repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
dongzhuoyao/embedding-propagation | [
"1f14947bbd8be8a9950e7c4093fbfed0536809b9"
] | [
"src/models/ssl_wrapper.py"
] | [
"\"\"\"\nFew-Shot Parallel: trains a model as a series of tasks computed in parallel on multiple GPUs\n\n\"\"\"\nimport copy\nimport numpy as np\nimport os\nfrom .base_ssl import oracle\nfrom scipy.stats import sem, t\nimport torch\nimport pandas as pd\nimport torch.nn.functional as F\nimport tqdm\nfrom src.tools.m... | [
[
"torch.zeros",
"numpy.unique",
"torch.cat",
"numpy.arange",
"numpy.asarray",
"torch.load",
"numpy.concatenate",
"scipy.stats.t.ppf",
"torch.no_grad",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yavuzdrmzksr/SD-FSIC | [
"29eb91c4d6e5ef91b1a7e4e3c425b0b751ba877b"
] | [
"models/FCModel.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import *\nimport misc.utils as utils\n\nfrom .CaptionModel import CaptionModel\n\nclass LSTMCore(nn.Module):\n ... | [
[
"torch.div",
"torch.nn.Dropout",
"torch.sigmoid",
"torch.LongTensor",
"torch.max",
"torch.nn.Embedding",
"torch.multinomial",
"torch.tanh",
"torch.nn.Linear",
"torch.exp",
"torch.FloatTensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rhgao/ObjectFolder | [
"91e56bcbf3dcea9b5604f53055133ddbb00fcfe2"
] | [
"AudioNet_model.py"
] | [
"import torch\ntorch.autograd.set_detect_anomaly(True)\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nfrom AudioNet_utils import *\n\nclass DenseLayer(nn.Linear):\n def __init__(self, in_dim: int, out_dim: int, activation: str = 'relu', *args, **kwargs) -> None:\n self.activa... | [
[
"torch.nn.init.calculate_gain",
"torch.linspace",
"torch.autograd.set_detect_anomaly",
"torch.cat",
"torch.split",
"torch.nn.Identity",
"torch.nn.functional.relu",
"torch.nn.init.zeros_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TheYargonaut/lucre | [
"1abd472993df01b443ab4811379dfe52e18cf790"
] | [
"Main.py"
] | [
"from ChartWidget import ChartWidget\nfrom EditGroupWindow import editGroupCb\nfrom Format import FormatMan\nfrom Group import GroupMan\nfrom GroupControlWidget import GroupList\nfrom ImportLedgerWindow import importLedgerCb\nfrom Ledger import Ledger\nfrom PlotSettings import PlotSettings\nfrom PlotSettingsWidget ... | [
[
"pandas.plotting.register_matplotlib_converters"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ChristopherDaigle/daigle_dist | [
"e716bdd8ce357461160b7a12510525738f914cdf"
] | [
"daigle_dist/Gaussiandistribution.py"
] | [
"import math\nimport matplotlib.pyplot as plt\nfrom .Generaldistribution import Distribution\n\nclass Gaussian(Distribution):\n\t\"\"\" Gaussian distribution class for calculating and \n\tvisualizing a Gaussian distribution.\n\t\n\tAttributes:\n\t\tmean (float) representing the mean value of the distribution\n\t\ts... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.hist",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xigang/ml | [
"1d8891b5bc66af8506694ba2d618624ae37f59a6"
] | [
"tensorflow/linear_model.py"
] | [
"import tensorflow as tf\n\n# linear_model: y = W*x + b\nW = tf.Variable([.1], dtype=tf.float32)\nb = tf.Variable([-.1], dtype=tf.float32)\n\nx = tf.placeholder(tf.float32, name='x')\ny = tf.placeholder(tf.float32, name='y')\n\n# create linear model\nlinear_model = W * x + b\n\n# create loss model\nwith tf.name_sco... | [
[
"tensorflow.summary.FileWriter",
"tensorflow.Variable",
"tensorflow.placeholder",
"tensorflow.global_variables_initializer",
"tensorflow.train.GradientDescentOptimizer",
"tensorflow.summary.merge_all",
"tensorflow.name_scope",
"tensorflow.Session",
"tensorflow.square",
"ten... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
iag0g0mes/t2_fis_driving_style | [
"7f62ac3e67e65e7bd1273a2f845eb05820e95b70"
] | [
"analysis/analysis_results.py"
] | [
"import numpy as np\nfrom typing import Any, Dict, List, Tuple, NoReturn\n\n\nimport argparse\nimport os\n\nimport pandas as pd\nimport json\n\nfrom sklearn.metrics import silhouette_score \nfrom sklearn.metrics import calinski_harabasz_score \nfrom sklearn.metrics import davies_bouldin_score \n\ndef parse_argument... | [
[
"sklearn.metrics.silhouette_score",
"numpy.asarray",
"numpy.unique",
"numpy.squeeze",
"numpy.min",
"numpy.quantile",
"numpy.max",
"numpy.std",
"sklearn.metrics.davies_bouldin_score",
"numpy.mean",
"numpy.where",
"sklearn.metrics.calinski_harabasz_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
e5120/EDAs | [
"acf86fa35182b8fe0cd913d6fb46280b2f9e6e46"
] | [
"eda/optimizer/replacement/restricted_tournament.py"
] | [
"import numpy as np\n\nfrom eda.optimizer.replacement import ReplacementBase\n\n\nclass RestrictedTournament(ReplacementBase):\n \"\"\"\n A class of restricted tournament replacement (RTR).\n \"\"\"\n def __init__(self, dim, replace_rate=0.5, window_size=2, fix_size=True):\n super(RestrictedTourn... | [
[
"numpy.arange",
"numpy.argmax",
"numpy.argmin",
"numpy.sum",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
qwe79137/JumpStarter | [
"e59ee341f31d7cc9fde05b6f395d29d4d63130e4"
] | [
"detector/cs_anomaly_detector.py"
] | [
"import numpy as np\nimport os\nfrom algorithm.cluster import cluster\nfrom algorithm.cvxpy import reconstruct\nfrom algorithm.sampling import localized_sample\nfrom cvxpy.error import SolverError\nfrom multiprocessing import Process, Event, Queue\nfrom threading import Thread\n\nmax_seed = 10 ** 9 + 7\n\n\nclass C... | [
[
"numpy.random.seed",
"numpy.arange",
"numpy.round",
"numpy.array",
"numpy.mat",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MBasting/SRGAN | [
"29b70557b940f5e73ecbb917c0b92176245e9c13"
] | [
"Discriminator.py"
] | [
"import torch.nn as nn\n\n\nclass DiscriminatorBlock(nn.Module):\n\n def __init__(self, in_channels, hidden_channel, kernel, stride, alpha=0.2):\n super(DiscriminatorBlock, self).__init__()\n\n self.conv1 = nn.Conv2d(in_channels, hidden_channel, kernel, stride)\n self.batchnorm = nn.BatchNor... | [
[
"torch.nn.Conv2d",
"torch.nn.Flatten",
"torch.nn.Sigmoid",
"torch.nn.Linear",
"torch.nn.LeakyReLU",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Hoon1029/jeju-bus-stations-clustering | [
"c227ea5e8ddb9cfae4bc005df560423eebd8d23b"
] | [
"lib/work.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom multiprocessing import Pool\n\ndef parallelize(func, df, core = 8):\n df_split = np.array_split(df, core)\n pool = Pool(core)\n df = pd.concat(pool.map(func, df_split))\n pool.close()\n pool.join()\n return df"
] | [
[
"numpy.array_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RuohanW/il_baseline_fork | [
"824d5117e573292e707d648b087f1e10253cc8d6"
] | [
"irl/util.py"
] | [
"import torch\nimport numpy as np\nfrom collections.abc import Iterable\nfrom sklearn.metrics.pairwise import cosine_similarity\nimport logging, os, sys\n\nfrom torch import Tensor\n\n\nclass LabelSmoothing(torch.nn.Module):\n \"\"\"\n NLL loss with label smoothing.\n \"\"\"\n def __init__(self, smoothi... | [
[
"torch.nn.functional.log_softmax",
"numpy.asarray",
"sklearn.metrics.pairwise.cosine_similarity",
"torch.no_grad",
"torch.cuda.is_available",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fishial/Object-Detection-Model | [
"4792f65ea785156a8e240d9cdbbc0c9d013ea0bb"
] | [
"train_scripts/classification/auto_train_cross.py"
] | [
"# coding=utf-8\nfrom __future__ import absolute_import, division, print_function\n\nimport sys\n\n# Change path specificly to your directories\nsys.path.insert(1, '/home/codahead/Fishial/FishialReaserch')\n\nfrom module.classification_package.src.model import FcNet\nfrom module.classification_package.src.model imp... | [
