repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
elda27/prepnet | [
"0f05018969496321aaa770b7e22bda858dab0ad6"
] | [
"tests/test_category.py"
] | [
"from collections import OrderedDict\n\nimport pytest\nimport pandas as pd\nimport numpy as np\n\nfrom prepnet.category.ordinal_converter import OrdinalConverter\nfrom prepnet.category.onehot_converter import OnehotConverter\n\n\ndef test_ordinal_converter():\n input_series = pd.Series(['frame', 'frame', 'old', ... | [
[
"pandas.testing.assert_frame_equal",
"pandas.testing.assert_series_equal",
"pandas.Series",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
LiYingwei/ghost-network | [
"6b0b521ff6e9d1f4c3f25cb25518968047b5cca0",
"c9731b60ce5a7e9590b24cb98c8ddfce30cf465e"
] | [
"networks/core/ens3_inception_v3.py",
"nets/resnet_v2_50.py"
] | [
"import os\n\nimport tensorflow.contrib.slim.nets as nets\n\nfrom config import config as FLAGS\n\n_CHECKPOINT_NAME = 'ens3_adv_inception_v3.ckpt'\ncheckpoint_path = os.path.join(\n FLAGS.checkpoint_path,\n _CHECKPOINT_NAME\n)\n\narg_scope = nets.inception.inception_v3_arg_scope(weight_decay=0.0)\nfunc = nets... | [
[
"tensorflow.contrib.slim.nets.inception.inception_v3_arg_scope"
],
[
"tensorflow.contrib.slim.python.slim.nets.resnet_utils.conv2d_same",
"tensorflow.contrib.slim.python.slim.nets.resnet_utils.stack_blocks_dense",
"tensorflow.contrib.layers.python.layers.layers.softmax",
"tensorflow.contri... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.13",
"1.10",
"1.12"
]
}
] |
trendelkampschroer/PyEMMA | [
"ee5784d5c1c5bc070fe2e9e6ad4f24b36185dc20"
] | [
"pyemma/msm/tests/birth_death_chain.py"
] | [
"\n# Copyright (c) 2015, 2014 Computational Molecular Biology Group, Free University\n# Berlin, 14195 Berlin, Germany.\n# All rights reserved.\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# * Redistributi... | [
[
"numpy.diag",
"scipy.sparse.diags",
"numpy.cumsum",
"numpy.all",
"numpy.cumprod",
"numpy.transpose",
"numpy.diag_indices",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"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"
... |
500swapnil/flappybird-ai | [
"8def6b05327e8b06eaea20158800e8f3bcebe0cb"
] | [
"neuroEvolution.py"
] | [
"from itertools import cycle\nimport random\nimport sys\nimport math\nimport numpy as np\nfrom collections import namedtuple\nfrom itertools import count\nimport tensorflow as tf\nfrom PIL import Image\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Input, Activation, Bat... | [
[
"tensorflow.keras.models.load_model",
"tensorflow.keras.layers.Activation",
"tensorflow.keras.models.Sequential",
"numpy.reshape",
"tensorflow.keras.layers.Dense",
"numpy.argmax",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
chaoticrnd/FaceyMcFaceFace | [
"c9639bfd7d9f776bb054d94e836e4aff58c75284"
] | [
"src/evaluate.py"
] | [
"from keras.models import Sequential, model_from_json\nfrom keras.layers import Activation, Flatten, Dense, Dropout\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.utils import np_utils\nfrom keras import layers\nfrom sklearn.model_selection import train_test_split\nimport pandas as pd\nfrom pandas impor... | [
[
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jiaweiM/plot-note | [
"5b4147795aa03665fd51df7558b79f927101fbbf"
] | [
"src/plotly_test/webgl_go.py"
] | [
"import numpy as np\nimport plotly.graph_objects as go\n\nN = 100000\n\nfig = go.Figure()\n\nfig.add_trace(\n go.Scattergl(\n x=np.random.randn(N),\n y=np.random.randn(N),\n mode='markers',\n marker=dict(\n line=dict(\n width=1,\n color='DarkSl... | [
[
"numpy.random.randn"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
h-aboutalebi/maxent | [
"5da2715bb6a9695cccd91b142b0ba5b927719850"
] | [
"explore/algos/ddpg/ddpg.py"
] | [
"import random\nimport numpy as np\nimport tensorflow as tf\nimport gym\nimport time\nfrom algos.ddpg import core\nfrom algos.ddpg.core import get_vars\nfrom spinup.utils.logx import EpochLogger\n\n\nclass ReplayBuffer:\n \"\"\"\n A simple FIFO experience replay buffer for DDPG agents.\n \"\"\"\n\n def ... | [
[
"numpy.random.seed",
"tensorflow.reduce_mean",
"numpy.clip",
"tensorflow.assign",
"tensorflow.stop_gradient",
"tensorflow.global_variables_initializer",
"tensorflow.variable_scope",
"numpy.random.randn",
"tensorflow.train.AdamOptimizer",
"tensorflow.Session",
"tensorflo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
manu-sk/rnn_chatbot | [
"4cdb880c0f329ab4b68069db5d9b6d868c18dfc4"
] | [
"man.py"
] | [
"# things we need for NLP\nimport nltk\nfrom nltk.stem.lancaster import LancasterStemmer\nstemmer = LancasterStemmer()\n\n# things we need for Tensorflow\nimport numpy as np\nimport tflearn\nimport tensorflow as tf\nimport random\nimport nltk\n\nimport json\nwith open('intents.json') as json_data:\n intents = js... | [
[
"tensorflow.reset_default_graph",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
DandelionCN/tensorflow | [
"1712002ad02f044f7569224bf465e0ea00e6a6c4"
] | [
"tensorflow/python/ops/distributions/distribution.py"
] | [
"# Copyright 2016 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.util.tf_inspect.getdoc",
"tensorflow.python.ops.array_ops.rank",
"tensorflow.python.framework.tensor_shape.TensorShape",
"tensorflow.python.ops.array_ops.concat",
"tensorflow.python.ops.array_ops.shape",
"tensorflow.python.ops.distributions.kullback_leibler.kl_divergence... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.5",
"1.7"
]
}
] |
YigitGunduc/self-driving-car | [
"2be31f6473c911cf004236ce0874cb2da8fe8ad1"
] | [
"src/datasetsampler.py"
] | [
"from typing import Callable\n\nimport pandas as pd\nimport torch\nimport torch.utils.data\nimport torchvision\n\n\nclass ImbalancedDatasetSampler(torch.utils.data.sampler.Sampler):\n\n \"\"\"\n @author : Ming\n see github repo : https://github.com/ufoym/imbalanced-dataset-sampler\n\n Samples elements r... | [
[
"torch.DoubleTensor",
"pandas.DataFrame",
"torch.multinomial"
]
] | [
{
"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": []
}
] |
SparshaSaha/Handwritten-Number-Recognition-With-Image-Segmentation | [
"fc7afc2b3095c2a0bb03f3d72c164459e735c5d7"
] | [
"Character_Segmenter_Utilities.py"
] | [
"\n# coding: utf-8\n\n# In[1]:\n\n\nimport tensorflow as tf\nimport tflearn\nfrom tflearn.layers.conv import conv_2d,max_pool_2d\nfrom tflearn.layers.core import input_data,dropout,fully_connected\nfrom tflearn.layers.estimator import regression\nimport scipy.io\nimport numpy as np\nimport matplotlib.pyplot as pypl... | [
[
"numpy.matrix",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
monikadelekta/Automatically-Detecting-Malaria | [
"5c645ccbdb5b6e2dadada69490f06f03d3e9bdd7"
] | [
"Evaluation_Tool.py"
] | [
"#!/usr/bin/env python\r\n\"\"\"\r\nEVALUATION TOOL FOR TESTING AUTOMATIC MALARIA DETECTION CODE\r\nWRITTEN BY MONIKA DELEKTAs\r\n\"\"\"\r\nimport cv2, numpy as np, os\r\nimport pandas as pd\r\nfrom skimage import measure\r\nfrom prettytable import PrettyTable\r\n\r\ndef infectedMask(im):\r\n \"\"\"\r\n Creat... | [
[
"numpy.array",
"numpy.zeros",
"pandas.DataFrame",
"numpy.ones"
]
] | [
{
"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": []
}
] |
mzapotoczny/dependency-parser | [
"e37f94e23cb61d6658774f5f9843219df331eb74",
"e37f94e23cb61d6658774f5f9843219df331eb74"
] | [
