repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
vkso/FER | [
"b7207341139ff451753a4c4640530e915673fc7c"
] | [
"train.py"
] | [
"import myMethod as myMethod\nfrom datetime import datetime\nfrom customParameters import *\nfrom tensorflow import keras\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport os\nimport argparse\n\n# python train.py --gpus 1 --model myModel --train_name fc1024\nparser = argparse.ArgumentParser(descript... | [
[
"tensorflow.keras.callbacks.ModelCheckpoint",
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.title",
"tensorflow.distribute.MirroredStrategy",
"matplotlib.pyplot.subplot",
"tensorflow.keras.callbacks.TensorBoard",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
flavorfan/deep-learning-coursera | [
"6aa1274a450fcb7a57c04072fe3bcf416501bdc6"
] | [
"Sequence Models/Trigger word detection/rnn_model.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.mlab as mlab\n\nimport pyaudio\n\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.callbacks import TensorBoard\nfrom keras.models import Model, load_model, Sequential\nfrom keras.layers import Dense, Activation, Dropout, Input, Masking,... | [
[
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kortemaki/OpenNMT-py | [
"fa793257966c23280e5a72bd43e56a1e998e47f7"
] | [
"onmt/modules/GlobalAttention.py"
] | [
"import torch\nimport torch.nn as nn\n\nfrom onmt.Utils import aeq, sequence_mask\n\nclass GlobalAttention(nn.Module):\n \"\"\"\n Global attention takes a matrix and a query vector. It\n then computes a parameterized convex combination of the matrix\n based on the input query.\n\n Constructs a unit m... | [
[
"torch.nn.Softmax",
"torch.cat",
"torch.nn.Tanh",
"torch.nn.Linear",
"torch.bmm"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rkarp/polars | [
"106bf5802126702cee8bc5bc21f2392bd5eebe98"
] | [
"py-polars/tests/test_series.py"
] | [
"from polars import Series\nfrom polars.datatypes import *\nimport polars as pl\nimport numpy as np\nimport pytest\nimport pyarrow as pa\n\n\ndef create_series() -> \"Series\":\n return Series(\"a\", [1, 2])\n\n\ndef test_to_frame():\n assert create_series().to_frame().shape == (2, 1)\n\n\ndef test_bitwise_op... | [
[
"numpy.array",
"numpy.exp",
"numpy.multiply"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BrancoLab/LocomotionControl | [
"6dc16c29c13b31f6ad70af954a237e379ee10846"
] | [
"draw/tracking.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom typing import Union\n\nfrom myterial import grey_dark\n\n\nclass Tracking:\n \"\"\"\n Renders tracking as a 2D trace\n \"\"\"\n\n def __init__(\n self,\n x: Union[pd.Series, np.ndarray],\n y: Union[p... | [
[
"matplotlib.pyplot.gca"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fujitaushizu/ArknightsAutoHelper | [
"d0bb47b4a141c791369f89093cef27fa25d2cad2"
] | [
"imgreco/stage_ocr.py"
] | [
"from functools import lru_cache\nimport cv2\nimport numpy as np\nfrom . import resources\nimport zipfile\nfrom . import common\nfrom util.richlog import get_logger\nimport config\n\n\nidx2id = ['-', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J',\n 'K', ... | [
[
"numpy.asarray",
"numpy.where",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chqiwang/sa-nmt | [
"0793130c916483f2a93c85d73c6ed4831da05146"
] | [
"train_wkd.py"
] | [
"import os\nimport time\nimport logging\nfrom argparse import ArgumentParser\nimport tensorflow as tf\nimport yaml\n\nfrom evaluate import Evaluator\nfrom models import *\nfrom utils import DataReader, AttrDict, available_variables, expand_feed_dict\n\n\nclass BreakLoopException(Exception):\n pass\n\n\ndef wrap_... | [
[
"tensorflow.Graph",
"tensorflow.get_variable",
"tensorflow.summary.FileWriter",
"tensorflow.train.latest_checkpoint",
"tensorflow.contrib.framework.load_checkpoint",
"tensorflow.global_variables",
"tensorflow.ConfigProto",
"tensorflow.global_variables_initializer",
"tensorflow.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
skiran252/FARM | [
"8460d78910a20d19a5da12de6e9bff11f68332a7"
] | [
"farm/file_utils.py"
] | [
"\"\"\"\nUtilities for working with the local dataset cache.\nThis file is adapted from the AllenNLP library at https://github.com/allenai/allennlp\nCopyright by the AllenNLP authors.\n\"\"\"\nfrom __future__ import absolute_import, division, print_function, unicode_literals\n\nimport json\nimport logging\nimport o... | [
[
"torch.hub._get_torch_home",
"numpy.meshgrid"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NrctcV/BigQuery-Antifraud-reporting | [
"6167a44159f939c5993423b1196992d6c6ecc34f"
] | [
"main.py"
] | [
"\nimport datetime\nimport numpy as np\nimport pandas as pd\nimport google.oauth2.credentials\nimport pandas_gbq\n\n\n# Setup gcloud SDK\n# login to gcloud\n# gcloud auth application-default login\n# print token\n# fill oauth2 creds and project name\n# install tqdm\n# install xlxswriter\n\n\ndef create_date():\n ... | [
[
"pandas.ExcelWriter",
"pandas.pivot_table"
]
] | [
{
"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": []
}
] |
EleutherAGI/summarisation | [
"d432873e1ba171f47371b8b0df7235478b52ca99"
] | [
"preprocess_tldr_dataset.py"
] | [
"import json\nfrom collections import OrderedDict, Counter\nfrom transformers import GPT2TokenizerFast\nfrom tqdm import tqdm\nfrom sklearn.model_selection import train_test_split\n\ndata = []\nwith open('./data/tldr-training-data.jsonl') as f:\n for line in f:\n data.append(json.loads(line))\n\ndef view_... | [
[
"sklearn.model_selection.train_test_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KantiCodes/flatland-rl | [
"fcc10e83d2548470ebaa5540b967db0940eb30dd"
] | [
"baselines/reinforcement_learning/multi_agent_training.py"
] | [
"from datetime import datetime\nimport os\nimport random\nimport sys\nfrom argparse import ArgumentParser, Namespace\nfrom pathlib import Path\nfrom pprint import pprint\n\nimport psutil\nfrom flatland.utils.rendertools import RenderTool\nfrom torch.utils.tensorboard import SummaryWriter\nimport numpy as np\nimport... | [
[
"numpy.random.seed",
"numpy.power",
"numpy.min",
"numpy.round",
"numpy.max",
"numpy.std",
"numpy.mean",
"torch.utils.tensorboard.SummaryWriter",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lumbric/pyam | [
"a73fc6a78871988cd842e52111c00879cf90882b"
] | [
"pyam/plotting.py"
] | [
"import itertools\nimport warnings\n\ntry:\n import cartopy\n cartopy_message = 'all good!'\nexcept ImportError as e:\n cartopy = None\n cartopy_message = str(e)\n\nimport matplotlib.pyplot as plt\nimport matplotlib.colors as colors\nimport matplotlib.cm as cmx\nimport matplotlib.patches as mpatches\nim... | [
[
"pandas.concat",
"numpy.unique",
"matplotlib.pyplot.ylim",
"matplotlib.patches.Rectangle",
"matplotlib.pyplot.get_cmap",
"matplotlib.colors.Normalize",
"pandas.DataFrame",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.colorbar",
"numpy.argwhere",
"matplotlib.pyplot.x... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Shriyam-Avasthi/Virtual-Whiteboard | [
