repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
boblandsky/Streamlit_tutorial | [
"53e6dac8ec20f275d213937acc5907038fe04d55"
] | [
"learning_materials/uber_pickups.py"
] | [
"import streamlit as st\nimport pandas as pd\nimport numpy as np\n\n\nst.title('Uber pickups in NYC')\n\nDATE_COLUMN = 'date/time'\nDATA_URL = ('https://s3-us-west-2.amazonaws.com/'\n 'streamlit-demo-data/uber-raw-data-sep14.csv.gz')\n\n@st.cache\ndef load_data(nrows):\n data = pd.read_csv(DATA_URL, nrow... | [
[
"pandas.read_csv",
"pandas.to_datetime",
"numpy.histogram"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
adwasser/dynamics | [
"85d33b367f19a9dab467c16290ede781a831f673"
] | [
"dynamics/orbconvert.py"
] | [
"import numpy as np\n\n'''\nAll angles in radians.\n\nαβγδεζηθικλμνξοπρστυφχψω\nΑΒΓΔΕΖΗΘΙΚΛΜΝΞΟΠΡΣΤΥΦΧΨΩ\n\na : semi-major axis\ne : eccentricity\ni : inclination\nω : argument of periapsis\nΩ : longitude of the ascending node\nf : true anomaly\n\nx, y, z : cartesian position\nvx, vy, vz : cartesian velocity\n'''\n... | [
[
"numpy.square",
"numpy.sqrt",
"numpy.cos",
"numpy.sin",
"numpy.arctan2",
"numpy.cross",
"numpy.array",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TaroNakasendo/MaskEraser | [
"373af686194aff716f53785e40252beae7b26cff"
] | [
"training.py"
] | [
"###############################################################################\n# MIT License\n#\n# Copyright (c) 2021, Jun So Intec Inc. All rights reserved.\n#\n# Author & Contact: Jun So (so_jun@intec.co.jp)\n###############################################################################\nimport argparse\nimpo... | [
[
"torch.Tensor",
"torch.load",
"numpy.random.seed",
"numpy.full",
"numpy.random.permutation",
"torch.no_grad",
"torch.stack",
"torch.device",
"numpy.array",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
UManitoba-BMS/UM-BMID | [
"85eb8d2afa06c8b539c79bfcbca5a5c9c2ce976f"
] | [
"run/logreg_analysis.py"
] | [
"\"\"\"\r\nTyson Reimer\r\nUniversity of Manitoba\r\nSeptember 14th, 2019\r\n\"\"\"\r\n\r\nimport os\r\nimport numpy as np\r\nfrom sklearn.metrics import roc_auc_score\r\n\r\nfrom umbmid import get_proj_path, get_script_logger\r\nfrom umbmid.loadsave import load_pickle\r\nfrom umbmid.ai.logreg import LogisticRegres... | [
[
"sklearn.metrics.roc_auc_score",
"numpy.abs",
"numpy.linspace",
"numpy.logical_and",
"numpy.fft.ifft",
"numpy.size",
"numpy.std",
"numpy.mean",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
deepqmc/deeperwin | [
"1c514c972ee1df3cc5c8c3ecaf9408befd157341"
] | [
"src/paper_plots/fig1_deeperwin_vs_paulinet.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nfrom deeperwin.wandb_utils import get_all_eval_energies\n\ndf_full = get_all_eval_energies(\"schroedinger_univie/Benchmark_vs_PauliNet\", print_progress=True)\ndf_full['molecule'] = df_full['physical.name'].str.replace('HChain10', 'H10')\n\nmolecules = ['LiH', '... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.gca",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.ylim",
"numpy.arange",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.close",
"matplotlib.pyplot.bar",
"matplotlib.pyplot.xticks",
"matplotlib.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
young-oct/OCT-sparse-estimation-with-CBPDN-framework | [
"980d8e9606fb4df01332f88f0de0eceed4ecfca4"
] | [
"scripts/lamba_compare.py"
] | [
"# -*- coding: utf-8 -*-\n# @Time : 2021-04-26 7:00 p.m.\n# @Author : young wang\n# @FileName: lamba_compare.py\n# @Software: PyCharm\n\n'''this script generates images for the figure 3 as seen in\nthe paper. Sparse reconstructions of the same OCT\nmiddle ear image using the same learned dictionary for\nvarious... | [
[
"matplotlib.patches.Circle",
"numpy.seterr",
"numpy.max",
"matplotlib.rcParams.update",
"matplotlib.pyplot.close",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HemangRajvanshy/DLSpaceShooter | [
"1a84f40bbcb8abbc09e380ed289fb5060dc1df10"
] | [
"DQNCode.py"
] | [
"import sys\nimport os\nimport cv2\nimport tensorflow as tf\nimport random\nimport numpy as np\nimport MLModifiedSpaceShooter as game\nfrom collections import deque\n\nGAME = 'spaceShooter' # the name of the game being played for log files\nACTIONS = 4 # number of valid actions 1. Move Left 2. Move Right 3. Shoot... | [
[
"tensorflow.nn.max_pool",
"numpy.max",
"tensorflow.train.AdamOptimizer",
"tensorflow.nn.conv2d",
"tensorflow.Variable",
"numpy.reshape",
"numpy.stack",
"numpy.argmax",
"tensorflow.square",
"tensorflow.train.Saver",
"numpy.zeros",
"tensorflow.matmul",
"tensorflow... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
GeorgKunk/MachineLearning | [
"085ff7908710ac1f83e01816ba72122b32a6915d"
] | [
"MLP_MNIST.py"
] | [
"import matplotlib.pyplot as plt\nfrom sklearn.datasets import fetch_mldata\nfrom sklearn.neural_network import MLPClassifier\nimport numpy as np\nimport pickle\nimport os.path\nimport math\n\n\nmnist = fetch_mldata(\"MNIST original\")\nX, y = mnist.data / 255., mnist.target\nX_train, X_test = X[:60000], X[60000:]\... | [
[
"sklearn.neural_network.MLPClassifier",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.subplots",
"sklearn.datasets.fetch_mldata",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
landon23/prsm | [
"0e8a7e7a7b3b3e09f38fe7e1417d2264dcc374fd"
] | [
"prsm/simulation_tools.py"
] | [
"import numpy as np\n\ndef make_data(N, M, sigmas):\n Z = np.random.normal(size = (N, M)) / np.sqrt(N)\n Z = Z * np.sqrt(sigmas)\n return Z\n\ndef diagonalize_cov(X):\n U, S, V = np.linalg.svd(X) #X is N x M. U is N x N, V is M x M.\n S = S*S\n return U, S, V\n\ndef samples(N, M, sigmas, n=1):\n ... | [
[
"numpy.linalg.svd",
"numpy.zeros",
"numpy.random.normal",
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
duynht/FIW_KRT | [
"bc07ba242ccaf762a55c80204d7da05d55847ec5"
] | [
"src/scripts/w_gp_ac_gan.py"
] | [
"from six.moves import xrange # pylint: disable=redefined-builtin\nimport fiwtools.data.preprocessing as dp\nimport tensorflow as tf\nimport argparse\nimport numpy as np\n\n\"\"\"updated @ 2018-02-09\"\"\"\n\n\n# Define Random Initilization\ndef xavier_init(size, std=1):\n in_dim = size[0]\n xavier_stddev = ... | [
[
"tensorflow.nn.softmax_cross_entropy_with_logits",
"tensorflow.concat",
"tensorflow.cast",
"tensorflow.diag",
"tensorflow.nn.l2_loss",
"tensorflow.train.AdamOptimizer",
"tensorflow.Variable",
"tensorflow.gradients",
"tensorflow.train.MomentumOptimizer",
"tensorflow.Session"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
rodogi/pyscisci | [
"c6b0c3487ddc1cfa7b1bb727da6b27ad742a2595"
] | [
"pyscisci/metrics/sleepingbeauty.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\n.. module:: sleepingbeauty\n :synopsis: Calculate the sleeping beauty coefficient.\n\n.. moduleauthor:: Alex Gates <ajgates42@gmail.com>\n \"\"\"\n\nimport sys\n\nimport pandas as pd\nimport numpy as np\n\n# determine if we are loading from a jupyter notebook (to make pretty pro... | [
[
"numpy.arange",
"numpy.argmax",
"numpy.Series",
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DAIKICHI-PSC/MIIL_DATASET_CREATER | [
"81560e15b4752a0c68b63d9c4a435e656ff53c9f"
] | [
"MIIL_DATASET_CREATER_B.py"
] | [
"# -*- coding: utf-8 -*-\r\n\r\nimport os #OS関連処理用モジュールの読込\r\nimport sys #システム関連処理用モジュールの読込\r\nimport time #時間関連処理用モジュールの読込\r\nimport numpy as np #行列処理用モジュールの読込\r\nimport math as mt #各種計算用モジュールの読込\r\nimport cv2 #画像処理用モジュールの読込\r\nimport glob #ファイルパス一括取得用モジュールの読込\r\nfrom PySide2 import QtCore, QtGui, QtWidgets, QtMul... | [
[
"numpy.copy",
"numpy.float32",
"numpy.array",
"numpy.zeros",
