1=CC=C(C=C1)O\"\n\n P,H_vap = calculate_PandHvap(smiles,T=298,NIST_db='NIST_Hvap.json')\n print(P,H_vap)\n\n\n# Returns a matrix of graphical separ... | [
[
"numpy.dot",
"numpy.log",
"numpy.where",
"numpy.fill_diagonal"
],
[
"tensorflow.compat.v2.random.set_seed",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JaviPardox/fk-trajectory-analysis | [
"deaac9521732970200fba0cec865ee046b30d8dd"
] | [
"MESSI_FK.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Dec 16 01:14:23 2019\n\n@author: Javier Pardo\n https://www.linkedin.com/in/javier-pardo-fernandez-87b565124/\n javiyupipa@gmail.com\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.integrate as scp\nfrom scipy import optim... | [
[
"numpy.sqrt",
"numpy.linspace",
"scipy.optimize.root",
"scipy.integrate.solve_ivp",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.axes",
"matplotlib.animation.FuncAnimation",
"matplotlib.pyplot.close",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.3",
"1.9",
"1.5",
"1.7",
"1.0",
"1.2",
"1.8"
],
"tensorflow": []
}
] |
pcrespov/osparc-services | [
"cd8fb7be86a91cfd2a39019be3600ee8d60ab55a"
] | [
"services/dy-dash/cc-rabbit-2d/src/cc-rabbit-2d/cc-rabbit-2d.py"
] | [
"# pylint: disable=global-statement\n\nimport asyncio\nimport logging\nimport os\nimport sys\nimport json\nfrom shutil import copyfile\n\nfrom flask import Flask, render_template, Blueprint, Response\nimport pandas as pd\nimport numpy as np\nfrom simcore_sdk import node_ports\n\nimport tempfile\nimport matplotlib.p... | [
[
"pandas.concat",
"pandas.read_csv",
"numpy.arange",
"matplotlib.pyplot.get_cmap",
"matplotlib.pyplot.colorbar",
"matplotlib.pyplot.ioff",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.close",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
saaryab/dataHack2017-teamSAMI | [
"97f187ddb40bea7cc3a56ba8ab3ee6f5fdfb5e7d"
] | [
"svm/svm_multi.py"
] | [
"\nfrom sklearn import svm\nimport numpy as np\n# from sklearn.metrics import confusion_matrix\n# from sklearn.model_selection import cross_val_score\nimport threading\n\n\nclass SvmMulti:\n\n def __init__(self, logger, full_x_train, full_y_train, x_test, y_test):\n self.__x_train = full_x_train\n ... | [
[
"numpy.zeros",
"sklearn.svm.SVC"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
FabianKP/cgn | [
"9963e60c4a4bf4f3869e43d1dfbe11da74887ba5",
"9963e60c4a4bf4f3869e43d1dfbe11da74887ba5",
"9963e60c4a4bf4f3869e43d1dfbe11da74887ba5"
] | [
"tests/unit/test_cnls_solve/test_logger.py",
"tests/unit/test_cnls_solve/test_cgn_state.py",
"cgn/regop/operators/identity_operator.py"
] | [
"\nimport numpy as np\nimport time\n\nfrom cgn.cnls_solve.logger import Logger\nfrom cgn.cnls_solve.cnls_solution import CNLSSolution, OptimizationStatus\n\n\ndef test_logger_instance():\n logger = Logger(verbosity=3, filename=\"test.log\")\n cost_start = \"[COST AT START]\"\n logger.print_preamble(cost_st... | [
[
"numpy.random.randn"
],
[
"numpy.isclose",
"numpy.random.randn",
"numpy.ones"
],
[
"numpy.identity"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SABSR3-Group-2/PKLibaryGroup2 | [
"99b065e75aed0a5e640f218455b9c9e452613fa6"
] | [
"plot.py"
] | [
"#format\n#graph = plot() #initialise plot class\n#graph.adddata([100,50,25,12,6,3,1,0.5],[0,2,3,4,5,6,7,9]) #add a single series as a list folowed by the time series\n#graph.adddata([data],[timesereies]) \n#graph.adddata([[1,2,3,5,8,13,21],[15,4,3,2,1,2,3]],[1,2,3,4,5,6,7]) #add two sets of data with a common ti... | [
[
"pandas.concat",
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"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": []
}
] |
rdrews-dev/ReMeltRadar | [
"7492323e7a412d35506c5c4f53333a9aea12a562",
"7492323e7a412d35506c5c4f53333a9aea12a562"
] | [
"Src/CApRES/plot_test_data.py",
"Src/ApRES/Rover/HF/create_testing_report.py"
] | [