"9c0fa39319d360094a380f3d5faf3a6f26531256"
] | [
"GUI.py"
] | [
"from ui_GUI import *\r\n# from PySide2 import *\r\nimport sys \r\nimport cv2\r\nfrom PySide2.QtGui import QPixmap\r\nfrom PySide2 import QtGui\r\nfrom functools import partial\r\nimport numpy as np\r\nimport mouse\r\nfrom MultiThreading import MainThread\r\nfrom Whiteboard import WhiteBoard\r\nfrom Tools import To... | [
[
"numpy.interp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
e2crawfo/dps | [
"968a87ed8580f58b46e75463d13a5966f4e772eb"
] | [
"dps/train.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nimport time\nfrom contextlib import ExitStack\nimport numpy as np\nfrom pprint import pformat\nimport datetime\nimport os\nimport pandas as pd\nimport dill\nfrom collections import defaultdict\nimport traceback\nimport json\nimport subprocess... | [
[
"pandas.DataFrame.from_records",
"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": []
}
] |
wilsonify/sktime | [
"68395d44bd3f46b0801c506e23e889dd54999d29"
] | [
"examples/scripts/dictionary_based_classification.py"
] | [
"# -*- coding: utf-8 -*-\n# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.11.1\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# # Dictio... | [
[
"sklearn.metrics.accuracy_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
violet-zct/fairseq | [
"5fd9b555428f004f72d4fe89e2a9d2c863c07581",
"5fd9b555428f004f72d4fe89e2a9d2c863c07581"
] | [
"fairseq/modules/typed_transformer_layer.py",
"fairseq/data/monolingual_dataset.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom fairseq import utils\nfrom fairseq.modules import LayerNo... | [
[
"torch.nn.functional.dropout",
"torch.nn.init.constant_",
"torch.cat",
"torch.nn.Linear",
"torch.nn.init.xavier_uniform_"
],
[
"torch.LongTensor",
"numpy.array",
"numpy.lexsort"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
XiangqianMa/AI-Competition-HuaWei | [
"d479b772f446033d32a124b80a1f9cd835988020"
] | [
"losses/CE_label_smooth.py"
] | [
"import torch\nimport torch.nn as nn\n\n\nclass CrossEntropyLabelSmooth(nn.Module):\n \"\"\"Cross entropy loss with label smoothing regularizer.\n\n Reference:\n Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVPR 2016.\n Equation: q_i = (1 - epsilon) * a_i + epsilon / N.\n\n ... | [
[
"torch.nn.LogSoftmax",
"torch.ones",
"torch.sort",
"torch.Tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
FeliMe/autoseg | [
"627a6b2bda3f6da8ea7c65742b9e9d3b7d6cc845"
] | [
"uas_mood/utils/data_utils.py"
] | [
"from PIL import Image\nimport matplotlib.pyplot as plt\nimport nibabel as nib\nimport numpy as np\nfrom skimage.exposure import equalize_hist\nfrom skimage.transform import resize\nimport torch\nfrom torchvision import transforms\n\n\ndef plot(image, f=None):\n plt.axis(\"off\")\n plt.imshow(image, cmap=\"gr... | [
[
"matplotlib.pyplot.imshow",
"numpy.pad",
"numpy.eye",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"numpy.transpose",
"numpy.dtype",
"torch.tensor",
"numpy.zeros_like",
"matplotlib.pyplot.axis",
"numpy.moveaxis",
"matplotlib.pyplot.show",
"numpy.fl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wj1tr0y/openpose | [
"b0971af64080e36992b588becdd920823fac179d"
] | [
"1_extract_pose.py"
] | [
"'''\n@Author: Jilong Wang\n@Date: 2019-01-10 14:12:01\n@LastEditors: Jilong Wang\n@Email: jilong.wang@watrix.ai\n@LastEditTime: 2019-01-15 11:16:42\n@Description: file content\n'''\n# From Python\n# It requires OpenCV installed for Python\nimport sys\nimport cv2\nimport os\nfrom sys import platform\nimport numpy a... | [
[
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yangpuhai/Granularity-in-DST | [
"1d9a42966ebda675d71b4b54412133cef63ec931"
] | [
"MGL_BERTDST/BERTDST_utils/MultiWOZ_data_utils.py"
] | [
"import numpy as np\nimport json\nfrom torch.utils.data import Dataset\nimport torch\nimport random\nimport re\nfrom copy import deepcopy\nfrom collections import OrderedDict\nfrom .fix_label import fix_general_label_error\nfrom .fix_value import fix_value_dict\nfrom .fix_value import fix_time\n\nEXPERIMENT_DOMAINS... | [
[
"numpy.array",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
typicasoft/transformers | [
"a1a8ffa5126ced93c12dfb677cbe3a069f48dcf3"
] | [
"tests/test_modeling_tf_common.py"
] | [
"# coding=utf-8\n# Copyright 2019 HuggingFace 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 appl... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.keras.models.load_model",
"tensorflow.zeros",
"tensorflow.equal",
"tensorflow.debugging.assert_near",
"tensorflow.config.list_physical_devices",
"torch.no_grad",
"numpy.random.randint",
"tensorflow.keras.Input",
"tensorflow.saved_... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"1.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1... |
sgarofoli/tf-quant-finance | [
"0dafa7379100b343e22ef2d4185e442f8520f8a6"
] | [
"tf_quant_finance/math/random_ops/stateless.py"
] | [
"# Copyright 2019 Google LLC\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# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed ... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.argsort",
"tensorflow.shape",
"tensorflow.compat.v1.name_scope"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PranithChowdary/DataQ | [
"1070038b14714156c4a9a7c06f606e155d6272b1"
] | [
"downloader.py"
] | [
"import pandas as pd\nfrom io import BytesIO\nimport base64\n\n\ndef to_excel(df):\n output = BytesIO()\n writer = pd.ExcelWriter(output, engine='xlsxwriter')\n df.to_excel(writer, index = False)\n writer.save()\n processed_data = output.getvalue()\n return processed_data\n\n\ndef get_table_downlo... | [
[
"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": []
}
] |
NKrvavica/fqs | [
"d95d684d867dcb89d0a3853569d12f1f955f1d5d"
] | [
"test_quadratic_roots.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jan 3 11:14:52 2019\n\n@author: NKrvavica\n\"\"\"\n\nimport timeit\nimport numpy as np\nimport fqs\n\n\ndef eig_roots(p):\n '''Finds cubic roots via numerical eigenvalue solver\n `npumpy.linalg.eigvals` from a 3x3 companion matrix'''\n a, b = (p[:, 1]/p[:, ... | [
[
"numpy.linalg.eigvals",
"numpy.sort",
"numpy.roots",
"numpy.random.rand",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tamasorosz/artap | [
"e8df160bfc9c378c3fc96b0b86e92d75d89cf26b"
] | [
"examples/mechanical_design/gear_design.py"
] | [
"from artap.problem import Problem\nfrom artap.algorithm_genetic import NSGAII\nfrom artap.results import Results\n\nimport matplotlib.pyplot as plt\n\n\nclass GearDesignProblem(Problem):\n \"\"\"\n Example from K.DEb Multi-objective evolutionary optimization problems, Wiley, 2001.\n pp 434.\n\n The obj... | [
[
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jordanopensource/arabic-ocr-studygroup | [
"3a39593ec28976c4209c813c87d0f37db72dcc03"
] | [
"starting-code.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef to_categorical (y, num_classes=None):\n y = np.array(y, dtype='int').ravel()\n if not num_classes: num_classes = np.max(y) + 1\n n = y.shape[0]\n categorical = np.zeros((n, num_classes))\n categorical[np.arange(n), y] = 1\n return categ... | [