"libs/Theano/theano/tensor/nnet/nnet.py",
"libs/Theano/theano/gpuarray/tests/test_basic_ops.py"
] | [
"\"\"\"\nProvides neural-network specific Ops.\n\nNotes\n-----\nTODO: factor this out into a neural-network toolbox.\n\nWe register all optimization with the gpu tag as we don't\nimplement all the intermediate case on the GPU (in particular\nAdvancedSubtensor). So to make sure it run well on the gpu with\nfast_comp... | [
[
"numpy.log",
"numpy.allclose",
"numpy.all",
"numpy.argmax",
"numpy.zeros_like",
"numpy.exp",
"numpy.zeros",
"numpy.sum",
"numpy.empty"
],
[
"numpy.asarray",
"numpy.eye",
"numpy.int32",
"numpy.dtype",
"numpy.all",
"numpy.random.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lavish619/MLP-Mixer-PyTorch | [
"4fb4de37c824645ef100d6dcbaf56349f138daa7"
] | [
"mlp_mixer.py"
] | [
"import torch\nfrom torch import nn\n\nclass MLP_Block(nn.Module):\n def __init__(self, num_features, expansion, dropout):\n super(MLP_Block, self).__init__()\n num_hidden = int(expansion*num_features)\n self.fc1 = nn.Linear(num_features, num_hidden)\n self.dropout = nn.Dropout(dropou... | [
[
"torch.nn.Sequential",
"torch.nn.Dropout",
"torch.nn.GELU",
"torch.transpose",
"torch.mean",
"torch.randn",
"torch.nn.Conv2d",
"torch.nn.LayerNorm",
"torch.nn.Linear",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yassersouri/task-driven-object-detection | [
"0815eff73e61c24020967f6640a2e3a988b09f3c"
] | [
"src/coco_tasks/graph_experiments.py"
] | [
"import os\nfrom typing import Optional, Union\n\nimport torch\nfrom tensorboardX import SummaryWriter\nfrom torch import Tensor\nfrom torch.optim import Optimizer\nfrom torch.optim.lr_scheduler import StepLR\nfrom torch.utils.data import DataLoader\nfrom tqdm import tqdm\n\nfrom coco_tasks.graph_datasets import (\... | [
[
"torch.no_grad",
"torch.utils.data.DataLoader",
"torch.optim.lr_scheduler.StepLR"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
meta-soul/MetaSpore | [
"e6fbc12c6a3139df76c87215b16f9dba65962ec7"
] | [
"demo/search/src/test/test_IR_eval.py"
] | [
"#\n# Copyright 2022 DMetaSoul\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 agr... | [
[
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rsprouse/phonlab | [
"12bc58c90fa8c1509d851a75c64965ce018de834"
] | [
"phonlab/array.py"
] | [
"import numpy as np\n\ndef nonzero_groups(a, minlen=1, include_any=None):\n '''Return groups of indexes where elements are non-zero. This function\nis similar to numpy's `nonzero()`, but instead of returning a single array of\nindexes, a tuple of consecutive groups of indexes is returned. Unlike\n`nonzero()` onl... | [
[
"numpy.hstack",
"numpy.abs",
"numpy.arange",
"numpy.all",
"numpy.asanyarray",
"numpy.diff"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ZhengLiangliang1996/Speech-Recogniton-Tool-Box | [
"0a2353d990e3f0a3057a747ff52fd3a066d4289d"
] | [
"train/modified_rnn/modified_residual_main.py"
] | [
"\"\"\"\nAuthor: Liangliang ZHENGT\nTime: 2020/02/26\nfile: stimuli3_main.py\n\"\"\"\n\n\nimport argparse\nimport sys\nimport time\nimport datetime\nimport os\nimport _pickle as pickle\nsys.path.append('../..')\n\nimport numpy as np\nimport tensorflow as tf\n# from models.brnn import brnn\nfrom models.vis_model.mod... | [
[
"tensorflow.compat.v1.logging.set_verbosity"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pleyad/atmt | [
"7edd94b308d9225b0ceb465dae280c7a79e8bf77"
] | [
"translate_deployment.py"
] | [
"import os\nimport logging\nimport argparse\nimport numpy as np\nfrom tqdm import tqdm\n\nimport torch\nfrom torch.serialization import default_restore_location\nfrom tqdm.utils import _environ_cols_wrapper\n\nfrom seq2seq import models, utils\nfrom seq2seq.data.dictionary import Dictionary\nfrom seq2seq.data.datas... | [
[
"torch.ones",
"torch.cat",
"torch.manual_seed",
"torch.serialization.default_restore_location",
"torch.no_grad",
"torch.where",
"torch.topk",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
er432/TASSELpy | [
"2273d2252786679e023d1279f0c717a29ddd6d35"
] | [
"TASSELpy/net/maizegenetics/matrixalgebra/Matrix/DoubleMatrix.py"
] | [
"from TASSELpy.java.lang.Object import Object\nimport TASSELpy.net.maizegenetics.matrixalgebra.decomposition.EigenvalueDecomposition\nimport TASSELpy.net.maizegenetics.matrixalgebra.decomposition.SingularValueDecomposition\nfrom TASSELpy.utils.helper import make_sig\nfrom TASSELpy.utils.Overloading import javaOverl... | [
[
"numpy.float64"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yudongqiu/GomokuWeb | [
"c7d887733250f4b69efd04edf72a22bbd8257206"
] | [
"standard_rule/app/AI/AI_debug.py"
] | [
"#!/usr/bin/env python\n\nfrom __future__ import print_function, division\nimport itertools, time, copy\nimport collections, random\nimport os, pickle\nimport numba\nimport numpy as np\nimport tflearn\n\nboard_size = 15\nestimate_level = 3\ndef strategy(state):\n \"\"\" AI's strategy \"\"\"\n\n \"\"\" Informa... | [
[
"numpy.random.seed",
"numpy.max",
"numpy.iinfo",
"numpy.argsort",
"numpy.array",
"numpy.zeros",
"numpy.sum",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RKDSOne/scikit-image | [
"baa67eafcace9cde1b94ad2d467e2f2e0468e759",
"d0415e6df475157705fd1ef2af69b16e4f7e38cc"
] | [
"skimage/exposure/tests/test_exposure.py",
"skimage/morphology/convex_hull.py"
] | [
"import numpy as np\nfrom numpy.testing import assert_array_almost_equal as assert_close\nimport skimage\nfrom skimage import data\nfrom skimage import exposure\nfrom skimage.color import rgb2gray\nfrom skimage.util.dtype import dtype_range\n\n\n# Test histogram equalization\n# ===========================\n\n# sque... | [
[
"numpy.square",
"numpy.polyfit",
"numpy.testing.run_module_suite",
"numpy.log",
"numpy.dstack",
"numpy.array",
"numpy.testing.assert_array_almost_equal"
],
[
"numpy.unique",
"scipy.spatial.Delaunay",
"numpy.arctan2",
"numpy.argsort",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
playing-code/ANCE_test | [
"80ae493af4e771274153ba5ce0d5b1793b1d7e11"
] | [
"data/DPR_data.py"
] | [
"from os.path import join\r\nimport sys\r\nsys.path += ['../']\r\nimport argparse\r\nimport json\r\nimport os\r\nimport random\r\nimport numpy as np\r\nimport torch\r\nfrom torch.utils.data import Dataset, TensorDataset\r\nfrom model.models import MSMarcoConfigDict, ALL_MODELS\r\nimport csv\r\nfrom utils.util impor... | [
[
"torch.utils.data.TensorDataset",
"numpy.array",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Eethir/Kobos-and-Dragons | [
"41299c490704f82f50fa3bc215f575b95b5dbdcb"
] | [
"random_utils.py"
] | [
"from numpy.random import choice\n\n\ndef from_dungeon_level(table, dungeon_level):\n for (value, level) in reversed(table):\n if dungeon_level >= level:\n return value\n\n return 0\n\n\ndef random_choice_from_dict(choice_dict):\n choices = list(choice_dict.keys())\n chances = list(cho... | [
[
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ChristianMurphy/shap | [
"6e04be3f4a9c7d61ada301366e4273a096e5c1f5"
] | [
"shap/maskers/_fixed_composite.py"
] | [
"import numpy as np\nfrom ._masker import Masker\n\nclass FixedComposite(Masker):\n def __init__(self, masker):\n \"\"\" Creates a Composite masker from an underlying masker and returns the original args along with the masked output.\n\n Parameters\n ----------\n masker: object\n ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sYeaLumin/SketchGNN | [