"import matplotlib.pyplot as plt\nimport matplotlib.patches as mpatches\nimport numpy as np\nimport pathlib\nimport pyapres \n\nucl_blue = (0, 151/255, 169/255)\nucl_orange = (234/255, 118/255, 0)\nucl_green = (181/255, 189/255, 0)\nucl_red = (213/255, 0, 50/255)\n\nline_colors = (ucl_blue, ucl_orange, ucl_green, u... | [
[
"numpy.abs",
"numpy.size",
"matplotlib.pyplot.subplots",
"numpy.sqrt"
],
[
"numpy.abs",
"numpy.sqrt",
"matplotlib.patches.Rectangle",
"matplotlib.pyplot.subplots",
"numpy.size"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
B-C-WANG/DeepLearningMoleculeEnergyPredict | [
"349c8f0ff156b8ccbcaec3af1108b62b604f117a"
] | [
"dlmep/DatasetOffer.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport numpy as np\nimport pickle\nfrom sklearn.model_selection import train_test_split\nfrom deepchem.data import NumpyDataset\nimport tensorflow as tf\nimport deepchem as dc\nimport time\nimport os\nfrom dlmep.DatasetMaker import *\nfrom VDE.AtomSpace import atom_index_trans_reverse\n... | [
[
"numpy.concatenate",
"numpy.array",
"numpy.zeros",
"sklearn.model_selection.train_test_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rstaten/fiberassign | [
"d336e7df37380dcb09054acf7a89491701dcec73"
] | [
"py/fiberassign/assign.py"
] | [
"# Licensed under a 3-clause BSD style license - see LICENSE.rst\n# -*- coding: utf-8 -*-\n\"\"\"\nfiberassign.assign\n=====================\n\nFunctions and classes for computing and writing fiber assignment.\n\n\"\"\"\nfrom __future__ import absolute_import, division, print_function\n\nimport os\n\nimport re\n\ni... | [
[
"numpy.isin",
"numpy.dtype",
"numpy.full",
"numpy.all",
"numpy.ones",
"numpy.copy",
"numpy.frombuffer",
"numpy.any",
"numpy.argsort",
"numpy.array",
"numpy.zeros",
"numpy.where",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
neu-spiral/SeverityScoreForROP | [
"4892f18022965c06d3a5dbefe535883ffa002fc6"
] | [
"generatePartitions.py"
] | [
"# This file is loading and preparing the data and labels for the cross-validation partitions\n# Parameter\n# ----------\n# K : scalar\n# number of folds\n# numOfRep : scalar\n# number of re-partition times\n\n# Return\n# --------------\n#\n#\n\nimport numpy as np\nfrom sklearn.cross_validation import ... | [
[
"numpy.reshape",
"scipy.io.loadmat",
"numpy.ones",
"numpy.concatenate",
"numpy.int_",
"sklearn.preprocessing.normalize",
"numpy.append",
"scipy.io.savemat",
"numpy.array",
"numpy.where"
]
] | [
{
"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"... |
upupming/dragon | [
"245f71996004b386ae764eb8f76603233d8a6763"
] | [
".notes/test.py"
] | [
"import numpy as np\n\na = [\n [1, 2, 3],\n [4, 5, 6]\n]\n\nb = [\n [7, 8, 9],\n [10, 11, 12]\n]\n\nc = [\n [90, 90, 98],\n [43, 45, 43]\n]\n\ncow = np.array(a)\n\nsheep = np.array(b)\n\nhare = np.array(c)\n\ntotal = np.append(\n np.append(cow, sheep, axis=1),\n hare,\n axis=1\n)\nprint(t... | [
[
"numpy.append",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
faizankshaikh/AmongUs_StatsChecker | [
"345b8ccecb8ea7f511165666e31e7d67c5c5446b"
] | [
"app.py"
] | [
"import os\nimport cv2\nimport easyocr\nimport numpy as np\nimport pandas as pd\nimport streamlit as st\nimport matplotlib.pyplot as plt\n\nfrom glob import glob\nfrom typing import Union\nfrom io import BytesIO, StringIO\nfrom scipy.stats import norm, percentileofscore\n\nif not os.path.exists('processed_images'):... | [
[
"pandas.read_csv",
"pandas.Series",
"scipy.stats.norm.pdf",
"matplotlib.pyplot.subplots",
"numpy.ones",
"scipy.stats.percentileofscore",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"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... |
tyomj/product-pricing | [
"ba554c63459ea83445a70b944527c8ee42af0341",
"ba554c63459ea83445a70b944527c8ee42af0341"
] | [
"prod_pricing/components/detector.py",
"prod_pricing/evaluation.py"
] | [
