repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
Dmytro-Skorniakov/pyvenn | [
"b3d3057d155f3f11e37a93b55305f2b6929cf608"
] | [
"demo.py"
] | [
"# coding: utf-8\n\n# ipython notebook requires this\n# %matplotlib inline\n\n# python console requires this\nimport matplotlib\nmatplotlib.use('Agg')\n\nimport matplotlib.pyplot as plt\nfrom pyvenn import venn\n\nlabels = venn.get_labels([range(10), range(5, 15)], fill=['number', 'logic'])\nfig, ax = venn.venn2(la... | [
[
"matplotlib.use",
"matplotlib.pyplot.close"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tzaumiaan/mono_depth_assisted_tracking_by_detection | [
"33a4ca8c5550d30111269848eab4148da2deaeea"
] | [
"inference_pipeline/inference_core.py"
] | [
"\"\"\"Inference core module.\n\nThis module is the top level wrapper for the whole detection-by-tracking\nframework. The wrapper takes care of a set of building blocks, including\ndetector, estimator, DA solver, tracker, and a bunch of unitilies to \nprovide a clean interface to be executed frame by frame.\n\n\"\"... | [
[
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Meistereder29/rl-course | [
"7816088830da9dbd143abe50b51f5acd52f25c35"
] | [
"ex07-fa/playground.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jun 22 17:10:20 2020\n\n@author: Gregor\n\"\"\"\nimport numpy as np\n\nx1 = -12\n\n\nfor i in range(20):\n y1 = int(np.round((x1+12)*19/18))\n x2 = -7\n for j in range(20):\n y2 = int(np.round((x2+7)*19/14))\n x2 +=(14/19)\n state = 20 *... | [
[
"numpy.round"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shangoma/Hate_Speech | [
"597795d40e3516ec83feb33e40c1d544c58deb74"
] | [
"evaluator.py"
] | [
"'''Scripts for evaluation,\n metrics: (macro) F1, AUC, FNED, FPED\n\n Because the data is skewed distributed, therefore,\n we use the macro f1 score to measure the performance.\n'''\nimport pandas as pd\nfrom sklearn import metrics\nfrom sklearn.utils.class_weight import compute_class_weight\nfrom sklearn... | [
[
"pandas.read_csv",
"numpy.unique",
"numpy.asarray",
"sklearn.utils.shuffle",
"sklearn.metrics.confusion_matrix",
"sklearn.metrics.roc_curve",
"sklearn.metrics.auc",
"sklearn.metrics.f1_score",
"sklearn.metrics.accuracy_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
jjpalacio/tflearn | [
"5c23566de6e614a36252a5828d107d001a0d0482"
] | [
"tflearn/data_utils.py"
] | [
"# -*- coding: utf-8 -*-\nfrom __future__ import division, print_function, absolute_import\n\nimport os\nimport random\nimport numpy as np\nfrom PIL import Image\nimport pickle\nimport csv\nimport warnings\nimport tensorflow as tf\ntry: #py3\n from urllib.parse import urlparse\n from urllib import request\nex... | [
[
"numpy.unique",
"numpy.asarray",
"numpy.fliplr",
"numpy.reshape",
"numpy.flipud",
"numpy.ones",
"numpy.max",
"numpy.std",
"numpy.mean",
"tensorflow.contrib.learn.python.learn.preprocessing.text.VocabularyProcessor",
"numpy.shape",
"numpy.array",
"tensorflow.pyth... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
snakedragon/udacity-dlnd | [
"2e5550f183f4eeb7d7c4a91f022df54f0f63c6f3"
] | [
"tv-script-generation/load_word2vec.py"
] | [
"\nimport os\nimport tensorflow as tf\nimport numpy as np\nfrom collections import Counter\nfrom itertools import chain\n\nembedding_dim = 100\nfname = 'data/glove.6B.%dd.txt'%embedding_dim\n\nglove_index_dict = {}\n\n\nwith open(fname, 'r') as fp:\n glove_symbols = len(fp.readlines())\n\nglove_embedding_weights... | [
[
"numpy.asarray",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Biles430/FPF_PIV | [
"66fa80dbd8414c1c6c522f74eb858c4a4725dde9"
] | [
"piv_outer.py"
] | [
"import pandas as pd\nfrom pandas import DataFrame\nimport numpy as np\nimport PIV\nimport h5py\nimport matplotlib.pyplot as plt\nimport hotwire as hw\n\n################################################\n# PURPOSE\n# 1. Compute Integral Parameters\n# 2. Outer Normalize\n# 3. Plot\n##########################... | [
[
"matplotlib.pyplot.legend",
"pandas.read_hdf",
"matplotlib.pyplot.contourf",
"matplotlib.pyplot.title",
"matplotlib.pyplot.figure",
"numpy.arange",
"matplotlib.pyplot.hold",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.colorbar",
"matplotlib.pyplot.quiverkey",
"matplotl... | [
{
"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": []
}
] |
gohsyi/learning-with-noisy-labels | [
"bd6086131389d6d3c9998e1908fe6e4a17ae7deb"
] | [
"run_svm.py"
] | [
"import datetime\nimport numpy as np\nimport multiprocessing as mp\n\nfrom sklearn.svm import SVC\n\nfrom utils import logger\nfrom utils.dataloader import DataLoader\nfrom utils.misc import set_global_seeds, make_arg_list\n\nCLASS_WEIGHTS = [0.1, 0.2, 0.25, 0.33, 0.5, 1.0, 2.0, 3.0, 4.0, 5.0, 10.0]\n\n\ndef find_b... | [
[
"numpy.min",
"numpy.max",
"numpy.std",
"numpy.mean",
"sklearn.svm.SVC"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AlwaysGemini/PaddleSeg | [
"a8c38bd6eca539b8fa470b8d59f7b22e6daf3a94"
] | [
"paddleseg/models/shufflenet_slim.py"
] | [
"# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless req... | [
[
"numpy.random.random",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
netx-repo/byteps | [
"95bbc0dabd76453504a2a73c6fcfeb47242d5ca7"
] | [
"examples-byteps/tensorflow/transformer/official/vision/image_classification/imagenet_preprocessing.py"
] | [
"# Copyright 2016 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.concat",
"tensorflow.stack",
"tensorflow.cast",
"tensorflow.minimum",
"tensorflow.image.decode_and_crop_jpeg",
"tensorflow.image.random_flip_left_right",
"tensorflow.data.TFRecordDataset",
"tensorflow.io.VarLenFeature",
"tensorflow.data.Options",
"tensorflow.ima... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhuchen03/influence | [
"fec7d4759da4843e356976f00e2af95cf0ea3078"
] | [
"scripts/run_spam_experiment.py"
] | [
"from __future__ import division\nfrom __future__ import print_function\nfrom __future__ import absolute_import\nfrom __future__ import unicode_literals \n\nimport os\nimport math\nimport numpy as np\nimport pandas as pd\nimport sklearn.linear_model as linear_model\n\nimport scipy\nimport sklearn\n\nimport influen... | [
[
"numpy.savez",
"numpy.random.seed",
"numpy.random.choice",
"numpy.copy",
"tensorflow.reset_default_graph",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
OE9NAT/bacharbeit | [
"067b0fc81cd306233cd97e124395ac898a82f092"
] | [
"program/sequenz/FIDseq.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Dec 9 09:37:39 2021\n\n@author: luki\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.fftpack import fft, fftshift, fftfreq\nimport limr\n\nl = limr.limr('./pulseN_test_USB.cpp');\n\nl.noi = -1 ... | [
[
"matplotlib.pyplot.plot",
"scipy.fftpack.fft",
"matplotlib.pyplot.ylabel",
"numpy.std",
"numpy.mean",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"numpy.where",
"scipy.fftpack.fftshift",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mgaldieri/flask-pyaffective | [
"dc7b878dde2cb56d975f8d0d091b7b1e4e2d2b00"
] | [
"utils/sensors.py"
] | [
"# -*- coding:utf-8 -*-\n__author__ = 'mgaldieri'\n\nimport uuid\n\nimport numpy as np\n\nfrom pyaffective.emotions import OCC, OCEAN\n\n\nclass InfluenceValue:\n DIRECT = 1\n INVERSE = -1\n\n def __init__(self, weight=1.0, relation=None):\n self.weight = weight\n self.relation = relation if ... | [
[
"numpy.average"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
buvta/HarvestText | [
