repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
Reinhold83/21datalab-1 | [
"69972048e624cecaa86b864829979d3164193b8a"
] | [
"plugins/threshold.py"
] | [
"import numpy\n\n\nfrom system import __functioncontrolfolder\n\nimport modelhelper as mh\n\nfrom timeseries import TimeSeries\nimport dates\nfrom utils import Profiling\n\nimport json\n\n\n\nthresholdScorer = {\n \"name\":\"thresholdScorer\",\n \"type\":\"function\",\n \"functionPointer\":\"threshold.thre... | [
[
"numpy.isfinite",
"numpy.asarray",
"numpy.full",
"numpy.logical_or",
"numpy.copy",
"numpy.any",
"numpy.searchsorted",
"numpy.logical_and",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
laoma023012/TensorFlow-practice | [
"2b02167307eca3950cc7e49c7c50510ff5ccb92e"
] | [
"tensorflow/python/keras/optimizer_v2/adadelta.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.util.tf_export.keras_export",
"tensorflow.python.framework.ops.convert_to_tensor_v2",
"tensorflow.python.training.training_ops.resource_apply_adadelta",
"tensorflow.python.keras.backend_config.epsilon",
"numpy.array",
"tensorflow.python.training.training_ops.resource_spa... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.2",
"2.3",
"2.4",
"2.9",
"2.5",
"2.8",
"2.10"
]
}
] |
ntyz/Qcover | [
"08b44e58b0504db23f33612a57464bee6e2edde3"
] | [
"Qcover/backends/circuitbytensor.py"
] | [
"import itertools\r\nimport os\r\nimport time\r\nimport warnings\r\nfrom collections import defaultdict, Callable\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom multiprocessing import Pool, cpu_count\r\nimport quimb as qu\r\nfrom multiprocessing import cpu_count\r\nfrom Qcover.backends import Back... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Tariq96/grad_pro | [
"26b98ae71a70ba4cd48984898cfe7be6892f33d9"
] | [
"noplotv2.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\nfrom mpl_toolkits.mplot3d import Axes3D\nimport time \nimport matplotlib.animation as animation\n\nimport math \nfrom threading import Thread\n#-------custom---------------\nfrom angleEngine import vector ,angle\n\n\n_CONNECTION = [\n [0, 1], [1, 2], [2, ... | [
[
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
reazulhoque/numba-dppy | [
"f8b3cc81b65ba75d126c8b3cb603d752eb681c7e",
"f8b3cc81b65ba75d126c8b3cb603d752eb681c7e"
] | [
"numba_dppy/compiler.py",
"numba_dppy/examples/auto_offload_examples/sum-5d.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.copyto"
],
[
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
arunimasundar/Supervised-Learning-of-Procedures | [
"3ae012cf4ee24a11f7cd35fd2ce04817c9eaa298"
] | [
"exercise_main.py"
] | [
"# -*- coding: UTF-8 -*-\nimport cv2 as cv\nimport argparse\nimport numpy as np\nimport time\nfrom utils import choose_run_mode, load_pretrain_model, set_video_writer\nfrom Pose.pose_visualizer import TfPoseVisualizer\nfrom Action.recognizer import load_action_premodel, framewise_recognize,output\nfrom pathlib impo... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
s274001/PINA | [
"beb33f0da20581338c46f0c525775904b35a1130"
] | [
"pina/adaptive_functions/adaptive_linear.py"
] | [
"import torch\nfrom torch.nn.parameter import Parameter\n\nclass AdaptiveLinear(torch.nn.Module):\n '''\n Implementation of soft exponential activation.\n Shape:\n - Input: (N, *) where * means, any number of additional\n dimensions\n - Output: (N, *), same shape as the input\n Pa... | [
[
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Passer-D/GameAISDK | [
"a089330a30b7bfe1f6442258a12d8c0086240606"
] | [
"Modules/server/rainbow/model/memory.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nTencent is pleased to support the open source community by making GameAISDK available.\n\nThis source code file is licensed under the GNU General Public License Version 3.\nFor full details, please refer to the file \"LICENSE.txt\" which is provided as part of this source code pack... | [
[
"numpy.power",
"torch.zeros",
"torch.cat",
"torch.tensor",
"torch.stack",
"numpy.random.uniform",
"numpy.array",
"numpy.zeros",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
berkeleyflow/flow | [
"bed5ec959aaf0eaa8dbc7fa03f0c3fd3f0184b80"
] | [
"tests/fast_tests/test_vehicles.py"
] | [
"import unittest\nimport os\nimport numpy as np\n\nfrom flow.core.vehicles import Vehicles\nfrom flow.core.params import SumoCarFollowingParams, NetParams, InitialConfig\nfrom flow.controllers.car_following_models import IDMController, \\\n SumoCarFollowingController\nfrom flow.controllers.lane_change_controller... | [
[
"numpy.testing.assert_array_almost_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DesperateMaker/TrackR-CNN | [
"5917836dc879fe3a18a68ed8e6eaf3110029afcb"
] | [
"datasets/MOT/MOT15.py"
] | [
"import numpy as np\n\nfrom datasets.MOT.MOT_common import MOTDetectionDataset, MOTDataset\nfrom datasets.Loader import register_dataset\nfrom datasets.util.Util import username\n\nNAME = \"2DMOT2015\"\nNAME_DETECTION = \"2DMOT2015_detection\"\nDEFAULT_PATH = \"/fastwork/\" + username() + \"/mywork/data/2DMOT2015/\... | [
[
"numpy.zeros",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CVPaul/pytorch | [
"bd4902d81f75168bdcdfcd4055f96a0602de97e3"
] | [
"test/forward_backward_compatibility/check_forward_backward_compatibility.py"
] | [
"import argparse\nimport datetime\nimport re\nimport sys\nimport warnings\nfrom collections import defaultdict\n\nimport torch\nfrom torch._C import parse_schema\n\n\n# The date specifies how long the allowlist exclusion should apply to.\n#\n# - If we NEVER give BC guarantee for an operator, you can put the\n# ... | [
[
"torch._C._get_operator_version_map",
"torch._C._jit_get_custom_class_schemas",
"torch._C.parse_schema",
"torch._C._jit_get_all_schemas"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chuckwong13/Ax | [
"e034105ad42f8d67dc161341afd6d8adb822a5c2"
] | [
"ax/storage/sqa_store/decoder.py"
] | [
"#!/usr/bin/env python3\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\nfrom collections import OrderedDict, defaultdict\nfrom enum import Enum\nfrom typing import List, Optional, ... | [
[
"pandas.read_json"
]
] | [
{
"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": []
}
] |
TaoHaoTian/federated-recommender-system | [
"65a151238e1a419fc713d26fa11ecfe4536d94ee"
] | [
"src/federator-draft/helpers.py"
] | [
"import itertools\nfrom sklearn.metrics import dcg_score\nimport math\nimport pandas as pd\nimport numpy as np\nimport scipy.sparse as sp\nfrom lightfm.data import Dataset\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import MinMaxScaler, RobustScaler\nfrom collections import defaultdict\n\nfrom defi... | [
[
"matplotlib.pyplot.legend",
"pandas.DataFrame",
"numpy.concatenate",
"numpy.mean",
"sklearn.preprocessing.MinMaxScaler",
"pandas.read_csv",
"matplotlib.pyplot.tight_layout",
"numpy.unique",
"numpy.arange",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.title",
"nump... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"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"... |
chriszhenghaochen/CCnet | [
"34df9a7bc82802a8eeb5d8e8e11a0409052b1dde",
"34df9a7bc82802a8eeb5d8e8e11a0409052b1dde"
] | [
"other_setting/tmp-frcn3/model/test.py",
"other_setting/tmp-frcn2/model/train_val.py"
] | [
