repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
MapleLeafKiller/affinity-loss | [
"9ce933fd2fd94928a2231f39b7f3302fcd9a6388"
] | [
"cnn_cifar_optuna_affinity.py"
] | [
"import tensorflow as tf\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.callbacks import LearningRateScheduler, Callback\nimport tensorflow.keras.backend as K\nfrom tensorflow.contrib.tpu.python.tpu import keras_support\nfrom affinity_loss_tpu import *\nfrom d... | [
[
"tensorflow.contrib.cluster_resolver.TPUClusterResolver",
"tensorflow.keras.layers.AveragePooling2D",
"tensorflow.keras.layers.Activation",
"tensorflow.keras.layers.GlobalAveragePooling2D",
"tensorflow.keras.models.Model",
"tensorflow.keras.layers.Conv2D",
"tensorflow.keras.callbacks.L... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
AnTao97/LGM | [
"95dc5c2e814f6bf27baae73a7e75578cb6dab659"
] | [
"indoor_scene/models/dynamics_aware_utils.py"
] | [
"\"\"\"\n\nDynamics-aware Adversarial Attack of 3D Sparse Convolution Network\n\n@Author: \n An Tao,\n Pengliang Ji\n\n@Contact: \n ta19@mails.tsinghua.edu.cn, \n jpl1723@buaa.edu.cn\n \n@Time: \n 2022/1/23 9:32 PM\n\n\"\"\"\n\nimport torch\nimport torch.nn.functional as F\nfrom torch_scatter impo... | [
[
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
XenonLamb/higan | [
"c08e2081413c3319b712c2f7193ac8013f601382"
] | [
"utils/visualizer.py"
] | [
"# python 3.7\n\"\"\"Utility functions for visualizing results on html page.\"\"\"\n\nimport base64\nimport os.path\nimport cv2\nimport numpy as np\n\n__all__ = [\n 'get_grid_shape', 'get_blank_image', 'load_image', 'save_image',\n 'add_text_to_image', 'fuse_images', 'HtmlPageVisualizer', 'VideoReader',\n ... | [
[
"numpy.zeros",
"numpy.sqrt",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
softsys4ai/neural-distiller | [
"12863bd8b69cf73c67ead5e14dbd2122c6db01ec"
] | [
"src/pruning/prune_experiments.py"
] | [
"from pruning.prune_util import load_dataset, load_model, compile_model, train_model, save_model_h5, \\\n evaluate_model_size, format_experiment_name, evaluate_percentage_of_zeros\n\nfrom pruning.pruner import Pruner\n\nimport tensorflow as tf\nimport tensorflow_model_optimization as tfmot\n\nimport numpy as np\... | [
[
"tensorflow.keras.callbacks.TensorBoard"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
CalebEverett/trax | [
"77b6e8e3830f0994481ed78e57e3070ed98e41e4"
] | [
"trax/supervised/training_test.py"
] | [
"# coding=utf-8\n# Copyright 2021 The Trax Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by app... | [
[
"numpy.arange",
"numpy.ones_like",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
juancroldan/datamart | [
"9ec3b99f36192f812edd74ad2262bebccc22bc66"
] | [
"datamart/materializers/parsers/json_parser.py"
] | [
"from pandas.io.json import json_normalize\nimport json\n\nfrom datamart.materializers.parsers.parser_base import *\n\n\nclass JSONParser(ParserBase):\n\n def get_all(self, url: str) -> typing.List[pd.DataFrame]:\n \"\"\"\n Parses json and returns result\n\n Params:\n - url: (str)... | [
[
"pandas.io.json.json_normalize"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"0.19",
"0.24",
"0.20",
"0.25"
],
"scipy": [],
"tensorflow": []
}
] |
harizMunawar/REI | [
"ff0cb47eba9134078636ecc29efb152f29463e31"
] | [
"helpers/excel_handlers.py"
] | [
"import pandas as pd\nimport json\nfrom sekolah.models import Kelas\nfrom helpers import active_semester, active_tp\n\ndef append_df_to_excel(filename, df, sheet_name='Sheet1', startrow=None,\n truncate_sheet=False, \n **to_excel_kwargs):\n # ignore [engine] parameter ... | [
[
"pandas.read_excel",
"pandas.ExcelWriter"
]
] | [
{
"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": []
}
] |
matthewmcampbell/connect4RL | [
"c39db321813165c73fdc595b8eeb145672516771"
] | [
"frontend/streamlit_app.py"
] | [
"import numpy as np\nimport streamlit as st\nimport requests\nimport json\n\n# GLOBAL CONFIG\nIMG_FOLDER = \"./frontend/imgs/\"\nNROWS = 6\nNCOLS = 7\n\n\n# Setup API query structures and perform a cold call at app load.\n# This will make the gameplay smoother once the user starts.\nhost_address = st.secrets['host_... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vhgnguyen/occlusion_behavior_planning | [
"33dea1fb9c5e3274e4482ca0d9eeb56f9beaa11e"
] | [
"src/stuffs/risk_functions.py"
] | [
"from scipy.stats import mvn\n\nimport numpy as np\nimport math\n\nimport _param as param\nimport gaussian as gaussian\n\n\ndef collisionEventSeverity(ego_vx, obj_vx, method='sigmoid', gom_rate=1,\n min_weight=param._SEVERITY_MIN_WEIGHT_CONST,\n quad_weight=param.... | [
[
"numpy.exp",
"numpy.array",
"scipy.stats.mvn.mvnun",
"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": [],
... |
wangyibin/biowy | [
"a534f35fc6f96fe1b3a6ca78853a5aa076337328"
] | [
"apps/numparse.py"
] | [
"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\n\n\"\"\"\nA library of number parse.\n\"\"\"\nimport logging\nimport numpy as np\nimport os\nimport os.path as op\nimport sys\n\n#from scipy import stats\n\n\nclass OrdNum(object):\n \"\"\"\n Return the corresponding ordinal number of a number.\n Such as 21... | [
[
"numpy.median",
"numpy.argmax",
"numpy.mean",
"numpy.bincount"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mwsmith2/recognit | [
"849c66754971a66a12b0b57d205a7873e0fb8eae"
] | [
"examples/quickplot.py"
] | [
"import os\n\nimport matplotlib\nmatplotlib.use('PDF')\nimport matplotlib.cm as cm\nimport matplotlib.pyplot as plt\nfrom sklearn.lda import LDA\nimport numpy as np\nfrom collections import defaultdict\nfrom scipy.spatial.distance import cdist\n\nfrom recognit import load\nfrom recognit import pca\nfrom recognit im... | [
[
"matplotlib.use",
"scipy.spatial.distance.cdist"
]
] | [
{
"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"
... |
2326wz/sharp-in | [
"520056ed923f1eb0b8bf8e06b0e959ff0ce73997"
] | [
"telegram-bot/core/unet/u_net.py"
] | [
"from tensorflow.keras import Model\nfrom tensorflow.keras.layers import Input, Concatenate, Convolution2D, MaxPooling2D, UpSampling2D\nfrom pathlib import Path\nfrom tensorflow.keras.optimizers import Adam\nimport cv2\nimport os\nimport time\nimport numpy as np\nfrom core.config import get_config\n\ncrop_size = ge... | [
[
"tensorflow.keras.layers.Concatenate",
"numpy.expand_dims",
"tensorflow.keras.layers.UpSampling2D",
"tensorflow.keras.Model",
"numpy.zeros_like",
"tensorflow.keras.layers.Convolution2D",
"tensorflow.keras.layers.MaxPooling2D",
"numpy.zeros",
"tensorflow.keras.layers.Input"
]
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
joleroi/gammapy | [
"c4e0c4bd74c79d30e0837559d18b7a1a269f70d9"
] | [
"gammapy/scripts/iterative_source_detect.py"
] | [
"# Licensed under a 3-clause BSD style license - see LICENSE.rst\nfrom __future__ import (absolute_import, division, print_function,\n unicode_literals)\nfrom ..utils.scripts import get_parser\n\n__all__ = ['iterative_source_detect']\n\n\ndef main(args=None):\n parser = get_parser(iterativ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Jeanca64091/CoronavirusML | [