"import sys\n\nimport numpy\nimport torch\nfrom datasets import letterbox\nfrom general import non_max_suppression, scale_coords\nfrom models.experimental import attempt_load\n\nsys.path.append('/product-pricing/yolov5')\nsys.path.append('/product-pricing/yolov5/utils')\n\n\nclass YoloV5Predictor:\n def __init__... | [
[
"numpy.ascontiguousarray",
"torch.from_numpy"
],
[
"pandas.read_csv",
"matplotlib.pyplot.ylim",
"numpy.cumsum",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlim",
"numpy.mean",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
muskanmahajan37/aa | [
"df28842a8594870db3282682b1261af5058af832"
] | [
"Scripts/create_latexTable_2col.py"
] | [
"\"\"\"\nScript creates a latex table for manuscript (two-column)\n\nNotes\n-----\n Author : Zachary Labe\n Date : 22 February 2018\n\"\"\"\n\n### Import modules\nimport numpy as np\nimport datetime\nfrom tabulate import tabulate\n\n### Define directories\ndirectorydata = '/surtsey/zlabe/simu/'\ndirectoryda... | [
[
"numpy.arange",
"numpy.array",
"numpy.genfromtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HugoFara/pyswarms | [
"73ac4bb138bd767604d2729a9cb32656fced9759"
] | [
"pyswarms/backend/swarms.py"
] | [
"# -*- coding: utf-8 -*-\n\n\"\"\"\nSwarm Class Backend.\n\nThis module implements a Swarm class that holds various attributes in\nthe swarm such as position, velocity, options, etc. You can use this\nas input to most backend cases.\n\"\"\"\n\n# Import modules\nimport numpy as np\nfrom attr import attrib, attrs\nfr... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jthermiz/spiketoolkit | [
"41dd987c719d7297f01c11ed2152818602bd45a7"
] | [
"spiketoolkit/preprocessing/normalize_by_quantile.py"
] | [
"from spikeextractors import RecordingExtractor\nimport numpy as np\nfrom spikeextractors.extraction_tools import check_get_traces_args\n\nclass NormalizeByQuantileRecording(RecordingExtractor):\n\n preprocessor_name = 'NormalizeByQuantile'\n installed = True # check at class level if installed or not\n i... | [
[
"numpy.concatenate",
"numpy.random.RandomState",
"numpy.quantile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mrkraimer/testPvaPy | [
"7d09095bc76bf0a86d8d664c85757ab8369485c8"
] | [
"matplot/ellipse.py"
] | [
"#!/usr/bin/env python\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom pvaccess import *\nfrom pvaccess import DOUBLE\n\nmin = 0.0\nmax = 1.0\nnpts = 1000\ninc = (max-min)/npts\nt = np.arange(min, max, inc)\na = 2.0\nb = 3.0\nx = a*np.cos(2*np.pi*t)\ny = b*np.sin(2*np.pi*t)\nchan = Channel(\"x\")\npv =... | [
[
"numpy.arange",
"numpy.cos",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TiffanyHsuuuu/adapter | [
"9352bc33af5c7aea0ea4675c05ea5c3b049aa95c"
] | [
"src/transformers/adapters/models/gpt2.py"
] | [
"from typing import Union\n\nimport torch\nfrom torch import nn\n\nfrom ..composition import AdapterCompositionBlock, parse_composition\nfrom ..heads import ClassificationHead, MultiLabelClassificationHead\nfrom ..model_mixin import InvertibleAdaptersMixin, ModelAdaptersMixin\nfrom .bert import (\n BertEncoderAd... | [
[
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Suuuuuzy/php-proxy-app | [
"449621731680b58729f8ad72051247137510cb44"
] | [
"gpu_attack.github.io/bar graph/bar graph.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nfrom scipy.stats import t\n\n# fig =\nN = 17\nWebgl_means = (14.08, 14.19, 16.04, 15.91, 15.95, 15.08, 16.18, 14.30, 14.08, 16.52, 14.52, 15.10, 22.81, 16.67, 15.59, 14.42, 14.58)\nSoft_means = (15.61, 15.07, 17.76, 16.07... | [
[
"numpy.arange",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.subplots",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
EduardoRomero83/TreeExplorationOfDefoNet | [
"817b42538a2c92f2122ee584b628cc0f37bd34c7"
] | [
"defonet_predict.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jul 14 15:56:30 2021\n\n@author: zhang.9325\n\"\"\"\n\n\nimport tensorflow as tf\nimport cv2\nimport numpy as np\nimport os\nimport shutil\nfrom keras.preprocessing.image import img_to_array\n\n\n\ndef get_prediction(file):\n model = tf.keras.models.load_model('mo... | [