[
"numpy.logical_and",
"numpy.arange",
"matplotlib.pyplot.subplots",
"numpy.max",
"numpy.mean",
"numpy.array",
"matplotlib.pyplot.show",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jesperiksson/SoccermaticsForPython | [
"aeb6cdfd4dfd0acfc15e0d47024693c01ec241d8"
] | [
"classes.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Oct 24 15:00:30 2020\n\n@author: jesper\n\"\"\"\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.colors as colors\nimport seaborn as sn\n\nclass Table():\n # Makes a table at the end of a season\n... | [
[
"matplotlib.pyplot.legend",
"matplotlib.colors.PowerNorm",
"numpy.min",
"numpy.arange",
"pandas.DataFrame",
"numpy.random.poisson",
"matplotlib.pyplot.ylabel",
"numpy.max",
"matplotlib.pyplot.xlabel",
"numpy.array",
"numpy.sum",
"matplotlib.pyplot.show",
"matplo... | [
{
"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": []
}
] |
easai/stat | [
"b0485454889531af15073d6b654c5a8ac70e6a98"
] | [
"src/stat/variation.py"
] | [
"\"\"\"\n変動係数\nCV = Var[X]/E[X]\nscipy.stats.variation(array)\n\"\"\"\n\nfrom scipy import stats\n\nBiden=[28.2,28.3,25.6]\nSanders=[17.5,17.6,15.7]\nWarren=[21.3,20.6,22.7]\n\nprint(stats.variation(Biden))\nprint(stats.variation(Sanders))\nprint(stats.variation(Warren))\n\n\"\"\"\n0.04567194460232075\n0.0515582710... | [
[
"scipy.stats.variation"
]
] | [
{
"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"
... |
rbehal/CMED-Image-Analysis | [
"6fed72a381357fd964e5f8aab05c9810c419e681"
] | [
"ImageViewer.py"
] | [
"from PyQt5.QtGui import QImage, QPixmap, QPainter\nfrom PyQt5.QtWidgets import QApplication, QWidget\nfrom PyQt5.QtCore import QTimer\nfrom PyQt5 import QtCore, QtGui, QtWidgets\n\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg\nfrom matplotlib.figure import Figur... | [
[
"matplotlib.figure.Figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
weimegan/fireroad053 | [
"45b82ee29798cf51cbea3ad5fc1d4a8fbdd08507"
] | [
"combos.py"
] | [
"import json\nimport csv\nimport pandas as pd\nimport numpy as np\nimport itertools\n\nf = open('finaldata/parsedsp21_dummy.json')\ndata = json.load(f)\n\nclassescsv = pd.read_csv('finaldata/parsedsp21_actual_classes.csv')\n\n#print(classescsv['id'])\nindToId = dict()\nfor i in range(len(classescsv)):\n indToId[... | [
[
"pandas.read_csv",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
smartmzl/Quanlse | [
"7d5d00d5401d801aeb7cbcee381ccdd07331e8a7"
] | [
"Quanlse/QOperation/RotationGate.py"
] | [
"#!/usr/bin/python3\n# -*- coding: utf8 -*-\n\n# Copyright (c) 2021 Baidu, Inc. 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/... | [
[
"numpy.sqrt",
"numpy.eye",
"numpy.cos",
"numpy.sin",
"numpy.exp",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cmougan/Novartis2021 | [
"72a6f088929a5a4546760f4a453ec4a77faf5856"
] | [
"NN_files/nnet.py"
] | [
"import pandas as pd\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import Dataset\nimport numpy as np\nfrom gauss_rank_scaler import GaussRankScaler\nfrom sklearn.model_selection import train_test_split\n\nimport random\nimport os\n\n\nrandom.seed(0)\n\n\nclass ReadDataset(Dataset):\n \"\"\"Read ... | [
[
"torch.nn.BatchNorm1d",
"torch.nn.Dropout",
"pandas.read_csv",
"torch.cat",
"sklearn.model_selection.train_test_split",
"torch.nn.Linear",
"torch.nn.SELU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
michaeljohnclancy/spikeforest2 | [
"93bdde2c570aef9426b3d7bceb69f3605c9f005a"
] | [
"working/tests/kilosort2_crash_tests/thisoneworks_ks2_boyden_singularity.py"
] | [
"#!/usr/bin/env python\n\nimport numpy as np\nfrom spikeforest2 import sorters\nfrom spikeforest2 import processing\nimport hither_sf as hither\nimport kachery as ka\nimport os\n\nos.environ['HITHER_USE_SINGULARITY'] = 'TRUE'\n\nrecording_path = 'sha1dir://49b1fe491cbb4e0f90bde9cfc31b64f985870528.paired_boyden32c/9... | [
[
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ChZPan/CT-image-DeepLearningRegression | [
"38c4e2ca5427affa0cc628b34c14b85e01dbb33c"
] | [
"src/resnet50.py"
] | [
"import numpy as np\nimport tensorflow as tf\nfrom keras import backend as K\nfrom keras.layers import Dense, Dropout, Flatten, Conv2D, Input, Add, \\\n Activation, ZeroPadding2D, BatchNormalization, \\\n AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D\nfrom keras.... | [
[
"tensorflow.norm"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"1.0",
"1.2"
]
}
] |
samuelbroscheit/kge | [
"208f310d199aa3c2059467ee24c28cae86bbc10b"
] | [
"kge/util/dump.py"
] | [
"import time\nimport os\nfrom collections import OrderedDict\nimport sys\nimport torch\nimport csv\nimport yaml\nimport re\nimport socket\nimport copy\n\nfrom kge.job import Trace\nfrom kge import Config\n\n\n## EXPORTED METHODS #####################################################################\n\n\ndef add_dump... | [
[
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JanS97/PlaNet | [
"c03c1d5b51fd20d1ec907f6591856d283092767e"
] | [
"main.py"
] | [
"import argparse\nfrom math import inf\nimport os\nimport numpy as np\nimport torch\nfrom torch import nn, optim\nfrom torch.distributions import Normal\nfrom torch.distributions.kl import kl_divergence\nfrom torch.nn import functional as F\nfrom torchvision.utils import make_grid, save_image\nfrom tqdm import tqdm... | [
[
"torch.randn_like",
"torch.load",
"torch.zeros",
"torch.cat",
"numpy.asarray",
"torch.no_grad",
"torch.cuda.is_available",
"torch.device",
"torch.ones",
"numpy.zeros",
"torch.nn.functional.pad",
"torch.optim.Adam",
"torch.full",
"torch.distributions.Normal",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hthieu166/selab-aic20-track-2 | [
"5a87a075e64711388e06fc22171ee314cca1ae10"
] | [
"src/losses/triplet_loss_online_utils.py"
] | [
"from itertools import combinations\n\nimport numpy as np\nimport torch\n\n\ndef pdist(vectors):\n distance_matrix = -2 * vectors.mm(torch.t(vectors)) + vectors.pow(2).sum(dim=1).view(1, -1) + vectors.pow(2).sum(\n dim=1).view(-1, 1)\n return distance_matrix\n\n\nclass PairSelector:\n \"\"\"\n Im... | [
[
"torch.t",
"numpy.logical_not",
"torch.LongTensor",
"numpy.logical_and",
"numpy.random.choice",
"numpy.argmax",
"numpy.array",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
liyidi/MPT | [
"76c1376d73ade2ecb5bb1bdd171e6f4d266951e5"
] | [
"MPAtt/model/mobileNet.py"
] | [
"'''MobileNetV3 in PyTorch.\nSee the paper \"Inverted Residuals and Linear Bottlenecks:\nMobile Networks for Classification, Detection and Segmentation\" for more details.\n'''\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn import init\n\n\n\nclass hswish(nn.Module):\n def f... | [
[
"torch.nn.Sequential",
"torch.nn.BatchNorm1d",
"torch.nn.init.constant_",
"torch.randn",
"torch.nn.functional.avg_pool2d",
"torch.nn.Conv2d",
"torch.nn.functional.relu6",
"torch.nn.Linear",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.init.normal_",
"torch.nn.BatchNorm2d",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
