repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
waitaminutewhoareyou/Project-Euler | [
"83c9cd62e4ca4abb0344079d588fb7fa170ca545"
] | [
"Solvers/p213 - Pending.py"
] | [
"'''\n\n\nA 30×30 grid of squares contains 900 fleas, initially one flea per square.\nWhen a bell is rung, each flea jumps to an adjacent square at random (usually 4 possibilities, except for fleas on the edge of the grid or at the corners).\n\nWhat is the expected number of unoccupied squares after 50 rings of the... | [
[
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nikonikolov/oyster | [
"b2f880c644afd732ccee50b2a0c6d7c26809146a"
] | [
"launch_experiment.py"
] | [
"\"\"\"\nLauncher for experiments with PEARL\n\n\"\"\"\nimport os\nimport pathlib\nimport numpy as np\nimport click\nimport json\nimport torch\n\nfrom rlkit.envs import ENVS\nfrom rlkit.envs.wrappers import NormalizedBoxEnv\nfrom rlkit.envs.render_wrapper import RenderWrapper\nfrom rlkit.torch.sac.policies import T... | [
[
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Hipkevin/KText | [
"7eda5ff5071116dc35d6598fb0a3f5cdcde722af"
] | [
"KTextTool/analyze.py"
] | [
"from sklearn.feature_extraction.text import TfidfVectorizer\nfrom .preProcess import prePro\nimport numpy as np\n\nfrom gensim import corpora, models\nfrom sklearn.metrics.pairwise import cosine_similarity\n\n\ndef TfIdf(words_ls, topN):\n \"\"\"\n 查看文档关键词\n :param words_ls: 语料\n :param topN: 前n个关键词\n ... | [
[
"numpy.argsort",
"numpy.argmin",
"sklearn.feature_extraction.text.TfidfVectorizer",
"numpy.mat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
weiliu-ai/AUDA | [
"68288a50b78805cab82b91572118d97f04caa8a1"
] | [
"xmuda/models/scn_unet.py"
] | [
"import torch\nimport torch.nn as nn\n\nimport sparseconvnet as scn\n\nDIMENSION = 3\n\n\nclass UNetSCN(nn.Module):\n def __init__(self,\n in_channels,\n m=16, # number of unet features (multiplied in each layer)\n block_reps=1, # depth\n residual... | [
[
"torch.arange",
"torch.randint",
"torch.rand",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JiazeWang/lung_nodule_detector | [
"920e7caa95e2dfd2d95bf39113aacd65f7726a9b",
"920e7caa95e2dfd2d95bf39113aacd65f7726a9b"
] | [
"preprocess/prepare_mhd_hku.py",
"dlung_v1/test.py"
] | [
"import os\nimport shutil\nimport numpy as np\nimport SimpleITK as sitk\nimport scipy.ndimage\nfrom scipy.ndimage.measurements import label\nfrom scipy.ndimage.interpolation import zoom\nfrom scipy.ndimage.morphology import binary_dilation,generate_binary_structure\nfrom skimage.morphology import convex_hull_image\... | [
[
"numpy.absolute",
"numpy.expand_dims",
"pandas.read_csv",
"numpy.clip",
"numpy.isnan",
"numpy.min",
"numpy.save",
"numpy.round",
"numpy.max",
"numpy.copy",
"numpy.concatenate",
"numpy.floor",
"numpy.array",
"numpy.where",
"numpy.sum"
],
[
"numpy.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
helene-t/pyleecan | [
"8362de9b0e32b346051b38192e07f3a6974ea9aa",
"8362de9b0e32b346051b38192e07f3a6974ea9aa"
] | [
"Tests/Methods/Geometry/test_arc_meth.py",
"Tests/Validation/Simulation/test_EM_IPMSM_FL_001.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport pytest\n\nfrom pyleecan.Classes.Arc1 import Arc1\nfrom pyleecan.Classes.Arc2 import Arc2\nfrom pyleecan.Classes.Arc3 import Arc3\nfrom numpy import pi, array, exp, sqrt\n\n\n# For AlmostEqual\nDELTA = 1e-6\n\nsplit_test = list()\n# 1) Arc1, 1 Intersection\nsplit_test.append(\n ... | [
[
"numpy.exp",
"numpy.sqrt"
],
[
"matplotlib.pyplot.close",
"numpy.array",
"numpy.linspace",
"matplotlib.pyplot.gcf"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
keithoffer/pylinac | [
"8cdd9b867133725da3baecb27e7c0d89c6b59a11",
"8cdd9b867133725da3baecb27e7c0d89c6b59a11"
] | [
"pylinac/log_analyzer.py",
"tests/test_planar_imaging.py"
] | [
"\"\"\"\nThe log analyzer module reads and parses Varian linear accelerator machine logs, both Dynalogs and Trajectory logs. The module also\ncalculates actual and expected fluences as well as performing gamma evaluations. Data is structured to be easily accessible and\neasily plottable.\n\nUnlike most other module... | [
[
"matplotlib.pyplot.autoscale",
"numpy.cumsum",
"numpy.nan_to_num",
"numpy.concatenate",
"numpy.max",
"numpy.round",
"numpy.mean",
"numpy.nanmean",
"numpy.histogram",
"numpy.where",
"numpy.intersect1d",
"numpy.size",
"numpy.std",
"numpy.diff",
"numpy.inse... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vrishank97/AlgoTrading | [
"41dd44f73d97267283032ed433dd0bfb3bd6c638"
] | [
"build/lib/algotrader/scraper.py"
] | [
"import pandas as pd\nimport sys\nsys.path.append('../')\nimport numpy as np\nimport os\nimport quandl\n\ndef getNifty():\n\tnifty_list = pd.read_csv(\"../Historical data/Nifty50list.csv\")\n\tquandl.ApiConfig.api_key = \"FDEDsMbK1E2t_PMf7X3M\"\n\t\n\tfor stock in nifty_list[\"Symbol\"]:\n\t try:\n\t prin... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
yiwei-prowler/GPflow | [
"de08879228e4077f4476279d208fd23634508a8b",
"de08879228e4077f4476279d208fd23634508a8b"
] | [
"doc/source/notebooks/basics/regression.pct.py",
"tests/gpflow/likelihoods/test_multiclass.py"
] | [
"# ---\n# jupyter:\n# jupytext:\n# formats: ipynb,.pct.py:percent\n# text_representation:\n# extension: .py\n# format_name: percent\n# format_version: '1.3'\n# jupytext_version: 1.4.0\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---... | [
[
"numpy.sqrt",
"numpy.linspace",
"numpy.genfromtxt",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlim",
"tensorflow.random.set_seed",
"matplotlib.pyplot.figure"
],
[
"numpy.log",
"numpy.allclose",
"tensorflow.zeros",
"tensorflow.ones",
"numpy.ones",
"numpy.r... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yudhik11/StackOverflow_UserQuery | [
"b3c88ed18304078901497a9a0dc99c232e1ffac4"
] | [
"app/utils/run.py"
] | [
"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport time\nimport re\nimport warnings; warnings.simplefilter('ignore')\nfrom sklearn.utils import shuffle\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\nfrom sklearn.model_selection import train_test_split\n... | [
[
"tensorflow.keras.preprocessing.sequence.pad_sequences",
"numpy.where",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
chrisdembia/pydy | [
"009d7ae57218dc985a041244e15154dfc32cf872"
] | [
"pydy/codegen/code.py"
] | [
"#!/usr/bin/env python\n\n# standard library\nimport os\nimport subprocess\nimport importlib\nimport random\nfrom itertools import chain\n\n# external libraries\nimport numpy as np\nfrom sympy import lambdify, numbered_symbols, cse, symbols\nfrom sympy.printing.ccode import CCodePrinter\ntry:\n from sympy.printi... | [
[
"numpy.asarray",
"numpy.linalg.solve"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
foxtrotmike/mine-pytorch | [
"87c6b491d4398df6353a31daecccd4b86230f2f1"
] | [
"mine/models/gan.py"
] | [
"import torch\nimport torch.nn as nn\nimport numpy as np\nimport itertools\n\nfrom mine.models.layers import LinearDiscriminator, LinearGenerator, DCGanDiscriminator, DCGanGenerator\nfrom mine.models.adaptive_gradient_clipping import adaptive_gradient_clipping_\n\nimport pytorch_lightning as pl\nimport torchvision\... | [
[
"torch.randint",
"torch.ones",
"matplotlib.pyplot.scatter",
"torch.cat",
"torch.zeros",
"torch.nn.BCELoss",