"numpy.where",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bzamecnik/halftone | [
"acd2af0afaa68d516b1c72ecbe07b67769be74e6"
] | [
"halftone/__init__.py"
] | [
"from typing import Tuple, Union, Callable\n\nimport numpy as np\nimport PIL.Image\n\nImgType = Union[PIL.Image.Image, np.ndarray]\n\n\ndef halftone(img: ImgType, spot_fn: Callable[[int, int], float]) -> ImgType:\n is_pil = isinstance(img, PIL.Image.Image)\n if is_pil:\n img = array_from_pil(img)\n ... | [
[
"numpy.sqrt",
"numpy.abs",
"numpy.arange",
"numpy.cos",
"numpy.sin",
"numpy.sign",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
iuga/kronos | [
"a8c93bda094c90286199e3a08386f4cdb2b0c317"
] | [
"kronos/training/train.py"
] | [
"import numpy as np\n\n\ndef training_non_prepare(X, y):\n \"\"\"\n Dummy function for the prepare option in the generator. It wont do anything with the data.\n It only prints the X,y values.\n \"\"\"\n return X, y\n\n\nclass Generators(object):\n \"\"\"\n Training Generators Utilities and Help... | [
[
"numpy.array",
"numpy.random.shuffle"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ddddwee1/RetinaFace | [
"91c2103c1af241e9f5f8aaac996df5124b7ec25c"
] | [
"img_process.py"
] | [
"import cv2 \nimport numpy as np \nimport config \nfrom multiprocessing import Pool \nfrom multiprocessing.pool import ThreadPool\nfrom skimage import transform as trans\n\ndef get_scale(img):\n\tim_shape = img.shape\n\ttarget_size = config.img_target_size\n\tmax_size = config.img_max_size\n\tim_size_min = np.min(i... | [
[
"numpy.min",
"numpy.round",
"numpy.max",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
takuseno/mvc-drl | [
"2d9dede864d4018ec91a7d69c7435f885e5fc7d5"
] | [
"tests/controllers/test_td3.py"
] | [
"import numpy as np\nimport unittest\n\nfrom mvc.models.buffer import Buffer\nfrom mvc.controllers.td3 import TD3Controller\nfrom unittest.mock import MagicMock\nfrom tests.test_utils import DummyNetwork, DummyNoise, DummyMetrics\n\n\nclass TD3ControllerTest(unittest.TestCase):\n def setUp(self):\n self.n... | [
[
"numpy.random.random"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
danielhanchen/cuml | [
"fab74ca94fdbc5b49281660ce32a48cfd3d66f46"
] | [
"python/cuml/test/test_tsne.py"
] | [
"\nfrom cuml.manifold import TSNE\n\nfrom sklearn.manifold.t_sne import trustworthiness\nfrom sklearn import datasets\nimport pandas as pd\nimport numpy as np\nimport cudf\nimport pytest\n\ndataset_names = ['digits', 'boston', 'iris', 'breast_cancer',\n 'diabetes', 'wine']\n\n\n@pytest.mark.parametr... | [
[
"numpy.isnan",
"sklearn.manifold.t_sne.trustworthiness",
"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": []
}
] |
rob-luke/mne-python | [
"e3fec3ff414017a93b4d7ae7d6698c06b22ed3b4"
] | [
"mne/preprocessing/tests/test_flat.py"
] | [
"# Author: Eric Larson <larson.eric.d@gmail.com>\n#\n# License: BSD-3-Clause\n\nimport datetime\nimport os.path as op\nimport numpy as np\nimport pytest\n\nfrom mne import create_info\nfrom mne.datasets import testing\nfrom mne.io import RawArray, read_raw_fif\nfrom mne.preprocessing import annotate_flat\n\ndata_pa... | [
[
"numpy.random.RandomState",
"numpy.diff"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gsmartensson/avra_public | [
"17e947606596e6594ec01d54ef38c992becf9395"
] | [
"model/model.py"
] | [
"import numpy as np\nimport torch.nn as nn\nimport torch\nfrom model.modules import ResidualAttentionNet,conv_block\n\nclass AVRA_rnn(nn.Module):\n '''\n A recurrent convolutional neural network to predict either PA, GCA-F or MTA of an input brain volume.\n Combines the CNN part (a residual attention netwo... | [
[
"torch.nn.Dropout",
"torch.ones",
"torch.nn.LSTM",
"torch.nn.Linear",
"numpy.shape",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jaejun-lee/amzbinimg | [
"6f549656a9e9a24873d2212458a72682d30d2017"
] | [
"src/explore_dataset.py"
] | [
"'''\nThis script is written to facilitate exploration of the images and associated\njson files. The purpose is to gain insights into the data to guide decisions\non model development. Some functions may have value later in the project but\nthat is not the main goal.\n'''\nfrom collections import Counter\nfrom coll... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gorosgobe/dsae-torch | [
"8db2e7ac55748995b23fe8cc4c4cdb76ac22c8f8"
] | [
"dsae.py"
] | [
"\"\"\"\nThis module implements, using PyTorch, the deep spatial autoencoder architecture presented in [1].\nReferences:\n [1]: \"Deep Spatial Autoencoders for Visuomotor Learning\"\n Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, Pieter Abbeel\n Available at: https://arxiv.org/pdf/1509... | [
[
"torch.ones",
"torch.zeros",
"torch.nn.Conv2d",
"torch.sum",
"torch.arange",
"torch.tensor",
"torch.nn.Tanh",
"torch.nn.Linear",
"torch.nn.Sigmoid",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU",
"torch.nn.MSELoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cleverhans-lab/model-extraction-iclr | [
"a74c506d5b1b67901a7e0663fba02ddb44f762a6",
"805205287876423621baca9d5e990edfe68ea803",
"805205287876423621baca9d5e990edfe68ea803"
] | [
"dfmenetwork/lenet.py",
"analysis/private_knn.py",
"MixMatch-pytorch/train.py"
] | [
"# This part is borrowed from https://github.com/huawei-noah/Data-Efficient-Model-Compression\n\nimport torch.nn as nn\n\n\nclass LeNet5(nn.Module):\n\n def __init__(self):\n super(LeNet5, self).__init__()\n\n self.conv1 = nn.Conv2d(1, 6, kernel_size=(5, 5))\n self.relu1 = nn.ReLU()\n ... | [
[
"torch.nn.Linear",
"torch.nn.MaxPool2d",
"torch.nn.ReLU",
"torch.nn.Conv2d"
],
[
"numpy.arange",
"numpy.log10",
"numpy.searchsorted"
],
[
"torch.mean",
"torch.nn.CrossEntropyLoss",
"torch.softmax",
"numpy.random.beta",
"numpy.random.seed",
"torch.load",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pabloduque0/cnn_deconv_viz | [
"3fc3d8a9dbad8e8e28d4df4023bdb438e4c9cf85"
] | [
"preprocessing/imageparser.py"
] | [
"import os\nimport gzip\nimport shutil\nimport cv2\nimport numpy as np\nimport subprocess\nfrom scipy.stats import norm\nfrom constants import *\nimport matplotlib.pyplot as plt\nimport SimpleITK\n\nclass ImageParser():\n\n def __init__(self, path_utrech='../Utrecht/subjects',\n path_singapore='.... | [
[
"numpy.swapaxes",
"numpy.expand_dims",
"numpy.min",
"matplotlib.pyplot.subplots",
"numpy.percentile",
"numpy.ones",
"numpy.concatenate",
"numpy.max",
"numpy.std",
"numpy.asanyarray",
"numpy.mean",
"numpy.ptp",
"matplotlib.pyplot.subplots_adjust",
"numpy.rave... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
choumartin1234/Music-Eye | [
"059b43fd21f7e7bf6c84cb35a03fd936e64b59a5"
] | [
"models.py"
] | [
"import numpy as np\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\ndefault_config = {\n 'cnn_ch_in': 1,\n 'cnn_ch_out': 384, # for now cnn_ch_out need to be the same as cnn_ch_in\n 'cnn_kernel_size': 15,\n 'cnn_stride': 1,\n 'cnn_padding': 0,\n # output length : (ch_... | [
[
"torch.nn.Dropout",
"torch.cat",
"torch.zeros",
"torch.nn.ModuleList",
"torch.nn.Embedding",
"torch.nn.LayerNorm",
"torch.nn.TransformerEncoderLayer",
"torch.nn.Linear",
"torch.nn.Conv1d",
"torch.nn.TransformerEncoder",
"torch.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zoltuz/bioscrape | [
"bf0f1d4b6f68f265fc208733b75ec16c36a28688"
] | [
"tests/test_initialize_model.py"
] | [
"from bioscrape.simulator import *\nfrom bioscrape.types import *\nimport numpy as np\nimport pytest\n\ntimepoints = np.arange(0, 100, .1)\n\n#This test confirms that calling model.py_initialize twice before simulation does not cause bugs\n# This test was written due to a bug found where the c_propensities vector ... | [