[
"torch.nn.CrossEntropyLoss",
"torch.load",
"torch.utils.data.DataLoader",
"torch.nn.Identity",
"torch.utils.tensorboard.SummaryWriter"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dot9aitclass/pytorch-CycleGAN-and-pix2pix | [
"e977cf9dd2f934097e2ebc403791fa30b6028448"
] | [
"models/pix2pix_model.py"
] | [
"import torch\nfrom .base_model import BaseModel\nfrom . import networks\n\n\nclass Pix2PixModel(BaseModel):\n \"\"\" This class implements the pix2pix model, for learning a mapping from input images to output images given paired data.\n\n The model training requires '--dataset_mode aligned' dataset.\n By ... | [
[
"torch.nn.L1Loss",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tcliang-tw/meda-env | [
"e0ba12a5ae85ea98d5a4dee9d21bb9c51510a675"
] | [
"meda_env/evaluate.py"
] | [
"#!/usr/bin/python\nimport os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\nimport warnings\nwarnings.filterwarnings('ignore')\nfrom pathlib import Path\nimport argparse\nimport time\nimport tensorflow as tf\nfrom stable_baselines.common import make_vec_env\nfrom stable_baselines.common.vec_env import DummyVecEnv\nfro... | [
[
"tensorflow.test.is_gpu_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jiangxiaoshuaiya/BertHub | [
"41eeba73c89ecf2ed2b77616c2aa40c7795495dd"
] | [
"example/haihua/ToyBert/utils.py"
] | [
"#!/usr/local/Cellar/python/3.7.4_1\n# -*- coding: utf-8 -*-\n# @File : utils.py\n# @Author : 姜小帅\n# @Moto : 良好的阶段性收获是坚持的重要动力之一\n# @Contract: Mason_Jay@163.com\nimport os\nimport json\nimport torch\nimport random\nimport numpy as np\nimport datetime\n\n\n# input examples for multiple choice reading comprehen... | [
[
"torch.manual_seed",
"torch.cuda.manual_seed",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MyrrhDev/SocioMark | [
"dd2f61a17f999b203581919349becd20e4f1f1b5"
] | [
"backend/app/server/controllers/post.py"
] | [
"import cv2\nimport numpy as np\nimport tempfile\nfrom PIL import Image\nfrom bson.objectid import ObjectId\nfrom fastapi import HTTPException\nfrom ..database import posts_collection, users_collection\nfrom .like import get_all_likes_on_post\nfrom .comment import get_all_comments_on_post\nfrom .steganography.hash ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
FZJ-IEK3-VSA/HiSim | [
"e9b3a69c6db331523b9ed5ac7aa6f57f9b4798b2"
] | [
"hisim/components/generic_heat_pump.py"
] | [
"# Generic/Built-in\nimport numpy as np\nimport copy\nimport matplotlib\nimport seaborn\nfrom math import pi\n\n# Owned\nimport hisim.utils as utils\nfrom hisim.components.weather import Weather\nfrom hisim import component as cp\nfrom hisim.loadtypes import LoadTypes, Units\nfrom hisim.simulationparameters import ... | [
[
"numpy.polyfit",
"matplotlib.rc"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
techiaith/docker-wav2vec2-xlsr-ft-cy | [
"4f7656e232ec890b877caf66596bf0359c63b3f5"
] | [
"train/python/train_kenlm.py"
] | [
"import os\nimport io\nimport sys\nimport json\nimport yaml\nimport shlex\nimport subprocess\n\nimport torch\nimport torchaudio\nimport optuna\nimport text_preprocess\n\nfrom pathlib import Path\nfrom ctcdecode import CTCBeamDecoder\nfrom datasets import load_dataset, load_metric, set_caching_enabled\nfrom transfor... | [
[
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sappelhoff/mne-python | [
"3d4a4f66232448de542ac076df7b57115644978a"
] | [
"mne/tests/test_epochs.py"
] | [
"# -*- coding: utf-8 -*-\n# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>\n# Denis Engemann <denis.engemann@gmail.com>\n# Stefan Appelhoff <stefan.appelhoff@mailbox.org>\n#\n# License: BSD-3-Clause\n\nfrom copy import deepcopy\nfrom functools import partial\nfrom io import BytesIO\nimport... | [
[
"numpy.dot",
"numpy.in1d",
"pandas.DataFrame",
"numpy.max",
"numpy.random.randn",
"numpy.mean",
"numpy.zeros_like",
"numpy.any",
"numpy.where",
"numpy.random.default_rng",
"numpy.testing.assert_equal",
"numpy.allclose",
"numpy.unique",
"numpy.arange",
"n... | [
{
"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": []
}
] |
zhangyongzhe20/nlpia | [
"2662b6e4a8d0668e39221199af73b2acdfef6fec"
] | [
"src/nlpia/book/examples/ch03-2.py"
] | [
"\n# coding: utf-8\n\n# In[1]:\n\n\nfrom nltk.tokenize import TreebankWordTokenizer\n\nsentence = \"The faster Harry got to the store, the faster Harry, the faster, would get home.\"\ntokenizer = TreebankWordTokenizer()\ntoken_sequence = tokenizer.tokenize(sentence.lower())\nprint(token_sequence)\n\n\n# In[2]:\n\n\... | [
[
"sklearn.feature_extraction.text.TfidfVectorizer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
noraj/shit | [
"81186df44d22a3e690aff6025048cce2a4f7b77f"
] | [
"tests/test_hide.py"
] | [
"#!/usr/bin/env python2\nimport os\nimport sys\nimport unittest\nimport numpy as np\nfrom scipy.misc import lena\n\n\nBASE_DIR = os.path.abspath(os.path.dirname(__file__))\nsys.path.append(os.path.join(BASE_DIR, '..', 'shit'))\nimport hide\n\n\nclass HideTest(unittest.TestCase):\n ###############################... | [
[
"scipy.misc.lena"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.19",
"0.18",
"0.12",
"0.17",
"0.16"
],
"tensorflow": []
}
] |
defineHong/mmdetection_hwcar | [
"9f7fa5682867e6cef03568f2c8cd8cab0aa602ac"
] | [
"mmdet/models/detectors/yolact_det.py"
] | [
"import torch\n\nfrom mmdet.core import bbox2result\nfrom ..builder import DETECTORS, build_head\nfrom .single_stage import SingleStageDetector\n\n\n@DETECTORS.register_module()\nclass YOLACT_DET(SingleStageDetector):\n \"\"\"Implementation of `YOLACT <https://arxiv.org/abs/1904.02689>`_\"\"\"\n\n def __init_... | [
[
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PTYin/product-search | [
"41044128f9b066e81257e141b454f427bec8157b"
] | [
"src/AEM/Model.py"
] | [
"import torch\nfrom torch import nn\nfrom common.loss import nce_loss\nimport math\n\n\nclass AttentionLayer(nn.Module):\n def __init__(self, input_dim, head_num, model_name: str):\n super(AttentionLayer, self).__init__()\n self.input_dim = input_dim\n self.head_num = head_num\n self.... | [
[
"torch.nn.init.uniform_",
"torch.ones",
"torch.max",
"torch.less",
"torch.zeros",
"torch.sum",
"torch.nn.Embedding",
"torch.exp",
"torch.nn.Linear",
"torch.nn.init.xavier_uniform_",
"torch.no_grad",
"torch.nn.init.zeros_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
radioactivebean0/baboon-tracking | [
"062351c514073aac8e1207b8b46ca89ece987928"
] | [
"src/baboon_tracking/stages/motion_detector/compute_moving_foreground.py"
] | [
"\"\"\"\r\nComputes the moving foreground using the subcomponents previously computed\r\n\"\"\"\r\nimport math\r\nimport numpy as np\r\n\r\nfrom baboon_tracking.mixins.foreground_mixin import ForegroundMixin\r\nfrom baboon_tracking.mixins.frame_mixin import FrameMixin\r\nfrom baboon_tracking.mixins.history_of_dissi... | [
[
"numpy.greater_equal",
"numpy.logical_and",
"numpy.greater"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
konstantinschulz/lm-evaluation-harness | [
"b0acb3379d2fa8e15561cea033be422bff144f30"
] | [
"lm_eval/tasks/winogrande.py"
] | [
"\"\"\"\nWinoGrande: An Adversarial Winograd Schema Challenge at Scale\nhttps://arxiv.org/pdf/1907.10641.pdf\n\nWinoGrande is a collection of 44k problems, inspired by Winograd Schema Challenge\n(Levesque, Davis, and Morgenstern 2011), but adjusted to improve the scale and\nrobustness against the dataset-specific b... | [
[
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zehao99/CEIT | [
"06f5a409a93073bb7cfd22afb3a39f500e5a24d8"
] | [
"CEIT/models/mesh.py"
] | [