"c40f54083cc62a63170b1dc323e960d418bf1f56"
] | [
"tools/draw_sketch.py"
] | [
"import os\nimport ndjson\nimport cv2 as cv\nimport numpy as np\nfrom math import log\nfrom operator import truediv\nfrom scipy.special import comb\nimport xml.etree.ElementTree as xml\n\n\nwhite = (256,256,256)\ngray = (100,100,100)\ncolors = [\n (255,0,0),\n (255,255,0),\n (255,0,255),\n (0,255,255),\... | [
[
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jkschin/YOLO | [
"e4ef2f6499784129ba57855be2adbfa812eac0ba"
] | [
"utils.py"
] | [
"import tensorflow as tf\nimport numpy as np\nimport cv2\nfrom nms import *\n\nFLAGS = tf.app.flags.FLAGS\n\ndef draw_boxes(img, bboxes, classes, idx_to_txt):\n h, w, _ = img.shape\n for i, box in enumerate(bboxes):\n scale_img = [h, w, h, w]\n box = [int(a*b) for a,b in zip(box, scale_img)]\n ... | [
[
"numpy.expand_dims"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WangChen0902/noise2noise-paddle | [
"20e9218eaafc526c19efcf8853c87fb1618b3963"
] | [
"src/datasets.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport paddle\nimport paddle.nn.functional as F\nfrom paddle.vision.transforms import functional as tvF\nfrom paddle.io import Dataset, DataLoader\n\n# from utils import load_hdr_as_tensor\n\nimport os\nfrom sys import platform\nimport numpy as np\nimport random\n... | [
[
"numpy.amax",
"numpy.random.seed",
"numpy.clip",
"matplotlib.use",
"numpy.random.poisson",
"numpy.random.normal",
"numpy.random.uniform",
"numpy.array",
"numpy.sum",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
samsipe/SatelliteConstellationCreator | [
"6acc75d652e0d34fa27c1e5bca29beb71a18b0f2"
] | [
"satellite_constellation/visualiser.py"
] | [
"import matplotlib.pyplot as plt\nfrom satellite_constellation.Constellation import *\nfrom satellite_constellation.utils import *\n\ndef draw_walker(walker_constellation):\n r = walker_constellation.altitude + heavenly_body_radius[walker_constellation.focus]\n\n t = np.linspace(0, 2 * math.pi, 100)\n\n x1... | [
[
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Seong-JiHyeon/ESG-investment | [
"80f2d992bbe3c36a73e4482087b87817113c1145"
] | [
"Data_Creation/ESG_Financial_Segment.py"
] | [
"# Databricks notebook source\nimport os\nfrom pprint import pprint\nimport pandas as pd\nimport numpy as np\nimport pyspark.sql.functions as F\nfrom pyspark.sql import Window\nimport sys\nimport datetime\n\n# COMMAND ----------\n\ndef show_df_rounded(df, places=4, rows=20):\n dtypes = {k: v for k, v in df.dtype... | [
[
"pandas.DataFrame",
"pandas.date_range"
]
] | [
{
"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": []
}
] |
mingbowan/audio | [
"703285275d68f68579915b20905fb96036a65903"
] | [
"test/test_functional.py"
] | [
"from __future__ import absolute_import, division, print_function, unicode_literals\nimport math\nimport os\n\nimport torch\nimport torchaudio\nimport torchaudio.functional as F\nimport torchaudio.transforms as T\nimport pytest\nimport unittest\nimport common_utils\n\nfrom torchaudio.common_utils import IMPORT_LIBR... | [
[
"torch.jit.script",
"torch.linspace",
"torch.Size",
"torch.ones",
"numpy.allclose",
"torch.testing.assert_allclose",
"torch.zeros",
"torch.hamming_window",
"torch.randn",
"torch.sin",
"torch.tensor",
"numpy.ceil",
"torch.rand",
"torch.allclose",
"torch.g... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AtrejuArtax/aironsuit | [
"9246d3d12fdee3c07a7748a9e6bccca83a21deda"
] | [
"aironsuit/suit.py"
] | [
"import math\nimport os\nimport pickle\nimport tempfile\nimport warnings\nfrom inspect import getfullargspec\n\nimport hyperopt\nimport numpy as np\nimport pandas as pd\nfrom hyperopt import Trials, STATUS_OK, STATUS_FAIL\n\nfrom aironsuit.callbacks import init_callbacks, get_basic_callbacks\nfrom aironsuit.design.... | [
[
"numpy.concatenate",
"pandas.DataFrame",
"numpy.random.default_rng"
]
] | [
{
"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": []
}
] |
lqjlqj1997/GCN-AAE | [
"e1fc3b26f49469963cca84dafe9d35bf76d5e96c"
] | [
"data_gen/rotation.py"
] | [
"import math\nimport numpy as np\n\n\ndef rotation_matrix(axis, theta):\n \n \"\"\"\n Return the rotation matrix associated with counterclockwise rotation about\n the given axis by theta radians.\n \"\"\"\n \n if( ( np.abs(axis).sum() < 1e-6 ) or ( np.abs(theta) < 1e-6 ) ):\n return np.e... | [
[
"numpy.dot",
"numpy.abs",
"numpy.asarray",
"numpy.eye",
"numpy.linalg.norm",
"numpy.cos",
"numpy.sin",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
csarron/mmf | [
"50c325a39aa6797472b5a91b90da8e1afe3b559c"
] | [
"mmf/models/mmbt.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\n\n# MMBTModel, ModalEmbeddings is copied from [1]\n# as we have internal dependency on transformers v2.3.\n# These will be removed when we upgrade to package v2.5+.\n# [1]: https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_mmbt.py ... | [
[
"torch.nn.CrossEntropyLoss",
"torch.nn.Dropout",
"torch.ones",
"torch.cat",
"torch.nn.Linear",
"torch.jit.is_scripting",
"torch.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aabadie/joblib | [
"fe0799ad62cf7bc154c82c78406015e27e685991"
] | [
"joblib/pool.py"
] | [
"\"\"\"Custom implementation of multiprocessing.Pool with custom pickler.\n\nThis module provides efficient ways of working with data stored in\nshared memory with numpy.memmap arrays without inducing any memory\ncopy between the parent and child processes.\n\nThis module should not be imported if multiprocessing i... | [
[
"numpy.lib.stride_tricks.as_strided",
"numpy.asarray",
"numpy.byte_bounds"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BlackHC/causal-bald | [
"8dd526064bba005325dcc975386333c73004f9b1",
"8dd526064bba005325dcc975386333c73004f9b1"
] | [
"causal_bald/library/datasets/ihdp_cov.py",
"causal_bald/library/plotting.py"
] | [
"import torch\nimport pyreadr\nimport requests\nimport numpy as np\n\nfrom pathlib import Path\n\nfrom torch.utils import data\n\nfrom sklearn import preprocessing\nfrom sklearn import model_selection\n\n_CONTINUOUS_COVARIATES = [\n \"bw\",\n \"b.head\",\n \"preterm\",\n \"birth.o\",\n \"nnhealth\",\... | [
[
"numpy.hstack",
"numpy.asarray",
"torch.from_numpy",
"sklearn.model_selection.train_test_split",
"sklearn.preprocessing.StandardScaler",
"numpy.random.default_rng"
],
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.imread",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
modelarious/Artificial_Net_Simple | [
"13f9397f8f0854fba93dde4b6c4dc75e13724735"
] | [
"optimizationFuncs.py"
] | [
"import tensorflow as tf\nimport numpy as np\n\ndef check_Tests(A, B):\n #could be achieved with argmaxes as well\n if list in [type(A), type(B)]:\n return \"Instead of numpy array, received list\"\n \n if A.shape != B.shape:\n return \"Shapes of two inputs didn't match\"\n \n percen... | [
[
"tensorflow.nn.softmax_cross_entropy_with_logits",
"numpy.random.choice",
"numpy.arange",
"tensorflow.global_variables_initializer",
"numpy.argmax",
"numpy.equal",
"tensorflow.Session",
"tensorflow.train.AdamOptimizer",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
tardis-sn/tardisnuclear_old | [
"35d3b7508033b2b0069be7e04249c66c8fb1b1fa"
] | [
"tardisnuclear/models/base.py"
] | [