[
"numpy.arange",
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
naveen521kk/manimce-deprecated | [
"52a0cb0e49f79cb48f78b51c724f049d522fc465"
] | [
"manim/scene/scene_file_writer.py"
] | [
"\"\"\"The interface between scenes and ffmpeg.\"\"\"\n\n__all__ = [\"SceneFileWriter\"]\n\n\nimport numpy as np\nfrom pydub import AudioSegment\nimport shutil\nimport subprocess\nimport os\nfrom time import sleep\nimport datetime\nfrom PIL import Image\nfrom pathlib import Path\n\nfrom .. import config, logger\nfr... | [
[
"numpy.ceil"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nrodrigoc/Numeric-methods | [
"dd7a98802ccdc51bfc60344f46b43c29651c9aaa"
] | [
"InterpolacaoPolinomial/SegundaParte.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\n\n# Metodo de Newton\n# Pn(x) = d0 + d1(x-x0) + d2(x-x0)*(x-x2) + ...\n# d0 = x0\n# d1 = f[x0,x1] = (f[x1] - f[x0]) / x1 - x0\n# d2 = f[x0, x1, x2] = (f[x1,x2] - f[x0,x1]) / x2 - x0\n\n# Definição dos parâmetros\nx = np.array([1991.0, 1996, 2000])\nf = np.array(... | [
[
"numpy.append",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
speleophysics/landlab | [
"44ec782e075ab132bf626f92e771bc786152d505"
] | [
"landlab/components/erosion_deposition/erosion_deposition.py"
] | [
"import numpy as np\n\nfrom landlab.utils.return_array import return_array_at_node\n\nfrom ..depression_finder.lake_mapper import _FLOODED\nfrom .cfuncs import calculate_qs_in\nfrom .generalized_erosion_deposition import _GeneralizedErosionDeposition\n\nROOT2 = np.sqrt(2.0) # syntactic sugar for precalculated squa... | [
[
"numpy.sqrt",
"numpy.nonzero",
"numpy.power",
"numpy.amin",
"numpy.flipud",
"numpy.concatenate",
"numpy.zeros_like",
"numpy.exp",
"numpy.zeros",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dex85/ga-learner-dsmp-repo | [
"e693d19c9f07d2951f2aa9120bbb4b0b48809617"
] | [
"ga_mars_crater/code.py"
] | [
"# --------------\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import MinMaxScaler\nimport pandas as pd\n\n# Code starts here\ndf = pd.read_csv(path)\nprint(df.head(5))\nX = df.drop(['attr1089'], axis = 1)\ny = df['attr1089']\n\nX_train, X_test, y_train, y_test = train_test_spli... | [
[
"sklearn.metrics.roc_auc_score",
"pandas.read_csv",
"sklearn.linear_model.LogisticRegression",
"sklearn.ensemble.RandomForestClassifier",
"sklearn.model_selection.train_test_split",
"sklearn.ensemble.VotingClassifier",
"sklearn.tree.DecisionTreeClassifier",
"sklearn.preprocessing.M... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Hemantr05/sar-pytorch | [
"00144dd8017bee781ca8c784ce939e870dac98b4"
] | [
"train.py"
] | [
"'''\nTHis is the main training code.\n'''\nimport os\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\" # set GPU id at the very begining\nimport argparse\nimport random\nimport math\nimport torch\nimport torch.nn.parallel\nimport torch.optim as optim\nimport torch.utils.data\nimport torch.nn.functional as F\nfrom torc... | [
[
"torch.optim.lr_scheduler.LambdaLR",
"torch.nn.functional.nll_loss",
"torch.multiprocessing.freeze_support",
"torch.load",
"torch.manual_seed",
"torch.set_grad_enabled",
"torch.cuda.is_available",
"torch.device",
"torch.nn.DataParallel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
esgario/BBAVectors-Oriented-Object-Detection | [
"3d0e5bb5c12fb52558bd5daf61d9a9528d94bd39"
] | [
"bbavectors/train.py"
] | [
"import os\nimport cv2\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport loss\nfrom . import func_utils\nfrom bbavectors import WORK_DIR\n\n\ndef collater(data):\n out_data_dict = {}\n for name in data[0]:\n out_data_dict[name] = []\n for sample in data:\n for name in sample:... | [
[
"torch.enable_grad",
"torch.load",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.from_numpy",
"torch.optim.lr_scheduler.ExponentialLR",
"torch.nn.DataParallel",
"torch.no_grad",
"torch.cuda.is_available",
"torch.stack",
"torch.cuda.device_count"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KarlesP/Simple-flask-CSV-processing | [
"78aa41f37246b3f8969a588e9a902f97ee76358e"
] | [
"app.py"
] | [
"from flask import Flask, render_template, request\r\nimport pandas as pd\r\nimport jinja2\r\n\r\n#define function\r\n##def new_io_table(io_t,factor=\"no\"):\r\n\r\n #Calculate row sum and column sum\r\n ##c_sum = io_t.sum(axis=0)\r\n ##r_sum = io_t.sum(axis=1)\r\n\r\n #Append the new row (which is the ... | [
[
"pandas.concat",
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
lmjohns3/py-rbm | [
"d79f1c7c892ea309c4bcb88443b4372b51f5845c"
] | [
"lmj/rbm.py"
] | [
"# Copyright (c) 2011 Leif Johnson <leif@leifjohnson.net>\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, c... | [
[
"numpy.dot",
"numpy.linalg.norm",
"numpy.random.randn",
"numpy.random.rand",
"numpy.exp",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Myzhar/zed-examples | [
"3c035a711f9bce7250f38346c80ddfd9afd140bd"
] | [
"tutorials/tutorial 3 - depth sensing/python/depth_sensing.py"
] | [
"########################################################################\n#\n# Copyright (c) 2020, STEREOLABS.\n#\n# All rights reserved.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n# \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n# LIMITED TO, THE IMPLIED WARRA... | [
[
"numpy.isnan",
"numpy.isinf"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GabeBolton/hievpy | [
"7b663f053a00eaf4508fed08b570cfd8f48a7cbf"
] | [
"hievpy/__init__.py"
] | [
"import urllib.request\nimport urllib.parse\nimport json\nimport pandas as pd\nimport requests\nimport io\nimport tqdm\nfrom pathlib import Path\nimport os\nimport shutil\nfrom hievpy.utils import *\n\n\n# --------------------------------------------------------------------------------------------------------------... | [
[
"pandas.concat",
"pandas.read_csv",
"pandas.to_datetime",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
UrosOgrizovic/SimpleGoogleQuickdraw | [
"9565cff13ab4afdde9ebc25697790aacac7bec0c"
] | [
"image_operations.py"
] | [
"import numpy as np\nfrom PIL import Image\nfrom io import BytesIO\nimport base64\nfrom matplotlib import pyplot as plt\n\n\ndef load_images(file_path, how_many=-1, is_transfer_learning=False):\n \"\"\"\n\n :param file_path:\n :param how_many: number of images to load from file (loads all images for -1)\n ... | [
[
"matplotlib.pyplot.imshow",
"numpy.pad",
"numpy.reshape",
"numpy.random.shuffle",
"numpy.save",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.clf",
"numpy.load",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MohammedQaraad/Advanced-Feature-Selection-Toolbox | [
"7929744234a7172674add16fac5424b2cb59073d"
] | [
"AFS/obwoa.py"
] | [
"#[2018]-\"Parameter estimation of solar cells diode models by an improved opposition-based whale optimization algorithm\"\r\n\r\nimport numpy as np\r\nfrom numpy.random import rand\r\nfrom FS.functionHO import Fun\r\n\r\n\r\ndef init_position(lb, ub, N, dim):\r\n X = np.zeros([N, dim], dtype='float')\r\n for... | [
[
"numpy.cos",
"numpy.ones",
"numpy.concatenate",
"numpy.size",
"numpy.random.rand",
"numpy.argsort",
"numpy.exp",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Darkgaja/edGNN | [
"a7d6bce2f84fccdc2e09b642afe584aa0fb96d81"
] | [
"core/data/dortmund.py"
] | [
"import os\nimport torch\nimport zipfile\nimport requests\nimport numpy as np\n\nfrom dgl import DGLGraph\n\nfrom core.data.constants import GRAPH, LABELS, N_CLASSES, N_RELS, N_ENTITIES\nfrom core.models.constants import GNN_NODE_LABELS_KEY, GNN_NODE_ATTS_KEY, GNN_EDGE_FEAT_KEY\nfrom core.models.constants import GN... | [
[