"numpy.argmax",
"torch.rand",
"matplotlib.pyplot.suptitle",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
atztogo/kspclib | [
"d842df2cb1a0c5294d184f85e62bcde1c22308a0"
] | [
"python/test/conftest.py"
] | [
"import os\nimport pytest\nimport numpy as np\n\ncurrent_dir = os.path.dirname(os.path.abspath(__file__))\n\n\n@pytest.fixture(scope='session')\ndef nacl_lattice():\n x = 5.6903014761756712 / 2\n lattice = [[0, x, x], [x, 0, x], [x, x, 0]]\n return lattice\n\n\n@pytest.fixture(scope='session')\ndef sio2_la... | [
[
"numpy.reshape",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
EIHW/Speech_Separation_DC | [
"30730bf801c3e4fca52012eae0529526d4f547f1"
] | [
"grog/evaluation/plot.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import MaxNLocator\n\nN_METRICS = 4\n\ndef mean_metrics(eval_result, name='SDR'):\n return list(map(lambda source_result: np.nanmean(source_result[name]), eval_result))\n\ndef mean_all_metrics(eval_result):\n cases = map(lambda sour... | [
[
"numpy.array",
"matplotlib.pyplot.subplots",
"numpy.mean",
"matplotlib.ticker.MaxNLocator",
"numpy.nanmean",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dataloop-ai/keras_yolo3 | [
"2aca217c8f8e240c6cffa0d1389e971dbe162b35"
] | [
"yolo3/model.py"
] | [
"\"\"\"YOLO_v3 Model Defined in Keras.\"\"\"\n\nfrom functools import wraps\n\nimport numpy as np\nimport tensorflow as tf\nfrom keras import backend as K\nfrom keras.layers import Conv2D, Add, ZeroPadding2D, UpSampling2D, Concatenate, MaxPooling2D, Dropout, GaussianNoise\nfrom keras.layers.advanced_activations imp... | [
[
"tensorflow.boolean_mask",
"tensorflow.is_nan",
"numpy.expand_dims",
"numpy.maximum",
"numpy.minimum",
"tensorflow.image.non_max_suppression",
"tensorflow.is_inf",
"numpy.argmax",
"numpy.floor",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
shhoalex/revgraph | [
"7060945aa46fbd9584861715f15b6fc8037ba53f"
] | [
"tests/core/functions/operations/math/test_matmul.py"
] | [
"import unittest\n\nimport numpy as np\n\nfrom revgraph.core.values.variable import Variable\nfrom revgraph.core.functions.operations.math.matmul import MatMul\n\n\nclass MatMulTestCase(unittest.TestCase):\n def test_dot_product_of_matrix_with_same_shape(self):\n a = Variable([[1,2,3],\n ... | [
[
"numpy.array",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ykk648/TrafficFlowPrediction | [
"bcbd5e6ea3a75fcd3618393620a883f194342c38"
] | [
"data_process/func.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport os\nimport numpy as np\nfrom functools import partial\nimport matplotlib.pyplot as plt\n\ndef get_data(sensor_node):\n DIR_PATH = 'E:/PycharmProjects/TDRL/data/'\n pathNames = []\n # sensor_node_data = np.empty([1,288])\n\n for dirName in os.list... | [
[
"numpy.linspace",
"matplotlib.pyplot.bar",
"matplotlib.pyplot.xticks",
"numpy.array",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ProGamerGov/Dream-Creator | [
"7acb45daf464b0a299edd72327ce52ee9cf3f9ff"
] | [
"utils/tile_utils.py"
] | [
"import torch\n\n\n# Create blend masks\ndef create_lin_mask(overlap, special_overlap, is_special, d, d2, rotate, c, device):\n mask_tensors = []\n if is_special:\n ones_size = special_overlap\n mask_tensors += [torch.zeros(d - (special_overlap + overlap), device=device)]\n else:\n one... | [
[
"torch.linspace",
"torch.ones",
"torch.zeros",
"torch.cat",
"torch.is_tensor",
"torch.ones_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
linaashaji/CP2A | [
"9ffd8b90d224b68502cd9b4bc54f90126db661ae"
] | [
"data/dataloader.py"
] | [
"from torch.utils.data import Dataset, DataLoader\nimport pickle\nfrom sklearn.preprocessing import OneHotEncoder\nfrom data.preprocessing import pad_sequence\nimport numpy as np\nimport pandas as pd\nimport torch\n\nclass SimulatedPIEDataset(Dataset):\n \n def __init__(self,\n path, split, da... | [
[
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KiLJ4EdeN/Streamlit_Minimal_ML | [
"1fe8fee54e4831d734abf2cf306a3627a6683b0b"
] | [
"service.py"
] | [
"# user interface\nimport tensorflow as tf\nimport streamlit as st\nfrom PIL import Image\nimport numpy as np\nfrom preprocessing import preprocess_image\n\n\nmodel = tf.keras.models.load_model('models/mnist.h5')\n\n# UI\nst.write(\"MNIST digit prediction\")\n\n# html input with an extra extension checker\nfile = s... | [
[
"tensorflow.keras.models.load_model",
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
maheshwarigagan/nlp-architect | [
"f6466edfd3ec6fe7d3682ec54306a1c65980d288"
] | [
"examples/supervised_sentiment/example_ensemble.py"
] | [
"# ******************************************************************************\n# Copyright 2017-2018 Intel Corporation\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# ... | [
[
"tensorflow.python.keras.preprocessing.text.Tokenizer",
"numpy.argmax",
"sklearn.model_selection.train_test_split",
"tensorflow.python.keras.preprocessing.sequence.pad_sequences"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.5",
"1.7",
"1.10",
"1.4"
]
}
] |
ggsdc/cornflow-examples | [
"05b4a87eb4dc2389f00f8a9d7f766aef942b9da8"
] | [
"python-examples/gui/dance/model.py"
] | [
"import pulp\nimport pytups as pt\nfrom cornflow_client import group_variables_by_name\nimport networkx as nx\nimport matplotlib.pyplot as plt\nimport matplotlib.colors as colors\n\n\ndef build_model(data, dataset_name=''):\n nodes = data['nodes']\n pairs = data['pairs']\n num_max_colors = len(nodes)\n ... | [
[
"matplotlib.pyplot.savefig"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pedrombmachado/nengo | [
"abc85e1a75ce2f980e19eef195d98081f95efd28"
] | [
"nengo/connection.py"
] | [
"import numpy as np\n\nfrom nengo.base import NengoObject, NengoObjectParam, ObjView\nfrom nengo.dists import DistOrArrayParam\nfrom nengo.ensemble import Ensemble, Neurons\nfrom nengo.exceptions import ValidationError\nfrom nengo.learning_rules import LearningRuleType, LearningRuleTypeParam\nfrom nengo.neurons imp... | [
[
"numpy.asarray",
"numpy.array",
"numpy.zeros",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adolf69/mmdetection | [
"e47c0759b2bbf57bc604338aa389e22e2247f76c"
] | [
"mmdet/datasets/coco.py"
] | [
"import itertools\nimport logging\nimport os.path as osp\nimport tempfile\n\nimport mmcv\nimport numpy as np\nfrom mmcv.utils import print_log\nfrom pycocotools.coco import COCO\nfrom pycocotools.cocoeval import COCOeval\nfrom terminaltables import AsciiTable\n\nfrom mmdet.core import eval_recalls\nfrom .builder im... | [
[
"numpy.arange",
"numpy.array",
"numpy.zeros",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IoannisKansizoglou/cite | [
"0c3e8f04bd559858f3edb7ffb6c381f85f26a2b2"
] | [
"data_loader.py"
] | [
"import numpy as np\nimport h5py\nimport os\nimport pickle\nfrom tqdm import tqdm\n\ndef load_word_embeddings(word_embedding_filename, embedding_length):\n with open(word_embedding_filename, 'r') as f:\n tok2idx = {}\n vecs = [np.zeros(embedding_length, np.float32)]\n for i, line in enumerat... | [
[
"numpy.sum",
"numpy.random.choice",
"numpy.random.shuffle",
"numpy.ceil",
"numpy.array",
"numpy.zeros",
"numpy.where",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
marximus/trackviz | [
"f06a5ee21f5cc9bdd49f62f7a2ad6de4e70f243f"
] | [
"examples/static_2d_color_frame.py"
] | [