[
"tensorflow.keras.models.load_model",
"numpy.expand_dims"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
mrcagney/gtfstk | [
"b379eba941f5bea6c88b7001a15fb66ccb1c4264"
] | [
"tests/test_cleaners.py"
] | [
"from pandas.util.testing import assert_frame_equal, assert_series_equal\nimport numpy as np\n\nfrom .context import gtfstk, sample\nfrom gtfstk import *\n\n\ndef test_clean_column_names():\n f = sample.routes.copy()\n g = clean_column_names(f)\n assert_frame_equal(f, g)\n\n f = sample.routes.copy()\n ... | [
[
"pandas.util.testing.assert_frame_equal",
"pandas.util.testing.assert_series_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
moeyensj/atm_data | [
"38e86c5478b40500b112cdeefce8dba22ac55fe6"
] | [
"paper1/runs/sigma15/runMultiFit_run4a.py"
] | [
"if __name__ == \"__main__\":\n \n ### TRACK 3 - 3 Parameters ###\n RUN_NAME = \"run4a\"\n \n import os\n import yaml\n import numpy as np\n import pandas as pd\n import sqlite3 as sql\n import warnings\n\n import sys\n sys.path.append(\"/gscratch/astro/moeyensj/atm/atm\")\n\n ... | [
[
"numpy.radians",
"pandas.read_sql"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
AceCoooool/LEDNet | [
"8887545ef0c0eba8b8e5d92f9452764d7bd55bb3"
] | [
"model/basic.py"
] | [
"import torch\nfrom torch import nn\nimport torch.nn.functional as F\n\n\n# helper function\ndef channel_shuffle(x, groups):\n b, n, h, w = x.shape\n channels_per_group = n // groups\n\n # reshape\n x = x.view(b, groups, channels_per_group, h, w)\n x = torch.transpose(x, 1, 2).contiguous()\n\n # f... | [
[
"torch.transpose",
"torch.nn.Dropout2d",
"torch.cat",
"torch.randn",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.functional.relu",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.functional.interpolate",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RedBeansAndRice/pyshgp | [
"99edc9f683af79fb04631881c27282c91fb33f84"
] | [
"tests/push/test_types.py"
] | [
"import pytest\nimport numpy as np\n\nfrom pyshgp.push.types import PushType, PushInt, PushStr, Char\nfrom pyshgp.validation import PushError\n\n\nclass TestPushType:\n\n def test_is_instance(self):\n assert PushInt.is_instance(5)\n assert PushInt.is_instance(np.int64(100))\n assert not Push... | [
[
"numpy.str_",
"numpy.int64"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yevheniyc/Python | [
"262842477793d65c2b382ca810867fd24a415576"
] | [
"1h_NGL_3D_Viewer/pyscripts/pdbmapper/InputProcessor.py"
] | [
"'''\n InputProcessor.py - Yevheniy Chuba - 6/1/2017\n Currently Parses input CSV file and extracts: Sequence ID, Group, Sequence.\n It then returns the following data structure for further pre-processing:\n {group_id: [['seq_id', 'seq], ... ],\n ...\n }\n\n Usage:\n input_... | [
[
"pandas.read_excel",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
qiweizhen/BANG | [
"d1a8c702d67df420cb3cd949f6180b18af1103c2"
] | [
"bang/bang/ngram_masked_s2s.py"
] | [
"# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License.\n\nimport os\nimport numpy as np\n\nimport torch\n\nfrom collections import OrderedDict\nfrom fairseq import utils\nfrom fairseq.data import (\n data_utils,\n Dictionary,\n PadDataset,\n TokenBlockDataset,\n)... | [
[
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yuan-xiaohan/nnUnet_OnWindows | [
"5cb561df75f22ee564592393a26837bd6b9e6fef"
] | [
"nnunet/training/dataloading/dataset_loading.py"
] | [
"# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany\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# ... | [
[
"numpy.random.choice",
"numpy.unique",
"numpy.save",
"numpy.concatenate",
"numpy.savez_compressed",
"numpy.copy",
"numpy.random.random_integers",
"numpy.load",
"numpy.array",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dyth/generative_models | [