"torch.LongTensor",
"numpy.unique",
"torch.from_numpy",
"numpy.argwhere",
"torch.FloatTensor",
"numpy.array",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kenzie-q/greykite | [
"9fb670b5325dd252759a490b31aa4d9494fdd68b",
"9fb670b5325dd252759a490b31aa4d9494fdd68b"
] | [
"greykite/sklearn/estimator/prophet_estimator.py",
"greykite/tests/sklearn/estimator/test_one_by_one_estimator.py"
] | [
"# BSD 2-CLAUSE LICENSE\n\n# Redistribution and use in source and binary forms, with or without modification,\n# are permitted provided that the following conditions are met:\n\n# Redistributions of source code must retain the above copyright notice, this\n# list of conditions and the following disclaimer.\n# Redis... | [
[
"sklearn.exceptions.NotFittedError"
],
[
"pandas.concat",
"pandas.to_datetime"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"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",
"... |
SarahEAdams/reddit | [
"4cf0e4b18f0241a0836a87d9d362f724cd80abe8"
] | [
"run.py"
] | [
"#!/usr/bin/env python3\n\n'''\nAuthors: Daniel M. Low\nLicense: Apache 2.0\n'''\n\n\nimport sys\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import classification_report, confusion_matrix\nfrom sklearn import preprocessing\nfrom sklearn.featur... | [
[
"pandas.concat",
"sklearn.model_selection.GridSearchCV",
"numpy.unique",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"numpy.concatenate",
"sklearn.feature_selection.SelectKBest",
"numpy.round",
"numpy.array",
"sklearn.preprocessing.LabelEncoder",
"sk... | [
{
"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": []
}
] |
JeanKossaifi/scikit-image | [
"dea355288db8bd3340353f2be6969f72dbc7dc7e",
"dea355288db8bd3340353f2be6969f72dbc7dc7e"
] | [
"skimage/viewer/tests/test_tools.py",
"skimage/feature/censure.py"
] | [
"from collections import namedtuple\n\nimport numpy as np\nfrom numpy.testing import assert_equal\nfrom numpy.testing.decorators import skipif\nfrom skimage import data\nfrom skimage.viewer import ImageViewer, viewer_available\nfrom skimage.viewer.canvastools import (\n LineTool, ThickLineTool, RectangleTool, Pa... | [
[
"numpy.testing.assert_equal",
"numpy.array",
"numpy.testing.decorators.skipif",
"numpy.transpose"
],
[
"scipy.ndimage.filters.maximum_filter",
"numpy.nonzero",
"numpy.zeros_like",
"numpy.column_stack",
"scipy.ndimage.filters.minimum_filter",
"numpy.zeros",
"numpy.su... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"0.15",
"1.4",
"0.10",
"1.3",
"0.19",
"1.5",
"0.18",
... |
PNNL-CompBio/kansas-native-prairie | [
"40e24da4b0b89d7a568b0c4c8d94a3f6da3ea766"
] | [
"FunTaxaCount/src/funtaxacount.py"
] | [
"import pandas as pd\nimport numpy as np\nimport argparse, sys, re\n\norf_names = ['ORF_ID', 'Contig', 'COG', 'KO'] #, 'product']\n\ndef merge_orf_and_funtax( orf_file, funtax_file ):\n \"\"\"\n Takes an orf file and a funtaxa file and returns the merge\n \"\"\"\n orf_df = pd.read_table(orf_file, header... | [
[
"pandas.read_table",
"pandas.concat",
"pandas.to_numeric"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ivek1312/ricercando | [
"fca8a2fba701adfba36b4fd6b2a5261b013c6da0"
] | [
"ricercando/queries.py"
] | [
"from itertools import chain\nfrom functools import lru_cache\n\nimport pandas as pd\n\nfrom .db import _check_table\nfrom .query_base import query, query_async, _check_freq\nfrom .util import aslist\n\n\n__all__ = ['distinct_values', 'all_nodes', 'all_tables', 'nodes_for_table',\n 'tables_for_node', 'tab... | [
[
"pandas.Timestamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aalvesan/NPLM_package | [
"9656f8b28efae2b32c94df2548c39319bcde2935"
] | [
"src/NPLM/PLOTutils.py"
] | [
"import glob, json, h5py, math, time, os\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.font_manager as font_manager\nfrom scipy.stats import norm, expon, chi2, uniform, chisquare\n \ndef Plot_Analysis_tdistribution(tvalues_BkgOnly, tvalues, df, rmin, rmax, bins=35, verbose=False, save=F... | [
[
"matplotlib.pyplot.legend",
"scipy.stats.norm.ppf",
"numpy.expand_dims",
"numpy.sqrt",
"numpy.linspace",
"numpy.around",
"matplotlib.pyplot.plot",
"numpy.concatenate",
"numpy.mean",
"numpy.var",
"numpy.exp",
"numpy.ones_like",
"numpy.std",
"matplotlib.pyplot... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ViniciusLimaFernandes/systems-and-signals | [
"372f309bdbce8eecc2b3e70cf009bf83f1a74e04"
] | [
"ado1/plot_graph.py"
] | [
"import matplotlib as mpl\nmpl.use('Agg')\nimport matplotlib.pyplot as plt\n\ndef plotWithYAxis(yPoints, yLabel, graphTitle, fileName):\n fig = plt.figure(figsize=(10, 8))\n ax = fig.add_subplot(111)\n\n ax.plot(yPoints)\n \n plt.ylabel(yLabel)\n plt.title(graphTitle)\n\n fig.savefig('./graphs/graph{}.png'.f... | [
[
"matplotlib.pyplot.title",
"matplotlib.use",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KiLJ4EdeN/ProGENI | [
"a62ca35d82d7302ec4659d633cddab4c052309fd"
] | [
"ProGENI.py"
] | [
"#!/usr/bin/env python\r\n\r\n\"\"\"\r\nImplementation fo ProGENI\r\n\"\"\"\r\nimport os\r\nimport sys\r\nimport argparse\r\n#import time\r\nimport warnings\r\nimport numpy as np\r\nfrom numpy import mean\r\nimport pandas as pd\r\nimport scipy.stats as ss\r\nfrom scipy import sparse\r\nfrom scipy.stats.mstats impor... | [
[
"pandas.read_csv",
"scipy.stats.mstats.zscore",
"numpy.random.seed",
"numpy.isnan",
"numpy.arange",
"scipy.stats.pearsonr",
"scipy.sparse.csr_matrix",
"pandas.DataFrame",
"scipy.stats.mstats.gmean",
"sklearn.preprocessing.normalize",
"scipy.sparse.csr_matrix.dot",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
"0.17",
"1.3",
... |
tjturnage/MetPy | [
"096be48de7f3ff895b689a881fe7b3b1acb4da12"
] | [
"tests/test_xarray.py"
] | [
"# Copyright (c) 2018,2019 MetPy Developers.\n# Distributed under the terms of the BSD 3-Clause License.\n# SPDX-License-Identifier: BSD-3-Clause\n\"\"\"Test the operation of MetPy's XArray accessors.\"\"\"\nfrom collections import OrderedDict\nfrom unittest.mock import patch, PropertyMock\n\nimport numpy as np\nim... | [
[
"numpy.linspace",
"numpy.asarray",
"numpy.arange",
"numpy.isnan",
"numpy.ones",
"numpy.array",
"numpy.zeros",
"numpy.testing.assert_array_almost_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
j40903272/MalConv-keras | [
"79f358445807aff3be66b0c03510c365ec461573"
] | [
"src/train.py"
] | [
"from os.path import join\nimport argparse\nimport pickle\nimport warnings\nimport pandas as pd\nfrom keras.callbacks import ModelCheckpoint, EarlyStopping\nfrom keras.models import load_model\n\nimport utils\nfrom malconv import Malconv\nfrom preprocess import preprocess\n\nwarnings.filterwarnings(\"ignore\")\n\np... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
yoshinaga0106/sr | [
"f822100a671bf2c0b683ccea857303f069412316"
] | [
"sranodec/silency.py"
] | [
"import numpy as np\nfrom scipy import stats\n\nfrom sranodec.util import marge_series, series_filter\n\n\nclass Silency(object):\n def __init__(self, amp_window_size, series_window_size, score_window_size):\n self.amp_window_size = amp_window_size\n self.series_window_size = series_window_size\n ... | [
[
"numpy.log",
"numpy.sqrt",
"numpy.fft.fft",
"numpy.abs",
"numpy.fft.ifft",
"numpy.mean",
"numpy.var"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ueshin/apache-arrow | [
"0a2cf3ac164d18d1cbf08470be51f336e35073a7"
] | [
"python/examples/plasma/sorting/sort_df.py"
] | [
"# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); y... | [
[
"pandas.concat",
"numpy.split",
"numpy.allclose",
"numpy.isfortran",
"pandas.DataFrame",
"numpy.sort",
"numpy.concatenate",
"numpy.random.bytes",
