repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
codeskipper/radon-report | [
"91d51dc8ac968af0cff5eacf1753b7e7b58fcd37"
] | [
"radon-report.py"
] | [
"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\n'''\n radon-report.py\n\n Calculate and report radon statistics from measurements\n Works with exported raw data in csv format from Airthings Wave sensors\n Follows Norwegian guidelines as described in https://dsa.no/radon/slik-maler-du-radon#\n'''\n\nimpo... | [
[
"matplotlib.pyplot.gca",
"pandas.read_csv",
"pandas.to_datetime",
"matplotlib.pyplot.savefig",
"pandas.Timedelta",
"matplotlib.pyplot.show",
"matplotlib.pyplot.style.use"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
XinLuETH/characterization-of-response-matrix | [
"70b9d22d050d855214d080ba8f5155f977f5d1cd"
] | [
"measure_response_matrix/core/pco_gui.py"
] | [
"__author__ = 'Polychronis Patapis'\r\nfrom PyQt4 import QtCore, QtGui\r\nfrom core.pco_definitions import PixelFly\r\nfrom threading import Thread\r\nimport os, time, sys, pickle\r\nimport pyqtgraph as pg\r\nfrom astropy.io import fits\r\nimport numpy as np\r\nimport matlab.engine\r\nfrom queue import Empty\r\nimp... | [
[
"numpy.log",
"numpy.max",
"numpy.std",
"numpy.average",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nithinsubbiah/iree | [
"719856b9f45a5e3685a37bd87db62081a53cb4f9"
] | [
"runtime/bindings/python/tests/system_api_test.py"
] | [
"# Copyright 2019 The IREE Authors\n#\n# Licensed under the Apache License v2.0 with LLVM Exceptions.\n# See https://llvm.org/LICENSE.txt for license information.\n# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception\n\n# pylint: disable=unused-variable\n\nimport logging\nimport os\nimport re\nimport tempfile\... | [
[
"numpy.array",
"numpy.testing.assert_allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
escorciav/deep-action-proposals | [
"c14f512febc1abd0ec40bd3188a83e4ee3913535"
] | [
"daps/utils/test/test_segment.py"
] | [
"import unittest\n\nimport numpy as np\n\nimport daps.utils.segment as segment\n\n\nclass test_segment_utilities(unittest.TestCase):\n def test_format(self):\n a = np.array([[10, 3]])\n res = np.array([[10, 12]])\n np.testing.assert_array_equal(segment.format(a, 'd2b'), res)\n\n def test_... | [
[
"numpy.array",
"numpy.random.rand",
"numpy.may_share_memory"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ChristopherHaynes/Atari-2600-Deep-Learning-Agent | [
"02ccd83701a4eeb7af160b0ff2cdb258a2338048"
] | [
"UnitTests.py"
] | [
"import unittest\nimport tensorflow as tf\nimport numpy as np\n\nfrom DeepQNetwork import DeepQnetwork\nfrom ExperienceReplay import ExperienceReplay\nfrom PreProcessor import PreProcessor\nfrom ResultsRecorder import ResultsRecorder\n\n# Test the functionality of the Deep Q Network\nclass TestDQN(unittest.TestCase... | [
[
"numpy.ones",
"tensorflow.global_variables_initializer",
"tensorflow.Session",
"numpy.array",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
xj361685640/meshio-1 | [
"16d330809919f828e01e60809f386400a72620f1"
] | [
"src/meshio/abaqus/_abaqus.py"
] | [
"\"\"\"\nI/O for Abaqus inp files.\n\"\"\"\nimport pathlib\nfrom itertools import count\n\nimport numpy as np\n\nfrom ..__about__ import __version__\nfrom .._common import num_nodes_per_cell\nfrom .._exceptions import ReadError\nfrom .._files import open_file\nfrom .._helpers import register_format\nfrom .._mesh im... | [
[
"numpy.concatenate",
"numpy.arange",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hse-ml-da/drink_cider | [
"96e76c8f1429b7776b3b9055ed98b4835dd2b6a9"
] | [
"src/api/dialogue.py"
] | [
"from logging import getLogger\nfrom os.path import join, exists\nfrom typing import Dict\n\nimport torch\nfrom transformers import AutoTokenizer\n\n\nclass Dialogue:\n __path_to_model = join(\"src\", \"resources\", \"dialogue_model_weights.pth\")\n __n_context_tokens = 512\n\n def __init__(self):\n ... | [
[
"torch.no_grad",
"torch.cat",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Watemlifts/covid19-dashboard | [
"cf5e15b2bd64131f268565bd669bc55eb78c6bd5"
] | [
"_notebooks/covid_helpers.py"
] | [
"import functools\nimport os\nimport re\nfrom typing import Tuple, List\nfrom urllib import request\n\nimport numpy as np\nimport pandas as pd\nimport altair as alt\n\ndata_folder = (os.path.join(os.path.dirname(__file__), 'data_files')\n if '__file__' in locals() else 'data_files')\n\nCOL_REGION = 'C... | [
[
"numpy.dot",
"pandas.merge",
"pandas.read_csv",
"numpy.ones_like",
"pandas.Series",
"numpy.linspace",
"pandas.concat",
"pandas.to_datetime",
"pandas.DataFrame",
"pandas.set_option",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MilesCranmer/Sapsan | [
"4d21954baf196ede2d4dafc765aed98a0cfca21b"
] | [
"sapsan/lib/estimator/cnn/cnn3d_estimator.py"
] | [
"\"\"\"\nSpacial 3d encoder estimator\n\n - uses pytorch_estimator for backend\n\nFor example see sapsan/examples/cnn_example.ipynb\n\"\"\"\nimport json\nimport numpy as np\n\nimport torch\n\nfrom sapsan.core.models import EstimatorConfig\nfrom sapsan.lib.estimator.torch_backend import TorchBackend\nfrom sapsan.... | [
[
"torch.nn.SmoothL1Loss",
"torch.optim.lr_scheduler.ReduceLROnPlateau",
"torch.nn.Linear",
"torch.nn.MaxPool3d",
"torch.nn.Conv3d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
abatula/yellowbrick | [
"b1deb4cbf78e57f9c31b895b16f73c35c9b06936"
] | [
"yellowbrick/__init__.py"
] | [
"# yellowbrick\n# A suite of visual analysis and diagnostic tools for machine learning.\n#\n# Author: Rebecca Bilbro <rbilbro@districtdatalabs.com>\n# Created: Wed May 18 10:46:33 2016 -0400\n#\n# Copyright (C) 2016 District Data Labs\n# For license information, see LICENSE.txt\n#\n# ID: __init__.py [0c5ba04] be... | [
[
"matplotlib.rcParams.copy"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jiazheng-xing/Swin_Multimodal | [
"7bc41977fe7d8d4f0091852c63a6a32a0fada0fb"
] | [
"modules/V1-vilt.py"
] | [
"# Code for \"ActionCLIP: ActionCLIP: A New Paradigm for Action Recognition\"\n# arXiv:\n# Mengmeng Wang, Jiazheng Xing, Yong Liu\n\nfrom re import S\nimport torch\nfrom torch import nn\nfrom collections import OrderedDict\nfrom torch.nn.modules import dropout\nfrom torch.nn.utils.rnn import pad_packed_sequence, pa... | [
[
"torch.nn.functional.softmax",
"torch.cat",
"torch.zeros",
"torch.nn.Embedding",
"torch.multinomial",
"torch.no_grad",
"torch.full_like",
"torch.nn.Dropout",
"torch.ones",
"torch.nn.MultiheadAttention",
"torch.sqrt",
"torch.eq",
"torch.arange",
"torch.sigmoi... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
EMBEDDIA/media_eval_vctm | [
"17111065f4a4435485d018cf81586a00cea8afff"
] | [
"make_submission.py"
] | [
"import argparse\nimport pandas as pd\nfrom tqdm import tqdm\nfrom PIL import Image\nimport numpy as np\nfrom contextualized_topic_models.datasets.dataset import CTMDataset\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom utils import load_model\nfrom sentence_transformers import SentenceTransformer, u... | [
[
"sklearn.metrics.pairwise.cosine_similarity",
"pandas.isnull"
]
] | [
{
"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": []
}
] |
playgrdstar/primer_pythonblender | [
"733170a23eadd14c18c0458c3400b0fa83055bcf"
] | [