"867f8e72579c60001719ede7211b86743c669fe6",
"867f8e72579c60001719ede7211b86743c669fe6"
] | [
"2020-11/20080862.py",
"2020-11/201503821.py"
] | [
"from sklearn.linear_model import LinearRegression \nfrom sklearn.preprocessing import PolynomialFeatures \nfrom sklearn.metrics import mean_squared_error, r2_score\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport random\n\n#----------------------------------------------------------------------------... | [
[
"sklearn.metrics.r2_score",
"matplotlib.pyplot.scatter",
"numpy.linspace",
"numpy.asarray",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.title",
"sklearn.preprocessing.PolynomialFeatures",
"matplotlib.pyplot.savefig",
"sklearn.metrics.mean_squared_error",
"matplotlib.pyplot... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ankitshah009/argus-freesound | [
"4faf8f192035b413e8946bda3555474cb9ad8237"
] | [
"src/argus_models.py"
] | [
"import torch\n\nfrom argus import Model\nfrom argus.utils import deep_detach, deep_to\n\nfrom src.models import resnet\nfrom src.models import senet\nfrom src.models.feature_extractor import FeatureExtractor\nfrom src.models.simple_kaggle import SimpleKaggle\nfrom src.models.simple_attention import SimpleAttention... | [
[
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
siouxmathware/ijsbeer-ai | [
"35b47a7735bb29dd0018b09ece683f57d8da0585"
] | [
"models/nlp/mocks/mock_bert.py"
] | [
"from models.nlp.mocks import mock_bert_layer, mock_tokenizer\nimport numpy as np\n\n\nclass MockHistory:\n def __init__(self, categories):\n all_categories = categories + ['loss']\n all_categories = all_categories + [f'val_{cat}' for cat in all_categories]\n self.history = {cat.lower(): [i]... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Sindhuja-R-21/class-110 | [
"cf87dd5225ea49a55f4ecbab2778893f936e0eee"
] | [
"main.py"
] | [
"import plotly.figure_factory as ff\nimport plotly.graph_objects as go\nimport statistics\nimport random\nimport pandas as pd\nimport csv\n\ndf=pd.read_csv(\"data.csv\")\ndata=df[\"temp\"].to_list()\npopulation_mean=statistics.mean(data)\nstd_deviation=statistics.stdev(data)\nprint(\"Population Mean = \",population... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
hvy/optuna-core | [
"be9df49424aa4022cfcec7d9423768cc39c73ae6"
] | [
"optuna_core/samplers/_random.py"
] | [
"from typing import Any\nfrom typing import Dict\nfrom typing import Optional\n\nimport numpy\n\nimport optuna_core\nfrom optuna_core import distributions\nfrom optuna_core.distributions import BaseDistribution\nfrom optuna_core.samplers._base import BaseSampler\nfrom optuna_core.trial import FrozenTrial\n\n\nclass... | [
[
"numpy.round",
"numpy.log",
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pysimu/pysimu | [
"646432bc96be199165b112a77b5ff650b97152ba",
"646432bc96be199165b112a77b5ff650b97152ba"
] | [
"examples/notebooks/smib_milano_ex8p1_v1/smib_milano_ex8p1_avr_pss.py",
"pysimu/ssa.py"
] | [
"import numpy as np\nimport numba\nfrom pysimu.nummath import interp\n\n\nclass smib_milano_ex8p1_avr_pss_class: \n def __init__(self): \n\n self.t_end = 20.000000 \n self.Dt = 0.001000 \n self.decimation = 10.000000 \n self.itol = 0.000000 \n self.solvern = 1 \n self.im... | [
[
"scipy.optimize.fsolve",
"numpy.abs",
"numpy.eye",
"numpy.dtype",
"numpy.ones",
"numpy.ceil",
"numpy.copy",
"numpy.zeros",
"numpy.vstack"
],
[
"numpy.linalg.solve"
]
] | [
{
"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"... |
NinaCalvi/OKBC | [
"e25ad0296137ed354593c74509b077a22f60425e",
"e25ad0296137ed354593c74509b077a22f60425e"
] | [
"preprocessing.py",
"get_turk_useful_res.py"
] | [
"import argparse\nimport logging\nimport os\nimport pickle\nimport sys\n\nimport numpy as np\n\nimport kb\nimport template_builder\nimport utils\n\n\ndef get_input(fact, y, template_obj_list,add_ids):\n if (add_ids):\n x = [fact[0],fact[1],fact[2]]\n else:\n x = []\n for template in template_... | [
[
"numpy.savetxt",
"numpy.array"
],
[
"pandas.read_csv",
"pandas.DataFrame",
"numpy.mean",
"pandas.DataFrame.from_dict",
"pandas.set_option"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lucienwang1009/tensorflow-onnx | [
"cb016ef5b2483b78b0c0ceea23652d4a6a142cf0"
] | [
"tests/test_onnx_shape_inference.py"
] | [
"# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT license.\n\n\"\"\"Unit Tests for shape inference.\"\"\"\n\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport numpy as np\nfrom onnx import TensorProto\nfrom ... | [
[
"numpy.array",
"numpy.random.random"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
abinashpanda/pgmpy | [
"bd4019f0b711eae95217cda82087d3ef90e2457a"
] | [
"pgmpy/factors/discrete/CPD.py"
] | [
"#!/usr/bin/env python3\n\"\"\"Contains the different formats of CPDs used in PGM\"\"\"\nfrom __future__ import division\n\nfrom itertools import product\nfrom warnings import warn\nimport numbers\n\nimport numpy as np\n\nfrom pgmpy.factors.discrete import DiscreteFactor\nfrom pgmpy.extern import tabulate\nfrom pgm... | [
[
"numpy.array",
"numpy.prod",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hoxbug/Computational_Geometry | [
"9672afa17f9a7c79e2285adb0398a45873e657f2"
] | [
"plotpoints.py"
] | [
"import matplotlib.pyplot as plt\nfrom convexhull import Point, readDataPts, isPtOnSegment, segmentIntn\n\ndef plot_points(listPts, plane, color = 'red', size = 4):\n \"\"\"Plots a given list of (x, y) values on a given figure\"\"\"\n x = []\n y = []\n for points in listPts:\n x.append(points[0])... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
brettgohre/still_life_rendering_gqn | [
"66053435f7b9e729bbebbef34c171a8fb72e1630"
] | [
"preprocess/make_dataset.py"
] | [
"import tensorflow as tf\nimport numpy as np\nimport os\nfrom glob import glob\nfrom skimage.io import imread\n\n\n\ndef make_dataset():\n\n # First 130 scenes. 12 facing away from wall.\n final_frames = []\n final_viewpoints = []\n frames = np.ndarray(shape=(12, 6, 64, 64, 3), dtype=float)\n viewpoi... | [
[
"tensorflow.convert_to_tensor",
"numpy.random.choice",
"numpy.asarray",
"numpy.arange",
"numpy.ndarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
AndrewReynen/WaveformGUI | [
"22a08dbc4215e50685dfdb68cd8729f7d539d76b"
] | [
"lazylyst/Plugins/Locate.py"
] | [
"from __future__ import print_function\r\nimport warnings\r\n\r\nimport numpy as np\r\nimport scipy.optimize as optimize\r\nwarnings.simplefilter(\"ignore\", optimize.OptimizeWarning)\r\n\r\nfrom StationMeta import unitConversionDict\r\n\r\n# Get the velocity and delay values based on the current source...\r\n# ...... | [
[
"numpy.abs",
"numpy.unique",
"scipy.optimize.curve_fit",
"numpy.array",
"numpy.where",
"numpy.empty"
]
] | [
{
"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"... |
qiujunlin/Segmentation | [
"b1514ca33bdf35737426de89850349aaf4ef59d4"
] | [
"dataset/DatasetUtro.py"
] | [