"from ..readmesh import read_mesh_from_csv\nfrom ..util.utilities import get_config, PointStack, Comp, quicksort\nimport numpy as np\n\n\nclass MeshObj(object):\n \"\"\"\n Mesh object for Calculation\n \"\"\"\n\n def __init__(self, mesh_obj=None, electrode_num=None, electrode_center_list=None, electrode... | [
[
"numpy.abs",
"numpy.linalg.inv",
"numpy.linalg.det",
"numpy.copy",
"numpy.mean",
"numpy.shape",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NolanYuu/espnet | [
"73c43ac034d7a44a3bf1a10dc4079884df57de43"
] | [
"espnet2/tasks/abs_task.py"
] | [
"from abc import ABC\nfrom abc import abstractmethod\nimport argparse\nfrom dataclasses import dataclass\nfrom distutils.version import LooseVersion\nimport functools\nimport logging\nimport os\nfrom pathlib import Path\nimport sys\nfrom typing import Any\nfrom typing import Callable\nfrom typing import Dict\nfrom ... | [
[
"torch.autograd.set_detect_anomaly",
"torch.load",
"numpy.min",
"torch.cuda.current_device",
"torch.utils.data.DataLoader",
"torch.multiprocessing.spawn.SpawnContext",
"torch.multiprocessing.get_context",
"numpy.max",
"numpy.mean",
"torch.distributed.get_rank",
"torch.d... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
geexie/mlir-extensions | [
"16f2a157d479e959dcd76428b1407a678abdff3f"
] | [
"numba_dpcomp/mlir/tests/test_numpy.py"
] | [
"# Copyright 2021 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# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or ... | [
[
"numpy.dot",
"numpy.random.RandomState",
"numpy.asarray",
"numpy.arange",
"numpy.dtype",
"numpy.concatenate",
"numpy.seterr",
"numpy.add",
"numpy.cov",
"numpy.atleast_2d",
"numpy.iscomplexobj",
"numpy.mean",
"numpy.testing.assert_allclose",
"sklearn.datasets... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tongni1975/Hands-On-Machine-Learning-on-Google-Cloud-Platform | [
"156950530e64462bcbeebbb5d58eaf5561ed62db"
] | [
"Chapter13/rnn_hwr.py"
] | [
"from __future__ import absolute_import\r\nfrom __future__ import division\r\nfrom __future__ import print_function\r\n\r\nimport tensorflow as tf\r\nfrom tensorflow.contrib import rnn\r\n\r\nfrom tensorflow.examples.tutorials.mnist import input_data\r\nmnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True... | [
[
"tensorflow.matmul",
"tensorflow.nn.softmax",
"tensorflow.unstack",
"tensorflow.contrib.rnn.BasicLSTMCell",
"tensorflow.cast",
"tensorflow.placeholder",
"tensorflow.global_variables_initializer",
"tensorflow.train.GradientDescentOptimizer",
"tensorflow.contrib.rnn.static_rnn",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
nikitacs16/d_bi_att_flow | [
"8d984cc8c0dc1112dbad61198caa616f83b54234"
] | [
"basic/graph_handler.py"
] | [
"import gzip\nimport json\nfrom json import encoder\nimport os\n\nimport tensorflow as tf\n\nfrom basic.evaluator import Evaluation, F1Evaluation\nfrom my.utils import short_floats\nfrom metrics.evaluate_off import evaluate\nimport pickle\n\n\nclass GraphHandler(object):\n def __init__(self, config, model):\n ... | [
[
"tensorflow.train.get_checkpoint_state",
"tensorflow.all_variables",
"tensorflow.trainable_variables",
"tensorflow.initialize_all_variables",
"tensorflow.train.Saver",
"tensorflow.get_default_graph"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
fr42k/gap-wgan-gp | [
"4e373c43d606a1b83f76893d93f9cf8be8cd460d"
] | [
"py3/wgan_gp_pytorch.py"
] | [
"import os, sys\nsys.path.append(os.getcwd())\n\nimport time\nimport tflib as lib\nimport tflib.save_images\nimport tflib.cifar10\nimport tflib.plot\nimport tflib.inception_score\n\nimport numpy as np\n\n\nimport torch\nimport torchvision\nfrom torch import nn\nfrom torch import autograd\nfrom torch import optim\n\... | [
[
"torch.nn.ConvTranspose2d",
"numpy.multiply",
"torch.randn",
"torch.nn.Conv2d",
"torch.nn.Tanh",
"numpy.concatenate",
"torch.nn.Linear",
"torch.FloatTensor",
"torch.rand",
"torch.cuda.is_available",
"torch.nn.BatchNorm2d",
"torch.nn.LeakyReLU",
"numpy.mean",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jeff41404/transformers | [
"825925dfaa5bd6f1dfb92a597ca89d69720772b6"
] | [
"src/transformers/modeling_bart.py"
] | [
"# coding=utf-8\n# Copyright 2020 The Facebook AI Research Team Authors and The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.or... | [
[
"torch.nn.functional.softmax",
"torch.zeros",
"torch.nn.functional.dropout",
"torch.cat",
"torch.nn.Embedding",
"torch.tanh",
"torch.no_grad",
"torch.cuda.is_available",
"torch.finfo",
"torch.nn.Dropout",
"torch.nn.CrossEntropyLoss",
"torch.tensor",
"numpy.sin",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jfreissmann/tespy | [
"70bf8da9fd8521a1177613a894829cd1fa78c663"
] | [
"src/tespy/networks/network.py"
] | [
"# -*- coding: utf-8\n\n\"\"\"Module for tespy network class.\n\nThe network is the container for every TESPy simulation. The network class\nautomatically creates the system of equations describing topology and\nparametrization of a specific model and solves it.\n\n\nThis file is part of project TESPy (github.com/o... | [
[
"pandas.read_csv",
"numpy.absolute",
"pandas.Series",
"numpy.linalg.inv",
"numpy.arange",
"numpy.linalg.norm",
"pandas.DataFrame",
"numpy.ones",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
smittal10/Alfred_OnDevice | [
"7c10663e2056e6dc4dcdc943e8f70602c71089d4"
] | [
"models/nn/transformer.py"
] | [
"\"\"\"\nThe implementation of the Transformer encoder and decoder is adopted from Yu-Hsiang Huang's implementation available in:\nhttps://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/master/transformer/Layers.py\n\"\"\"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport... | [
[
"torch.nn.Dropout",
"torch.nn.functional.softmax",
"torch.ones",
"torch.nn.Embedding",
"torch.nn.LayerNorm",
"torch.nn.Linear",
"torch.matmul",
"torch.nn.init.xavier_uniform_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
he-actlab/waveq.code | [
"a205d3fa22a41d5f4fc1ef1e5698c4f1dbb11e6a"
] | [
"code/examples/classifier_compression/sinreq_v2_svhn_runcode/run_sinreq-Learn_svhn.py"
] | [
"#!/usr/bin/python\r\n\r\n# developer: Ahmed Taha Elthakeb\r\n# email: (a1yousse@eng.ucsd.edu)\r\n\r\n\"\"\"\r\n[21-oct-2018]\r\n- test case: alexnet \r\n- changing reward function to be func(val_acc + train_acc) on 10k images \r\n[29-Dec-2019]\r\n- SinReQ + Learning the frequency of sinreq regularizer\r\n- adding ... | [
[
"tensorflow.nn.softmax_cross_entropy_with_logits",
"tensorflow.get_variable",
"tensorflow.metrics.accuracy",
"tensorflow.FixedLenFeature",
"tensorflow.gfile.Exists",
"tensorflow.cast",
"pandas.DataFrame",
"tensorflow.nn.l2_loss",
"tensorflow.train.AdamOptimizer",
"tensorflo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
Eduardof0nt/Lab-4-Visio-n | [
"803fb60973748148ca3c4da21e75c1d372744df2"
] | [
"Ejercicio-2/Ejercicio-2.py"
] | [
"import matplotlib\nimport matplotlib.pyplot as plt\n\nimport skimage.filters\nfrom skimage import io\nimport os\n\nimage1 = io.imread(os.getcwd() + '/Ejercicio-2/Img/ruido02.jpg', as_gray=False)\n\nfilteredImage1 = skimage.filters.gaussian(image1, multichannel=False, sigma=2)\n\nfig, ax = plt.subplots(1, 1)\nax.im... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Nikeliza/MaskTextSpotter | [
"d1a02d4c2d5111e7846c53227353a84f8588aaf5"
] | [
"maskrcnn_benchmark/modeling/backbone/mobilenet_v2.py"
] | [
"import torch.nn as nn\nfrom maskrcnn_benchmark.modeling.make_layers import inverted_residual_sequence, conv2d_bn_relu6\n\n\nclass MobileNetV2(nn.Module):\n def __init__(self, args):\n super(MobileNetV2, self).__init__()\n\n # s1, s2 = 2, 2\n # if args.DOWNSAMPLING == 16:\n # s1, ... | [