"numpy.random.randn"
]
] | [
{
"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": []
}
] |
jingli9111/vissl | [
"bdf9e69ce678c5b2f5eaa599a02f2310ec15e3e2"
] | [
"vissl/utils/io.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\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.\n\nimport json\nimport logging\nimport os\nimport pickle\nimport re\nimport time\nfrom urllib.parse import urlparse\n\nimport numpy as n... | [
[
"numpy.load",
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
udeshmg/GTA-RL | [
"3e4d159116d2f11eb66bd6f242a720dae7de74e8"
] | [
"nets/attention_model.py"
] | [
"import torch\nfrom torch import nn\nfrom torch.utils.checkpoint import checkpoint\nimport math\nfrom typing import NamedTuple\nfrom utils.tensor_functions import compute_in_batches\n\nfrom nets.graph_encoder import GraphAttentionEncoder\nfrom torch.nn import DataParallel\nfrom utils.beam_search import CachedLookup... | [
[
"torch.softmax",
"torch.Tensor",
"torch.isnan",
"torch.cat",
"torch.zeros_like",
"torch.is_tensor",
"torch.log_softmax",
"torch.nn.Linear",
"torch.tanh",
"torch.nonzero",
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DEVESHTARASIA/BMW-YOLOv3-Training-Automation | [
"0849267f0e5471c875ab968b1856592049edda4e"
] | [
"src/train_utils.py"
] | [
"import re\nimport json\nimport random\nimport numpy as np\n\nfrom cv2 import cv2\nfrom tqdm import tqdm\nfrom exception_utils import *\nfrom shutil import copy2, copyfile\nfrom pathlib import Path, PurePath\nfrom sklearn.model_selection import train_test_split\n\n\ndef copy_weights_file(source: Path, destination: ... | [
[
"numpy.argsort"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
blakermchale/robot-control | [
"b2d0109950d5abb4b12055ce7a3645ca9cc07b20"
] | [
"robot_control/robot_control/cli/shell.py"
] | [
"#!/usr/bin/env python3\n'''\nShell\n======\nShell interface for calling ROS2 actions.\n'''\nimport rclpy\nfrom cmd2 import Cmd2ArgumentParser, with_argparser\nfrom .common import complete_action_call, ClientShell\nfrom .drone_client import DroneClient\nfrom ..utils.structs import Frame\nfrom argparse import Argume... | [
[
"numpy.deg2rad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Jiaqi-beep/cs451-practicals | [
"9e94422d3a6446cffb6a2edbf350fe3c9f41bc1d"
] | [
"p09-learning-curves.py"
] | [
"import numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction import DictVectorizer\nfrom sklearn.linear_model import SGDClassifier\nfrom shared import dataset_local_path, simple_boxplot\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.utils import resampl... | [
[
"matplotlib.pyplot.title",
"sklearn.linear_model.SGDClassifier",
"sklearn.model_selection.train_test_split",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlim",
"numpy.std",
"numpy.mean",
"matplotlib.pyplot.fill_between",
"sklearn.utils.resample... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
siavelis/Awesome-GANs | [
"9add33fdfcb9fead75c37dd7afdbede625a303c9"
] | [
"datasets.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import division\n\nimport os\nimport cv2\nimport h5py\nimport numpy as np\nimport tensorflow as tf\n\nfrom glob import glob\nfrom tqdm import tqdm\nfrom multiprocessing import Pool\nfrom sklearn.model_selection import tr... | [
[
"numpy.true_divide",
"tensorflow.FixedLenFeature",
"numpy.asarray",
"numpy.ndarray",
"numpy.concatenate",
"numpy.max",
"tensorflow.python_io.TFRecordOptions",
"tensorflow.train.Int64List",
"numpy.arange",
"tensorflow.decode_raw",
"tensorflow.data.TFRecordDataset",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"1.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1... |
EnergyModels/caes | [
"7bb3e7f9cfbfcc2e781918333f6be3b718ca0743"
] | [
"examples/pipe_flow/friction_coefficient.py"
] | [
"from caes import friction_coeff\nimport CoolProp.CoolProp as CP # http://www.coolprop.org/coolprop/HighLevelAPI.html#propssi-function\nfrom math import pi\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# fixed inputs\nT = 290 # [K]\np = 15.0 # pressures [MPa]... | [
[
"pandas.Series",
"numpy.arange",
"matplotlib.pyplot.savefig",
"pandas.DataFrame",
"matplotlib.pyplot.close"
]
] | [
{
"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": []
}
] |
Akilditu/pyronear-webapp | [
"b953afc78aea008859c1dde0bc8a69d3fa7db145"
] | [
"app/alerts.py"
] | [
"# Copyright (C) 2021, Pyronear contributors.\n\n# This program is licensed under the Apache License version 2.\n# See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0.txt> for full license details.\n\n\"\"\"\nThe following Python file is dedicated to the \"Alerts and Infrastructure\" view of the dashb... | [
[
"pandas.read_json"
]
] | [
{
"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": []
}
] |
bottlenome/cutmix | [
"1bc006b5f40c284a6bf4ef881906e2109a27ef6c"
] | [
"cutmix/utils.py"
] | [
"import numpy as np\nimport torch\nfrom torch.nn.modules.module import Module\n\n\nclass CutMixCrossEntropyLoss(Module):\n def __init__(self, size_average=True):\n super().__init__()\n self.size_average = size_average\n\n def forward(self, input, target):\n if len(target.size()) == 1:\n ... | [
[
"torch.nn.LogSoftmax",
"numpy.sqrt",
"torch.zeros",
"numpy.clip",
"numpy.int",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sanixa/GS-WGAN-custom | [
"77660d4ba9f0e8ae949d76c29bb1b11d0afd9f96"
] | [
"source/mc_attack.py"
] | [
"import os\n\nimport numpy as np\nimport argparse\nimport glob\nimport cv2\nimport time\nimport sys\nimport scipy\nfrom sklearn.decomposition import PCA\n\nimport torch\nfrom torch.autograd import Variable\nfrom torch.utils.data import DataLoader, Dataset, TensorDataset\nimport torchvision.transforms as transforms\... | [
[
"torch.set_default_tensor_type",
"numpy.amax",
"numpy.log",
"torch.load",
"numpy.amin",
"numpy.arange",
"torch.utils.data.DataLoader",
"scipy.spatial.distance.cdist",
"numpy.random.shuffle",
"torch.tensor",
"numpy.concatenate",
"torch.cuda.is_available",
"torch.... | [
{
"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"
... |
jeanlks/ARP | [
"5e7f9987f4caba67dce7d136945c3c1d6301ebaa",
"5e7f9987f4caba67dce7d136945c3c1d6301ebaa"
] | [
"finalProject/KNNVariation.py",
"finalProject/XGBOOST.py"
] | [
"import pandas as pd\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.neighbors import KNeighborsClassifier\nimport matplotlib.pyplot as plt\n\ndataset = pd.read_csv(\"crimes2016HOMICIDEandMOTORandNARCOTICS.csv\",sep=\",\")\n\ndataset.dropna(inplace=True)\n\n#Splitting dependent and independent variables\... | [
[
"pandas.read_csv",
"matplotlib.pyplot.title",
"sklearn.model_selection.train_test_split",
"sklearn.neighbors.KNeighborsClassifier",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show",
"sklearn.metrics.accuracy_score"
],
[
"pandas.read_csv",
"sklearn.model_selection.train_te... | [
{
"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... |
christophernhill-dev/fall-2021-12.010 | [
"5b417379d436896c6c20886780a93a4876797a1c"
] | [
"lec22/step4.py"
] | [
"#!/usr/bin/env python\n\n#\n# From N processes, each with their own id (rank), use MPI library to report\n# each process rank, total number of processes and the name of the host machine\n# on which the process resides.\n#\n\nfrom mpi4py import MPI\nimport numpy as np\n\ncomm = MPI.COMM_WORLD\nrank = comm.Get_ra... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Purewhite2019/CS214-Project-1 | [
"446ef99ba638d907afcf735266430a4c51197493"
] | [
"zyj_code/task_scheduler.py"
] | [