"8f3aed5662d52b28e965cc67e7924415b06d82df"
] | [
"GANs/DCGAN.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\nfrom torch.autograd import Variable\nfrom torchvision.utils import save_image\nimport os\nimport argparse\nimport time\nimport json\nfrom datetime import datetime\n\nfrom... | [
[
"torch.nn.Dropout2d",
"torch.nn.Conv2d",
"torch.nn.BCELoss",
"torch.nn.Tanh",
"torch.nn.Linear",
"torch.nn.Sigmoid",
"torch.nn.Upsample",
"torch.nn.LeakyReLU",
"torch.nn.functional.interpolate",
"torch.cuda.is_available",
"torch.device",
"torch.nn.BatchNorm2d",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alexchungio/Video-Quality-Diagnosis | [
"8e407e6c62b3ea3a8902cf81acef9685e14b3d42"
] | [
"joint_detect_blur_noise.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#------------------------------------------------------\n# @ File : joint_detect_blur_noise.py\n# @ Description: \n# @ Author : Alex Chung\n# @ Contact : yonganzhong@outlook.com\n# @ License : Copyright (c) 2017-2018\n# @ Time : 2020/12/24 下午2:... | [
[
"numpy.fft.fft2",
"numpy.fft.ifft2",
"numpy.abs",
"matplotlib.pyplot.subplots",
"numpy.fft.fftshift",
"numpy.fft.ifftshift",
"numpy.mean",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MansonX/mbrl-lib | [
"2c37143f7353858d20f8d3def1d7bd031ef24514"
] | [
"mbrl/algorithms/m2ac.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\nimport os\nfrom typing import Optional, Tuple, cast\n\nimport gym\nimport hydra.utils\nimport numpy as np\nimport... | [
[
"torch.Generator",
"numpy.logical_not",
"numpy.reshape",
"numpy.ceil",
"numpy.float32",
"torch.device",
"numpy.argsort",
"numpy.zeros",
"numpy.random.default_rng"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Josecontis/opinion_target_extraction | [
"daa971c1724eb53589d8f44c74a3eba928c125b8"
] | [
"ProgettoTesi/src/processing_data.py"
] | [
"import pandas as pd\nimport re\n\n\n# metodo per estrapolare la colonna delle frasi da un file csv\ndef csv_to_column_list(file_csv, sentence_col):\n dfP = pd.read_csv(file_csv, encoding ='ISO-8859-1') # lettura file csv\n sentence = dfP[sentence_col].tolist() # sentence contiene una lista di frasi preleva... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
maelso/devito | [
"d45932b08733ea7437e6a83fff816ee20d3fbada"
] | [
"examples/seismic/model.py"
] | [
"import numpy as np\nfrom sympy import sin, Abs, finite_diff_weights\n\n\nfrom devito import (Grid, SubDomain, Function, Constant,\n SubDimension, Eq, Inc, Operator, div)\nfrom devito.builtins import initialize_function, gaussian_smooth, mmax, mmin\nfrom devito.tools import as_tuple\n\n__all__ = ... | [
[
"numpy.log",
"numpy.abs",
"numpy.sqrt",
"numpy.min",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ardunn/latmats | [
"9eabbd404041cd706dac443dda18bf4809835d3b"
] | [
"latmats/tasks/tester.py"
] | [
"import tqdm\nimport copy\nimport pickle\nimport datetime\n\nimport numpy as np\nfrom sklearn.model_selection import train_test_split, KFold, LeaveOneOut\nfrom sklearn.metrics import mean_absolute_error, r2_score, explained_variance_score, mean_squared_error\n\nfrom latmats.tasks.loader import load_e_form, load_exp... | [
[
"sklearn.model_selection.LeaveOneOut",
"sklearn.model_selection.train_test_split",
"sklearn.model_selection.KFold",
"sklearn.metrics.mean_squared_error",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pabloppp/CarND-Advanced-Lane-Lines | [
"c81435e6f16211b89c9405f31bc745a623634a57"
] | [
"src/test_pipeline.py"
] | [
"import cv2\nimport matplotlib.pyplot as plt\n\nfrom src.utils.image_pipeline import undistort, birds_eye_view, pipeline\n\ntest_img = cv2.imread('../test_images/test8.jpg')\n\noutput_image, unwarped, M, Minv, d_pre, d_post = pipeline(test_img, debug=True)\ncv2.imwrite('../output_images/test8_pipeline.jpg', output_... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Vermankayak/Self-Driving-Car | [