awesome-archive/mixmatch | [
"77bf67ddf15fa51b6784d5aad1a4793b43352f7f"
] | [
"scripts/check_split.py"
] | [
"#!/usr/bin/env python\n\n# Copyright 2019 Google LLC\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# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by ... | [
[
"tensorflow.round"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"1.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1... |
AngelLiang/hacking-influxdb-python | [
"d5d12499f3755199d5eedd8b363450f1cf4073bd"
] | [
"influxdb/_dataframe_client.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"DataFrame client for InfluxDB.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport math\nfrom collections import defaultdict\n\nimport pandas as pd\nimport numpy as ... | [
[
"pandas.concat",
"pandas.to_datetime",
"pandas.Series",
"pandas.DataFrame",
"numpy.int64",
"pandas.Timestamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zaxtax/arviz | [
"c78deefeeb355d3cee11a93fc148f9198dde8b35"
] | [
"arviz/tests/external_tests/test_data_cmdstan.py"
] | [
"# pylint: disable=no-member, invalid-name, redefined-outer-name\n# pylint: disable=too-many-lines\nimport os\n\nimport numpy as np\nimport pytest\n\nfrom ... import from_cmdstan\n\nfrom ..helpers import check_multiple_attrs\n\n\nclass TestDataCmdStan:\n @pytest.fixture(scope=\"session\")\n def data_directory... | [
[
"numpy.arange",
"numpy.array",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wecacuee/habitat-sim | [
"973ab45c08e8b6d7e578db87b25700fbfdd10a02"
] | [
"tests/test_simulator.py"
] | [
"import multiprocessing\nimport os.path as osp\nimport random\n\nimport magnum as mn\nimport numpy as np\nimport pytest\n\nimport examples.settings\nimport habitat_sim\n\n\ndef test_no_navmesh_smoke(sim):\n sim_cfg = habitat_sim.SimulatorConfiguration()\n agent_config = habitat_sim.AgentConfiguration()\n #... | [
[
"numpy.array",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
geosharma/PyNite | [
"efffccdbff6727d3b271ba2937e35892d9df8c00"
] | [
"Examples/large_grid_frame/create_frame_data.py"
] | [
"## -*- coding: utf-8 -*-\n\"\"\"\nMIT License\n\nCopyright (c) 2020 tamalone1\n\"\"\"\nimport numpy as np\nimport itertools, csv, os\n\n# Nodes coordinates in a 3D rectangle\nx_values = np.linspace(0, 10, 5)\ny_values = np.linspace(0, 50, 11)\nz_values = np.linspace(0, 10, 5)\n\n# Create a 3D grid of nodes (list o... | [
[
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MrSyee/rl_algorithms | [
"5b5276982032f8a8a614b9466849b7b3ef245b3e"
] | [
"rl_algorithms/common/abstract/agent.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"Abstract Agent used for all agents.\n\n- Author: Curt Park\n- Contact: curt.park@medipixel.io\n\"\"\"\n\nfrom abc import ABC, abstractmethod\nimport argparse\nimport os\nimport shutil\nimport subprocess\nfrom typing import Tuple, Union\n\nimport gym\nfrom gym.spaces import Discrete\n... | [
[
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
William-An/WaveMusician | [
"d1829e51f8a6d7fee2ff5571375b7488681796bb"
] | [
"WaveForms/main.py"
] | [
"import dwf\nimport time\nimport sys\nimport random\nfrom mido import MidiFile\nfrom musicConstants import NOTES_FREQ\nfrom midi2Cmd import MidiFileParser\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n##\n# \n# @description MusicWave for Analog Discovery Device 2, using WaveForm SDK\n#\n# @author William\... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lbasek/named-entity-recognition | [
"d21e41442b67161285efe02a6cb032ce63b8ecf2"
] | [
"evaluation.py"
] | [
"import itertools\nimport sys\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn.metrics import confusion_matrix, precision_recall_fscore_support\n\nfrom utils.classification_report import classification_report\nfrom utils.plot_confusion_matrix_util import plot_confusion_matrix\n\n\ndef evaluate(m... | [
[
"numpy.set_printoptions",
"sklearn.metrics.confusion_matrix",
"matplotlib.pyplot.savefig",
"sklearn.metrics.precision_recall_fscore_support",
"numpy.argmax",
"matplotlib.pyplot.close",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mzzhong2/isce2 | [
"7e9c86910afcbe3e39815ebf5ecc744e0c9caee8"
] | [
"contrib/geo_autoRIFT/geogrid/GeogridOptical.py"
] | [
"#!/usr/bin/env python3\n\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Copyright 2019 California Institute of Technology. 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 Li... | [
[
"numpy.dot",
"numpy.abs",
"numpy.min",
"numpy.linalg.norm",
"numpy.round",
"numpy.max",
"numpy.ceil",
"numpy.floor",
"numpy.cross",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jlezama/disentangling-jacobian | [
"c570945055c735a15b9adba093b7c688c7310aad"
] | [
"unsupervised_disentangling/utils.py"
] | [
"import torch\nimport torch.nn as nn\n\nfrom PIL import Image\nimport matplotlib\nmatplotlib.use('agg')\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport argparse\n\n\ndef to_img(x):\n x = 1-x.clamp(0, 1)\n x = x.view(x.size(0), 1, 28, 28)\n return x\n\ndef show(img, outfname=None):\n npimg =... | [
[
"torch.mean",
"matplotlib.pyplot.imshow",
"torch.cat",
"matplotlib.use",
"torch.t",
"torch.nn.MSELoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LEOCUIZHIHAO/segcarpoint | [
"42d78cde1f28b0c705f7755356610cf3039c3caf"
] | [
"bcl_caffe/layers/bcl_layers.py"
] | [
"\nfrom pathlib import Path\nimport pickle\nimport shutil\nimport time, timeit\nimport numpy as np\nimport torch\nimport torchplus\n\nfrom google.protobuf import text_format\nimport second.data.kitti_common as kitti\nfrom second.builder import target_assigner_builder, voxel_builder\nfrom second.pytorch.core import ... | [
[
"numpy.expand_dims",
"numpy.minimum",
"numpy.squeeze",
"torch.utils.data.DataLoader",
"numpy.concatenate",
"numpy.mean",
"numpy.exp",
"numpy.where",
"numpy.square",
"numpy.clip",
"numpy.unique",
"numpy.arange",
"numpy.eye",
"numpy.stack",
"numpy.sin",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
carpedkm/vedatad | [
"55f8dced57f698ee9fc0da9bcf471d171e718d0c"
] | [
"vedacore/image/photometric.py"
] | [
"import cv2\nimport numpy as np\n\n\ndef imnormalize(img, mean, std, to_rgb=True):\n \"\"\"Normalize an image with mean and std.\n\n Args:\n img (ndarray): Image to be normalized.\n mean (ndarray): The mean to be used for normalize.\n std (ndarray): The std to be used for normalize.\n ... | [
[
"numpy.full_like",
"numpy.right_shift",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PranavBharadwaj-1328/Image_Filtering_methods | [
"a608c81a47f85adb38604f8f8d9503f5bf6555f7"
] | [
"tophat.py"
] | [
"import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef openimg():\n F = input()\n img = cv2.imread(F,0)\n kernel = np.ones((5,5),np.uint8)\n opening = cv2.morphologyEx(img,cv2.MORPH_TOPHAT,kernel)\n plt.subplot(121),plt.imshow(img)\n plt.title('Original'),plt.xticks([]),plt.yticks... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.title",
"numpy.ones",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