"import imageio\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nimport trackviz.static\n\n\ntracks = pd.read_csv('sample_data/ant_tracking_res.csv').rename(columns={'frame': 't'})\nimage = imageio.get_reader('sample_data/ant_dataset.mp4').get_next_data()\n\nfig, ax = trackviz.static.trajectory_2d(tracks, c... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
bhaavanmerchant/inception-v3 | [
"3710086a3b8c868da03cec72c994569a3a9ca670"
] | [
"data/relation_tag_to_id.py"
] | [
"# encoding: utf-8\n\nimport numpy as np\nimport random\n\ndef main(filepath):\n data = np.loadtxt(filepath, delimiter=\",\", dtype=np.str)\n id_index = -1\n classes = set()\n datasets = {}\n testsets = {}\n for line in data:\n image = line[0]\n label = line[1]\n if not label ... | [
[
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
marinimau/spark_fraud_detection | [
"e39e83532cb9f53e8d674c7a54a2afdc18e84057"
] | [
"spark_fraud_detection/preprocessing.py"
] | [
"#\n# spark_fraud_detection copyright © 2021 - all rights reserved\n# Created at: 15/05/21\n# By: mauromarini\n# License: MIT\n# Repository: https://github.com/marinimau/spark_fraud_detection\n# Credits: @marinimau (https://github.com/marinimau)\n#\n\nimport pandas as pd\nimport numpy as np\nfrom scipy ... | [
[
"pandas.concat",
"scipy.stats.zscore"
]
] | [
{
"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": [
"0.13",
"1.6",
"0.14",
"1.10",
"0... |
linncy/Tester-Automation | [
"24287cb0bbaa4a264b620c0dfb0d42a777c539b5"
] | [
"main.py"
] | [
"import sys, os, random\nfrom PyQt5.QtCore import pyqtSlot\nfrom PyQt5.QtWidgets import QApplication, QWidget, QMainWindow, QPushButton, QMessageBox, QTableView\nfrom PyQt5.QtCore import Qt\nfrom PyQt5 import QtGui\nfrom ui_mainwindow import Ui_MainWindow\nimport visa\nimport csv\nimport matplotlib\nmatplotlib.use(... | [
[
"matplotlib.use",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nitinshyamk/pylds | [
"1b0b866181203890d7b8eebe1069a746574d1531",
"1b0b866181203890d7b8eebe1069a746574d1531"
] | [
"examples/poisson_lds.py",
"examples/diagonal_meanfield.py"
] | [
"from __future__ import division\nimport numpy as np\nimport numpy.random as npr\nimport matplotlib.pyplot as plt\n\nfrom scipy.stats import poisson\n\nfrom pybasicbayes.util.text import progprint_xrange\nfrom pylds.models import DefaultPoissonLDS\n\nnpr.seed(0)\n\n# Parameters\nD_obs = 10\nD_latent = 2\nT = 5000\n... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.tight_layout",
"numpy.ones_like",
"numpy.random.seed",
"matplotlib.pyplot.figure",
"numpy.arange",
"numpy.eye",
"numpy.cos",
"numpy.sin",
"numpy.ones",
"scipy.stats.poisson",
"numpy.random.randn",
"matplotlib.pyplot... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tuscan-chicken-wrap/rmllib | [
"bc5e9374367757fac17ab30143ad4e17aaec44a6"
] | [
"rmllib/data/generate/edge_rejection_generator.py"
] | [
"'''\n Joel Pfeiffer\n jpfeiffe@gmail.com\n'''\nimport numpy as np\nimport numpy.random as rnd\nimport pandas\nfrom scipy.sparse import csr_matrix\nimport time\n\ndef edge_rejection_generator(labels, negative_acception_probability=.9, density=.05, **kwargs):\n '''\n Generates a network given a set of la... | [
[
"numpy.logical_or",
"numpy.array",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
docbrisky/coronary-occlusion | [
"ddfaeba8b919c84fe655346fe3c4fc955961241b"
] | [
"second-training.py"
] | [
"import numpy as np\r\nimport os\r\nimport shutil\r\nimport wfdb\r\nimport keras\r\nfrom keras.models import load_model\r\nfrom keras.layers import Dense\r\nfrom keras.models import Model\r\nfrom keras.callbacks import ModelCheckpoint\r\nfrom keras.optimizers import SGD, Adam\r\nimport os.path\r\nimport tensorflow ... | [
[
"numpy.load",
"numpy.zeros",
"numpy.sum",
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yenh-cs/adventofcode2021 | [
"55d425483a4b24140083cfd7b17df05a7a90e130"
] | [
"day4/puzzle2.py"
] | [
"import numpy as np \n\ndef getRandomNumbers(fileName):\n with open(fileName, 'r', encoding='utf-8') as rad:\n number_line = rad.read()\n\n random_nums = number_line.rstrip('\\n').split(',')\n return [int(s) for s in random_nums]\n\ndef getBoards(fileName):\n numbers = []\n with open(fileName,... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hajians/UQ | [
"bfab2e2a1f3f410ef98792c151ae4b5d4a9aa340"
] | [
"examples/clustering.py"
] | [
"#! /usr/bin/env python2.7\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.cluster import KMeans, AffinityPropagation, MeanShift, SpectralClustering\n\nfrom UQuant.SemilinearSystem import SemiLinSystem\n\n## reading data\n# \"results/samples-11-v0.0.dat\"\ndf = pd.read_csv(\"results/uq_pcn.dat... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.tight_layout",
"pandas.read_csv",
"sklearn.cluster.KMeans",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.show",
"matplotlib.pyplot.xticks",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
jiang1hong2xie/111 | [
"905c5f2ba84c2683892d423b622e7b9a77df9d89"
] | [
"data/build_errorchecker_data.py"
] | [
"\n\n'''\nFile: generate_sequence.py\nProject: utils\nFile Created: Monday, 24th December 2018 12:23:26 pm\nAuthor: xiaofeng (sxf1052566766@163.com)\n-----\nLast Modified: Monday, 24th December 2018 12:23:37 pm\nModified By: xiaofeng (sxf1052566766@163.com>)\n-----\n 2018.06 - 2018 Latex Math, Latex Math\n'''\nfrom... | [
[
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.title",
"sklearn.cluster.KMeans",
"scipy.stats.norm.pdf",
"tensorflow.python.platform.gfile.GFile",
"tensorflow.gfile.GFile",
"numpy.load",
"numpy.stack",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"numpy.std",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
neuro-evolution/alectrnn | [
"f39476b6eb3f4270c5f7f2f93ebcc5940b9c39e4"
] | [
"examples/alectrnn_experiment_template.py"
] | [
"\"\"\"\nThis is an example script that shows how you can run ALECTRNN using evolutionary\nstrategies. Running the script requires the evostrat package from github:\nhttps://github.com/Nathaniel-Rodriguez/evostrat\n\nIt also requires a working installation of mpi4py, with some accompanying\nMPI software like MPICH ... | [
[
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kuzhamuratov/deep-landscape | [
"255999cb2f87bc72b68fe29483a4fb7605f0e26b"
] | [
"superres/src/models/modules/ade20k_segm/base.py"
] | [
"\"\"\"Modified from https://github.com/CSAILVision/semantic-segmentation-pytorch\"\"\"\n\nimport os\n\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom scipy.io import loadmat\nfrom torch.nn.modules import BatchNorm2d\n\nfrom . import resnet\n\n# constants\nARCH_ENCOD... | [
[
"torch.nn.functional.softmax",
"torch.nn.Dropout2d",
"torch.cat",
"torch.load",
"torch.zeros",
"torch.no_grad",
"torch.nn.functional.interpolate",
"torch.cuda.is_available",
"torch.nn.modules.BatchNorm2d",
"pandas.read_csv",
"scipy.io.loadmat",
"torch.LongTensor",
... | [
{
"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"... |
Yuki-F-HCU/filterdesigner | [
"bb735d507da0338b2925f84e54df091ce1c32f95",
"bb735d507da0338b2925f84e54df091ce1c32f95"
] | [
"filterdesigner/IO/_loadmat.py",
"filterdesigner/FIRDesign/_kaiserord.py"
] | [
