code stringlengths 31 1.05M | apis list | extract_api stringlengths 97 1.91M |
|---|---|---|
import os
import argparse
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Input
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from softlearning.utils.keras import PicklableModel
from softlearning.models.convnet import convnet_model
from softlearn... | [
"argparse.ArgumentParser",
"softlearning.models.feedforward.feedforward_model",
"softlearning.utils.keras.PicklableModel",
"tensorflow.keras.optimizers.Adam",
"numpy.arange",
"tensorflow.keras.layers.Input",
"softlearning.models.convnet.convnet_model",
"os.path.join",
"numpy.random.shuffle"
] | [((823, 845), 'numpy.arange', 'np.arange', (['num_samples'], {}), '(num_samples)\n', (832, 845), True, 'import numpy as np\n'), ((850, 876), 'numpy.random.shuffle', 'np.random.shuffle', (['indices'], {}), '(indices)\n', (867, 876), True, 'import numpy as np\n'), ((1141, 1197), 'tensorflow.keras.layers.Input', 'Input', ... |
# -*- coding: utf-8 -*-
"""PCA concordance.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1J13PIl7JVDX3jkkMC6ai-9FZippStPe8
"""
import pandas as pd
import numpy as np
from scipy.stats import weightedtau, kendalltau, spearmanr, pearsonr
def _ge... | [
"pandas.DataFrame",
"numpy.linalg.eigh",
"scipy.stats.weightedtau",
"numpy.dot"
] | [((772, 798), 'numpy.linalg.eigh', 'np.linalg.eigh', (['dot_matrix'], {}), '(dot_matrix)\n', (786, 798), True, 'import numpy as np\n'), ((1058, 1130), 'pandas.DataFrame', 'pd.DataFrame', (['eigen_vec'], {'index': 'dot_matrix.index', 'columns': 'eigen_val.index'}), '(eigen_vec, index=dot_matrix.index, columns=eigen_val.... |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
A package of compatibility functions helping to ease the transition between
old MIRI data model and the new one. The module serves two purposes:
1) To provide replacements for methods which are not implemented in the
new data model. If any of these methods are stil... | [
"miri.datamodels.dqflags.insert_value_column",
"os.path.split",
"numpy.array"
] | [((4621, 4682), 'numpy.array', 'np.array', (['[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]'], {}), '([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]])\n', (4629, 4682), True, 'import numpy as np\n'), ((4739, 4800), 'numpy.array', 'np.array', (['[[1.0, 1.0, 1.0], [2.0, 2.0, 2.0], [1.0, 1.0, 1.0]]'], {}), '([[1... |
"""
Exercise: Linear Regression using Adagrad
@auth: <NAME>
@date: 2018/09/18
"""
# --------------------------------------------------------------------------------
# 1.Import packages
# --------------------------------------------------------------------------------
import copy
import matplotlib.pyplot as plt
import ... | [
"matplotlib.pyplot.tight_layout",
"numpy.meshgrid",
"numpy.sum",
"matplotlib.pyplot.get_cmap",
"numpy.std",
"matplotlib.pyplot.close",
"numpy.power",
"copy.copy",
"numpy.mean",
"numpy.loadtxt",
"numpy.linspace",
"numpy.random.rand",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig"... | [((1131, 1141), 'numpy.mean', 'np.mean', (['d'], {}), '(d)\n', (1138, 1141), True, 'import numpy as np\n'), ((1154, 1163), 'numpy.std', 'np.std', (['d'], {}), '(d)\n', (1160, 1163), True, 'import numpy as np\n'), ((2745, 2784), 'numpy.linspace', 'np.linspace', (['x_left', 'x_right', 'num_level'], {}), '(x_left, x_right... |
from collections import defaultdict
import h5py
import numpy as np
import pandas as pd
from itertools import product
systems = ['H2', 'LiH', 'Be', 'B', 'Li2', 'C']
ansatzes = ['SD-SJ', 'SD-SJBF', 'MD-SJ', 'MD-SJBF']
def get_mean_err(energies):
return energies.mean(), energies.mean(axis=0).std() / np.sqrt(energie... | [
"h5py.File",
"collections.defaultdict",
"pandas.Series",
"itertools.product",
"pandas.concat",
"numpy.sqrt"
] | [((344, 361), 'collections.defaultdict', 'defaultdict', (['list'], {}), '(list)\n', (355, 361), False, 'from collections import defaultdict\n'), ((367, 423), 'h5py.File', 'h5py.File', (['f"""../data/raw/data_pub_small_systems.h5"""', '"""r"""'], {}), "(f'../data/raw/data_pub_small_systems.h5', 'r')\n", (376, 423), Fals... |
import numpy