"# --------------------------------------------------------\n# Tensorflow Faster R-CNN\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Xinlei Chen\n# --------------------------------------------------------\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __fu... | [
[
"numpy.hstack",
"numpy.maximum",
"numpy.minimum",
"numpy.random.seed",
"numpy.min",
"numpy.reshape",
"numpy.tile",
"numpy.sort",
"numpy.round",
"numpy.max",
"numpy.array",
"numpy.where"
],
[
"numpy.random.get_state",
"tensorflow.multiply",
"tensorflo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
mholowko/Solaris | [
"25f65e72667f1e92e0d5c26bc9cbe159a6a15ace",
"25f65e72667f1e92e0d5c26bc9cbe159a6a15ace"
] | [
"synbio_rbs/demo/Round0/codes/kernels_pairwise.py",
"notebook/rec_design/Clustering/results/HSN_util.py"
] | [
"import numpy as np\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.preprocessing import LabelEncoder, OneHotEncoder, normalize\nfrom codes.embedding import Embedding\n\ndef phi(x, y, l, j_x, j_y, d):\n \"\"\"Calculate spectrum features for spectrum kernel.\n\n phi is a mapping of a... | [
[
"numpy.asarray",
"sklearn.feature_extraction.text.CountVectorizer",
"sklearn.preprocessing.normalize",
"sklearn.preprocessing.LabelEncoder"
],
[
"numpy.log",
"numpy.sum",
"numpy.unique",
"numpy.arange",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.subplot",
"matp... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yutake27/HMDM | [
"a16c6e77cae9509ccf49140171797680068709aa"
] | [
"src/categorize_target_by_template_quality.py"
] | [
"import argparse\nfrom pathlib import Path\nfrom typing import List\n\nimport numpy as np\nimport pandas as pd\n\n\ndef categorize_by_label_distribution(group: pd.DataFrame,\n label: str,\n dif_threshold: float = 0.1,\n ... | [
[
"pandas.concat",
"pandas.read_csv",
"pandas.merge",
"pandas.Series",
"pandas.DataFrame",
"numpy.std",
"numpy.var"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
eigotateishi/GOST_Urban | [
"3e2a940c5064e8e16ebaced5ff27b25a98a1b4ef"
] | [
"src/UrbanRaster.py"
] | [
"#-------------------------------------------------------------------------------\n# Calculate urban areas from gridded population data\n# Benjamin P Stewart, April 2019\n# Purpose is to create high density urban clusters and urban cluster above minimum\n# density and total population thresholds\n#-------------... | [
[
"numpy.amax",
"scipy.ndimage.median_filter",
"numpy.dstack",
"pandas.DataFrame",
"numpy.nansum",
"scipy.ndimage.generic_filter",
"scipy.stats.mode",
"numpy.where",
"scipy.sparse.csgraph.connected_components"
]
] | [
{
"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": []
}
] |
johnnylu305/Simple-does-it-weakly-supervised-instance-and-semantic-segmentation | [
"083d94e8fdb804ef3ca860df4281b8b3ddc6fbb1"
] | [
"Simple_does_it/Dataset/load.py"
] | [
"import numpy as np\nimport matplotlib as mlp\nimport PIL\nfrom PIL import Image\nimport tqdm\n\nmlp.use('Agg')\n\n# standard output format\nSPACE = 35\n\n# tqdm parameter\nUNIT_SCALE = True\n\n\nclass Load:\n def __init__(self, is_train, dataset, set_name, label_dir_name,\n img_dir_name, width, ... | [
[
"numpy.asarray",
"matplotlib.use",
"numpy.array",
"numpy.expand_dims"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chychen/nba_scrip_generation | [
"942df59cc0426aa30b54a0e09c0f646aa8fd4f18"
] | [
"src/ref/C_WGAN_RNN_ResBlock/Generator.py"
] | [
"\"\"\"\nmodeling\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport time\nimport shutil\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.contrib import rnn\nfrom tensorflow.contrib import layers\nfrom utils impor... | [
[
"tensorflow.concat",
"tensorflow.stack",
"tensorflow.train.AdamOptimizer",
"tensorflow.summary.scalar",
"tensorflow.get_collection",
"tensorflow.gradients",
"tensorflow.train.get_or_create_global_step",
"tensorflow.add",
"tensorflow.name_scope",
"tensorflow.contrib.layers.x... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
jpeumesmo/poc | [
"530cbba732cfae0e7cf0e2badbad6e754e3b91ac"
] | [
"header.py"
] | [
"import numpy as np\nimport cv2\nimport imutils\n\ndef getArea(image):\n im2, contours, hierarchy = cv2.findContours(image,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)\n cnts = sorted(contours, key=cv2.contourArea, reverse=True)[:1]\n cnt = cnts[0]\n return cv2.contourArea(cnt)\n\ndef avaliar(image, vertices)... | [
[
"numpy.array",
"numpy.zeros_like",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hearteam/Linebot_project | [
"8df61f1c59c197126f0385c1ec1cf65a29a80cec",
"532b81d3c8bd1a658e0ec8f1bf473ee3fa4d232d"
] | [
"nets/yolo4.py",
"yolo.py"
] | [
"from collections import OrderedDict\r\n\r\nimport torch\r\nimport torch.nn as nn\r\n\r\nfrom nets.CSPdarknet import darknet53\r\n\r\n\r\ndef conv2d(filter_in, filter_out, kernel_size, stride=1):\r\n pad = (kernel_size - 1) // 2 if kernel_size else 0\r\n return nn.Sequential(OrderedDict([\r\n (\"conv\"... | [
[
"torch.cat",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.Upsample",
"torch.nn.LeakyReLU",
"torch.nn.BatchNorm2d"
],
[
"numpy.expand_dims",
"torch.load",
"torch.cat",
"numpy.asarray",
"numpy.concatenate",
"torch.no_grad",
"numpy.shape",
"torch.cuda... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
oknkc8/CSE4020 | [
"3dde2443d75b98d3e448c957dffef74329aeeb68"
] | [
"class_assignment_2/viewer.py"
] | [
"import glfw\nfrom OpenGL.GL import *\nfrom OpenGL.GLU import *\nimport numpy as np\n\ny_rotate = 0\nx_rotate = 0\nz_rotate = 0.\n\nscene_x_pos = 0.\nscene_y_pos = 0.\nscene_x = 0.\nscene_y = 0.\n\ng_fViewDistance = 9.\ng_Width = 600\ng_Height = 600\n\ng_nearPlane = 5\ng_farPlane = 1000.\n\nzoom = 3.\n\nflag_left_p... | [
[
"numpy.cos",
"numpy.transpose",
"numpy.sin",
"numpy.delete",
"numpy.append",
"numpy.identity",
"numpy.cross",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AlanJiang98/v2e | [
"129236584d5ff2d9597912d1ebf9d2758478b870"
] | [
"scripts/single_linearly_moving_dot.py"
] | [
"# generates moving dot(s)\n\n# use it like this:\n#v2e --synthetic_input=scripts.moving_dot --disable_slomo --dvs_aedat2=v2e.aedat --output_width=346 --output_height=260\n\n# NOTE: There are nonintuitive effects of low contrast dot moving repeatedly over the same circle:\n# The dot initially makes events and then ... | [
[
"numpy.arange",
"numpy.log",
"numpy.exp",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
axelniklasson/adalyzer | [
"be577f26255b1117a62653b51b3494d8c593835e"
] | [
"backend/location_optimisation.py"
] | [
"from sklearn import cluster\nimport numpy as np\nimport datetime\n#import matplotlib\n#matplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\n\nclass Location:\n\n\tvehicle_data = None\n\n\t@staticmethod\n\tdef get_data():\n\t\tif Location.vehicle_data is not None:\n\t\t\treturn Location.vehicle_data.tolist()\n... | [
[
"numpy.empty",
"numpy.zeros",
"sklearn.cluster.KMeans"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
trias702/fairseq | [
"00ee7869aad92979b5939f379b554b4cfead9dcf"
] | [
"fairseq_cli/train.py"
] | [
"#!/usr/bin/env python3 -u\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\"\"\"\nTrain a new model on one or across multiple GPUs.\n\"\"\"\n\nimport argparse\nimport logging\nimpor... | [
[
"torch.autograd.profiler.record_function",
"torch.autograd.profiler.emit_nvtx",
"torch.cuda.profiler.profile",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Archjbald/PoseStylizer | [
"95aae02d1f4ac83536d91b8db5f78d12e7830f97",