"metashapes.py"
] | [
"import bpy\nimport math\nimport random \nimport numpy as np\n\n# Functions to compute grid points for a sphere, pyramid and square \n\ndef sphere(n,r):\n theta =(math.pi)/n\n phi = (2*math.pi)/n\n\n vertices = []\n\n for stack in range(n):\n stackRadius = math.sin(theta*stack) * r\n for s... | [
[
"numpy.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Tuebel/PVN3D | [
"7f0551238135d3f71a06fa1df8137e466f55c8b8"
] | [
"pvn3d/datasets/linemod/linemod_dataset.py"
] | [
"#!/usr/bin/env python3\nimport os\nimport cv2\nimport pcl\nimport torch\nimport os.path\nimport numpy as np\nimport torchvision.transforms as transforms\nfrom PIL import Image\nfrom common import Config\nimport pickle as pkl\nfrom lib.utils.basic_utils import Basic_Utils\nimport yaml\nimport scipy.io as scio\nimpo... | [
[
"numpy.dot",
"numpy.clip",
"numpy.isnan",
"numpy.cos",
"numpy.random.shuffle",
"numpy.sin",
"numpy.concatenate",
"numpy.deg2rad",
"numpy.transpose",
"numpy.add",
"numpy.repeat",
"numpy.array",
"numpy.zeros",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HiroIshida/mohou | [
"05fbf1f186f32d139423e3679feab73a27b7d6a4"
] | [
"mohou/propagator.py"
] | [
"from typing import List, Optional\n\nimport numpy as np\nimport torch\n\nfrom mohou.constant import CONTINUE_FLAG_VALUE\nfrom mohou.encoding_rule import EncodingRule\nfrom mohou.model import LSTM\nfrom mohou.types import ElementDict, TerminateFlag\n\n\nclass Propagator:\n lstm: LSTM\n encoding_rule: Encoding... | [
[
"numpy.vstack",
"torch.from_numpy",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
icr-ctl/cougarvision | [
"8b5190ae0f59f2e0fe19ddba3c3d5f680948ee5a"
] | [
"stream_detect.py"
] | [
"import sys\nimport threading\nimport time \nimport humanfriendly\nimport json \nimport uuid\nimport warnings\nimport logging\n\nimport yaml\nimport numpy as np\nimport torch \nfrom torchvision import transforms\nimport cv2 \nfrom PIL import Image\nimport cougarvision_utils.alert as alert_util\nimport cougarvision_... | [
[
"torch.jit.load",
"torch.softmax",
"numpy.asarray",
"torch.no_grad",
"torch.topk"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
n-jun-k2/GPU-train | [
"ee6b93147727b5c6ed1c8a207d86f94b73105f71"
] | [
"Docker/python/packages/utils/anomaly_scores.py"
] | [
"import numpy as np\nimport pandas as pd\n\ndef anormaly_scores(original_df: pd.DataFrame, reduced_df: pd.DataFrame):\n \"\"\"異常スコア関数\n\n Args:\n - original_df:元の特徴量ベクトル\n - reduced_df:次元削減されたベクトル\n\n return:\n - loss:再構成誤差（0 ~ 1）\n \"\"\"\n loss = np.sum( (np... | [
[
"numpy.max",
"numpy.array",
"pandas.Series",
"numpy.min"
]
] | [
{
"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": []
}
] |
hyperionfalling/final_assignment | [
"b6c4d7bada3c4da014af8dbf86c1b476877f670e"
] | [
"T3Dtestnet.py"
] | [
"\"\"\"\n-*- coding:utf-8 -*-\n@Time :2019/12/24 上午10:14\n@Author :wts\n@File :3Dtestnet.py\n@Version：1.0\n\"\"\"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom utils import Mish\nfrom net import DenseConv3DDownProjectionUnit, DenseConv3DUpProjectionUnit, PixelShuffle3D, NonLocalBlo... | [
[
"torch.tensor",
"torch.nn.Conv3d",
"torch.nn.Upsample",
"torch.rand",
"torch.cuda.is_available",
"torch.nn.init.kaiming_normal_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dsperax/dasxlib | [
"dd4b8e639631cc46ea8913a2fc91cf10911d199e"
] | [
"dasxlib/layers.py"
] | [
"#As redes neurais são feitas de camadas(layers). Cada uma\n#dessas camadas precisa passar a entrada (forward) e propagar gradientes\n#para tras(backward). Ex -> uma rede neural pode ter o seguinte formato:\n# inputs -> Linear -> Tanh -> Linear -> output\nfrom typing import Callable, Dict\n\nimport numpy as np\n\nf... | [
[
"numpy.tanh",
"numpy.random.randn",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vpeopleonatank/OBBDetection | [
"86fb098d8d2ff3fc3cc447714d89a44c0939614a"
] | [
"setup.py"
] | [
"#!/usr/bin/env python\nimport os\nimport subprocess\nimport time\nfrom setuptools import find_packages, setup\n\nimport torch\nfrom torch.utils.cpp_extension import (BuildExtension, CppExtension,\n CUDAExtension)\n\n\ndef readme():\n with open('README.md', encoding='utf-8')... | [
[
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rlebras/pytorch-pretrained-BERT | [
"002e0f88e7533846a6d3b80df1268f270524ff9d"
] | [
"pytorch_pretrained_bert/modeling.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/l... | [
[
"torch.nn.Softmax",
"torch.nn.Dropout",
"torch.sigmoid",
"torch.ones",
"torch.nn.CrossEntropyLoss",
"torch.load",
"torch.zeros",
"torch.sqrt",
"torch.zeros_like",
"torch.nn.Embedding",
"torch.nn.Tanh",
"torch.nn.Linear",
"torch.matmul",
"torch.arange",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
simonsmh/www | [
"1741545e636540b9eb250840347f091082fe301a"
] | [
"api/yolo_minimal/detect.py"
] | [
"import cv2\nimport numpy as np\nimport torch\n\nfrom .models import Darknet\nfrom .utils import (\n letterbox,\n non_max_suppression,\n scale_coords,\n get_file_location,\n)\n\n\ndef detect_init(cfg=\"yolov3-spp-buoy.cfg\", weights=\"best_buoy.pt\", img_size=512):\n cfg, weights = get_file_location(... | [
[
"torch.load",
"numpy.ascontiguousarray",
"torch.from_numpy",
"numpy.frombuffer",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RichardYang40148/pyACA-1 | [
"870d100ed232cca5a890570426116f70cd0736c8",
"870d100ed232cca5a890570426116f70cd0736c8",
"870d100ed232cca5a890570426116f70cd0736c8"
] | [
"pyACA/PitchSpectralHps.py",
"pyACA/ToolGammatoneFb.py",
"pyACA/computeNoveltyFunction.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\ncomputes the maximum of the Harmonic Product Spectrum\n\n Args:\n X: spectrogram (dimension FFTLength X Observations)\n f_s: sample rate of audio data\n\n Returns:\n f HPS maximum location (in Hz)\n\"\"\"\n\nimport numpy as np\n\n \ndef PitchSpectralHps(X, f_s): \n ... | [
[
"numpy.arange",
"numpy.zeros"
],
[
"numpy.log",
"numpy.sqrt",
"numpy.arange",
"numpy.cos",
"numpy.sin",
"scipy.signal.lfilter",
"numpy.exp",
"numpy.zeros"
],
[
"scipy.signal.filtfilt",
"numpy.abs",
"numpy.sqrt",
"scipy.signal.find_peaks",
"numpy.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
... |
zfit/tfphasespace | [
"7131fbd687cb5dd1ce53fd4a590dcfd5d1af572f"
] | [
"phasespace/fromdecay/genmultidecay.py"
] | [
"from __future__ import annotations\r\n\r\nimport itertools\r\nfrom collections.abc import Callable\r\n\r\nimport tensorflow as tf\r\nimport tensorflow.experimental.numpy as tnp\r\nfrom particle import Particle\r\n\r\nfrom phasespace import GenParticle\r\n\r\nfrom .mass_functions import DEFAULT_CONVERTER\r\n\r\n\r\... | [
[
"tensorflow.experimental.numpy.log",
"tensorflow.cast",
"tensorflow.experimental.numpy.max",
"tensorflow.unique_with_counts",
"tensorflow.experimental.numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tom-gall/tvm-bench | [
"547f574c2ce065f26d3d66ae78587cc82d0010a0",
"547f574c2ce065f26d3d66ae78587cc82d0010a0"
] | [
"mobilenet-v2-1.0-acl-float.py",
"mobilenet-v3-large-float.py"
] | [
"import os\nimport numpy as np\nimport tvm\nfrom PIL import Image\nfrom tvm import te\nfrom tvm.contrib import graph_executor\nfrom tvm import relay\nfrom tvm.runtime import container\nfrom tvm.runtime import vm as vm_rt\nfrom tvm.relay import testing\nfrom tvm.relay import vm\nfrom tvm.relay.op.contrib import arm_... | [