"870a00cba856a83a00105fcb4cc6de06387a9729"
] | [
"harvesttext/word_discover.py"
] | [
"import jieba\nimport jieba.analyse\nimport logging\nimport networkx as nx\nimport numpy as np\nimport pandas as pd\nfrom collections import defaultdict\nfrom tqdm import tqdm\nfrom .resources import get_baidu_stopwords\nfrom .algorithms.word_discoverer import WordDiscoverer\nfrom .algorithms.entity_discoverer impo... | [
[
"numpy.log",
"pandas.Series",
"numpy.sqrt",
"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": []
}
] |
Originofamonia/pylon | [
"e26202b2c1cfbb8b5c444f840763f0ce839f048a"
] | [
"trainer/callbacks/base_cb.py"
] | [
"import os\nfrom collections import defaultdict\nfrom functools import partial\n\nimport pandas as pd\nimport torch\n\nfrom ..numpy_writer import *\nfrom ..params_grads import *\nfrom ..save import *\nfrom ..stateful import *\nfrom ..types import *\n\n\ndef set_order(order):\n \"\"\"decorator to set callback's m... | [
[
"pandas.merge",
"torch.utils.tensorboard.SummaryWriter",
"pandas.DataFrame",
"torch.load"
]
] | [
{
"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": []
}
] |
WhiteboardLiveCoding/ImageSegmentation | [
"bf0f47320a2a455d21a4f5dc1163f1bb8157989c"
] | [
"image_segmentation/picture.py"
] | [
"import logging\n\nimport cv2\nimport numpy as np\n\nfrom image_segmentation.extended_image import ExtendedImage\nfrom image_segmentation.line import Line\n\nLOGGER = logging.getLogger()\n\n\nclass Picture(ExtendedImage):\n INDENTATION_THRESHOLD = 50\n ARTIFACT_PERCENTAGE_THRESHOLD = 0.08\n MINIMUM_LINE_OV... | [
[
"numpy.concatenate",
"numpy.copy",
"numpy.zeros_like",
"numpy.equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kashifcap/image_captioning | [
"233ff629eb7424eea88bd49b4b9d701f247dc111"
] | [
"utils/image_processing.py"
] | [
"import math\nimport numpy as np\nimport tensorflow as tf\nfrom PIL import Image, ImageOps\nfrom keras.preprocessing import image\nfrom keras.applications.inception_v3 import preprocess_input\n\n#%%\n\n# Image-Preprocessing Function : takes path of the image as the only argument\ndef preprocess(image_path):\n \n... | [
[
"numpy.reshape",
"numpy.expand_dims",
"tensorflow.keras.preprocessing.image.load_img"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
fcharras/copain | [
"19633e0240a50e69236328c1a03ba97e00cc74f0"
] | [
"copain/nn.py"
] | [
"import torch\nimport torch.nn as nn\nfrom copain.utils import WeightInitializer\n\n\nclass CopainANN(nn.Module):\n def __init__(\n self,\n n_actions,\n input_dim,\n nb_values_per_dim,\n starting_nb_embeddings,\n nb_embeddings_step,\n depth,\n embedding_siz... | [
[
"torch.nn.Sequential",
"torch.nn.Dropout",
"torch.zeros",
"torch.vstack",
"torch.nn.Linear",
"torch.nonzero",
"torch.arange",
"torch.nn.ReLU",
"torch.nn.EmbeddingBag"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
areding/6420-pymc | [
"181ee40b8bf4a2c9fb237c4d388c4f62ea41bfeb"
] | [
"original_examples/Codes4Unit5/norcau.py"
] | [
"g# -*- coding: utf-8 -*-\r\n\"\"\"\r\nBayes estimator delta(x) for x=2,\r\nfor Normal-Cauchy Model\r\nCreated on Thu Dec 28 11:56:56 2017\r\n@author: bv20\r\n\"\"\"\r\n\r\nfrom scipy import integrate, inf, exp\r\nx = 2\r\nnum = lambda th: th * exp(-0.5*(x-th)**2)/(1+th**2)\r\ndenom = lambda th: exp(-0.5*(x-th)**2)... | [
[
"scipy.integrate.quad",
"scipy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Shumway82/tf_base | [
"09f60f773f14526281d36cf764c33e90791fb18d"
] | [
"tfcore/utilities/utils.py"
] | [
"import glob\nimport io\nimport json\nimport os\nimport re\nimport shutil\nimport zipfile\nfrom collections import deque\nfrom inspect import signature\nfrom keras import backend as K\n\nimport numpy as np\nimport scipy\nimport tensorflow as tf\nfrom tensorflow.contrib.framework.python.framework import checkpoint_u... | [
[
"tensorflow.concat",
"numpy.asarray",
"tensorflow.reduce_sum",
"tensorflow.cast",
"tensorflow.pad",
"tensorflow.nn.conv2d",
"numpy.clip",
"tensorflow.get_collection",
"tensorflow.extract_image_patches",
"tensorflow.square",
"tensorflow.train.Saver",
"numpy.zeros",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"1.0",
"0.19",
"0.18",
"1.2",
"0.12",
"0.10",
"0.17",
"0.16"
],
"tensorflow": [
"1.10"
]
}
] |
ADALabUCSD/DeepPostures | [
"f51acc8fea2aa76fe0150f87284f624840016095"
] | [
"MSSE-2021/commons.py"
] | [
"# Copyright 2021 Supun Nakandala. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by... | [
[
"tensorflow.layers.conv2d",
"tensorflow.__version__.split",
"tensorflow.concat",
"tensorflow.transpose",
"tensorflow.nn.rnn_cell.LSTMCell",
"tensorflow.reshape",
"tensorflow.layers.dense",
"tensorflow.nn.bidirectional_dynamic_rnn",
"tensorflow.variable_scope",
"numpy.array"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
Flolight/100DaysOfMLCode | [
"0b9bc8944f7d18b2ca5f1ea282cea322ad64bc5e"
] | [
"courses/MachineLearningAZ_Python_HandsOn/regressions/decision_tree/decision_tree_regression.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jan 16 11:32:57 2020\n\n@author: flo\n\"\"\"\n\n# Decision Tree regression\n\n# Importing the libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Importing dataset\ndataset = pd.read_csv('Position_Salaries.... | [
[
"pandas.read_csv",
"sklearn.tree.DecisionTreeRegressor",
"matplotlib.pyplot.title",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.xlabel",
"numpy.array",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Abhishek-Aditya-bs/Streaming-Spark-For-Machine-Learning | [
"ba95a7d2d6bb15bacfbbf5b3c95317310b36d54f"
] | [
"models/deepImageSVM.py"
] | [
"from typing import List\n\nimport numpy as np\nimport warnings\n\nfrom joblibspark import register_spark\n\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.utils import parallel_backend\nfrom sklearn.metrics import log_loss, precision_score, recall_score\n\nfrom pyspark.sql.dataframe import DataFrame\... | [
[
"numpy.isnan",
"numpy.arange",
"sklearn.metrics.confusion_matrix",
"sklearn.utils.parallel_backend",
"numpy.load",
"numpy.array",
"sklearn.linear_model.SGDClassifier"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gdsfactory/ubc | [
"f780778a06dad80c3e0df36c534d88000adc1c87"
] | [
"ubcsp/mzi_spectrum.py"
] | [
"\"\"\"\nbased on https://github.com/SiEPIC-Kits/SiEPIC_Photonics_Package\n\"\"\"\n\nimport numpy as np\nfrom ubcsp.waveguide import beta, neff, wavelength_um\n\n\ndef mzi_spectrum(\n L1_um,\n L2_um,\n wavelength_um=wavelength_um,\n beta=beta,\n alpha=1e-3,\n neff=neff,\n n1=2.4,\n n2=-1,\n ... | [
[
"numpy.exp",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
poliyev/poptimizer | [
"71935c4365b0572e65b6d3172f925701dda283db",
"71935c4365b0572e65b6d3172f925701dda283db"
] | [
"poptimizer/data/adapters/html/parser.py",
"poptimizer/data/adapters/gateways/tests/test_cbr.py"
] | [
"\"\"\"Парсер html-таблиц.\"\"\"\nfrom datetime import datetime\nfrom typing import Callable, List, Union\n\nimport aiohttp\nimport bs4\nimport pandas as pd\n\nfrom poptimizer.data.adapters.html import description\nfrom poptimizer.shared import connections\n\nDescriptions = List[description.ColDesc]\nParseFuncType ... | [
[
"pandas.read_html"
],
[
"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": []
},
{
"matplotlib": [],
"nump... |
souptc/pytorch | [
"c8fd1bbc1101d76903144c03424fc325ee92cfe2"
] | [
"torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py"
] | [