"import numpy as np\nfrom DAG_scheduler import DAGScheduler\nfrom data_loader import DataLoader\n\nclass TaskScheduler:\n def __init__(self, file_path=\"../ToyData.xlsx\", threshold=5):\n dag_scheduler = DAGScheduler(file_path)\n data_loader = DataLoader(file_path)\n # depth based task set\n... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sashkarivkind/rnn_for_lm | [
"7ee083791331d418899fb9ce636265312e5ac126"
] | [
"scripts/minRnn_utils.py"
] | [
"import numpy as np\nimport tensorflow as tf\nimport time\nimport re\nimport random\nimport sys\nimport scipy.cluster.hierarchy as schi\nimport pickle\nfrom termcolor import colored\nimport matplotlib.pyplot as plt\n\ndef prep_flatFromBatch(fun_name_list,x_in,y_in):\n fun_list=list(map(eval,fun_name_list))\n ... | [
[
"numpy.linalg.eigvals",
"numpy.linspace",
"matplotlib.pyplot.plot",
"numpy.random.randn",
"numpy.mean",
"numpy.float16",
"numpy.diff",
"scipy.cluster.hierarchy.linkage",
"numpy.triu",
"numpy.zeros",
"numpy.log",
"numpy.log10",
"numpy.argsort",
"numpy.array",... | [
{
"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"
... |
nickcordella/CarND-Advanced-Lane-Lines-Submission4 | [
"038c11e187401b9bc88d4c6b9e1d53042bc50d3e"
] | [
"Advanced_Lane_Finding.py"
] | [
"\n# coding: utf-8\n\n# ## Import Packages\n\n# In[1]:\n\n\n#importing some useful packages\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport numpy as np\nimport cv2\nimport glob\nget_ipython().run_line_magic('matplotlib', 'inline')\n\n\n# ## Calibrate Camera\n\n# In[2]:\n\n\n# Make a list ... | [
[
"matplotlib.pyplot.imshow",
"numpy.polyfit",
"numpy.linspace",
"numpy.concatenate",
"matplotlib.image.imread",
"numpy.max",
"numpy.zeros_like",
"numpy.mean",
"numpy.hstack",
"numpy.copy",
"numpy.argmax",
"numpy.float32",
"matplotlib.pyplot.subplots_adjust",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ahleighton/nighres | [
"bc01463241a03a88569b3ba56e195127788b5639"
] | [
"nighres/statistics/segmentation_statistics.py"
] | [
"import numpy as np\nimport nibabel as nb\nimport os\nimport sys\nimport nighresjava\nfrom ..io import load_volume, save_volume\nfrom ..utils import _output_dir_4saving, _fname_4saving, \\\n _check_topology_lut_dir, _check_available_memory\n\n\ndef segmentation_statistics(segmentation, intensity=... | [
[
"numpy.nanmax",
"numpy.nanmin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Savidude/ai-chess | [
"79239f42c1e71bf0581c9ee2cd115282da163ae8"
] | [
"main.py"
] | [
"from agent import Agent\nfrom train import train\n\nimport torch\nimport torch.multiprocessing as mp\n\nfrom brain.piece_selector import pCNN\nfrom brain.move_selector import mCNN\n\nTEAM_WHITE = \"w\"\nTEAM_BLACK = \"b\"\n\nNUM_PROCESSES = 16\n\n\ndef main():\n device = torch.device(\"cuda\" if torch.cuda.is_a... | [
[
"torch.multiprocessing.set_start_method",
"torch.cuda.is_available",
"torch.multiprocessing.Process"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
staadecker/advent-of-code-2020 | [
"9ab0703099c750d90e4fa167438f8fc97302c81b"
] | [
"17/main.py"
] | [
"import numpy as np\n\nFILENAME = \"input.txt\"\n\nclass NeighbourIterator:\n def __init__(self, pos):\n self.pos = pos\n self.offset = None\n\n def increment_offset(self):\n ow, oz, ox, oy = self.offset\n\n oy = (oy + 1) % 3\n if oy == 0:\n ox = (ox + 1) % 3\n ... | [
[
"numpy.any",
"numpy.bitwise_xor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ddland/TIS-TN-python | [
"d1f7d864c09f0af907697e5d81d66a24c08814ad"
] | [
"TN_code/fourier/fourier_coeff.py"
] | [
"#!/usr/bin/env python\n\nimport logging\n\ntry:\n from sympy import pi, cos, sin, sqrt, arg, exp, Abs, S\n from sympy import integrate, Rational, nfloat, conjugate, Symbol\n from sympy.functions.special.delta_functions import Piecewise\nexcept ImportError as e:\n print(\"sympy kon niet geïmporteerd wor... | [
[
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tomera95/IML.HUJI | [
"1207ed57faba2871f92e8a6e5c8346de32a66bac"
] | [
"IMLearn/learners/classifiers/linear_discriminant_analysis.py"
] | [
"from typing import NoReturn\nfrom ...base import BaseEstimator\nimport numpy as np\nfrom numpy.linalg import det, inv\n\n\nclass LDA(BaseEstimator):\n \"\"\"\n Linear Discriminant Analysis (LDA) classifier\n\n Attributes\n ----------\n self.classes_ : np.ndarray of shape (n_classes,)\n The di... | [
[
"numpy.log",
"numpy.unique",
"numpy.linalg.inv",
"numpy.argmax",
"numpy.mean",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KyunghyunLee/tf-a3c-gpu-python3.6.5 | [
"7537bed64dcd0c44b3bd0ecc916a0c5d4c51f703"
] | [
"commons/ops.py"
] | [
"import tensorflow as tf\n\nclass Conv2d(object) :\n def __init__(self,name,input_dim,output_dim,k_h=4,k_w=4,d_h=2,d_w=2,\n stddev=0.02, data_format='NCHW') :\n with tf.variable_scope(name) :\n assert(data_format == 'NCHW' or data_format == 'NHWC')\n self.w = tf.get_v... | [
[
"tensorflow.nn.convolution",
"tensorflow.matmul",
"tensorflow.shape",
"tensorflow.maximum",
"tensorflow.nn.moments",
"tensorflow.truncated_normal_initializer",
"tensorflow.nn.conv2d_transpose",
"tensorflow.constant_initializer",
"tensorflow.variable_scope",
"tensorflow.pad"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hegdepashupati/sleep-tracking | [
"7bce27e6519f2265541a9020aca8620c9cfaf45f"
] | [
"sleep_tracking/features/base_features.py"
] | [
"from sleep_tracking.utils import get_root_directory\n\nimport os\nfrom abc import ABC, abstractmethod\n\nimport pandas as pd\nimport numpy as np\nfrom typing import List\n\n\nclass AbstractFeature(ABC):\n WINDOW_SIZE = 10 * 30 - 15\n EPOCH_DURATION = 30\n\n def __init__(self, name):\n self.name = n... | [
[
"pandas.concat",
"numpy.amax",
"numpy.amin",
"numpy.atleast_2d",
"numpy.interp",
"numpy.where"
]
] | [
{
"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": []
}
] |
spamz23/mljar-supervised | [
"e9713eda80f9f1e11debd67c7dc2af1e5d7e7f83"
] | [
"examples/scripts/regression.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom supervised.automl import AutoML\n\n\ndf = pd.read_csv(\"./tests/data/housing_regression_missing_values_missing_target.csv\")\nx_cols = [c for c in df.columns if c != \"MEDV\"]\nX = df[x_cols]\ny = df[\"MEDV\"]\n\nprint(\"y\", y[:10])\n\nprint(X.shape)\n\nautoml = AutoM... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Shannic/IML.HUJI | [
"46f1484ba8ab485525ee82fe30bd5fa156a1e256"
] | [
"exercises/fit_gaussian_estimators.py"
] | [
"from IMLearn.learners import UnivariateGaussian, MultivariateGaussian\nimport numpy as np\nimport plotly.graph_objects as go\nimport plotly.io as pio\n\npio.templates.default = \"simple_white\"\n\n\ndef test_univariate_gaussian():\n # Question 1 - Draw samples and print fitted model\n mu = 10\n X = np.ran... | [
[
"numpy.random.seed",
"numpy.linspace",
"numpy.random.multivariate_normal",
"numpy.random.normal",
"numpy.argmax",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TomDLT/celer | [
"4429a23c8c637e0c5d3a784143f6a49f01ab092a"
] | [
"setup.py"
] | [
"from setuptools.command.build_ext import build_ext\nfrom setuptools import dist, setup, Extension, find_packages\nimport os\ndist.Distribution().fetch_build_eggs(['numpy>=1.12'])\nimport numpy as np # noqa\n\ndescr = 'Fast algorithm with dual extrapolation for sparse problems'\n\nversion = None\nwith open(os.path... | [
[
"numpy.get_include"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
immerrr/numpy | [
"e9f27b9ad506a8e37565abf72c0e9f23254595de"
] | [
"numpy/core/tests/test_ufunc.py"
] | [
"from __future__ import division, absolute_import, print_function\n\nimport sys\n\nimport numpy as np\nfrom numpy.testing import *\nimport numpy.core.umath_tests as umt\nimport numpy.core.operand_flag_tests as opflag_tests\nfrom numpy.compat import asbytes\nfrom numpy.core.test_rational import *\n\nclass TestUfunc(... | [