import pandas
from tec.ic.ia.p1 import g08_data
from tec.ic.ia.pc1 import g08
from sklearn.svm import LinearSVC
from sklearn.svm import SVC
def non_shuffling_train_test_split(X, y, test_size=0.2):
i = int((1 - test_size) * X.shape[0]) + 1
X_train, X_test = numpy.split(X, [i])
y_train, y_test =... | [
"tec.ic.ia.p1.g08_data.shaped_data2",
"tec.ic.ia.pc1.g08.generar_muestra_pais",
"sklearn.svm.LinearSVC",
"numpy.split"
] | [((279, 298), 'numpy.split', 'numpy.split', (['X', '[i]'], {}), '(X, [i])\n', (290, 298), False, 'import numpy\n'), ((321, 340), 'numpy.split', 'numpy.split', (['y', '[i]'], {}), '(y, [i])\n', (332, 340), False, 'import numpy\n'), ((464, 494), 'tec.ic.ia.p1.g08_data.shaped_data2', 'g08_data.shaped_data2', (['dataset'],... |
import os
import sys
import argparse
from PIL import Image
import numpy as np
import cv2
import torch
from torch.backends import cudnn
import torchvision.transforms as transforms
import network
from optimizer import restore_snapshot
from datasets import cityscapes
from config import assert_and_infer_cfg
parser = arg... | [
"network.get_net",
"numpy.zeros_like",
"argparse.ArgumentParser",
"os.makedirs",
"numpy.argmax",
"config.assert_and_infer_cfg",
"torchvision.transforms.Normalize",
"os.path.exists",
"PIL.Image.open",
"numpy.where",
"torch.cuda.empty_cache",
"torch.nn.DataParallel",
"torch.no_grad",
"os.pat... | [((317, 360), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""demo"""'}), "(description='demo')\n", (340, 360), False, 'import argparse\n'), ((851, 895), 'config.assert_and_infer_cfg', 'assert_and_infer_cfg', (['args'], {'train_mode': '(False)'}), '(args, train_mode=False)\n', (871, 895),... |
import pandas as pd
import numpy as np2
def build(args):
# Get medians
def get_medians(df_p, last):
df_res = df_p.iloc[-last:].groupby(["param"]).median().reset_index()["median"][0]
return df_res
def medians_params(df_list, age_group, last):
params_def = ["age", "beta", "IFR", "Re... | [
"pandas.read_csv",
"pandas.DataFrame",
"numpy.median"
] | [((709, 785), 'pandas.read_csv', 'pd.read_csv', (['args.params_data_path'], {'encoding': '"""unicode_escape"""', 'delimiter': '""","""'}), "(args.params_data_path, encoding='unicode_escape', delimiter=',')\n", (720, 785), True, 'import pandas as pd\n'), ((827, 896), 'pandas.DataFrame', 'pd.DataFrame', (["params_data_BO... |
from __future__ import print_function
import numpy as np
import tensorflow as tf
from agents.base_agent import BaseAgent
from agents.network.base_network_manager import BaseNetwork_Manager
# from agents.network import ac_network
from agents.network import ac_actor_network
from agents.network import ac_critic_network
f... | [
"experiment.write_summary",
"agents.network.ac_critic_network.AC_Critic_Network",
"tensorflow.global_variables_initializer",
"tensorflow.Session",
"numpy.expand_dims",
"numpy.random.RandomState",
"tensorflow.set_random_seed",
"agents.network.ac_actor_network.AC_Actor_Network",
"numpy.mean",
"numpy... | [((574, 615), 'numpy.random.RandomState', 'np.random.RandomState', (['config.random_seed'], {}), '(config.random_seed)\n', (595, 615), True, 'import numpy as np\n'), ((6207, 6253), 'numpy.reshape', 'np.reshape', (['reward_batch', '(self.batch_size, 1)'], {}), '(reward_batch, (self.batch_size, 1))\n', (6217, 6253), True... |
# -*- coding: utf-8 -*-
"""
@author: <NAME>
"""
import numpy as np
from scipy.stats import norm
import chip_local_search as cls
import matplotlib.pyplot as plt
from scipy.stats import multinomial
import generative_model_utils as utils
import parameter_estimation as estimate_utils
from spectral_clustering import spectr... | [
"generative_model_utils.event_dict_to_aggregated_adjacency",
"matplotlib.pyplot.yscale",
"numpy.argmax",
"numpy.logspace",
"parameter_estimation.compute_sample_mean_and_variance",
"numpy.histogram",
"numpy.mean",
"numpy.unique",
"numpy.copy",
"generative_model_utils.calc_block_pair_size",
"numpy... | [((1239, 1302), 'generative_model_utils.event_dict_to_aggregated_adjacency', 'utils.event_dict_to_aggregated_adjacency', (['num_nodes', 'event_dict'], {}), '(num_nodes, event_dict)\n', (1279, 1302), True, 'import generative_model_utils as utils\n'), ((1425, 1502), 'spectral_clustering.spectral_cluster', 'spectral_clust... |