imanpalsingh/projection-pursuit | [
"307ad765d447e81dce909dfa9778db1610704315"
] | [
"skpp/tests/test_skpp.py"
] | [
"# run with python(3) -m pytest\n\nimport numpy\nimport pytest\nimport time\n\nfrom sklearn.utils import estimator_checks\nfrom sklearn.utils.testing import assert_equal\nfrom sklearn.utils.testing import assert_less\nfrom sklearn.utils.testing import assert_almost_equal\nfrom sklearn.utils.testing import assert_ar... | [
[
"numpy.dot",
"numpy.arange",
"numpy.eye",
"sklearn.utils.testing.assert_raises",
"numpy.random.shuffle",
"sklearn.utils.testing.assert_less",
"numpy.random.randn",
"sklearn.utils.estimator_checks.check_estimator",
"numpy.random.rand",
"sklearn.utils.testing.assert_array_equ... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
c-d-leonard/N5K | [
"99d844621f2436aaf56fc98484e309043d4b7bd1"
] | [
"timer.py"
] | [
"import sys\nsys.path.append(\"fftlogx/\")\nimport numpy as np\nimport time\nimport n5k\n\n\ndef time_run(cls, config, niter):\n c = cls(config)\n c.setup()\n ts = np.zeros(niter+1)\n for i in range(niter+1):\n t0 = time.time()\n c.run()\n tf = time.time()\n ts[i] = tf-t0\n ... | [
[
"numpy.savez",
"numpy.sqrt",
"numpy.std",
"numpy.mean",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AGrigis/pynet | [
"d0e6a3e6e954ae0e59fddfe85fe12ce0ef1e6fe4"
] | [
"pynet/plotting/image.py"
] | [
"# -*- coding: utf-8 -*-\n##########################################################################\n# NSAp - Copyright (C) CEA, 2019\n# Distributed under the terms of the CeCILL-B license, as published by\n# the CEA-CNRS-INRIA. Refer to the LICENSE file or to\n# http://www.cecill.info/licences/Licence_CeCILL-B_V1... | [
[
"numpy.concatenate",
"numpy.transpose",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TUDelft-CITG/OpenTNSim | [
"7d3566c9027fe6874b9196e03aafd70e4f5919f5"
] | [
"opentnsim/corelock2.py"
] | [
"\"\"\"Main module.\"\"\"\n\n# package(s) related to time, space and id\nimport json\nimport logging\nimport uuid\n\n# you need these dependencies (you can get these from anaconda)\n# package(s) related to the simulation\nimport simpy\nimport random\nimport networkx as nx\nimport numpy as np\n\n# spatial libraries\... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
radroid/project-one-sentiment-analysis | [
"cff3ebfe7a2d3ab6bc4fa6b93669aee995d5b43b"
] | [
"train/train.py"
] | [
"import argparse\nimport json\nimport os\nimport pickle\nimport sys\nimport sagemaker_containers\nimport pandas as pd\nimport torch\nimport torch.optim as optim\nimport torch.utils.data\n\nfrom model import LSTMClassifier\n\ndef model_fn(model_dir):\n \"\"\"Load the PyTorch model from the `model_dir` directory.\... | [
[
"torch.load",
"torch.manual_seed",
"torch.utils.data.TensorDataset",
"torch.utils.data.DataLoader",
"torch.from_numpy",
"torch.nn.BCELoss",
"torch.cuda.is_available",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhanwenchen/Scene-Graph-Benchmark.pytorch | [
"c86475bcbdaefcc1656a2890194355c2b32aa694"
] | [
"maskrcnn_benchmark/modeling/roi_heads/relation_head/model_transformer.py"
] | [
"\"\"\"\nBased on the implementation of https://github.com/jadore801120/attention-is-all-you-need-pytorch\n\"\"\"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nfrom maskrcnn_benchmark.modeling.utils import cat\nfrom .utils_motifs import obj_edge_vectors, to_onehot, nms_o... | [
[
"torch.nn.Softmax",
"torch.nn.Dropout",
"torch.nn.functional.softmax",
"torch.LongTensor",
"numpy.sqrt",
"torch.cat",
"numpy.power",
"torch.nn.utils.rnn.pad_sequence",
"torch.nn.init.xavier_normal_",
"torch.arange",
"torch.nn.Embedding",
"torch.nn.LayerNorm",
"t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lcwong0928/pyna | [
"44210812268cb3dbbaaee8caa58e48e4c47372e9"
] | [
"python/vcf/optimizer.py"
] | [
"import pandas as pd\nimport re\nimport json\nfrom pandas import ExcelWriter\n\nstandard = {'PIK3R1', 'PTEN', 'PIK3CG', 'TP53',\n 'PTPN11', 'PIK3CA', 'RB1', 'PDGFRA', 'MET',\n 'ATRX', 'CDK4', 'EGFR', 'IDH1', 'NF1',\n 'CDKN2A', 'MDM4', 'MDM4', 'MDM2', 'CDK6', 'LTBP4'}\n\n\ndef score(... | [
[
"pandas.isnull",
"pandas.ExcelWriter"
]
] | [
{
"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": []
}
] |
lawrenceyan/mango-explorer | [
"ea16f2a27c51e9e5e0f79d491828ad250f970452"
] | [
"mango/account.py"
] | [
"# # ⚠ Warning\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT\n# LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN\n# NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES O... | [
[
"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": []
}
] |
vasslitvinov/arkouda | [
"7751a512bd93211c4739d859462a7f1ae9ff8b4a"
] | [
"benchmarks/setops.py"
] | [
"#!/usr/bin/env python3 \n\nimport time, argparse\nimport numpy as np\nimport arkouda as ak\n\nOPS = ('intersect1d', 'union1d', 'setxor1d', 'setdiff1d')\nTYPES = ('int64',)\n\ndef time_ak_setops(N_per_locale, trials, dtype):\n print(\">>> arkouda setops\")\... | [
[
"numpy.isclose",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lucaslingle/memn2n | [
"50007bc79d0bdaaf25723d78e86a1ee57bcf2237"
] | [
"babi_dataset_utils.py"
] | [
"import os\nimport re\nimport numpy as np\nimport pickle\nimport math\nimport errno\nimport collections\n\nclass Sentence:\n def __init__(self, string):\n self.string = string\n\n def get_tokens(self, drop_punctuation=True):\n if drop_punctuation:\n tokens = re.findall(r\"[\\w]+\", se... | [
[
"numpy.random.shuffle",
"numpy.ones",
"numpy.concatenate",
"numpy.random.permutation",
"numpy.array",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PiotrGrzybowski/ProbabilisticMachineLearning | [
"c835a1bdf7ab1b2e58bcf90ae02b7405c9c72977"
] | [
"Lab2/task3.py"
] | [
"import numpy as np\nimport itertools\n\n\ndef student_application(tries, successes, probabilities):\n faculties = set(np.arange(tries))\n combinations = set(itertools.combinations(faculties, successes))\n\n return np.sum([np.prod([probabilities[i] for i in combination]) *\n np.prod([1 - ... | [
[
"numpy.arange",
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
e-koch/regions | [
"d1a6dd34def9442133065041974b2e33cafaf1cf"
] | [
"regions/_utils/wcs_helpers.py"
] | [
"# Licensed under a 3-clause BSD style license - see LICENSE.rst\n# (taken from photutils: should probably migrate into astropy.wcs)\nfrom __future__ import absolute_import, division, print_function, unicode_literals\nimport numpy as np\nfrom astropy import units as u\nfrom astropy.coordinates import UnitSphericalR... | [
[
"numpy.arctan2",
"numpy.hypot"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
brian220/sketch2pointcloud | [
"55f9011dff89963af57d1bb842f763d2fa3603d2"
] | [
"layers/gcn.py"
] | [
"# -*- coding: utf-8 -*-\n#\n# Developed by Chao Yu Huang <b608390.cs08g@nctu.edu.tw>\n# Lot's of codes are borrowed from treeGCN: \n# https://github.com/seowok/TreeGAN\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.init as init\nimport math\n\nclass TreeGCN(nn.Module):\n def __init__(self, batch, depth... | [
[
"torch.nn.Linear",