"import scipy.io as io\r\nimport numpy as np\r\nfrom typing import List, Tuple\r\n\r\ndef loadmat(filename:str, mdict:dict, byte_order:str=None, \r\n matlab_compatible:bool=False, verify_compressed_data_integrity:bool=True, \r\n variable_names=None)->dict:\r\n \r\n \"\"\"\r\n Load MAT... | [
[
"scipy.io.loadmat"
],
[
"numpy.min",
"numpy.ceil",
"numpy.log10",
"numpy.array",
"scipy.signal.kaiser_beta"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
pm15ma/RMG-Py | [
"ca2f663c711ec45012afc911138716aaf0049296",
"ca2f663c711ec45012afc911138716aaf0049296"
] | [
"rmgpy/data/thermo.py",
"rmgpy/data/statmech.py"
] | [
"#!/usr/bin/env python3\n\n###############################################################################\n# #\n# RMG - Reaction Mechanism Generator #\n# ... | [
[
"numpy.std",
"numpy.array",
"numpy.sum",
"numpy.average"
],
[
"numpy.arange",
"numpy.array",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TimO96/RE-CEM | [
"a3e90a50a9c409d80d76b5f878a7d90cffafa181"
] | [
"cem/train.py"
] | [
"# train.py -- Initialize Dataset class with training functionalities.\n\n# (C) 2020 Changes by UvA FACT AI group CEM-I\n\nfrom argparse import ArgumentParser\nfrom os import system, path\n\nfrom torch import mean, argmax, save, cuda, manual_seed\nfrom torch.optim import SGD, Adam, Adadelta, Adagrad\nfrom torch.nn ... | [
[
"torch.nn.CrossEntropyLoss",
"torch.manual_seed",
"torch.cuda.is_available",
"torch.nn.MSELoss",
"torch.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
renansantosmendes/benchmark_tests | [
"106f842b304a7fc9fa348ea0b6d50f448e46538b"
] | [
"pymoo/algorithms/adapted_genetic_algorithm.py"
] | [
"import os\nimport time\nimport numpy as np\n\nfrom pymoo.model.algorithm import Algorithm\nfrom pymoo.model.duplicate import DefaultDuplicateElimination, NoDuplicateElimination\nfrom pymoo.model.individual import Individual\nfrom pymoo.model.initialization import Initialization\nfrom pymoo.model.mating import Mati... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
swazer/clusterfuzz | [
"5fd7878d5beac105e601585843086d575778ade8"
] | [
"src/python/bot/fuzzers/ml/rnn/generate.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# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed ... | [
[
"tensorflow.train.import_meta_graph",
"numpy.percentile",
"tensorflow.Session",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
pombreda/pyamg | [
"ecd464de4d16e16bc905d84df181025ddf3c1958",
"ecd464de4d16e16bc905d84df181025ddf3c1958"
] | [
"Examples/Diffusion/demo_anisotropic_convergence.py",
"Examples/ComplexSymmetric/my_vis.py"
] | [
"\"\"\"\nTest the convergence of a 100x100 anisotropic diffusion equation\n\"\"\"\nimport numpy\nimport scipy\n\nfrom pyamg.gallery import stencil_grid\nfrom pyamg.gallery.diffusion import diffusion_stencil_2d\nfrom pyamg.strength import classical_strength_of_connection\nfrom pyamg.classical.classical import ruge_s... | [
[
"scipy.rand",
"numpy.random.seed"
],
[
"scipy.sparse.csc_matrix",
"scipy.sparse.coo_matrix",
"numpy.hstack",
"scipy.zeros",
"scipy.sparse.eye",
"numpy.issubdtype",
"numpy.arange",
"scipy.sparse.csr_matrix",
"scipy.mean",
"numpy.diff",
"scipy.rand",
"scip... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Intrinsically-Disordered/TBD | [
"cf3c95c58388822f7f53925fe69cb5764bb6a0e0"
] | [
"tbd/model.py"
] | [
"\"\"\"\nModel to classifiy protein sequence as ordered or disordered.\n\"\"\"\nimport numpy as np\nimport pickle\nfrom sklearn.model_selection import train_test_split\nimport tensorflow as tf\n\nHEIGHT = 40\nWIDTH = 20\n\n\ndef read_data(infile):\n \"\"\"Read data array.\n\n Args:\n infile (str): path... | [
[
"numpy.hstack",
"tensorflow.keras.losses.CategoricalCrossentropy",
"tensorflow.keras.Input",
"numpy.random.seed",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.Conv2D",
"tensorflow.keras.Sequential",
"sklearn.model_selection.train_test_split",
"tensorflow.keras.metr... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
thefullstackninja/plotly_express_tutorial | [
"23b706269d997058197db9600c4ec953e89ab5e2"
] | [
"lineplots/all_stocks_lineplot.py"
] | [
"import plotly_express as px\nimport pandas as pd\n\n# import data\nstocks_df = pd.read_csv(\"../data/all_stocks_5yr.csv\")\n\nprint(stocks_df.head())\n\nall_stock_prices = px.line(data_frame=stocks_df,\n x='date', y='high',color='Name',\n title=\"Line plot of Sto... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Noixas/Evaluating-Bias-In-Dutch-Word-Embeddings | [
"e4c002c10ba0d73a2ae43bef76d8c315b2d511de",
"e4c002c10ba0d73a2ae43bef76d8c315b2d511de"
] | [
"Code/compute_sonar_test.py",
"Code/Finetuning/debias_finetunning.py"
] | [
"# To add a new cell, type '# %%'\n# To add a new markdown cell, type '# %% [markdown]'\n# %%\nfrom IPython import get_ipython\n\n# %% [markdown]\n# # Streamlined testing for word embeddings\n\n# %%\nimport numpy as np\nimport pandas as pd\nfrom numpy import linalg\nimport fasttext.util\nfrom gensim.models.fasttext... | [
[
"matplotlib.rc"
],
[
"numpy.linalg.norm",
"numpy.save",
"numpy.load",
"numpy.array",
"sklearn.decomposition.PCA"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mitmul/kornia | [
"810e5189408cf97e81449e4a11454d803038a3f6"
] | [
"kornia/geometry/epipolar/scene.py"
] | [
"\"\"\"Module to generate synthetic 3d scenes.\"\"\"\nfrom typing import Dict\n\nimport torch\nimport kornia\n\nfrom kornia.geometry import epipolar\n\n\ndef generate_scene(num_views: int, num_points: int) -> Dict[str, torch.Tensor]:\n # Generate the 3d points\n points3d = torch.rand(1, num_points, 3) # NxMx... | [
[
"torch.norm",
"torch.empty",
"torch.min",
"torch.rand",
"torch.where",
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
goriccardo/gluon-ts | [
"77273999d5e7b09b14f98a0773355fb19e3efcb4"
] | [
"test/distribution/test_mixture.py"
] | [
"# Third-party imports\nimport mxnet as mx\nimport numpy as np\nimport pytest\n\n# First-party imports\nfrom gluonts.gluonts_tqdm import tqdm\nfrom gluonts.model.common import Tensor, NPArrayLike\nfrom gluonts.distribution.distribution import Distribution\nfrom gluonts.distribution import (\n Gaussian,\n Stud... | [
[
"numpy.allclose",
"numpy.linspace",
"numpy.abs",
"numpy.random.normal",
"numpy.random.uniform",
"matplotlib.pyplot.show",
"numpy.histogram",
"matplotlib.pyplot.hist"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jaked0626/CMSC122-Edu-Project | [
"e1e6c9523825aa41c8ada4f5623ca198c996321f"
] | [
"nctq.py"
] | [
"# Starting from nctq state comparison homepage, crawls all policy\n# databases and returns a dictionary mapping the year to a pandas \n# dataframe containing policy info per year. \n# To get dataframes, run crawler() in terminal.\nhome_url = \"https://www.nctq.org/yearbook/home\"\n\nimport bs4\nimport csv\nimport ... | [
[
"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": []
}
] |
maciejczechowski/CarND-Behavioral-Cloning-P3 | [
"c84f932237c8144828a060f2b438dd240560e88d"
] | [
"drive.py"
] | [
"import argparse\nimport base64\nfrom datetime import datetime\nimport os\nimport shutil\n\nimport numpy as np\nimport socketio\nimport eventlet\nimport eventlet.wsgi\nfrom PIL import Image\nfrom flask import Flask\nfrom io import BytesIO\nfrom keras.models import load_model\nimport h5py\nfrom keras import __versio... | [
[
"numpy.asarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ahwillia/tensorflow | [