"import numpy as np\n\nfrom astropy.modeling import FittableModel, Parameter\nfrom astropy import units as u\nfrom pyne import nucname\nimport pandas as pd\n\nfrom tardisnuclear.ejecta import Ejecta, msun_to_cgs\n\nfrom tardisnuclear.nuclear_data import DecayRadiation\n\nmpc_to_cm = u.Mpc.to(u.cm)\n\nclass BaseEner... | [
[
"numpy.trapz",
"numpy.sum",
"pandas.DataFrame",
"pandas.concat"
]
] | [
{
"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": []
}
] |
MetOffice/forest-nwcsaf-regrid | [
"8ba055a71e584fa668df2e7d013c5cea37aa95a4"
] | [
"bin/tiling.py"
] | [
"\"\"\"Wrap forest.geo to make an easier interface\"\"\"\nimport numpy as np\nimport geo\n\n\ndef web_mercator(lons, lats):\n \"\"\"Similar to forest.geo.web_mercator but preserves array shape\"\"\"\n if (lons.ndim == 1):\n gx, _ = geo.web_mercator(\n lons,\n np.zeros(len(lons), d... | [
[
"numpy.ma.masked_invalid"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.13",
"1.16",
"1.9",
"1.18",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
palouda/DisasterResponse | [
"ca22017b54a224d0d716af3d31c4ee392d0e2993"
] | [
"app/run.py"
] | [
"import json\nimport plotly\nimport pandas as pd\n\nfrom nltk.stem import WordNetLemmatizer\nfrom nltk.tokenize import word_tokenize, sent_tokenize\nfrom nltk import pos_tag, word_tokenize\nimport nltk\n\nfrom sqlalchemy import create_engine\n\nfrom sklearn.base import BaseEstimator, TransformerMixin\n\nfrom flask ... | [
[
"pandas.read_sql_table",
"pandas.Series",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
OUnke/SpookyNet | [
"d57b1fc02c4f1304a9445b2b9aa55a906818dd1b"
] | [
"spookynet/modules/swish.py"
] | [
"import torch\nimport torch.nn as nn\n\n\nclass Swish(nn.Module):\n \"\"\"\n Swish activation function with learnable feature-wise parameters:\n f(x) = alpha*x * sigmoid(beta*x)\n sigmoid(x) = 1/(1 + exp(-x))\n For beta -> 0 : f(x) -> 0.5*alpha*x\n For beta -> inf: f(x) -> max(0, alpha*x)\n\n ... | [
[
"torch.nn.init.constant_",
"torch.sigmoid",
"torch.Tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Nirj2004/face_recognition_nirj2004 | [
"5ef602ad330243d7ad72f8c9f6690c8926415585"
] | [
"face-recogniton-develop/api.py"
] | [
"# -*- coding: utf-8 -*-\r\n\r\nimport PIL.Image\r\nimport dlib\r\nimport numpy as np\r\nfrom PIL import ImageFile\r\n\r\ntry:\r\n import face_recognition_models\r\nexcept Exception:\r\n print(\"Please install `face_recognition_models` with this command before using `face_recognition`:\\n\")\r\n print(\"pi... | [
[
"numpy.array",
"numpy.linalg.norm",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
inbalbeka/attention-analysis | [
"4044a5fb914c49c41de2a536d33702d60e6e0a3d"
] | [
"extract_attention.py"
] | [
"\"\"\"Runs BERT over input data and writes out its attention maps to disk.\"\"\"\n\nimport argparse\nimport os\nimport numpy as np\nimport tensorflow as tf\n\nfrom bert import modeling\nfrom bert import tokenization\nimport bpe_utils\nimport utils\n\n\nclass Example(object):\n \"\"\"Represents a single input sequ... | [
[
"tensorflow.train.init_from_checkpoint",
"tensorflow.placeholder",
"tensorflow.global_variables_initializer",
"tensorflow.Session",
"tensorflow.trainable_variables",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
PeterRabbit95/pymoca | [
"e10d7b14538062a144026be2c6fa3e0d1a8cfad3"
] | [
"src/pymoca/tree.py"
] | [
"#!/usr/bin/env python\n\"\"\"\nTools for tree walking and visiting etc.\n\"\"\"\n\nfrom __future__ import print_function, absolute_import, division, unicode_literals\n\nimport copy # TODO\nimport logging\nimport sys\nfrom collections import OrderedDict\nfrom typing import Union, Iterable\nimport os\n\nimport nump... | [
[
"numpy.all"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
intbio/seqaln_tools | [
"9576856e65d2dfd5d268c885b30955195b3fe96b"
] | [
"examples/seq_filtering/compare_seqs.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jun 13 13:09:07 2013\n\n@author: alexeyshaytan\nHere we compare yeast and Xenopus (1kx5) histone sequences.\nThe gis are:\n 1kx5 yeast\nH3 27573749 6319482\nH4 27573750 6319481\nH2A 27573751 398366187\nH2B 27573752 6319471\n\n\"\"\"\n\nimport sys\nimport os... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
matt-stack/PhysiCell_GPU | [
"e1e07680b99f5c624448f2dfa607cfdbf0176c2f"
] | [
"beta/cell_tracks.py"
] | [
"#\n# cell_tracks.py - plot 2-D cell tracks associated with PhysiCell .svg files\n#\n# Usage:\n# python cell_tracks.py <max # of .svg frames>\n# \n# Dependencies include matplotlib and numpy. We recommend installing the Anaconda Python3 distribution.\n#\n# Examples (run from directory containing the .svg files):\n... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show",
"matplotlib.pyplot.title",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
megvii-research/ICD | [
"fdda393088fa31ac6dc9ddbd7ec3e7008ea32ff4"
] | [
"megengine_release/layers/basic/functional.py"
] | [
"# -*- coding: utf-8 -*-\n# This repo is licensed under the Apache License, Version 2.0 (the \"License\")\n#\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n#\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS,... | [
[
"numpy.finfo"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
prakharverma/markovflow | [
"9b7fafc199dae2f7f3207c2945fd43f674386dc1",
"9b7fafc199dae2f7f3207c2945fd43f674386dc1",
"9b7fafc199dae2f7f3207c2945fd43f674386dc1",
"9b7fafc199dae2f7f3207c2945fd43f674386dc1"
] | [
"docs/notebooks/importance_weighted_vi.py",
"markovflow/posterior.py",
"markovflow/kernels/periodic.py",
"tests/unit/test_emission_model.py"
] | [
"# ---\n# jupyter:\n# jupytext:\n# cell_markers: '\"\"\"'\n# formats: ipynb,py:percent\n# text_representation:\n# extension: .py\n# format_name: percent\n# format_version: '1.3'\n# jupytext_version: 1.7.1\n# kernelspec:\n# display_name: Python 3\n# language: python\n#... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.constant",
"numpy.linspace",
"matplotlib.pyplot.subplots",
"numpy.cos",
"numpy.std",
"numpy.random.randn",
"numpy.mean",
"tensorflow.optimizers.Adam"
],
[
"tensorflow.TensorShape",
"tensorflow.nn.softmax",
"tensorflow.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
Imperssonator/afm-cnn | [
"67f757cb38cf595b32f768f26d4a6d646fbb1b36",
"67f757cb38cf595b32f768f26d4a6d646fbb1b36"
] | [
"scripts/clean_afm_db.py",
"mnist/mnist_cnn_train_multi_gpu.py"
] | [
"#!/usr/bin/env python\n\nimport os\nimport click\nimport pandas as pd\nimport numpy as np\n\n@click.command()\n@click.argument('orig_csv', type=click.Path())\n@click.argument('out_csv', type=click.Path())\ndef clean_afm_db(orig_csv, out_csv):\n \n df_mg = pd.read_csv(orig_csv) # open csv to dataframe\n d... | [
[
"pandas.read_csv"
],
[
"tensorflow.device",
"tensorflow.concat",
"tensorflow.train.ExponentialMovingAverage",
"tensorflow.train.AdamOptimizer",
"tensorflow.group",
"tensorflow.summary.scalar",
"tensorflow.add_n",
"tensorflow.contrib.slim.prefetch_queue.prefetch_queue",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
... |
VladyslavHladchenko/tf-pose-estimation | [
"5b87f5b0a207016dbe1721450dfc9e9f482bff02"
] | [
"show_gesture.py"
] | [
"import matplotlib.pyplot as plt\nfrom matplotlib.animation import FuncAnimation\nimport argparse\nimport pickle\n\nfig, ax = plt.subplots()\nln, = plt.plot([], [], 'ro')\n\n\ndef get_frames(filename):\n frames = []\n with open(filename, \"rb\") as file:\n while True:\n try:\n ... | [