from fastText import load_model
import joblib
import numpy as np
import sys
def isEnglish(s):
try:
s.encode(encoding='utf-8').decode('ascii')
except UnicodeDecodeError:
return False
else:
return True
def is_fence_word(w_embed, center1, center2):
distance1 = np.linalg.norm(w_emb... | [
"joblib.load",
"fastText.load_model",
"numpy.linalg.norm"
] | [((300, 343), 'numpy.linalg.norm', 'np.linalg.norm', (['(w_embed - centers[cluster1])'], {}), '(w_embed - centers[cluster1])\n', (314, 343), True, 'import numpy as np\n'), ((360, 403), 'numpy.linalg.norm', 'np.linalg.norm', (['(w_embed - centers[cluster2])'], {}), '(w_embed - centers[cluster2])\n', (374, 403), True, 'i... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Reads a folder of .md files (with file name formatting of export_notes.pm)
and rewrites the mendeley sqlite database notes table with the corresponding
document_id notes
"""
from __future__ import print_function, division
__author__ = "<NAME>"
__license__ = "MIT"
__v... | [
"pandas.DataFrame",
"os.path.abspath",
"argparse.ArgumentParser",
"os.path.basename",
"numpy.where",
"sqlite3.connect",
"os.path.join"
] | [((2389, 2418), 'sqlite3.connect', 'sqlite3.connect', (['DATABASE_LOC'], {}), '(DATABASE_LOC)\n', (2404, 2418), False, 'import sqlite3\n'), ((3546, 3585), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '""""""'}), "(description='')\n", (3569, 3585), False, 'import argparse\n'), ((3761, 3795)... |
#!python
# -*- coding: utf-8 -*-
__version__ = "$Revision: 1.8 $"
# $Source: /home/mechanoid/projects/py/cv/bottle/template-matcher/RCS/main.py,v $
#
# OS : GNU/Linux 4.10.3-1-ARCH
# COMPILER : Python 3.6.0
#
# AUTHOR : <NAME>
#
# http://www.mechanoid.kiev.ua
# e-mail : <EMAIL>
# - - - - - - - - - - - - -... | [
"os.path.basename",
"cv2.cvtColor",
"cv2.imwrite",
"numpy.nonzero",
"cv2.imread",
"numpy.max",
"numpy.where",
"cv2.rectangle",
"os.path.join",
"os.listdir",
"cv2.matchTemplate"
] | [((688, 741), 'cv2.matchTemplate', 'cv2.matchTemplate', (['img', 'img_tpl', 'cv2.TM_CCOEFF_NORMED'], {}), '(img, img_tpl, cv2.TM_CCOEFF_NORMED)\n', (705, 741), False, 'import cv2\n'), ((764, 781), 'numpy.max', 'np.max', (['match_map'], {}), '(match_map)\n', (770, 781), True, 'import numpy as np\n'), ((1282, 1332), 'num... |
import os
import pandas as pd
import pickle
import ast
import numpy as np
def open_dict_txt(dict_filename):
file = open(dict_filename, "r")
contents = file.read()
dictionary = ast.literal_eval(contents)
file.close()
return dictionary
DATASETS = ['sensorless_drive',
'segment',
... | [
"pandas.DataFrame",
"pandas.DataFrame.from_dict",
"os.path.exists",
"numpy.array",
"ast.literal_eval",
"os.path.join",
"os.listdir"
] | [((189, 215), 'ast.literal_eval', 'ast.literal_eval', (['contents'], {}), '(contents)\n', (205, 215), False, 'import ast\n'), ((2438, 2476), 'os.path.join', 'os.path.join', (['gdrive_rpath', 'MODEL_NAME'], {}), '(gdrive_rpath, MODEL_NAME)\n', (2450, 2476), False, 'import os\n'), ((6264, 6293), 'pandas.DataFrame', 'pd.D... |
__license__ = "MIT"
__author__ = "<NAME> (BGT) @ Johns Hopkins University"
__startdate__ = "2016.01.11"
__name__ = "cnn"
__module__ = "Network"
__lastdate__ = "2016.01.19"
__version__ = "0.01"
# To-do:
# - Need to make stepsize adaptive
# - Perturb the results every 100 mini_batches and choose the best solution a... | [
"numpy.sum",
"numpy.random.randn",
"numpy.argmax",
"numpy.roll",
"numpy.asarray",
"numpy.einsum",
"numpy.zeros",
"numpy.equal",
"numpy.prod",
"numpy.mean",
"numpy.arange",
"numpy.matmul",
"numpy.random.shuffle",
"numpy.sqrt"
] | [((2125, 2157), 'numpy.random.randn', 'np.random.randn', (['self.n_features'], {}), '(self.n_features)\n', (2140, 2157), True, 'import numpy as np\n'), ((2409, 2441), 'numpy.random.randn', 'np.random.randn', (['self.n_features'], {}), '(self.n_features)\n', (2424, 2441), True, 'import numpy as np\n'), ((2868, 2895), 'n... |
__description__ = \
"""
Base class for simulating genotype phenotype maps from epistasis models.