"42992df96b72c69a5d2a5139f3d8f97e39acae9b"
] | [
"train.py"
] | [
"import os\nimport tensorflow as tf\nfrom tensorflow.core.protobuf import saver_pb2\nimport driving_data\nimport model\n\nLOGDIR = './save' #save folder stores the final model files at the end of training\n#The models traied by AAIC course are in save copy folder and we can use them instead of training a new model.... | [
[
"tensorflow.InteractiveSession",
"tensorflow.subtract",
"tensorflow.initialize_all_variables",
"tensorflow.nn.l2_loss",
"tensorflow.summary.merge_all",
"tensorflow.train.AdamOptimizer",
"tensorflow.trainable_variables",
"tensorflow.get_default_graph",
"tensorflow.train.Saver",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
AdityaKane2001/flax | [
"addfe9c26549dceafa3a4fa6882cc527e3344337"
] | [
"flax/deprecated/nn/linear.py"
] | [
"# Copyright 2022 The Flax 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 applicable law or a... | [
[
"numpy.max",
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
StefanStegmueller/fakecheck | [
"519f86b088a279aec51681dabdb05e9d0f597e31"
] | [
"src/utils.py"
] | [
"#!/usr/bin/python\n\nimport os\nimport json\nimport datetime\nimport tensorflow as tf\nfrom tensorflow.data import Dataset\n\n \ndef write_json_data(data, path):\n with open(path, 'w') as fout:\n json.dump(data, fout)\n \n \ndef read_json_data(path):\n with open(path) as json_file:\n ... | [
[
"tensorflow.keras.callbacks.ModelCheckpoint",
"tensorflow.data.TFRecordDataset",
"tensorflow.data.Dataset.list_files",
"tensorflow.train.Features",
"tensorflow.keras.callbacks.TensorBoard"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
riaz/neptuneml-toolkit | [
"6c68ba6d02a3d52116e6e8ca23d5f755693ae3d8"
] | [
"src/neptuneml_toolkit/modelzoo/feature_generator/node_embedding.py"
] | [
"import dgl\nimport torch as th\nimport torch.nn as nn\n\ndef initializer(emb):\n emb.uniform_(-1.0, 1.0)\n return emb\n\nclass NodeEmbedding(nn.Module):\n r\"\"\"\n Learnable node embeddings as initial node features\n\n Parameters\n ----------\n node_counts : dict[ntype: str, ncount: int]\n ... | [
[
"torch.mean",
"torch.nn.Parameter",
"torch.zeros",
"torch.nn.ModuleDict",
"torch.nn.Embedding",
"torch.nn.Embedding.from_pretrained"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
carolineyuchen/MMRIV | [
"2b3f13a453f2b4542bfe8820f1a7a63f7725db4e",
"2b3f13a453f2b4542bfe8820f1a7a63f7725db4e"
] | [
"MMR_IVs/rkhs_model_LMO_nystr_zoo.py",
"MMR_IVs/nn_model_zoo.py"
] | [
"import os, sys\nimport autograd.numpy as np\nfrom autograd import value_and_grad\nfrom scipy.optimize import minimize\nfrom util import get_median_inter_mnist, Kernel, load_data, ROOT_PATH, jitchol, _sqdist, \\\n remove_outliers, nystrom_decomp, chol_inv, bundle_az_aw, visualise_ATEs\nfrom joblib import Paralle... | [
[
"matplotlib.pyplot.figure",
"scipy.optimize.minimize",
"matplotlib.pyplot.close",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.ylabel"
],
[
"torch.cat",
"torch.randperm",
"numpy.concatenate",
"matplotlib.pyplot.plot",
"numpy.mean",
"torch.eye",
"torch.tensor",... | [
{
"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"
... |
ylin00/pandas | [
"dc1c849f0cf4f1b8c5602a54f5231f6b57d1d913"
] | [
"pandas/core/generic.py"
] | [
"from __future__ import annotations\n\nimport collections\nfrom datetime import timedelta\nimport functools\nimport gc\nfrom io import StringIO\nimport json\nimport operator\nimport pickle\nimport re\nfrom typing import (\n TYPE_CHECKING,\n Any,\n Callable,\n Dict,\n FrozenSet,\n Hashable,\n Li... | [
[
"pandas.util._validators.validate_bool_kwarg",
"pandas.core.dtypes.inference.is_hashable",
"numpy.unique",
"numpy.asanyarray",
"pandas.core.dtypes.common.is_re_compilable",
"pandas.concat",
"pandas.core.dtypes.common.is_list_like",
