repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
zyjia/tensorlayer-chinese | [
"f55986e9ef3aaf0f59dae8e4e7a84812868bce33"
] | [
"tensorlayer/models/squeezenetv1.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nSqueezeNet for ImageNet.\n\"\"\"\n\nimport os\n# import numpy as np\nimport tensorflow as tf\nfrom .. import _logging as logging\nfrom ..layers import (Layer, Conv2d, InputLayer, MaxPool2d, ConcatLayer, DropoutLayer, GlobalMeanPool2d)\nfrom ..files import maybe_download_and_extract... | [
[
"tensorflow.variable_scope"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
connahKendrickMMU/KerasLandmarkingToAndroid | [
"64de938e5b2c0aefdf6510ddf095358b147da082"
] | [
"TrainBuildTo android/ConvertKerasModelToTensorGraphAndroid.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Mar 31 11:39:31 2017\r\n\r\n@author: 12102083\r\n\"\"\"\r\n\r\nimport os\r\nimport numpy \r\nimport pandas \r\nfrom sklearn.utils import shuffle \r\nfrom keras.wrappers.scikit_learn import KerasRegressor \r\nfrom sklearn.model_selection import cross_val_score ... | [
[
"tensorflow.constant",
"tensorflow.assign",
"tensorflow.contrib.session_bundle.exporter.classification_signature",
"tensorflow.global_variables_initializer",
"tensorflow.python.framework.graph_util.convert_variables_to_constants",
"tensorflow.contrib.session_bundle.exporter.Exporter",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
mfa/wandb-allennlp | [
"29ebba81cdbd83653350d00911c4a54d8da9def1"
] | [
"tests/models/dummy.py"
] | [
"from typing import List, Tuple, Union, Dict, Any, Optional\nimport torch\nimport allennlp\nfrom allennlp.models import Model\nfrom allennlp.data.vocabulary import Vocabulary\nfrom allennlp.data.dataset_readers import DatasetReader\nfrom allennlp.data.fields import TensorField\nfrom allennlp.data.instance import In... | [
[
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
songqiang/autogluon | [
"529d7cc65fad411622072aa0349215a15e1e901c"
] | [
"core/src/autogluon/core/metrics/__init__.py"
] | [
"import copy\nfrom abc import ABCMeta, abstractmethod\nfrom functools import partial\n\nimport scipy\nimport scipy.stats\nimport sklearn.metrics\n\nfrom . import classification_metrics\nfrom .util import sanitize_array\nfrom ..constants import PROBLEM_TYPES_REGRESSION, PROBLEM_TYPES_CLASSIFICATION, QUANTILE\nfrom .... | [
[
"scipy.stats.spearmanr",
"scipy.stats.pearsonr"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16",
"1.8"
... |
aokellermann/gym-minesweeper | [
"d071ab103c912f2dd08b2a83129b4ca6df3b3617"
] | [
"gym_minesweeper/tests/minesweeper_test.py"
] | [
"\"\"\"Tests for minesweeper env implementation.\"\"\"\nfrom unittest.mock import patch\n\nimport numpy as np\nimport numpy.testing as npt\nimport pytest\nfrom PIL import Image\n\nfrom gym_minesweeper import MinesweeperEnv, SPACE_UNKNOWN, \\\n DEFAULT_REWARD_WIN, DEFAULT_REWARD_LOSE, DEFAULT_REWARD_CLEAR, DEFAUL... | [
[
"numpy.testing.assert_array_equal",
"numpy.sort"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
esamuel1/pymapd | [
"8a4c5093b9d864d3356880bab846cbb6f50d2127"
] | [
"tests/test_data_no_nulls_cpu.py"
] | [
"\"\"\"\nThe intent of this file is to be a full integration test. Whenever possible,\nadd a datatype to the main _tests_table_no_nulls function, so that the tests\nwill evaluate not only that a data type works, but that it works in the\npresence of the other data types as well in the same dataframe/database table\... | [
[
"pandas.DataFrame.equals",
"pandas.read_sql",
"numpy.isclose"
]
] | [
{
"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": []
}
] |
bbidong/enas | [
"759d081ae73ac0a971aa69f51b67a4d78fec6b03"
] | [
"src/cifar10/main.py"
] | [
"#-*- coding: utf-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport cPickle as pickle\nimport shutil\nimport sys\nimport time\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom src import utils\nfrom src.utils import Logger... | [
[
"tensorflow.Graph",
"tensorflow.train.SingularMonitoredSession",
"numpy.reshape",
"tensorflow.train.CheckpointSaverHook",
"tensorflow.ConfigProto",
"tensorflow.train.Saver",
"tensorflow.app.run"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
ian-double-u/solar | [
"a455c901947df11202d70235aca6968f2e764fcb"
] | [
"surfrad_data_prep.py"
] | [
"import pandas as pd\nimport requests\nfrom requests.auth import HTTPBasicAuth\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\n\npath_list = Path('C:\\\\Users\\\\Admin\\\\Desktop\\\\').glob('**/*.dat')\npaths = []\n\nfor path in path_list:\n paths.append(str(path))\n \nframes = [] # hold ... | [
[
"pandas.concat",
"pandas.read_csv",
"numpy.mean",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
gorilux/incubator-mxnet | [
"8ca4f5088072a9b0c50562a476d9892c83d0af48"
] | [
"python/mxnet/ndarray/numpy/_op.py"
] | [
"# pylint: disable=C0302\n# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.... | [
[
"numpy.true_divide",
"numpy.logical_xor",
"numpy.minimum",
"numpy.asarray",
"numpy.around",
"numpy.nan_to_num",
"numpy.dtype",
"numpy.bitwise_xor",
"numpy.arctan2",
"numpy.polyval",
"numpy.divide",
"numpy.hypot",
"numpy.right_shift",
"numpy.fmod",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yl-1993/mmhuman3d | [
"61a7427b7882d5e5f5fe623272a5c455c3d3b009"
] | [
"mmhuman3d/models/losses/prior_loss.py"
] | [
"import itertools\nimport os\nimport pickle\nimport sys\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom mmhuman3d.core.conventions.joints_mapping.standard_joint_angles import (\n STANDARD_JOINT_ANGLE_LIMITS,\n TRANSFORMATION_AA_TO_SJA,\n TRANSFORMATION_SJ... | [
[
"torch.cat",
"torch.pow",
"torch.norm",
"torch.einsum",
"torch.argmin",
"numpy.stack",
"torch.tensor",
"numpy.linalg.det",
"torch.nn.functional.relu",
"torch.ones_like",
"numpy.linalg.inv",
"torch.min",
"torch.deg2rad",
"torch.zeros_like",
"torch.exp",
... | [
{
"matplotlib": [],
"numpy": [
"1.11",
"1.10",
"1.12",
"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": [],
... |
jhyuuu/pytorch_image_classification | [
"20a5585d06a6e8aedffb3d8b86614c467ae4710b"
] | [
"pytorch_image_classification/utils/env_info.py"
] | [
"import torch\nimport yacs.config\n\nfrom pytorch_image_classification.config.config_node import ConfigNode\n\n\ndef get_env_info(config: yacs.config.CfgNode) -> yacs.config.CfgNode:\n info = {\n 'pytorch_version': str(torch.__version__),\n 'cuda_version': torch.version.cuda or '',\n 'cudnn_... | [
[
"torch.cuda.device_count",
"torch.cuda.get_device_capability",
"torch.backends.cudnn.version",
"torch.cuda.get_device_name"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JXKun980/TransUNet | [
"78fca7d1a87a13bd4d7d95fa5e6598587b09b78a"
] | [
"select_permutations.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Sep 14 15:50:28 2017\n\n@author: bbrattol\n\"\"\"\nimport argparse\nfrom tqdm import trange\nimport numpy as np\nimport itertools\nfrom scipy.spatial.distance import cdist\n\n\nparser = argparse.ArgumentParser(description='Train network on Imagenet')\nparser.add_argu... | [
[
"scipy.spatial.distance.cdist",
"numpy.save",
"numpy.delete",
"numpy.array",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
mappercore/mapper-core | [
"39af3685beecce14cdb55b951f2d5556cdd28f76"
] | [
"app/enhanced_mapper/mapper.py"
] | [