"""
__author__ = "<NAME>"
from .distribution import DistributionSimulation
from epistasis.mapping import encoding_to_sites, assert_epistasis
from epistasis.matrix import get_model_matrix
import gpmap
from gpmap import Gen... | [
"gpmap.utils.genotypes_to_mutations",
"numpy.zeros",
"gpmap.utils.mutations_to_genotypes",
"epistasis.matrix.get_model_matrix",
"epistasis.mapping.encoding_to_sites"
] | [((2126, 2176), 'epistasis.mapping.encoding_to_sites', 'encoding_to_sites', (['self.order', 'self.encoding_table'], {}), '(self.order, self.encoding_table)\n', (2143, 2176), False, 'from epistasis.mapping import encoding_to_sites, assert_epistasis\n'), ((4089, 4115), 'numpy.zeros', 'np.zeros', (['self.epistasis.n'], {}... |
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import h5py
m = 1
l_a = 1
l_r = 0.5
C_a = 1
C_r = 0.5
alpha = 1
beta = 0.5
N = 40
D = 2
num_steps = 4000
np.random.seed(3)
# q_prime = f(q)
z = np.zeros([3*N, D, num_steps]) #[x,v,a] = [x, f] = [q, a]
x = z[:N, :, :]
... | [
"h5py.File",
"numpy.random.seed",
"numpy.sum",
"numpy.zeros",
"numpy.arange",
"numpy.exp",
"numpy.random.rand"
] | [((205, 222), 'numpy.random.seed', 'np.random.seed', (['(3)'], {}), '(3)\n', (219, 222), True, 'import numpy as np\n'), ((246, 277), 'numpy.zeros', 'np.zeros', (['[3 * N, D, num_steps]'], {}), '([3 * N, D, num_steps])\n', (254, 277), True, 'import numpy as np\n'), ((428, 440), 'numpy.arange', 'np.arange', (['N'], {}), ... |
from numpy import inner
from numpy.linalg import norm
def cosine_similarity(a, b):
na, nb = norm(a), norm(b)
if na == .0 or nb == .0:
return .0
return inner(a, b)/(norm(a)*norm(b))
| [
"numpy.linalg.norm",
"numpy.inner"
] | [((98, 105), 'numpy.linalg.norm', 'norm', (['a'], {}), '(a)\n', (102, 105), False, 'from numpy.linalg import norm\n'), ((107, 114), 'numpy.linalg.norm', 'norm', (['b'], {}), '(b)\n', (111, 114), False, 'from numpy.linalg import norm\n'), ((173, 184), 'numpy.inner', 'inner', (['a', 'b'], {}), '(a, b)\n', (178, 184), Fal... |
import unittest
import numpy as np
from spectralcluster import laplacian
from spectralcluster import utils
LaplacianType = laplacian.LaplacianType
class TestComputeLaplacian(unittest.TestCase):
"""Tests for the compute_laplacian function."""
def test_affinity(self):
matrix = np.array([[3, 4], [-4, 3], [6, ... | [
"unittest.main",
"spectralcluster.utils.compute_affinity_matrix",
"numpy.allclose",
"numpy.array",
"numpy.array_equal",
"spectralcluster.laplacian.compute_laplacian"
] | [((1980, 1995), 'unittest.main', 'unittest.main', ([], {}), '()\n', (1993, 1995), False, 'import unittest\n'), ((289, 334), 'numpy.array', 'np.array', (['[[3, 4], [-4, 3], [6, 8], [-3, -4]]'], {}), '([[3, 4], [-4, 3], [6, 8], [-3, -4]])\n', (297, 334), True, 'import numpy as np\n'), ((350, 387), 'spectralcluster.utils.... |
# common import abbreviations
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
import pandas as pd
import patsy
import itertools as it
import collections as co
import functools as ft
import os.path as osp
import glob
import textwrap
# finally, a better idiom for warni... | [
"numpy.random.seed",
"numpy.sum",
"numpy.argmax",
"numpy.empty",
"numpy.mean",
"numpy.arange",
"matplotlib.pyplot.gca",
"itertools.cycle",
"pandas.set_option",
"numpy.round",
"numpy.unique",
"numpy.set_printoptions",
"numpy.meshgrid",
"matplotlib.ticker.FixedLocator",
"numpy.max",
"num... | [((349, 424), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {'category': 'FutureWarning', 'module': '"""sklearn"""'}), "('ignore', category=FutureWarning, module='sklearn')\n", (372, 424), False, 'import sklearn, warnings\n'), ((805, 852), 'numpy.set_printoptions', 'np.set_printoptions', ([],... |
# Author: <NAME>
import unittest
import numpy as np
from PySeismoSoil.class_site_factors import Site_Factors as SF
from PySeismoSoil.class_frequency_spectrum import Frequency_Spectrum as FS
class Test_Class_Site_Factors(unittest.TestCase):
'''
Unit test for Site_Factors class
'''
def test_range_check... | [
"numpy.meshgrid",
"unittest.TextTestRunner",
"PySeismoSoil.class_site_factors.Site_Factors",
"numpy.allclose",
"PySeismoSoil.class_site_factors.Site_Factors._range_check",
"PySeismoSoil.class_site_factors.Site_Factors._search_sorted",
"scipy.interpolate.RegularGridInterpolator",