"2c1f2847c2afbc6f23ef7040d49c71ffaa8b669c"
] | [
"tensorflow/python/kernel_tests/scatter_nd_ops_test.py"
] | [
"# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.ops.array_ops.placeholder",
"tensorflow.python.ops.variables.Variable",
"tensorflow.python.ops.array_ops.zeros",
"numpy.random.randn",
"tensorflow.python.ops.gradients_impl.gradients",
"tensorflow.python.eager.context.executing_eagerly",
"numpy.random.randint",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"2.7",
"1.4",
"2.6",
"1.13",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"2.2",
"2.10"
]
}
] |
kirkjules/machine-learned-timeseries | [
"7aedec0fe04807fef1cf5e79a929652101d467f7"
] | [
"htp/helper.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nfrom loguru import logger\nfrom htp.analyse import evaluate, indicator\n\nlogger.enable(\"htp.api.oanda\")\n\n\ndef main(data_mid):\n\n # periods = list(range(3, 101, 1))\n periods = [3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 15, 16, 20, 24, 25, 28, 30, ... | [
[
"pandas.concat",
"pandas.HDFStore"
]
] | [
{
"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": []
}
] |
dhruvbhq/qiskit-terra | [
"74a6d0d409d42a83f0be56e39274d07f56f1a6d1"
] | [
"test/python/compiler/test_disassembler.py"
] | [
"# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2017, 2019.\n#\n# This code is licensed under the Apache License, Version 2.0. You may\n# obtain a copy of this license in the LICENSE.txt file in the root directory\n# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\n#\n# Any modificatio... | [
[
"numpy.sqrt",
"numpy.testing.assert_allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
colinxs/OpenMDAO | [
"a9a52be29281a23a102c64b577066ee5fc70f4b4"
] | [
"openmdao/core/test/test_check_derivatives.py"
] | [
"\"\"\" Testing for Problem.check_partial_derivatives and check_total_derivatives.\"\"\"\n\nimport unittest\nfrom six import iteritems, StringIO, PY3\nfrom six.moves import cStringIO as StringIO\n\nimport numpy as np\n\nfrom openmdao.api import Group, Component, IndepVarComp, Problem, ScipyGMRES, \\\n ... | [
[
"numpy.cos",
"numpy.sin",
"numpy.ones",
"numpy.array",
"numpy.exp",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
varun1423/solo-learn_SSL | [
"f0e2bd3a278ff468049f68641700e03059afdf6f"
] | [
"solo/methods/vicreg.py"
] | [
"# Copyright 2021 solo-learn development team.\n\n# Permission is hereby granted, free of charge, to any person obtaining a copy of\n# this software and associated documentation files (the \"Software\"), to deal in\n# the Software without restriction, including without limitation the rights to use,\n# copy, modify,... | [
[
"torch.nn.Linear",
"torch.nn.ReLU",
"torch.nn.BatchNorm1d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ybarancan/BEV_feat_stitch | [
"f8999715a334a6cf63d3a202d3ea06c16f2fc486"
] | [
"dataset_creator.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Oct 14 11:17:20 2019\n\n@author: cany\n\"\"\"\n\n#import matplotlib.pyplot as plt\n\nimport os\nfrom PIL import Image\nimport numpy as np\nimport logging\n\nfrom nuscenes.nuscenes import NuScenes\n\nfrom nuscenes.map_expansion.map_api import N... | [
[
"numpy.arange",
"numpy.uint8",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aborodya/dawp | [
"0b34507e4b09eda677815728b46d800eedfeba3d"
] | [
"python36/03_stf/EURIBOR_analysis.py"
] | [
"#\n# Analyzing Euribor Interest Rate Data\n# Source: http://www.emmi-benchmarks.eu/euribor-org/euribor-rates.html\n# 03_stf/EURIBOR_analysis.py\n#\n# (c) Dr. Yves J. Hilpisch\n# Derivatives Analytics with Python\n#\nimport pandas as pd\nfrom GBM_returns import *\n\n# Read Data for Euribor from Excel file\n\n\ndef ... | [
[
"pandas.read_excel"
]
] | [
{
"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": []
}
] |
myunyui22/smart-social-distancing-dev | [
"2b71c4330420758a3ff6833923cf2ef81cdebdb1"
] | [
"api/routers/cameras.py"
] | [
"import base64\nimport cv2 as cv\nimport logging\nimport os\nimport shutil\nimport re\nimport numpy as np\n\nfrom fastapi import APIRouter, status\nfrom starlette.exceptions import HTTPException\nfrom typing import Dict, Optional\nfrom pathlib import Path\n\nfrom libs.utils.camera_calibration import (get_camera_cal... | [
[
"numpy.savetxt",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
andizq/star-forming-regions | [
"4a2b33ad5ae75473a5b023eed712c4009e99ca3c",
"4a2b33ad5ae75473a5b023eed712c4009e99ca3c"
] | [
"examples/two_sources/overlap_submodels.py",
"sf3dmodels/grid/fillgrid.py"
] | [
"\"\"\"\nBuilding global model from 2 submodels\n======================================\n\nBasic docstring explaining example\n\"\"\"\n#------------------\n#Import the package\n#------------------\nfrom sf3dmodels import Model, Plot_model as Pm\nimport sf3dmodels.utils.units as u\nimport sf3dmodels.rt as rt\nfrom s... | [
[
"numpy.mean"
],
[
"numpy.hstack",
"numpy.linspace",
"numpy.asarray",
"scipy.spatial.Delaunay",
"numpy.append",
"numpy.where",
"numpy.random.uniform",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
amirgholipour/alibi-detect | [
"04c0574d98c5ad0ac9e3c4376ce4e91c456bfbde"
] | [
"alibi_detect/utils/tensorflow/kernels.py"
] | [
"import tensorflow as tf\nimport numpy as np\nfrom . import distance\nfrom typing import Optional, Union\nfrom scipy.special import logit\n\n\nclass GaussianRBF(tf.keras.Model):\n def __init__(self, sigma: Optional[tf.Tensor] = None, trainable: bool = False) -> None:\n \"\"\"\n Gaussian RBF kernel:... | [
[
"tensorflow.keras.backend.floatx",
"tensorflow.constant",
"tensorflow.concat",
"tensorflow.reduce_mean",
"tensorflow.reshape",
"tensorflow.cast",
"tensorflow.math.exp",
"tensorflow.math.log",
"tensorflow.math.sigmoid",
"scipy.special.logit",
"tensorflow.reduce_all",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Amchuz/ISL-to-Malayalam-using-OpenPose | [
"a2fa32690b8a1064e8b71dc808998c6d0dfc94d6"
] | [
"src/utils/lib_classifier.py"
] | [
"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport numpy as np\nimport sys\nimport os\nimport pickle\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import ListedColormap\nfrom collections import deque\nimport cv2\nfrom PIL import Image, ImageDraw, ImageFont, ImageFilter\nfrom sklearn.model_selection im... | [
[
"sklearn.neural_network.MLPClassifier",
"sklearn.naive_bayes.GaussianNB",
"sklearn.ensemble.RandomForestClassifier",
"sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis",
"sklearn.gaussian_process.kernels.RBF",
"sklearn.neighbors.KNeighborsClassifier",
"sklearn.tree.DecisionTr... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LDJWJ/kagglebook | [
"8f0c7613f746f9e025a017d0afd561f6f21f2ab8"
] | [
"ch03/ch03-04-time_series.py"
] | [
"import numpy as np\nimport pandas as pd\n\n# -----------------------------------\n# 와이드 포맷, 롱 포맷\n# -----------------------------------\n\n# 와이드 포맷의 데이터 읽기\ndf_wide = pd.read_csv('../input/ch03/time_series_wide.csv', index_col=0)\n# 인덱스의 형태를 날짜형으로 변경\ndf_wide.index = pd.to_datetime(df_wide.index)\n\nprint(df_wide.... | [
[
"pandas.read_csv",
"pandas.to_datetime",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
rwright88/covid | [
"11b799e3c3f7d4c205aef925600cf63719f2998c"
] | [
"covid/data_old.py"
] | [