"import torch\nimport glob\nimport os\nimport sys\nimport numpy as np\nfrom torchvision import transforms\nfrom torchvision.transforms import functional as F\n#import cv2\nfrom PIL import Image\nimport random\nfrom imgaug import augmenters as iaa\nimport imgaug as ia\nclass Dataset(torch.utils.data.Dataset):\n\n ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
renll/ComerNet | [
"d466e9b0c69f74003654c1cb0bcf6e5c591eaa9f"
] | [
"create_data.py"
] | [
"# -*- coding: utf-8 -*-\nimport copy\nimport json\nimport os\nimport re\nimport shutil\nimport urllib\nfrom collections import OrderedDict\nfrom io import BytesIO\nfrom zipfile import ZipFile\nimport difflib\nimport numpy as np\n\nnp.set_printoptions(precision=3)\n\nnp.random.seed(2)\n\n\n'''\nMost of the codes ar... | [
[
"numpy.set_printoptions",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
navies/neural_multistyle_transfer | [
"e122d57e4dee82a8076fec84b9a143cab8236df5"
] | [
"neural_multistyle/model.py"
] | [
"import torch\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport torchvision.models as models\nfrom tqdm import tqdm\n\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n\ndef gram_matrix(x):\n \"\"\"Computes the gram matrix of the input\n\n Parameters\n ----------... | [
[
"torch.nn.Sequential",
"torch.tensor",
"torch.nn.functional.mse_loss",
"torch.optim.LBFGS",
"torch.no_grad",
"torch.cuda.is_available",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ngocphucck/SimpleMLP | [
"e05cfce7e8f2e823f491c6564fa6c2c2e0cef420"
] | [
"optimizer.py"
] | [
"import numpy as np\n\n\nclass SGD(object):\n def __init__(self, parameters, grads, lr):\n self.parameters = parameters\n self.grads = grads\n self.lr = lr\n\n def zero_grad(self):\n for key in self.grads.keys():\n self.grads[key] = np.zeros(self.grads[key].shape)\n\n ... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ChristianIngwersen/BombermanRL | [
"6cad61708211d74fbc1e16776a579861b614f360"
] | [
"evolutionarystrategies/evolutionarystrategy.py"
] | [
"import torch\nimport numpy as np\n#import pathos.multiprocessing as mp\nimport multiprocessing as mp\n\nclass EvolutionaryStrategy:\n\n def __init__(self, model, fitness, impact, processes=4, populationsize=10, learning_rate=0.5):\n self.model = model(transfer = True)\n self.processes = processes\... | [
[
"torch.manual_seed",
"torch.randint",
"torch.randn"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tgaillard1/ucaip-labs | [
"44b2d8ec017793e40ae1a26b6b6a505d18bdf002"
] | [
"src/model_training/model.py"
] | [
"# Copyright 2021 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed ... | [
[
"tensorflow.keras.layers.Concatenate",
"tensorflow.keras.layers.Embedding",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.experimental.preprocessing.CategoryEncoding",
"tensorflow.expand_dims",
"tensorflow.keras.Model",
"tensorflow.keras.layers.Input"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
shonenkov/TPU-Star | [
"184ca912e4c3e6300af0156213ed792997d1fcc4",
"184ca912e4c3e6300af0156213ed792997d1fcc4"
] | [
"tpu_star/experiment/torch_tpu.py",
"tpu_star/utils.py"
] | [
"# -*- coding: utf-8 -*-\nimport os\nimport random\nimport time\n\nimport torch\nimport numpy as np\n\nfrom .torch_gpu import TorchGPUExperiment\n\n\nclass TorchTPUExperiment(TorchGPUExperiment):\n\n def __init__(\n self,\n model,\n optimizer,\n scheduler,\n criterion,\n ... | [
[
"torch.manual_seed",
"torch.cuda.manual_seed",
"numpy.random.seed"
],
[
"torch.utils.data.DataLoader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fgolemo/kubric | [
"a8b6bc8260add2f516e4805929dcb17f223974ba"
] | [
"kubric/datasets/movid.py"
] | [
"# Copyright 2021 The Kubric Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or... | [
[
"numpy.min",
"numpy.round",
"numpy.max",
"numpy.floor",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NoahEmbedded/EmbeddedKWD | [
"2380d56b0b75bae4fedeb60885358332766f7319"
] | [
"Modell/CSV/CSVErstellen.py"
] | [
"import csv\nimport time\nimport tensorflow as tf\nimport tensorflow.keras.models\nfrom tensorflow.keras.preprocessing.image import load_img,img_to_array\nfrom numpy import expand_dims\nfrom os import listdir\n\ndef ladeBild(pfad):\n bild = load_img(path = pfad,color_mode = 'grayscale')\n array = img_to_array... | [
[
"tensorflow.lite.Interpreter",
"numpy.expand_dims",
"tensorflow.keras.preprocessing.image.img_to_array",
"tensorflow.keras.preprocessing.image.load_img"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.4",
"2.3",
"2.5",
"2.6"
]
}
] |
leyp1/darts | [
"edeb1810f0a5e63ddef2b6db2a997c6c9428c51d"
] | [
"darts/tests/models/forecasting/test_NBEATS.py"
] | [
"import shutil\nimport tempfile\n\nimport numpy as np\n\nfrom darts.logging import get_logger\nfrom darts.tests.base_test_class import DartsBaseTestClass\nfrom darts.utils import timeseries_generation as tg\n\nlogger = get_logger(__name__)\n\ntry:\n from darts.models.forecasting.nbeats import NBEATSModel\n\n ... | [
[
"numpy.array",
"numpy.average"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MichalOleszak/tsaugur | [
"367d04081395e691bacc725133c2b247453ae464"
] | [
"tsaugur/models/holt_winters.py"
] | [
"import itertools\nimport warnings\nimport numpy as np\nfrom statsmodels.tsa.holtwinters import ExponentialSmoothing\n\nfrom tsaugur.utils import data_utils, model_utils\nfrom tsaugur.models import base_model\nfrom tsaugur.metrics import get_metric\n\n\nclass HoltWinters(base_model.BaseModel):\n \"\"\"\n Holt... | [
[
"numpy.nanargmin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
afcarl/Useful-python | [
"5d1947052fb25b2388704926e4692511cc162031"
] | [
"Scikit-learn/sklearn_tutorial_notebooks/fig_code/sgd_separator.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.datasets.samples_generator import make_blobs\n\ndef plot_sgd_separator():\n # we create 50 separable points\n X, Y = make_blobs(n_samples=50, centers=2,\n random_state=0, clu... | [
[
"numpy.array",
"numpy.linspace",
"sklearn.linear_model.SGDClassifier",
"matplotlib.pyplot.show",
"matplotlib.pyplot.axes",
"numpy.ndenumerate",
"sklearn.datasets.samples_generator.make_blobs",
"numpy.meshgrid",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SergioAlvarezB/ml-numpy | [
"bf450b0d48b52c56fd3d124a5b41f2b99594ea3b"
] | [
"models/svm.py"
] | [
"import numpy as np\n\nfrom utils import kernels\n\n\nclass SVM:\n \"\"\"SVM classifier class.\n\n Implements a SVM classification model. The algorithm minimize the dual form\n cost using a projected version of gradient descent.\n\n Parameters\n ----------\n kernel : `str`, optional\n Kerne... | [
[
"numpy.dot",
"numpy.clip",
"numpy.atleast_2d",
"numpy.linalg.pinv",
"numpy.random.rand",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tp5uiuc/PyElastica | [
"37db35137b198d1c0756e058ec1635a3675fab22"
] | [
"examples/JointCases/hinge_joint.py"
] | [
"__doc__ = \"\"\"Hinge joint example, for detailed explanation refer to Zhang et. al. Nature Comm. methods section.\"\"\"\n\nimport numpy as np\nimport sys\n\n# FIXME without appending sys.path make it more generic\nsys.path.append(\"../../\")\nfrom elastica import *\nfrom examples.JointCases.external_force_class_... | [