"unittest.TestLoader",... | [((2728, 2762), 'PySeismoSoil.class_site_factors.Site_Factors._search_sorted', 'SF._search_sorted', (['(24)', 'z1000_array'], {}), '(24, z1000_array)\n', (2745, 2762), True, 'from PySeismoSoil.class_site_factors import Site_Factors as SF\n'), ((2816, 2850), 'PySeismoSoil.class_site_factors.Site_Factors._search_sorted',... |
#!/usr/bin/env python
import numpy.ma as ma
import os,sys, subprocess, math, datetime
from os.path import basename
import numpy as np
import time as tt
import gdal
import h5py
from datetime import timedelta,datetime
from gdalconst import GDT_Float32, GA_Update
from osgeo import ogr, osr
#TODO: change for handling of ... | [
"numpy.amin",
"numpy.ones",
"gdal.GetDriverByName",
"os.path.exists",
"numpy.max",
"numpy.reshape",
"h5py.File",
"numpy.average",
"os.path.basename",
"os.path.realpath",
"os.system",
"numpy.min",
"datetime.datetime.fromtimestamp",
"sys.exit",
"numpy.ma.masked_equal",
"gdal.Open",
"nu... | [((462, 491), 'os.path.basename', 'os.path.basename', (['fileAbsPath'], {}), '(fileAbsPath)\n', (478, 491), False, 'import os, sys, subprocess, math, datetime\n'), ((716, 743), 'h5py.File', 'h5py.File', (['fileAbsPath', '"""r"""'], {}), "(fileAbsPath, 'r')\n", (725, 743), False, 'import h5py\n'), ((762, 791), 'gdal.Get... |
# coding=utf-8
# Copyright 2022 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | [
"absl.testing.absltest.main",
"numpy.random.seed",
"numpy.isnan",
"numpy.sin",
"numpy.testing.assert_array_almost_equal",
"sofima.map_utils.compose_maps",
"numpy.zeros_like",
"sofima.map_utils.fill_missing",
"connectomics.common.bounding_box.BoundingBox",
"numpy.isfinite",
"sofima.map_utils.to_r... | [((8599, 8614), 'absl.testing.absltest.main', 'absltest.main', ([], {}), '()\n', (8612, 8614), False, 'from absl.testing import absltest\n'), ((1300, 1397), 'scipy.interpolate.griddata', 'interpolate.griddata', (['data_points', 'coord_map[0, 0, ...][valid]', 'query_points'], {'method': '"""linear"""'}), "(data_points, ... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys
import numpy as np
# =============== function =============== #
def split_n(line, length):
"""
n 文字毎に分割する関数
Args:
line (str): 対象文字列
length (int): 分割後の個別の文字列の長さ
Returns:
list
"""
return [line[i:i+length] for i in range(0, len(line), length)]
... | [
"numpy.array",
"numpy.dot",
"sys.stderr.write",
"numpy.delete",
"sys.exit"
] | [((3304, 3320), 'numpy.array', 'np.array', (['coords'], {}), '(coords)\n', (3312, 3320), True, 'import numpy as np\n'), ((4284, 4304), 'numpy.array', 'np.array', (['new_coords'], {}), '(new_coords)\n', (4292, 4304), True, 'import numpy as np\n'), ((5429, 5467), 'numpy.delete', 'np.delete', (['self._coords', 'idx_remove... |
# Authors: <NAME>
"""
This example shows the most simple way of using a solver.
We solve free vibration of a simple oscillator::
m \ddot{u} + k u = 0, u(0) = u_0, \dot{u}(0) = \dot{u}_0
using the CVODE solver, which means we use a rhs function of \dot{u}.
Solution::
u(t) = u_0*cos(sqrt(k/m)*t)+\dot{u}_0... | [
"scikits.odes.ode",
"numpy.sqrt"
] | [((799, 834), 'scikits.odes.ode', 'ode', (['"""cvode"""', 'rhseqn'], {'old_api': '(False)'}), "('cvode', rhseqn, old_api=False)\n", (802, 834), False, 'from scikits.odes import ode\n'), ((1164, 1175), 'numpy.sqrt', 'sqrt', (['(k / m)'], {}), '(k / m)\n', (1168, 1175), False, 'from numpy import asarray, cos, sin, sqrt\n... |
import unittest
from imgaug.augmenters.meta import Augmenter, Sequential
import numpy as np
from autoPyTorch.pipeline.components.setup.augmentation.image.ImageAugmenter import ImageAugmenter
class TestImageAugmenter(unittest.TestCase):
def test_every_augmenter(self):
image_augmenter = ImageAugmenter()
... | [
"unittest.main",
"numpy.random.randint",
"autoPyTorch.pipeline.components.setup.augmentation.image.ImageAugmenter.ImageAugmenter"
] | [((2253, 2268), 'unittest.main', 'unittest.main', ([], {}), '()\n', (2266, 2268), False, 'import unittest\n'), ((303, 319), 'autoPyTorch.pipeline.components.setup.augmentation.image.ImageAugmenter.ImageAugmenter', 'ImageAugmenter', ([], {}), '()\n', (317, 319), False, 'from autoPyTorch.pipeline.components.setup.augment... |
# Licensed under an MIT style license -- see LICENSE.md
try:
import pepredicates as pep
PEP = True
except ImportError:
PEP = False
import numpy as np
import pandas as pd
from pesummary.core.plots.figure import ExistingFigure