[
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.plot",
"matplotlib.animation.FuncAnimation",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
michaelfaerber/scholarly-trends | [
"ea4a57a684bde4c7f7bd6858a2c9fa4197bb57c0"
] | [
"Sparsity Statistics/sparsity_stats_wiki.py"
] | [
"\"\"\" This module is used to create a data frame with all WIKIPEDIA URLS which occur at least 100 times in the whole corpus (the\ncommon words which occur 50000 times or more are later discarded), gets sparsity stats\n\nUNCOMMENT and COMMENT relevant lines in drop_useless_phrases and remove_more_phrases_and_sort ... | [
[
"pandas.merge",
"pandas.read_pickle"
]
] | [
{
"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": []
}
] |
mayanks888/CenterTrack | [
"ea225eb64332aab9d7b803d976be2d63ade9c6d3"
] | [
"src/demo_mayank_onnx_prac.py"
] | [
"import torch\nimport torchvision\nimport torch.onnx\n# from models.model import create_model, load_model\nfrom lib.model.model import create_model, load_model\n# from model.model import create_model, load_model\n\ninput = torch.randn(1, 3, 512, 512)\n## mobilenetv2_10\n# model = create_model('mobilenetv2_10', head... | [
[
"torch.randn",
"torch.onnx.export"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nathanbegbie/COSMOS | [
"2185ee4d217d5982f5b2112b7efbde33a206ca0b"
] | [
"detectron2_changes/modeling/meta_arch/build.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\nimport torch\n\nfrom detectron2.utils.registry import Registry\n\nMETA_ARCH_REGISTRY = Registry(\"META_ARCH\") # noqa F401 isort:skip\nMETA_ARCH_REGISTRY.__doc__ = \"\"\"\nRegistry for meta-architectures, i.e. the whole model.\n\nThe registered object will be ca... | [
[
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mkondel/crypto-signal | [
"b9f176c945bcc46e88ed9f93e3e0ab61c6fec682"
] | [
"app/indicators/sma.py"
] | [
"\"\"\" SMA Indicator\n\"\"\"\n\nimport math\n\nimport pandas\nfrom talib import abstract\n\nfrom indicators.utils import IndicatorUtils\n\n\nclass SMA(IndicatorUtils):\n def analyze(self, historical_data, period_count=15,\n hot_thresh=None, cold_thresh=None, all_data=False):\n \"\"\"Pe... | [
[
"pandas.concat"
]
] | [
{
"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": []
}
] |
aseyboldt/pysundials-cffi | [
"ccdcd0fd0252285a5440c397619c57378a32d33a"
] | [
"sunode/solver.py"
] | [
"from typing import overload, Union, Optional, Callable, Dict, Any\nimport logging\nimport weakref\n\nimport numpy as np\nimport xarray as xr\n\nimport sunode\nfrom sunode.basic import CPointer, ERRORS, lib, ffi, check, check_ptr, Borrows, check_code\nfrom sunode.dtypesubset import _as_dict\nfrom sunode.matrix impo... | [
[
"numpy.array",
"numpy.zeros",
"numpy.broadcast_to"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
m7xedhoward/crankshaft | [
"ac02bf88807f0e952cab70c7ad9700d1ef8a527c"
] | [
"src/py/crankshaft/crankshaft/regression/glm/iwls.py"
] | [
"import numpy as np\nimport numpy.linalg as la\nfrom scipy import sparse as sp\nfrom scipy.sparse import linalg as spla\nfrom spreg.utils import spdot, spmultiply\nfrom .family import Binomial, Poisson\n\ndef _compute_betas(y, x):\n \"\"\"\n compute MLE coefficients using iwls routine\n\n Methods: p189, It... | [
[
"numpy.dot",
"numpy.sqrt",
"numpy.linalg.inv",
"scipy.sparse.csr_matrix",
"numpy.zeros"
]
] | [
{
"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"... |
srakrn/2017-204111 | [
"1985d4c73fabd5f08f54b922e73a9306e09c77a5"
] | [
"lib/python3.6/site-packages/mpl_toolkits/axes_grid1/parasite_axes.py"
] | [
"from __future__ import (absolute_import, division, print_function,\n unicode_literals)\n\nimport six\n\nimport warnings\n\nimport matplotlib\nrcParams = matplotlib.rcParams\nimport matplotlib.artist as martist\nimport matplotlib.transforms as mtransforms\nimport matplotlib.collections as mco... | [
[
"matplotlib.transforms.Bbox.union",
"matplotlib.axes.subplot_class_factory",
"numpy.arange",
"matplotlib.transforms.IdentityTransform",
"matplotlib.pyplot.gcf",
"matplotlib.pyplot.draw_if_interactive",
"numpy.meshgrid",
"matplotlib.transforms.blended_transform_factory",
"numpy.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ishihara1989/chainer | [
"4663aa25ffcac07f2d972b2eda59edf6314ea8cb",
"4663aa25ffcac07f2d972b2eda59edf6314ea8cb"
] | [
"chainer/functions/loss/softmax_cross_entropy.py",
"tests/chainer_tests/functions_tests/activation_tests/test_softplus.py"
] | [
"import numpy\nimport six\n\nimport chainer\nfrom chainer import cuda\nfrom chainer import function\nfrom chainer.functions.activation import log_softmax\nfrom chainer.utils import type_check\nfrom chainer import variable\n\n\ndef _broadcast_to(array, shape):\n if hasattr(numpy, \"broadcast_to\"):\n retur... | [
[
"numpy.rollaxis",
"numpy.expand_dims",
"numpy.maximum",
"numpy.arange",
"numpy.broadcast_to",
"numpy.broadcast_arrays",
"numpy.exp",
"numpy.empty"
],
[
"numpy.random.uniform",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dwief-usgs/pydrip | [
"84cebab0420ab47073569564482de534d61cdaf9"
] | [
"pydrip/drip_sources.py"
] | [
"\"\"\"Access dam removal data from sources.\n\nThis module uses ScienceBase and Figshare APIs to retrieve most current\nversions of source data for the Dam Removal Information Portal (DRIP).\nSource datasets include the USGS Dam Removal Science Database and the\nAmerican Rivers Dam Removal Database. Functions in ... | [
[
"numpy.isnan",
"pandas.read_csv",
"pandas.to_numeric",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Ruikang07/VIM-LPR-keras | [
"0ab5da1b673e41f9a492cd9fee7d886f61d6e0aa"
] | [
"AOLP_data_preprocess/prepare_npy_datafile_for_AOLP_LE.py"
] | [
"import os\r\nfrom os import path\r\nimport numpy as np\r\nfrom numpy import save\r\nimport cv2\r\n\r\n\r\nclass_dict = { '0':0,\r\n '1':1,\r\n '2':2,\r\n '3':3,\r\n '4':4,\r\n '5':5,\r\n '6':6,\r\n '7':7,\r\n ... | [
[
"numpy.array",
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
andreysay/CarND-Advanced-Lane-Lines | [
"57810c305ae0f89d4a8322f1a23c0f04c0c10a57"
] | [
"Thresholding.py"
] | [
"import cv2\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport numpy as np\n\ndef threshold_rel(img, lo, hi):\n vmin = np.min(img)\n vmax = np.max(img)\n \n vlo = vmin + (vmax - vmin) * lo\n vhi = vmin + (vmax - vmin) * hi\n return np.uint8((img >= vlo) & (img <= vhi)) * 25... | [
[
"numpy.absolute",
"numpy.min",
"numpy.power",
"numpy.uint8",
"numpy.max",
"numpy.zeros_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
danamoroz/WoundsKP2D | [
"77f8ea9d97afe5147f8c01e087c9223de6ad0cf9"
] | [
"scripts/eval_sift.py"
] | [
"# Example usage: python scripts/eval_sift.py --input_dir /data/datasets/kp2d/HPatches/\n\nimport argparse\nimport os\nimport pickle\nimport random\nimport subprocess\n\nimport cv2\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom termcolor import colored\nfrom torch.utils.data import DataLoader, Datas... | [
[
"torch.utils.data.DataLoader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aakepley/spectral-cube | [
"b93d1b4c9a6fb25bccda7b6ec45d7d2c6110b8f1"
] | [
"spectral_cube/tests/test_spectral_cube.py"
] | [