"import numpy as np\nfrom typing import List, Dict, Tuple\nfrom itertools import combinations\nfrom sklearn.preprocessing import MinMaxScaler\n\nfrom .cover import Cover\nfrom .oracle import _check_clustering_object, cluster_points, map_overlap_cluster_to_interval\nfrom .graph import EnhancedGraph, Graph, AbstractG... | [
[
"numpy.min",
"numpy.reshape",
"numpy.median",
"numpy.linalg.norm",
"numpy.max",
"numpy.std",
"numpy.mean",
"numpy.sum",
"sklearn.preprocessing.MinMaxScaler"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
brightsparc/mlflow | [
"31e4f969d3b65a2fd3e246e88a23433b72904d49",
"31e4f969d3b65a2fd3e246e88a23433b72904d49"
] | [
"tests/pyfunc/test_model_export_with_loader_module_and_data_path.py",
"examples/xgboost/train.py"
] | [
"import os\nimport pickle\nimport yaml\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nimport six\nimport sklearn.datasets\nimport sklearn.linear_model\nimport sklearn.neighbors\n\nimport mlflow\nimport mlflow.pyfunc\nfrom mlflow.pyfunc import PyFuncModel\nimport mlflow.pyfunc.model\nimport mlflow.sklear... | [
[
"pandas.DataFrame"
],
[
"matplotlib.use",
"sklearn.datasets.load_iris",
"sklearn.model_selection.train_test_split",
"sklearn.metrics.log_loss",
"sklearn.metrics.accuracy_score"
]
] | [
{
"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... |
WhiteCrow/zipline | [
"ae540c57bac7fa43118dcfde95f5af9ae7efaee4"
] | [
"zipline/algorithm.py"
] | [
"#\n# Copyright 2015 Quantopian, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or... | [
[
"numpy.isnan",
"pandas.DatetimeIndex",
"pandas.DataFrame",
"numpy.all",
"pandas._libs.tslib.normalize_date",
"pandas.Timestamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ver0z/detect-waste | [
"7dbe029022d71e3f3643fc76d302fef684390d37"
] | [
"efficientdet/train.py"
] | [
"#!/usr/bin/env python\n\"\"\" EfficientDet Training Script\n\nThis script was started from an early version of the PyTorch ImageNet example\n(https://github.com/pytorch/examples/tree/master/imagenet)\n\nNVIDIA CUDA specific speedups adopted from NVIDIA Apex examples\n(https://github.com/NVIDIA/apex/tree/master/exa... | [
[
"torch.cuda.synchronize",
"torch.distributed.init_process_group",
"torch.cuda.set_device",
"torch.manual_seed",
"torch.cuda.empty_cache",
"torch.nn.SyncBatchNorm.convert_sync_batchnorm",
"torch.cuda.amp.autocast.to",
"torch.no_grad",
"torch.nn.parallel.DistributedDataParallel"
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zisikons/deep-rl | [
"3c39a194d048618a2a3962cdf5f4b1825e789a22"
] | [
"core/Noise.py"
] | [
"import numpy as np\nclass OUNoise(object):\n def __init__(self, act_dim, num_agents, act_low, act_high, mu=0.0,\n theta=0.15, max_sigma=0.7, min_sigma=0.05, decay_period=2500):\n # Parameters\n self.mu = mu\n self.theta = theta\n self.sigma = max_si... | [
[
"numpy.ones",
"numpy.random.randn",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Luodian/MADAN | [
"7a2918da44f5203b72652bc4cba0e70057482114"
] | [
"cyclegan/options/base_options.py"
] | [
"import argparse\nimport os\n\nimport torch\nfrom util import util\n\n\nclass BaseOptions():\n\tdef __init__(self):\n\t\tself.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n\t\tself.initialized = False\n\t\n\tdef initialize(self):\n\t\tself.parser.add_argument('--dataroot'... | [
[
"torch.cuda.set_device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
drMJ/roman | [
"9650e73ec6fbb2d8044aa1bbf89fd671843ea54e",
"9650e73ec6fbb2d8044aa1bbf89fd671843ea54e"
] | [
"roman/ur/realtime/urlib.py",
"test/arm_sim_test.py"
] | [
"################################################################################################################################\n## Redirects the UR functions needed by the control script to the simulator.\n###########################################################################################################... | [
[
"scipy.spatial.transform.Rotation.from_rotvec",
"numpy.greater",
"numpy.subtract",
"numpy.linalg.norm",
"numpy.concatenate",
"numpy.add"
],
[
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.5",
"1.2",
"1.3",
"1.4"
],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
modscripps/mixsea | [
"b9962e1fd86da509d6649d1e766d8daeb440656f"
] | [
"mixsea/overturn.py"
] | [
"import gsw\nimport numpy as np\n\n\ndef nan_eps_overturn(\n depth,\n t,\n SP,\n lon,\n lat,\n **kwargs,\n):\n \"\"\"\n Calculate turbulent dissipation based on the Thorpe scale method attempting to deal NaN values in the input data.\n It does this by removing all NaN values in the input ... | [
[
"numpy.minimum",
"numpy.asarray",
"numpy.flipud",
"numpy.cumsum",
"numpy.zeros_like",
"numpy.any",
"numpy.mean",
"numpy.fix",
"numpy.square",
"numpy.hstack",
"numpy.unique",
"numpy.arange",
"numpy.size",
"numpy.diff",
"numpy.isnan",
"numpy.full_like"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
joshwalawender/PypeIt | [
"f952cbb2aaee640b5c585be823884a237b441e8e"
] | [
"pypeit/scripts/ql_mos.py"
] | [
"#!/usr/bin/env python\n#\n# See top-level LICENSE file for Copyright information\n#\n# -*- coding: utf-8 -*-\n\"\"\"\nThis script runs PypeIt on a set of MultiSlit images\n\"\"\"\nimport argparse\n\nfrom pypeit import msgs\n\nimport warnings\n\ndef parser(options=None):\n\n parser = argparse.ArgumentParser(desc... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
henry-eigen/weightnorm | [
"017d6288262d5a4e2ffcfd909709bbf8059698f3"
] | [
"keras_2/weightnorm.py"
] | [
"from keras import backend as K\nfrom keras.optimizers import SGD,Adam\nimport tensorflow as tf\n\n# adapted from keras.optimizers.SGD\nclass SGDWithWeightnorm(SGD):\n def get_updates(self, loss, params):\n grads = self.get_gradients(loss, params)\n self.updates = []\n\n lr = self.lr\n ... | [
[
"tensorflow.reduce_sum",
"tensorflow.sqrt",
"tensorflow.square"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"1.4",
"2.2",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1.2",
"2.... |
khalidsaifullaah/tutorials | [
"cf10bed4dd5dd6b069f8f102e2a532a4d03fcf43"
] | [
"beginner_source/blitz/neural_networks_tutorial.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nNeural Networks\n===============\n\nNeural networks can be constructed using the ``torch.nn`` package.\n\nNow that you had a glimpse of ``autograd``, ``nn`` depends on\n``autograd`` to define models and differentiate them.\nAn ``nn.Module`` contains layers, and a method ``forward(i... | [
[
"torch.nn.Linear",
"torch.randn",
"torch.nn.Conv2d",
"torch.nn.MSELoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
goldpink/NLP_multiclass_classification | [
"ecdd75a12487233e1439e9c8cb1a7b488fcdc852"
] | [
"data_loader.py"
] | [
"import os\nimport copy\nimport json\nimport logging\n\nimport torch\nfrom torch.utils.data import TensorDataset\nlogger = logging.getLogger(__name__)\n\n\nclass InputExample(object):\n \"\"\"\n A single training/test example for simple sequence classification.\n \"\"\"\n\n def __init__(self, guid, text... | [
[
"torch.utils.data.TensorDataset",
"torch.load",
"torch.save",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DanPorter/Dans_Diffaction | [
"74aea3d2b54d841271f22841f405a9a7c6fa1c81"
] | [