"torch.FloatTensor",
"torch.nn.LeakyReLU",
"torch.nn.init.calculate_gain"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SouppuoS/segan | [
"90d174e1e2277cb6a1711fe0fe94646cf0c116da"
] | [
"segan_module.py"
] | [
"import torch\nimport torch.nn as nn\n\nclass wavnetlike(nn.Module):\n def __init__(self, stride=2, kernal_size=31, channel_size=[], rev=False):\n super(wavnetlike, self).__init__()\n\n self.num_layer = len(channel_size) - 1\n self.cnn = nn.ModuleList([])\n self.skipTns = nn.M... | [
[
"torch.nn.BatchNorm1d",
"torch.cat",
"torch.nn.PReLU",
"torch.nn.ModuleList",
"torch.randn",
"torch.nn.Sigmoid",
"torch.nn.Linear",
"torch.nn.LeakyReLU",
"torch.nn.Conv1d",
"torch.nn.ConvTranspose1d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
peerschuett/lattice_net | [
"bedee4c7e4adf5ae191a408597c058f2638c96cc"
] | [
"latticenet_py/misc/compute_class_frequency.py"
] | [
"#!/usr/bin/env python3.6\n\nimport torch\nfrom torch.autograd import Function\nfrom torch import Tensor\n\nimport sys\nimport os\nimport numpy as np\n# http://wiki.ros.org/Packages#Client_Library_Support\nimport rospkg\nrospack = rospkg.RosPack()\nsf_src_path=rospack.get_path('surfel_renderer')\nsf_build_path=os.p... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Pandinosaurus/clana | [
"270da7fff0c09f690c8b9595bc29405eb9fdd244"
] | [
"clana/clustering.py"
] | [
"\"\"\"Everything about clustering classes of a confusion matrix.\"\"\"\n\n# Core Library\nimport logging\nimport random\nfrom typing import Any, List, TypeVar, Union\n\n# Third party\nimport numpy as np\n\n# First party\nimport clana.utils\n\ncfg = clana.utils.load_cfg()\nlogger = logging.getLogger(__name__)\n\n\n... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dnnspark/dsynth | [
"4dd0d502e143f0aece5739084ee5a45346608554"
] | [
"dsynth/util/object_loader.py"
] | [
"\"\"\"\n(ext) Carbon copy of:\n https://vhub.vicarious/vicarious/dataset/blob/master/dataset/util/object_loader.py\n\nWavefront .obj file loader.\n\nSupports basic material and texture properties.\nVertex colors are not supported.\n\"\"\"\n\nimport re\nimport os\nimport numpy as np\nimport logging\nimport math\... | [
[
"numpy.min",
"numpy.asarray",
"numpy.arccos",
"numpy.max",
"numpy.iinfo",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WilsonYangLiu/TCGADownload | [
"d3af7e3fecacd0703c4c82ebd889593a5ffb2e41"
] | [
"script/TPM.py"
] | [
"from __future__ import print_function, division\r\n\r\n#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n\r\n'''\r\nCopyright (c) 2016 Wei-Xin Liu\r\n\r\nPermission is hereby granted, free of charge, to any person obtaining a copy\r\nof this software and associated documentation files (the \"Software\"), to deal... | [
[
"pandas.Series",
"pandas.read_csv",
"numpy.sum",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
dylansnow/vimba_pose_detection | [
"2aeac00cb678f8a7f490f52be3fe5f87573703db"
] | [
"Vimba_5.0/VimbaPython/Source/vimba/frame.py"
] | [
"\"\"\"BSD 2-Clause License\n\nCopyright (c) 2019, Allied Vision Technologies GmbH\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n1. Redistributions of source code must retain the above copyr... | [
[
"numpy.ndarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
congvmit/mipkit | [
"d65a5083852dcfc5db766175aa402a5e3a506f21"
] | [
"examples/test_debugger.py"
] | [
"\"\"\"\n The MIT License (MIT)\n Copyright (c) 2021 Cong Vo\n \n Permission is hereby granted, free of charge, to any person obtaining a copy\n of this software and associated documentation files (the \"Software\"), to deal\n in the Software without restriction, including without limitation the rights\n to use, co... | [
[
"torch.ones",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sizhky/detr | [
"54f18a0b3a3be69be4c451567ea730c731c7ad48"
] | [
"main.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\nimport argparse\nimport datetime\nimport json\nimport random\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nfrom torch.utils.data import DataLoader, DistributedSampler\n\nimport datasets\nimport util.misc as utils\... | [
[
"torch.utils.data.DistributedSampler",
"numpy.random.seed",
"torch.load",
"torch.manual_seed",
"torch.utils.data.SequentialSampler",
"torch.utils.data.DataLoader",
"torch.utils.data.RandomSampler",
"torch.optim.AdamW",
"torch.save",
"torch.nn.parallel.DistributedDataParalle... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
elopezphy/basic_dash | [
"deda0997d5b7e5378a0b3124791f9bfee4cee4cd"
] | [
"app.py"
] | [
"import dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport pandas as pd\n\ndata = pd.read_csv('data/avocado.csv')\n\ndata = data.query(\"type == 'conventional' and region == 'Albany'\")\ndata[\"Date\"] = pd.to_datetime(data[\"Date\"], format=\"%Y-%m-%d\")\ndata.sort_values(\"Date\... | [
[
"pandas.read_csv",
"pandas.to_datetime"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
NithinKumaraNT/DNN_Quantizer | [
"3a6885f77aabb9b539e554a34a1c7ad358a39336"
] | [
"examples/Optimize_Quant.py"
] | [
"\"\"\"\nAn example that learns the optimal approximate uniform symmetric mid-even quantizer for a given data distribution. \nWe use Stochastic gradient descent for optimization of the range. The #steps used for quantization is a fixed design\nparameter. We test it with:\n\n I) Normal distributed data\n II)... | [
[
"numpy.sqrt",
"numpy.linspace",
"matplotlib.pyplot.plot",
"numpy.random.randn",
"numpy.random.laplace",
"tensorflow.reset_default_graph",
"scipy.stats.laplace.pdf",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.title",
"tensorflow.pow",
"tensorflow.global_variables_ini... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
meokz/psd-tools | [
"64197a1c0f75b8d1b3bcfaaae7fa2b97e34ffb1e"
] | [
"src/psd_tools/composer/blend.py"
] | [
"\"\"\"\nBlending module.\n\nCheck Blending_ section of W3C recommendation for blending mode definitions.\n\n.. _Blending: https://www.w3.org/TR/compositing/#blending\n\"\"\"\nfrom __future__ import absolute_import, unicode_literals\nimport logging\n\nfrom psd_tools.utils import new_registry\nfrom psd_tools.constan... | [
[
"numpy.expand_dims",
"numpy.maximum",
"numpy.minimum",
"numpy.sqrt",
"numpy.abs",
"numpy.min",
"numpy.stack",
"numpy.max",
"numpy.copy",
"numpy.zeros_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jkhu29/SoCo | [
"1cef465ce5bdc975a72d3d869147ebeb6031781d"
] | [
"main_linear.py"
] | [
"# --------------------------------------------------------\n# SoCo\n# Copyright (c) 2021 Microsoft\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Yue Gao\n# --------------------------------------------------------\n\n\nimport json\nimport os\nimport time\n\nimport torch\nimport torch.bac... | [
[
"torch.distributed.init_process_group",
"torch.cuda.set_device",
"torch.load",
"torch.nn.functional.cross_entropy",
"torch.no_grad",
"torch.utils.tensorboard.SummaryWriter",
"torch.distributed.get_rank",
"torch.distributed.get_world_size",