"from __future__ import print_function, absolute_import, division\n\nimport six\nimport operator\nimport itertools\nimport warnings\nimport mmap\nfrom distutils.version import LooseVersion\nimport sys\n\nimport pytest\n\nimport astropy\nfrom astropy.io import fits\nfrom astropy import units as u\nfrom astropy.wcs i... | [
[
"numpy.testing.assert_equal",
"numpy.log",
"numpy.random.random",
"numpy.isfinite",
"numpy.isnan",
"numpy.arange",
"numpy.median",
"numpy.indices",
"numpy.percentile",
"numpy.testing.assert_array_equal",
"numpy.testing.assert_almost_equal",
"numpy.all",
"numpy.n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Hamidreza3252/CarND-Behavioral-Cloning-P3 | [
"10eb66fbc5c8266fe43aa04aee6f8e460215df09"
] | [
"selfDrivingCarModules.py"
] | [
"import csv\nimport cv2\nimport numpy as np\nimport glob\nfrom sklearn import preprocessing\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelBinarizer\nimport tensorflow as tf\nimport re\nimport os\nfrom datetime import datetime\nimport sys\nimport shutil\nimport matplot... | [
[
"numpy.asarray",
"matplotlib.pyplot.subplots",
"sklearn.model_selection.train_test_split",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
apnkv/mondrian-forest | [
"17cb1cf68fcdef77dead37e15ce865c56a5bf82a"
] | [
"data_loaders.py"
] | [
"import h5py\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.datasets import load_svmlight_file\nfrom sklearn.preprocessing import MinMaxScaler\n\n\ndef rescale(X_train, X_test):\n scaler = MinMaxScaler()\n X_train = scaler.fit_transform(X_train)\n ... | [
[
"pandas.read_csv",
"sklearn.model_selection.train_test_split",
"numpy.array",
"sklearn.datasets.load_svmlight_file",
"sklearn.preprocessing.MinMaxScaler"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Jalilnkh/MU-Net | [
"fdb4948b04707b92f3fae19c757d18509701ea6c"
] | [
"MobV2-U-NetSSBC2020.py"
] | [
"import numpy as np\nfrom config import *\nfrom tensorflow.keras.callbacks import EarlyStopping\nimport cv2\nfrom glob import glob\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom tensorflow.keras.callbacks import ModelCheckpoint\nimport os\nfrom... | [
[
"matplotlib.pyplot.imshow",
"numpy.expand_dims",
"tensorflow.reduce_sum",
"numpy.squeeze",
"numpy.concatenate",
"tensorflow.numpy_function",
"tensorflow.keras.layers.Concatenate",
"tensorflow.keras.optimizers.Nadam",
"tensorflow.keras.layers.Conv2D",
"tensorflow.keras.layer... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
ZiQiangXie/Table-tennis-timing-action-positioning | [
"b0b9ad2ce08f0ab0a185a688c949c50b5125803e"
] | [
"tools/utils.py"
] | [
"# Copyright (c) 2020 PaddlePaddle 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 re... | [
[
"numpy.split",
"numpy.expand_dims",
"numpy.pad",
"numpy.asarray",
"numpy.arange",
"numpy.stack",
"pandas.DataFrame",
"numpy.concatenate",
"numpy.percentile",
"numpy.frombuffer",
"matplotlib.cm.ScalarMappable",
"numpy.argpartition",
"numpy.argsort",
"numpy.lo... | [
{
"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": []
}
] |
NeuralProsthesisLab/unlock | [
"0c4d95abdab288d3e657ca2db867b06f755f26ff"
] | [
"unlock/bci/acquire/file_signal.py"
] | [
"# Copyright (c) James Percent, Byron Galbraith and Unlock contributors.\n# All rights reserved.\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# 1. Redistributions of source code must retain the above copyr... | [
[
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NicholasDominic/The-NucleoNets | [
"83530c2e3bf746c08e9045aeff82435fca5162e1"
] | [
"NucleoNetV2/Proposed DNN Model (v2) - Evaluation.py"
] | [
"#!/usr/bin/env python\r\n# coding: utf-8\r\nimport torch\r\nfrom ast import literal_eval as lev\r\nfrom pandas import read_csv, DataFrame as df\r\nfrom sklearn.model_selection import train_test_split as tts\r\nfrom sklearn.metrics import mean_squared_log_error as msle\r\nfrom matplotlib import pyplot as plt\r\nfro... | [
[
"torch.nn.Softmax",
"torch.abs",
"pandas.read_csv",
"torch.load",
"torch.cat",
"torch.nn.Embedding",
"torch.nn.Linear",
"torch.nn.AvgPool2d",
"torch.nn.init.normal_",
"torch.no_grad",
"torch.nn.ReLU",
"torch.nn.MSELoss",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
mmalekzadeh/honest-but-curious-nets | [
"0493a2e3bf681990a2b1a0bb588dc45bc09740d7"
] | [
"hbcnets/trainers.py"
] | [
"import torch\nfrom torch import optim, nn\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader, TensorDataset\nfrom . import constants\nfrom . import utils \n\ndef get_data_loader(args, dataset, train=True):\n if constants.DATASET == \"utk_face\":\n if train:\n ... | [
[
"torch.nn.Softmax",
"torch.nn.CrossEntropyLoss",
"torch.nn.NLLLoss",
"torch.nn.functional.softmax",
"torch.Tensor",
"torch.nn.functional.log_softmax",
"torch.utils.data.TensorDataset",
"torch.utils.data.DataLoader",
"torch.tensor",
"torch.log"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
asalt/gpgrouper | [
"88c8d1187efd7d258b87abf615565e632c5bea9c"
] | [
"gpgrouper/subfuncts.py"
] | [
"# ===============================================================================#\nimport re, sys\nimport os\nimport csv\nimport numpy as np\nfrom datetime import datetime\nimport itertools\nfrom collections import namedtuple, defaultdict, OrderedDict, deque\nimport six\n\nif six.PY3:\n from configparser impor... | [
[
"numpy.isnan"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
20dzhong/pyScan | [
"219885f921b52ef70d9fbc5ad4c0f271e2755d0b"
] | [
"functions/utils.py"
] | [
"\nfrom functions.transform import *\nfrom skimage.filters import threshold_local\nimport imutils\n\nimport cv2\nimport numpy as np\n\n\ndef cancel_noise(img):\n # blur and make image binary\n img = cv2.GaussianBlur(img, (5, 5), 0)\n img = cv2.bilateralFilter(img, 9, 75, 75)\n img = cv2.medianBlur(img, ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bogiebro/funsor | [
"c15eaf7019e34c647630ed3da89001e620a972fa"
] | [
"test/examples/test_sensor_fusion.py"
] | [
"# Copyright Contributors to the Pyro project.\n# SPDX-License-Identifier: Apache-2.0\n\nfrom collections import OrderedDict\n\nimport pytest\nimport torch\n\nimport funsor.torch.distributions as dist\nimport funsor.ops as ops\nfrom funsor.cnf import Contraction\nfrom funsor.domains import bint, reals\nfrom funsor.... | [
[
"torch.randn",
"torch.eye",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jimgreen/Viscid | [
"41e19fee8576c5e3fa9c758c48731bc25e1db1b9"
] | [
"viscid/readers/athena.py"
] | [
"\"\"\" athena reader common stuff \"\"\"\n\nfrom __future__ import print_function\nimport re\nimport os\nfrom operator import itemgetter\n\nimport numpy as np\ntry:\n import numexpr\n _has_numexpr = True\nexcept ImportError:\n _has_numexpr = False\n\nfrom viscid import field\nfrom viscid import grid\nfrom... | [
[
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SenhuWong/PySPOD | [
"d37b415a6376b6a13d84ce6b25125bcaa64bdbb8"
] | [
"tests/test_earthquakes.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n'''\n\tThis file is subject to the terms and conditions defined in\n\tfile 'LICENSE.txt', which is part of this source code package.\n\n\tWritten by Dr. Gianmarco Mengaldo, May 2020.\n'''\n\n\n# python libraries\nimport os\nimport sys\nimport shutil\nimport subproce... | [
[
"numpy.ceil",
"numpy.array",
"numpy.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