"Dans_Diffraction/classes_orbitals.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\n Orbitals class \"classes_orbitals.py\"\n Build an ionic compound or molecule from ions with defined atomic orbitals\n\n orbital = Orbital('4d4')\n atom = Atom('Co3+')\n compound = CompoundString('Ca2RuO4')\n\n Orbital:\n individual atomic orbital with properties, n,l and fill\n... | [
[
"numpy.min",
"numpy.asarray",
"numpy.unique",
"numpy.ceil",
"numpy.floor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RadwaSK/deep-text-recognition-benchmark | [
"cea2e093c4b4e3f0144f1eefa7cd899419375d92"
] | [
"dataset.py"
] | [
"import os\nimport sys\nimport re\nimport six\nimport math\nimport lmdb\nimport torch\n\nfrom natsort import natsorted\nfrom PIL import Image\nimport numpy as np\nfrom torch.utils.data import Dataset, ConcatDataset, Subset\nfrom torch._utils import _accumulate\nimport torchvision.transforms as transforms\n\n\nclass... | [
[
"torch.cat",
"numpy.tile",
"torch.utils.data.ConcatDataset",
"torch.FloatTensor",
"torch._utils._accumulate",
"numpy.transpose",
"torch.utils.data.Subset"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Synerise/recsys-challenge-2021 | [
"f8e8005a1553c14bae16951d787d6864094f7a3b"
] | [
"src/bert_finetuning.py"
] | [
"import os\nimport argparse\nimport logging\nimport yaml\nfrom transformers import DistilBertForMaskedLM, DistilBertTokenizer, DataCollatorForLanguageModeling\nimport numpy as np\nfrom transformers import Trainer\nfrom transformers import TrainingArguments\nfrom read_dataset_utils import all_features_to_idx\nfrom d... | [
[
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vbob/CarSpeedDetection | [
"a8862317ce3868ca35e747c5450383c3fbea9bc3"
] | [
"trackCarNeuralNework.py"
] | [
"import cv2\nimport sys\nimport time\nimport tensorflow as tf\nimport numpy as np\n\nvideo = cv2.VideoCapture(\"v7.mp4\")\n\nif not video.isOpened():\n print(\"Could not open video\")\n sys.exit()\n\ntotalFPS = 0\ntotalFrames = 0\ntimer = cv2.getTickCount()\n\nbtn_down = False\n\ndef get_points(im):\n # Se... | [
[
"tensorflow.device",
"tensorflow.import_graph_def",
"numpy.uint16",
"tensorflow.Session",
"tensorflow.GraphDef",
"tensorflow.gfile.FastGFile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
Investimentos-do-Vitor/Beta-ibov-calculator | [
"ab122310b7915d09f338bf1f689d6c05e3351759"
] | [
"Analise.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Nov 1 12:43:07 2020\nfor Python 3.7\n\n@author: vitor\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\n#import pandas_datareader as wb\nimport matplotlib.pyplot as plt\nimport math\n\n\nticker = 'TRPL4 Historical Data.csv'\ntickername = 'TRPL4'\n\n#Lê os dados do... | [
[
"pandas.read_csv",
"matplotlib.pyplot.title",
"pandas.DataFrame",
"numpy.mean",
"matplotlib.pyplot.hist",
"matplotlib.pyplot.xlabel",
"numpy.sum",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
rosario-riccio/MLWeatherLabeling | [
"8dd0b43ac877647d70d6c96c79c9a7f660e88982"
] | [
"mlMain1.py"
] | [
"#mlMain1.py\nimport numpy as np\nimport csv\nfrom keras.models import Sequential\nfrom keras.layers import Dense,Dropout\nfrom keras.optimizers import SGD\nfrom keras.utils import to_categorical\nfrom keras.models import load_model\nimport pandas as pd\nimport tensorflow as tf\nimport glob\nimport os\nimport sys\n... | [
[
"matplotlib.pyplot.legend",
"pandas.concat",
"pandas.read_csv",
"matplotlib.pyplot.title",
"sklearn.model_selection.train_test_split",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
olivier2311/Quantropy | [
"5d678a802adb4720c17e6ae4c313b1e37db8f313"
] | [
"quantitative_analysis/time_series_analysis/time_series_behaviors.py"
] | [
"import numpy as np\nfrom numpy.random import randn\nimport pandas as pd\nimport statsmodels.tsa.stattools as ts\n\n\nclass TimeSeriesBehavior:\n \"\"\"\n This class provides functions to determine the behavior of a time series,\n specifically whether it is\n - A random walk. It has no memory. Examp... | [
[
"numpy.subtract",
"numpy.log",
"numpy.random.randn"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
haoyuying/PaddleSeg | [
"6607d88df39500330a7b6ed160b4626d9f38df66"
] | [
"contrib/EISeg/eiseg/scripts/annotations_conversion/coco_lvis.py"
] | [
"import cv2\nimport pickle\nimport numpy as np\nfrom pathlib import Path\nfrom tqdm import tqdm\n\nfrom data.datasets import LvisDataset, CocoDataset\nfrom util.misc import get_bbox_from_mask, get_bbox_iou\nfrom scripts.annotations_conversion.common import get_masks_hierarchy, get_iou, encode_masks\n\n\ndef create_... | [
[
"numpy.full_like",
"numpy.max",
"numpy.logical_not",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DmitriyValetov/ai-testing-platform | [
"6ddb453db247b571082202d247672c674e20b13d"
] | [
"tests/test_main.py"
] | [
"import os\nimport io\nimport time\nimport json\nimport socket\nimport base64\nimport psutil\nimport shutil\nimport sqlite3\nimport tempfile\nimport requests\nimport subprocess\nimport numpy as np\nimport pandas as pd\n\n\nfrom calc_metrics import write_img_return_base64\nfrom db import empty_db, execute_query, exe... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
josephhic/AutoDot | [
"9acd0ddab9191b8a90afc6f1f6373cf711b40b89"
] | [
"Investigation/condition_functions.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Nov 15 07:11:05 2019\n\n@author: thele\n\"\"\"\n\nimport scipy.signal as signal\nimport pickle\nimport numpy as np\nfrom .scoring.Last_score import final_score_cls\nfrom skimage.feature import blob_log\nimport time\n\ndef mock_peak_check(anchor,minc,maxc,configs,**kw... | [
[
"numpy.expand_dims",
"numpy.maximum",
"scipy.signal.find_peaks",
"numpy.minimum",
"numpy.linalg.norm",
"numpy.all",
"numpy.copy",
"numpy.random.uniform",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"1.5",
"1.2",
"1.7",
"1.3",
"1.8"
],
"tensorflow": []
}
] |
biomass-dev/biomass | [
"789a747bb293a52eaf65ce2c441d063d7a6d0671"
] | [
"biomass/dynamics/temporal_dynamics.py"
] | [
"import os\nfrom dataclasses import dataclass\nfrom math import isnan\nfrom typing import List, Optional\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom matplotlib.axes._axes import _log as matplotlib_axes_logger\n\nfrom ..exec_model import ExecModel, ModelObject\nfrom ..plotting import MultipleOb... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.gca",
"numpy.empty_like",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.figure",
"matplotlib.axes._axes._log.setLevel",
"numpy.max",
"matplotlib.pyplot.xlim",
"numpy.nanmean",
"matplotlib.pyplot.c... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bmistry4/nalm-benchmark | [
"273c95cc75241f56e48bcd0b18b043969ef82004"
] | [
"stable_nalu/functional/golden_ratio_base.py"
] | [
"import math\nimport torch\n\ngolden_ratio = (1 + math.sqrt(5)) / 2.\ntanh = lambda x: (torch.pow(golden_ratio, 2 * x) - 1) / (torch.pow(golden_ratio, 2 * x) + 1)\nsigmoid = lambda x: 1 / (1 + torch.pow(golden_ratio, -x))\n"
] | [
[
"torch.pow"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sjtuytc/AAAI21-RoutineAugmentedPolicyLearning | [
"7192f0bf26378d8aacb21c0220cc705cb577c6dc",
"7192f0bf26378d8aacb21c0220cc705cb577c6dc"
] | [
"make_demo_discover_rt/baseline_a2c.py",
"make_demo_discover_rt/sq_rt_proposal.py"
] | [