"pandas.compat.numpy.function.validate_cum_func_wi... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
bchangala/nitrogen | [
"94f8828a51aa536fe93fe6a8bdd8da04eb6fdce8"
] | [
"nitrogen/basis/sincDVR.py"
] | [
"import numpy as np\n\ndef _sincDVR(start,stop,num):\n \"\"\" \n Calculate sinc DVR grid and operators\n \n Parameters\n ----------\n start : float\n Grid start value\n stop : float\n Grid stop value. Must be larger than start\n num : int\n Number of grid points. Must be... | [
[
"numpy.sinc",
"numpy.sqrt",
"numpy.linspace",
"numpy.ndarray",
"numpy.ndim",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
korepwx/mlcomp | [
"b39f64d700531792da72175c8daaa10be5c73ad1"
] | [
"tests/report/components/test_training_metrics.py"
] | [
"# -*- coding: utf-8 -*-\nimport json\nimport unittest\n\nimport numpy as np\n\nfrom mlcomp.report import loss_accuracy_curve\n\n\nclass TrainingMetricsTestCase(unittest.TestCase):\n\n def test_loss_accuracy_curve(self):\n # generate the test payload\n steps = np.arange(101)\n loss = np.exp(... | [
[
"numpy.min",
"numpy.asarray",
"numpy.arange",
"numpy.random.normal",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MNRKhan/aps360-project | [
"1d91a4262c95cd6b5610aae16e1a30f2749a4373",
"1d91a4262c95cd6b5610aae16e1a30f2749a4373"
] | [
"modules/extractNet_resnet_prelu.py",
"modules/saving_pretrain.py"
] | [
"\nimport torchvision\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom model_blocks import *\n\nencode_out_r = []\n\n\ndef hook_r(module, input, output):\n encode_out_r.append(output)\n\n\n\nclass extractNet_resnet_prelu(nn.Module):\n def __init__(self, act_type = 'R', r_size = 50):... | [
[
"torch.cat"
],
[
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PaulKuin/uvotpy | [
"a507576534588468391afff7a0653262d21d7337"
] | [
"uvotpy/uvotplot.py"
] | [
"'''\n These are function for making plots of UVOT grism stuff.\n \n binplot(*args, **kwargs)\n Bin up the arrays with the keyword bin=<number>\n \n waveAccPlot( wave_obs,pix_obs, wave_zmx, pix_zmx, disp_coef, acc, order=None) \n display a figure of the accuracy of the wavelength solution\n... | [
[
"numpy.abs",
"numpy.linspace",
"numpy.meshgrid",
"numpy.asarray",
"scipy.interpolate.bisplrep",
"scipy.interpolate.bisplev",
"numpy.arange",
"numpy.cos",
"numpy.empty",
"numpy.sin",
"numpy.ones",
"numpy.int",
"matplotlib.pylab.plot",
"numpy.array",
"nump... | [
{
"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"
... |
yanivbl6/deep-griffinlim-iteration | [
"b96165c0c11e00bff1e033f93aeca6fafe9833d3"
] | [
"train.py"
] | [
"import warnings\nfrom collections import defaultdict\nfrom pathlib import Path\nfrom typing import Any, Dict, Optional, Sequence, Tuple\n\nimport numpy as np\nimport scipy.io as scio\nimport torch\nimport torch.optim.lr_scheduler as lr_scheduler\nfrom numpy import ndarray\nfrom torch import Tensor, nn\nfrom torch.... | [
[
"torch.normal",
"torch.mean",
"torch.optim.lr_scheduler.ReduceLROnPlateau",
"torch.cuda.set_device",
"torch.zeros",
"torch.load",
"torch.cuda.device_count",
"torch.no_grad",
"torch.device",
"torch.nn.DataParallel",
"torch.nn.L1Loss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ongchi/DoseComparison | [
"ea22c92b8bb7f43d870382a2bfcb9d0ab1f62020"
] | [
"DoseComparison.py"
] | [
"#!/usr/bin/env python\n'''\nDoseComparison\n numerical evaluation of dose distributions comparison\n\n:Date: 2017-01-02\n:Version: 1.0.0\n:Author: ongchi\n:Copyright: Copyright (c) 2016, ongchi\n:License: BSD 3-Clause License\n'''\nimport numpy as np\n\n\ndef DoseComparison(refimg, tstimg, delta_r=1, delta_d=0.... | [
[
"numpy.ones_like",
"numpy.nditer",
"numpy.sqrt",
"numpy.abs",
"numpy.arange",
"numpy.bitwise_not",
"numpy.cos",
"numpy.ma.empty_like",
"numpy.alltrue",
"numpy.ma.array",
"numpy.array",
"numpy.meshgrid",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
babubaskaran/pands-problem-set | [