[
"torch.nn.Sequential",
"torch.nn.init.constant",
"torch.nn.init.xavier_normal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rio26/clustering | [
"8d4a3eb0648de448cc880aa3a79a6b764d67be7a"
] | [
"clustering/snmf-clustering.py"
] | [
"import matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.gridspec as gridspec\nimport utils, SNMF\nimport numpy as np\nfrom sklearn.metrics.pairwise import chi2_kernel,sigmoid_kernel, laplacian_kernel,polynomial_kernel\n\n## ALL datasets: \n## 2 clusters: 'happy', 'be2', 'hm', ... | [
[
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.scatter",
"sklearn.metrics.pairwise.sigmoid_kernel",
"matplotlib.pyplot.subplot",
"numpy.argmax",
"numpy.random.rand",
"matplotlib.pyplot.subplots_adjust",
"matplotlib.pyplot.show",
"numpy.zeros",
"matplotlib.pyplot.figur... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rolando-archive/theusual-kaggle-seeclickfix-ensemble | [
"6879d4e5686443a3ad7d7b91062c51d7241e9125"
] | [
"Bryan/misc.py"
] | [
"\"\"\"\r\n=================================================================================================\r\nMisc code snippets used in the ipython console throughout the project for development and exploration,\r\nbut NOT directly referenced in the final program execution.\r\n===================================... | [
[
"sklearn.datasets.dump_svmlight_file",
"sklearn.tree.DecisionTreeRegressor",
"numpy.asarray",
"scipy.sparse.csr_matrix",
"numpy.ones"
]
] | [
{
"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"... |
ttngu207/Li-2015a | [
"a7a4f78aadc8574284d702bf3474b661bcdd7ef1"
] | [
"pipeline/plot/unit_characteristic_plot.py"
] | [
"import numpy as np\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport itertools\nimport pandas as pd\n\nfrom pipeline import experiment, ephys, psth\nfrom pipeline.plot.util import (_plot_with_sem, _extract_one_stim_dur, _get_units_hemisphere, _get_clustering_method,\n ... | [
[
"numpy.abs",
"numpy.linspace",
"numpy.random.choice",
"numpy.isnan",
"numpy.logical_and",
"matplotlib.path.Path",
"matplotlib.pyplot.subplots",
"pandas.DataFrame",
"numpy.sin",
"numpy.cos",
"numpy.std",
"numpy.mean",
"numpy.nanmean",
"numpy.array",
"nump... | [
{
"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": []
}
] |
weiwang1206/indigo | [
"463d89b09699a57bfdfbae351646df6a60040b90"
] | [
"env/sender.py"
] | [
"# Copyright 2018 Francis Y. Yan, Jestin Ma\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 applica... | [
[
"numpy.percentile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PuneethKouloorkar/SPC-Water-Model | [
"faa18fe344dee47eb8eb8e939bd15c5876af12e4"
] | [
"plot_traj.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nx = np.genfromtxt('trajectory', usecols = [1])\ny = np.genfromtxt('trajectory', usecols = [2])\n\n#plotting only the oxygen particle's trajectory\nox = x[::3]\noy = y[::3]\n\nfor i in range(len(ox)-1):\n plt.scatter(ox[i],oy[i],c='k',marker = 'o')\n plt.... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.pause",
"matplotlib.pyplot.scatter",
"numpy.genfromtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jpeg729/pytorch-bits | [
"5d107094042c27472dfb7dee77506b603f5d3e45"
] | [
"models.py"
] | [
"\nfrom torch import nn\nimport nn as custom\n\n\nclass Model(nn.Module):\n def __init__(self, input_size=1, layers=[\"LSTM_51\"], output_size=1, sigmoid=None, tanh=None, biases=True):\n super(Model, self).__init__()\n self.input_size = input_size\n self.output_size = output_size\n se... | [
[
"torch.nn.Linear"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KIPAC/NWayMatch | [
"3cc4dfd8816dfb373a3f1174801549a05a2baf52"
] | [
"python/nway/nway.py"
] | [
"\"\"\" Proof of concept for n-way matching using footprint detection\n\nSome terminology:\n\ncell : A small area, used to find matches, The size\n should be about the same as the maximum match radius.\n\nsource : As per DM, per-catalog detection\n\nmatchWcs : The WCS used to define the cells\n\nsubReg... | [
[
"numpy.hstack",
"numpy.sqrt",
"numpy.unique",
"numpy.ones",
"numpy.full",
"numpy.ceil",
"numpy.argmax",
"numpy.mean",
"numpy.argsort",
"numpy.array",
"numpy.zeros",
"numpy.sum",
"numpy.histogram2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ali-senguel/fairo-explore | [
"893481da270eed1e6d504c71e483d685ca9218d1"
] | [
"polymetis/polymetis/python/polymetis/robot_client/metadata.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.\nfrom typing import List\nfrom dataclasses import dataclass\nimport io\n\nimport torch\n\nimport polymetis\nimport polymetis_pb2\nfrom p... | [
[
"torch.jit.script"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BrisClimate/flood-cascade | [
"660c29275a87785153d0f107ed23104fcbcbddee"
] | [
"submit_scripts/fuse_gridded/generate_param_maps_3choices.py"
] | [
"# Script to produce maps of FUSE parameter sets, based on parameter transfer / regionalisation\n# Peter Uhe\n# June 2019\n#\n\nimport numpy as np\nimport pickle,glob,os,sys\nfrom netCDF4 import Dataset\n# Import libraries for plotting:\nimport matplotlib.pyplot as plt\nimport matplotlib.cm\nimport cartopy.crs as c... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.savefig",
"numpy.ones",
"matplotlib.pyplot.colorbar",
"numpy.ma.masked_where",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.13",
"1.16",
"1.9",
"1.18",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bjrnfrdnnd/panel-test | [
"4609a259e749825b2a2012d8a7e48ed8e8a78deb"
] | [
"nmrtools/plt.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom nmrtools.math import lorentz\n\n\"\"\"TODO: rethink plot routines. There are two main flavors:\n * plot peaklists, either as stick plots or with Lorentzians\n * plot lineshapes\n \nDifferent routes:\n peaklist -> stick plot\n peaklist -> Lo... | [
[
"matplotlib.pyplot.gca",
"numpy.linspace",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.plot",
"numpy.append",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
samadejacobs/moses | [
"1fda9a06ef645f533191990cd10834e52ec29a37"
] | [
"scripts/run.py"
] | [
"import argparse\nimport os\nimport pandas as pd\nimport importlib.util\nimport sys\n\nfrom moses.models_storage import ModelsStorage\n\ndef load_module(name, path):\n spec = importlib.util.spec_from_file_location(name, path)\n module = importlib.util.module_from_spec(spec)\n spec.loader.exec_module(module... | [
[
"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": []
}
] |
younggeun-kim/Styleformer | [
"cb2dd2d169727ad968b3a482d3790008b7865af1"
] | [
"training/networks_Generator.py"
] | [
"import math\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch_utils import misc\nfrom torch_utils import persistence\nfrom torch_utils.ops import upfirdn2d\nfrom torch_utils.ops import bias_act\nfrom torch_utils.ops import fma\nfrom torch.utils.checkpoint import ... | [
[
"torch.autograd.profiler.record_function",
"numpy.log2",
"numpy.sqrt",
"torch.nn.InstanceNorm1d",
"torch.zeros",
"torch.randn",
"torch.cat",
"torch.tensor",
"torch.matmul",
"torch.FloatTensor",
"torch.nn.functional.leaky_relu",
"numpy.float32",
"torch.clamp"
]... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
M3nin0/iris-analysis | [
"2562e611f1535a33bee76fdc281c58bfd74fbca3"
] | [
"Process.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nclass Process(object):\n \n def __init__(self, data):\n self.data = data\n\n def plotSetosa(self):\n # Exibindo o comprimento da sépala da iris setosa\n plt.plot(self.data[:50, 0], 'r-', marker = 'o', ms = 6, label = 'Comp. Sépala... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