"95aae02d1f4ac83536d91b8db5f78d12e7830f97"
] | [
"tool/create_pair.py",
"models/PoseCutNet.py"
] | [
"import pandas as pd\nimport pose_utils\nfrom itertools import permutations\n\nLABELS = ['nose', 'neck', 'Rsho', 'Relb', 'Rwri', 'Lsho', 'Lelb', 'Lwri',\n 'Rhip', 'Rkne', 'Rank', 'Lhip', 'Lkne', 'Lank', 'Leye', 'Reye', 'Lear', 'Rear']\n\nMISSING_VALUE = -1\n\ndef give_name_to_keypoints(array):\n re... | [
[
"pandas.read_csv",
"pandas.unique"
],
[
"torch.tensor",
"torch.flip",
"numpy.zeros",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fenz-org/mlperf_inference_results_v0.7 | [
"2e38bec7f8df806283802a69db3d0038a37d026e",
"2e38bec7f8df806283802a69db3d0038a37d026e"
] | [
"closed/CentaurTechnology/code/python-code/python/main_compliance-test04b-bug-workaround.py",
"open/Inspur/code/ssd-mobilenet/tensorrt/calibrator.py"
] | [
"\"\"\"\nmlperf inference benchmarking tool\n\"\"\"\n\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport argparse\nimport array\nimport collections\nimport glob\nimport json\nimport logging\nimport os\nimport shutil\nimport sys\nimport threadin... | [
[
"numpy.array",
"numpy.mean",
"numpy.percentile"
],
[
"numpy.ascontiguousarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
garyzhao/FRGAN | [
"8aeb064fc93b45d3d8e074c5253b4f7a287582f4"
] | [
"common/models/analogy.py"
] | [
"import torch\nimport torch.nn as nn\n\n\ndef _compute_layer_config(img_size):\n min_size = img_size\n block_num = 0\n while min_size >= 8:\n min_size /= 2\n block_num += 1\n return block_num, min_size\n\n\ndef _make_upsample_layer(in_channels, out_channels, filter_size):\n return nn.Se... | [
[
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.nn.Tanh",
"torch.nn.Sigmoid",
"torch.nn.Linear",
"torch.nn.AvgPool2d",
"torch.nn.Upsample",
"torch.nn.LeakyReLU",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jarrelscy/pytarsier | [
"19ebea61caf85f151c5e31755ecb5a2f507dde04"
] | [
"vistarsier.py"
] | [
"#!/usr/bin/python3\n\nimport sys\nimport os\nimport subprocess\nimport numpy as np\nimport nibabel as nib\nimport colormaps\nimport time\n\nFIXED_OUT_N4 = '/dicom/n4out-fixed.nii.gz'\nFLOATING_OUT_N4 = '/dicom/n4out-floating.nii.gz'\nFIXED_OUT_BET = '/dicom/betout-fixed.nii.gz'\nFLOATING_OUT_BET = '/dicom/betout-f... | [
[
"numpy.max",
"numpy.abs",
"numpy.dtype",
"numpy.min"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bepnye/bran | [
"e5c56d4ecd3e327e88124f35cb3004fae759d5af",
"e5c56d4ecd3e327e88124f35cb3004fae759d5af"
] | [
"src/evaluation/utils/plot_attention.py",
"src/models/text_encoders.py"
] | [
"from __future__ import division\nfrom __future__ import print_function\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nimport re\nimport string\nimport argparse\nimport sys\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-v', '--numpy_file', required=True, default='attention_weights.np... | [
[
"numpy.load",
"matplotlib.pyplot.subplots",
"numpy.random.choice"
],
[
"tensorflow.layers.conv1d",
"tensorflow.not_equal",
"tensorflow.concat",
"tensorflow.add",
"tensorflow.nn.embedding_lookup",
"tensorflow.nn.dropout"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
neonnnnn/sparsepoly | [
"244b89d80c7a59c0a31e33671fb47e8f8df8c82c"
] | [
"sparsepoly/regularizer/omegacs.py"
] | [
"import numpy as np\nfrom numba import float64, boolean\nfrom numba.experimental import jitclass\nfrom .utils import norm\nfrom math import sqrt\n\nspec = [\n (\"_norms\", float64[:]),\n (\"_cache\", float64[:]),\n (\"_dcache\", float64[:]),\n (\"_cache_all_subsets\", float64)\n]\n\n\n@jitclass(spec)\nc... | [
[
"numpy.dot",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
c-w-m/learning_python | [
"8f06aa41faf9195d978a7d21cbb329280b0d3200"
] | [
"src/PYnative/exercise/NumPy/Q8.py"
] | [
"# Following is the 2-D array. Print max from axis 0 and min from axis 1\n\n# My Solution\nimport numpy as np\n\nsampleArray = np.array([[34, 43, 73], [82, 22, 12], [53, 94, 66]])\n\nprint(\"Printing Original array\")\nprint(sampleArray)\n\nprint(\"\\nPrinting amin of Axis 1\")\nprint(np.min(sampleArray, axis=1))\n... | [
[
"numpy.max",
"numpy.array",
"numpy.min"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
data301-2020-winter2/course-project-group_1014 | [
"b4f504908ec558b33b10fa14d2731244e183616f"
] | [
"analysis/scripts/.ipynb_checkpoints/project_functions-checkpoint.py"
] | [
"import numpy as np\nimport pandas as pd\n\ndef load_and_process(df):\n\n # Method Chain 1 (Load data and deal with missing data)\n \n df1 = (pd.read_excel(df).rename(columns={\"Jitter(Abs)\": \"Jitter_ms\"})\n .sort_values(\"subject#\", ascending=True)\n .reset_index(drop=True)\n ... | [
[
"pandas.read_excel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
anno-domini-207/cs207-FinalProject | [
"d95922f787eba737baf7c9bda8ef974fa4de9b70",
"d95922f787eba737baf7c9bda8ef974fa4de9b70"
] | [
"tests/test_newton.py",
"tests/test_AutoDiff.py"
] | [
"import sys\nimport os\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../AnnoDomini')))\n\n#from AnnoDomini.AutoDiff import AutoDiff as AD\nfrom AnnoDomini.AutoDiff import AutoDiff as AD\nfrom AnnoDom... | [
[
"numpy.round"
],
[
"numpy.log",
"numpy.sqrt",
"numpy.arctan",
"numpy.arcsin",
"numpy.cosh",
"numpy.arccos",
"numpy.sinh",
"numpy.sin",
"numpy.round",
"numpy.cos",
"numpy.tan",
"numpy.testing.assert_raises",
"numpy.exp",
"numpy.array",
"numpy.tanh... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hajicj/muscima | [
"f6f3d014761442af52a108bb873786a41d6de4b3"
] | [
"scripts/add_staffline_symbols.py"
] | [
"#!/usr/bin/env python\n\"\"\"The script ``add_staffline_symbols.py`` takes as input a CVC-MUSCIMA\n(page, writer) index and a corresponding CropObjectList file\nand adds to the CropObjectList staffline and staff objects.\"\"\"\nfrom __future__ import print_function, unicode_literals\nfrom __future__ import divisio... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.show",
"numpy.zeros",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aarora8/snowfall | [
"4484fb52a7a2d9a067f77dbbaee8dad7946db185"
] | [
"egs/librispeech/asr/simple_v1/bpe_ctc_att_conformer_decode.py"
] | [
"#!/usr/bin/env python3\n\n# Copyright 2021 Xiaomi Corporation (Author: Guo Liyong)\n# Apache 2.0\n\nimport argparse\nimport logging\nimport os\nimport random\nimport re\nimport sys\n\nfrom pathlib import Path\nfrom typing import Union\n\nimport k2\nimport numpy as np\nimport torch\n\nfrom snowfall.data import Libr... | [
[
"torch.load",
"torch.argsort",
"torch.no_grad",
"torch.device",
"torch.clamp",
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NathanSandford/PypeIt | [
"89470d27422b7f8662642060b5687a5b2fda27ed"
] | [
"pypeit/scripts/trace_edges.py"
] | [
"#!/usr/bin/env python\n#\n# See top-level LICENSE file for Copyright information\n#\n# -*- coding: utf-8 -*-\n\"\"\"\nTrace slit edges for a set of images.\n\"\"\"\n\ndef parse_args(options=None, return_parser=False):\n\n import argparse\n from pypeit.spectrographs import available_spectrographs\n\n parse... | [
[
"numpy.arange",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pleiszenburg/pyIGRF | [
"3a1f394e2df6702432b29d2a7edded4d63cb14da"
] | [
"src/pyCRGI/array/_coeffs.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport numba as nb\nimport numpy as np\n\nfrom .._coeffs import GH\nfrom .._debug import typechecked\n\n\nGH = np.array(GH, dtype = 'f8')\nSH = 13 # maximum number of spherical harmonics\n\n\n@nb.njit('i8(f8,f8[:,:,:])')\ndef _get_coeff(year, gh):\n \"\"\"\n Processes coefficients\... | [