"# Copyright 2022 Cruise LLC\nimport logging\nimport warnings\nfrom collections import OrderedDict\nfrom typing import Union, Iterable, Dict\n\nimport torch\nimport torch.distributed as dist\nimport torch.distributed.algorithms.model_averaging.averagers as averagers\nimport torch.distributed.algorithms.model_averag... | [
[
"torch.distributed.new_subgroups",
"torch.distributed.get_world_size",
"torch.distributed.algorithms.model_averaging.utils.average_parameters_or_parameter_groups"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tangwei94/statmech_tm_solver.jl | [
"001288de3643c9cd962a14e739efb5257656a682"
] | [
"helpers/triangular_AF_ising_fulldiag/plot_triangular_ising_fulldiag_Ek.py"
] | [
"import numpy as np \nimport matplotlib \nimport matplotlib.pyplot as plt \nimport io \n\nmatplotlib.rcParams['mathtext.fontset'] = 'stix'\nmatplotlib.rcParams['font.family'] = 'STIXGeneral'\nplt.rcParams['font.size'] = 15\n\nf = io.open(\"result_triangular_ising_fulldiag.txt\", \"r\")\ndata = np.loadtxt(f)\nf.clos... | [
[
"numpy.log",
"numpy.sqrt",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.close",
"numpy.angle",
"numpy.loadtxt",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lindenmp/NASA_aus_firedata | [
"e65b058983d64daa1ff662e6c19bb7fa77f0637a"
] | [
"preprocessing.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport os, sys\nimport pandas as pd\nimport numpy as np\nimport numpy.matlib\nimport scipy as sp\n\nimport geopandas as gpd\n\n# Plotting\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n\n# Load map of australia using postcode data\n\n# In[2]:\n\n\... | [
[
"pandas.concat",
"numpy.logical_and",
"numpy.zeros",
"matplotlib.pyplot.subplots"
]
] | [
{
"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": []
}
] |
nemodrive/path_generation | [
"64d36342e46a83ed0ade5801bb69370d41d9ecbb"
] | [
"utils/save_training.py"
] | [
"# AndreiN, 2019\n\nimport os\nimport torch\nimport numpy as np\nimport shutil\nimport itertools\nimport glob\nimport re\n\n\ndef get_training_data_path(model_dir, best=False, index=None):\n if best:\n return os.path.join(model_dir, \"training_data_best.pt\")\n\n if index is not None:\n fld = os... | [
[
"torch.load",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
huangyh09/limix | [
"bed5b8e0aaa9b11f19bdd13b76d21510e56064be",
"bed5b8e0aaa9b11f19bdd13b76d21510e56064be"
] | [
"limix/qc/_covariance.py",
"limix/stats/_confusion.py"
] | [
"def normalise_covariance(K, out=None):\n \"\"\"\n Variance rescaling of covariance matrix 𝙺.\n\n Let n be the number of rows (or columns) of 𝙺 and let\n mᵢ be the average of the values in the i-th column.\n Gower rescaling is defined as\n\n .. math::\n\n 𝙺(n - 1)/(𝚝𝚛𝚊𝚌𝚎(𝙺) - ∑mᵢ).... | [
[
"numpy.copyto",
"numpy.asarray"
],
[
"numpy.asarray",
"numpy.arange",
"numpy.empty_like",
"numpy.concatenate",
"numpy.std",
"numpy.mean",
"numpy.searchsorted",
"numpy.argsort",
"numpy.where",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hyoseupjang/Fractal_Practice | [
"4a7221f25b705364aafe1b0ce796672a5daa7b21"
] | [
"Visualize.py"
] | [
"import numpy as np\nfrom PIL import Image\nxres,yres=np.fromfile('data.dat',dtype=np.int32,count=2,offset=0)\ndata=np.fromfile('data.dat',dtype=np.int32,offset=8)\nprint('File loaded...')\ndata=np.array(data.reshape(yres,xres),dtype=np.uint8)\nprint('Converted to uint8... Rendering. ')\nImage.fromarray(data,'L').s... | [
[
"numpy.fromfile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
awhite862/kelvin | [
"a2e5c3acd8bbf9a9b3e88f321849374e7070f000"
] | [
"kelvin/hubbard_system.py"
] | [
"import logging\nimport numpy\nfrom cqcpy import ft_utils\nfrom cqcpy.ov_blocks import one_e_blocks\nfrom cqcpy.ov_blocks import two_e_blocks\nfrom cqcpy.ov_blocks import two_e_blocks_full\nfrom cqcpy import utils\nfrom .system import System\n\neinsum = numpy.einsum\n\n\nclass HubbardSystem(System):\n \"\"\"Hubb... | [
[
"numpy.diag",
"numpy.hstack",
"numpy.ix_",
"numpy.einsum",
"numpy.linalg.eigh",
"numpy.identity",
"numpy.argsort",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MarcRoigVilamala/TwoOutput3DResNet | [
"71a98cefc050c134d3bc027b3a3144e121374776"
] | [
"TwoOutput3DResNet/main.py"
] | [
"import os\nimport json\nimport torch\nfrom torch import nn\nfrom torch import optim\nfrom torch.optim import lr_scheduler\n\nfrom TwoOutput3DResNet.opts import parse_opts\nfrom TwoOutput3DResNet.model import generate_model\nfrom TwoOutput3DResNet.Transformations.spatial_transforms import get_spatial_transform, get... | [
[
"torch.nn.CrossEntropyLoss",
"torch.optim.lr_scheduler.ReduceLROnPlateau",
"torch.load",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.optim.SGD"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
unionai/flyteevents-datahub | [
"972dd7c68c9f07b934c7c948068da429e9ce1813"
] | [
"flytelineage/tests/test_glue.py"
] | [
"import pytest\nimport moto\nimport boto3\n\n\n@pytest.fixture(scope=\"function\")\ndef moto_s3():\n with moto.mock_s3():\n s3 = boto3.resource(\"s3\", region_name=\"us-east-1\")\n s3.create_bucket(\n Bucket=\"bucket\",\n )\n yield s3\n\n\n@pytest.fixture(scope=\"module\")\... | [
[
"numpy.array",
"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": []
}
] |
yurymalkov/ann-benchmarks | [
"e5fa90cc5eee529a4c2650c2daf7589eca78bc20"
] | [
"ann_benchmarks/algorithms/kgraph.py"
] | [
"from __future__ import absolute_import\nimport os\nimport numpy\nimport pykgraph\nfrom ann_benchmarks.constants import INDEX_DIR\nfrom ann_benchmarks.algorithms.base import BaseANN\n\nclass KGraph(BaseANN):\n def __init__(self, metric, index_params, save_index):\n if type(metric) == unicode:\n ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MASILab/pyPheWAS | [
"10cf320a24dc9d81a3b09aa38d3d4de8c2e1fcd5"
] | [
"deprecated/pyProWAS.py"
] | [
"from collections import Counter\nimport getopt\nimport math\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_pdf import PdfPages\nimport numpy as np\nimport os\nimport pandas as pd\nimport scipy.stats\nimport statsmodels.api as sm\nimport statsmodels.formula.api as smf\nfrom tqdm import tqdm\nimp... | [
[
"matplotlib.pyplot.gca",
"pandas.merge",
"pandas.read_csv",
"numpy.isfinite",
"numpy.isnan",
"pandas.DataFrame",
"numpy.sort",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.xlabel",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ColinWine/Multi-modal-Multi-label-Facial-Action-Unit-Detection-with-Transformer | [
"93871bed9078d5bf6b4bb37407c9dce87c569b55"
] | [
"models/dual_sformer.py"
] | [
"\"\"\"\nCode from\nhttps://github.com/zengqunzhao/Former-DFER\n\"\"\"\nfrom einops import rearrange, repeat\nfrom torch import nn, einsum\nimport math\nimport torch\nfrom torchvision import models\nfrom .loss import CCCLoss,AULoss,FocalLoss_Ori\nfrom torch.functional import F\nimport numpy as np\nfrom collections ... | [
[
"torch.load",
"torch.tanh",
"torch.flatten",
"torch.finfo",
"torch.pow",
"torch.nn.CrossEntropyLoss",
"torch.nn.Dropout",
"torch.add",
"torch.einsum",
"torch.randn",
"torch.tensor",
"torch.nn.Sequential",
"torch.nn.BatchNorm1d",
"torch.nn.init.constant_",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
andrewsalij/pymatgen | [
"7b6809c783ef356d437d65e8d6b733333a16d381"
] | [
"pymatgen/analysis/tests/test_pourbaix_diagram.py"
] | [