[
"numpy.std",
"numpy.mean"
],
[
"numpy.std",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KianPardoe/RocketPy | [
"d3d835fa7fd71fea636132616b2eea8917729875"
] | [
"rocketpy/Rocket.py"
] | [
"# -*- coding: utf-8 -*-\n\n__author__ = \"Giovani Hidalgo Ceotto, Franz Masatoshi Yuri\"\n__copyright__ = \"Copyright 20XX, Projeto Jupiter\"\n__license__ = \"MIT\"\n\nimport re\nimport math\nimport bisect\nimport warnings\nimport time\nfrom datetime import datetime, timedelta\nfrom inspect import signature, getso... | [
[
"numpy.log",
"numpy.radians",
"numpy.sqrt",
"numpy.arctan",
"numpy.arcsin",
"numpy.cos",
"numpy.sin",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ChenglongChen/seq2seq-keyphrase-pytorch | [
"942b480a3d54069d35c65c84b35d970efa1660ff"
] | [
"utils.py"
] | [
"from os.path import join, dirname\nimport numpy as np\nimport time\nimport sys,logging\nimport matplotlib\nmatplotlib.use('agg')\nimport matplotlib.pyplot as plt\nimport time\n\ndef time_usage(func):\n def wrapper(*args, **kwargs):\n beg_ts = time.time()\n retval = func(*args, **kwargs)\n e... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"numpy.asarray",
"matplotlib.use",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"numpy.std",
"numpy.log10",
"numpy.mean",
"matplotlib.pyplot.fill_b... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
OCHA-DAP/pa-covid-dataviz | [
"9d738c536945ba1060bb4f607dd8cdcb7bf4f2b2"
] | [
"run.py"
] | [
"import argparse\nimport json\nimport logging\nimport sys\nfrom os import getenv\nfrom os.path import join\nfrom shutil import rmtree\n\nimport pygsheets\nfrom google.oauth2 import service_account\n\nimport pandas as pd\nfrom hdx.facades.keyword_arguments import facade\nfrom hdx.hdx_configuration import Configurati... | [
[
"pandas.ExcelWriter"
]
] | [
{
"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": []
}
] |
Dog0320/BERT-Text-Classification | [
"60a1b54b4a23fabf3765077dfc4f1d4a3a3ededf"
] | [
"evaluate.py"
] | [
"import argparse\nimport os\n\nimport numpy as np\nimport torch\nfrom torch.utils.data import DataLoader\nfrom tqdm import tqdm\nfrom transformers import BertTokenizer, BertConfig\n\nfrom model import Model\nfrom utils.data_utils import ClassificationDataset, glue_processor, prepare_data\n\ndevice = torch.device('c... | [
[
"numpy.random.seed",
"torch.load",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.no_grad",
"torch.cuda.manual_seed_all",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KayleighRutherford/bcbio-nextgen | [
"9be442305a54c601e4d3f5c7b09d8f9835c677fd"
] | [
"bcbio/install.py"
] | [
"\"\"\"Handle installation and updates of bcbio-nextgen, third party software and data.\n\nEnables automated installation tool and in-place updates to install additional\ndata and software.\n\"\"\"\nfrom __future__ import print_function\nimport argparse\nimport collections\nimport contextlib\nimport datetime\nimpor... | [
[
"matplotlib.use",
"matplotlib.matplotlib_fname"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rsumner33/numba | [
"67de7f12300fb17949f1120df5b7bb1e88196abd"
] | [
"numba/tests/test_svml.py"
] | [
"from __future__ import division, print_function\n\nimport math\nimport numpy as np\nimport subprocess\nimport numbers\nimport importlib\nimport sys\n\nfrom .support import TestCase, tag, override_env_config\nimport numba\nfrom numba.compiler import compile_isolated, Flags\nfrom numba import unittest_support as uni... | [
[
"numpy.testing.assert_almost_equal",
"numpy.float64",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
michalkouril/altanalyze | [
"e721c79c56f7b0022516ff5456ebaa14104c933b"
] | [
"visualization_scripts/umap_learn_single/distances.py"
] | [
"# Author: Leland McInnes <leland.mcinnes@gmail.com>\n#\n# License: BSD 3 clause\nimport numpy as np\nimport numba\n\n_mock_identity = np.eye(2, dtype=np.float64)\n_mock_ones = np.ones(2, dtype=np.float64)\n\n\n@numba.njit(fastmath=True)\ndef euclidean(x, y):\n \"\"\"Standard euclidean distance.\n\n ..math::\... | [
[
"numpy.sqrt",
"numpy.abs",
"numpy.arcsin",
"numpy.eye",
"numpy.cos",
"numpy.sin",
"numpy.ones",
"numpy.sum",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rahmanifard/lifelib | [
"27527d6c7b3076dc3b272729d99437dfd9cfa088"
] | [
"lifelib/projects/simplelife/plot_simplelife.py"
] | [
"\"\"\"\n:mod:`simplelife` cashflow\n==========================\n\nDraw a graph of liability cashflows of a simple whole life policy\n\n*Run this sample now!* |binder simplelife_demo|\n\n.. include:: /binderlinks.rst\n :start-after: Begin binder simplelife_demo\n :end-before: End binder simplelife_demo\n\n\"\"\... | [
[
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
asaiyed10/rasa_core | [
"a6b36e5d3979bea015737d829c21711653ddb893"
] | [
"tests/conftest.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport logging\nfrom builtins import str\nimport os\n\nimport matplotlib\nimport pytest\nfrom pytest_localserver.http import WSGIServer\n\nfrom rasa_core import... | [
[
"matplotlib.use"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
0x4849/cmput366 | [
"ed1def624f568057aeb38c4950cc43ed9dd5a855"
] | [
"Assignment3MountainCar/Qlearning-starter.py"
] | [
"#-------------------------------\n# MountainCar Lab - Programming Assignment 3\n# Author: Brad Harrison\n# Date: November 28th 2014\n#-------------------------------\n\nimport mountaincar\nfrom Tilecoder import numTilings, tilecode, numTiles\nimport Tilecoder\nimport numpy\nimport random\nimport pylab\n#from pylab... | [
[
"numpy.dot",
"numpy.std",
"numpy.argmax",
"numpy.mean",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
danakianfar/Examining-Cooperation-in-Visual-Dialog-Models | [
"2108bff9b95a4ccde5dbccd89d7146ec70cbc200"
] | [
"train.py"
] | [
"import torch\nimport argparse\nfrom IO import DataLoader\nimport models\nfrom torch.autograd import Variable\nimport math\nfrom random import shuffle\n\n\nparser = argparse.ArgumentParser()\n# Data option\nparser.add_argument('-input_img', type=str, default='data/data_img.h5')\nparser.add_argument('-input_data', t... | [
[
"torch.LongTensor",
"torch.save",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sweeneyyang/surge-examples | [
"dfc2bbab477495d67233350323cdf81492f733b1"
] | [
"global/setplot.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import print_function\n\nimport os\n\nimport numpy\nimport matplotlib.pyplot as plt\nimport datetime\n\nimport clawpack.visclaw.colormaps as colormap\nimport clawpack.visclaw.gaugetools as gaugetools\nimport clawpack.clawutil.data as clawutil\nimport clawpack... | [
[
"matplotlib.pyplot.gca",
"matplotlib.pyplot.title",
"matplotlib.pyplot.subplots_adjust"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
geloescht/LIP_JPPNet | [
"4e0064fe4e696a943bdca1d8ae244ac58ad00969"
] | [
"utils/utils.py"
] | [
"from PIL import Image\nimport numpy as np\nimport tensorflow as tf\nimport os\nimport scipy.misc\nfrom scipy.stats import multivariate_normal\nimport matplotlib.pyplot as plt\n\nn_classes = 20\n# colour map\nlabel_colours = [(0,0,0)\n # 0=Background\n ,(128,0,0),(255,0,0),(0,85,0),(17... | [