baidu/information-extraction | [
"748c8915858ce8b82c3ce5a828cf0d79c54874d9"
] | [
"bin/p_classification/p_infer.py"
] | [
"# -*- coding: utf-8 -*-\n########################################################\n# Copyright (c) 2019, 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 Licens... | [
[
"numpy.set_printoptions",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Aitical/ADspeech2face | [
"2e811ff8cc7333729f4b77d1b1067296253e8e38"
] | [
"test_scripts/stylegan.py"
] | [
"import torch\nfrom torchvision.utils import save_image\nfrom models.stylegan2_pytorch import ModelLoader\n\n\nloader = ModelLoader(\n base_dir = '/home/aitical/Documents/paper_with_code/speech2face/stylegan2/', # path to where you invoked the command line tool\n name = 'default' # the pro... | [
[
"torch.randn",
"torch.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
reppertj/earworm | [
"5c3d457e2c09ce96be75fcb19cd9acf819b84c4b"
] | [
"backend/app/app/tests/utils/utils.py"
] | [
"import random\nfrom numpy.random import default_rng\nimport numpy as np\nimport string\nfrom typing import Dict, List\n\nfrom fastapi.testclient import TestClient\n\nfrom app.core.config import settings\n\nrng = default_rng(42)\n\ndef random_lower_string() -> str:\n return \"\".join(random.choices(string.ascii_... | [
[
"numpy.linalg.norm",
"numpy.random.default_rng"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jiang1997/mmcv | [
"571e3e5fc75c23b45cbd9b00011af094357c5f1d"
] | [
"mmcv/runner/fp16_utils.py"
] | [
"import functools\nimport warnings\nfrom collections import abc\nfrom inspect import getfullargspec\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nfrom mmcv.utils import TORCH_VERSION, digit_version\nfrom .dist_utils import allreduce_grads as _allreduce_grads\n\ntry:\n # If PyTorch version >= 1.6.... | [
[
"torch.cuda.amp.autocast"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
digitalinteraction/openmovement-python | [
"d07dcd66d5c402436ee1113ed2862f6c41aa61b1"
] | [
"src/openmovement/load/csv_load.py"
] | [
"import csv\nimport re\nimport datetime\nimport time\n\nimport numpy as np\nimport pandas as pd\n\nfrom openmovement.load.base_data import BaseData\n\n\n# Normalize the column labels (e.g. 'Time' to 'time'; 'Accel-X (g)' to 'accel_x'; 'Gyro-Z (d/s)' to 'gyro_z')\ndef _normalize_label(label):\n if label is None:\... | [
[
"numpy.arange",
"numpy.median",
"pandas.DataFrame",
"numpy.insert",
"pandas.to_numeric"
]
] | [
{
"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": []
}
] |
mcharsley/google-cloud-cpp | [
"934b05112555e661035930c10ebc0a8011d519ac"
] | [
"google/cloud/storage/benchmarks/storage_throughput_plots.py"
] | [
"#!/usr/bin/env python3\n# Copyright 2020 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 ap... | [
[
"scipy.stats.mannwhitneyu",
"pandas.set_option",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"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... |
1617226214/hybrid_space | [
"2ad8946d44fb61c72787f10f4596a08ca80b5960"
] | [
"tester.py"
] | [
"import os\r\nimport sys\r\nimport json\r\nimport torch\r\nimport pickle\r\nimport logging\r\nimport argparse\r\n\r\nimport evaluation\r\nfrom model import get_model\r\nfrom validate import norm_score, cal_perf\r\n\r\nimport util.data_provider as data\r\nfrom util.text2vec import get_text_encoder\r\nimport util.met... | [
[
"torch.save",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WDZRMPCBIT/SCoRE | [
"c426e58c253f5d97fc4ad0e0fea9606f70cff872",
"c426e58c253f5d97fc4ad0e0fea9606f70cff872",
"c426e58c253f5d97fc4ad0e0fea9606f70cff872"
] | [
"sqa/pytorch_transformers/convert_pytorch_checkpoint_to_tf.py",
"sqa/pytorch_transformers/convert_transfo_xl_checkpoint_to_pytorch.py",
"sqa/pytorch_transformers/modeling_roberta.py"
] | [
"# coding=utf-8\r\n# Copyright 2018 The HuggingFace Inc. team.\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n# http://www.apache.org/licenses/LICENSE-2.0\r\n#\... | [
[
"tensorflow.keras.backend.set_value",
"numpy.allclose",
"torch.load",
"tensorflow.zeros_initializer",
"tensorflow.variables_initializer",
"tensorflow.reset_default_graph",
"tensorflow.Session",
"tensorflow.trainable_variables",
"tensorflow.dtypes.as_dtype"
],
[
"torch.s... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wbwatkinson/determined | [
"f9e099e06746a79a2eaf51a89acc264fc5c301e1"
] | [
"harness/determined/keras/_tf_keras_trial.py"
] | [
"import inspect\nimport logging\nimport pathlib\nimport pickle\nimport random\nimport sys\nfrom abc import abstractmethod\nfrom typing import Any, Dict, List, Optional, cast\n\nimport h5py\nimport numpy as np\nimport tensorflow as tf\nfrom packaging import version\nfrom tensorflow.keras.models import Model\nfrom te... | [
[
"tensorflow.python.keras.saving.hdf5_format.load_optimizer_weights_from_hdf5_group",
"tensorflow.compat.v1.ConfigProto",
"tensorflow.executing_eagerly",
"tensorflow.compat.v1.get_default_graph",
"numpy.random.seed",
"tensorflow.config.experimental.set_memory_growth",
"tensorflow._tf2.e... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"2.9",
"2.5",
"2.8",
"2.10"
]
}
] |
ShenYujun/genforce | [
"2ad04974cfaeba20b93c806531f987f06cc5c328"
] | [
"converters/stylegan2ada_tf_official/dnnlib/tflib/autosummary.py"
] | [
"# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all intellectual property\n# and proprietary rights in and to this software, related documentation\n# and any modifications thereto. Any use, reproduction, disclosure or\n# distribution of this softwa... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.device",
"tensorflow.is_finite",
"tensorflow.control_dependencies",
"tensorflow.zeros",
"tensorflow.reduce_sum",
"tensorflow.stack",
"tensorflow.cast",
"tensorflow.is_variable_initialized",
"tensorflow.add_n",
"tensorflow.summ... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
YashBit/generalized-hindsight | [
"2ba2e1ba0caa20b71d01c8ad70fbc7f47d61bd6c"
] | [
"rlkit/torch/sac/sac_gher.py"
] | [
"from collections import OrderedDict\n\nimport numpy as np\nimport torch\nimport torch.optim as optim\nfrom torch import nn as nn\n\nimport rlkit.torch.pytorch_util as ptu\nfrom rlkit.core.eval_util import create_stats_ordered_dict\nfrom rlkit.torch.torch_rl_algorithm import TorchTrainer\n\n\nclass SACTrainer(Torch... | [
[
"torch.nn.MSELoss",
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
apacha/Mensural-Detector | [
"d924a651bca5ccb97c7b45861b9ef5ef6e4cb26e"
] | [
"position_classification/train_model.py"
] | [
"import argparse\nimport datetime\nimport os\nfrom time import time\n\nimport keras\nimport numpy\nimport numpy as np\nfrom IPython import get_ipython\nfrom keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau, TensorBoard\nfrom keras.preprocessing.image import ImageDataGenerator, DirectoryItera... | [
[
"numpy.argmax",
"numpy.math.ceil"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fmarberg/optking | [
"c50f3f89bcdbb66f779be57277fcd91a74ad677e"
] | [
"optking/displace.py"
] | [
"import numpy as np\nimport logging\n\nfrom . import intcosMisc\nfrom .exceptions import AlgError, OptError\nfrom . import optparams as op\nfrom .linearAlgebra import abs_max, rms, symm_mat_inv\n\n# Functions in this file displace.py\n#\n# displace_molsys: Displace each fragment. Displace dimer coordinates.\n# d... | [
[
"numpy.dot",
"numpy.copy",
"numpy.absolute",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Kaoschuks/CrypTen | [
"c181518da7e2cec14402e64ccc0bf55743b3f765"
] | [
"test/test_gradients.py"