"# coding: utf-8\n# Copyright (c) Pymatgen Development Team.\n# Distributed under the terms of the MIT License.\n\n\nimport logging\nimport multiprocessing\nimport os\nimport unittest\nimport warnings\n\nimport numpy as np\nfrom monty.serialization import loadfn\n\nfrom pymatgen.core import SETTINGS\nfrom pymatgen.... | [
[
"numpy.array",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JohnSpencerTerry/superset | [
"597b020168411892853949f09608884b9afad963"
] | [
"superset/views/core.py"
] | [
"# 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 2.0 (the\n# \"License\"); y... | [
[
"pandas.DataFrame.from_records"
]
] | [
{
"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": []
}
] |
HelloImRobert/mmdetection | [
"223235c90fc644bb2f04fa92c770b83f320db7d2"
] | [
"ext/utils/model_zoo.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\nimport os\nimport sys\n\nimport torch\n\nfrom ssd.utils.dist_util import is_main_process, synchronize\n\ntry:\n from torch.hub import _download_url_to_file\n from torch.hub import urlparse\n from torch.hub import HASH_REGEX\nexcept I... | [
[
"torch.utils.model_zoo.urlparse",
"torch.utils.model_zoo.HASH_REGEX.search",
"torch.utils.model_zoo._download_url_to_file",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zren96/rlpyt | [
"7e29587d29219f7af80868f7c85e38bea80ed2cf"
] | [
"rlpyt/agents/qpg/sac_vision_agent.py"
] | [
"\nimport numpy as np\nimport torch\nfrom collections import namedtuple\nfrom torch.nn.parallel import DistributedDataParallel as DDP\n\nfrom rlpyt.agents.base import BaseAgent, AgentStep, RecurrentAgentMixin\nfrom rlpyt.agents.qpg.base import AgentInfo, AgentInfoRnn\nfrom rlpyt.models.qpg.conv import QConvLSTMMode... | [
[
"numpy.exp",
"torch.no_grad",
"torch.nn.parallel.DistributedDataParallel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fighting41love/zincbase | [
"40d68bcb50e8a509cafe7496545f172cc3559406"
] | [
"zincbase/utils/calc_auc_roc.py"
] | [
"\"\"\"Calculate the Area-Under-the-Curve Receiver Operating Characteristic\n\nA funny measure that combines precision and recall. Sklearn can't agree how\nto implement it for multiclass; this version is from fbrundu on\nhttps://github.com/scikit-learn/scikit-learn/issues/3298\n\"\"\"\n\nfrom sklearn.metrics import... | [
[
"sklearn.metrics.roc_auc_score",
"sklearn.preprocessing.LabelBinarizer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JiaweiSheng/FAAN | [
"b439b829506c4e2e9044a6b2ab7f3d844f445a95"
] | [
"trainer.py"
] | [
"from collections import defaultdict\r\nfrom torch import optim\r\nfrom collections import deque\r\nfrom args import read_options\r\nfrom data_loader import *\r\nfrom matcher import *\r\nfrom tensorboardX import SummaryWriter\r\nimport os\r\nfrom tqdm import tqdm\r\n\r\n\r\nclass Trainer(object):\r\n def __init_... | [
[
"torch.optim.Adam"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
iosifidisvasileios/AdaFair | [
"5e4ad12a670a767dd8aaf4f7d0a68b871d1f4d1a"
] | [
"Competitors/SMOTEBoost.py"
] | [
"\"\"\"Weight Boosting\n\nThis module contains weight boosting estimators for both classification and\nregression.\n\nThe module structure is the following:\n\n- The ``BaseWeightBoosting`` base class implements a common ``fit`` method\n for all the estimators in the module. Regression and classification\n only di... | [
[
"sklearn.utils.validation.check_is_fitted",
"sklearn.metrics.r2_score",
"numpy.exp",
"numpy.where",
"numpy.random.randint",
"sklearn.utils.validation.has_fit_parameter",
"numpy.finfo",
"numpy.full",
"numpy.argmax",
"sklearn.base.is_classifier",
"sklearn.base.is_regresso... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
thimo72/haystack | [
"85571cdd15f1c9592cf28121187ffef7d4827f83"
] | [
"haystack/document_stores/milvus2.py"
] | [
"import logging\nimport warnings\nfrom typing import TYPE_CHECKING, Any, Dict, Generator, List, Optional, Union\n\nif TYPE_CHECKING:\n from haystack.nodes.retriever.base import BaseRetriever\n\nimport numpy as np\n\nfrom scipy.special import expit\nfrom tqdm import tqdm\n\ntry:\n from pymilvus import FieldSch... | [
[
"numpy.asarray",
"numpy.array",
"numpy.linalg.norm"
]
] | [
{
"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": [],
... |
TaskeHAMANO/deblur | [
"4b4badccdbac8fe9d8f8b3f1349f3700e31b5d7b"
] | [
"deblur/workflow.py"
] | [
"# ----------------------------------------------------------------------------\n# Copyright (c) 2015, The Deblur Development Team.\n#\n# Distributed under the terms of the BSD 3-clause License.\n#\n# The full license is in the file LICENSE, distributed with this software.\n# ---------------------------------------... | [
[
"numpy.array",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HermasTV/mmfu | [
"dc14f0c06dbff3f1c92606ff11fc30d782ea23ef"
] | [
"tests/face_cropper.py"
] | [
"import argparse\nimport os\nimport numpy as np\nfrom cv2 import cv2\nfrom face_utils.detection import Detector\nfrom face_utils.cropping import cropping\n\n\nap = argparse.ArgumentParser()\nap.add_argument(\"-i\", \"--image\", required = False, help = \"Path to the input image\")\nap.add_argument(\"-d\", \"--model... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yuchenhou/eagle | [
"8050b4c023d3bef4bd80c9ad6b10615ba54eb953"
] | [
"elephant/clean.py"
] | [
"import pandas\n\n\ndef main():\n links = pandas.read_csv('../resources/' + 'Newman-Cond_mat_95-99-co_occurrence.txt', sep=' ', header=None)\n links.to_csv('../graph/' + 'authors.tsv', sep='\\t', index=False, header=False)\n\n\nif __name__ == '__main__':\n main()\n"
] | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
seanbruno/vinyl_inventory | [
"93783089319a1e6228e4fe0e9c3522a7483fafdd"
] | [
"csv_to_html.py"
] | [
"#!/usr/local/bin/python\n\nimport sys, getopt\nimport pandas as pd\n\ndef usage():\n\tprint ('csv_to_html.py -h -i <input_csv> -o <output_html>')\n\tsys.exit(2)\n\ndef main(argv):\n\ttry:\n\t\topts, args = getopt.getopt(argv,\"hi:o:\",[\"help\",\"input_csv=\",\"output_html=\"])\n\texcept getopt.GetoptError as err:... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
tbredbenner/unsupervised_learning_of_dense_shape_correspondence | [
"440643d633a6db3f947ac71a247c8083cb3aeadc"
] | [
"Single Pair Experiment/models_self_supervised.py"
] | [
"import tensorflow as tf\nimport numpy as np\n\nfrom ops_self_supervised import *\n\nflags = tf.app.flags\nFLAGS = flags.FLAGS\n\n\ndef fmnet_model(phase, part_shot, model_shot, part_dist_map , model_dist_map, part_evecs,\tpart_evecs_trans, model_evecs, model_evecs_trans):\n\t\"\"\"Build FM-net model.\n\n\tArgs:\n\... | [
[
"tensorflow.variable_scope",
"tensorflow.matmul"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
ansin218/gutereise | [
"af64d93bc02f0870671dcfe9bfacb87dec835584"
] | [
"misc/price_scraper_v1.py"
] | [
"from amadeus import Client\nimport pandas as pd\nimport datetime as dt\nimport glob\n\napi_file = open('API_KEY.txt', 'r')\napi_credentials = api_file.read()\nc_id = api_credentials.split(' ', 1)[0]\nc_secret = api_credentials.split(' ', 1)[1]\napi_file.close()\n\namadeus = Client(\n client_id = c_id,\n clie... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
yongzx/lm-evaluation-harness | [
"26f0233fa4b6ca5b2d663a017dc4352ac528648a"
] | [
"lm_eval/evaluator.py"
] | [
"import collections\nimport itertools\nimport random\nimport lm_eval.metrics\nimport lm_eval.models\nimport lm_eval.tasks\nimport lm_eval.base\nimport numpy as np\nfrom lm_eval.utils import positional_deprecated\n\n\n@positional_deprecated\ndef simple_evaluate(model, model_args=None, tasks=[],\n ... | [