[
"tensorflow.train.get_checkpoint_state",
"tensorflow.squeeze",
"tensorflow.image.resize",
"tensorflow.one_hot",
"numpy.array",
"numpy.zeros",
"tensorflow.compat.v1.name_scope"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LucianaMarques/mlbench-benchmarks | [
"1310a782894a3aa664cfb813ca7590e69ad2c674"
] | [
"examples/resnet_cifar10_mpi.py"
] | [
"r\"\"\"Example of using mlbench : CIFAR10 + Resnet20 + MPI + GPU\n\n.. code-block:: bash\n mpirun -n 2 --oversubscribe python resnet_cifar10_mpi.py --run_id 1\n\"\"\"\nimport argparse\nimport json\nimport os\n\nfrom mlbench_core.controlflow.pytorch import TrainValidation\nfrom mlbench_core.controlflow.pytorch.c... | [
[
"torch.optim.lr_scheduler.MultiStepLR",
"torch.utils.data.DataLoader",
"torch.nn.modules.loss.CrossEntropyLoss",
"torch.distributed.get_rank",
"torch.distributed.get_world_size"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
trinhcon/scikit-learn | [
"14d8a9688b2d9d23094a69f3f05ab1f77316b43b"
] | [
"sklearn/neighbors/_kde.py"
] | [
"\"\"\"\nKernel Density Estimation\n-------------------------\n\"\"\"\n# Author: Jake Vanderplas <jakevdp@cs.washington.edu>\n\nimport numpy as np\nfrom scipy.special import gammainc\nfrom ..base import BaseEstimator\nfrom ..utils import check_random_state\nfrom ..utils.validation import _check_sample_weight, check... | [
[
"numpy.log",
"numpy.sqrt",
"scipy.special.gammainc",
"numpy.asarray",
"numpy.searchsorted"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ai-nick/PyTorch-NEAT | [
"825941ee7cfc2a7222e9ae1ed97422634e56ce4d"
] | [
"tests/test_cppn.py"
] | [
"# Copyright (c) 2018 Uber Technologies, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by appli... | [
[
"torch.full",
"numpy.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BloonCorps/IAP2022 | [
"11a481790878defad0d2974b81ae109168306077"
] | [
"MullerPotential/script/plot_data_and_noise.py"
] | [
"import numpy as np\nfrom functions import *\nfrom sys import exit\nimport argparse\nfrom scipy.interpolate import BSpline\nfrom scipy import optimize\nimport matplotlib as mpl\nfrom matplotlib import cm\nimport matplotlib.pyplot as plt\n\nargparser = argparse.ArgumentParser()\nargparser.add_argument(\"--alpha\", t... | [
[
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.xlim",
"numpy.random.rand",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.tick_params",
"numpy.vstack",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NikolayVaklinov10/Python_Challenges | [
"a1052e1d527004bbb6072a3cbc802065469f66ae"
] | [
"Numpy/Floor,Ceil_and_Rint.py"
] | [
"\"\"\"\nfloor\nThe tool floor returns the floor of the input element-wise.\nThe floor of is the largest integer where\n\n.\n\nimport numpy\n\nmy_array = numpy.array([1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9])\nprint numpy.floor(my_array) #[ 1. 2. 3. 4. 5. 6. 7. 8. 9.]\n\nceil\nThe tool ceil retu... | [
[
"numpy.ceil",
"numpy.set_printoptions",
"numpy.rint",
"numpy.floor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
obliviateandsurrender/pennylane | [
"b9db331b2e15a6b710fe7e595168fb1d9e7af4d6"
] | [
"pennylane/math/single_dispatch.py"
] | [
"# Copyright 2018-2021 Xanadu Quantum Technologies Inc.\r\n\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n\r\n# http://www.apache.org/licenses/LICENSE-2.0\r\n\r\n# Unles... | [
[
"scipy.linalg.block_diag",
"numpy.arange",
"numpy.cumsum",
"numpy.stack",
"numpy.any",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.12",
"0.10"
],
"tensorflow": []
}
] |
cool-RR/pandas | [
"80d37adcc3d9bfbbe17e8aa626d6b5873465ca98"
] | [
"pandas/core/indexing.py"
] | [
"from typing import Hashable, List, Tuple, Union\n\nimport numpy as np\n\nfrom pandas._libs.indexing import _NDFrameIndexerBase\nfrom pandas._libs.lib import item_from_zerodim\nfrom pandas.errors import AbstractMethodError\nfrom pandas.util._decorators import Appender\n\nfrom pandas.core.dtypes.common import (\n ... | [
[
"pandas.Series",
"numpy.asarray",
"pandas.core.indexers.check_array_indexer",
"numpy.ix_",
"pandas.core.dtypes.common.is_numeric_dtype",
"pandas.errors.AbstractMethodError",
"pandas.core.dtypes.concat.concat_compat",
"pandas.core.dtypes.common.is_iterator",
"pandas.core.dtypes.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Sirui-Xu/Arena | [
"6ae9e69d795919f8775ded5d2dd6d6b60ae8ffea"
] | [
"examples/rl_ppo/utils/logx.py"
] | [
"\"\"\"\n\nSome simple logging functionality, inspired by rllab's logging.\n\nLogs to a tab-separated-values file (path/to/output_directory/progress.txt)\n\n\"\"\"\nimport json\nimport joblib\nimport shutil\nimport numpy as np\nimport torch\nimport os.path as osp, time, atexit, os\nimport warnings\nfrom .mpi_tools ... | [
[
"numpy.concatenate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TerryZ7/bert-sentiment | [
"10c06a62372896e21cf119f488fe835b130723f3"
] | [
"train.py"
] | [
"import config\nimport dataset\nimport engine\nimport torch\nimport pandas as pd\nimport torch.nn as nn\nimport numpy as np\n\nfrom model import BERTBaseUncased\nfrom sklearn import model_selection\nfrom sklearn import metrics\nfrom transformers import AdamW\nfrom transformers import get_linear_schedule_with_warmup... | [
[
"pandas.read_csv",
"torch.utils.data.DataLoader",
"sklearn.model_selection.train_test_split",
"torch.device",
"numpy.array",
"sklearn.metrics.accuracy_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
dhaberl/ComputeSUV | [
"3b746e89a066fce9f57065baea402d4812d8b805"
] | [
"compute_suv.py"
] | [
"\"\"\"\nCOMPUTATION OF STANDARDIZED UPTAKE VALUES BASED ON BODY WEIGHT (SUVbw)\nReference:\n[1] https://github.com/mvallieres/radiomics/blob/master/Utilities/computeSUVmap.m\n\"\"\"\n\nfrom datetime import datetime\nimport numpy as np\nimport pydicom\nimport pandas as pd\n\n\ndef _assert_time_format(time):\n \"... | [
[
"numpy.asarray",
"numpy.log",
"numpy.isnan",
"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": []
}
] |
lilleswing/SMAC3 | [
"9890e0cdc4ce898de25fa00d794cbe0af83a09c2",
"9890e0cdc4ce898de25fa00d794cbe0af83a09c2"
] | [
"smac/epm/base_epm.py",
"test/test_intensify/test_successive_halving.py"
] | [
"import copy\nimport typing\nimport warnings\n\nimport numpy as np\n\nfrom sklearn.decomposition import PCA\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.exceptions import NotFittedError\n\nfrom smac.configspace import ConfigurationSpace\nfrom smac.utils.constants import VERY_SMALL_NUMBER\nfrom smac... | [
[
"numpy.hstack",
"numpy.sum",
"numpy.isnan",
"numpy.nan_to_num",
"numpy.tile",
"numpy.mean",
"numpy.zeros",
"sklearn.decomposition.PCA",
"sklearn.preprocessing.MinMaxScaler"
],
[
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sinakm/AnomalyDetector | [
"68a0a4d7de4fdc34181b79a8267ab24d8001580c"
] | [
"cycle_detector/train.py"
] | [
"# ------------------------------------------ This app is a cycle detector --------------------------------------------\r\n# Simple API. JSON Request with following format:\r\n# {\r\n# 'year': \"2020\",\r\n# 'month': \"12\",\r\n# 'day': \"24\",\r\n\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nfro... | [
[
"tensorflow.keras.models.load_model",
"tensorflow.keras.callbacks.ModelCheckpoint",
"numpy.abs",