[
"numpy.array",
"numpy.zeros",
"numpy.cross"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rosechung-unity3d/Robotics-Object-Pose-Estimation | [
"f33075d7e5f9476e10eddee055f6150aeb4efb66"
] | [
"Model/pose_estimation/pose_estimation_estimator.py"
] | [
"import copy\nimport os\nimport logging\nfrom pose_estimation.logger import Logger\nfrom .storage.checkpoint import EstimatorCheckpoint\n\nfrom pose_estimation.model import PoseEstimationNetwork\nfrom pose_estimation.train import train_model\nfrom pose_estimation.evaluate import evaluate_model\nfrom pose_estimation... | [
[
"torch.load",
"torch.cuda.is_available",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dasturge/eisen-core | [
"09056f1e6aff450ef402b35b10ef96a7d4a3ff87"
] | [
"eisen/datasets/camus.py"
] | [
"import os\nimport torch\nimport copy\n\nfrom torch.utils.data import Dataset\n\n\nclass CAMUS(Dataset):\n \"\"\"\n This object implements the capability of reading CAMUS data. The CAMUS dataset is a dataset of ultrasound\n images of the heart. Further information about this dataset can be found on the off... | [
[
"torch.is_tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
neurallayer/fos | [
"92c3cd485a45c2243900c881b6625c4453f6a359"
] | [
"test/test_metrics.py"
] | [
"# pylint: disable=E1101, C0116, C0114\nimport torch\nfrom fos.metrics import BinaryAccuracy, ConfusionMetric\n\ndef test_accuracy():\n metric = BinaryAccuracy()\n y_pred = torch.randn(100, 10, 10)\n value = metric(y_pred, y_pred > 0.)\n assert value == 1.0\n value = metric(y_pred, y_pred < 0.)\n ... | [
[
"torch.randn",
"torch.FloatTensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GuillaumeSimo/autoforecast | [
"7205ce5f426b2950f7de2877303fb5999edf63be"
] | [
"autoforecast/metrics/metrics.py"
] | [
"import numpy as np\nfrom sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error, mean_squared_error\n\n\ndef encode(data, col=\"bank\"):\n map_col_to_col_id = {col: col_id for col_id, col in enumerate(data[col].unique())}\n data[f\"{col}_token\"] = data[col].map(map_col_to_col_id)\n re... | [
[
"numpy.log",
"numpy.sqrt",
"numpy.abs",
"sklearn.metrics.mean_absolute_error",
"sklearn.metrics.mean_squared_error",
"sklearn.metrics.mean_absolute_percentage_error",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jan-xu/2d-slam | [
"11b0e5e0157578d342270aea6465d673cd2de470"
] | [
"icp.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\nimport numpy as np\nfrom sklearn.neighbors import NearestNeighbors\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\ndef wraptopi(phi):\n return np.mod(phi + np.pi, 2*np.pi) - np.pi\n\ndef best_fit_transform(A... | [
[
"numpy.dot",
"numpy.linspace",
"numpy.arctan2",
"numpy.max",
"numpy.mean",
"numpy.any",
"numpy.random.randint",
"numpy.hstack",
"numpy.linalg.svd",
"numpy.eye",
"numpy.sin",
"numpy.linalg.det",
"numpy.copy",
"sklearn.neighbors.NearestNeighbors",
"numpy.z... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
matteoacrossi/adapt_ic-povm | [
"1c9a0b4b98fafff478aed66686692ec97c0342ae"
] | [
"tomography_script.py"
] | [
"import pandas as pd\nimport numpy as np\nfrom qiskit import execute, Aer\nimport tomography.workinglib as wl\nimport tomography.likelihood_maximisation as lm\nimport networkx as nx\nimport tomography.hilbert_graph_tools as ht\nfrom povm.povm_operator import POVMOperator\nimport qiskit.quantum_info as qi\nimport it... | [
[
"pandas.merge",
"pandas.concat",
"pandas.DataFrame",
"pandas.read_json",
"pandas.read_pickle"
]
] | [
{
"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": []
}
] |
DavidBert/N7-techno-IA | [
"a43105be602282ac3a564066ef588d46a7e4251f"
] | [
"code/developpement/train_mnist.py"
] | [
"import argparse\nfrom statistics import mean\n\nimport torch\nimport torchvision\nimport torchvision.transforms as transforms\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.utils.tensorboard import SummaryWriter\nfrom tqdm import tqdm\n\nfrom mnist_net import MNIST... | [
[
"torch.nn.CrossEntropyLoss",
"torch.utils.data.DataLoader",
"torch.no_grad",
"torch.utils.tensorboard.SummaryWriter",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zlWang573/ggnnForSentimentTreebank | [
"351b6a0f34248c10116a7a49d88d651083df6fc7"
] | [
"utils/train.py"
] | [
"import torch\nimport torch.tensor\n\nfrom torch.autograd import Variable\n\ndef train(epoch, dataloader, net, criterion, optimizer, opt):\n net.train()\n \"\"\"\n 以下m_node为当前图节点数量\n lable为节点与子树上最远叶子节点距离，lable == 0为叶子节点\n lable并不参与运算，用于统计信息\n \"\"\"\n for i, (m_node, adj_matrix, annotat... | [
[
"torch.autograd.Variable",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ydavidchen/pytorch_dl_challenge | [
"3feab77f6d40709805e3d2b94d5b50f4a1109c78"
] | [
"lessons_and_tutorials/02_intro_to_neuralnet/2.10_perceptron_algorithm.py"
] | [
"# Section 2.10: Perceptron algorithm\n\nimport numpy as np\n\nLEARN_RATE = 0.01;\nNUM_EPOCHS = 25;\nSEED = 42;\n\nnp.random.seed(SEED)\n\ndef stepFunction(t):\n if t >= 0: return 1\n return 0\n\ndef prediction(X, W, b):\n pred = stepFunction((np.matmul(X,W)+b)[0]);\n return pred;\n\ndef perceptronStep(... | [
[
"numpy.matmul",
"numpy.random.rand",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SakshayMahna/Robotics-Mechanics | [
"3fa4b5860c4c9b4e22bd8799c0edc08237707aef"
] | [
"Part-12-RobotJacobian/tests/test_transformations.py"
] | [
"\"\"\"\nUnit Testing Rigid Body Transformations\n\"\"\"\n\nimport unittest\nimport numpy as np\nfrom tools.transformations import *\n\nclass TestTransformations(unittest.TestCase):\n\n def test_transl(self):\n x = 1\n y = 2\n z = 3\n\n transformation = transl(x, y, z)\n actual... | [
[
"numpy.testing.assert_almost_equal",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anandkamat05/TDEOC | [
"11749457c3a7550e11ba1acc4784e8545f8087aa"
] | [
"baselines/common/atari_wrappers.py"
] | [
"import numpy as np\nfrom collections import deque\nimport gym\nfrom gym import spaces\nimport cv2\n\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 No-op is assumed to be action 0.\n \"\"\"... | [
[
"numpy.sign",
"numpy.array",
"numpy.zeros",
"numpy.concatenate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kotania/impy | [
"85ddd5764a3c1f955fae8d1b9422b619d4d12a4d"
] | [
"impy/kinematics.py"
] | [
"\"\"\" This module handles transformations between Lorentz frames and\ndifferent inputs required by the low-level event generator interfaces.\n\n\n@Hans @Sonia: we need to come up with some sort general handling\nof different inputs. Hans suggested to mimic a similar behavior as for\ncolors in matplotlib. That one... | [
[
"numpy.sqrt",
"numpy.random.choice",
"numpy.asarray",
"numpy.argsort",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
patel-zeel/lab | [
"cc0df2c03196863041e78fa4179445341e86958c"
] | [
"tests/util.py"
] | [
"import logging\nfrom itertools import product\n\nimport jax.numpy as jnp\nimport numpy as np\nimport plum\nimport pytest\nimport tensorflow as tf\nimport torch\nfrom autograd.core import VJPNode, getval\nfrom autograd.tracer import trace_stack, new_box\nfrom plum import Dispatcher, Union\n\nimport lab as B\nfrom l... | [