"torch.nn.parallel.DistributedDataPara... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Hossam86/Statistics-Using-Python | [
"25fa7fb574c0cc8af48ea780da033a34b14affe8"
] | [
"confidence_interval_2.py"
] | [
"import numpy as np\nimport pandas as pd\n\nimport matplotlib\nimport matplotlib.pyplot as plt\n\nimport scipy.stats\nimport scipy.optimize\nimport scipy.spatial\n\npoll=pd.read_csv(\"Statistics-Using-Python\\data\\poll.csv\")\npoll.info()\nprint (poll.vote.value_counts(normalize=True))\n\n#sampling func\n# =======... | [
[
"pandas.read_csv",
"numpy.random.rand",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
sbitters/enrichTSS | [
"c1b8d18c8e6f08926725290c233a04a22e41dfaf"
] | [
"modGFF.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# 2017-07-30\n# STB\n\nimport sys\nimport regex as re\nimport pandas as pd\nfrom argparse import ArgumentParser\nfrom file_read_backwards import FileReadBackwards\n\n\nMIT_license = \"\"\"Copyright 2017 Sven T. Bitters (sven.bitters@gmail.com)\n\nPermission is he... | [
[
"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": []
}
] |
desh2608/css | [
"a595f40085f2a8de9a62f9d4e34950ab55a7d27e"
] | [
"css/models/conv_tasnet.py"
] | [
"#!/usr/bin/env python3\n# This module is taken from: https://github.com/JusperLee/Conv-TasNet/blob/master/Conv_TasNet_Pytorch/Conv_TasNet.py\n\nimport torch\n\nDEFAULT_CONV_TASNET_CONF = {\n \"num_filters\": 512,\n \"filter_length\": 16,\n \"bottleneck_channels\": 128,\n \"conv_channels\": 512,\n \"... | [
[
"torch.nn.Sequential",
"torch.mean",
"torch.transpose",
"torch.nn.Softmax",
"torch.nn.BatchNorm1d",
"torch.ones",
"torch.zeros",
"torch.sqrt",
"torch.nn.PReLU",
"torch.unsqueeze",
"torch.nn.Sigmoid",
"torch.nn.Conv1d",
"torch.chunk",
"torch.nn.ReLU",
"to... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
scivision/apexpy | [
"a2e919fd9ea9a65d49c4c22c9eb030c8ccf48386"
] | [
"tests/test_Apex.py"
] | [
"# -*- coding: utf-8 -*-\n\nfrom __future__ import division, absolute_import, unicode_literals\n\nimport datetime as dt\nimport warnings\n\nimport numpy as np\nimport pytest\nfrom numpy.testing import assert_allclose\n\nfrom apexpy import fortranapex as fa\nfrom apexpy import Apex, ApexHeightError, helpers\n\n\n###... | [
[
"numpy.allclose",
"numpy.append",
"numpy.testing.assert_allclose",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
markvilar/Trajectory | [
"1879bec9c0383576464d92772f2b802cd2cbd725"
] | [
"Python/georeference.py"
] | [
"import copy\n\nfrom typing import Dict, List, Tuple\n\nimport matplotlib\nmatplotlib.use(\"TkAgg\")\nimport matplotlib.pyplot as plt\nplt.style.use(\"./Styles/Scientific.mplstyle\")\nimport matplotlib.patches as patches\nimport msgpack\nimport numpy as np\nimport quaternion as quat\n\nimport optimization\n\nfrom c... | [
[
"numpy.min",
"matplotlib.use",
"matplotlib.pyplot.subplots",
"numpy.stack",
"numpy.max",
"numpy.array",
"matplotlib.pyplot.style.use",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WagnerLabPapers/Waskom_PNAS_2017 | [
"ef7ec8513c61ef031e09f9e67a3d061b038f8db0"
] | [
"sup_figure_4.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nfrom plotutils import set_style, savefig, get_colormap, get_subject_order\n\nfrom figure_2 import plot_brains, plot_hists\n\n\ndef setup_figure():\n\n f = plt.figure(figsize=(7, 2.8))\n\n brain_gs = plt.GridSpec(3, 4, .13, .13, .87, .99, .05, .05)\n b... | [
[
"numpy.arange",
"matplotlib.pyplot.GridSpec",
"numpy.array_split",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hanaecarrie/pisap | [
"958f53dbc28afc6fb84c7f3d678c8549307ef9f5"
] | [
"pisap/base/image.py"
] | [
"##########################################################################\n# XXX - Copyright (C) XXX, 2017\n# Distributed under the terms of the CeCILL-B license, as published by\n# the CEA-CNRS-INRIA. Refer to the LICENSE file or to\n# http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html\n# for details.\n... | [
[
"numpy.abs",
"numpy.asarray",
"numpy.ndarray",
"numpy.ones",
"numpy.iscomplex"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
maryamghr/whatshap | [
"61f64612751af3c0882582e6e879c6e6a90a3556"
] | [
"whatshap/cli/compare.py"
] | [
"\"\"\"\nCompare two or more phasings\n\"\"\"\nimport logging\nimport math\nfrom collections import defaultdict\nfrom contextlib import ExitStack\nimport dataclasses\nfrom itertools import chain, permutations\nfrom typing import Set, List, Optional, DefaultDict, Dict\n\nfrom whatshap.vcf import VcfReader, VcfVarian... | [
[
"matplotlib.backends.backend_pdf.PdfPages",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.gca",
"matplotlib.use",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.close",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.hist",
"matplotlib.pyplot.ylabe... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
snoop2head/Open_stock_liquidity | [
"a5450774c50f14433915c37d057fa20a8196cdb9"
] | [
"app_pandas_to_dataframe_or_to_excel.py"
] | [
"import pandas as pd\nfrom pandas import ExcelWriter\n\n\ncode_df = pd.read_html('http://kind.krx.co.kr/corpgeneral/corpList.do?method=download&searchType=13', header=0)[0]\n\n# 종목코드가 6자리이기 때문에 6자리를 맞춰주기 위해 설정해줌\ncode_df.종목코드 = code_df.종목코드.map('{:06d}'.format)\n\n# 우리가 필요한 것은 회사명과 종목코드이기 때문에 필요없는 column들은 제외해준다.\n... | [
[
"pandas.ExcelWriter",
"pandas.read_html",
"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": []
}
] |
subhayuroy/vedo | [
"4c4686fe14f42b2d79d4e138afed362813ede4d3"
] | [
"vedo/mesh.py"
] | [
"import numpy as np\nimport os\nimport vtk\nimport vedo\nfrom vedo.colors import printc, getColor, colorMap\nfrom vedo.utils import isSequence, flatten, mag, buildPolyData, numpy2vtk, vtk2numpy\nfrom vedo.pointcloud import Points\nfrom deprecated import deprecated\n\n__doc__ = (\"\"\"Submodule to manage polygonal m... | [
[
"numpy.dot",
"numpy.split",
"numpy.log",
"numpy.ascontiguousarray",
"numpy.asarray",
"numpy.linalg.norm",
"numpy.arccos",
"numpy.argmin",
"numpy.cross",
"numpy.array",
"numpy.zeros",
"numpy.isin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cuishuhao/HDA | [
"1733ca74eee7839b455e9ffd7a169bc54b272745"
] | [
"scripts/train_ssda.py"
] | [
"import argparse\nimport os\nimport os.path as osp\nimport sys\nsys.path.append(\".\")\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader\nfrom torch.autograd import Variable\nimport random\nimport pdb\nimport math\nfrom distutils.version... | [
[
"torch.abs",
"torch.mean",
"torch.nn.Sequential",
"torch.nn.Softmax",
"torch.max",
"torch.nn.CrossEntropyLoss",
"torch.cat",
"torch.utils.data.DataLoader",
"torch.sum",
"torch.std",
"torch.no_grad",
"torch.pow",
"torch.squeeze"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chrinide/pyscf | [
"8ea26f650566faac6621af0101441becaf7fe399"
] | [
"pyscf/hessian/rks.py"
] | [
"#!/usr/bin/env python\n# Copyright 2014-2019 The PySCF Developers. 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/LIC... | [
[
"numpy.dot",
"numpy.allclose",
"numpy.einsum",
"numpy.asarray",
"numpy.linalg.norm",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ShanghuoLi/Adam-Ginsburg-pyspeckit | [