"import time\nimport functools\nimport tensorflow as tf\n\nfrom baselines import logger\n\nfrom baselines.common import set_global_seeds, explained_variance\nfrom baselines.common import tf_util\nfrom baselines.common.policies import build_policy\n\nfrom baselines.a2c.utils import Scheduler, find_trainable_variable... | [
[
"tensorflow.train.RMSPropOptimizer",
"tensorflow.reduce_mean",
"tensorflow.gradients",
"tensorflow.placeholder",
"tensorflow.squeeze",
"tensorflow.ConfigProto",
"tensorflow.global_variables_initializer",
"tensorflow.clip_by_global_norm",
"tensorflow.variable_scope"
],
[
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensor... |
Razoff/prepa_cahier | [
"86bd4671a50ad06ee247089ae01d50c1a2479415"
] | [
"game.py"
] | [
"import headers\nimport pgn_move_processing\nimport networkx as nx\nimport matplotlib.pyplot as plt\nfrom networkx.drawing.nx_pydot import graphviz_layout\n\n\"\"\"\nGame class. A game is a header object and and list of half_move objects\n\"\"\"\nclass Game:\n def __init__(self, header, moves):\n self.hea... | [
[
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DRL-CASIA/Perception | [
"a0e7d3957267ce92a82b03ab3eca96916d22c4f2"
] | [
"demo/main.py"
] | [
"##v4版本可以识别多车并对应,并行运算\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Aug 20 12:53:40 2020\r\n\r\n@author: Administrator\r\n\"\"\"\r\nimport sys\r\nsys.path.append(\"./angle_classify\")\r\nsys.path.append(\"./armor_classify\")\r\nsys.path.append(\"./car_classify\")\r\nimport numpy as np\r\nimport cv2\r\nfrom... | [
[
"numpy.int0",
"numpy.sqrt",
"torch.Tensor",
"torch.max",
"numpy.linalg.inv",
"numpy.set_printoptions",
"torch.from_numpy",
"torch.unsqueeze",
"torch.tensor",
"numpy.shape",
"torch.cuda.is_available",
"numpy.transpose",
"numpy.argsort",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ptklx/segmentation_models.pytorch | [
"16c68a7e6bff9644b97f340d67912c4785219818"
] | [
"my_timm/models/dla.py"
] | [
"\"\"\" Deep Layer Aggregation and DLA w/ Res2Net\nDLA original adapted from Official Pytorch impl at:\nDLA Paper: `Deep Layer Aggregation` - https://arxiv.org/abs/1707.06484\n\nRes2Net additions from: https://github.com/gasvn/Res2Net/\nRes2Net Paper: `Res2Net: A New Multi-scale Backbone Architecture` - https://arx... | [
[
"torch.nn.Sequential",
"torch.cat",
"torch.nn.functional.dropout",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.AvgPool2d",
"torch.nn.BatchNorm2d",
"torch.split",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mlberkeley/multiarchy | [
"18ceae308efe67ad3e575a3e76a784af036b25a6",
"18ceae308efe67ad3e575a3e76a784af036b25a6"
] | [
"multiarchy/algorithms/ddpg.py",
"multiarchy/distributions/gaussian.py"
] | [
"\"\"\"Author: Brandon Trabucco, Copyright 2019, MIT License\"\"\"\n\n\nfrom multiarchy.algorithms.algorithm import Algorithm\nimport tensorflow as tf\n\n\nclass DDPG(Algorithm):\n\n def __init__(\n self,\n policy,\n target_policy,\n qf,\n target_qf,\n ... | [
[
"tensorflow.concat",
"tensorflow.reduce_mean",
"tensorflow.keras.losses.logcosh",
"tensorflow.stop_gradient",
"tensorflow.GradientTape"
],
[
"tensorflow.concat",
"tensorflow.shape",
"tensorflow.math.log",
"tensorflow.math.exp",
"tensorflow.split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
scrime-u-bordeaux/dereverberation-ml | [
"ba335d400bf2235afabd4151fdae4906c0cd87a8"
] | [
"src/datagen/utils.py"
] | [
"# -*- coding: utf-8 -*-\n\n\"\"\"Set and get audio properties\"\"\"\n\nimport src.utils.path as pth\nimport src.utils.logger as log\n\nimport numpy as np\nimport fleep\n\n\n## Useful to avoid picking non audio files ##\n\"\"\"\nfrom mimetypes import guess_type\n\ndef __is_audio_file(fpath):\n \\\"\"\"Return Tru... | [
[
"numpy.iinfo"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fatiando/v0.1 | [
"1ab9876b247c67834b8e1c874d5b1d86f82802e2"
] | [
"_static/cookbook/seismic_srtomo_sparse.py"
] | [
"\"\"\"\nSeismic: 2D straight-ray tomography of large data sets and models using\nsparse matrices\n\nUses synthetic data and a model generated from an image file.\n\nSince the image is big, use sparse matrices and a steepest descent solver\n(it doesn't require Hessians).\n\nWARNING: may take a long time to calculat... | [
[
"numpy.std",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
varun-jois/KAIR | [
"90c04671c6eb32a6765edfec94f7db3ba1f53f1e"
] | [
"main_test_bsrgan.py"
] | [
"import os.path\nimport logging\nimport torch\n\nfrom utils import utils_logger\nfrom utils import utils_image as util\n# from utils import utils_model\nfrom models.network_rrdbnet import RRDBNet as net\n\n\n\"\"\"\nSpyder (Python 3.6-3.7)\nPyTorch 1.4.0-1.8.1\nWindows 10 or Linux\nKai Zhang (cskaizhang@gmail.com)\... | [
[
"torch.cuda.current_device",
"torch.cuda.empty_cache",
"torch.cuda.is_available",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eridgd/texar | [
"9c699e8143fd8ecb5d65a41ceef09c45832b9258"
] | [
"examples/transformer/bleu_tool.py"
] | [
"# Copyright 2018 The Tensor2Tensor Authors.\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 applic... | [
[
"numpy.float32"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shelizi/GPT2-Chinese | [
"8d4989e058453caf06e2a6ef5173b258d7fc3336"
] | [
"generate.py"
] | [
"import torch\nimport torch.nn.functional as F\nimport os\nimport argparse\nfrom tqdm import trange\nfrom transformers import GPT2LMHeadModel\nimport transformers\n\ndef is_word(word):\n for item in list(word):\n if item not in 'qwertyuiopasdfghjklzxcvbnm':\n return False\n return True\n\n\n... | [
[
"torch.nn.functional.softmax",
"torch.softmax",
"torch.LongTensor",
"torch.load",
"torch.tensor",
"torch.no_grad",
"torch.sort",
"torch.cuda.is_available",
"torch.topk"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eonu/sigment | [
"926237642f1fa5b63921ddf82341a4bbd7243394"
] | [
"lib/sigment/transforms.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport numpy as np, librosa\nfrom itertools import chain\nfrom math import ceil\nfrom copy import copy\nfrom .base import _Base\nfrom .internals import _Validator\n\n__all__ = [\n 'Transform', 'Identity',\n 'GaussianWhiteNoise',\n 'TimeStretch', 'PitchShift',\n 'EdgeCrop', 'R... | [
[
"numpy.hstack",
"numpy.abs",
"numpy.arange",
"numpy.asfortranarray",
"numpy.percentile",
"numpy.append",
"numpy.apply_along_axis",
"numpy.searchsorted",
"numpy.flip",
"numpy.add.accumulate",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
visiope/simpeg | [
"94295102afc664c001f77c88f902772e06a467c0"
] | [
"SimPEG/VRM/ProblemVRM.py"
] | [
"from SimPEG import Problem, mkvc, Maps, Props, Survey\nfrom SimPEG.VRM.SurveyVRM import SurveyVRM\nimport numpy as np\nimport scipy.sparse as sp\n\n############################################\n# BASE VRM PROBLEM CLASS\n############################################\n\n\nclass Problem_BaseVRM(Problem.BaseProblem):\n... | [
[
"numpy.matrix",
"numpy.dot",
"numpy.hstack",
"numpy.abs",
"numpy.linspace",
"numpy.min",
"numpy.reshape",
"numpy.arange",
"numpy.sqrt",
"scipy.sparse.block_diag",
"scipy.sparse.diags",
"numpy.ones",
"scipy.sparse.csr_matrix.dot",
"numpy.shape",
"scipy.sp... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
zhangrj91/DarkPose | [
"dd8403633b64936e73a3d8d44d4b34f422d6a6a0"
] | [
"src/architecture/model_prune.py"
] | [
"# Does human brain prune the useless neural cells?\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nimport logging\nlogger = logging.getLogger(__name__)\n\nclass Empty_Cell(nn.Module):\n r\"\"\"\n This class is used to replace the useless `Cell` object whose output has no computing ... | [