[
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HANDS-Research-Group/HNN_Soil_Reaction_Front | [
"17f65b18ddf3a93bde69111786912850702406ab"
] | [
"final.py"
] | [
"import matplotlib.style\nimport matplotlib as mpl\nmpl.style.use('classic')\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nplt.rcParams['axes.facecolor'] = 'white'\nplt.rcParams['figure.facecolor'] = 'white'\nplt.rcParams['font.family'] = 'sans-serif'\nplt.rcParams['font.sans-serif'] = ... | [
[
"matplotlib.pyplot.legend",
"numpy.dot",
"pandas.DataFrame",
"tensorflow.compat.v1.math.exp",
"tensorflow.compat.v1.train.Saver",
"pandas.read_csv",
"matplotlib.pyplot.tight_layout",
"tensorflow.compat.v1.train.AdamOptimizer",
"matplotlib.style.use",
"tensorflow.compat.v1.t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
rsriram315/eds_covid-19 | [
"528695a430ff13c9dcc6e969ebf1f7988e26c434"
] | [
"src/features/build_features.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Aug 22 10:32:53 2020\n\n@author: Sriram\n\"\"\"\n\nimport numpy as np\nfrom sklearn import linear_model\nimport pandas as pd\nfrom scipy import signal\n\n# we define the linear regression object\nreg=linear_model.LinearRegression(fit_intercept=True)\n\ndef get_doubli... | [
[
"numpy.arange",
"pandas.read_csv",
"numpy.array",
"sklearn.linear_model.LinearRegression"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
silky/mpld3 | [
"12151b57d8f245c3538f3c19e34d71caf8e65a59"
] | [
"examples/drag_points.py"
] | [
"\"\"\"\nDraggable Points Example\n========================\nThis example shows how a D3 plugin can be created to make plot elements\ndraggable. A stopPropagation command is used to allow the drag behavior\nand pan/zoom behavior to work in tandem.\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\nimpor... | [
[
"numpy.random.normal",
"matplotlib.pyplot.subplots",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dkoes/md-scripts | [
"2002a9e8eafaf2d203334285e47fa1637d22286d"
] | [
"mdrmsdplot.py"
] | [
"#!/usr/bin/env python3\n\nimport sys, MDAnalysis\nimport numpy as np\nfrom os.path import splitext\nfrom MDAnalysis.analysis.rms import *\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\nimport seaborn as sns\nimport argparse\n\nparser = argparse.ArgumentParser(description='Generate pairwise RMSD heatmap ... | [
[
"matplotlib.pylab.tight_layout",
"numpy.set_printoptions",
"matplotlib.pylab.title",
"matplotlib.pylab.figure",
"matplotlib.pylab.ylabel",
"matplotlib.pylab.gca",
"matplotlib.pylab.savefig",
"matplotlib.pylab.xlabel",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lena-u/flair | [
"821de2e1d5446a9308fbad6f4d51bd4e9614ec02"
] | [
"flair/embeddings/token.py"
] | [
"import hashlib\nfrom abc import abstractmethod\nfrom pathlib import Path\nfrom typing import List, Union\nfrom collections import Counter\nfrom functools import lru_cache\n\nimport torch\nfrom bpemb import BPEmb\nfrom transformers import XLNetTokenizer, T5Tokenizer, GPT2Tokenizer, AutoTokenizer, AutoConfig, AutoMo... | [
[
"torch.ones",
"torch.enable_grad",
"torch.nn.LSTM",
"torch.cat",
"torch.zeros",
"torch.nn.utils.rnn.pack_padded_sequence",
"torch.nn.Embedding",
"torch.tensor",
"torch.nn.utils.rnn.pad_packed_sequence",
"torch.no_grad",
"torch.FloatTensor",
"torch.nn.init.xavier_uni... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bkoch4142/pytorch-sequence-models | [
"ba61e79066b220bd2f34d787d89bad7c87d4004e"
] | [
"src/models/sentiment_clf.py"
] | [
"import torch\nimport torch.nn as nn\nimport sys\nfrom models.rnn import RNN\nfrom models.lstm import LSTM\nfrom models.gru import GRU\n\n\nclass SentimentClassifier(nn.Module):\n def __init__(self, vocab_sz, n_hidden, rnn_type='RNN'):\n super(SentimentClassifier, self).__init__()\n\n self.embeddin... | [
[
"torch.mean",
"torch.nn.Dropout",
"torch.max",
"torch.cat",
"torch.nn.Embedding",
"torch.nn.Linear",
"torch.squeeze"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MGibsonint/nncf_pytorch | [
"1c1ee370460d2e4531c2bf353c7b89ccc659fa38",
"1c1ee370460d2e4531c2bf353c7b89ccc659fa38"
] | [
"nncf/quantization/init_precision.py",
"nncf/sparsity/magnitude/algo.py"
] | [
"import itertools\nfrom collections import OrderedDict\nfrom pathlib import Path\nfrom typing import List, Dict, Union\n\nimport os\nimport torch\nfrom bisect import bisect_left\nfrom operator import itemgetter\nfrom torch import Tensor, nn\nfrom torch.nn.modules.loss import _Loss\n\nfrom nncf.debug import is_debug... | [
[
"torch.norm",
"torch.Tensor",
"torch.load",
"matplotlib.pyplot.savefig",
"torch.save",
"matplotlib.pyplot.figure"
],
[
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AWehrhahn/CATS | [
"40b9f21ffccda8f70f9d1a9d7335102083847ce3",
"40b9f21ffccda8f70f9d1a9d7335102083847ce3"
] | [
"cats/least_squares/least_squares.py",
"cats/data_modules/marcs.py"
] | [
"\"\"\"Generic interface for least-square minimization.\"\"\"\nfrom __future__ import division, print_function, absolute_import\n\nfrom warnings import warn\n\nimport numpy as np\nfrom numpy.linalg import norm\n\nfrom scipy.sparse import issparse, csr_matrix\nfrom scipy.sparse.linalg import LinearOperator\nfrom sci... | [
[
"numpy.dot",
"numpy.concatenate",
"scipy.optimize._lsq.least_squares.check_tolerance",
"scipy.optimize._lsq.common.in_bounds",
"numpy.all",
"numpy.any",
"numpy.iscomplexobj",
"scipy.optimize._lsq.common.make_strictly_feasible",
"scipy.optimize._lsq.dogbox.dogbox",
"scipy.sp... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
aashrithbandaru/fmltc | [
"3b95626583d4004d06c542992cf8e35967dcada5"
] | [
"model_trainer.py"
] | [
"# Copyright 2020 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed ... | [
[
"tensorflow.python.summary.summary_iterator.summary_iterator"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"1.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1... |
mspayam/MeTooIran | [
"63c2ae2dbb06ef2d6836840e66af21a025668491"
] | [
"MeTooIran.py"
] | [
"import tweepy\nfrom tweepy import OAuthHandler\nimport pandas as pd\n\n\naccess_token = ''\naccess_token_secret = ''\nAPI_key = ''\nAPI_key_secret = ''\n\n\nauth = tweepy.OAuthHandler(API_key, API_key_secret)\nauth.set_access_token(access_token, access_token_secret)\n\n\napi = tweepy.API(auth, wait_on_rate_limit=T... | [
[
"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": []
}
] |
RichardoMrMu/facial-emotion-recognition | [
"d8abd1bcf685eaeb55f844b21e2fda5ebfa25a00"
] | [
"code/models/swin_transformer.py"
] | [
"# -*- coding:utf-8 -*-\n# @Time : 2021/8/1 16:53\n# @Author : Richardo Mu\n# @FILE : swin_transformer.PY\n# @Software : PyCharm\n\n\n\"\"\" Swin Transformer\nA PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`\n - https://arxiv.org/pdf/2103.14030\nCode/weights... | [
[
"torch.nn.Softmax",
"torch.nn.Dropout",
"torch.nn.Sequential",
"torch.cat",
"torch.zeros",
"torch.arange",
"torch.nn.Linear",
"torch.nn.Identity",
"torch.utils.checkpoint.checkpoint",
"torch.jit.is_scripting",
"torch.flatten",
"torch.roll",
"torch.nn.AdaptiveAvg... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aks2203/deep-thinking | [
"089fc5d04a0997ccdbad601b3e025f547a8b6327"
] | [
"deepthinking/models/dt_net_1d.py"
] | [
"\"\"\" dt_net_1d.py\n DeepThinking 1D convolutional neural network.\n\n Collaboratively developed\n by Avi Schwarzschild, Eitan Borgnia,\n Arpit Bansal, and Zeyad Emam.\n\n Developed for DeepThinking project\n October 2021\n\"\"\"\n\nimport torch\nfrom torch import nn\n\nfrom .blocks import Basic... | [