from pesummary.utils.utils import logger
__author__ = ["<NAME> <<EMAIL>>"]
def... | [
"pandas.DataFrame",
"pepredicates.plot_predicates",
"pesummary.utils.utils.logger.debug",
"pepredicates.is_BNS",
"pepredicates.is_BBH",
"pepredicates.rewt_approx_massdist_redshift",
"pepredicates.is_NSBH",
"pepredicates.is_MG",
"pesummary.utils.utils.RedirectLogger",
"numpy.round"
] | [((2503, 2517), 'pandas.DataFrame', 'pd.DataFrame', ([], {}), '()\n', (2515, 2517), True, 'import pandas as pd\n'), ((3486, 3528), 'pepredicates.rewt_approx_massdist_redshift', 'pep.rewt_approx_massdist_redshift', (['samples'], {}), '(samples)\n', (3519, 3528), True, 'import pepredicates as pep\n'), ((5572, 5620), 'pes... |
#!/usr/bin/env python
# Copyright 2016 NeuroData (http://neurodata.io)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required ... | [
"vtk.vtkVolumeProperty",
"dipy.viz.actor.line",
"numpy.median",
"vtk.vtkVolume",
"dipy.viz.window.Renderer",
"vtk.vtkPiecewiseFunction",
"vtk.vtkNIFTIImageReader",
"vtk.vtkSmartVolumeMapper",
"os.path.split",
"dipy.viz.window.record",
"vtk.vtkColorTransferFunction"
] | [((1913, 1930), 'dipy.viz.window.Renderer', 'window.Renderer', ([], {}), '()\n', (1928, 1930), False, 'from dipy.viz import window, actor\n'), ((2036, 2052), 'dipy.viz.actor.line', 'actor.line', (['fibs'], {}), '(fibs)\n', (2046, 2052), False, 'from dipy.viz import window, actor\n'), ((2512, 2577), 'dipy.viz.window.rec... |
#coding=utf-8
import numpy as np
class Vector(object):
def __init__(self):
self._array = np.array([0., 0.],dtype=np.float64)
@property
def x(self):
return self._array[0]
@property
def y(self):
return self._array[1]
| [
"numpy.array"
] | [((102, 140), 'numpy.array', 'np.array', (['[0.0, 0.0]'], {'dtype': 'np.float64'}), '([0.0, 0.0], dtype=np.float64)\n', (110, 140), True, 'import numpy as np\n')] |
import numpy as np
def compress_image(image, num_values):
"""Compress an image using SVD and keeping the top `num_values` singular values.
Args:
image: numpy array of shape (H, W)
num_values: number of singular values to keep
Returns:
compressed_image: numpy array of shape (H, W)... | [
"numpy.linalg.svd",
"numpy.zeros_like",
"numpy.expand_dims"
] | [((438, 458), 'numpy.zeros_like', 'np.zeros_like', (['image'], {}), '(image)\n', (451, 458), True, 'import numpy as np\n'), ((714, 734), 'numpy.linalg.svd', 'np.linalg.svd', (['image'], {}), '(image)\n', (727, 734), True, 'import numpy as np\n'), ((1001, 1032), 'numpy.expand_dims', 'np.expand_dims', (['U[:, i]'], {'axi... |
from numpy import zeros, trim_zeros, ones, linspace, concatenate, std,\
roll, diff, any, digitize, savetxt, append, array, savez_compressed, mean,\
sqrt
from pickle import dump
import matplotlib.pyplot as plt
from ShapeGen import *
from CrossSection import *
from flow import *
from sediment import *
import sys
from o... | [
"matplotlib.pyplot.show",
"numpy.concatenate",
"matplotlib.pyplot.plot",
"numpy.roll",
"numpy.zeros",
"matplotlib.pyplot.axis",
"os.path.exists",
"numpy.ones",
"numpy.mean",
"numpy.linspace",
"sys.exit"
] | [((2179, 2191), 'numpy.zeros', 'zeros', (['(t + 1)'], {}), '(t + 1)\n', (2184, 2191), False, 'from numpy import zeros, trim_zeros, ones, linspace, concatenate, std, roll, diff, any, digitize, savetxt, append, array, savez_compressed, mean, sqrt\n'), ((2195, 2207), 'numpy.zeros', 'zeros', (['(t + 1)'], {}), '(t + 1)\n',... |
import matplotlib.animation as animation
import matplotlib.cm as cm
import matplotlib.pylab as plt
from nilearn.image import load_img
import numpy as np
import os as os
import pandas as pd
# plot exemplar prediction set
def draw_image_masks(brain_img, true_mask, predicted_mask):
my_cmap_predict = cm.jet
my_cm... | [
"matplotlib.pylab.show",
"os.makedirs",
"matplotlib.pylab.imshow",
"nilearn.image.load_img",
"matplotlib.pylab.title",
"os.path.exists",
"os.walk",
"matplotlib.pylab.plot",
"matplotlib.pylab.close",
"numpy.random.rand",
"matplotlib.pylab.tight_layout",
"os.path.join",
"matplotlib.pylab.figur... | [((455, 490), 'matplotlib.pylab.imshow', 'plt.imshow', (['brain_img'], {'cmap': '"""Greys"""'}), "(brain_img, cmap='Greys')\n", (465, 490), True, 'import matplotlib.pylab as plt\n'), ((513, 614), 'matplotlib.pylab.imshow', 'plt.imshow', (['predicted_mask'], {'cmap': 'my_cmap_predict', 'interpolation': '"""none"""', 'cl... |