[
"torch.nn.functional.softmax",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yashpatel5400/dl-playground | [
"acf71dab5bb29b253bb28b966115d72d18b76a8e"
] | [
"tf_tutorial.py"
] | [
"import nltk\nfrom nltk.tokenize import word_tokenize\nfrom nltk.stem import WordNetLemmatizer\n\nimport numpy as np\nimport random\nimport pickle\nfrom collections import Counter\n\nimport tensorflow as tf\n\nlemmatizer = WordNetLemmatizer()\nnum_lines = 10000000\n\ndef create_lexicon(pos, neg):\n lexicon = []\... | [
[
"tensorflow.nn.relu",
"tensorflow.nn.softmax_cross_entropy_with_logits",
"tensorflow.matmul",
"tensorflow.reduce_mean",
"tensorflow.cast",
"tensorflow.placeholder",
"tensorflow.initialize_all_variables",
"tensorflow.Session",
"tensorflow.train.AdamOptimizer",
"tensorflow.ar... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
fratambot/api-template-2 | [
"1bbb67c21298835e24328169cedb862dae3cf481"
] | [
"app/models/algebra/array.py"
] | [
"import numpy as np\n\n\ndef get_random(dim=5):\n arr = np.random.rand(dim)\n return arr\n"
] | [
[
"numpy.random.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hdert/2018.py | [
"66fc5afc853af2ed5d6b2fc5f280e73be200a542",
"66fc5afc853af2ed5d6b2fc5f280e73be200a542"
] | [
"classes/Examples/graphing6.py",
"classes/Plotty.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nh = [77.1, 75.5, 76.9, 77.4, 77.9, 78.9, 79.2, 80.5, 81.9, 82.3, 83.5, 84.1, 84.4, 84.6,\n 85.1, 85.3 ,85.7, 85.5, 85.2, 84.6, 83.9, 83.5, 83.0, 82.4, 81.8, 81.5, 81.3, 81.9,\n 82.1, 83.5, 83.5, 85.1, 85.8, 86.5, 86.5, 86.0, 85.9, 85.4, 85.2, 85.1, 85.0, 84... | [
[
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.title",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.subplot",
"numpy.array",
"matplotlib.pyplot.show"
],
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.scatter"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
opconty/Transformer_STR | [
"f6c7521618d9640cc78135e38ed003075c686753"
] | [
"utils/model_util.py"
] | [
"#-*- coding: utf-8 -*-\n#'''\n# @date: 2020/5/18 下午6:06\n#\n# @author: laygin\n#\n#'''\nimport math\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom torch.nn.init import xavier_uniform_\nfrom torch.nn.init import constant_\nfrom torch.nn.init import xavier_normal_\nimport copy\nimport torch.nn.functi... | [
[
"numpy.expand_dims",
"torch.zeros",
"torch.sin",
"numpy.concatenate",
"numpy.fill_diagonal",
"numpy.square",
"torch.nn.Dropout",
"torch.ones",
"numpy.arange",
"torch.from_numpy",
"numpy.stack",
"torch.bmm",
"torch.arange",
"numpy.zeros",
"torch.cos",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tmarkn/covid-twitter | [
"741661d1440663f4fce5c436f314e0b0c90cc027"
] | [
"graphs.py"
] | [
"import json\nimport datetime\nimport numpy as np\nimport dateutil.parser\nimport operator\nimport pytz\nimport matplotlib.pyplot as plt\n\ndef deEmojify(inputString):\n return inputString.encode('ascii', 'ignore').decode('ascii')\n\n# turn save to True to save the graphs as .png images\nsave = False\nfilePath='... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.xticks",
"numpy.delete",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.bar",
"numpy.array",
"matplotlib.pyplot.show",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
physimals/quantiphyse | [
"34f40424941414ce139c4612a903de3f24883576"
] | [
"quantiphyse/packages/core/smoothing/process.py"
] | [
"\"\"\"\nQuantiphyse - Analysis processes for data smoothing\n\nCopyright (c) 2013-2020 University of Oxford\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/li... | [
[
"numpy.isfinite"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Vergenter/familyWealth | [
"eefbc8d3e7cbe6e59add97dffb35ac3a19f17ea9"
] | [
"web_scraper/every_item_by_country_in_usd/web_scraper.py"
] | [
"# source: https://www.thepythoncode.com/article/convert-html-tables-into-csv-files-in-python\n\nimport requests\nimport pandas as pd\nfrom bs4 import BeautifulSoup as bs\n\nUSER_AGENT = \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/44.0.2403.157 Safari/537.36\"\n# US english\nLANG... | [
[
"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": []
}
] |
MIC-Surgery-Heidelberg/HyperGUI_1.0 | [
"0ee8e0da85049076bb22a542d15d6c3adf6ea106"
] | [
"HyperGuiModules/csv_saver.py"
] | [
"from HyperGuiModules.utility import *\nimport numpy as np\nimport os\n\n\nclass CSVSaver:\n def __init__(self, csv_frame, listener):\n self.root = csv_frame\n\n # Listener\n self.listener = listener\n\n self.ogr_butt = None\n self.ogrp_butt = None\n self.normr_butt = No... | [
[
"numpy.savetxt",
"numpy.flipud",
"numpy.ma.is_masked",
"numpy.asarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Wi11iamDing/toad | [
"0b6973e910c337b779b6c95087f6d24b89a20eed"
] | [
"toad/stats_test.py"
] | [
"import pytest\nimport numpy as np\nimport pandas as pd\n\nfrom .stats import IV, WOE, gini, gini_cond, entropy_cond, quality, _IV, VIF\n\n\nnp.random.seed(1)\n\nfeature = np.random.rand(500)\ntarget = np.random.randint(2, size = 500)\nA = np.random.randint(100, size = 500)\nB = np.random.randint(100, size = 500)\n... | [
[
"numpy.random.seed",
"numpy.isnan",
"pandas.DataFrame",
"numpy.random.rand",
"numpy.array",
"numpy.random.randint"
]
] | [
{
"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": []
}
] |
lulongfei-luffy/once-for-all | [
"d3c5f5f613bb3454fd18043e1d217f583db9f4b0"
] | [
"ofa/elastic_nn/networks/ofa_mbv3.py"
] | [
"# Once for All: Train One Network and Specialize it for Efficient Deployment\n# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han\n# International Conference on Learning Representations (ICLR), 2020.\n\nimport copy\nimport random\n\nimport torch\n\nfrom ofa.elastic_nn.modules.dynamic_layers import DynamicM... | [
[
"torch.squeeze"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AutumnWormSun/pyGAT | [
"9bd7c5c738b8919153694c9390d92b4c9d99a33b"
] | [
"layers.py"
] | [
"import numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass GraphAttentionLayer(nn.Module):\n \"\"\"\n Simple GAT layer, similar to https://arxiv.org/abs/1710.10903\n \"\"\"\n def __init__(self, in_features, out_features, dropout, alpha, concat=True):\n su... | [
[
"torch.nn.functional.softmax",
"torch.mm",
"torch.nn.Dropout",
"torch.empty",
"torch.Size",
"torch.nn.functional.dropout",
"torch.cat",
"torch.zeros",
"torch.ones",
"torch.isnan",
"torch.nn.init.xavier_normal_",
"torch.sparse_coo_tensor",
"torch.matmul",
"to... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kazuto1011/dusty-gan | [
"63ea1757660806cd04976b24fc7733ab26b2a3a1",
"63ea1757660806cd04976b24fc7733ab26b2a3a1"
] | [
"models/gans/dcgan_eqlr.py",
"evaluate_reconstruction.py"
] | [
"import models.ops.common as ops\nimport torch\nfrom torch import nn\n\n\nclass Proj(nn.Sequential):\n def __init__(self, in_ch, out_ch, kernel=(4, 16)):\n super().__init__(\n ops.EqualLR(nn.ConvTranspose2d(in_ch, out_ch, kernel, 1, 0, bias=False)),\n ops.FusedLeakyReLU(out_ch),\n ... | [
[
"torch.nn.ConvTranspose2d",
"torch.randn",
"torch.nn.ModuleDict",
"torch.nn.Conv2d",
"torch.tanh"
],
[
"torch.randn_like",
"torch.abs",
"torch.optim.lr_scheduler.LambdaLR",