[
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gfournier/aikit | [
"23257f365a4f387cbb86f0ed3994b696a81b57c6",
"23257f365a4f387cbb86f0ed3994b696a81b57c6"
] | [
"aikit/transformers/base.py",
"aikit/tools/helper_functions.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jan 22 10:47:48 2018\n\n@author: Lionel Massoulard\n\"\"\"\nimport numpy as np\nimport pandas as pd\n\nimport scipy.sparse as sps\n\nimport scipy.stats\nfrom statsmodels.nonparametric.kernel_density import KDEMultivariate\nfrom scipy.interpolate import interp1d\n\nfr... | [
[
"sklearn.ensemble.RandomForestRegressor",
"sklearn.cluster.KMeans",
"pandas.DataFrame",
"numpy.max",
"numpy.exp",
"sklearn.ensemble.RandomForestClassifier",
"numpy.unique",
"numpy.arange",
"scipy.interpolate.interp1d",
"numpy.float32",
"numpy.log1p",
"numpy.zeros",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0... |
insdeep/keras | [
"616201255b434a224fd83430d89810744a6ebc22"
] | [
"tests/keras/test_activations.py"
] | [
"import pytest\nimport numpy as np\nfrom numpy.testing import assert_allclose\n\nfrom keras import backend as K\nfrom keras import activations\n\n\ndef get_standard_values():\n '''\n These are just a set of floats used for testing the activation\n functions, and are useful in multiple tests.\n '''\n ... | [
[
"numpy.absolute",
"numpy.ones_like",
"numpy.max",
"numpy.size",
"numpy.vectorize",
"numpy.testing.assert_allclose",
"numpy.tanh",
"numpy.exp",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
arnaujc91/pandas | [
"7d2f5ce59551a52643318f3e4e9cd1b8058a6e44"
] | [
"pandas/core/groupby/categorical.py"
] | [
"from typing import (\n Optional,\n Tuple,\n)\n\nimport numpy as np\n\nfrom pandas.core.algorithms import unique1d\nfrom pandas.core.arrays.categorical import (\n Categorical,\n CategoricalDtype,\n recode_for_categories,\n)\nfrom pandas.core.indexes.api import CategoricalIndex\n\n\ndef recode_for_gro... | [
[
"numpy.sort",
"pandas.core.algorithms.unique1d",
"pandas.core.arrays.categorical.CategoricalDtype",
"pandas.core.arrays.categorical.recode_for_categories",
"pandas.core.arrays.categorical.Categorical"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
anssilaukkarinen/trh-1 | [
"2c7de539209660b370c62bf0948b97c0083c9719"
] | [
"mi.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Aug 20 14:38:09 2021\n\nMould index according to the Finnish Mould Growth Model\n\nPython implementation: Anssi Laukkarinen 2011-2016\n\"\"\"\n\nimport numpy as np\n\n\ndef MI(dataT, dataRH, MGspeedclass, MGmaxclass, Cmat):\n\n # Array for mold index values is cre... | [
[
"numpy.max",
"numpy.log",
"numpy.exp",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sstcam/CHECLabPy | [
"c67bf0b190ba4b799d4da150591d602e16b1d6b0"
] | [
"CHECLabPy/plotting/spe.py"
] | [
"from CHECLabPy.plotting.setup import Plotter\nfrom matplotlib import pyplot as plt\n\n\nclass SpectrumFitPlotter(Plotter):\n def __init__(self, n_illuminations, **kwargs):\n super().__init__(**kwargs)\n plt.close(self.fig)\n self.n_illuminations = n_illuminations\n self.fig = plt.fig... | [
[
"matplotlib.pyplot.subplot2grid",
"matplotlib.pyplot.close",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
soum-sr/EmojifySentences | [
"6ef5ba2d067e338c446608dfd0e191e2a4cb01a3"
] | [
"run.py"
] | [
"import os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n\nimport numpy as np\nfrom buildModel import define_model,pretrained_embedding_layer\nfrom utils import read_glove_vecs,sentences_to_indices,label_to_emoji\n\nmaxLen = 10\n\nword_to_index, index_to_word, word_to_vec_map = read_glove_vecs('glove.6B.50d.txt')\n\nm... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rickyHong/wavenet-vocoder-line-repl | [
"1813ea82a64724960f1f928b9ba05768457bc5a5"
] | [
"synthesis.py"
] | [
"# coding: utf-8\n\"\"\"\nSynthesis waveform from trained WaveNet.\n\nusage: synthesis.py [options] <checkpoint> <dst_dir>\n\noptions:\n --hparams=<parmas> Hyper parameters [default: ].\n --preset=<json> Path of preset parameters (json).\n --length=<T> ... | [
[
"torch.LongTensor",
"torch.load",
"torch.zeros",
"torch.from_numpy",
"torch.set_num_threads",
"torch.FloatTensor",
"numpy.isscalar",
"torch.cuda.is_available",
"numpy.load",
"numpy.repeat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nadamian/AGATI | [
"7fc56135c03dcfa627f128e81b807a4e3dc3ea1a"
] | [
"tracker.py"
] | [
"from csv import writer as csvwriter\nfrom numpy import percentile, max as npmax, min as npmin, array as nparray\nfrom cv2 import CAP_PROP_FPS, VideoCapture\nfrom os import path as ospath\nimport matplotlib.pyplot as plt\nfrom draw import draw\nfrom scipy import stats\nfrom TrackingObjects import Line\nfrom math im... | [
[
"matplotlib.pyplot.title",
"numpy.min",
"matplotlib.pyplot.savefig",
"numpy.percentile",
"matplotlib.pyplot.plot",
"numpy.max",
"scipy.stats.linregress",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel",
"numpy.array",
"matplotlib.pyplot.figure"
]
] | [
{
"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"
... |
broilo/Summaries | [
"2befdf1484bf2feff11aaf9465ff902424025ab0"
] | [
"Hands-on_ML/chap4/elastic_net-regularization.py"
] | [
"import numpy as np\nfrom sklearn.linear_model import ElasticNet\n\nnp.random.seed(42)\n\nm = 20\n\nX = 3*np.random.rand(m, 1)\ny = 1+0.5*X+np.random.randn(m, 1)/1.5\nX_new = np.linspace(0, 3, 100).reshape(100, 1)\n\nelastic_net = ElasticNet(alpha=0.1, l1_ratio=0.5)\nelastic_net.fit(X, y)\nelastic_net.predict([[1.5... | [
[
"numpy.random.seed",
"numpy.linspace",
"sklearn.linear_model.ElasticNet",
"numpy.random.randn",
"numpy.random.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BogdanKandra/image-watermarking | [
"961647d4441f6a269e003ce5ad3184d2e2c39f34"
] | [
"main.py"
] | [
"\"\"\"\nCreated on Fri Jan 5 23:35:11 2018\n\n@author: Bogdan Kandra\n\nWatermarking Project Driver Program\n\"\"\"\n\nimport cv2\nimport watermarking as wm\nimport projutils as pj\nimport numpy as np\n\ndef lsb_test():\n \"\"\"Runs a test for the Least Significant Bit watermarking technique.\n\n Arguments:... | [
[
"numpy.rint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
manuel-delverme/client | [
"6928bce379f7418fe3a6d59f85589bb2da51b543"
] | [
"functional_tests/lightning/train-ddp.py"
] | [
"#!/usr/bin/env python\n\nimport os\n\nimport pytorch_lightning as pl\nfrom pytorch_lightning import LightningModule\nfrom pytorch_lightning.loggers import WandbLogger\nimport torch\nfrom torch.utils.data import DataLoader, Dataset\nimport wandb\n\n\nclass RandomDataset(Dataset):\n def __init__(self, size, num_s... | [
[
"torch.randn",
"torch.utils.data.DataLoader",
"torch.nn.Linear",
"torch.stack",
"torch.ones_like",
"torch.optim.lr_scheduler.StepLR"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
charnley/qml | [
"b6de1539708a5f073428dcef95b36d54494411ef"
] | [
"test/test_mrmp.py"
] | [
"# MIT License\n#\n# Copyright (c) 2018 Silvia Amabilino\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, co... | [
[
"numpy.linspace",
"numpy.reshape",
"numpy.arange",
"numpy.all",
"numpy.load",
"numpy.loadtxt",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hinton024/Mathematical-Physics | [
"fabe34d0fb1492ad177c9e7be99e1dbe718fda69"