[
"torch.nn.Sequential",
"torch.nn.ReLU",
"torch.cat",
"torch.nn.Conv1d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Anonymous-px/ID2445_DFFT | [
"89a1a482c1b9d5a664dc9e77536ac8c65dc6b614"
] | [
"mmdet/models/backbones/DFFTNet.py"
] | [
"# --------------------------------------------------------\n# Swin Transformer\n# Copyright (c) 2021 Microsoft\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Ze Liu, Yutong Lin, Yixuan Wei\n# --------------------------------------------------------\n\nfrom numpy.core.numeric import cross... | [
[
"torch.nn.Dropout",
"torch.nn.ReLU6",
"torch.zeros",
"torch.nn.init.constant_",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.nn.Upsample",
"torch.nn.functional.interpolate",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yotamfr/prot2vec | [
"eaee36f9e3929054b1c324acd053a52d0e7be2bd"
] | [
"src/python/word2vec.py"
] | [
"import os\nimport sys\nimport operator\nimport numpy as np\nimport pandas as pd\n\nfrom shutil import copyfile\n\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nfrom torch.nn.modules.loss import _Loss\n\nfrom itertools import combinations\n\nfrom pymongo.errors import CursorNotFound\nfro... | [
[
"sklearn.cluster.KMeans",
"torch.load",
"numpy.ndarray",
"torch.nn.Embedding",
"sklearn.manifold.TSNE",
"numpy.where",
"torch.save",
"pandas.read_csv",
"numpy.unique",
"torch.from_numpy",
"torch.nn.Sigmoid",
"numpy.copy",
"torch.bmm",
"numpy.zeros",
"mat... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
xjtuchenchao888/xjtuchenchao888.github.io | [
"71dd380be3be17b22874d9add511b436852e9d67"
] | [
"backend/tf_inference.py"
] | [
"import tensorflow as tf\nimport numpy as np\n\nfrom backend.config import id2name\n\nPATH_TO_CKPT = 'models/ssdlite_mobilenet_v2.pb'\n\ndef load_model():\n detection_graph = tf.Graph()\n with detection_graph.as_default():\n od_graph_def = tf.GraphDef()\n with tf.gfile.GFile(PATH_TO_CKPT, 'rb') ... | [
[
"tensorflow.Graph",
"numpy.expand_dims",
"tensorflow.import_graph_def",
"tensorflow.gfile.GFile",
"tensorflow.Session",
"tensorflow.GraphDef"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Annusha/unsup_temp_embed | [
"2fd98b4d70d6180cb9f4a5adc107c8a24dd256bb"
] | [
"ute/models/training_embed.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"Implementation of training and testing functions for embedding.\"\"\"\n\n__all__ = ['training', 'load_model']\n__author__ = 'Anna Kukleva'\n__date__ = 'August 2018'\n\nimport torch\nimport torch.backends.cudnn as cudnn\nfrom os.path import join\nimport time\nimport numpy as np\nimpor... | [
[
"torch.manual_seed",
"numpy.random.seed",
"torch.cuda.manual_seed",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JustaTinyDot/BiTr-Unet | [
"52c1a68a9fd1cc7968e43d3f89ef700bcd71d60d"
] | [
"evaluation/evaluation.py"
] | [
"#Modified from the following:\n# -*- coding: utf-8 -*-\n# Implementation of Wang et al 2017: Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks. https://arxiv.org/abs/1709.00382\n\n# Author: Guotai Wang\n# Copyright (c) 2017-2018 University College London, United Kingdom. A... | [
[
"numpy.multiply",
"numpy.asarray",
"scipy.ndimage.distance_transform_edt",
"numpy.max",
"numpy.copy",
"scipy.ndimage.shift",
"numpy.sum"
]
] | [
{
"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"... |
devYaoYH/hackillinois_2020 | [
"36fdcd2e4848d2b7f513ee729dc124dbdbeb3125"
] | [
"example_histogram.py"
] | [
"import h5py\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\ncwd = os.getcwd()\n\n#Open the data file\nfilepath = cwd + '\\\\demo.hdf'\nf = h5py.File(filepath, 'r')\n\n#Show all channels available in file\nchanIDs = f['DYNAMIC DATA']\n\nprint(\"Channels available in this data file\")\nprint(list(ch... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.hist",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
claudiodtbarros/GEM | [
"82ba349e18bb700a9380db2827ad3beb45ff1731"
] | [
"gem/embedding/lle.py"
] | [
"disp_avlbl = True\nimport os\nif 'DISPLAY' not in os.environ:\n disp_avlbl = False\n import matplotlib\n matplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\nimport matplotlib.pyplot as plt\nimport networkx as nx\nimport numpy as np\nimport scipy.io as sio\nimport scipy.sparse as sp\nimport scipy.spa... | [
[
"matplotlib.use",
"numpy.linalg.norm",
"scipy.sparse.linalg.svds",
"sklearn.preprocessing.normalize",
"matplotlib.pyplot.show",
"numpy.zeros"
]
] | [
{
"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"
... |
llealgt/PySyft | [
"76c91adde068ed930cff7ca9249ab06e08210e97"
] | [
"syft/core/frameworks/torch/__init__.py"
] | [
"from .hook import TorchHook\nfrom .tensor import _SyftTensor, _LocalTensor, _PointerTensor\nfrom .tensor import _FixedPrecisionTensor, _TorchTensor, _PlusIsMinusTensor, _GeneralizedPointerTensor\nfrom .tensor import _SPDZTensor, _SNNTensor\n\n__all__ = ['TorchHook', '_SyftTensor', '_LocalTensor',\n '_Poi... | [
[
"torch._command_guard",
"torch.tensorvar_methods.append"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
k59047318/ML-final-project | [
"6adcc6fd830279d368dcc506f476aff873a36678"
] | [
"code/build_vocabulary.py"
] | [
"from PIL import Image\nimport numpy as np\nfrom cyvlfeat.sift.dsift import dsift\nfrom cyvlfeat.kmeans import kmeans\nfrom time import time\n\nimport pdb\n\n#This function will sample SIFT descriptors from the training images,\n#cluster them with kmeans, and then return the cluster centers.\n\ndef build_vocabulary... | [
[
"numpy.concatenate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CookiePPP/hifi-gan | [
"688af111556b39d5f105870a1f292190396fb6b2"
] | [
"train.py"
] | [
"import warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\nimport itertools\nimport os\nimport time\nimport argparse\nimport json\nimport torch\nimport torch.nn.functional as F\nfrom torch.utils.tensorboard import SummaryWriter\nfrom torch.utils.data import DistributedSampler, DataLoader\nimp... | [
[
"torch.utils.data.DistributedSampler",
"torch.distributed.init_process_group",
"torch.cuda.manual_seed",
"torch.multiprocessing.spawn",
"torch.nn.functional.l1_loss",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.cuda.empty_cache",
"torch.optim.lr_scheduler.Exponen... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
robjordan/gps-accuracy | [
"fbf5a094c96dcbb692065a928c95d3702d0152b0"
] | [
"gps-accuracy.py"
] | [
"import argparse\nimport gpxpy\nimport gpxpy.gpx\nfrom pyproj import Proj\nimport numpy as np\nfrom scipy.spatial import cKDTree\nimport itertools\nimport math\nimport statistics as st\nfrom datetime import datetime, timezone, MINYEAR\n\n# assume England - change this if you live elsewhere\nUTMZ = '30U'\nmyProj = P... | [
[
"numpy.median",
"numpy.percentile",
"numpy.max",
"numpy.mean",
"scipy.spatial.cKDTree"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nairoukh-code/Python_Projects | [
"9a0e2adb6e352b301ed9e542be9c9f1cd16b95b0"
] | [
"NLP/project.py"
] | [
"from nltk.corpus import stopwords\nfrom sklearn.model_selection import train_test_split\nimport pandas as pd\nfrom nltk.corpus import words\nfrom sklearn.model_selection import KFold\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.metrics import accuracy_score\n\n# open and clean true and fake tweet... | [