abael/eli5 | [
"e2f2acfeac5f7a13f13b314e486069660287d7b0"
] | [
"tests/test_sklearn_treeinspect.py"
] | [
"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\n\nfrom sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n\nfrom eli5.formatters.trees import tree2text\nfrom eli5.sklearn.treeinspect import get_tree_info\n\n\ndef test_tree2dict():\n X = [[1, 1], [0, 2], [0, 3], [1, 3], [2, 3], [... | [
[
"sklearn.tree.DecisionTreeClassifier"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
L-Net-1992/ColossalAI | [
"b167258b6a29bcadcedc26322c7b0a3de29628a8"
] | [
"colossalai/tensor/colo_tensor.py"
] | [
"from .op_wrapper import _COLOSSAL_OPS\nfrom copy import copy\nimport torch\nfrom colossalai.tensor import TensorSpec\nfrom .const import TensorType\nfrom colossalai.tensor import distspec\nfrom colossalai.tensor.dist_spec_mgr import DistSpecManager\nfrom colossalai.tensor.distspec import _DistSpec\nfrom torch.over... | [
[
"torch.Tensor._make_subclass",
"torch.overrides.get_default_nowrap_functions",
"torch._C.DisableTorchFunction",
"torch.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ptannor/Audio2Face | [
"98b501791e74601a72466d191730904474b74f42"
] | [
"code/test/AiSpeech/lib/tensorflow/api_tensorflow.py"
] | [
"# encoding=utf-8\nimport os\nimport numpy as np\nimport scipy.io.wavfile as wavfile\n# from audiolazy.lazy_lpc import lpc\nfrom ctypes import *\n# import time\n\ndll = cdll.LoadLibrary(r'D:\\audio2bs\\audio2bs_v3_1015\\step3_train\\LPC.dll')\nwav_path = r'D:\\audio2bs\\audio2bs_v3_1015\\step3_train\\wav\\1015_3_16... | [
[
"numpy.hstack",
"numpy.expand_dims",
"numpy.ndarray",
"numpy.save",
"numpy.concatenate",
"numpy.hanning",
"numpy.array",
"numpy.zeros",
"scipy.io.wavfile.read"
]
] | [
{
"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"... |
p0l0satik/avatar-dataset-toolkit | [
"dc39c4a6790347e40f354bbd04b28639b06d12b9"
] | [
"convert/asl_to_ros.py"
] | [
"import numpy as np\nimport pandas as pd\nimport cv2\nimport argparse\n\nimport rosbag\nimport rospy\nfrom sensor_msgs.msg import Imu\nfrom cv_bridge import CvBridge, CvBridgeError\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n\n parser.add_argument('--input_path', help='Directory with A... | [
[
"numpy.isnan",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
hkfrei/pythonRemoteSensing | [
"c8681d859313ee5ad01e5b9753f8c43462268624"
] | [
"scripts/ndvi_diff.py"
] | [
"import numpy\nimport rasterio\nimport gdal\nprint('all modules imported')\n\n# path to the folder with the ndvi rasters\nbase_path = \"/Users/hk/Downloads/gaga/\"\n\n# shapefile with forest mask\nforest_mask = base_path + \"waldmaske_wgs84.shp\"\n\n# initialize the necessary rasters for the ndvi calculation.\nndvi... | [
[
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lgbouma/betty | [
"1541ca6cbcc91f34780012fbc514e0cded51a495"
] | [
"tests/test_fit_line.py"
] | [
"import pytest\nimport numpy as np, matplotlib.pyplot as plt, pymc3 as pm\nimport pickle, os, corner\n\nfrom os.path import join\nfrom pymc3.backends.tracetab import trace_to_dataframe\nfrom betty.paths import TESTRESULTSDIR, BETTYDIR\n\nn_cpu = os.cpu_count()\n\n@pytest.mark.skip(reason=\"This is a boiler-plate te... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.tight_layout",
"numpy.linspace",
"matplotlib.pyplot.title",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"numpy.random.normal",
"matplotlib.pyplot.close",
"matplotlib.pyplot.xlabel",
"m... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
amanwalia92/VisionChess | [
"c57219b3b7ce1fd98b27573aa0a8658ceabd0593"
] | [
"Testing Code/goodFeatures.py"
] | [
"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Nov 16 05:43:41 2016\n\n@author: aman\n\"\"\"\n\nimport numpy as np\nimport cv2\nfrom matplotlib import pyplot as plt\n\nfilename = '/home/aman/Pictures/Computer_Vision/Project/1.jpg'\n\nimg = cv2.imread(filename)\ngray = cv2.cvtColor(img,cv2.... | [
[
"numpy.int0"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mariomarine/tripadvisor-scraper | [
"13ac5c9e8ac1ec8e4dcf3ac23f16498e2ac49822"
] | [
"tripadvisor_scraper.py"
] | [
"'''\n A script to scrape restaurant reviews from tripadvisor.com or tripadvisor.com using Selenium.\n\n Author: Ryan Buchmeier <rwbleo@gmail.com>\n License: MIT\n\n Installation:\n - Install `selenium` and `pandas` using pip\n - Install PhantomJS or get Chrome- or Firefox webdriver... | [
[
"pandas.DataFrame.from_records"
]
] | [
{
"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": []
}
] |
chenwenhang/Short-term-Prediction-of-Demand-for-Ride-hailing-Services | [
"d0cc73643314d6daac4d7cd39adf8208b30989a3"
] | [
"Bike/TimeSeries/data-pre-processing.py"
] | [
"import pandas as pd\nimport datetime as dt\n\n\ndef time_transformation(obj_dt_time, time_interval):\n # transform the datetime.time object to time period\n \"\"\"\n :param obj_dt_time: datetime.time object\n :param time_interval: in minute, e.g. 15 minutes\n :return: time period in minutes index fr... | [
[
"pandas.read_csv",
"pandas.DatetimeIndex"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
Pandinosaurus/3D-ResNets-PyTorch | [
"540a0ea1abaee379fa3651d4d5afbd2d667a1f49"
] | [
"inference.py"
] | [
"import time\nimport json\nfrom collections import defaultdict\n\nimport torch\nimport torch.nn.functional as F\n\nfrom utils import AverageMeter\n\n\ndef get_video_results(outputs, class_names, output_topk):\n sorted_scores, locs = torch.topk(outputs,\n k=min(output_topk, len... | [
[
"torch.stack",
"torch.mean",
"torch.nn.functional.softmax",
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jgrewe/relacsed_nix | [
"8c5bf486018891baf886635608677faf2487fd48"
] | [
"rlxnix/plugins/efish/filestimulus.py"
] | [
"import os\nimport nixio\nimport logging\nimport numpy as np\nfrom numpy import full\nfrom scipy.interpolate import interp1d\n\nfrom .efish_ephys_repro import EfishEphys\nfrom ...utils.util import convert_path\n\n\nclass FileStimulus(EfishEphys):\n _repro_name = \"FileStimulus\"\n\n def __init__(self, repro_r... | [
[
"numpy.array",
"scipy.interpolate.interp1d"
]
] | [
{
"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"
... |
fontclos/pytorch-transformers | [
"ef555657591d89616a86f27a6f62461b0285475d"
] | [
"examples/run_generation_gutenberg.py"
] | [
"#!/usr/bin/env python3\n# coding=utf-8\n# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University 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 fil... | [
[
"torch.nn.functional.softmax",
"numpy.random.seed",
"torch.zeros",
"torch.manual_seed",
"torch.topk",
"torch.tensor",
"torch.no_grad",
"torch.sort",
"torch.cuda.manual_seed_all",
"torch.cuda.is_available",
"pandas.to_pickle",
"torch.cuda.device_count"
]
] | [
{
"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": []
}
] |
Wsine/filterfuzz | [
"bcbe4b144b875a23f866be1a3aae77bf81f5f5fc"
] | [
"datasets/gtsrb.py"
] | [
"# source: https://raw.githubusercontent.com/tomlawrenceuk/GTSRB-Dataloader/master/gtsrb_dataset.py\n\nimport torch\nimport os\nimport pandas as pd\nfrom torch.utils.data import Dataset\nimport numpy as np\nfrom PIL import Image\n\n\nclass GTSRB(Dataset):\n base_folder = 'GTSRB'\n\n def __init__(self, root_di... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
NightmareNyx/RL-Project | [