"\"\"\"Functions to create datasets, Covid Tracking Project\"\"\"\n\nimport io\n\nimport numpy as np\nimport pandas as pd\nimport requests\n\nfrom covid.utils import fill_dates\n\n\ndef get_state():\n \"\"\"Get state data from Covid Tracking project\"\"\"\n url = \"http://covidtracking.com/api/states/daily.cs... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
AhmedARezk/spatio_temporal_analysis_interactive_maps | [
"216474c56206272891913336d9d71aa5cdf648b7"
] | [
"analysis_functions.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# In[8]:\n\n\ndef hotspots(variable, perms):\n \n \n '''\n The first section Imports all the libraries and dependencies needed for the analysis\n ''' \n # base libraries\n import numpy as np\n import pandas as pd\n import geopandas as gpd\n ... | [
[
"pandas.merge",
"pandas.read_csv",
"numpy.random.seed",
"numpy.linspace",
"numpy.arange",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
felix990302/nnlib | [
"618c9f860ff2298ed2e0bbcb249ae74eeb8a408b"
] | [
"nnlib/l_layer/forward.py"
] | [
"import numpy as np\n\nfrom nnlib.utils.activation import relu, sigmoid\n\n\ndef linear_forward(A_prev, W, b):\n \"\"\"\n Implement linear part of forward propagation\n\n Arguments:\n\n A_prev -- activations from previous layer or input data\n\n W -- weight matrix\n\n b -- bias vector\n\n Retur... | [
[
"numpy.matmul",
"numpy.random.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NganMrn/ml-100d | [
"a9e321129ea8c7b6f6e79ac4542e37a61a10f786"
] | [
"Work/Day-1_Data-PreProcessing.py"
] | [
"##### Data PreProcessing #####\n## Step 1: Importing the libraries\nimport numpy as np\nimport pandas as pd\n\n## Step 2: Importing dataset\ndataset = pd.read_csv('datasets/Data.csv')\nX = dataset.iloc[ : , :-1].values\nY = dataset.iloc[ : , 3].values\nprint(\"import data ------------------------------------------... | [
[
"pandas.read_csv",
"sklearn.preprocessing.OneHotEncoder",
"sklearn.impute.SimpleImputer",
"sklearn.model_selection.train_test_split",
"sklearn.preprocessing.StandardScaler",
"sklearn.preprocessing.LabelEncoder"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
0x00b1/napari | [
"01c24e320dc3d8daaae80a383f95fc0587c98ee4"
] | [
"napari/_qt/_tests/test_progress.py"
] | [
"import os\nimport sys\nfrom contextlib import contextmanager\n\nimport numpy as np\nimport pytest\n\npytest.importorskip('qtpy', reason='Cannot test progress without qtpy.')\n\nfrom napari._qt.widgets.qt_progress_bar import ProgressBar # noqa\nfrom napari.qt import progrange, progress # noqa\n\nSHOW = bool(sys.p... | [
[
"numpy.random.random"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tjyuyao/ice-learn | [
"99087181d2d15cb55a3c34004550179366ce601a"
] | [
"ice/api/transforms/semseg.py"
] | [
"import os.path as osp\n\nimport numpy as np\nfrom ice.api.transforms.random import RandomROI, _rng\nfrom ice.llutil.dictprocess import dictprocess\nfrom ice.llutil.file_client import FileClient\n\nfrom .image.io import imfrombytes\nfrom .image.spatial import Crop\n\n\n@dictprocess\ndef LoadAnnotation(\n seg... | [
[
"numpy.max",
"numpy.sum",
"torch.from_numpy",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pokemonjs/Paddle | [
"00586cf3683c0c5e635b88eead5a8ec47b88d018"
] | [
"python/paddle/tests/test_vision_models.py"
] | [
"# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless re... | [
[
"numpy.random.random"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mathildebadoual/energym | [
"bcdba783ea50a2c3adb9e6c86ecdfb1949bd59a5"
] | [
"tests/test_energy_market_battery_env_V0.py"
] | [
"import numpy as np\nimport unittest\nimport gym\nimport energym\n\n\nclass TestEnergyMarketEnv(unittest.TestCase):\n def setUp(self):\n self.env = gym.make('energy_market_battery-v0')\n\n def test_as_gym_env(self):\n ob_space = self.env.observation_space\n ob = self.env.reset()\n ... | [
[
"numpy.isscalar"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
renzotello10/bookSageMaker | [
"dbf8a8dbfe427abb0f68506f8be63de49220b5c6"
] | [
"sdkv1/ch13/multi_model/sklearn-boston-housing.py"
] | [
"\nimport pandas as pd\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_squared_error, r2_score\nfrom sklearn.externals import joblib\nimport argparse, os\n\ndef model_fn(model_dir):\n model = joblib.load(os.path.join(model... | [
[
"sklearn.externals.joblib.dump",
"pandas.read_csv",
"sklearn.metrics.r2_score",
"sklearn.model_selection.train_test_split",
"sklearn.metrics.mean_squared_error",
"sklearn.linear_model.LinearRegression"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
dzambrano/mmtracking | [
"a4824091a0b24eb0add16a9233fec3be73ad6e32",
"ec7a2e36fbf99effed4602a4df929f495efe73c5"
] | [
"tests/test_models/test_forward/test_sot_forward.py",
"tools/sot_siamrpn_param_search.py"
] | [
"# Copyright (c) OpenMMLab. All rights reserved.\nimport copy\nfrom collections import defaultdict\n\nimport pytest\nimport torch\n\nfrom .utils import _demo_mm_inputs, _get_config_module\n\n\n@pytest.mark.parametrize('cfg_file',\n ['sot/siamese_rpn/siamese_rpn_r50_1x_lasot.py'])\ndef test_s... | [
[
"torch.no_grad",
"torch.cat"
],
[
"numpy.arange",
"torch.cuda.current_device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LSchultebraucks/regression-model-accuracy | [
"3ddeac308a7d0b8e639525e1ddb539066072f57a"
] | [
"regression_holdhout.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn import datasets, linear_model\nfrom sklearn.metrics import mean_squared_error, r2_score\nimport matplotlib.pyplot as plt\nfrom math import fabs\n\ndef main():\n\tdata_set = datasets.load_boston()\n\n\tfeatures, target = split_features_target(data_se... | [
[
"matplotlib.pyplot.legend",
"numpy.random.seed",
"sklearn.metrics.mean_squared_error",
"matplotlib.pyplot.plot",
"sklearn.linear_model.Ridge",
"sklearn.datasets.load_boston",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fogoke/blusky | [
"bbefd799fa3e4215896006f8de51ce057e47e23e"
] | [
"blusky/transforms/tests/test_cascade_2d.py"
] | [
"from os import path\n\nfrom keras.models import Model\nfrom keras.layers import Input\nimport numpy as np\nfrom PIL import Image\nfrom scipy.signal import convolve2d\nimport unittest\n\nfrom blusky.wavelets.morlet2d import Morlet2D\nfrom blusky.transforms.cascade_2d import Cascade2D\nimport blusky.datasets as data... | [
[
"numpy.abs",
"scipy.signal.convolve2d",
"numpy.max",
"numpy.testing.assert_almost_equal",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
RamonAlvarez1/Image-processing | [
"98ca4fd334023dec5ed0e66231af9da23c5ed130"
] | [
"edge_detection/edge_dectection.py"
] | [
"import cv2\nimport numpy as np\nimport math\nIMG_NAME = 'ape.jpeg'\nRES_IMG = (500,500) \n\n#This generate a padding of zeros that surround the image\ndef getPadding(img, img_axb, convo_axb, zeros):\n for i in range(img_axb[0]):\n for j in range(img_axb[1]):\n zeros[i+int((convo_axb[0]-1)/2), ... | [
[
"numpy.arctan2",
"numpy.max",
"numpy.copy",
"numpy.array",
"numpy.zeros",
"numpy.sum",
"numpy.hypot"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ekerazha/tensorforce | [
"4cfea271190244b5e1efbd92afb880128c53f90c",
"4cfea271190244b5e1efbd92afb880128c53f90c"
] | [
"tensorforce/models/pg_prob_ratio_model.py",
"tensorforce/tests/test_preprocessing.py"
] | [