] | [
"ppt/MP Lab Practicals/Trapezoidal_and_simpson/trep_simp.py"
] | [
"import numpy as np\nimport math\nimport matplotlib.pyplot as plt\nimport scipy.integrate as integrate\ndef trapeziodal(func,a,b,n):\n y=[]\n h=(b-a)/n\n for i in range(n+1): \n y.append(func(a+i*h)) #y at limit points\n trp=h*(func(a)+func(b))/2\n for j in range(1,len(y)-1):\n trp=... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.title",
"numpy.arange",
"matplotlib.pyplot.yscale",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.grid",
"scipy.integrate.quad",
"matplotlib.pyplot.xscale",
"numpy.array",
"matplotlib.pyplot.sh... | [
{
"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"... |
aleromar/udaML | [
"88fea10010b2151678008770709614b902d9074d"
] | [
"predict.py"
] | [
"import argparse\nimport nnutils\nimport pyutils\nimport pandas as pd\nimport torch\nfrom PIL import Image\nimport numpy as np\n\ndef initPredictParser(parser):\n parser.add_argument(dest='imageDir',action='store', help='Path to image')\n parser.add_argument(dest='checkpoint',action='store', help='Model check... | [
[
"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": []
}
] |
spcolin/transformation | [
"0a5a988bcd4005014f59c013d9f725b6379b50c3"
] | [
"evaluate.py"
] | [
"import sys\nsys.path.append('core')\n\nfrom PIL import Image\nimport argparse\nimport os\nimport time\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport matplotlib.pyplot as plt\n\nfrom core import datasets\nfrom core.utils import flow_viz\nfrom core.utils import frame_utils\n\nfrom core.ra... | [
[
"torch.load",
"torch.sum",
"numpy.concatenate",
"torch.no_grad",
"numpy.mean",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CMU-IDS-2020/fp-carambola-of-evolution | [
"440bd33a1f1cc40f22a32a0db80279649e1036e6"
] | [
"streamlit_app.py"
] | [
"import re\n#import time\nimport nltk\nimport pickle\nimport string\nimport numpy as np\nimport pandas as pd\nimport altair as alt\nimport streamlit as st\nimport tensorflow as tf\nfrom sklearn.manifold import TSNE\nfrom tensorflow.keras import layers\nimport tensorflow_datasets as tfds\nfrom scipy.spatial.distance... | [
[
"tensorflow.keras.layers.Dropout",
"pandas.read_csv",
"numpy.abs",
"tensorflow.keras.layers.experimental.preprocessing.TextVectorization",
"tensorflow.keras.layers.Dense",
"numpy.append",
"tensorflow.keras.layers.LSTM",
"numpy.diff",
"numpy.load",
"numpy.array",
"numpy.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
goodxue/CenterNet | [
"50e1726664337fb988542e3c2247a4c57ef74334"
] | [
"combinatorial_optim/sko/SA.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# @Time : 2019/8/17\n# @Author : github.com/guofei9987\n\nimport numpy as np\nfrom .base import SkoBase\nfrom sko.operators import mutation\n\ndef get_new_constellation(x):\n size_n = x.shape[0]\n ret = []\n for i in range(size_n):\n tmp = np.ran... | [
[
"numpy.log",
"numpy.sqrt",
"numpy.abs",
"numpy.ones",
"numpy.tan",
"numpy.sign",
"numpy.random.normal",
"numpy.random.rand",
"numpy.random.uniform",
"numpy.array",
"numpy.exp",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hoel-bagard/yolact | [
"028fd121e94c18531243a73eb4c0d443fc38a079"
] | [
"data/config.py"
] | [
"from math import sqrt\n\nimport torch\n\nfrom yolact.backbone import ResNetBackbone, VGGBackbone, ResNetBackboneGN, DarkNetBackbone\n\n# for making bounding boxes pretty\nCOLORS = ((244, 67, 54),\n (233, 30, 99),\n (156, 39, 176),\n (103, 58, 183),\n (63, 81, 181),\n ... | [
[
"torch.nn.functional.softmax",
"torch.nn.functional.relu"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
palmergroup-tutorial/Python-force-field-parameterization-workflow | [
"43fac675f7474230ff8c6669230f1606d6c5bd8f"
] | [
"objective/force_matching/force_matching.py"
] | [
"# Python standard library:\nimport numpy as np \nimport multiprocessing as mp \nimport sys \nimport logging\nimport time \nimport os \nimport itertools \nimport shutil\n\n# local library: \nimport IO.check_file \nimport IO.check_type\nimport IO.reader \nimport IO.user_provided \n\n# Third-party library: \n\n# de... | [
[
"numpy.var",
"numpy.array",
"numpy.average",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hanbioinformatica/owe2a | [
"f572866ef3bc75689d2d571cb393c6d60480655b"
] | [
"week2MatPlotLib/MatPlotLibDemo/demo_1_eenvoudigePlot.py"
] | [
"import matplotlib.pyplot as plt\n\ny_values = [3,5,7,10,12,15,18,20]\nplt.plot(y_values)\nplt.show()"
] | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
m-cip/sdepy | [
"9b8f4a39f0314f4ddd8f788c3f47a46ab3752080"
] | [
"sdepy/tests/test_montecarlo.py"
] | [
"\"\"\"\n===================================================================\nFORMAL (FAST) AND QUANTITATIVE (SLOW) TESTS ON THE MONTECARLO CLASS\n===================================================================\n\"\"\"\nfrom .shared import *\n\nimport scipy\nimport scipy.stats\nimport scipy.interpolate\n\nmonte... | [
[
"scipy.stats.norm",
"scipy.stats.uniform",
"scipy.stats.trapz"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Smallflyfly/VGGFace2-pytorch | [
"2d5db07505a0ce402e9e11003b1bf8d205e41d3c"
] | [
"extractor_demo.py"
] | [
"#!/usr/bin/env python\n\nimport argparse\nimport os\nimport sys\nimport torch\nimport torch.nn as nn\n\nimport datasets\nimport models.resnet as ResNet\nimport models.senet as SENet\nfrom trainer import Trainer, Validator\nfrom extractor import Extractor\nimport utils\n\nconfigurations = {\n 1: dict(\n m... | [
[
"torch.nn.CrossEntropyLoss",
"torch.cuda.manual_seed",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.backends.cudnn.version",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
likesum/bpn | [
"bf4cc2b78c461f99cdc7ac91453b1c4cd3aad9b6"
] | [
"utils/utils.py"
] | [
"\"\"\"Various utility functions.\"\"\"\n\nimport sys\nimport time\nimport signal\nimport numpy as np\n\nclass LogWriter:\n def __init__(self, filename):\n self._log = open(filename, 'a')\n\n def log(self, data, numit=None):\n \"\"\"Log output in standard format.\"\"\"\n if numit is None:... | [
[
"numpy.load",
"numpy.savez"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gitter-badger/traffic | [
"69c97491ec29b2413669441bb5d26d984bd539f2"
] | [
"tests/test_flight.py"
] | [
"# fmt: off\n\nimport zipfile\nfrom typing import Optional, cast\n\nimport pandas as pd\nimport pytest\nfrom traffic.algorithms.douglas_peucker import douglas_peucker\nfrom traffic.core import Flight, Traffic\nfrom traffic.data import eurofirs, navaids, runways\nfrom traffic.data.samples import (airbus_tree, belevi... | [
[
"pandas.DataFrame.from_records",
"pandas.Timedelta",
"pandas.Timestamp",
"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": []
}
] |
yy2111/iSPY | [
"454ed4ec6a18b9e2dd3e13114b1263760054401e"
] | [
"models/cnn_feature.py"
] | [
"\n# coding: utf-8\n\n# In[1]:\nfrom __future__ import print_function\n\nimport os\n# os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"2\"\n\n\n# In[2]:\n\n\n\n\nimport os\nimport sys\nimport numpy as np\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.prepro... | [
[
"numpy.asarray",
"numpy.argmax",
"numpy.array",
"numpy.where",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tobiasholenstein/dace | [
"38fb56d12b59aa8dfe8bb1ff0068e29c5c75efc9",
"38fb56d12b59aa8dfe8bb1ff0068e29c5c75efc9"
] | [
"tests/inline_nonsink_access_test.py",