'''
This file containts the run algorithm for the transit search
'''
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from matplotlib import colors
import itertools
import numpy as np
import multiprocessing as mp
import os
from tqdm import tqdm
import pickle
from transitleastsquares import transi... | [
"pickle.dump",
"transitleastsquares.transitleastsquares",
"numpy.abs",
"numpy.sum",
"matplotlib.colors.PowerNorm",
"numpy.argmax",
"numpy.shape",
"numpy.argsort",
"matplotlib.pyplot.figure",
"numpy.mean",
"numpy.arange",
"matplotlib.pyplot.tight_layout",
"os.path.join",
"multiprocessing.cp... | [((1974, 2060), 'numpy.arange', 'np.arange', (['self.TDurLower', '(self.TDurHigher + self.TDurStepSize)', 'self.TDurStepSize'], {}), '(self.TDurLower, self.TDurHigher + self.TDurStepSize, self.\n TDurStepSize)\n', (1983, 2060), True, 'import numpy as np\n'), ((10637, 10664), 'numpy.array', 'np.array', (['self.AllMod... |
from __future__ import absolute_import
from __future__ import division
from builtins import zip
from builtins import range
import scipy.spatial as spa
import scipy.linalg as lin
import numpy as np
def prepare_input(file_name): #pylint: disable=unused-argument
"""Read in from list of files and output them as... | [
"numpy.cross",
"numpy.any",
"numpy.linalg.det",
"numpy.arange",
"numpy.array",
"builtins.zip",
"scipy.linalg.norm",
"numpy.dot",
"scipy.spatial.ConvexHull",
"builtins.range",
"numpy.vstack"
] | [((997, 1022), 'scipy.spatial.ConvexHull', 'spa.ConvexHull', (['data_list'], {}), '(data_list)\n', (1011, 1022), True, 'import scipy.spatial as spa\n'), ((1457, 1489), 'numpy.arange', 'np.arange', (['three_points.shape[1]'], {}), '(three_points.shape[1])\n', (1466, 1489), True, 'import numpy as np\n'), ((2369, 2381), '... |
# MIT License
#
# Copyright (c) 2021 <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge,... | [
"numpy.arctan2",
"dsorlib.utils.wrapAngle",
"numpy.linalg.norm",
"numpy.array",
"numpy.cos",
"numpy.sin"
] | [((2735, 2765), 'numpy.array', 'np.array', (['[self._yc, self._xc]'], {}), '([self._yc, self._xc])\n', (2743, 2765), True, 'import numpy as np\n'), ((2779, 2809), 'numpy.array', 'np.array', (['[self._ys, self._xs]'], {}), '([self._ys, self._xs])\n', (2787, 2809), True, 'import numpy as np\n'), ((2828, 2851), 'numpy.lin... |
"""
You can use this script to evaluate prediction files (valpreds.npy). Essentially this is needed if you want to, say,
combine answer and rationale predictions.
"""
import numpy as np
import json
import os
# get gt labels
labels = {
'val_rationale': [],
'test_rationale': [],
'val_answer': [],
'test_a... | [
"json.loads",
"numpy.mean",
"numpy.array",
"numpy.exp",
"os.path.join"
] | [((673, 692), 'numpy.array', 'np.array', (['labels[k]'], {}), '(labels[k])\n', (681, 692), True, 'import numpy as np\n'), ((1651, 1660), 'numpy.exp', 'np.exp', (['x'], {}), '(x)\n', (1657, 1660), True, 'import numpy as np\n'), ((483, 499), 'json.loads', 'json.loads', (['line'], {}), '(line)\n', (493, 499), False, 'impo... |
import h5py
import numpy
import scipy.sparse
import scipy.linalg
import sys
from afqmctools.utils.io import (
to_qmcpack_complex,
from_qmcpack_complex
)
from afqmctools.hamiltonian.io import (
write_sparse_basic,
write_sparse_chol_chunk
)
def get_dset_simple(fh5, name):... | [
"h5py.File",
"numpy.abs",
"numpy.tril",
"afqmctools.hamiltonian.io.write_sparse_basic",
"numpy.zeros",
"afqmctools.utils.io.from_qmcpack_complex",
"numpy.argsort",
"afqmctools.utils.io.to_qmcpack_complex",
"numpy.array",
"numpy.real",
"numpy.unique"
] | [((20756, 20791), 'numpy.zeros', 'numpy.zeros', (['nkp'], {'dtype': 'numpy.int32'}), '(nkp, dtype=numpy.int32)\n', (20767, 20791), False, 'import numpy\n'), ((25629, 25664), 'numpy.zeros', 'numpy.zeros', (['nkp'], {'dtype': 'numpy.int32'}), '(nkp, dtype=numpy.int32)\n', (25640, 25664), False, 'import numpy\n'), ((25683... |
from pysb.tools.sensitivity_analysis import \
PairwiseSensitivity, InitialsSensitivity
from pysb.examples.tyson_oscillator import model
import numpy as np
import numpy.testing as npt
import os
from pysb.simulator.scipyode import ScipyOdeSimulator
import tempfile
import shutil
from nose.tools import raises
class T... | [
"pysb.simulator.scipyode.ScipyOdeSimulator",
"numpy.average",
"shutil.rmtree",
"numpy.testing.assert_almost_equal",