[
"pandas.concat",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"sklearn.model_selection.KFold",
"sklearn.tree.DecisionTreeClassifier",
"sklearn.metrics.accuracy_score"
]
] | [
{
"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": []
}
] |
Ahmedjjj/MiDaS | [
"915e88ecad177f04fb84b2f3cdf6892b8c603b07"
] | [
"midas/midas_net.py"
] | [
"\"\"\"MidashNet: Network for monocular depth estimation trained by mixing several datasets.\nThis file contains code that is adapted from\nhttps://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py\n\"\"\"\nimport torch\nimport torch.nn as nn\n\nfrom .base_model ... | [
[
"torch.nn.ReLU",
"torch.nn.Conv2d",
"torch.nn.Identity",
"torch.squeeze"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pnarvor/nephelae_base | [
"d5f1abeae0b0473b895b4735f182ddae0516a1bd"
] | [
"tests/mapping/map_resolution01.py"
] | [
"#! /usr/bin/python3\n\n# changing process priority (linux only)\nimport os\n# os.nice(-19) # probably a bit harsh (requires sudo)\n\nimport sys\nsys.path.append('../../')\nimport numpy as np\nimport numpy.fft as npfft\nimport matplotlib.pyplot as plt\nfrom matplotlib import animation\nimport time\nfrom PIL impor... | [
[
"numpy.sqrt",
"matplotlib.pyplot.subplots",
"numpy.array",
"numpy.zeros",
"numpy.sum",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
neurokernel/neurodriver | [
"9dcafdeddfbcde928e3c688d9240cdc1da40aa1b"
] | [
"neurokernel/LPU/InputProcessors/StepInputProcessor.py"
] | [
"import numpy as np\n\nfrom .BaseInputProcessor import BaseInputProcessor\n\n\nclass StepInputProcessor(BaseInputProcessor):\n\n def __init__(self, variable, uids, val, start, stop,\n input_file = None, input_interval = 1,\n sensory_file = None, sensory_interval = 1):\n sup... | [
[
"numpy.isscalar",
"numpy.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
giocard/yeast_segmentation | [
"0b1b2d8d2c71ff1d0959b286851245ffee868c6f"
] | [
"mrcnn/my_inference.py"
] | [
"import os\n\nos.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\nos.environ['KERAS_BACKEND'] = 'tensorflow'\n\nseed = 123\nfrom keras import backend as K\n\nimport numpy as np\n\nnp.random.seed(seed)\nimport tensorflow as tf\n\ntf.set_random_seed(seed)\n\nimport random\... | [
[
"pandas.read_csv",
"numpy.expand_dims",
"numpy.random.seed",
"numpy.arange",
"tensorflow.set_random_seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
jselvam11/numpyro | [
"42ed07f5b0c761b5fa0c951e4fe64cdd6b5d0723"
] | [
"test/contrib/test_funsor.py"
] | [
"# Copyright Contributors to the Pyro project.\n# SPDX-License-Identifier: Apache-2.0\n\nfrom collections import OrderedDict\nfrom functools import partial\n\nimport numpy as np\nfrom numpy.testing import assert_allclose\nimport pytest\n\nfrom jax import random\nimport jax.numpy as jnp\n\nfrom funsor import Tensor,... | [
[
"numpy.random.choice",
"numpy.arange",
"numpy.stack",
"numpy.random.normal",
"numpy.random.rand",
"numpy.testing.assert_allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DS4A-84/DS4A_Group84_Project | [
"6f9244689156b818e4081727fef574caa038c419"
] | [
"code/imdb_intersect.py"
] | [
"import json, requests\nimport string\nimport re\nimport gzip\nimport shutil\nimport urllib.request \nimport pandas as pd\n# import imdbpy\n\n\n# Downloading IMDB dataset of names\nimdb_url = 'https://datasets.imdbws.com/name.basics.tsv.gz'\nimdb_file = requests.get(imdb_url, stream=True)\nopen('../data/namebasics.... | [
[
"pandas.merge",
"pandas.read_csv",
"pandas.to_numeric"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
tingelst/game | [
"2e9acc1d3052e4135605211a622aa8613ee56949"
] | [
"python/parameterizations.py"
] | [
"import sys\nsys.path.append('../build')\nimport versor as vsr\nimport numpy as np\nnp.set_printoptions(linewidth=120)\nimport matplotlib\nmatplotlib.use('Qt5Agg')\nimport matplotlib.pyplot as plt\n\n\ndef create_motor(d_lims=(0, 1), th_lims=(0, np.pi/2)):\n translator = (vsr.Vec(*np.random.random(3)).unit()\n ... | [
[
"matplotlib.pyplot.semilogy",
"numpy.random.random",
"numpy.sqrt",
"numpy.inner",
"matplotlib.use",
"numpy.set_printoptions",
"numpy.linalg.norm",
"numpy.linalg.pinv",
"numpy.random.normal",
"numpy.random.uniform",
"numpy.array",
"matplotlib.pyplot.show",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aidkilda/understanding-drl-navigation | [
"0d637c2390a935ec1182d4f2d5165644d98d6404"
] | [
"src/internal_representation_analysis/decoder/scene_visualizer.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\n\n\nclass SceneVisualizer(object):\n def __init__(self, env):\n\n self.env = env\n self.scene_scope = env.scene_name\n self.locations_x = [l[0] for l in env.locations]\n self.locations_y = [l[1] for l in env.locations]\n sel... | [
[
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.close",
"matplotlib.pyplot.show",
"matplotlib.pyplot.arrow"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
augmento-ai/quant-reseach | [
"6b3bc4c01a8d533dfa1826d59aa90fbc4c6f98cd",
"6b3bc4c01a8d533dfa1826d59aa90fbc4c6f98cd"
] | [
"src/analysis_helper.py",
"examples/4_basic_strategy_example.py"
] | [
"import numpy as np\nimport numba as nb\n\n\n@nb.jit(\"(f8[:])(f8[:], f8[:])\", nopython=True, nogil=True, cache=True)\ndef nb_safe_divide(a, b):\n\t# divide each element in a by each element in b\n\t# if element b == 0.0, return element = 0.0\n\tc = np.zeros(a.shape[0], dtype=np.float64)\n\tfor i in range(a.shape[... | [
[
"numpy.ones",
"numpy.sign",
"numpy.random.normal",
"numpy.std",
"numpy.mean",
"numpy.float64",
"numpy.zeros",
"numpy.sum"
],
[
"matplotlib.pyplot.gca",
"matplotlib.dates.DateFormatter",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.subplots_adjust",
"matp... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
craterkamath/Leaf_Disease_detection | [
"75b8b27db9bcdca57ed78c2752b339b73edcd4bf"
] | [
"example.py"
] | [
"\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom skimage import data, img_as_float\nfrom skimage.segmentation import (morphological_chan_vese,\n\t\t\t\t\t\t\t\t morphological_geodesic_active_contour,\n\t\t\t\t\t\t\t\t inverse_gaussian_gradient,\n\t\t\t\t\t\t\t\t checkerboard_level_set)\nimport skimag... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.subplots",
"numpy.copy",
"matplotlib.pyplot.show",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DarkSZChao/Big-Little_NN_Strategies | [
"5821765c5ed1a2cbdfe7d9586df7bd36e08fa6fd"
] | [
"Model_Training/MOTION_Detector/Tools/lr_draw.py"
] | [
"# -- coding: utf-8 --\nimport math\nimport os\nimport random\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras import Input, Model\nfrom tensorflow.keras.layers import Conv1D, MaxPooling1D, Dense, Flatten\n\ntry: # import for pycharm project directory\n from... | [
[
"tensorflow.keras.callbacks.ModelCheckpoint",
"matplotlib.pyplot.legend",
"tensorflow.keras.Input",
"tensorflow.keras.losses.SparseCategoricalCrossentropy",
"matplotlib.pyplot.ylim",
"tensorflow.keras.layers.Conv1D",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.callbacks.Lear... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
shreyansh26/DL-Code-Repository | [
"f1974eedc1fef54b2d274703390a22721e46f502"
] | [
"GANs/gan/gan.py"
] | [
"import argparse\nimport os\n\nimport torch\nimport torch.functional as F\nimport torch.nn as nn\nimport torchvision.transforms as transforms\nfrom torch.autograd import Variable\nfrom torchvision import datasets\nfrom torchvision.utils import save_image\n\nfrom model import Discriminator, Generator\n\nCUDA_AVAILAB... | [