[
"numpy.matrix",
"numpy.logical_xor",
"numpy.core.umath_tests.inner1d",
"numpy.dtype",
"numpy.all",
"numpy.max",
"numpy.core.umath_tests.test_signature",
"numpy.any",
"numpy.power.reduce",
"numpy.negative",
"numpy.bool_",
"numpy.conjugate",
"numpy.arange",
"n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
luoyaxiong/epfl-deeplearning-project | [
"33a4bdeb764c2c1f645bfb1317e1238374ce2c15"
] | [
"proj2/code/test.py"
] | [
"\"\"\"\nTest file of our framework.\n\nAuthor: Tao Sun, Wenlong Deng, Yaxiong Luo\n\"\"\"\n\nfrom module import *\nfrom torch import FloatTensor, LongTensor\nimport math\nimport numpy as np\n\n\ndef reshapeLabel(label):\n \"\"\"\n Reshape 1-D [0,1,...] to 2-D [[1,-1],[-1,1],...].\n \"\"\"\n n = label.s... | [
[
"torch.FloatTensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
prkhrsrvstv1/cupy | [
"ea86c8225b575af9d2855fb77a306cf86fd098ea",
"ea86c8225b575af9d2855fb77a306cf86fd098ea",
"ea86c8225b575af9d2855fb77a306cf86fd098ea"
] | [
"cupy/_manipulation/basic.py",
"tests/cupyx_tests/linalg_tests/test_solve.py",
"tests/cupyx_tests/fallback_mode_tests/test_fallback.py"
] | [
"import numpy\n\nfrom cupy import _core\nfrom cupy._core import _fusion_interface\nfrom cupy._core import fusion\nfrom cupy._sorting import search\n\n\ndef copyto(dst, src, casting='same_kind', where=None):\n \"\"\"Copies values from one array to another with broadcasting.\n\n This function can be called for ... | [
[
"numpy.can_cast"
],
[
"numpy.linalg.inv"
],
[
"numpy.__version__.split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"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",
... |
nguyenngoctongan/lstm-svo-extractor | [
"0d8f62aa14213e348248ca59188187ed1988e4af"
] | [
"lstm.py"
] | [
"import torch\r\nimport torch.nn as nn\r\nimport torch.optim as optim\r\nfrom torchtext import data\r\nfrom torchtext.datasets import SequenceTaggingDataset\r\nimport numpy as np\r\nimport time\r\nimport random\r\nimport pandas as pd\r\nfrom torchtext.data import Dataset, Example, Field\r\nfrom torchtext.data.itera... | [
[
"torch.nn.CrossEntropyLoss",
"pandas.read_csv",
"torch.nn.Dropout",
"torch.nn.LSTM",
"torch.zeros",
"torch.load",
"torch.nn.Embedding",
"torch.nn.Linear",
"torch.nn.init.normal_",
"torch.no_grad",
"torch.cuda.is_available",
"torch.FloatTensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
BinLiu777/pycorrector | [
"06a54e5a0d847a81e78f904573229af57a7aa6fa"
] | [
"seq2vec/bert_vec_quanti.py"
] | [
"import numpy as np\nimport torch\n#from transformers import BertModel, BertConfig, BertTokenizer\nfrom transformers_quant.modeling_bert_quant import BertModel\nfrom transformers import BertConfig, BertTokenizer\nfrom scipy.spatial.distance import cosine\n\n#from albert.albert_total import get_albert_total\n#from m... | [
[
"numpy.array",
"scipy.spatial.distance.cosine",
"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"
... |
cysimon/pandas | [
"ce34c1cee70021792b0c1b0db2c899069bbf0c1d"
] | [
"pandas/core/arrays/_mixins.py"
] | [
"from __future__ import annotations\n\nfrom functools import wraps\nfrom typing import (\n Any,\n Optional,\n Sequence,\n Type,\n TypeVar,\n Union,\n cast,\n)\n\nimport numpy as np\n\nfrom pandas._libs import lib\nfrom pandas._typing import (\n F,\n Shape,\n)\nfrom pandas.compat.numpy imp... | [
[
"pandas.util._validators.validate_bool_kwarg",
"pandas.Series",
"numpy.asarray",
"pandas.core.dtypes.common.is_dtype_equal",
"pandas._libs.lib.is_scalar",
"numpy.concatenate",
"pandas.core.indexers.check_array_indexer",
"numpy.where",
"pandas.errors.AbstractMethodError",
"p... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.0",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
kerengaiger/mnist_autoencoder | [
"19d7c347897a7f1ced684a04146b052940884e5f"
] | [
"utils.py"
] | [
"import os\nimport pathlib\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\n\n\ndef add_noise(images, noise_var):\n # add random noise to the input images\n noisy_imgs = images + noise_var * torch.randn(*images.shape)\n # Clip the images to be between 0 and 1\n noisy_imgs = np.clip(... | [
[
"torch.randn",
"numpy.squeeze",
"matplotlib.pyplot.subplots",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ZGainsforth/InterstellarXAS | [
"481a008cf3f14b5a0b5bc84be7b12981711f7234",
"481a008cf3f14b5a0b5bc84be7b12981711f7234"
] | [
"MasterXRayDB/MasterXMMDB.py",
"MasterXRayDB/MasterXRayDB.py"
] | [
"from copy import deepcopy\nimport os, sys, shutil\nimport time\nimport requests\nimport tarfile\nimport yaml\n\nimport streamlit as st\n\nimport astropy.units as u\nfrom astropy.coordinates import SkyCoord\nfrom astropy.io import fits\nfrom astroquery.esa.xmm_newton import XMMNewton\nimport dustmaps\nfrom dustmaps... | [
[
"numpy.log",
"numpy.arctan",
"numpy.min",
"numpy.arange",
"numpy.max",
"numpy.meshgrid"
],
[
"numpy.arange",
"numpy.log",
"numpy.meshgrid",
"numpy.arctan"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
x5g/MNIST | [
"8f0be405c5f1fa30dbdb1e00d3fb7202d5da975b"
] | [
"input_data.py"
] | [
"# #!/usr/bin/python\n# # coding:utf-8\n\n# 用于下载和读取MNIST数据的函数\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nimport gzip\nimport os\nimport tensorflow.python.platform\nimport numpy\nfrom six.moves import urllib\nfrom six.moves import xrange # pylint... | [
[
"tensorflow.as_dtype",
"numpy.multiply",
"numpy.arange",
"numpy.dtype",
"numpy.random.shuffle",
"numpy.frombuffer",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"1.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1... |
Prepaire/DeepPurpose | [
"6f19d44fb64e72d2b85995e2b87e2679a888c3ea"
] | [
"DeepPurpose/PPI.py"
] | [
"import torch\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nfrom torch.utils import data\nfrom torch.utils.data import SequentialSampler\nfrom torch import nn \n\nfrom tqdm import tqdm\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom time import time\nfrom skl... | [
[
"sklearn.metrics.roc_auc_score",
"torch.cat",
"torch.load",
"numpy.asarray",
"torch.utils.data.DataLoader",
"sklearn.metrics.mean_squared_error",
"matplotlib.pyplot.plot",
"torch.set_grad_enabled",
"torch.cuda.is_available",
"torch.device",
"sklearn.metrics.f1_score",
... | [
{
"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"
... |
marin-oz/HIA_Audition | [
"af34855cbefe08fc825e0f63077bea2092e01b3a"
] | [
"src/models/test_model.py"
] | [
"from tensorflow.keras.optimizers import SGD\nimport pandas as pd\nimport os\nimport numpy as np\n\nimport sys\nsys.path.append('..')\n\nfrom definition import DatasetDefinition\nfrom models.load_data import DataLoader\nfrom models.conv_models import ModelConstruction\n\ndef model_test_all(model_name='m5', test_gro... | [
[
"pandas.DataFrame",
"tensorflow.keras.optimizers.SGD"
]
] | [
{
"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": [
"1.10",
"2.7",
"2.2",
... |
eraldoluis/LeNER-Br | [
"e0cb65fc0b82ca1cab34297c6dec8a8664f2f3da"
] | [
"classScores.py"
] | [
"'''\nCode entirely written by the authors of leNER-Br paper\n'''\n\n# This file was developed as part of the project reported in the paper below.\n# We kindly request that users cite our paper in any publication that is \n# generated as a result of the use of our source code or our dataset.\n# \n# Pedro H. Luz de ... | [
[
"sklearn.metrics.f1_score",
"sklearn.metrics.classification_report"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
programatt/POCS | [