[
"tensorflow.constant",
"numpy.matmul",
"numpy.indices",
"numpy.transpose",
"torch.tensor",
"numpy.random.randn",
"numpy.random.rand",
"numpy.testing.assert_allclose",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.4",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1.2",
"2.... |
LynXies/lab3 | [
"8c35daecfde8a6aae6fdc9ccdfa9f097552e1743"
] | [
"lab2.py"
] | [
"import sqlite3\r\nfrom bottle import route, run, debug, template, request\r\nimport requests\r\nimport json\r\nfrom bs4 import BeautifulSoup\r\nimport pandas as pd\r\nurl = 'https://xakep.ru/'\r\nheaders = {\"accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0... | [
[
"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": []
}
] |
naesseth/nestedsmc | [
"b94633c14c2fd335b143d2bd264fd4900ed278cd"
] | [
"src/lgss/runBootstrap.py"
] | [
"import helpfunctions as hlp\nimport numpy as np\nfrom optparse import OptionParser\n\n\ndef runBootstrap(d, tauPhi, N, nrRuns):\n r\"\"\"Run bootstrap particle filtering on high-dimensional LGSS.\n \n Parameters\n ----------\n d : int\n State dimension.\n tauPhi : float\n Measuremen... | [
[
"numpy.dot",
"numpy.arange",
"numpy.max",
"numpy.mean",
"numpy.exp",
"numpy.zeros",
"numpy.sum",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Franck-Dernoncourt/meta_cross_nlu_qa | [
"98f0af07988f24d9c7827030765246c6f67a0f4d"
] | [
"nlu/meta_learner_l2l_no_acc.py"
] | [
"from torch import optim\nfrom torch import nn\nimport torch\n\nfrom sklearn.metrics import f1_score, precision_score, recall_score\nfrom copy import deepcopy\nfrom tqdm import tqdm\nimport learn2learn as l2l\n\n\ndef accuracy(logits, targets):\n intent_corrects = 0\n for j in range(len(logits)):\n tru... | [
[
"sklearn.metrics.f1_score",
"sklearn.metrics.precision_score",
"torch.no_grad",
"sklearn.metrics.recall_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
smit2k14/awkward-array | [
"a2645fdaed1a6997c4677ae47cbb2cd0663e8a21"
] | [
"awkward/persist.py"
] | [
"#!/usr/bin/env python\n\n# BSD 3-Clause License; see https://github.com/scikit-hep/awkward-array/blob/master/LICENSE\n\nimport base64\nimport fnmatch\nimport importlib\nimport json\nimport numbers\nimport os\nimport pickle\nimport types\nimport zipfile\nimport zlib\nfrom itertools import count\ntry:\n from coll... | [
[
"numpy.frombuffer",
"numpy.isfinite",
"numpy.dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jmhuer/computer-vision-framework | [
"508c8efe0bf4d983d533f0547210b2732d5e9620"
] | [
"distance.py"
] | [
"# distance.py\nfrom math import sqrt\nfrom scipy.spatial.distance import euclidean\n\ndef get_shoulder_dist_from_pe(candidate, subset):\n ''' From the pose estimation results (cadidate and subset) extract shoulder points and calculate their euclidean distance.\n '''\n distances = []\n # Check if there ... | [
[
"scipy.spatial.distance.euclidean"
]
] | [
{
"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"
... |
jkznst/detectron2 | [
"790f1894134bb85b897b0912367ee54a24caf2b2"
] | [
"projects/SixDPose/sixdpose/pvnet_pose_utils.py"
] | [
"import numpy as np\nimport cv2\n\n\ndef pnp(points_3d, points_2d, camera_matrix, method=cv2.SOLVEPNP_ITERATIVE):\n try:\n dist_coeffs = pnp.dist_coeffs\n except:\n dist_coeffs = np.zeros(shape=[8, 1], dtype='float64')\n\n assert points_3d.shape[0] == points_2d.shape[0], 'points 3D and points... | [
[
"numpy.dot",
"numpy.expand_dims",
"numpy.cos",
"numpy.sin",
"numpy.concatenate",
"numpy.float32",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
amaotone/pygtm | [
"94cd5effc10a565cb111235faec96790cc4d2bbe"
] | [
"pygtm/gtm.py"
] | [
"import numpy as np\nfrom scipy.spatial.distance import cdist\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.decomposition import PCA\n\n\nclass GTM(BaseEstimator, TransformerMixin):\n def __init__(self, n_components=2, n_rbfs=10, sigma=1, alpha=1e-3, n_grids=20, method='mean',\n ... | [
[
"numpy.log",
"numpy.linspace",
"scipy.spatial.distance.cdist",
"numpy.linalg.pinv",
"numpy.identity",
"numpy.fill_diagonal",
"numpy.exp",
"sklearn.decomposition.PCA"
]
] | [
{
"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"
... |
tacaswell/silx | [
"67f0ac8d3fcb5764c23b2210becfe2052f98061d"
] | [
"silx/gui/plot3d/scene/primitives.py"
] | [
"# coding: utf-8\n# /*##########################################################################\n#\n# Copyright (c) 2015-2019 European Synchrotron Radiation Facility\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Soft... | [
[
"numpy.nanmax",
"numpy.isnan",
"numpy.nanmin",
"numpy.dtype",
"numpy.mean",
"numpy.equal",
"numpy.iinfo",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ludehsar/bangla-frequency-calculator | [
"8c5d2da0bf6f214f89c812a80c4287a6aa88de36"
] | [
"main.py"
] | [
"import matplotlib.font_manager as fm\nimport matplotlib.pyplot as plt\nimport matplotlib.font_manager as fm\nimport numpy as np\n\nfreq = {}\n\n\ndef read_file(filename='input.txt'):\n f = open(filename, \"r\", encoding=\"utf-8\")\n for character in f.read():\n if (character in freq):\n fre... | [
[
"matplotlib.font_manager.fontManager.ttflist.extend",
"matplotlib.font_manager.FontProperties",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.ylabel",
"matplotlib.font_manager.createFontList",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.tick_params",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WeatherGod/geopandas | [
"891c5cd73c604862fbefd014cc6536faf571d260"
] | [
"geopandas/base.py"
] | [
"from warnings import warn\n\nimport numpy as np\nimport pandas as pd\nfrom pandas import Series, DataFrame, MultiIndex\nfrom pandas.core.indexing import _NDFrameIndexer\nfrom shapely.geometry import box, MultiPoint, MultiLineString, MultiPolygon\nfrom shapely.ops import cascaded_union, unary_union\nimport shapely.... | [
[
"pandas.notnull",
"pandas.Series",
"pandas.MultiIndex.from_tuples",
"pandas.DataFrame",
"numpy.array"
]
] | [
{
"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": []
}
] |
austinkwillis/flexmatcher | [
"c771cea696014f62bf919ecf678835d8c655d04f"
] | [
"examples/movie_schemas.py"
] | [
"import pandas as pd\n\nimport flexmatcher\n# Let's assume that the mediated schema has three attributes\n# movie_name, movie_year, movie_rating\n\n# creating one sample DataFrame where the schema is (year, Movie, imdb_rating)\nvals1 = [['year', 'Movie', 'imdb_rating'],\n ['2001', 'Lord of the Rings', '8.8'... | [
[
"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": []
}
] |
void-main/Paddle | [
"fabdb43c94c20b9fdf5ce87438f710e680f2588f"
] | [
"python/paddle/fluid/tests/unittests/npu/test_reduce_any_op_npu.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 ... | [
[
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
danielballan/edrixs | [
"57fbd11ba9aaeaa393c3e2f06af41e4e386749e4"
] | [
"edrixs/basis_transform.py"
] | [