"sklearn.model_selection.train_test_split",
"numpy.max",
"tensorflow.keras.optimizers.Adam",
"numpy.array",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
AlanGanem/scikit-density | [
"87b3a9793f91d2c3a2845019c0dd93848d998f9d"
] | [
"skdensity/ensemble.py"
] | [
"# AUTOGENERATED! DO NOT EDIT! File to edit: notebooks/01_ensemble.ipynb (unless otherwise specified).\n\n__all__ = ['QuantileCalibrator', 'identity_func', 'HistogramEstimator', 'IDENTITY_TRANSFORMER', 'minkowski_similarity',\n 'ClassificationKernelEstimator', 'expected_likelihood', 'inverese_log_node_var... | [
[
"sklearn.utils.class_weight.compute_sample_weight",
"numpy.take",
"numpy.linspace",
"sklearn.preprocessing.QuantileTransformer",
"numpy.around",
"numpy.concatenate",
"numpy.mean",
"numpy.digitize",
"numpy.exp",
"numpy.histogram",
"numpy.where",
"sklearn.preprocessin... | [
{
"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"
... |
mihaic/psyneulink | [
"3d2fc3117c82bccc92fc585add330b0f9b35c830",
"3d2fc3117c82bccc92fc585add330b0f9b35c830"
] | [
"psyneulink/components/projections/modulatory/learningprojection.py",
"psyneulink/components/mechanisms/processing/defaultprocessingmechanism.py"
] | [
"# Princeton University licenses this file to You under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License. You may obtain a copy of the License at:\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to... | [
[
"numpy.diag",
"numpy.array",
"numpy.expand_dims"
],
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
akawashiro/pytorch-pfn-extras | [
"4f96ec4095a50cb6dfb84a3c946b745b277d222b"
] | [
"tests/pytorch_pfn_extras_tests/runtime_tests/test_jit_runtime.py"
] | [
"import types\nimport warnings\n\nimport torch\nimport torch.nn.functional as F\n\nimport pytorch_pfn_extras as ppe\nimport pytorch_pfn_extras.utils.comparer as _comp\nfrom pytorch_pfn_extras.onnx._as_output import trace\n\n\nclass JITRuntime(ppe.runtime.PyTorchRuntime):\n\n def move_module(self, module):\n\n ... | [
[
"torch.nn.Linear",
"torch.jit.trace_module",
"torch.rand",
"torch.nn.functional.l1_loss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
swisscom/ai-research-fairsourcing | [
"9056179cbdcfaf6304244a0940cc15522b1e8428"
] | [
"team.py"
] | [
"\"\"\"Copyright © 2020-present, Swisscom (Schweiz) AG.\nAll rights reserved.\n\nTeam, used for modelling the behavior of an organisational unit.\nThe Team class contains the implementation of the team object, using all the other classes.\nIts function is to make the link between the different other elements of thi... | [
[
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.cm.get_cmap",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim",
"pandas.DataFrame",
"matplotlib.pyplot.gcf",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.xticks",
"numpy.average",
"matplotlib.pyplot.show",
"matplotlib... | [
{
"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": []
}
] |
hitlic/torchility | [
"c28701d8c93955ad115d364b35b680a60ecfd360"
] | [
"torchility/utils.py"
] | [
"from matplotlib import pyplot as plt\nimport numpy as np\nimport itertools\nfrom typing import Iterable\nimport torch\n\nclass TensorList(list):\n \"\"\"\n list of tensors\n \"\"\"\n @property\n def device(self):\n if len(self) > 0:\n return self[0].device\n else:\n ... | [
[
"matplotlib.pyplot.text",
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.title",
"matplotlib.pyplot.figure",
"numpy.arange",
"matplotlib.pyplot.grid",
"torch.device",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
qqiang00/practical-pytorch | [
"c9b07c41e5bfbc970d6e2369bd3df4b618ab725a"
] | [
"seq_auto_encoder/train.py"
] | [
"from data import *\nfrom model import *\nfrom torch.autograd import Variable\nimport torch\nfrom torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence\nfrom masked_cross_entropy import *\nimport time\nimport datetime\nimport math\nimport argparse\nimport os\n\nargparser = argparse.ArgumentParser()\na... | [
[
"matplotlib.ticker.MultipleLocator",
"torch.LongTensor",
"torch.zeros",
"torch.load",
"numpy.power",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.plot",
"torch.save",
"matplotlib.pyplot.show",
"numpy.sum",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
akrakman/hackillinois | [
"a4cfb107aefb95ff987ba159e3c7867a9d10ce9a"
] | [
"gpaData.py"
] | [
"import pandas as pd\ndf = pd.read_csv(\"https://waf.cs.illinois.edu/discovery/gpa.csv\")\n\n\ndf_removedup = df.groupby(['Year', 'Term', 'YearTerm','Subject','Number','Course Title','Primary Instructor']).agg(Aplus = ('A+', 'sum'), A=('A', 'sum'), Aminus = ('A-', 'sum'), Bplus = ('B+', 'sum'), B = ('B', 'sum'), Bm... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
RadioAstronomySoftwareGroup/simpleDS | [
"2c1805a044dff63678c7453bcaf4ffe51b1f99cf"
] | [
"simpleDS/tests/test_cosmo.py"
] | [
"# -*- mode: python; coding: utf-8 -*\n# Copyright (c) 2018 rasg-affiliates\n# Licensed under the 3-clause BSD License\n\"\"\"Tests for cosmo module.\n\nRelevent Cosmology units and transforms for Power Spectrum estimation.\n\nAll cosmological calculations and converions follow from Liu et al 2014a\nPhys. Rev. D 90... | [
[
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
process-asl/process-asl | [
"777ae9ccf290e3e3ce133d339801b2aa7dd5a558"
] | [
"procasl/externals/nistats/regression.py"
] | [
"# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n\"\"\"\nThis module implements some standard regression models: OLS and WLS\nmodels, as well as an AR(p) regression model.\n\nModels are specified with a design matrix and are fit using their\n'fit' ... | [
[
"numpy.dot",
"scipy.linalg.pinv",
"numpy.log",
"numpy.linalg.matrix_rank",
"numpy.sqrt",
"numpy.abs",
"numpy.asarray",
"numpy.finfo",
"numpy.transpose",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.12",
"0.14",
"0.15"
],
"tensorflow": []
}
] |
ogrisel/scikits-image | [
"cfc1dfa16b1e94dba42f2f5766a41cbbede03f7d"
] | [
"skimage/feature/greycomatrix.py"
] | [
"\"\"\"\nCompute grey level co-occurrence matrices (GLCMs) and associated\nproperties to characterize image textures.\n\"\"\"\n\nimport numpy as np\nimport skimage.util\n\nfrom ._greycomatrix import _glcm_loop\n\n\ndef greycomatrix(image, distances, angles, levels=256, symmetric=False,\n normed=Fals... | [
[
"numpy.abs",
"numpy.sqrt",
"numpy.ascontiguousarray",
"numpy.apply_over_axes",
"numpy.transpose",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rishusiva/CustomerChurnPrediction_ANN | [
"886780eea408511998f2c2f7ea940cd6b9fb9d42"
] | [
"Preprocessing_Data/Cleaning_dataset.py"
] | [
"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport warnings\nwarnings.filterwarnings('ignore')\n\ndf=pd.read_csv(\"Customer_churn.csv\")\n\ndf.drop('customerID',axis=1,inplace=True)\n\n#print(df.dtypes)\n\n#print(df.TotalCharges.values)\n\ndf[pd.to_numeric(df.TotalCharges,errors='coer... | [
[
"pandas.read_csv",
"pandas.to_numeric",
"pandas.get_dummies"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
jrising/research-common | [
"2b930d29fa0b16b3b08b33b7d8fffa583ccde94f"
] | [
"python/geogrid/spacegrid.py"
] | [