"torch.nn.Parameter",
"torch.nn.parallel.DataParallel",
"torch.randn",
"torch.utils.data.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"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",
"... |
dinaatia/gender_novels | [
"35158916967fc0f748ce601e1453af6e4eeff7fa"
] | [
"gender_novels/analysis/visualizations/datagraphs_functions.py"
] | [
"import matplotlib.pyplot as plt\nimport seaborn as sns\nfrom gender_novels.corpus import Corpus\n\n\ndef plt_pubyears(pub_years,corpus_name):\n '''\n Creates a histogram displaying the frequency of books that were published within a 20 year \n period\n :param years: list\n RETURNS a pyplot histogram... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.subplot2grid",
"matplotlib.pyplot.bar",
"matplotlib.pyplot.subplots_adjust",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.pie",
"matplotlib.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
amzn/image-to-recipe-transformers | [
"96e257e910c79a5411c3f65f598dd818f72fc262"
] | [
"src/eval.py"
] | [
"# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.\n# SPDX-License-Identifier: Apache-2.0\n\nimport numpy as np\nfrom config import get_eval_args\nimport random\nrandom.seed(1234)\nimport os\nimport pickle\nfrom utils.metrics import compute_metrics\nimport argparse\n\n\ndef computeAverageMetrics(... | [
[
"numpy.argsort",
"numpy.array",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zouxlin3/StockDataAnalysis | [
"65a8d76a148150b85883c096938ff315a6a4df1b"
] | [
"StockData.py"
] | [
"from typing import List\nimport os\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport calendar\nfrom math import pow\n\n\nclass StockData:\n def __init__(self, path: str): # path为csv数据文件所在路径\n self.path = os.path.normpath(path)\n\n self.filenames = {}\n\n def... | [
[
"pandas.read_csv",
"pandas.Series",
"matplotlib.pyplot.title",
"pandas.Timedelta",
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.bar",
"matplotlib.pyplot.xlabel",
"pandas.Timestamp",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yxgeee/BAKE | [
"07c4f668ea19311d5b50121026e73d2f035d5765"
] | [
"small_scale/train.py"
] | [
"from __future__ import print_function\r\n\r\nimport argparse\r\nimport csv\r\nimport os, logging\r\nimport random\r\n\r\nimport numpy as np\r\nimport torch\r\nfrom torch.autograd import Variable, grad\r\nimport torch.backends.cudnn as cudnn\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport torc... | [
[
"torch.set_rng_state",
"torch.nn.Softmax",
"torch.nn.functional.softmax",
"torch.max",
"torch.load",
"torch.no_grad",
"torch.cuda.is_available",
"torch.cuda.manual_seed_all",
"torch.nn.CrossEntropyLoss",
"torch.eye",
"torch.inverse",
"torch.get_rng_state",
"torc... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
spacecataz/HydroQuebecRemix | [
"5dc0a88a55def420728029255e241f13fb8c8d38"
] | [
"code/swtools.py"
] | [
"# Tools for working with IMP8/ISEE data\nimport os\nimport datetime as dt\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport spacepy.datamodel as dm\nimport spacepy.time as spt\nimport spacepy.datamanager as dman\nimport spacepy.plot as splot\n\n\ndef readIMP8plasmafile(fname):\n \"\"\"\n ftp://s... | [
[
"numpy.asarray",
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xinyuhuang97/LU3IN003-Projet | [
"c0b80752a6e4454108d0e300a344e2b985c325d2"
] | [
"projet/methode_generalise.py"
] | [
"import numpy as np\nM=4\nN=4\n\ngrille=np.full((M,N), -1)\ngrille[1][0]=0\nsequence1=[1,1]\nsequence2=[2,1]\n# non-colore -1\n# blanche 0\n# noire 1\n\n\"\"\"def annalyse_ligne(grille ,i):\n j=0\n case_j=-1\n nb_block=0\n while( j<N and grille[i][j]!=-1 ):\n if grille[i][j]==1:\n case... | [
[
"numpy.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ultrons/t5x | [
"e684a307fe62e4a088f457cc592c299cfb070794"
] | [
"t5x/models.py"
] | [
"# Copyright 2021 The T5X Authors.\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... | [
[
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
saadz-khan/stylegan2 | [
"9fa83f5213e1077d7e5d0d595a961618f3524156"
] | [
"run_generator.py"
] | [
"# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.\n#\n# This work is made available under the Nvidia Source Code License-NC.\n# To view a copy of this license, visit\n# https://nvlabs.github.io/stylegan2/license.html\n\nimport argparse\nimport numpy as np\nimport PIL.Image\nimport dnnlib\nimport dnnli... | [
[
"numpy.linspace",
"numpy.asarray",
"numpy.cos",
"numpy.sin",
"numpy.random.uniform",
"numpy.add",
"numpy.random.randn",
"numpy.load",
"numpy.array",
"numpy.zeros",
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nlinc1905/dsilt-ml-code | [
"d51fffd16e83f93ea7d49f65102e731abd3ba70c"
] | [
"03 Rule Learners/03_association_rules.py"
] | [
"\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport numpy as np\nfrom mlxtend.preprocessing import TransactionEncoder\nfrom mlxtend.frequent_patterns import apriori, association_rules\n\n#---------------------------------------------------------------------------------------------... | [
[
"pandas.read_excel",
"matplotlib.pyplot.show",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
mkiseljov/JONNEE | [
"3085493837842f1d10b01f2b269971e731beef16"
] | [
"DuoGAE/visualization.py"
] | [
"# Plot results\n\nimport time\nimport numpy as np\nfrom sklearn.manifold import TSNE\nimport matplotlib.pyplot as plt\nplt.style.use('ggplot')\n\n\ndef plot_results(results, test_freq, path='results.png', show=False):\n # Init\n plt.close('all')\n fig = plt.figure(figsize=(8, 8))\n\n x_axis_train = ran... | [
[
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.cm.get_cmap",
"matplotlib.pyplot.plot",
"sklearn.manifold.TSNE",
"matplotlib.pyplot.close",
"matplotlib.pyplot.show",
"matplotlib.pyplot.style.use",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
doncat99/FinanceDataCenter | [
"1538c8347ed5bff9a99a3cca07507a7605108124",
"1538c8347ed5bff9a99a3cca07507a7605108124"
] | [
"findy/database/plugins/baostock/quotes/bao_china_stock_kdata_recorder.py",
"findy/database/plugins/baostock/meta/bao_china_stock_meta_recorder.py"
] | [
"# -*- coding: utf-8 -*-\nimport time\n\nimport pandas as pd\n\nfrom findy import findy_config\nfrom findy.interface import Region, Provider, ChnExchange, EntityType\nfrom findy.database.schema import IntervalLevel, AdjustType\nfrom findy.database.schema.meta.stock_meta import Stock\nfrom findy.database.schema.data... | [
[
"pandas.to_datetime"
],
[
"pandas.to_datetime"
]
] | [
{
"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... |
owenfeehan/python-visualization-scripts | [
"96f14ac39f572cdea3838cde2d44a920a6383ddd"
] | [
"src/anchor_python_visualization/projection/_tsne.py"
] | [
"\"\"\"T-SNE projection.\"\"\"\n\n__author__ = \"Owen Feehan\"\n__copyright__ = \"Copyright (C) 2021 Owen Feehan\"\n__license__ = \"MIT\"\n__version__ = \"0.1\"\n\nimport pandas as pd\nfrom sklearn.manifold import TSNE\n\nfrom ._derive_utilities import derive_projected\nfrom ._pca import PCAProjection\nfrom .projec... | [
[
"sklearn.manifold.TSNE"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
blaylockbk/Web-Homepage | [
"f5ede8f7ea7c1a8af662069c8feca7a418d01cc7"
] | [
"HRRR_archive/hrrr_sfc_table.py"
] | [