"70b688ee7ffba6545acdbd66abf471df5f15dc6a"
] | [
"plot.py"
] | [
"# Author : Babu Baskaran \n# Date : 08/04/2019 Time : 22:00 pm\n# Solution for problem number 10\n# Version 1.0\n# reference https://matplotlib.org/tutorials/introductory/pyplot.html\n# import matplotlib for ploting\nimport matplotlib.pyplot as plt\n# assigning list of range 0,4 to x\nx = list(range (0,4))\n# as... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
abdullahmitkar/deep-reading-of-a-topic | [
"45ad7046f94321472e659f28019268a4b991da38"
] | [
"examples/run_gan.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.\n# Copyright (c) 2018, NVIDIA CORPORATION. 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... | [
[
"torch.utils.data.DataLoader",
"torch.no_grad",
"torch.cuda.manual_seed_all",
"torch.cuda.is_available",
"torch.device",
"torch.distributed.get_rank",
"torch.distributed.init_process_group",
"torch.utils.data.distributed.DistributedSampler",
"torch.distributed.barrier",
"to... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
FayW/maskrcnn-benchmark | [
"b7ab727c3a4223508f05589b2af5933ad2ba0a7c"
] | [
"demo/predictor.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\nimport cv2\nimport torch\nfrom torchvision import transforms as T\nimport time\nfrom maskrcnn_benchmark.modeling.detector import build_detection_model\nfrom maskrcnn_benchmark.utils.checkpoint import DetectronCheckpointer\nfrom maskrcnn_bench... | [
[
"torch.LongTensor",
"torch.zeros",
"torch.cat",
"matplotlib.pyplot.get_cmap",
"torch.tensor",
"numpy.copy",
"torch.no_grad",
"numpy.mean",
"torch.nonzero",
"torch.device",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
odarotto/start_urls_generation | [
"321e4b169f81d80d2e14500b7cee42d55eabd800"
] | [
"checking_url_tool.py"
] | [
"import logging\nimport time\nfrom os import name, sep\nimport re, requests\nimport urllib.parse as prs\nimport argparse\nimport pandas as pd\nfrom scrapy import Selector\n\ndef subdomain_to_name(domain):\n a = domain.split('|')\n arr = a[0].split('.')\n name = str(arr[0]).strip()\n suff = ''\n if le... | [
[
"pandas.DataFrame.from_dict"
]
] | [
{
"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": []
}
] |
johannah/iceview | [
"f42ce780eaa3d189e6b1a60eee9017b501f6075a"
] | [
"scripts/parse_gpx.py"
] | [
"import argparse\nimport sys\nimport os\nimport gpxpy\n# had to clone the latest github version of gpxfile to get it to work:\n# pip install git+https://github.com/tkrajina/gpxpy.git\nfrom glob import glob\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.basemap import Basemap\nfrom mpl_toolk... | [
[
"numpy.asarray",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Erfun76/insightface | [
"148cef36a43a055f68d2b6a475f4aa38625ad8b4"
] | [
"recognition/arcface_torch/verification.py"
] | [
"\"\"\"Helper for evaluation on the Labeled Faces in the Wild dataset \n\"\"\"\n\n# MIT License\n#\n# Copyright (c) 2016 David Sandberg\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Soft... | [
[
"numpy.asarray",
"sklearn.model_selection.KFold",
"numpy.concatenate",
"numpy.max",
"torch.no_grad",
"numpy.mean",
"torch.device",
"numpy.square",
"torch.distributed.init_process_group",
"numpy.arange",
"numpy.less",
"numpy.subtract",
"numpy.std",
"numpy.arg... | [
{
"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": [],
... |
nnnyt/CAT | [
"471f573dd51b9cc09339ea73241ad9ac9e5d0d8f"
] | [
"CAT/strategy/MFI_strategy.py"
] | [
"import numpy as np\n\nfrom CAT.strategy.abstract_strategy import AbstractStrategy\nfrom CAT.model import AbstractModel\nfrom CAT.dataset import AdapTestDataset\n\n\nclass MFIStrategy(AbstractStrategy):\n \"\"\"\n Maximum Fisher Information Strategy\n D-opt Strategy when using MIRT(num_dim != 1)\n \"\"\... | [
[
"numpy.linalg.det",
"numpy.argmax",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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