"ec77cb92194f54e85ffafee214982857bfe392f5"
] | [
"for_testing/test_agents/SomeOtherAgent/agent.py"
] | [
"import torch\nimport numpy as np\nfrom torch.distributions import Categorical\nfrom models import PolicyConv\n\n\nclass Agent(object):\n def __init__(self):\n self.train_device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n self.policy = PolicyConv(3, 128).to(self.train_device)\n se... | [
[
"numpy.expand_dims",
"torch.from_numpy",
"numpy.concatenate",
"torch.cuda.is_available",
"torch.device",
"torch.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DWvanderMeer/Copulas | [
"4a7bf29ba5bcb172fbaa412c77ea2bc507f822e4"
] | [
"tests/unit/univariate/test_base.py"
] | [
"from unittest import TestCase\n\nimport numpy as np\n\nfrom copulas.univariate.base import BoundedType, ParametricType, Univariate\nfrom copulas.univariate.beta import BetaUnivariate\nfrom copulas.univariate.gamma import GammaUnivariate\nfrom copulas.univariate.gaussian import GaussianUnivariate\nfrom copulas.univ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
FredHutch/find-cags | [
"079483eb2ce165180d9e4be54c4aa843b4b56328"
] | [
"ann_linkage_clustering/helpers.py"
] | [
"import boto3\nfrom collections import defaultdict\nimport gzip\nimport io\nimport json\nimport logging\nimport numpy as np\nimport os\nimport pandas as pd\nimport shutil\nimport sys\nimport time\nimport traceback\n\n\nclass TrackTrailing():\n\n def __init__(self, n=100, start=1000):\n self.cache = np.arr... | [
[
"pandas.Series",
"numpy.float16",
"pandas.DataFrame",
"numpy.max",
"numpy.mean"
]
] | [
{
"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": []
}
] |
ChangHeeHAN/PAIP2020 | [
"e42731ed2824f9f4a2e4a30f029dfea0f9efd25c"
] | [
"Main Task_MSI-H classification/dataset.py"
] | [
"import csv\nimport glob\nimport random\nfrom collections import Counter\n\nimport cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch.utils.data as data\nfrom torchvision import transforms\nfrom torchvision.utils import make_grid\nfrom imgaug import augmenters as iaa\n\nimport numpy as np\n\n\nc... | [
[
"matplotlib.pyplot.imshow",
"numpy.random.choice",
"numpy.squeeze",
"matplotlib.pyplot.get_cmap",
"numpy.concatenate",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.show",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DL-Metaphysics/DL-Fuzhiyuan | [
"b9d977a3b8f5301d951ab6dff8305d0987291086"
] | [
"multi_channel_bpr-master/src/multi_channel_bpr/run.py"
] | [
"\"\"\"\nEntry point for experiments on Bayesian Personalized Ranking\nfor Multi-Channel user feedback based on the paper\n\"Bayesian personalized ranking with multi-channel user feedback.\"\nby Loni, Babak, et al.\nin Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016.\n\"\"\"\nfrom datetime ... | [
[
"sklearn.model_selection.KFold"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jmamath/OOD-Generalization | [
"03fa834b63b522d2ba6d416d0da3c334b745a783"
] | [
"training_loops.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon May 10 07:01:52 2021\n\n@author: root\n\"\"\"\nfrom tqdm import trange\nimport torch \nimport numpy as np\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nfrom torch.autograd import grad\n\ndef get_device():\n if torch... | [
[
"numpy.random.beta",
"torch.Tensor",
"torch.zeros",
"torch.randperm",
"torch.sign",
"torch.min",
"torch.nn.functional.cross_entropy",
"torch.zeros_like",
"numpy.tile",
"torch.from_numpy",
"numpy.ones",
"torch.FloatTensor",
"torch.cuda.is_available",
"numpy.e... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aaristov/picasso | [
"08245bab8aebd8767468cd4944ef0ec73c76ba98"
] | [
"picasso/gui/filter.py"
] | [
"\"\"\"\n gui/filter\n ~~~~~~~~~~~~~~~~~~~~\n\n Graphical user interface for filtering localization lists\n\n :author: Joerg Schnitzbauer, 2015\n :copyright: Copyright (c) 2015 Jungmann Lab, Max Planck Institute of Biochemistry\n\"\"\"\n\n\nimport sys\nimport traceback\nfrom PyQt4 import QtCore, QtGu... | [
[
"matplotlib.backends.backend_qt4agg.NavigationToolbar2QT",
"matplotlib.pyplot.Figure",
"matplotlib.backends.backend_qt4agg.FigureCanvasQTAgg",
"numpy.isfinite",
"matplotlib.colors.LogNorm",
"matplotlib.pyplot.style.use"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cwm9cwm9/frc-docs | [
"87b501346ee998dbf770a02cbf143554876dae74"
] | [
"source/docs/software/advanced-controls/state-space/graph.py"
] | [
"import control as ct\nimport numpy as np\nimport scipy as sp\n\n\ndef lqr(*args, **kwargs):\n \"\"\"Solves for the optimal linear-quadratic regulator (LQR).\n For a continuous system:\n .. math:: xdot = A * x + B * u\n .. math:: J = \\\\int_0^\\\\infty (x^T Q x + u^T R u + 2 x^T N u) dt\n For a disc... | [
[
"numpy.linalg.solve",
"scipy.linalg.solve_continuous_are",
"matplotlib.pyplot.subplots",
"numpy.linalg.norm",
"scipy.linalg.solve_discrete_are",
"numpy.array",
"numpy.zeros",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.12",
"0.14",
"0.15"
],
"tensorflow": []
}
] |
hitliaomq/pyemto | [
"334903fbe626fa8b9f7e1f7a089b7c0e30edba46"
] | [
"pyemto/examples/refractroyDatabase.py"
] | [
"#!python\n#\n#Calculate the lattice constant and elastic constant of refractory HEAs\n\nimport os\nimport re\nimport shutil\nimport operator\nfrom itertools import combinations\nfrom pymatgen.core.periodic_table import Element\nimport scipy.constants\nfrom pyemto.latticeinputs.batch import batch_head\nfrom pyemto.... | [
[
"numpy.array",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DDD-FIT-CTU/CMOS-PLS | [
"21ac55b73c24a3f617159245a172b27deafe61e5"
] | [
"SBOX/MetaCentrum/drawHist.py"
] | [
"#!/usr/bin/python3\n#\n# Original code by Oliver W.:\n# https://stackoverflow.com/questions/26936094/python-load-data-and-do-multi-gaussian-fit\n#\n# Author: Jan Belohoubek\n# Date: 09/2020\n#\n# Plot fitted data\n#\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import optimize\n\nINDEX_INPUT... | [
[
"matplotlib.pyplot.legend",
"numpy.genfromtxt",
"matplotlib.pyplot.plot",
"scipy.optimize.leastsq",
"numpy.exp",
"numpy.array",
"matplotlib.pyplot.show"
]
] | [
{
"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"... |
nriesterer/cogsci-individualization | [
"da97bf0a6b53f440670e22ff591348f3d3fab230"
] | [
"analysis/plot_fig2_mReasoner_params.py"
] | [
"\"\"\" Produces figure 2, the distribution of mReasoner parameters.\n\n\"\"\"\n\nimport sys\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n\n# Parse command line arguments\nif len(sys.argv) != 2:\n print('usage: python3 plot_fig2_mReasoner_params.py <mReaso... | [
[
"pandas.merge",
"matplotlib.pyplot.tight_layout",
"numpy.linspace",
"numpy.arange",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"pandas.DataFrame",
"numpy.round",
"matplotlib.pyplot.show"
]
] | [
{
"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": []
}
] |
Infernolia/Computational_Statistics | [
"743d5b413d938480437b9a1a590e76925f7a506f"
] | [
"amin_function.py"
] | [
"import numpy as np\r\n\r\n\r\na = np.array(\r\n [\r\n [50,10,30],\r\n [60,5,40],\r\n [70,80,90]\r\n ]\r\n )\r\n\r\n# return minimum element from all the dimension\r\nprint(np.amin(a))\r\n\r\n#minimum from each column (axis=0 means colu... | [
[
"numpy.amin",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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