] | [
"#!/usr/bin/env python3\n\n# 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\n\nimport copy\nimport itertools\nimport logging\nimport unittest\nfrom collections import namedtuple\n\nim... | [
[
"torch.nn.functional.batch_norm",
"torch.autograd.set_detect_anomaly",
"torch.tensor",
"torch.arange",
"torch.nn.functional.max_pool2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
brightway-lca/brightway2-calc-copy | [
"770cb83954499199888bb7422442cbdc7ef553b3"
] | [
"tests/fixtures/create_fixtures.py"
] | [
"from bw_processing import create_datapackage, INDICES_DTYPE, UNCERTAINTY_DTYPE\nfrom fs.zipfs import ZipFS\nfrom fs.osfs import OSFS\nfrom pathlib import Path\nimport json\nimport numpy as np\n\n\nfixture_dir = Path(__file__).resolve().parent\n\n\ndef bw2io_example_database():\n try:\n import bw2data as ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wonbeomjang/kaggle | [
"b9dd12448f5438804412ac0480f06f16ea164549"
] | [
"facial-keypoints-detection/test.py"
] | [
"import os\nimport argparse\nimport torch\nimport torch.nn as nn\nfrom tqdm import tqdm\n\nfrom models import Model\nfrom dataset import get_test_loader\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument('--image_size', type=int, default=224, help='the height / width of the input image to network')\nparse... | [
[
"torch.no_grad",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LinXueyuanStdio/KGE-toolbox | [
"916842835e61ba99dde1409592977a2ec55f8aae",
"916842835e61ba99dde1409592977a2ec55f8aae"
] | [
"toolbox/nn/TuckERTNT.py",
"toolbox/nn/EchoE.py"
] | [
"import numpy as np\nimport torch\nimport torch.nn as nn\n\n\nclass TuckERTNT(nn.Module):\n def __init__(self, d, de, dr, dt, device=\"cpu\", input_dropout=0., hidden_dropout1=0., hidden_dropout2=0., **kwargs):\n super(TuckERTNT, self).__init__()\n\n self.device = device\n\n # Embeddings dim... | [
[
"torch.nn.BatchNorm1d",
"torch.nn.Dropout",
"torch.sigmoid",
"torch.ones",
"torch.nn.init.xavier_normal_",
"torch.nn.Embedding",
"torch.nn.BCELoss",
"torch.bmm",
"numpy.random.uniform"
],
[
"torch.nn.BatchNorm1d",
"torch.nn.Dropout",
"torch.abs",
"torch.nn.D... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jolares/ripe-banana-collector | [
"88c921d401697b007ee37f360dc05c4b56a046bf"
] | [
"python/unitytrainers/ppo/trainer.py"
] | [
"# # Unity ML-Agents Toolkit\n# ## ML-Agent Learning (PPO)\n# Contains an implementation of PPO as described (https://arxiv.org/abs/1707.06347).\n\nimport logging\nimport os\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom unityagents import AllBrainInfo, BrainInfo\nfrom unitytrainers.buffer import Buffer\nfr... | [
[
"tensorflow.summary.FileWriter",
"numpy.abs",
"tensorflow.set_random_seed",
"numpy.zeros_like",
"numpy.mean",
"tensorflow.variable_scope",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
samukie/fml-project-2021 | [
"20a424cca97cf8df6fb7c2ff4b41ce834031077d"
] | [
"bomberman_rl/agent_code/rule_based_agent/callbacks.py"
] | [
"from collections import deque\nfrom random import shuffle\n\nimport numpy as np\n\n\ndef look_for_targets(free_space, start, targets, logger=None):\n \"\"\"Find direction of closest target that can be reached via free tiles.\n\n Performs a breadth-first search of the reachable free tiles until a target is en... | [
[
"numpy.subtract",
"numpy.random.seed",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aroongta/Pedestrian_Trajectory_Prediction | [
"de368ef502391bbc87100314f96a3ab1f6b9959a"
] | [
"RNN/data/lstm_prototype_v1.py"
] | [
"# definition vanilla LSTM network233\n\n# import relevant libraries\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport matplotlib\nimport numpy as np\nimport trajectories\nimport loader\nimport argparse\nimport gc\nimport logging\nimport os\nimport sys\nimport time\nimport matplotlib.pyplot ... | [
[
"matplotlib.pyplot.legend",
"torch.nn.Dropout",
"numpy.sum",
"matplotlib.pyplot.title",
"numpy.asarray",
"matplotlib.pyplot.plot",
"torch.nn.Linear",
"torch.nn.LSTMCell",
"torch.save",
"torch.stack",
"torch.nn.ReLU",
"matplotlib.pyplot.show",
"torch.nn.MSELoss",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
modi975/multilayer-perceptron | [
"4cfc9bd79ec145f6ecd2c5f1c12e9df7156e5e70"
] | [
"utils.py"
] | [
"# Copyright 2017 Abien Fred Agarap\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 ag... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.tight_layout",
"numpy.reshape",
"numpy.load",
"tensorflow.argmax",
"sklearn.metrics.confusion_matrix",
"matplotlib.pyplot.colorbar",
"numpy.append",
"tensorflow.Session",
"matplotlib.pyplot.xlabe... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
QiJune/elasticdl | [
"6b01f5b32fd757badff96ed652662bd94afe9263",
"6b01f5b32fd757badff96ed652662bd94afe9263"
] | [
"elasticdl/python/data/recordio_gen/heart_recordio_gen.py",
"elasticdl/python/tests/embedding_test_module.py"
] | [
"# Copyright 2020 The ElasticDL Authors. All rights reserved.\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 ... | [
[
"pandas.read_csv",
"sklearn.model_selection.train_test_split",
"tensorflow.train.Features",
"tensorflow.train.FloatList",
"tensorflow.train.Int64List"
],
[
"tensorflow.keras.layers.Concatenate",
"tensorflow.keras.layers.Embedding",
"tensorflow.keras.layers.Dense",
"tensorfl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"... |
j3soon/tbparse | [
"4bd87404040fd85bdd72b89fefd21e9c6486d26a",
"4bd87404040fd85bdd72b89fefd21e9c6486d26a"
] | [
"tests/test_summary_reader/test_tensor.py",
"tests/test_summary_reader/test_edge_cases.py"
] | [
"import os\nimport tempfile\nfrom typing import List\n\nimport pytest\nimport tensorflow as tf\nfrom tbparse import SummaryReader\nfrom tensorboard.backend.event_processing.event_accumulator import TensorEvent\nfrom torch.utils.tensorboard import SummaryWriter\n\nN_RUNS = 3\nN_EVENTS = 5\n\n@pytest.fixture\ndef pre... | [
[
"tensorflow.make_ndarray",
"tensorflow.summary.scalar"
],
[
"torch.utils.tensorboard.SummaryWriter"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sen-pai/Sub-Sequence-Segmentation | [
"2cc421d5c608149d93ad09f19599bfc681cc7850"
] | [
"both_noisy_training_script.py"
] | [
"import os, sys\nimport numpy as np\nimport random\nimport copy\nimport shutil\nfrom collections import defaultdict\n\nimport matplotlib.pyplot as plt\n\nplt.rc(\"font\", size=8)\n\nimport argparse\nfrom tqdm import tqdm\n\nimport torch\nimport torch.nn as nn\nimport torchvision.utils\nimport torch.nn.functional as... | [
[
"numpy.random.seed",
"torch.nn.functional.l1_loss",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"matplotlib.pyplot.rc",
"torch.nn.functional.mse_loss",
"torch.cuda.is_available",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
an-kramer/exo-intraday | [
"7359f387534063080cf415feee241b3eda22bf91",
"7359f387534063080cf415feee241b3eda22bf91"
] | [
"model_full/parameter_estimation.py",
"model_full/model_specs.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCopyright 2020 by Anke Kramer\n\nLicensed under the MIT License as stated in the LICENSE file.\n\nThis code supplements the paper \"Exogenous factors for order arrivals \non the intraday electricity market\" by Anke Kramer and Rüdiger Kiesel.\n\"\"\"\n\n#%% ... | [
[
"numpy.isnan",
"numpy.array"
],
[
"numpy.hstack",
"scipy.interpolate.interp1d",
"numpy.diff",
"numpy.exp",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
... |
SarahMit/bullseye_pipeline | [
"4e538793037b9b15a603d1644253b49c7c57f0a0"
] | [
"bullseye_pipeline/utils.py"
] | [