"#!/usr/bin/env python\n\nimport numpy as np\n\ndef cb_op(oper_A, t_mat):\n \"\"\"\n Change the basis of an operator :math:`\\hat{O}` from one basis :math:`A`: :math:`\\\\psi^{A}_{i}` to another basis :math:`B`: :math:`\\\\phi^{B}_{j}`. \n\n .. math::\n\n O^{\\\\prime} = T^{\\dagger} O T, \n ... | [
[
"numpy.complex128",
"numpy.dot",
"numpy.sqrt",
"numpy.conj",
"numpy.cos",
"numpy.stack",
"numpy.sin",
"numpy.transpose",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
compsciencelab/pytorch-cifar | [
"4a526d0bbe53163b738602657cee220265ea6a55"
] | [
"models/densenet.py"
] | [
"'''DenseNet in PyTorch.'''\nimport math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass Bottleneck(nn.Module):\n def __init__(self, in_planes, growth_rate):\n super(Bottleneck, self).__init__()\n self.bn1 = nn.BatchNorm2d(in_planes)\n self.conv1 = nn.Conv2... | [
[
"torch.nn.Sequential",
"torch.cat",
"torch.randn",
"torch.nn.functional.avg_pool2d",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jlanga/exfi | [
"6cd28423213aba0ab8ac191e002396ddc84c4be3"
] | [
"tests/io/gfa1.py"
] | [
"#!/usr/bin/env python3\n\n\"\"\"tests.io.gfa1.py: Fragments of GFA1 files\"\"\"\n\nimport pandas as pd\n\n\nfrom exfi.io.gfa1 import \\\n HEADER_COLS, SEGMENT_COLS, LINK_COLS, CONTAINMENT_COLS, PATH_COLS, \\\n HEADER_DTYPES, SEGMENT_DTYPES, LINK_DTYPES, CONTAINMENT_DTYPES, PATH_DTYPES\n\nHEADER = pd.DataFram... | [
[
"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": []
}
] |
chidinzerem/chidinzerem.github.io | [
"0a5ac76b944531179e3d7f46abe45a0cce7ad1af"
] | [
"code/WEBSCRAPER PYTHON/core/logger.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# ------------------------\n# Penji OpDev Fall 2019\n# Open AI Based Logger\n# Modifications: Cory Paik\n# ------------------------\n\n# General\nimport os\nimport sys\nimport shutil\nimport os.path as osp\nimport json\nimport time\nimport datetime\nimport tempfile\n... | [
[
"tensorflow.python.util.compat.as_bytes",
"pandas.read_csv",
"tensorflow.train.summary_iterator",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
eqy/autotosis | [
"93fc800ed7c3b0fe0fcf58af90283de8d2036568"
] | [
"inference.py"
] | [
"import argparse\nimport ast\nimport os\nimport sys\nimport time\nfrom clip import Clip\nfrom artosisnet_transforms import crop_callbacks\nimport random\nimport shutil\nimport subprocess\n\nimport ffmpeg\nimport numpy as np\nimport torch\n\nsys.setrecursionlimit(10**6)\n\ndef _join_videos(listpath, outputpath):\n ... | [
[
"torch.cuda.device_count",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
The-Edgar/Marketing-Attribution-Models | [
"299a2e9097bb55da38d7c8d3cbb13e677b65efba"
] | [
"marketing_attribution_models/MAM.py"
] | [
"import numpy as np\nimport pandas as pd\nimport itertools\nimport math\nimport re\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport warnings\nplt.style.use('seaborn-white')\n\n\nclass MAM:\n \"\"\"\n MAM (Marketing Attribution Models) is a class inspired on the R Package ‘GameTheoryAllocation’ ... | [
[
"numpy.trunc",
"pandas.to_datetime",
"pandas.merge",
"pandas.Series",
"numpy.asarray",
"numpy.linalg.eig",
"numpy.linalg.inv",
"matplotlib.pyplot.subplots",
"pandas.DataFrame",
"numpy.linalg.pinv",
"pandas.melt",
"matplotlib.pyplot.xticks",
"numpy.array",
"n... | [
{
"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": []
}
] |
MrMiilk/SuperPoint | [
"a67bac07f6922677f0108b26d434bf4b61ee9de9"
] | [
"superpoint/models/classical_detectors.py"
] | [
"import tensorflow as tf\nimport numpy as np\nimport cv2\n\nfrom .base_model import BaseModel\nfrom .utils import box_nms\n\n\ndef classical_detector(im, **config):\n if config['method'] == 'harris':\n detections = cv2.cornerHarris(im, 4, 3, 0.04)\n\n elif config['method'] == 'shi':\n detections... | [
[
"numpy.int0",
"tensorflow.device",
"numpy.linspace",
"tensorflow.reduce_sum",
"numpy.random.rand",
"tensorflow.greater_equal",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
dashings/CAMVIS | [
"fb7e4e5d885ae227140f7ab40b5f47e730ec249b"
] | [
"models/long_range_perception/eval.py"
] | [
"import tqdm\nimport torch\nimport torch.nn as nn\n\nimport numpy as np\nfrom sklearn.metrics import roc_auc_score, roc_curve\n\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\nfrom .model import SimpleCNN\nfrom .utils import *\n\nmodel = torch.load('./model.pt', map_location=lambda storage, loc: storage... | [
[
"sklearn.metrics.roc_auc_score",
"matplotlib.pyplot.title",
"torch.zeros",
"torch.load",
"numpy.arange",
"numpy.flipud",
"matplotlib.pyplot.subplots",
"numpy.std",
"numpy.mean",
"torch.no_grad",
"numpy.where",
"matplotlib.pyplot.xlabel",
"numpy.random.RandomStat... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jacarvalho/mushroom-rl | [
"ba0a62454d771a1d3cacbec1ea9d71535f476b31"
] | [
"mushroom_rl/algorithms/actor_critic/classic_actor_critic/stochastic_ac.py"
] | [
"import numpy as np\n\nfrom mushroom_rl.algorithms.agent import Agent\nfrom mushroom_rl.approximators import Regressor\nfrom mushroom_rl.approximators.parametric import LinearApproximator\n\n\nclass StochasticAC(Agent):\n \"\"\"\n Stochastic Actor critic in the episodic setting as presented in:\n \"Model-F... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhiqiang-hu/bl_iot_sdk | [
"154ee677a8cc6a73e6a42a5ff12a8edc71e6d15d"
] | [
"toolchain/riscv/Linux/python/lib/python3.7/test/test_buffer.py"
] | [
"#\n# The ndarray object from _testbuffer.c is a complete implementation of\n# a PEP-3118 buffer provider. It is independent from NumPy's ndarray\n# and the tests don't require NumPy.\n#\n# If NumPy is present, some tests check both ndarray implementations\n# against each other.\n#\n# Most ndarray tests also check ... | [
[
"numpy.ndarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
leanth/OSUCoursework | [
"ccfbf5f9daa8f6d3818bb5e4cc8df7c5135a5f34"
] | [
"CS534-MachineLearning/hw2/perc-svm-demo/perc.py"
] | [
"import numpy as np\nimport sys\nimport math\n\nprec = 1e-4\nsign = lambda x: -1 if x < -prec else 1 if x > prec else 0\n\ndef perc(data, MIRA=False, aggressive=False, margin=0.5):\n\n weight = np.array([0.,0.,0.]) # must be float!\n avgw = np.array([0.,0.,0.])\n\n supp_vec = set()\n for i in range(1000... | [
[
"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": [],
... |
angusll/kaggle_greatbarrierreef | [
"cf1065833a8009be765f8d5d3f81a0c39485f312"
] | [
"csl_yolo/callbacks.py"
] | [
"import tensorflow as tf\r\nimport os\r\nimport math\r\n\r\nclass LearningRateReducer(tf.keras.callbacks.Callback):\r\n def __init__(self,lr_tune_dict={}):\r\n super(LearningRateReducer,self).__init__()\r\n self._lr_tune_dict=lr_tune_dict\r\n def on_epoch_end(self,epoch,logs={}):\r\n lr_t... | [
[
"tensorflow.zeros_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
mikedwhite/microstructural-fingerprinting-tools | [
"969ac9d032f82ca002846ac39017b7de04f50e85"
] | [
"mftools/assess/classify.py"
] | [
"import graphlearning as gl\nimport numpy as np\nfrom sklearn.cluster import KMeans, SpectralClustering\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.neighbors import NearestNeighbors\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.svm... | [