"# Copyright 2017 reinforce.io. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by ap... | [
[
"tensorflow.clip_by_value",
"tensorflow.concat",
"tensorflow.reduce_mean",
"tensorflow.reshape",
"tensorflow.minimum",
"tensorflow.exp",
"tensorflow.stop_gradient",
"tensorflow.make_template"
],
[
"numpy.concatenate",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
... |
safranchik/wrench | [
"f20135eb9b1d51b5bad92b3a910efd92235df356"
] | [
"wrench/classification/astra.py"
] | [
"import copy\r\nimport logging\r\nfrom typing import Any, Optional, Union, Callable, List\r\n\r\nimport numpy as np\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm.auto import trange\r\nfrom transformers import AutoTokenizer\r\n\r\n... | [
[
"torch.nn.Dropout",
"torch.softmax",
"torch.nn.functional.softmax",
"torch.sum",
"torch.nn.Sigmoid",
"torch.vstack",
"torch.nn.Linear",
"torch.no_grad",
"torch.log",
"torch.nn.functional.one_hot",
"torch.nn.ReLU",
"numpy.vstack",
"torch.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
smheidrich/scikit-image | [
"901ac307b68486d8289105c159ca702318bea5b0",
"e9cf8b850c4c2800cc221be6f1dfff6a2a32a4eb"
] | [
"skimage/future/graph/tests/test_rag.py",
"benchmarks/benchmark_rank.py"
] | [
"import numpy as np\nfrom skimage.future import graph\nfrom skimage._shared.version_requirements import is_installed\nfrom skimage import segmentation\nfrom skimage._shared import testing\n\n\ndef max_edge(g, src, dst, n):\n default = {'weight': -np.inf}\n w1 = g[n].get(src, default)['weight']\n w2 = g[n].... | [
[
"numpy.arange",
"numpy.linalg.norm",
"numpy.all",
"numpy.zeros_like",
"numpy.array",
"numpy.zeros"
],
[
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
patel-nisarg/SFM_from_CAD_Model | [
"ecececfd357ea8c4802619bbbb7a278c5f6b3989"
] | [
"main.py"
] | [
"from WorldPoints import WorldPointSet\r\nfrom baseline import Baseline\r\nfrom utils import *\r\nfrom view import ImageView\r\nfrom bundle_adjustment import BundleAdjustment\r\nfrom visualize import PointCloudVisualizer\r\nimport numpy as np\r\nimport os\r\nfrom datetime import datetime\r\nimport argparse\r\nimpor... | [
[
"numpy.savetxt",
"numpy.load",
"numpy.savez",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jeonghunn/EverytimeBot | [
"99b411adb11eeaec5c254805c759b63fa9934d3a"
] | [
"chatbot.py"
] | [
"import tensorflow as tf\nimport numpy as np\nimport math\nimport sys\n\nfrom config import FLAGS\nfrom model import Seq2Seq\nfrom dialog import Dialog\n\n\nclass ChatBotT:\n\n def __init__(self, voc_path, train_dir):\n\n self.dialog = Dialog()\n self.dialog.load_vocab(voc_path)\n\n self.mod... | [
[
"tensorflow.train.get_checkpoint_state",
"tensorflow.ConfigProto"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HeidiNeuhaeuser/pandas | [
"4ef033fe487df127ffb7ba186cee539e534a8bb4",
"4ef033fe487df127ffb7ba186cee539e534a8bb4"
] | [
"pandas/core/internals/concat.py",
"pandas/tests/test_take.py"
] | [
"from __future__ import annotations\n\nimport copy\nimport itertools\nfrom typing import (\n TYPE_CHECKING,\n Dict,\n List,\n Sequence,\n)\n\nimport numpy as np\n\nfrom pandas._libs import internals as libinternals\nfrom pandas._typing import (\n ArrayLike,\n DtypeObj,\n Manager,\n Shape,\n)... | [
[
"pandas.core.dtypes.common.is_extension_array_dtype",
"pandas.core.dtypes.common.is_dtype_equal",
"numpy.dtype",
"pandas.core.dtypes.common.is_datetime64tz_dtype",
"numpy.concatenate",
"pandas._libs.internals.get_blkno_placements",
"pandas.core.internals.managers.BlockManager",
"nu... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"1.5",
"2.0",
"1.3",
"1.4"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
... |
hagne/hrrr_sraper | [
"89313c5fa8579e9850197865839a9c3d9b82e788"
] | [
"hrrr_scraper/hrrr_lab.py"
] | [
"# -*- coding: utf-8 -*-\n\n# from atmPy.data_archives.NOAA_ESRL_GMD_GRAD.surfrad import surfrad\nfrom functools import partial\nimport ftplib\nimport pygrib\nimport xarray as xr\nimport numpy as np\nimport pathlib as pl\nimport shutil\nimport pandas as pd\nimport psutil\n\nimport multiprocessing as mp\nimport time... | [
[
"pandas.to_datetime",
"numpy.sqrt",
"numpy.cos",
"pandas.DataFrame",
"numpy.argsort",
"numpy.array",
"numpy.zeros"
]
] | [
{
"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": []
}
] |
KuKuXia/tensorlayer | [
"654de4a37892cde54495350f99f5f3b38b2c6eb3",
"654de4a37892cde54495350f99f5f3b38b2c6eb3",
"c5def14c4d66d150863f975d9001a5e1891d003f"
] | [
"tensorlayer/layers/dense/binary_dense.py",
"examples/basic_tutorials/tutorial_mnist_mlp_static.py",
"examples/deprecated_tutorials/tutorial_imagenet_inceptionV3_distributed.py"
] | [
"#! /usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport tensorflow as tf\nimport tensorlayer as tl\nfrom tensorlayer import logging\nfrom tensorlayer.decorators import deprecated_alias\nfrom tensorlayer.layers.core import Layer\nfrom tensorlayer.layers.utils import quantize\n\n__all__ = [\n 'BinaryDense',\n]\n\n\... | [
[
"tensorflow.nn.bias_add",
"tensorflow.matmul"
],
[
"tensorflow.optimizers.Adam",
"numpy.argmax",
"tensorflow.GradientTape"
],
[
"tensorflow.device",
"tensorflow.control_dependencies",
"tensorflow.gfile.Exists",
"tensorflow.cast",
"tensorflow.equal",
"tensorflow.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
... |
tidepool-org/data-science-tidepool-api-python | [
"48f4cfa1f6c4d4bc1f8dab27d0aecbe82ffc3ddc"
] | [
"data_science_tidepool_api_python/visualization/visualize_user_data.py"
] | [
"import datetime as dt\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport seaborn as sns\nsns.set_style(\"darkgrid\")\n\n\ndef plot_raw_data(user, start_date, end_date):\n \"\"\"\n Args:\n user: Tidepool_User\n start_date (dt.DateTime): start date to plot\n end_date (dt.Date... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
muhannad93/fourier_neural_operator | [
"80f2823f534b42203996e22dbe01fac668f3d7c1"
] | [
"utilities3.py"
] | [
"import torch\nimport numpy as np\nimport scipy.io\nimport h5py\nimport torch.nn as nn\n\nimport operator\nfrom functools import reduce\nfrom functools import partial\n\n#################################################\n#\n# Utilities\n#\n#################################################\ndevice = torch.device('cu... | [
[
"torch.mean",
"torch.abs",
"torch.nn.BatchNorm1d",
"torch.max",
"torch.sqrt",
"torch.nn.ModuleList",
"torch.min",
"torch.sum",
"torch.from_numpy",
"torch.fft.fftn",
"torch.nn.Linear",
"torch.std",
"torch.cuda.is_available",
"torch.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aws-samples/aws-open-data-analytics-notebooks | [
"680e9689e1b0ceb047960662d220564ae3ecbddb"
] | [
"experiments/notebooks/covid/covid.py"
] | [
"import urllib.request\nimport bs4\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom datetime import datetime, date, timedelta\nimport os.path\nfrom os import path\n\ndef linear_regression(df):\n fig, ax = plt.subplots(1, 3, figsize=(15,5))\n sns.regplot(x=... | [
[
"pandas.read_csv",
"matplotlib.pyplot.subplots",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
michalnand/reinforcement_learning_experiments | [
"105bcfacd30cfbd1bbd9893859fc7d203e0df1e9"
] | [
"experiments/atari_venture/models/ppo_curiosity/src/model_forward_target.py"
] | [