"# Brian Blaylock\n# August 14, 2017\n\n\nimport numpy as np\n\ntable = np.genfromtxt('https://api.mesowest.utah.edu/archive/HRRR/GRIB2Table_hrrr_2d.txt',\n delimiter=',',\n skip_header=4,\n names=True,\n dtype=None)\n\nall_headers ... | [
[
"numpy.genfromtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
umbc-sanjaylab/DeepPseudo_AAAI2021 | [
"89d6eaf57212ef77900008927b70a89fbe43b216",
"89d6eaf57212ef77900008927b70a89fbe43b216"
] | [
"Marginal_DeepPSeudo/get_validation_performance.py",
"Marginal_DeepPSeudo/import_data.py"
] | [
"''' The code is inspired by the code for DeepHit model. The github link of the code for DeepHit is https://github.com/chl8856/DeepHit. Reference: C. Lee, W. R. Zame, J. Yoon, M. van der Schaar, \"DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks,\" AAAI Conference on Artificial Intelligen... | [
[
"tensorflow.div",
"tensorflow.ConfigProto",
"tensorflow.global_variables_initializer",
"tensorflow.reset_default_graph",
"numpy.shape",
"tensorflow.log",
"tensorflow.contrib.layers.xavier_initializer",
"tensorflow.Session",
"tensorflow.train.Saver",
"numpy.mean"
],
[
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
... |
lazykyama/gpu-minicamp-examples | [
"20674b17286ffab727f0d6aa9538ee92218fa855"
] | [
"pytorch/native/pytorch_distributed_run_example.py"
] | [
"# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n# SPDX-License-Identifier: Apache-2.0\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 Licen... | [
[
"torch.nn.CrossEntropyLoss",
"torch.distributed.broadcast",
"torch.cuda.synchronize",
"torch.distributed.init_process_group",
"torch.utils.data.distributed.DistributedSampler",
"torch.zeros",
"torch.__version__.split",
"torch.utils.data.DataLoader",
"torch.tensor",
"torch.c... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SebastiaanZ/aoc-2019 | [
"e1fe4630b0f375be0b79398e07e23b9c0196efbb"
] | [
"solutions/day08/solution.py"
] | [
"from collections import Counter\nfrom operator import itemgetter\nfrom typing import List, Tuple\n\nimport numpy as np\n\n\ndef part_one(data: str) -> int:\n \"\"\"Find the layer with the least amount of `0`s and return number of `1`s * number of `2`s.\"\"\"\n min_layer = min((Counter(layer) for layer in zi... | [
[
"numpy.array",
"numpy.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MarcCoru/manim | [
"54ce7a446cc1884b1a9f2cd395aae3d482b24500"
] | [
"manimlib/mobject/coordinate_systems.py"
] | [
"import numpy as np\nimport numbers\n\nfrom manimlib.constants import *\nfrom manimlib.mobject.functions import ParametricFunction\nfrom manimlib.mobject.geometry import Arrow\nfrom manimlib.mobject.geometry import Line\nfrom manimlib.mobject.number_line import NumberLine\nfrom manimlib.mobject.svg.tex_mobject impo... | [
[
"numpy.arange",
"numpy.array",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fregu856/2D_detection | [
"1f22a6d604d39f8f79fe916fcdbf40b5b668a39a"
] | [
"utilities.py"
] | [
"import cv2\nimport numpy as np\nimport tensorflow as tf\n\n# function for drawing all ground truth bboxes of an image on the image:\ndef visualize_gt_label(img_path, label_path):\n class_to_color = {\"car\": (255, 191, 0),\n \"cyclist\": (0, 191, 255),\n \"pedestrian\":... | [
[
"numpy.maximum",
"numpy.minimum",
"numpy.ones",
"tensorflow.zeros_like",
"tensorflow.to_float",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
fengyouliang/wheat_detection | [
"d056123426a1260c29b486cbb8e44a88a0a3c5bc",
"d056123426a1260c29b486cbb8e44a88a0a3c5bc"
] | [
"tests/test_ops/test_merge_cells.py",
"mmdet/models/roi_heads/mask_heads/fused_semantic_head.py"
] | [
"\"\"\"\r\nCommandLine:\r\n pytest tests/test_merge_cells.py\r\n\"\"\"\r\nimport torch\r\nimport torch.nn.functional as F\r\n\r\nfrom mmdet.ops.merge_cells import (BaseMergeCell, ConcatCell,\r\n GlobalPoolingCell, SumCell)\r\n\r\n\r\ndef test_sum_cell():\r\n inputs_x = torch.... | [
[
"torch.randn",
"torch.nn.functional.max_pool2d",
"torch.nn.functional.interpolate"
],
[
"torch.nn.CrossEntropyLoss",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.nn.functional.interpolate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aakanksha888sahu/tensorflow | [
"2f6e53147b7b27b7289a892998a891e3dead440e"
] | [
"tensorflow/contrib/distribute/python/mirrored_strategy_test.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.python.framework.ops.device",
"tensorflow.python.eager.context.num_gpus",
"tensorflow.python.training.distribution_strategy_context.get_replica_context",
"tensorflow.python.ops.variable_scope.variable_creator_scope",
"tensorflow.python.eager.test.main",
"tensorflow.contrib.dist... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"2.7",
"2.6",
"1.4",
"1.13",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"2.2",
"2.10"
]
}
] |
yngtodd/mobil | [
"4a6479bfe0f6a29cc3e6ff4e75a98475ec74ae67"
] | [
"debug.py"
] | [
"'''Train CIFAR10 with PyTorch.'''\nfrom __future__ import print_function\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport torch.backends.cudnn as cudnn\n\nimport torchvision\nimport torchvision.transforms as transforms\n\nimport os\nimport argparse\n\n#fr... | [
[
"torch.nn.CrossEntropyLoss",
"torch.max",
"numpy.linspace",
"numpy.arange",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
stefanoregis/pycryptobot | [
"1edeaab46d2a7f9046a76f18527cc6d152b8707d"
] | [
"models/Trading.py"
] | [
"\"\"\"Technical analysis on a trading Pandas DataFrame\"\"\"\n\nimport json, math\nimport numpy as np\nimport pandas as pd\nimport re, sys\nfrom statsmodels.tsa.statespace.sarimax import SARIMAX\nfrom models.CoinbasePro import AuthAPI\n\nclass TechnicalAnalysis():\n def __init__(self, data=pd.DataFrame()):\n ... | [
[
"numpy.maximum",
"numpy.minimum",
"pandas.Series",
"pandas.DataFrame",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
janblechschmidt/DeepBSDE | [
"fd138083a78b15fd75bee4a5e65761ac41eb7d29"
] | [
"equation.py"
] | [
"import numpy as np\nimport tensorflow as tf\nfrom scipy.stats import multivariate_normal as normal\n\n\nclass Equation(object):\n \"\"\"Base class for defining PDE related function.\"\"\"\n\n def __init__(self, eqn_config):\n # Global parameters from config\n self.dim = eqn_config.dim\n ... | [
[
"numpy.sqrt",
"tensorflow.reduce_sum",
"numpy.exp",
"numpy.square",
"tensorflow.square",
"numpy.zeros",
"tensorflow.pow",
"numpy.power",
"scipy.stats.multivariate_normal.rvs",
"tensorflow.exp",
"numpy.sum",
"tensorflow.nn.relu",
"tensorflow.reduce_max",
"ten... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
git163/Cornell-MOE | [
"df299d1be882d2af9796d7a68b3f9505cac7a53e"
] | [
"pes/PES/target_function.py"
] | [
"import numpy as np\r\nimport numpy.random as npr\r\n\r\n\r\n\r\n#This file is used to store the function that the user would like to oprimize.\r\n#Users can define their own functions in this file. Here we define two synthetic\r\n#funtions. One is Hartmann6 and the other one is Branin Hoo. Users can define \r\n#th... | [
[
"numpy.exp",
"numpy.random.normal",
"numpy.array",
"numpy.cos"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ebentley17/Deniz_lab_code | [
"3cf13c769bed0ddf0abf0dc74213a9dec96bfabb"
] | [
"wrangling/tutorials/compile_fluorimeter_data_simple.py"
] | [