[
"sklearn.cluster.KMeans",
"sklearn.ensemble.RandomForestClassifier",
"sklearn.cluster.SpectralClustering",
"numpy.concatenate",
"numpy.max",
"numpy.int64",
"numpy.random.permutation",
"numpy.argwhere",
"sklearn.neighbors.NearestNeighbors",
"sklearn.svm.SVC",
"sklearn.pr... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
abcp4/coach | [
"ae6593bb33cf0ae3c5a4b3b351560dd6b47cd031"
] | [
"rl_coach/architectures/tensorflow_components/general_network.py"
] | [
"#\n# Copyright (c) 2017 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 ... | [
[
"tensorflow.losses.get_regularization_losses",
"tensorflow.concat",
"tensorflow.contrib.opt.ScipyOptimizerInterface",
"tensorflow.Variable",
"tensorflow.train.RMSPropOptimizer",
"tensorflow.losses.get_losses",
"tensorflow.assign",
"tensorflow.placeholder",
"tensorflow.losses.co... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
bchu/pandas | [
"5a150694731d2ecce670cca65760c472338a04fa"
] | [
"pandas/core/sparse/frame.py"
] | [
"\"\"\"\nData structures for sparse float data. Life is made simpler by dealing only\nwith float64 data\n\"\"\"\nimport warnings\n\nimport numpy as np\n\nfrom pandas._libs.sparse import BlockIndex, get_blocks\nimport pandas.compat as compat\nfrom pandas.compat import lmap\nfrom pandas.compat.numpy import function a... | [
[
"pandas.core.generic.NDFrame.__init__",
"pandas.compat.numpy.function.validate_cumsum",
"pandas.core.dtypes.missing.notna",
"numpy.concatenate",
"pandas.core.internals.construction.prep_ndarray",
"pandas.core.dtypes.common.is_scipy_sparse",
"pandas.compat.iteritems",
"pandas.core.f... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"1.4",
"1.1",
"1.5",
"1.2",
"0.24",
"1.0",
"0.25",
"1.3"
],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.1... |
SuwenJunliu/seisflows | [
"14d246691acf8e8549487a5db7c7cd877d23a2ae"
] | [
"seisflows/plugins/line_search/bracket.py"
] | [
"#\n# This is Seisflows\n#\n# See LICENCE file\n#\n#\n###############################################################################\n\n# Import numpy\nimport numpy as np\n\n# Local imports\nfrom seisflows.plugins.line_search import Base\nfrom seisflows.tools.math import backtrack2, polyfit2\n\n\nclass Bracket(Bas... | [
[
"numpy.log10",
"numpy.argmin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xebastien/TomoMIST | [
"c2a77757e25e7c16d392de56457b8b75872a2b64"
] | [
"pagailleIO.py"
] | [
"\nimport fabio\nimport fabio.edfimage as edf\nimport fabio.tifimage as tif\n#import edfimage\n\n#from PIL import Image\nimport numpy as np\nimport sys\nimport os\nimport configparser as ConfigParser\n\n\ndef openImage(filename):\n filename=str(filename)\n im=fabio.open(filename)\n imarray=im.data\n ret... | [
[
"numpy.amax",
"numpy.asarray",
"numpy.amin",
"numpy.mean",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
florisdesmedt/EfficientDet | [
"a840ca1be55ad84f9aa2517114e467a574c6fea9"
] | [
"generators/common.py"
] | [
"import numpy as np\nimport random\nimport warnings\nimport cv2\nfrom tensorflow import keras\n\nfrom utils.anchors import anchors_for_shape, anchor_targets_bbox\n\n\nclass Generator(keras.utils.Sequence):\n \"\"\"\n Abstract generator class.\n \"\"\"\n\n def __init__(\n self,\n ph... | [
[
"numpy.clip",
"numpy.ones",
"numpy.delete",
"numpy.array",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bxkftechteam/onnx-ml-demo | [
"91cd2b5674217233585870ff2b89c31a6cd2b960",
"91cd2b5674217233585870ff2b89c31a6cd2b960"
] | [
"converter/inference_hmm.py",
"train/infer.py"
] | [
"#!/usr/bin/env python3\n\n\"\"\"Run inference on HMM models\"\"\"\n\nimport sys\nfrom os import path\nimport numpy as np\nimport onnxruntime as ort\n\n\nif len(sys.argv) < 3:\n print(\"usage: {} <onnx_model_path> <N>\".format(\n sys.argv[0]))\n exit(1)\n\nonnx_model_path = sys.argv[1]\nN = int(sys.arg... | [
[
"numpy.random.randint"
],
[
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Armanfard-Lab/DSSL | [
"89c38ea299d4920c77fb80496b16be67c99bbea8"
] | [
"Code/train.py"
] | [
"\r\nfrom torch.utils.data import Dataset\r\nimport torch\r\nimport torch.nn as nn\r\nimport torchvision.datasets as dset\r\nimport torchvision.transforms as transforms\r\n\r\nfrom DSSL import DSSL\r\nfrom Network import AutoEncoder\r\n\r\nbatch_size = 500\r\ndataset_size = 70000\r\ntrain_set = dset.MNIST(root='/ho... | [
[
"torch.nn.init.zeros_",
"torch.utils.data.ConcatDataset",
"torch.utils.data.DataLoader",
"torch.nn.init.xavier_uniform_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Iamlegend-Imani/airbnb-plotly-dash-app | [
"837c93cded2f633d9ba9d7b0c8c75fd6a6c7d2a3"
] | [
"pages/predictionsbackup.py"
] | [
"# Imports from 3rd party libraries\nimport dash\nimport dash_bootstrap_components as dbc\nimport dash_core_components as dcc\nimport dash_html_components as html\nfrom dash.dependencies import Input, Output, State\nimport dash_daq as daq\nimport pandas as pd\nfrom datetime import date\nfrom tensorflow import keras... | [
[
"tensorflow.keras.models.load_model",
"numpy.array",
"numpy.arange",
"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": [
"1.10",
"2.7",
"2.2",
... |
bradkav/PBH_bounds | [
"b534defe718c2a40b1d8a66c9bdd6987e9c2a9f8"
] | [
"PlotPBHbounds.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport argparse\n\n\n#Specify the plot style\nmpl.rcParams.update({'font.size': 16,'font.family':'serif'})\nmpl.rcParams['xtick.major.size'] = 7\nmpl.rcParams['xtick.major.width'] = 1\nmpl.rcParams['xtick.minor.size'] = 3.5\nmpl.rcParam... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.gca",
"numpy.clip",
"matplotlib.pyplot.axhspan",
"matplotlib.pyplot.text",
"matplotlib.pyplot.style.use",
"matplotlib.pyplot.figure",
"numpy.logspace",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.savefig",
"numpy.log10",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Ros522/lazy-bot-tester | [
"30e7f50bcd7ca77e0ec9e11d069e047318ec2bdb"
] | [
"lazybot/collector/exchanges/bitflyer.py"
] | [
"import asyncio\nimport json\nfrom datetime import datetime\n\nimport numpy as np\nimport websockets\n\n\nclass BitFlyer:\n def __init__(self, tag=\"BITFLYERFX\", channel='lightning_executions_FX_BTC_JPY', retry=1, loop=None):\n self.loop = loop or asyncio.get_event_loop()\n self.tag = tag\n ... | [
[
"numpy.datetime64"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Miyoshichi/SimLight | [
"9f01dee5e324026bfdcdbe9f83cd29bbd447adda"
] | [
"SimLight/plottools/slidetools.py"
] | [
"# -*- coding: utf-8 -*-\n\n\"\"\"\nCreated on Nov 10, 2020\n@author: Zhou Xiang\n\"\"\"\n\nimport math\nfrom matplotlib.pyplot import bar\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom mpl_toolkits.axes_gr... | [
[
"numpy.sqrt",
"numpy.random.seed",
"numpy.min",
"numpy.linspace",
"numpy.meshgrid",
"matplotlib.pyplot.title",
"matplotlib.patches.Circle",
"numpy.max",
"numpy.random.rand",
"matplotlib.pyplot.rcParams.update",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