"import torch\nimport torch.nn as nn\n\nclass Model(torch.nn.Module):\n def __init__(self, input_shape):\n super(Model, self).__init__()\n\n self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n fc_size = (input_shape[1]//8) * (input_shape[2]//8)\n self.la... | [
[
"torch.nn.Sequential",
"torch.load",
"torch.nn.ELU",
"torch.nn.Conv2d",
"torch.nn.Flatten",
"torch.nn.Linear",
"torch.nn.init.orthogonal_",
"torch.cuda.is_available",
"torch.nn.init.zeros_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SeaOfOcean/EasyParallelLibrary | [
"cd2873fe04c86c62e55418129ba2f1dc83d222b4",
"93baaa851f5ce078b1c55032a27398a588ca4107"
] | [
"epl/runtime/gc/auto_gradient_checkpoint.py",
"epl/cluster.py"
] | [
"# Copyright 2021 Alibaba Group Holding Limited. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unle... | [
[
"tensorflow.contrib.graph_editor.select.compute_boundary_ts",
"tensorflow.contrib.graph_editor.get_backward_walk_ops",
"tensorflow.python.framework.ops._gradient_registry.lookup",
"tensorflow.python.framework.ops.get_default_graph"
],
[
"numpy.reshape",
"tensorflow.python.platform.tf_l... | [
{
"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... |
sgherbst/champ | [
"e2e9b7edb117ce45828ba1542e26870b41e81a66"
] | [
"tests/test_rf.py"
] | [
"from champ import *\n\ndef main():\n import matplotlib.pyplot as plt\n\n tf = s4p_to_tf('../data/peters_01_0605_B12_thru.s4p')\n imp = tf_to_imp(*tf)\n step = imp_to_step(*imp)\n pulse = center_pulse(*step_to_pulse(*step, 1/16e9))\n\n plt.stem(get_pulse_coeffs(*pulse, t_samp=1/16e9, n_pre=25, n_p... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Jeroendevr/xlwings | [
"0d9d1e50cd1c3917f1fa0470a2ac02a41016ff59"
] | [
"xlwings/_xlwindows.py"
] | [
"import os\nimport sys\n\n# Hack to find pythoncom.dll - needed for some distribution/setups (includes seemingly unused import win32api)\n# E.g. if python is started with the full path outside of the python path, then it almost certainly fails\ncwd = os.getcwd()\nif not hasattr(sys, 'frozen'):\n # cx_Freeze etc.... | [
[
"numpy.isnan"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
emorynlp/el | [
"dbe73d1ce6f2296a64fb013775d2691ae1ed90d4"
] | [
"elit/layers/embeddings/embedding.py"
] | [
"# ========================================================================\n# Copyright 2020 Emory University\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.a... | [
[
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BenikaHall/cudf | [
"d3f5add210293a4832dafb85f04cbb73149b9d54",
"d3f5add210293a4832dafb85f04cbb73149b9d54",
"d3f5add210293a4832dafb85f04cbb73149b9d54"
] | [
"python/cudf/cudf/tests/test_string.py",
"python/cudf/cudf/tests/test_dropna.py",
"python/cudf/cudf/core/reshape.py"
] | [
"# Copyright (c) 2018-2020, NVIDIA CORPORATION.\nimport re\nfrom contextlib import ExitStack as does_not_raise\nfrom sys import getsizeof\n\nimport cupy\nimport numpy as np\nimport pandas as pd\nimport pyarrow as pa\nimport pytest\n\nimport cudf\nfrom cudf import concat\nfrom cudf.core import DataFrame, Series\nfro... | [
[
"pandas.concat",
"pandas.Series",
"numpy.asarray",
"pandas.Index",
"pandas.DataFrame",
"numpy.dtype",
"numpy.random.rand",
"numpy.array",
"numpy.random.randint"
],
[
"pandas.date_range",
"pandas.Series",
"pandas.DataFrame",
"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": []
},
{
"matplotlib": [],
"nump... |
aqiugroup/AirSim | [
"b8ea885963d38f15a3176f9f0b4a4473ba21c4a6"
] | [
"PythonClient/computer_vision/qzc_rotate_flip_depth.py"
] | [
"# In settings.json first activate computer vision mode:\n# https://github.com/Microsoft/AirSim/blob/master/docs/image_apis.md#computer-vision-mode\n\nimport setup_path\nimport airsim\n\nimport pprint\nimport tempfile\nimport os\nimport sys\nimport time\n\nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.title",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xysmlx/antares | [
"262883d719f70978f93bbfdca92e32bc7a451de0"
] | [
"tuner/OpEvo/main.py"
] | [
"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport math\nimport logging\nimport random\nimport json\nimport pickle\nimport time\nimport numpy as np\nfrom itertools import combinations, permutations\nimport copy\n\nclass Parameter(object):\n \"\"\"Base class for all types of para... | [
[
"numpy.product",
"numpy.array",
"numpy.mean",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anjanadev96/NURBS_Diff | [
"21937912e8648c3c78416968401fe13458223b0d"
] | [
"torch_nurbs_eval/curve_eval.py"
] | [
"import torch\nimport numpy as np\nfrom torch import nn\nfrom torch.autograd import Function\nfrom torch.autograd import Variable\nfrom torch_nurbs_eval.curve_eval_cpp import forward as cpp_forward, backward as cpp_backward, pre_compute_basis as cpp_pre_compute_basis\nfrom torch_nurbs_eval.curve_eval_cuda import pr... | [
[
"torch.manual_seed",
"torch.linspace",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lukeWaninger/RiverRunner | [
"db977d9eccbc711b15678af3f94f1891375fb001"
] | [
"riverrunner/repository.py"
] | [
"\"\"\"\nmodule defining the class Repository\n\nThe repository class represents an abstraction for the user to interface with the backend. It provides\nstandard CRUD operations as defined below.\n\"\"\"\n\nimport datetime\nfrom builtins import list\n\nimport pandas as pd\nimport psycopg2\nfrom riverrunner import c... | [
[
"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": []
}
] |
bessszilard/automl | [
"ce0eec6f7f2a7d368c2e36f6f5953a99114b4f8e"
] | [
"efficientdet/hparams_config.py"
] | [
"# Copyright 2020 Google Research. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by... | [
[
"tensorflow.compat.v1.gfile.Open",
"tensorflow.compat.v1.io.gfile.GFile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
frnsys/maup | [
"2cd12184e3a0d60128c4991e7e8eb706e3a227bd"
] | [
"maup/indexed_geometries.py"
] | [
"import pandas\nfrom shapely.prepared import prep\nfrom shapely.strtree import STRtree\nfrom .progress_bar import progress\n\n\ndef get_geometries(geometries):\n return getattr(geometries, \"geometry\", geometries)\n\n\nclass IndexedGeometries:\n def __init__(self, geometries):\n self.geometries = get_... | [
[
"pandas.concat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
TheCBaH/tpu | [
"d546b90094284ad566b6a9658a9b369608317672"
] | [
"models/official/resnet/resnet_main.py"
] | [
"# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.contrib.cluster_resolver.TPUClusterResolver",
"tensorflow.contrib.tpu.bfloat16_scope",
"tensorflow.metrics.accuracy",
"tensorflow.control_dependencies",
"tensorflow.contrib.training.python.training.evaluation.checkpoints_iterator",
"tensorflow.cast",
"tensorflow.contrib.tpu... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
harish-garg/Machine-Learning | [
"de427f12c0203d70528e4964d8c96e4618463727"
] | [
"udacity/sklearn_practice/terrain_data/ClassifyNB.py"
] | [
"def classify(features_train, labels_train):\n ### import the sklearn module for GaussianNB\n ### create classifier\n ### fit the classifier on the training features and labels\n ### return the fit classifier\n\n\n ### your code goes here!\n from sklearn.naive_bayes import GaussianNB\n clf = Ga... | [
[
"sklearn.naive_bayes.GaussianNB"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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