"import math, csv\nimport numpy as np\n\nclass SpatialGrid(object):\n def __init__(self, x0_corner, y0_corner, sizex, sizey, ncols, nrows):\n self.y0_corner = y0_corner\n self.x0_corner = x0_corner\n self.sizex = sizex\n self.sizey = sizey\n self.ncols = ncols\n self.nro... | [
[
"numpy.abs",
"numpy.isnan",
"numpy.arange",
"numpy.diff",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SabariKumar/flare | [
"539257958a960341d6957f1745dffd308b2c1aa4",
"539257958a960341d6957f1745dffd308b2c1aa4"
] | [
"tests/ase_otf/otf_setup.py",
"tests/test_lmp.py"
] | [
"import numpy as np\nfrom copy import deepcopy\n\nfrom ase import units\nfrom ase.md.velocitydistribution import (MaxwellBoltzmannDistribution,\n Stationary, ZeroRotation)\n\nfrom flare.ase.otf_md import otf_md\nfrom flare.ase.logger import OTFLogger\n\nimport atom_setup, fla... | [
[
"numpy.random.seed"
],
[
"numpy.eye",
"numpy.array",
"numpy.abs",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
duxiaodan/pixel2style2pixel | [
"8881b2d5d08da404ddb3d02cafc9aa7fa4107153"
] | [
"utils/common.py"
] | [
"import cv2\r\nimport numpy as np\r\nfrom PIL import Image\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\n# Log images\r\ndef log_input_image(x, opts):\r\n\tif opts.label_nc == 0:\r\n\t\treturn tensor2im(x)\r\n\telif opts.label_nc == 1:\r\n\t\treturn tensor2sketch(x)\r\n\telse:\r\n\t\treturn tensor2map(x)\r\n\r\n\r\... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.title",
"numpy.unique",
"numpy.ones",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
priyankjaini/discFlowMH | [
"d48d9a3dace44520865584f6dafd476de29b556e"
] | [
"ising/gibbs/eval_ess_std.py"
] | [
"import os\nimport math\nimport torch\nimport pickle\nimport argparse\n\n# Path\nimport os, sys\ncurrentdir = os.path.dirname(os.path.realpath(__file__))\nparentdir = os.path.dirname(currentdir)\nbasedir = os.path.dirname(parentdir)\nsys.path.append(parentdir)\nsys.path.append(basedir)\n\n# MCMC\nfrom ess import ES... | [
[
"torch.mean",
"torch.load",
"torch.zeros",
"torch.manual_seed",
"torch.std",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gptune/GPTune | [
"7ed5c63275da2d9625880c8eae837b8eb2d2df81"
] | [
"examples/NewtonSketch/tune_gaussian.py"
] | [
"#! /usr/bin/env python\n\n# GPTune Copyright (c) 2019, The Regents of the University of California,\n# through Lawrence Berkeley National Laboratory (subject to receipt of any\n# required approvals from the U.S.Dept. of Energy) and the University of\n# California, Berkeley. All rights reserved.\n#\n# If you have ... | [
[
"numpy.argmin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Robolabo/EvoSwarmSim | [
"45f20f00b079a9481e324e091c46040182cf1d3c"
] | [
"spike_swarm_sim/controllers/controller.py"
] | [
"import numpy as np\r\nfrom spike_swarm_sim.register import sensors, actuators, controller_registry\r\nfrom spike_swarm_sim.utils import increase_time\r\n\r\nclass Controller:\r\n \"\"\" Base class for entity controllers. \"\"\"\r\n def __init__(self):\r\n raise NotImplementedError\r\n \r\n def s... | [
[
"numpy.cos",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
foolcatcora/tushare | [
"44b51564942386658cab6b111929495c85ad25cc"
] | [
"trader/trader.py"
] | [
"#!/usr/bin/env python\r\n# -*- coding:utf-8 -*- \r\n\r\n'''\r\nCreated on 2016年9月25日\r\n@author: Jimmy Liu\r\n@group : waditu\r\n@contact: jimmysoa@sina.cn\r\n'''\r\nimport six\r\nimport pandas as pd\r\nimport requests\r\nimport time\r\nfrom threading import Thread\r\nfrom tushare.trader import vars as vs\r\nfrom ... | [
[
"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": []
}
] |
IssamLaradji/BlockCoordinateDescent | [
"0be4013a3b6ec59f7f9a9f645ca2dd93e05f7147"
] | [
"trainval.py"
] | [
"from haven import haven_chk as hc\nfrom haven import haven_results as hr\nfrom haven import haven_utils as hu\nimport torch\nimport torchvision\nimport tqdm\nimport pandas as pd\nimport pprint\nimport itertools\nimport os\nimport pylab as plt\nimport exp_configs\nimport time\nimport numpy as np\n\nimport os\nimpor... | [
[
"numpy.array",
"numpy.zeros",
"numpy.where",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kamigaito/SLAHAN | [
"5ef981f1713f4586e2ec42c226555e95d7904147"
] | [
"scripts/tools/extract_glove_features.py"
] | [
"import argparse\nimport numpy as np\nimport h5py\nimport codecs\n\n# Option List\ndef option_parser():\n usage = \"Usage (train): python {} -i /path/to/input.txt -m path/to/model -e path/to/elmo.hdf5\"\n parser = argparse.ArgumentParser(usage=usage)\n parser.add_argument('-i', '--input_file', type=str, de... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AppliedDynamicMechanics/Deterministic-Policy-Gradient-Cell-Migration | [
"6a21ac608635d2fde243c8782354d5630678e69f"
] | [
"Moving_Source_DDPG_local.py"
] | [
"####Deep deterministic policy gradient\r\n\r\nimport numpy as np\r\nimport tensorflow as tf\r\nfrom collections import deque\r\nimport trfl\r\nimport os\r\nimport shutil\r\nfrom Continuum_Cellspace_Moving_Source import *\r\n\r\nNumber_Agent = 1\r\nExit.append( np.array([0.5, 0.5, 0.5]) ) ##Source\r\n\r\noutput_di... | [
[
"tensorflow.train.get_checkpoint_state",
"tensorflow.losses.mean_squared_error",
"tensorflow.concat",
"tensorflow.reduce_mean",
"numpy.clip",
"tensorflow.placeholder",
"tensorflow.trainable_variables",
"tensorflow.contrib.layers.fully_connected",
"tensorflow.ConfigProto",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
timstaley/simlightcurve | [
"801b8e89be5f332c652c2c61185fcbb9bfc18608"
] | [
"src/simlightcurve/curves/composite/gaussexp.py"
] | [
"from __future__ import absolute_import, division\nimport numpy as np\nfrom astropy.modeling import FittableModel, Parameter\nfrom simlightcurve.curves.powerlaw import Powerlaw\n\n\nclass GaussExp(FittableModel):\n inputs=('t',)\n outputs=('flux',)\n\n amplitude = Parameter()\n rise_tau = Parameter()\n ... | [
[
"numpy.ndim",
"numpy.exp",
"numpy.zeros_like",
"numpy.asarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Shruthi-Sampathkumar/active-learning | [
"05208790bc56f0aa5cade8b90e0533739e661b1b"
] | [
"utils/small_cnn.py"
] | [
"# Copyright 2017 Google Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed... | [
[
"tensorflow.set_random_seed",
"numpy.reshape",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
emabotv1/crypto-signal | [
"42208c2f0366c0fed0f756bf00571b09cf4b70d6"
] | [
"app/notification.py"
] | [
"\"\"\"Handles sending notifications via the configured notifiers\n\"\"\"\n\nimport copy\nimport json\nimport os\nimport re\nimport sys\nimport traceback\nimport datetime\nfrom time import sleep\n\nimport matplotlib\nimport matplotlib.dates as mdates\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as mti... | [
[
"numpy.nanmax",
"pandas.Series",
"numpy.linspace",
"numpy.asarray",
"numpy.nanmin",
"matplotlib.pyplot.rc",
"pandas.DataFrame",
"numpy.zeros_like",
"numpy.diff",
"matplotlib.pyplot.close",
"matplotlib.pyplot.figure",
"matplotlib.dates.DateFormatter",
"matplotlib... | [
{
"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": []
}
] |
justinbois/be150 | [
"96afe62ff40276f81d8a86eaa7b54d442517eec7"
] | [
"biocircuits/viz.py"
] | [