"tests/transformations/redundant_copy_test.py"
] | [
"# Copyright 2019-2020 ETH Zurich and the DaCe authors. All rights reserved.\nimport dace\nimport numpy as np\n\nsdfg = dace.SDFG('inline_nonsink_access_test')\nsdfg.add_array('A', [1], dace.float32)\nsdfg.add_array('B', [1], dace.float32)\n\nstate = sdfg.add_state()\nA = state.add_access('A')\nB = state.add_access... | [
[
"numpy.random.rand",
"numpy.array",
"numpy.linalg.norm"
],
[
"numpy.arange",
"numpy.array_equal",
"numpy.zeros_like",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
josehenriqueroveda/emergency-room | [
"238f08e78bf98709c5089f246d6624cac74a38ba"
] | [
"emergency-room.py"
] | [
"import simpy\nimport random\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n\nlogo = \"\"\"\n ______ _____ \n | ____| | __ \\ \n | |__ _ __ ___ ___ _ __ __ _ _... | [
[
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.show",
"pandas.DataFrame",
"matplotlib.pyplot.figure"
]
] | [
{
"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": []
}
] |
stevensdavid/OrGAN | [
"6ebcaf604ecbed75576326ad23222749ea31642b"
] | [
"src/models/patchgan.py"
] | [
"from typing import Tuple\n\nimport torch\nfrom torch import Tensor, nn\nfrom util.dataclasses import DataShape\nfrom util.pytorch_utils import ConditionalInstanceNorm2d\n\nfrom models.abstract_model import AbstractDiscriminator, AbstractGenerator\n\n\nclass Generator(AbstractGenerator):\n def __init__(\n ... | [
[
"torch.nn.Sequential",
"torch.nn.ConvTranspose2d",
"torch.cat",
"torch.nn.PReLU",
"torch.nn.Conv2d",
"torch.nn.Sigmoid",
"torch.tanh",
"torch.nn.InstanceNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zengzhixian/SoftPool_SVSE | [
"0edeed1ee788d21ff4cc5191c9189f49af41f57c"
] | [
"lib/modules/aggr/gpo.py"
] | [
"# coding=utf-8\nimport torch\nimport torch.nn as nn\nimport math\nfrom torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\n\n\ndef positional_encoding_1d(d_model, length):\n \"\"\"\n :param d_model: dimension of the model\n :param length: length of positions\n :return: length*d_model p... | [
[
"torch.nn.Softmax",
"torch.softmax",
"torch.zeros",
"torch.nn.GRU",
"torch.sum",
"torch.nn.Linear",
"torch.nn.utils.rnn.pad_packed_sequence",
"torch.mul",
"torch.where",
"torch.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zabir-nabil/darknet-fastapi-modelserver | [
"abac3f6515add300121802412eaef422f3f18c4f"
] | [
"serve.py"
] | [
"from typing import Optional\r\nfrom fastapi import FastAPI\r\nfrom pydantic import BaseModel\r\nfrom model_config import models\r\nimport numpy as np\r\nimport cv2\r\nimport darknet\r\n\r\n\r\napp = FastAPI()\r\n\r\nclass ModelServer():\r\n def __init__(self):\r\n self.nets = {}\r\n self.class_map... | [
[
"numpy.frombuffer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anisayari/matchvec | [
"74ed468ccc17a0430a7f617b9c14f43ac370875a"
] | [
"matchvec/classification_onnx.py"
] | [
"\"\"\"Classification Marque Modèle\"\"\"\nimport os\nimport json\nimport numpy as np\nfrom typing import List, Tuple\nfrom matchvec.utils import timeit\nimport onnxruntime\nfrom PIL import Image\nfrom matchvec.utils import timeit\nfrom matchvec.BaseModel import BaseModel\n\n\nCLASSIFICATION_MODEL = os.getenv('CLAS... | [
[
"numpy.max",
"numpy.array",
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ersilia-os/ersilia-automl-chem | [
"fabb1f05d17cff11ec0e084495eed4c0152f2f63",
"fabb1f05d17cff11ec0e084495eed4c0152f2f63",
"fabb1f05d17cff11ec0e084495eed4c0152f2f63",
"fabb1f05d17cff11ec0e084495eed4c0152f2f63"
] | [
"zairachem/tools/molmap/scripts/predict.py",
"zairachem/augmentation/augment.py",
"zairachem/automl/autogluon.py",
"zairachem/tools/fpsim2/FPSim2/FPSim2/FPSim2Cuda.py"
] | [
"import os\nimport sys\n\nroot = os.path.dirname(os.path.abspath(__file__))\nsys.path.append(os.path.join(root, \"../bidd-molmap/\"))\n\nimport pandas as pd\nimport numpy as np\n\nfrom molmap import MolMap\nfrom molmap import feature\nfrom molmap.model import load_model\n\nfile_name = sys.argv[1]\nmodel_path = sys.... | [
[
"pandas.read_csv"
],
[
"pandas.DataFrame",
"numpy.clip"
],
[
"numpy.array",
"pandas.DataFrame"
],
[
"numpy.zeros",
"numpy.ndarray",
"numpy.dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
... |
macabdul9/Fake-News-Detection | [
"1f049b578126d4a868e7650a3c2ce17b2c449487"
] | [
"utils.py"
] | [
"import pandas as pd\n\n# add label coloum to each row\n\nfake = pd.read_csv(\"./data/FakeNews/Fake.csv\")\nreal = pd.read_csv(\"./data/FakeNews/True.csv\")\n\nfake['label'] = [1]*fake.shape[0]\nreal['label'] = [0]*real.shape[0]\n\nfake.to_csv(\"./data/FakeNews/Fake.csv\")\nreal.to_csv(\"./data/FakeNews/True.csv\")... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
GunjanChourasia/WS_DAN_PyTorch | [
"6c12a1b5b0b8980e3b69d44474e0b5edb455570c"
] | [
"utils/utils.py"
] | [
"############################################################\n# File: utils.py #\n# Created: 2019-11-18 20:50:50 #\n# Author : wvinzh #\n# Email : wvinzh@qq.com #\n# -... | [
[
"torch.cuda.manual_seed",
"numpy.random.seed",
"torch.manual_seed",
"torch.no_grad",
"torch.cuda.manual_seed_all",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MohamedGassem/mohamedgassem.github.io | [
"673b2aa725c94f1341150fef91959d29eb90357c"
] | [
"ai-for-tetris/src/tetris/base/TetrisBase.py"
] | [
"import numpy as np\r\nimport pprint\r\nfrom copy import copy, deepcopy\r\n\r\n\r\n\r\nfrom src.tetris.base.Controls import Controls\r\nfrom src.tetris.base.Piece import Piece\r\n\r\n\r\nclass TetrisBase:\r\n \"\"\"\r\n Class that handles Tetris base game\r\n \"\"\"\r\n\r\n def __init__(self, par... | [
[
"numpy.random.RandomState",
"numpy.zeros",
"numpy.count_nonzero"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bueler/multilevel-stokes-geometry | [
"a7e0703f7e9605c67d8fa6b0b026c545f8af305e"
] | [
"py/experiments/humps.py"
] | [
"#!/usr/bin/env python3\n'''Demonstrate that extruded meshes can handle disconnected ice masses.'''\n\nimport numpy as np\nfrom firedrake import *\n\ndx = 1000.0\nmx, mz = 12, 4 # number of elements in extruded mesh\nice = [0,1,1,1,0,0,1,1,1,1,0,0] # =1 where ice is present in element\n... | [
[
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lostmsu/border | [
"473286035dd71c5afeaf0e0b47574b18ebb1f8c2"
] | [
"examples/atari_wrappers.py"
] | [
"import gym\nimport numpy as np\nfrom collections import deque\nfrom PIL import Image\nfrom multiprocessing import Process, Pipe\n\n# atari_wrappers.py\nclass NoopResetEnv(gym.Wrapper):\n def __init__(self, env, noop_max=30):\n \"\"\"Sample initial states by taking random number of no-ops on reset.\n ... | [
[
"numpy.sign",
"numpy.array",
"numpy.concatenate",
"numpy.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chenzuge1/RetinaNet-Conf | [
"350d9f6d7a6cd1303cac557fdfa9418fc7a6e84a"
] | [
"mmdet/models/losses/focal_loss.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmcv.ops import sigmoid_focal_loss as _sigmoid_focal_loss\n\nfrom ..builder import LOSSES\nfrom .utils import weight_reduce_loss\n\n\n# This method is only for debugging\ndef py_sigmoid_focal_loss(pred,\n target,\n ... | [
[
"torch.nn.functional.binary_cross_entropy_with_logits",
"torch.sigmoid",
"torch.log",
"torch.ones_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MoyGcc/hpcwild | [
"8ed35c3f188284af2a4dd0d68b09fbceb105c2ba"
] | [
"registration/lib/torch_functions.py"
] | [