"841e8f1d742f2ee6ff0fac5dc097e598ba62d74a"
] | [
"pyspeckit/spectrum/plotters.py"
] | [
"\"\"\"\n=======\nPlotter\n=======\n\n.. moduleauthor:: Adam Ginsburg <adam.g.ginsburg@gmail.com>\n\"\"\"\nfrom __future__ import print_function\nimport matplotlib\nimport matplotlib.figure\nimport numpy as np\nimport astropy.units as u\nimport copy\nimport inspect\nfrom astropy import log\n\ntry:\n from matplot... | [
[
"numpy.nanmax",
"matplotlib.cbook._BoundMethodProxy",
"numpy.abs",
"numpy.nanmin",
"numpy.concatenate",
"numpy.argsort"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
abdelq/pybaselines | [
"043aa7875efe1ca01c3e8e9ae7c57a67274aff06"
] | [
"tests/test_optimizers.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"Tests for pybaselines.optimizers.\n\n@author: Donald Erb\nCreated on March 20, 2021\n\n\"\"\"\n\nimport numpy as np\nfrom numpy.testing import assert_array_almost_equal\nimport pytest\n\nfrom pybaselines import optimizers\n\nfrom .conftest import get_data, AlgorithmTester\n\n\n@pytes... | [
[
"numpy.vstack",
"numpy.testing.assert_array_almost_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kipolovnikov/cooltools | [
"986fe95f96978f669204226d99e3c0a6fd5be208"
] | [
"cooltools/lib/common.py"
] | [
"import numpy as np\nimport pandas as pd\n\n\ndef assign_supports(features, supports, labels=False, suffix=\"\"):\n \"\"\"\n Assign support regions to a table of genomic intervals.\n\n Parameters\n ----------\n features : DataFrame\n Dataframe with columns `chrom`, `start`, `end`\n or `... | [
[
"pandas.Series"
]
] | [
{
"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": []
}
] |
msakai/chainer-compiler | [
"77190561408911b33904a20c47f734f38790cfdf"
] | [
"scripts/gen_extra_test.py"
] | [
"\"\"\"Yet another ONNX test generator for custom ops and new ops.\"\"\"\n\n\nimport chainer\nimport chainer.functions as F\nimport chainer.links as L\nimport numpy as np\nimport onnx\n\nimport onnx_script\nimport test_case\n\nimport gen_chainercv_op_tests\nimport sentiment\n\n\n_extract_value_info = onnx_script._e... | [
[
"numpy.split",
"numpy.expand_dims",
"numpy.random.random",
"numpy.pad",
"numpy.sqrt",
"numpy.abs",
"numpy.arange",
"numpy.squeeze",
"numpy.stack",
"numpy.ones",
"numpy.concatenate",
"numpy.prod",
"numpy.transpose",
"numpy.array",
"numpy.sum",
"numpy.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Tyuzu/opencv_snippets | [
"f6690baa72a3119b9545f5c031c523dccddf1281"
] | [
"corners.py"
] | [
"import numpy as np\nimport cv2\n\nimg = cv2.imread('k.jpg')\ngray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\ngray = np.float32(gray)\n\ncorners = cv2.goodFeaturesToTrack(gray, 100, 0.01, 10)\ncorners = np.int0(corners)\n\nfor corner in corners:\n x,y = corner.ravel()\n cv2.circle(img,(x,y),3,255,-1)\n \ncv2.... | [
[
"numpy.int0",
"numpy.float32"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adamnovak/biopython | [
"92772dd6add33e0b87ab593841f924f0f6f16090"
] | [
"Bio/Affy/CelFile.py"
] | [
"# Copyright 2004 by Harry Zuzan. All rights reserved.\n# This code is part of the Biopython distribution and governed by its\n# license. Please see the LICENSE file that should have been included\n# as part of this package.\n\n\"\"\"\nClasses for accessing the information in Affymetrix cel files.\n\nFunctions:\n... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hamdi-haddad/pyEIT | [
"30c47839c537dbdfb65f2b70daa68f4cc8e13d9a"
] | [
"examples/eit_dynamic_greit.py"
] | [
"# coding: utf-8\n\"\"\" demo using GREIT \"\"\"\n# Copyright (c) Benyuan Liu. All Rights Reserved.\n# Distributed under the (new) BSD License. See LICENSE.txt for more info.\nfrom __future__ import division, absolute_import, print_function\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport pyeit.mesh... | [
[
"numpy.real",
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
krrish94/learn_tensorflow | [
"b5725bfbd09911e7c7342ab76eea07e294d5573c"
] | [
"lstm_repetition_detection_classifier.py"
] | [
"# Tutorial from: https://jasdeep06.github.io/posts/Understanding-LSTM-in-Tensorflow-MNIST/\n\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.contrib import rnn\n\n\n\n# Seed RNG\nrng_seed = 12345\nnp.random.seed(rng_seed)\ntf.set_random_seed(rng_seed)\n\n# Declare constants\n\n# Dataset generation pa... | [
[
"tensorflow.nn.softmax_cross_entropy_with_logits",
"numpy.expand_dims",
"tensorflow.contrib.rnn.GRUCell",
"numpy.squeeze",
"tensorflow.cast",
"tensorflow.nn.l2_loss",
"numpy.random.randint",
"tensorflow.train.MomentumOptimizer",
"numpy.argmax",
"tensorflow.Session",
"te... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
chellabeatrixkiddo/keras-frcnn | [
"0b09f279f32143e084e2b884076ee51d9daac55d"
] | [
"test_frcnn.py"
] | [
"from __future__ import division\nimport os\nimport cv2\nimport numpy as np\nimport sys\nimport pickle\nfrom optparse import OptionParser\nimport time\nfrom keras_frcnn import config\nfrom keras import backend as K\nfrom keras.layers import Input\nfrom keras.models import Model\nfrom keras_frcnn import roi_helpers\... | [
[
"numpy.expand_dims",
"pandas.Series",
"pandas.DataFrame",
"numpy.max",
"numpy.argmax",
"numpy.transpose",
"numpy.array",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"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": []
}
] |
SensorUp/LDAR_Sim | [
"e341998257eb3c74497c950935c02bb81c01eb32"
] | [
"LDAR_Sim/src/initialization/campaigns.py"
] | [
"\nfrom math import floor\nfrom numpy import zeros\n# --- initialize Campaigns ---\n\n\ndef _add_method_campaign(campaign_s_t, d_per_campaign, timesteps, n_sites, m_name):\n n_campaigns = floor(timesteps/d_per_campaign)\n return campaign_s_t.update(\n {\n m_name: {\n 'current_... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TAU-MLwell/Marginal-Contribution-Feature-Importance | [
"956f401d3af0b9da7a607cc30304669f6b723d7c"
] | [
"mci/mci_values.py"
] | [
"import matplotlib.pyplot as plt\nfrom typing import Sequence, Tuple, Optional\nfrom mci.estimators.contribution_tracker import ContributionTracker\n\n\nclass MciValues:\n\n \"\"\"contain MCI values and project relevant plots from them\"\"\"\n\n def __init__(self,\n mci_values: Sequence[float]... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.barh",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.close",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sujaynagaraj/probML_project | [
"4b1fe9d18b949b01bc1948599ee2494875c7ab92"
] | [
"plotting_helpers.py"
] | [
"import matplotlib.pyplot as plt\n\ndef to_numpy(x):\n\t\treturn x.detach().cpu().numpy()\n\ndef make_quick_plot(batch_dict, example, extra_str=\"\"):\n\n plot_dict = {x: to_numpy(batch_dict[x]) for x in ('observed_data', 'observed_tp', 'data_to_predict', 'tp_to_predict', 'observed_mask')}\n\n plt.figure()\n ... | [
[
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.title",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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