"from nipype.interfaces.base import (\n traits,\n TraitedSpec,\n CommandLineInputSpec,\n CommandLine,\n File\n)\n\nimport os\n\ndef filter_labels(in_file, include_superlist, fixed_id=None, map_pairs_list=None):\n \"\"\"filters-out labels not in the include-superset. Merges labels within superset. ... | [
[
"numpy.logical_not",
"numpy.linspace",
"numpy.arange",
"numpy.argwhere",
"numpy.all",
"numpy.copy",
"numpy.any",
"scipy.ndimage.morphology.generate_binary_structure",
"numpy.ravel_multi_index",
"numpy.logical_and",
"numpy.zeros",
"scipy.ndimage.morphology.iterate_st... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"0.15",
"1.4",
"0.10",
"1.3",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16"
],
"tensorflow": [... |
ngoyal2707/tutel | [
"677cdd077293968852f41617e9f2a274a233a258"
] | [
"tutel/impls/moe_layer.py"
] | [
"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nfrom typing import TYPE_CHECKING, Any, Optional, Tuple, Union, cast\n\nimport torch\nfrom torch import Tensor\nimport torch.distributed as dist\nfrom torch.nn import ModuleList\nimport torch.nn.functional as F\n\nfrom ..impls.fast_dispatc... | [
[
"torch.mean",
"torch.nn.functional.softmax",
"torch.cat",
"torch.zeros",
"torch.sum",
"torch.finfo",
"torch.topk",
"torch.addmm",
"torch.distributed.get_rank",
"torch.nn.functional.relu",
"torch.empty_like",
"torch.nn.Parameter",
"torch.empty",
"torch.nn.Mod... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
as641651/LinearAlgera-Awareness-Benchmark | [
"1fbbca4229d5869f35a20193c6f23414fbc81ab7"
] | [
"Exp4-Algebraic-Manipulation/distributivity/distributivity_eq9_tf.py"
] | [
"import tensorflow as tf\nimport os\nimport time\nimport numpy as np\n\nclass bcolors:\n WARNING = '\\033[93m'\n ENDC = '\\033[0m'\n\n\n#Check if MKL is enabled\nimport tensorflow.python.framework as tff\nprint(bcolors.WARNING + \"MKL Enabled : \", tff.test_util.IsMklEnabled(), bcolors.ENDC)\n\n\n#Set threads... | [
[
"tensorflow.print",
"tensorflow.config.threading.set_inter_op_parallelism_threads",
"tensorflow.python.framework.test_util.IsMklEnabled",
"tensorflow.random.normal",
"tensorflow.config.threading.set_intra_op_parallelism_threads",
"tensorflow.config.run_functions_eagerly"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Yamahitsuji/ssd_keras | [
"67bdf4c612a32e36a918490b821e4b63c12687f2"
] | [
"PASCAL_VOC/get_data_from_XML.py"
] | [
"import numpy as np\nimport os\nfrom xml.etree import ElementTree\n\nclass XML_preprocessor(object):\n\n def __init__(self, data_path):\n self.path_prefix = data_path\n self.num_classes = 1\n self.data = dict()\n self._preprocess_XML()\n\n def _preprocess_XML(self):\n filena... | [
[
"numpy.asarray",
"numpy.hstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
StarsMyDestination/OpenPCDet | [
"a9bfdffb2c23f6fe7d4c19085b47ec35728d5884"
] | [
"pcdet/models/backbones_2d/salsanext_backbone.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass ResContextBlock(nn.Module):\n def __init__(self, in_filters, out_filters):\n super(ResContextBlock, self).__init__()\n self.conv1 = nn.Conv2d(in_filters, out_filters, kernel_size=(1, 1), stride=1)\n self.act1 = ... | [
[
"torch.nn.Dropout2d",
"torch.cat",
"torch.nn.Conv2d",
"torch.nn.PixelShuffle",
"torch.nn.AvgPool2d",
"torch.nn.LeakyReLU",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
evolution232634/vehicle_detection | [
"1f58aab8150df29a973ba1fd70ed226665df1450"
] | [
"research/object_detection/dataset_tools/create_pet_tf_record.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.app.run",
"tensorflow.gfile.GFile",
"tensorflow.train.Features",
"tensorflow.python_io.TFRecordWriter"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
C-bowman/mesa | [
"9ee9166e1f5598b43dc257c9d0b1d6903368e834"
] | [
"settings.py"
] | [
"\n\n# ----------------------------------------------------------------------------\n# directory settings\n# ----------------------------------------------------------------------------\n\n# directory where a reference SOLPS run is stored\nsolps_ref_directory = '/pfs/work/g2hjame/solps-iter/runs/TCV_58196_small/r... | [
[
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
stevelill/segreg | [
"71e213d8f36b7bf28d1975bd17b59f61d9dba174"
] | [
"segreg/data/_testing_util.py"
] | [
"\"\"\"\nRoutines to generate fake data to use for testing purposes.\n\"\"\"\n\n# Author: Steven Lillywhite\n# License: BSD 3 clause\n\n\nimport numpy as np\n\n\ndef generate_fake_data(num_data, x_min, x_max, seed=None):\n \"\"\"\n Generates an 1D array of fake data.\n\n Parameters\n ----------\n num... | [
[
"numpy.random.randn",
"numpy.random.random",
"numpy.random.seed",
"numpy.sort"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
iVibudh/stock-prediction | [
"234bd7b1e67e6e2d9c728dce1851e020aab0662e",
"234bd7b1e67e6e2d9c728dce1851e020aab0662e"
] | [
"email-updates-stonks-AI-ML/venv/Lib/site-packages/pandas_datareader/tests/av/test_av_time_series.py",
"email-updates-stonks-AI-ML/venv/Lib/site-packages/pandas_datareader/tests/test_utils.py"
] | [
"from datetime import datetime\nimport os\n\nimport pandas as pd\nimport pytest\n\nfrom pandas_datareader import data as web\nfrom pandas_datareader._utils import RemoteDataError\n\nTEST_API_KEY = os.getenv(\"ALPHAVANTAGE_API_KEY\")\nTEST_API_KEY = None if not TEST_API_KEY else TEST_API_KEY\n\npytestmark = [\n p... | [
[
"pandas.Index"
],
[
"pandas.to_datetime",
"pandas.Timestamp"
]
] | [
{
"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": []
},
{
"matplotlib": [],
"nump... |
arcaneknowledge/ArcanOth | [
"c5d55ef6277550b5e73e65bc0b3d126996773da6"
] | [
"tensorflow/load.py"
] | [
"import tensorflow.keras as keras \nimport tensorflow as tf\nfrom tensorflow.python.platform import gfile\n\"\"\"\nmodel = keras.models.load_model(\"mobilenet/saved_model/saved_model.pb\")\nmodel.load_weights(\"mobilenet/checkpoint\")\nprint(\" Model : \"+str(model.summary()))\n\"\"\"\n\nimport tensorflow as tf\nfr... | [
[
"tensorflow.compat.v1.Session",
"tensorflow.python.platform.gfile.FastGFile",
"tensorflow.import_graph_def",
"tensorflow.compat.v1.GraphDef"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LCAV/continuous-localization | [
"00d6ed4921291fb7a6c3ca1f9bf00724d28640e5"
] | [
"source/simulation.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nsimulation.py: Generate random trajectories and noisy distance estimates, reconstruct trajectory and save errors. \n\"\"\"\n\nimport argparse\nimport json\nimport logging\nimport os\nimport sys\nimport time\n\nimport cvxpy\nimport numpy as np\n\nfrom global_variables import DIM\nf... | [
[
"numpy.sqrt",
"numpy.multiply",
"numpy.isnan",
"numpy.linalg.norm",
"numpy.stack",
"numpy.save",
"numpy.full",
"numpy.load",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ethanr-2000/deep-fry-bot | [
"1a1598915ae216f3fa1cab0cf5620028cea22a64"
] | [
"src/deeppyer/__init__.py"
] | [
"from collections import namedtuple\nfrom io import BytesIO\nimport pkgutil\nfrom typing import Tuple\nfrom PIL import Image, ImageOps, ImageEnhance\nimport cv2\nimport numpy\n\n__all__ = ('Colour', 'ColourTuple', 'DefaultColours', 'deepfry')\n\nColour = Tuple[int, int, int]\nColourTuple = Tuple[Colour, Colour]\n\n... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bethgelab/decompose | [
"b6ad3e3d1a2d049f1853cdc309ad042293415ad1"
] | [
"decompose/distributions/exponentialAlgorithms.py"
] | [
"from typing import Dict\nimport tensorflow as tf\nimport numpy as np\nfrom tensorflow import Tensor\n\nfrom decompose.distributions.algorithms import Algorithms\n\n\nclass ExponentialAlgorithms(Algorithms):\n\n @classmethod\n def sample(cls, parameters: Dict[str, Tensor], nSamples: Tensor) -> Tensor:\n ... | [