"\"\"\"\nhttps://github.com/bharat-b7/IPNet\n\"\"\"\nimport torch\n\n\ndef batch_gather(arr, ind):\n \"\"\"\n :param arr: B x N x D\n :param ind: B x M\n :return: B x M x D\n \"\"\"\n dummy = ind.unsqueeze(2).expand(ind.size(0), ind.size(1), arr.size(2))\n out = torch.gather(arr, 1, dummy)\n ... | [
[
"torch.gather",
"torch.sparse_coo_tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vanitas-vanitatum/swarm-intelligence | [
"97bf985f545c612016fd6830092a6cd4ebd93cbe"
] | [
"src/constraints.py"
] | [
"import numpy as np\n\nfrom src.furnishing.room import RoomDrawer\n\n\nclass BaseConstraint:\n\n def maybe(self, other):\n return MaybeConstraint(self, other)\n\n def und(self, other):\n return UndConstraint(self, other)\n\n def nope(self):\n return NopeConstraint(self)\n\n def chec... | [
[
"numpy.all",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zykls/performative-prediction | [
"583d83f0a65f50214cef9cd24975d1a4578c0c18"
] | [
"experiments/icml2020/optimization.py"
] | [
"\"\"\"Logistic regression model\"\"\"\n\nimport numpy as np\nfrom strategic import best_response\n\n\ndef sigmoid(z):\n \"\"\"Evaluate sigmoid function\"\"\"\n return 1 / (1 + np.exp(-z))\n\n\ndef evaluate_loss(X, Y, theta, lam, strat_features=[], epsilon=0):\n \"\"\"Evaluate L2-regularized logistic regre... | [
[
"numpy.multiply",
"numpy.linalg.norm",
"numpy.copy",
"numpy.append",
"numpy.exp",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
asaleh33/ode_model_simulation | [
"04abf469ab121604592555bc82bf7b42a7225506"
] | [
"update/default/ode_model_simulation.py"
] | [
"##########################################################################################################\n# #\n# This code \"ODE model simulation\" is written by Ahmed Khalil for \"a model of lysosomal acidific... | [
[
"matplotlib.pyplot.legend",
"numpy.linspace",
"matplotlib.pyplot.plot",
"scipy.interpolate.interp2d",
"numpy.exp",
"matplotlib.pyplot.tight_layout",
"numpy.arange",
"scipy.integrate.solve_ivp",
"numpy.log",
"matplotlib.pyplot.title",
"matplotlib.pyplot.savefig",
"nu... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.3",
"1.9",
"1.5",
"1.7",
"1.0",
"1.2",
"1.8"
],
"tensorflow": []
}
] |
LazarValkov/GanModeCollapseEvaluation | [
"bc9ce08125bd409e1d9e3577954115a31caf6b3f"
] | [
"Run_ALI_DCGAN_cifar10.py"
] | [
"import numpy as np\nfrom main import run_app\nimport tensorflow as tf\nimport os\nfrom utils import pp\n\nclass AttributeDict(dict):\n def __getattr__(self, attr):\n return self[attr]\n def __setattr__(self, attr, value):\n self[attr] = value\n\nif __name__ == '__main__':\n start_index = 2 \... | [
[
"tensorflow.reset_default_graph"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
zurutech/anomaly-toolbox | [
"ee772898b66b8be86cfa300334fb8cf7b826dc4d"
] | [
"src/anomaly_toolbox/trainers/ganomaly.py"
] | [
"# Copyright 2021 Zuru Tech HK Limited. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required b... | [
[
"tensorflow.keras.models.load_model",
"tensorflow.concat",
"tensorflow.summary.scalar",
"tensorflow.zeros",
"tensorflow.shape",
"tensorflow.keras.losses.MeanSquaredError",
"tensorflow.keras.metrics.AUC",
"tensorflow.GradientTape",
"tensorflow.keras.optimizers.Adam",
"tensor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
kamleshbhalui/MADYPG | [
"b91c7af0b7a1382fed0a5fb3147f13594ddec71e"
] | [
"generate_yarnmodels/generate_nsamples.py"
] | [
"import generate\nimport numpy as np\nimport time\n\n# maxish = 1.0\n# minish = -0.25\n# rge = (np.linspace(0, 1, 24)**2 * (maxish - minish) + minish)\n# rge = rge - rge[np.argmin(np.abs(rge))]\n# rgexy = np.round(rge, 3)\n# rgea = np.round(np.linspace(-0.7, 0.7, 23), 3)\n# print(rgexy)\n# print(rgea)\n# print(len... | [
[
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Chris-george-anil/flower | [
"98fb2fcde273c1226cc1f2e1638c1e4d8f35815c"
] | [
"examples/simulation_pytorch/main.py"
] | [
"import flwr as fl\nfrom flwr.common.typing import Scalar\nimport ray\nimport torch\nimport torchvision\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nfrom collections import OrderedDict\nfrom pathlib import Path\nfrom typing import Dict, Callable, Optional, Tuple\nfrom dataset_utils i... | [
[
"torch.nn.CrossEntropyLoss",
"torch.max",
"torch.nn.Conv2d",
"torch.utils.data.DataLoader",
"numpy.atleast_1d",
"torch.nn.Linear",
"torch.nn.MaxPool2d",
"numpy.copy",
"torch.no_grad",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zthurman/FizzPyX | [
"e110d7dd1061b898ff31112686dfde929f850f92"
] | [
"examples/FizzPyXPlot.py"
] | [
"#!/usr/bin/env python\n# FizzPyX - FizzPyXPlot\n# Copyright (C) 2017 Zechariah Thurman\n# GNU GPLv3\n\n\nfrom __future__ import division\nfrom matplotlib.pyplot import figure, plot, title, xlabel, ylabel, xlim, ylim, savefig\nfrom numpy import argsort, abs, mean, arange, argmax\nfrom numpy.fft import fft, rfft, ff... | [
[
"numpy.abs",
"matplotlib.pyplot.title",
"numpy.fft.fft",
"numpy.fft.rfft",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.ylim",
"numpy.arange",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlim",
"numpy.argmax",
"numpy.mean",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eduardo98m/open-blacky | [
"7d0661fbd24dda6c4afc91f77b9f05b17f1aec59"
] | [
"src/src/terrain_gen.py"
] | [
"\"\"\"\n Authors: Amin Arriaga, Eduardo Lopez\n Project: Graduation Thesis: GIAdog\n\n File containing the code in charge of the automatic generation of simulated terrain.\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom typing import *\nfrom random import uniform\nfrom perlin_noise impo... | [
[
"matplotlib.pyplot.imshow",
"numpy.min",
"numpy.cos",
"numpy.sin",
"matplotlib.pyplot.show",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tobias-loew/CoolProp | [
"ea3a957701f587c8033b4f194401915992f73d68"
] | [
"dev/incompressible_liquids/CPIncomp/BaseObjects.py"
] | [
"from __future__ import division, print_function\nimport numpy as np\nfrom scipy.optimize._minimize import minimize\nfrom scipy.optimize.minpack import curve_fit\nimport sys\n\n# Here we define the types. This is done to keep the definitions at one\n# central place instead of hiding them somewhere in the data.\n\n\... | [
[
"numpy.nanmax",
"numpy.nanmin",
"numpy.polynomial.polynomial.polyval2d",
"numpy.any",
"numpy.nanstd",
"numpy.square",
"numpy.allclose",
"numpy.clip",
"numpy.reshape",
"numpy.finfo",
"numpy.copy",
"numpy.zeros",
"numpy.log",
"numpy.min",
"numpy.linalg.lst... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"0.15",
"1.4",
"0.16",
"1.0",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"0.10",
"0.17",
"1.3"
],
"tensorflow": [... |
ozemsbg/Mitty | [
"5fc6ad399c5366442034b59dfd1c0f9bab199a3f",
"5fc6ad399c5366442034b59dfd1c0f9bab199a3f"
] | [
"mitty/benchmarking/plot/byvsize.py",
"mitty/benchmarking/evcfbysize.py"
] | [
"import numpy as np\nfrom matplotlib import patches as mpatches\nfrom scipy import stats as ss\n\n\ndef bootstrap(p, n, ci=0.05):\n \"\"\"Given a fraction correct (p) and total count (n) (both arrays) and a confidence interval\n return us two arrays l, h indicating the lower and higher confidence limits\n\n :par... | [
[
"numpy.ceil",
"matplotlib.patches.Patch",
"numpy.array",
"scipy.stats.binom.ppf"
],
[
"matplotlib.pyplot.legend",
"matplotlib.use",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.subplots_adjust",
"numpy.savetxt",