"import warnings\n\nimport numpy as np\n\nimport matplotlib.streamplot\n\nimport bokeh.application\nimport bokeh.application.handlers\nimport bokeh.layouts\nimport bokeh.models\nimport bokeh.palettes\nimport bokeh.plotting\n\nimport colorcet\n\nfrom . import utils\n\n\ndef _ecdf_vals(data, formal=False, complementa... | [
[
"numpy.rollaxis",
"numpy.hstack",
"numpy.sqrt",
"numpy.linspace",
"numpy.unique",
"numpy.sort",
"numpy.concatenate",
"numpy.ma.masked_invalid",
"numpy.log10",
"numpy.asanyarray",
"numpy.searchsorted",
"numpy.transpose",
"numpy.mean",
"numpy.diff",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
marcusbonifacio/Reddit-post-datetime | [
"c3a13bf8362dcf2d6f53e38e14b541cbd7f0ba2a"
] | [
"posts.py"
] | [
"from psaw import PushshiftAPI\nfrom datetime import datetime, timedelta\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\ndef ceil_dt(dt, delta):\n return dt + (datetime.min - dt) % delta\n\n\napi = PushshiftAPI()\nstart_epoch = int(datetime(2020, 10, 1).timestamp())\nend_epoch = i... | [
[
"matplotlib.pyplot.gca",
"matplotlib.pyplot.title",
"pandas.DataFrame",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.show",
"matplotlib.pyplot.hist",
"matplotlib.pyplot.ylabel"
]
] | [
{
"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": []
}
] |
BradReesWork/cugraph | [
"9ddea03a724e9b32950ed6282120007c76482cbc"
] | [
"python/cugraph/cugraph/tests/utils.py"
] | [
"# Copyright (c) 2020-2021, NVIDIA CORPORATION.\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... | [
[
"pandas.read_csv",
"pandas.DataFrame",
"numpy.genfromtxt",
"numpy.array",
"numpy.random.RandomState",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
carlosrh18/DavinciBot | [
"d73a6b7f68d7bab25d134d3f85c6b63a86c206c5"
] | [
"bot_app/views.py"
] | [
"from django.shortcuts import render\nfrom django.http import JsonResponse\nfrom django.forms.models import model_to_dict\nfrom .models import Messages\nimport requests\nfrom twilio.twiml.messaging_response import MessagingResponse\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.http import HttpR... | [
[
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
quanvuong/baselines | [
"1831fe64ffae1ed19ca5f6df3ede4518e549fe48"
] | [
"baselines/ppo1/mlp_policy.py"
] | [
"from baselines.common.mpi_running_mean_std import RunningMeanStd\nimport baselines.common.tf_util as U\nimport tensorflow as tf\nimport gym\nfrom baselines.common.distributions import make_pdtype\n\nclass MlpPolicy(object):\n recurrent = False\n def __init__(self, name, *args, **kwargs):\n with tf.var... | [
[
"tensorflow.clip_by_value",
"tensorflow.concat",
"tensorflow.get_collection",
"tensorflow.zeros_initializer",
"tensorflow.placeholder",
"tensorflow.variable_scope",
"tensorflow.get_variable_scope"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
nobu513/recopi | [
"cd8c865a5e6eb093e4bcc04fdcd3956731422d4d"
] | [
"api/images/models.py"
] | [
"from django.db import models\nimport pandas as pd\nimport numpy as np\nimport keras\nimport tensorflow as tf\nfrom keras.preprocessing.image import img_to_array\nfrom django.conf import settings\nfrom keras.preprocessing import image\nfrom tensorflow.keras.models import load_model\nimport os\nfrom PIL import Image... | [
[
"tensorflow.keras.models.load_model",
"tensorflow.compat.v1.get_default_graph",
"numpy.expand_dims"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jsalvatier/Theano-1 | [
"457bae18a0d3284841b9bcae5ce4ceee22f33132"
] | [
"theano/tensor/shared_randomstreams.py"
] | [
"\"\"\"Define RandomStreams, providing random number variables for Theano\ngraphs.\n\"\"\"\n__docformat__ = \"restructuredtext en\"\n\nimport copy\nimport sys\n\nimport numpy\n\nfrom theano.gof import Container\nfrom theano.compile.sharedvalue import (SharedVariable, shared_constructor,\n ... | [
[
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
choice17/ransac_with_homography | [
"453bbb50b222cc268b264906c34bce03d58b19e7"
] | [
"app.py"
] | [
"import cv2\nimport numpy as np\nimport sys\nimport tkinter as tk\nimport tkinter.filedialog as F\nimport threading as pthread\nimport os\nimport time\nfrom homography import transformImage\nfrom ransac import stitching\nimport signal\n\ndebugLvl = 0.5 \n\nWIN = 'app'\nWINT = 'transform'\nTIME_MAX = 5\nH, W, C = 40... | [
[
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CMU-IDS-2020/fp-lyric-visualization | [
"621a36308ba9d19c965ec6cdfd9e0c5f07069b90"
] | [
"preprocessing/lyrics_preprocess.py"
] | [
"# Name of File: lyrics_preprocess.py\n# Date: November 14th, 2020\n# Purpose of File: to create a Panda dataframe called lyrics_dataframe of words by their location in a song\n# Tools used:\n# LyricsGenius (see https://pypi.org/project/lyricsgenius/)\n# HuggingFace Library (https://huggingface.co/transformers/quic... | [
[
"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": []
}
] |
DTUWindEnergy/PyWake | [
"6576bd8079d033cc4a1e7162cf8a803f3312fb57"
] | [
"py_wake/tests/test_sites/test_site.py"
] | [
"import os\nimport pytest\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom py_wake.tests import npt\nfrom py_wake.site._site import UniformWeibullSite, UniformSite\nfrom py_wake.site.shear import PowerShear\nfrom py_wake.site.xrsite import XRSite\nfrom py_wake.tests.test_files import tfp\nfrom py_wake.exa... | [
[
"matplotlib.pyplot.gca",
"numpy.linspace",
"numpy.arange",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.plot",
"numpy.arctan2",
"numpy.zeros_like",
"matplotlib.pyplot.close",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
schustda1/reagan | [
"204e13b4690d829135b6472520d5c6d2e44c5548",
"204e13b4690d829135b6472520d5c6d2e44c5548"
] | [
"reagan/subclass.py",
"reagan/smartsheets.py"
] | [
"from reagan.ssm_parameter_store import SSMParameterStore\nfrom pandas.io.json import json_normalize\nimport pandas as pd\nimport numpy as np\nimport os\n\n\nclass Subclass(SSMParameterStore):\n def __init__(self, verbose=0):\n super().__init__()\n self.verbose = verbose\n\n def vprint(self, obj... | [
[
"pandas.isnull",
"pandas.concat",
"pandas.DataFrame"
],
[
"pandas.merge",
"pandas.read_excel",
"pandas.ExcelFile",
"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": []
},
{
"matplotlib": [],
"nump... |
bendemeo/ample | [
"4ca2688ff2b0e5c8f7dcbc0a1f4ddc8927fac670"
] | [
"ample/sketch.py"
] | [
"import numpy as np\nfrom sklearn.preprocessing import normalize\nfrom sklearn.metrics.pairwise import pairwise_distances\nimport sys\n\nfrom .kmeanspp import kmeanspp\nfrom .utils import log\n\ndef gs(X, N, **kwargs):\n return gs_gap(X, N, **kwargs)\n\ndef gs_gap(X, N, k='auto', seed=None, replace=False,\n ... | [
[
"numpy.absolute",
"numpy.sqrt",
"numpy.random.seed",
"numpy.random.choice",
"numpy.nonzero",
"numpy.linalg.norm",
"numpy.max",
"numpy.random.normal",
"sklearn.preprocessing.normalize",
"numpy.argmin",
"numpy.floor",
"numpy.argsort",
"numpy.array",
"sklearn.m... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CvHadesSun/E2P | [
"e5732ca5a18d4ef389798d7f62a748b799c06158"
] | [
"refinenet/loss.py"
] | [
"from torch.nn import functional as F\r\nimport torch\r\nimport numpy as np\r\nfrom torch import nn\r\n# num_keypoints=8\r\n\r\ndef loss(input,target):\r\n # input [b,c,h,w]\r\n # target [b,c,3]\r\n\r\n R=1 #per grid height\r\n device=input.device\r\n N,C,H,W=input.shape\r\n valid=target[:,:,-1].c... | [