[
"tensorflow.less",
"tensorflow.ones_like",
"tensorflow.exp",
"tensorflow.zeros_like",
"tensorflow.distributions.Exponential",
"tensorflow.log",
"tensorflow.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
madhavjk/warehouse_safety_kit_detection | [
"744c88e6c72b3825b30fadd6a881a3fb0c4820b1"
] | [
"com_ineuron_apparel/predictor_yolo_detector/utils/google_utils.py"
] | [
"# This file contains google utils: https://cloud.google.com/storage/docs/reference/libraries\n# pip install --upgrade google-cloud-storage\n# from google.cloud import storage\n\nimport os\nimport platform\nimport subprocess\nimport time\nfrom pathlib import Path\n\nimport torch\n\n\ndef gsutil_getsize(url=''):\n ... | [
[
"torch.hub.download_url_to_file"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ctberthiaume/opedia_dataset_validator | [
"0d88aa0a37a203a894affed60961ab3f17ecdc93"
] | [
"src/opedia_dataset_validator/validator.py"
] | [
"from __future__ import unicode_literals\nfrom .error import error\nfrom io import open\nimport arrow\nimport os\nimport oyaml as yaml\nimport pandas as pd\nimport re\nimport sys\n\n# Load dataset file specifications\nspec_file_name = 'dataset_file_def.yaml'\nspec_file_path = os.path.join(os.path.dirname(__file__),... | [
[
"pandas.isna",
"pandas.read_excel",
"pandas.to_numeric"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"0.24",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
kajikentaro/ssd_keras | [
"86ac105a4e2e8be28605ca772e85842cc85748d3"
] | [
"main.py"
] | [
"from keras.applications.imagenet_utils import preprocess_input\nfrom keras.backend.tensorflow_backend import set_session\nfrom keras.preprocessing import image\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom imageio import imread\nimport tensorflow as tf\nfrom ssd_v2 import SSD300v2\nfrom ssd_utils impo... | [
[
"numpy.linspace",
"numpy.set_printoptions",
"tensorflow.ConfigProto",
"tensorflow.Session",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
ziyeshanwai/SSDR | [
"71ef96eabafbfe88d19955d47218a1557a396b31"
] | [
"SSDR.py"
] | [
"# SSDR Implementation in Python\n# Dalton Omens\n\nimport maya.api.OpenMaya as om\nimport pymel.core as pm\nimport numpy as np\nfrom scipy.optimize import lsq_linear\nfrom scipy.cluster.vq import vq, kmeans, whiten\nimport time\n\n\ndef kabsch(P, Q):\n \"\"\"\n Computes the optimal translation and rotation m... | [
[
"scipy.cluster.vq.whiten",
"numpy.square",
"numpy.linalg.svd",
"scipy.cluster.vq.vq",
"numpy.einsum",
"scipy.cluster.vq.kmeans",
"numpy.vstack",
"numpy.matmul",
"numpy.linalg.norm",
"numpy.linalg.det",
"scipy.optimize.lsq_linear",
"numpy.delete",
"numpy.mean",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.7",
"1.0",
"0.17",
"1.2",
"1.8"
],
"tensorflow": []
}
] |
znicholls/xarray | [
"98e96923293aa8d21e2339e5c890df8c2633493f"
] | [
"xarray/tests/test_dataset.py"
] | [
"import pickle\nimport sys\nimport warnings\nfrom copy import copy, deepcopy\nfrom io import StringIO\nfrom textwrap import dedent\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom pandas.core.indexes.datetimes import DatetimeIndex\n\nimport xarray as xr\nfrom xarray import (\n DataArray,\n Data... | [
[
"pandas.Series",
"numpy.linspace",
"numpy.asarray",
"pandas.MultiIndex.from_tuples",
"pandas.DataFrame",
"numpy.dtype",
"numpy.random.randn",
"numpy.mean",
"numpy.trapz",
"numpy.random.randint",
"numpy.testing.assert_equal",
"numpy.allclose",
"pandas.Categorical... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tianrui/coremltools | [
"f662dea15e3207bd02f44b32d8e35c2893c954a1"
] | [
"coremltools/test/neural_network/test_numpy_nn_layers.py"
] | [
"from __future__ import print_function\n\nimport itertools\nimport math\nimport os\nimport random\nimport shutil\nimport tempfile\nimport unittest\nimport uuid\nimport pytest\nfrom packaging import version\n\nimport numpy as np\nimport pytest\nimport tensorflow as tf\n\nimport coremltools\nimport coremltools.models... | [
[
"numpy.minimum",
"numpy.sqrt",
"tensorflow.image.non_max_suppression",
"numpy.exp",
"numpy.where",
"numpy.square",
"numpy.matmul",
"numpy.sin",
"numpy.ceil",
"tensorflow.Session",
"numpy.zeros",
"numpy.log",
"tensorflow.gather_nd",
"numpy.multiply",
"num... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
maxfrei750/paddle | [
"78d4a585f7cae4eeea3ec47f8d5e41de1dfef1f9",
"78d4a585f7cae4eeea3ec47f8d5e41de1dfef1f9"
] | [
"paddle/metrics/utilities.py",
"data_preparation/utilities.py"
] | [
"from typing import Literal, Optional\n\nimport torch\nfrom torch import Tensor\nfrom torchvision.ops import box_iou\n\n\ndef mask_iou(\n masks_prediction: Tensor,\n masks_target: Tensor,\n boxes_prediction: Tensor,\n boxes_target: Tensor,\n) -> Tensor:\n \"\"\"Calculates IoU matrix, based on instanc... | [
[
"torch.cat",
"torch.round",
"torch.zeros_like",
"torch.logical_and",
"torch.logical_or"
],
[
"pandas.read_csv",
"numpy.zeros_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Yao-14/stAnalysis | [
"d08483ce581f5b03cfcad8be500aaa64b0293f74"
] | [
"stRT/tdr/models/voxel/voxel_model.py"
] | [
"import numpy as np\nimport pyvista as pv\nfrom pyvista import PolyData, UnstructuredGrid\n\ntry:\n from typing import Literal\nexcept ImportError:\n from typing_extensions import Literal\n\nfrom typing import Optional, Union\n\nfrom ..utilities import add_model_labels, merge_models\n\n#######################... | [
[
"numpy.asarray",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xh-diagrams/xhorizon | [
"20b3f2f0f621ca2a31c9f6a1d5fcd06692a700ce"
] | [
"src/xhorizon/diagram_tools/diagram_classes.py"
] | [
"\n\"\"\"\nThis module defines the classes\n\tdiagram\n\tregion\n\tblock\nwhich are used to construct and plot diagrams.\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom xhorizon.diagram_tools import coord_transf as coord\nfrom xhorizon.diagram_tools.block_masks import rstar_minmax, uv_range\n... | [
[
"numpy.logical_and",
"numpy.sort",
"matplotlib.pyplot.plot",
"numpy.any",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nik849/ct-tools | [
"1218f62de1b8974651506ebf8fd9197c1b038a0a"
] | [
"ct-tools/recon.py"
] | [
"import axitom\nimport numpy as np\nimport glob\nfrom os.path import *\nimport matplotlib.pyplot as plt\n\n\nclass data(object):\n\n\n def __init__(self, dataset=None, xtekct=None, debug=None, **kwargs):\n self.debug = debug\n if dataset is not None:\n self.dataset = sorted([os.path.join... | [
[
"matplotlib.pyplot.title"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pwmdebuck/dymos-1 | [
"0fb3e91e8c32b34fca41e8c1ec9bec66f31af341",
"0fb3e91e8c32b34fca41e8c1ec9bec66f31af341"
] | [
"dymos/examples/racecar/problemSolver.py",
"dymos/examples/brachistochrone/test/test_brachistochrone_undecorated_ode.py"
] | [
"import numpy as np\nimport openmdao.api as om\nimport dymos as dm\nimport matplotlib.pyplot as plt\nfrom combinedODE import CombinedODE\nimport matplotlib as mpl\n\n#track curvature imports\nfrom scipy import interpolate\nfrom scipy import signal\nfrom Track import Track\nimport tracks\nfrom spline import getSplin... | [
[
"numpy.amax",
"numpy.linspace",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.rcParams.update",
"matplotlib.pyplot.tight_layout",
"matplotlib.cm.ScalarMappable",
"numpy.interp",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.subplots_adjust",
"numpy.amin",
"matplotlib.colo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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