"6450a27e3d3a9bb33e251f5d3508266445fe8136"
] | [
"src/panoptes/pocs/state/states/default/pointing.py"
] | [
"import numpy as np\nfrom panoptes.pocs.images import Image\nfrom panoptes.utils.time import wait_for_events\n\nMAX_EXTRA_TIME = 60 # second\n\n\ndef on_enter(event_data):\n \"\"\"Pointing State\n\n Take 30 second exposure and plate-solve to get the pointing error\n \"\"\"\n pocs = event_data.model\n\n... | [
[
"numpy.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yaxis1/Reinforcement-learning | [
"4e0da197cae68b38df02008d973b914894cd4f3f"
] | [
"ai/ai.py"
] | [
"#AI for the car\n\n# Importing libraries\nimport numpy as np #Working with arrays\nimport random #taking random samples\nimport os #load and save the model\nimport torch #only lib that can handle dynamic graphs\nimport torch.nn as nn#neural network\nimport torch.nn.functional as F #functions for nn \nimport torch.... | [
[
"torch.Tensor",
"torch.cat",
"torch.load",
"torch.nn.Linear",
"torch.nn.functional.smooth_l1_loss",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
craymichael/tensorflow-posit | [
"e29d1e58df25b984adec19abcaab688624ce169c"
] | [
"tensorflow/python/framework/function.py"
] | [
"# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.pywrap_tensorflow.TF_GetBuffer",
"tensorflow.python.framework.c_api_util.ScopedTFFunction",
"tensorflow.python.util.tf_inspect.ismethod",
"tensorflow.python.ops.variable_scope._get_default_variable_store",
"tensorflow.python.ops.array_ops.placeholder",
"tensorflow.python... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.5",
"1.13",
"1.7",
"1.12"
]
}
] |
JohnAnih/BMW-used-car-prediction | [
"17f6938716c6b0d09e419b464148beb44a8095c9"
] | [
"src/preprocessing/load_data.py"
] | [
"import logging\nimport pandas as pd\nfrom src.preprocessing import prepare_data as data\n\ndef load_processed(data_path: str):\n df = pd.read_csv(data_path)\n logging.debug(\"finished loading, start cleaning\")\n df= data.clean_column_names(df)\n logging.debug(\"cleaned up the column names\")\n df= ... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
josh-marsh/thursday | [
"0acb0511b8007091a409e674bb42c2c0fe8f8a82"
] | [
"thursday/experiments.py"
] | [
"import h5py\nimport numpy as np\nfrom pathlib import Path\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.linear_model import LogisticRegressionCV\nfrom sklearn.naive_bayes import GaussianNB\n\nfrom format_data import add_random\nfrom format_data import append_random\nfrom format_data import aug... | [
[
"numpy.asarray",
"numpy.expand_dims",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Haotianz94/Deep-Image-Matting-PyTorch | [
"9d7fa3029cce9e0f6081908a5698f2396848eb05"
] | [
"eval_parallel.py"
] | [
"import os\r\n\r\nimport cv2 as cv\r\nimport numpy as np\r\nimport torch\r\nfrom torchvision import transforms\r\nfrom tqdm import tqdm\r\nimport sys\r\n\r\nfrom config import device\r\nfrom data_gen import data_transforms\r\nfrom utils import ensure_folder\r\n\r\n\r\nIMG_FOLDER = 'data/image/{}'.format(sys.argv[1]... | [
[
"torch.no_grad",
"torch.zeros",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
markvasin/openvqa | [
"e9838e49c2c9a8128f61b6cc500839106f821c26"
] | [
"openvqa/models/vqabert/net.py"
] | [
"import torch\nimport torch.nn as nn\n\nfrom openvqa.models.vqabert.adapter import Adapter\nfrom openvqa.models.vqabert.vqa_bert import VQA_BERT, BERTPooler\nfrom openvqa.ops.layer_norm import LayerNorm\nfrom openvqa.utils.make_mask import make_mask\n\n\n# -------------------------\n# ---- Main VQA BERT Model ----\... | [
[
"torch.nn.Dropout",
"torch.nn.LSTM",
"torch.cat",
"torch.from_numpy",
"torch.nn.Embedding",
"torch.nn.Tanh",
"torch.tensor",
"torch.nn.Linear"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fzd9752/CarND-Semantic-Segmentation | [
"8c24ac3052ab078fb2f48bdc0fdaa6f1fc0eefb6"
] | [
"project_tests.py"
] | [
"'''\nYou should not edit project_tests.py as part of your submission.\n\nThis file is used for unit testing your work within main.py.\n'''\n\nimport sys\nimport os \nfrom copy import deepcopy\nfrom glob import glob\nfrom unittest import mock\n\nimport numpy as np\nimport tensorflow as tf\n\n\ndef test_safe(func):\... | [
[
"tensorflow.Graph",
"tensorflow.constant",
"tensorflow.zeros",
"tensorflow.placeholder",
"tensorflow.global_variables_initializer",
"tensorflow.Session",
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
clebsonpy/HydroComp | [
"9d17fa533e8a15c760030df5246ff531ddb4cb22"
] | [
"hidrocomp/statistic/pearson3.py"
] | [
"import numpy as np\n\nfrom hidrocomp import statistic as e\nfrom hidrocomp.statistic.stats_build import StatsBuild\nfrom scipy.stats import pearson3\nfrom lmoments3.distr import pe3\n\n\nclass Pearson3(StatsBuild):\n\n name = 'Pearson3'\n estimator = None\n parameter = {'loc': None, 'scale': None}\n\n ... | [
[
"scipy.stats.pearson3",
"scipy.stats.pearson3.fit"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pedrolarben/TimeSeriesForecasting-DeepLearning | [
"2fc5468bc7717e42feb686f85fac2fd7ec84c457"
] | [
"experiments_transformers/generate_data.py"
] | [
"# -*- coding: utf-8 -*-\nimport os\nimport requests\nimport json\nimport time\nimport random\nimport itertools\nimport numpy as np\nfrom tqdm import tqdm\nfrom preprocessing import (\n read_ts_dataset,\n normalize_dataset,\n moving_windows_preprocessing,\n denormalize,\n)\n\nNUM_CORES = 7\n\ndef notify... | [
[
"numpy.max",
"numpy.copy",
"numpy.min"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Fast-LM-WordEvalRu/SRU-LM | [
"dd69d6c7b7b6c0164e83a874aee5e6f6766070d5"
] | [
"sru_lm/core/char_embedder.py"
] | [
"import torch\nimport torch.nn as nn\n\nfrom .highway import Highway\n\n\nclass CharEmbedder(nn.Module):\n def __init__(self, char_embedding_dim, max_characters_per_token, n_characters, cnn_options, n_highway, output_dim):\n super(CharEmbedder, self).__init__()\n\n self.char_embedding = nn.Embeddin... | [
[
"torch.transpose",
"torch.max",
"torch.cat",
"torch.nn.Embedding",
"torch.nn.Linear",
"torch.nn.functional.relu",
"torch.nn.Conv1d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
btarjan/NeMo | [
"6a2bb4d19524b0bff198b3d9bbd116f82486a36e",
"6a2bb4d19524b0bff198b3d9bbd116f82486a36e"
] | [
"nemo/collections/asr/models/ctc_models.py",
"nemo/collections/nlp/modules/common/megatron/language_model.py"
] | [
"# Copyright (c) 2020, 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 obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless re... | [
[
"torch.no_grad",
"torch.cuda.is_available"
],
[
"torch.nn.Dropout",
"torch.nn.Parameter",
"torch.cat",
"torch.nn.ModuleDict",
"torch.nn.Embedding",
"torch.tensor",
"torch.tanh",
"torch.stack",
"torch.distributed.get_rank",
"torch.nn.functional.linear"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hwang595/distpytorchps | [
"4645ecba8c74e86f982d22fa9f38d2811f598054"
] | [
"src/model_ops/lenet.py"
] | [
"import torch\nfrom torch import nn\nimport torch.nn.functional as F\n\nimport pandas as pd\nimport numpy as np\nfrom torch.autograd import Variable\n\nfrom mpi4py import MPI\n\nimport sys\nsys.path.insert(0, '../compression')\nfrom compression import g_compress\n\n# we use LeNet here for our simple case\nclass LeN... | [
[
"torch.nn.CrossEntropyLoss",
"pandas.isnull",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.MaxPool2d",
"torch.nn.functional.relu",
"torch.nn.ReLU",
"torch.nn.functional.max_pool2d",
"torch.autograd.Variable"
]
] | [
{
"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": []
}
] |
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