leemengtaiwan/pytorch_lightning_applications | [
"8d277d0b7b740bcdc8e6ca39444ee3c0da23aa51"
] | [
"learnable_ai/vision/gan/core.py"
] | [
"# AUTOGENERATED! DO NOT EDIT! File to edit: notebooks/vision.gan.core.ipynb (unless otherwise specified).\n\n__all__ = ['logger', 'SPECTRAL_NORM', 'get_n_samplings', 'get_norm2d', 'get_activation', 'init_xavier_uniform',\n 'UpsampleConv2d', 'UnsqueezeLatent', 'SqueezeLogit', 'DownsampleConv2d', 'ConvGene... | [
[
"torch.nn.ConvTranspose2d",
"torch.randn",
"torch.nn.utils.spectral_norm",
"torch.nn.Conv2d",
"torch.nn.Tanh",
"torch.tensor",
"torch.nn.LeakyReLU",
"torch.nn.init.xavier_uniform_",
"torch.nn.GroupNorm",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HPI-Information-Systems/TimeEval | [
"9b2717b89decd57dd09e04ad94c120f13132d7b8",
"9b2717b89decd57dd09e04ad94c120f13132d7b8"
] | [
"timeeval_experiments/2021-11-26-runtime-benchmark-2.py",
"scripts/calculate_metric.py"
] | [
"#!/usr/bin/env python3\nimport logging\nimport random\nimport shutil\nimport sys\nfrom pathlib import Path\nfrom typing import List, Tuple\n\nimport numpy as np\nfrom durations import Duration\n\nfrom timeeval import TimeEval, Datasets, TrainingType\nfrom timeeval.constants import HPI_CLUSTER\nfrom timeeval.remote... | [
[
"numpy.random.rand",
"numpy.unique"
],
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
m-r-munroe/alphazero-general | [
"221422e81b01f3b532da210b193692fe125a974c"
] | [
"alphazero/envs/tafl/players.py"
] | [
"from hnefatafl.engine import Move, BoardGameException\nfrom alphazero.envs.tafl.tafl import get_action\nfrom alphazero.envs.tafl.fastafl import get_action as ft_get_action\nfrom alphazero.GenericPlayers import BasePlayer\nfrom alphazero.Game import GameState\n\nimport pyximport, numpy\npyximport.install(setup_args... | [
[
"numpy.get_include"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YangYunjia/cfdpost | [
"87199d1c2749c90ecdf18cd47a47a43aabff49c6"
] | [
"cfdpost/cfdresult.py"
] | [
"'''\nPost process of CFD results\n'''\nimport copy\nimport os\nimport platform\n\nimport numpy as np\nimport struct as st\n\n\nclass cfl3d():\n '''\n Extracting data from cfl3d results\n '''\n\n def __init__(self):\n print('All static method functions')\n pass\n\n @staticmethod\n de... | [
[
"numpy.expand_dims",
"numpy.sqrt",
"numpy.min",
"numpy.squeeze",
"numpy.max",
"numpy.mean",
"numpy.insert",
"numpy.transpose",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hal-314/fastinference | [
"03e86920825520d842cf4ad75e5c9daf4614a143"
] | [
"fastinference/onnx.py"
] | [
"# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/03_onnx.ipynb (unless otherwise specified).\n\n__all__ = ['fastONNX']\n\n# Cell\nfrom .soft_dependencies import SoftDependencies\nif not SoftDependencies.check()['onnxcpu']:\n raise ImportError(\"The onnxcpu or onnxgpu module is not installed.\")\n\n# Cell\nfrom f... | [
[
"torch.tensor",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
usaito/unbiased-implicit-rec-real | [
"ff1435ea82613b1ed5c2690b77c130ddf57c0b27"
] | [
"src/models/rec_eval.py"
] | [
"import bottleneck as bn\nimport numpy as np\n\nfrom scipy import sparse\n\n\n\"\"\"\nAll the data should be in the shape of (n_users, n_items)\nAll the latent factors should in the shape of (n_users/n_items, n_components)\n\n1. train_data refers to the data that was used to train the model\n2. heldout_data refers ... | [
[
"numpy.hstack",
"numpy.isfinite",
"numpy.arange",
"scipy.sparse.csr_matrix",
"numpy.zeros_like",
"numpy.argsort",
"numpy.logical_and",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
artificially-ai/clip-mania | [
"de612cbf94d0de7aa0d26e064e3d75b80909e776"
] | [
"tests/test_core/test_executor.py"
] | [
"import os\n\nfrom unittest import TestCase\n\nfrom pathlib import Path\n\nimport PIL\n\nimport torch\nimport clip\n\nimport numpy as np\n\nfrom clip_mania.core.executor import ModelExecutor\nfrom clip_mania.utils.data.preprocess import DatasetProcessor\n\n\nclass TestModelExecutor(TestCase):\n\n def setUp(self)... | [
[
"numpy.argmax",
"torch.no_grad",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nion-software/nionui | [
"082c7a3eb9547491a8e00e5dd700aeb2f8d6bc30"
] | [
"nion/ui/DrawingContext.py"
] | [
"\"\"\"\n DrawingContext module contains classes related to drawing context.\n\n DrawingContexts are able to be handled directly by the UI system or\n produce javascript or svg to do the drawing.\n\"\"\"\nfrom __future__ import annotations\n\n# standard libraries\nimport base64\nimport collections\nimport ... | [
[
"numpy.multiply",
"numpy.subtract",
"numpy.empty",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jcoughlin11/yt | [
"31565b56571609fc3afff156cda77dbff8fc986c"
] | [
"yt/visualization/tests/test_plotwindow.py"
] | [
"import os\nimport shutil\nimport tempfile\nimport unittest\nfrom collections import OrderedDict\n\nimport numpy as np\nfrom nose.tools import assert_true\n\nfrom yt.loaders import load_uniform_grid\nfrom yt.testing import (\n assert_array_almost_equal,\n assert_array_equal,\n assert_equal,\n assert_fna... | [
[
"numpy.arange",
"numpy.array",
"numpy.random.random"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dshea89/luminoth | [
"18607a4ca42fbeaf1c0e4dc7901f1f0467118253"
] | [
"luminoth/utils/bbox_transform_tf.py"
] | [
"import tensorflow.compat.v1 as tf\n\n\ndef get_width_upright(bboxes):\n with tf.name_scope('BoundingBoxTransform/get_width_upright'):\n bboxes = tf.cast(bboxes, tf.float32)\n x1, y1, x2, y2 = tf.split(bboxes, 4, axis=1)\n width = x2 - x1 + 1.\n height = y2 - y1 + 1.\n\n # Calc... | [
[
"tensorflow.compat.v1.stack",
"tensorflow.compat.v1.exp",
"tensorflow.compat.v1.concat",
"tensorflow.compat.v1.split",
"tensorflow.compat.v1.unstack",
"tensorflow.compat.v1.Session",
"tensorflow.compat.v1.placeholder",
"numpy.all",
"tensorflow.compat.v1.minimum",
"tensorflo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
azedarach/reanalysis-dbns | [
"160f405762fb33cfde38b1d3d63cc19e0bb3d591"
] | [
"src/reanalysis_dbns/indices/indopacific_sst.py"
] | [
"\"\"\"\nProvides routines for computing indices associated with Indo-Pacific SST.\n\"\"\"\n\n# License: MIT\n\nfrom __future__ import absolute_import, division\n\nimport os\n\nimport dask.array\nimport geopandas as gp\nimport numpy as np\nimport regionmask as rm\nimport scipy.linalg\nimport xarray as xr\n\nimport ... | [
[
"numpy.deg2rad",
"numpy.product"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Dhruv-Mohan/PoseEstimationForMobile | [
"5c17272be0336398d244c567eba80a2795135dc6"
] | [
"training/src/utils/pointIO.py"
] | [
"import numpy as np\n\ndef write_style_menpo(file_handle, pts):\n num_pts = pts.shape[0] # assuming pts is an nparray\n file_handle.write('version: 1\\nn_points: ' + str(num_pts) + '\\n{ \\n')\n for ptx, pty in pts:\n file_handle.write(str(ptx) + ' ' + str(pty) + '\\n')\n file_handle.write('}')\... | [
[
"numpy.asarray",
"numpy.reshape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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