"matplotlib.pyplot.sup... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
giacoballoccu/KGAT-Baseline | [
"99a9b4fe3e53ce21ac7746dc3f048ac84881fe17"
] | [
"Model/NFM.py"
] | [
"'''\nCreated on Dec 18, 2018\nTensorflow Implementation of the Baseline model, NFM, in:\nWang Xiang et al. KGAT: Knowledge Graph Attention Network for Recommendation. In KDD 2019.\n@author: Xiang Wang (xiangwang@u.nus.edu)\n'''\nimport tensorflow.compat.v1 as tf\nimport os\nos.environ['TF_CPP_MIN_LOG_LEVEL']='2'\n... | [
[
"tensorflow.compat.v1.square",
"tensorflow.compat.v1.nn.dropout",
"tensorflow.compat.v1.nn.sigmoid",
"tensorflow.compat.v1.train.AdamOptimizer",
"tensorflow.compat.v1.concat",
"tensorflow.compat.v1.reduce_mean",
"tensorflow.compat.v1.SparseTensor",
"tensorflow.compat.v1.matmul",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
smithjm0824/nostradamus | [
"66c1fd29912849dc01bf54e8f6f9ad98c851fb41"
] | [
"stock_watch/indicators.py"
] | [
"\"\"\"\nCopyright, Rinat Maksutov, 2017.\nLicense: GNU General Public License\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\n\n\"\"\"\nExponential moving average\nSource: http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_averages\nParams: \n data: pandas DataFrame\n peri... | [
[
"numpy.square",
"numpy.amax",
"numpy.abs",
"numpy.sqrt",
"pandas.Series",
"numpy.amin"
]
] | [
{
"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": []
}
] |
maxholloway/agent-exchange | [
"13eab47b2aa709f416fbd3866d08cdfc876fbb1a"
] | [
"examples/prisoners_dilemma/exchange.py"
] | [
"import numpy as np\nfrom typing import Callable, Sequence\nfrom agent_exchange.agent import Agent\n\nfrom agent_exchange.exchange import Exchange\nfrom utils import BufferList\n\nclass PrisonersDilemmaExchange(Exchange):\n \"\"\"\n Defines the exchange for a simple prisoner's\n dilemma game. In each round... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
suvrapratim98/Hoax-News-Detection | [
"4817be176aa2e35d81a176cc54b8ba1d3b83df3b"
] | [
"Hoax-news/classifier.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Aug 29 16:58:52 2018\n\n@author: rubitry\n\"\"\"\n\nimport DataPrep\nimport FeatureSelection\nimport numpy as np\nimport pandas as pd\nimport pickle\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.feature_extraction.text import TfidfTransfo... | [
[
"matplotlib.pyplot.legend",
"numpy.linspace",
"matplotlib.pyplot.step",
"sklearn.metrics.confusion_matrix",
"matplotlib.pyplot.plot",
"numpy.mean",
"sklearn.svm.LinearSVC",
"sklearn.metrics.f1_score",
"sklearn.metrics.classification_report",
"sklearn.cross_validation.KFold"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
khllkcm/templot | [
"9ba85f35c0e7a3a8c238071be4911c25c03a3883"
] | [
"templot/plot_aggregated_map.py"
] | [
"\"\"\"\nPlot Aggregated Map.\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nimport folium\nimport json\nimport pkg_resources\nimport os\nimport warnings\nimport matplotlib.pyplot as plt\nimport re\nfrom io import BytesIO\nimport base64\n\nDATA_PATH = pkg_resources.resource_filename('templot', 'data')\n\n\ndef... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.close",
"matplotlib.pyplot.bar",
"matplotlib.pyplot.xticks"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
daylightZhang/ECE5725-Final-Project | [
"d602334a7b579dece7bcff16d5ab395e95447a55"
] | [
"main_demo_old_version.py"
] | [
"# # main program for the project \n# # Author: Jingkai Zhang (jz544@cornell.edu) and Lanyue Fang (lf355@cornell.edu)\n# # Date: 2021.12.8\n# import threading\n# import RPi.GPIO as GPIO\n# import cv2\n# import numpy as np\n# # from GoBang.GUI import GoBang_GUI # display should be written in another file \n# from Ha... | [
[
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
maundersea/csv_tools | [
"b1625c3a72395da67305b35fe2f6ef7874e51607"
] | [
"csv_utility/csv_dummy.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# ----------------------------------------------------------------------\n# Name: csv_dummy.py\n# Description:\n#\n# Author: m.akei\n# Copyright: (c) 2020 by m.na.akei\n# Time-stamp: <2020-08-14 19:16:36>\n# Licence:\n# Copyright (c) 2021 Masahar... | [
[
"numpy.random.random",
"numpy.random.choice",
"pandas.DataFrame",
"numpy.random.rand",
"pandas.date_range",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
brunston/nextTwilight | [
"51b32cb211bdb7c7bddcfb98adceaa388fcfce33"
] | [
"isoENCAPSULATE.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 17 11:09:32 2014\n@author: B Poon (demure)\n\"\"\"\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\n#definitions\nfirst = r\"C:\\ast\\iso100Mfixed.txt\"\nthird = r\"C:\\ast\\iso10G.txt\"\nsecond = r\"C:\\ast\\iso1G.txt\"\nfirst_c = 'b.'\nthird_c = '... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"numpy.loadtxt",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jernsting/useful_layers | [
"707246e020722ee992e95151c43425ee59c14aae"
] | [
"test/test_blocks/test_ScalingBlock.py"
] | [
"import unittest\n\nimport torch\n\nimport useful_layers as ul\n\n\nclass ScalingBlockTest(unittest.TestCase):\n\n def test_se_block(self):\n layer = ul.layers.SqueezeAndExcitation2D(3)\n block = ul.blocks.ScalingBlock(layer)\n\n dummy_input = torch.rand(2, 3, 4, 4) # b, c, h, w\n bl... | [
[
"torch.rand",
"torch.equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wolfiex/transform | [
"1a51a522fa23bedc34859035671715cd6b497902"
] | [
"tensorflow_transform/schema_inference_test.py"
] | [
"# Copyright 2017 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by appl... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.constant",
"tensorflow.cast",
"tensorflow.compat.v1.Session",
"tensorflow.io.FixedLenFeature",
"tensorflow.compat.v1.placeholder",
"tensorflow.compat.v1.Graph",
"tensorflow.size"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
yosumkmk/2019_petfinder | [
"802281d20106d8faef8b165a3768e7a0564ce15d"
] | [
"src/features/build_features.py"
] | [
"# -*- coding: utf-8 -*-\nimport warnings\n\nwarnings.filterwarnings('ignore')\nimport numpy as np\nimport pandas as pd\nimport glob\nfrom src.util.log_util import set_logger\nfrom logging import StreamHandler, Formatter, getLogger, FileHandler, DEBUG, INFO, ERROR\nfrom sklearn.preprocessing import StandardScaler\n... | [
[
"sklearn.decomposition.TruncatedSVD",
"pandas.concat",
"numpy.isnan",
"numpy.arange",
"pandas.DataFrame",
"numpy.sort",
"numpy.round",
"sklearn.preprocessing.StandardScaler",
"numpy.where"
]
] | [
{
"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": []
}
] |
rahulgupta9202/ColossalAI | [
"993088d45eaa032e39cf5959df2a506f0663bc2e",
"993088d45eaa032e39cf5959df2a506f0663bc2e",
"993088d45eaa032e39cf5959df2a506f0663bc2e"
] | [
"tests/test_layers/test_2p5d/test_layer.py",
"colossalai/nn/layer/parallel_3d/_vit.py",
"colossalai/nn/data/_utils.py"
] | [
"from torch.nn import Parameter\n\nfrom colossalai.context.parallel_mode import ParallelMode\nfrom colossalai.core import global_context as gpc\nfrom colossalai.nn import (Linear2p5D, LayerNorm2p5D, TransformerSelfAttention2p5D, TransformerMLP2p5D,\n TransformerLayer2p5D)\nfrom colossalai.... | [
[
"torch.nn.Parameter"
],
[
"torch.nn.Softmax",
"torch.nn.Dropout",
"torch.cat",
"torch.zeros",
"torch.nn.Conv2d",
"torch.matmul",
"torch.chunk"
],
[
"numpy.rollaxis",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.