[
"torch.ceil",
"torch.cat",
"torch.nn.functional.cross_entropy",
"numpy.where",
"torch.nn.MSELoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cliffxuan/puzzle | [
"5de2e5bb4ceb9021566690b5eb58cc2ff5c2103e"
] | [
"energy_consumption/resampling.py"
] | [
"import argparse\n\nfrom scipy import signal\nimport pandas as pd\nimport numpy as np\n\n\ndef argument_parser():\n parser = argparse.ArgumentParser(description='resample time series data')\n parser.add_argument(\n 'filename', type=argparse.FileType('r'),\n help='name of the file to convert')\n ... | [
[
"pandas.read_csv",
"scipy.signal.resample",
"pandas.DataFrame",
"pandas.date_range"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"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"... |
shuyiii/A-Hybrid-of-Fast-rcnn-and-Bayesian-Model-for-Object-Detection | [
"20834a658b416507c7a78d5d0f09b15f9370a802"
] | [
"faster-rcnn-base-model/code/gaussian_parameter.py"
] | [
"import numpy as np\nimport glob\nimport os\nimport pandas as pd\npath='/fs/project/PAS1263/data/ILSVRC/train.csv'\nA=[];B=[];C=[];\ndf1=pd.read_csv(path)\ndf2=df1['filename'].values.tolist()\ndf3=df1.set_index(\"filename\")\ndflist=set(df2)\nfor file_name in list(dflist):\n temp=df3.loc[file_name,]\n d... | [
[
"pandas.read_csv",
"numpy.sqrt",
"numpy.save",
"numpy.std",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
haonguyen1915/rasa-lookup-table-nlu | [
"59cfe68a40fdf9b3a9a6ea6bf5ba18d7dedf500e"
] | [
"ngrams/make_dataset.py"
] | [
"import pandas as pd\nimport os\nfrom numpy.random import randint\nimport numpy as np\n\n\ndef load_dataset(fname):\n company_df = pd.read_csv(fname)\n num_open_addr = len(company_df.index)\n print(\"{} addresses in dataset {}\".format(num_open_addr, fname))\n return company_df\n\n\nif __name__ == \"__m... | [
[
"pandas.concat",
"pandas.read_csv",
"pandas.DataFrame",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
spacebel/MAJA | [
"3e5d20bc9c744c610e608cfcf1f4c5c738d4de9e"
] | [
"StartMaja/prepare_mnt/mnt/DEMInfo.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\nCopyright (C) 2016-2020 Centre National d'Etudes Spatiales (CNES), CSSI, CESBIO All Rights Reserved\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a co... | [
[
"numpy.std",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ptigas/genedisco | [
"e606abe31fc45ad71b1d5035860d40933fad8a4b"
] | [
"genedisco/active_learning_methods/acquisition_functions/margin_sampling_acquisition.py"
] | [
"\"\"\"\nCopyright 2021 Patrick Schwab, Arash Mehrjou, GlaxoSmithKline plc; Andrew Jesson, University of Oxford; Ashkan Soleymani, MIT\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n ... | [
[
"numpy.argsort"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
echoyi/pgmpy | [
"c37dda4401f23ec73fc5d17d957867cd62e588d3"
] | [
"pgmpy/utils/optimizer.py"
] | [
"import warnings\nfrom math import isclose\n\n\ntry: # pragma: no cover\n import torch\n\n optim = torch.optim\nexcept ImportError: # pragma: no cover\n optim = None\n\n\ndef pinverse(t):\n \"\"\"\n Computes the pseudo-inverse of a matrix using SVD.\n\n Parameters\n ----------\n t: torch.t... | [
[
"torch.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sladebot/traffiko-data | [
"459d13185057666a2dbee22031e3ecc549ff5e37"
] | [
"lib/sampling.py"
] | [
"from sklearn.cluster import KMeans, MiniBatchKMeans\nfrom sklearn.feature_extraction import DictVectorizer\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef k_means(dataset, cluster_size):\n X = np.array(dataset)\n print(\"Running k means with cluster size - \" + str(cluster_size))\n kmeans = ... | [
[
"matplotlib.pyplot.title",
"sklearn.cluster.KMeans",
"matplotlib.pyplot.xlabel",
"numpy.array",
"numpy.where",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GerardMJuan/BrainFortLib-neuroimage-hub | [
"14a2ea35a81d0f8daa0ec1d903274ff137ab0aaf"
] | [
"scripts/data_availability.py"
] | [
"\"\"\"\nCheck which images are available.\n\nScript that looks at the ADNIMERGE file and checks how many of the\nsubjects have either a MRI or a PET scan downloaded. It also checks for\nthe same file existance in the BIDS directory. The output of this Script is\na .csv file with the availabiliy information of all ... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
simeonschuez/pytorch-tutorial | [
"9ca21ddc12dfa98c360a57f66101f4e30aaf7072"
] | [
"tutorials/03-advanced/image_captioning/train.py"
] | [
"import argparse\nimport pandas as pd\nimport json\nimport configparser\nimport torch\nimport torch.nn as nn\nimport numpy as np\nimport os\nimport pickle\nfrom data_loader import get_loader\nfrom build_vocab import Vocabulary\nfrom model import EncoderCNN, DecoderRNN\nfrom torch.nn.utils.rnn import pack_padded_seq... | [
[
"torch.optim.Adam",
"torch.nn.CrossEntropyLoss",
"torch.nn.utils.rnn.pack_padded_sequence",
"pandas.DataFrame",
"torch.cuda.is_available"
]
] | [
{
"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": []
}
] |
BulyginMaksim/dlcourse_ai | [
"0391555b043e8b64faf3f0da76455d1bee4d3f55"
] | [
"assignments/assignment1/metrics.py"
] | [
"import numpy as np\n\n\ndef binary_classification_metrics(prediction, ground_truth):\n '''\n Computes metrics for binary classification\n\n Arguments:\n prediction, np array of bool (num_samples) - model predictions\n ground_truth, np array of bool (num_samples) - true labels\n\n Returns:\n pr... | [
[
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yashwarlord/fairness-comparison | [
"366a4e4bb70498ac7498e4a98f50e3585f7881d3"
] | [
"fairness/analysis/analysis-numerical-vs-binsensitive.py"
] | [
"import fire\nimport pandas as pd\nfrom ggplot import *\nimport sys\n\n##############################################################################\n\nimport pathlib\n\ndef main():\n o = pathlib.Path(sys.argv[1]).parts[-1].split('.')[0]\n f1 = pd.read_csv(sys.argv[1] + '_numerical-binsensitive.csv')\n f2... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Galaxies99/EE447-CourseData | [
"a5fd8658bf6b739a70f671a16e96afa3f929011b"
] | [
"Labs/Lab3/community/utils.py"
] | [
"import json\nimport pandas as pd\n\n\ndef dblp_dataset(file_path):\n edge_list = []\n with open(file_path, 'r') as f:\n # Headers\n for line in f.readlines():\n line = line.lstrip(' \\n\\t').rstrip(' \\n\\t')\n if line == \"\" or line[0] == '#':\n continue\n... | [
[
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
DianCh/SimMIM | [
"519ae7b0999b9d720daa61e3848cd41b8fbd9978"
] | [
"models/vision_transformer.py"
] | [
"# --------------------------------------------------------\n# SimMIM\n# Copyright (c) 2021 Microsoft\n# Licensed under The MIT License [see LICENSE for details]\n# Based on BEIT code bases (https://github.com/microsoft/unilm/tree/master/beit)\n# Written by Yutong Lin, Zhenda Xie\n# --------------------------------... | [
[
"torch.nn.Dropout",
"torch.linspace",
"torch.ones",
"torch.zeros",
"torch.cat",
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.zeros_like",
"torch.flatten",
"torch.nn.Linear",
"torch.nn.Identity",
"torch.arange",
"torch.meshgrid",
"torch.nn.functional.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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