"\"\"\"This is a simple script to compile data from the Deniz lab fluorimeter.\n\nYou can make a shortcut of this file. Double-click the file to run it. \nYou will be prompted to enter the path for a folder of .ifx data \nfiles. A .csv with the compiled data will be saved in the same folder. \n\"\"\"\n\nimport sys... | [
[
"numpy.isnan"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhangxiao339/document-ocr | [
"2c87c67691f76e947804a28df18957422c1f2c2d"
] | [
"single_word_ocr/load_saved_model.py"
] | [
"#!/usr/bin/env python\n#-*- coding:utf-8 -*-\n#author: wu.zheng midday.me\n\nimport tensorflow as tf\nimport cv2\nimport numpy as np\nimport json\nimport time\nimport os\n\nEXPORT_PATH = \"single_word_model/densenet_exported/1565353481\"\nVOCAB_PATH = './gbk.json'\n\n\ndef load_charset_map():\n char_set = json.lo... | [
[
"tensorflow.saved_model.loader.load",
"tensorflow.get_default_graph",
"numpy.pad",
"tensorflow.Session"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
ronaldogomes96/Acelera-Dev-DataScience | [
"3bdd7ae86a907db3339fdd7abc615ecaf5a683a6"
] | [
"Modulo 3/desafioModulo3.py"
] | [
"\nimport pandas as pd\nimport json\n\ndf = pd.read_csv('baseDesafio.csv')\n\n#Selecionando as colunas para o uso\ndadosImportantes = { 'pontuacao_credito' : df['pontuacao_credito'] ,\n 'estado_residencia' : df['estado_residencia']}\n\n#Criando um novo datframe com essa colunas\ndata = pd.DataFr... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
paragagrawal11/transformers | [
"18c32eeb21afa1d883094af54ba6321aa2d4c8db"
] | [
"src/transformers/models/electra/modeling_electra.py"
] | [
"# coding=utf-8\n# Copyright 2019 The Google AI Language 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.org/... | [
[
"torch.nn.Softmax",
"torch.nn.Dropout",
"torch.nn.CrossEntropyLoss",
"torch.ones",
"torch.zeros",
"torch.einsum",
"torch.from_numpy",
"torch.nn.Embedding",
"torch.nn.LayerNorm",
"tensorflow.train.load_variable",
"torch.nn.Linear",
"torch.matmul",
"torch.nn.BCEWi... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
malamleh93/quickNAT_pytorch | [
"e6076854ed4d8d0bdd36c8c1ff9a5a5cd27906a6"
] | [
"utils/preprocessor.py"
] | [
"import numpy as np\n\nORIENTATION = {\n 'coronal': \"COR\",\n 'axial': \"AXI\",\n 'sagital': \"SAG\"\n}\n\n\ndef rotate_orientation(volume_data, volume_label, orientation=ORIENTATION['coronal']):\n if orientation == ORIENTATION['coronal']:\n return volume_data.transpose((2, 0, 1)), volume_label.... | [
[
"numpy.gradient",
"numpy.unique",
"numpy.median",
"numpy.compress",
"numpy.zeros_like",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RamitPahwa/Knowledge-Distillation | [
"fc5ed25affc00ac1aa8be300748a4902aa85fb8e"
] | [
"data_loader.py"
] | [
"from __future__ import print_function\nfrom PIL import Image\nimport os\nimport os.path\nimport numpy as np\nimport sys\nif sys.version_info[0] == 2:\n import cPickle as pickle\nelse:\n import pickle\n\nimport torch.utils.data as data\n\nclass CIFARSel(data.Dataset):\n base_folder = 'cifar-10-batches-py'\... | [
[
"numpy.concatenate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WenheLI/npyjs | [
"7becdf6d53ae22eb2748cc207988ff8f2cce47d5"
] | [
"test/generate-test-data.py"
] | [
"import numpy as np\nimport json\n\nrecords = {}\n\nfor dimensions in [(10,), (65, 65), (100, 100, 100), (4, 4, 4, 4, 4)]:\n for dtype in [\"int8\", \"int16\", \"int64\", \"float32\", \"float64\"]:\n name = f\"./data/{'x'.join(str(i) for i in dimensions)}-{dtype}\"\n data = np.random.randint(0, 255... | [
[
"numpy.save",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JimothyJohn/PerceiverToolkit | [
"7f1e4b93a619f9b93000dc52ffbe4eeaf07d612b"
] | [
"OpticalFlow.py"
] | [
"#!/usr/bin/env python\n\n# Copyright 2021 DeepMind Technologies Limited\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# ... | [
[
"numpy.asarray",
"numpy.array",
"numpy.zeros",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
liwen-deepmotion/map_based_lidar_camera_calibration_tool | [
"d260380729b05b153c2efd1e76d4ae077c48c4b1"
] | [
"calibration_tool/vector/polygon_3d.py"
] | [
"\n\nimport numpy as np\n\nfrom vector.vector import Vector\n\n\nclass Polygon3D(Vector):\n\n def __init__(self, vertices=np.zeros((0, 3))):\n super().__init__(vertices)\n"
] | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Albertios/PythonInGIS_EagleOwl | [
"35cba102b2c93bb1a7b415e9460aa955d4816b32"
] | [
"Scripts/stackedBarChart.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\n\ncnames = [\n '#F0F8FF',\n '#FAEBD7',\n '#00FFFF',\n '#7FFFD4',\n '#F0FFFF',\n '#F5F5DC',\n '#FFE4C4',\n '#000000',\n '#FFEBCD',\n '#0000FF',\n '#8A2BE2',\n '#A52A2A',\n ... | [
[
"numpy.arange",
"matplotlib.pyplot.show",
"matplotlib.pyplot.bar"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
caicre/PrivMRF | [
"9ff82346ec6cf29889a4fe0d7cd9fdd480e7d5ab"
] | [
"domain.py"
] | [
"from functools import reduce\nimport numpy as np\n\nclass Domain:\n # attr_list specifies the order of axis\n def __init__(self, domain_dict, attr_list):\n self.dict = domain_dict\n self.attr_list = attr_list\n self.shape = [domain_dict[i]['domain'] for i in attr_list]\n\n\n def proje... | [
[
"numpy.reshape",
"numpy.random.normal",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AustinTSchaffer/seedpod_ground_risk | [
"694846fb1b19ebc3deb44a310c0e509e25c5f002"
] | [
"seedpod_ground_risk/pathfinding/theta_star.py"
] | [
"from heapq import heappop, heappush\nfrom typing import Union, List\n\nimport numpy as np\nfrom skimage.draw import line\n\nfrom seedpod_ground_risk.pathfinding.a_star import _reconstruct_path\nfrom seedpod_ground_risk.pathfinding.algorithm import Algorithm\nfrom seedpod_ground_risk.pathfinding.environment import ... | [
[
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
knarfamlap/tensor2tensor | [
"92ebc7152e0f4f42871251f17dbe6db8409d4fae"
] | [
"tensor2tensor/layers/common_image_attention_test.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Tensor2Tensor Authors.\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 requir... | [
[
"tensorflow.random_uniform",
"tensorflow.contrib.training.HParams",
"tensorflow.test.main",
"tensorflow.random_normal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
arthurflor/signatures | [
"e6ae5e5996df438e533420d80eda288d849db28c"
] | [
"src/classifier/tree.py"
] | [
"from sklearn.ensemble import RandomForestClassifier\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.model_selection import train_test_split\nimport util.data as data\nimport util.path as path\nimport numpy as np\nimport os\n\ndef random_split(test_size, features, labels):\n features_tr, features_... | [
[
"numpy.log",
"sklearn.ensemble.RandomForestClassifier",
"sklearn.model_selection.train_test_split",
"sklearn.tree.DecisionTreeClassifier",
"numpy.argmax",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mares29/rio-tiler | [
"72ddbaa7ff1a972774cdb94fea664a9d017409bf"
] | [
"tests/conftest.py"
] | [
"\"\"\"``pytest`` configuration.\"\"\"\n\nimport os\n\nimport pytest\n\nimport numpy\n\nimport rasterio\nfrom rasterio.io import MemoryFile\nfrom rasterio.transform import from_bounds\nfrom rasterio.enums import ColorInterp\n\nfrom rio_cogeo.cogeo import cog_translate\nfrom rio_cogeo.profiles import cog_profiles\n\... | [
[
"numpy.concatenate",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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