[
"torch.device",
"torch.randn",
"torch.cuda.is_available",
"torch.nn.BCELoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kellylab/genomic-surveillance-of-the-bronx | [
"14a58b89e99946c92287387c6ac1fb34c6c0cde4"
] | [
"scripts/demographics/figure1.py"
] | [
"\"\"\" \nMakes a figure providing an overview of our dataset with a focus on lineages\nlaid out as follows:\n\na - Patient metadata\nb - Donut plot of our lineage distributions vs the world\nc - Timeline of patient sampling vs lineages identified\nd - Choropleth of lineages by region\n\"\"\"\n\nimport matplotlib.p... | [
[
"pandas.read_csv",
"pandas.to_datetime",
"matplotlib.pyplot.tight_layout",
"matplotlib.colors.to_rgba",
"numpy.nan_to_num",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.Circle",
"pandas.DataFrame",
"matplotlib.pyplot.clf",
"matplotlib.rc... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Diriba-Getch/CNN-Multi-Label-Text-Classificati2on | [
"0792c0f244b8190e097da42e8719c8bb03573e14"
] | [
"text_cnn.py"
] | [
"# -*- coding:utf-8 -*-\n\nimport tensorflow as tf\n\n\ndef linear(input_, output_size, scope=None):\n \"\"\"\n Linear map: output[k] = sum_i(Matrix[k, i] * args[i] ) + Bias[k]\n :param input_: a tensor or a list of 2D, batch x n, Tensors.\n :param output_size: int, second dimension of W[i].\n :param... | [
[
"tensorflow.get_variable",
"tensorflow.device",
"tensorflow.concat",
"tensorflow.nn.max_pool",
"tensorflow.reduce_sum",
"tensorflow.cast",
"tensorflow.nn.sigmoid_cross_entropy_with_logits",
"tensorflow.nn.l2_loss",
"tensorflow.nn.conv2d",
"tensorflow.Variable",
"tensorf... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
markusgay/seldon-core | [
"b9ebfdfd63e5f7b23311b81ba78e36aa08e87640"
] | [
"python/seldon_core/serving_test_gen.py"
] | [
"\"\"\"Contains methods to generate a JSON file for Seldon API integration testing.\"\"\"\n\nimport os\nfrom typing import List, Optional, Union\n\nimport numpy as np\nimport pandas as pd\n\nRANGE_INTEGER_MIN = 0\nRANGE_INTEGER_MAX = 1\nRANGE_FLOAT_MIN = 0.0\nRANGE_FLOAT_MAX = 1.0\n\n\ndef _column_range(col: pd.Ser... | [
[
"numpy.where",
"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": []
}
] |
hangwudy/Mask_RCNN | [
"8b5d896076b994e2f9136054114c551a8cb3119f"
] | [
"samples/car_door/FusionNet.py"
] | [
"# coding: utf-8\n# # FusionNet for Car Door Detection and Pose Estimation\n# @author: Hang Wu\n# @date: 2018.12.20\n\n\nimport os\nimport sys\nimport json\nimport numpy as np\nimport skimage.io\nimport matplotlib.pyplot as plt\nimport csv\nfrom skimage.color import gray2rgb\nfrom keras.preprocessing.image import i... | [
[
"matplotlib.pyplot.imshow",
"numpy.expand_dims",
"matplotlib.pyplot.subplots",
"numpy.concatenate",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
muhammadidrees/covid19_data_analysis | [
"4764b0ad20771d319f2b1d5062bc7dc11e9c7243"
] | [
"code/data_preparation.py"
] | [
"# Before performing any analysis we need to check the data\n# and prepare it excluding null values and giving proper\n# format to values so our data can be clean and ready\n\nimport pandas as pd\nimport numpy as np\n\n# covert the data file into a dataframe so it's easy to manupilate\ncovid = pd.read_csv(\"data/co... | [
[
"pandas.read_csv",
"pandas.to_datetime"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
jpenrici/Computer_Graphics | [
"5ba268e9e75de0d7ad733a503400e52b66edc78b"
] | [
"NumPy_Training/img_histogram.py"
] | [
"# -*- Mode: Python3; coding: utf-8; indent-tabs-mpythoode: nil; tab-width: 4 -*-\n\nimport os\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nPATH = \"../Images/\"\nRED = 0\nGREEN = 1\nBLUE = 2\nALL = 3\n\n\ndef view(data, channel=ALL, title=\"histogram\"):\n\n R = data[:, :, RED].flatten()\n G ... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"numpy.arange",
"numpy.load",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.hist",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sergeiissaev/rxrx1-utils | [
"e3c1832dbb5b9396c81cd716a9680ccc0191ce09"
] | [
"rxrx/main.py"
] | [
"# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.contrib.cluster_resolver.TPUClusterResolver",
"tensorflow.contrib.tpu.bfloat16_scope",
"tensorflow.metrics.accuracy",
"tensorflow.control_dependencies",
"tensorflow.cast",
"tensorflow.train.cosine_decay_restarts",
"tensorflow.contrib.tpu.CrossShardOptimizer",
"tensorflo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
licTomasPerez/-Code-Thesis-Non-markovian-Dynamics | [
"bafda3eeb8b9e326c0fb33237cdd7fa8d1412195"
] | [
"Quantum States' distance Notebooks/bures-wooters.py"
] | [
"import qutip\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.optimize as opt\nimport pickle\n\n\ndef prod_basis(b1, b2):\n return [qutip.tensor(b,s) for b in b1 for s in b2]\n\ndef scalar_prod(op1,op2,rho0=None):\n if op1.dims[0][0]!=op1.dims[0][0]:\n return None\n if rho0 is Non... | [
[
"matplotlib.pyplot.legend",
"numpy.log",
"numpy.sqrt",
"matplotlib.pyplot.title",
"numpy.linspace",
"numpy.arccos",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DavidDworetzky/Pycasso | [
"4810445889d7309b10fc039b57f0c6026633229b"
] | [
"Pycasso/Core/Neural_Transfer.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom PIL import Image\nimport torchvision.transforms as transforms\nimport torchvision.models as models\nfrom io import BytesIO\nimport base64\nimport copy\nimport uuid\n\n#CONSTANTS\n# desired depth layers to comput... | [
[
"torch.nn.Sequential",
"torch.tensor",
"torch.nn.functional.mse_loss",
"torch.cuda.is_available",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nitred/img2svd | [
"16845e1e5f01964375af197acce7e0de9247652d"
] | [
"img2svd/__init__.py"
] | [
"\"\"\"Contains the functions for implementing img2svd.\"\"\"\nimport matplotlib.image as mpimg\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom skimage.io import imread\n\n\ndef get_svd_from_grayscale_image(imgpath, sigma_coverage_percentage=95, plot=True):\n \"\"\"Returns the compressed U, S and V.H ... | [
[
"numpy.diag",
"numpy.dot",
"numpy.linalg.svd",
"matplotlib.pyplot.subplots",
"numpy.cumsum",
"matplotlib.pyplot.show",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Sketos/PyAutoArray | [
"72dc7e8d1c38786915f82a7e7284239e5ce87624"
] | [
"test_autoarray/plot/mapper_voronoi/all.py"
] | [
"import autoarray as aa\nimport autoarray.plot as aplt\nimport numpy as np\n\ngrid_7x7 = aa.grid.uniform(shape_2d=(7, 7), pixel_scales=0.25)\ngrid_9 = aa.grid.manual_1d(\n grid=[\n [0.6, -0.3],\n [0.5, -0.8],\n [0.2, 0.1],\n [0.0, 0.5],\n [-0.3, -0.8],\n [-0.6, -0.5],\n ... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JavierEscobarOrtiz/skforecast | [
"a3af4a1dd4201c582f159d4e3a1734ed6d29b6c5"
] | [
"skforecast/model_selection/model_selection.py"
] | [
"################################################################################\n# skforecast.model_selection #\n# #\n# This work by Joaquin Amat Rodrigo is licensed under a Creative Comm... | [
[
"pandas.concat",
"sklearn.model_selection.ParameterSampler",
"pandas.DataFrame",
"sklearn.model_selection.ParameterGrid",
"numpy.array",
"numpy.random.RandomState"
]
] | [
{
"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": []
}
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.