"os.path.exists",
"tempfile.mkdtemp",
"numpy.array",
"numpy.linspace",
"nose.tools.raises",
"os.path.join",
"pysb.tools.sensitivity_analysis.PairwiseSensitivity",
"pysb.tools.sen... | [((6569, 6587), 'nose.tools.raises', 'raises', (['ValueError'], {}), '(ValueError)\n', (6575, 6587), False, 'from nose.tools import raises\n'), ((7262, 7280), 'nose.tools.raises', 'raises', (['ValueError'], {}), '(ValueError)\n', (7268, 7280), False, 'from nose.tools import raises\n'), ((7613, 7630), 'nose.tools.raises... |
import sys
import numpy as np
from scipy.misc import *
def to_cylindrical(image, camera_params):
F, k1, k2 = camera_params
cyl_img_pts = np.mgrid[:image.shape[0], :image.shape[1]].transpose(1,2,0).astype(np.float32)
x_cyl = cyl_img_pts[..., 1]
y_cyl = cyl_img_pts[..., 0]
# Convert from cylindric... | [
"numpy.dstack",
"numpy.sin",
"numpy.loadtxt",
"numpy.cos"
] | [((589, 602), 'numpy.sin', 'np.sin', (['theta'], {}), '(theta)\n', (595, 602), True, 'import numpy as np\n'), ((629, 642), 'numpy.cos', 'np.cos', (['theta'], {}), '(theta)\n', (635, 642), True, 'import numpy as np\n'), ((1257, 1318), 'numpy.dstack', 'np.dstack', (['[yPrime[..., np.newaxis], xPrime[..., np.newaxis]]'], ... |
from scipy import optimize
import numpy as np
def IR_constraint(Rtype,pH,pL,B,A,p):
if Rtype=='sqroot':
def f(x,a):
return pH*(x**(0.5)-B*x/(pH-pL))-(x-a)
def grad(x):
return pH*0.5*x**(-0.5)-pH*B/(pH-pL)-1
sol = np.zeros((A.size))
for i in range(0,... | [
"numpy.log",
"numpy.zeros",
"scipy.optimize.newton"
] | [((276, 292), 'numpy.zeros', 'np.zeros', (['A.size'], {}), '(A.size)\n', (284, 292), True, 'import numpy as np\n'), ((641, 657), 'numpy.zeros', 'np.zeros', (['A.size'], {}), '(A.size)\n', (649, 657), True, 'import numpy as np\n'), ((1016, 1032), 'numpy.zeros', 'np.zeros', (['A.size'], {}), '(A.size)\n', (1024, 1032), T... |
import pandas as pd
import numpy as np
import tensorflow as tf
import functools
from witwidget.notebook.visualization import WitConfigBuilder
from witwidget.notebook.visualization import WitWidget
# Creates a tf feature spec from the dataframe and columns specified.
def create_feature_spec(df, columns=None):
feat... | [
"functools.partial",
"tensorflow.train.Example",
"tensorflow.feature_column.numeric_column",
"numpy.dtype",
"tensorflow.TensorShape",
"witwidget.notebook.visualization.WitConfigBuilder",
"tensorflow.io.parse_example",
"tensorflow.io.FixedLenFeature"
] | [((2426, 2494), 'tensorflow.io.parse_example', 'tf.io.parse_example', ([], {'serialized': 'example_proto', 'features': 'feature_spec'}), '(serialized=example_proto, features=feature_spec)\n', (2445, 2494), True, 'import tensorflow as tf\n'), ((4104, 4180), 'functools.partial', 'functools.partial', (['tfexamples_input_f... |
import logging
import os
import numpy as np
import pandas as pd
import seaborn as sb
from matplotlib import pyplot as plt
from observers import dynamics_traj_observer
from utils import RMS, log_multivariate_normal_likelihood, reshape_pt1, \
reshape_dim1, interpolate
sb.set_style('whitegrid')
# Some useful plot... | [
"matplotlib.pyplot.title",
"numpy.arange",
"utils.log_multivariate_normal_likelihood",
"matplotlib.pyplot.fill_between",
"os.path.join",
"pandas.DataFrame",
"logging.warning",
"matplotlib.pyplot.close",
"utils.RMS",
"seaborn.set_style",
"utils.reshape_dim1",
"matplotlib.pyplot.show",
"matplo... | [((274, 299), 'seaborn.set_style', 'sb.set_style', (['"""whitegrid"""'], {}), "('whitegrid')\n", (286, 299), True, 'import seaborn as sb\n'), ((3097, 3130), 'numpy.zeros', 'np.zeros', (['(rollout_length + 1, 1)'], {}), '((rollout_length + 1, 1))\n', (3105, 3130), True, 'import numpy as np\n'), ((6731, 6762), 'utils.RMS... |
# coding:utf-8
import tensorflow as tf
import numpy as np
import time
from ReinforcementLearning.Modules.Environments.IEnv import IEnv
from ReinforcementLearning.Modules.Models.Models import DDPG_Model_v1, DDPG_Global_And_Local_Models_v1, \
DDPG_Model_v2_With_Reward_PreCorr
from ReinforcementLearning.Modules.Agents... | [
"threading.Thread",
"ReinforcementLearning.Modules.Models.Models.DDPG_Global_And_Local_Models_v1",
"tensorflow.train.Coordinator",
"ReinforcementLearning.Modules.Models.Models.DDPG_Model_v2_With_Reward_PreCorr",
"ReinforcementLearning.Modules.DataAnalysisTools.DataMonitor.LineConfig",
"copy.deepcopy",
"... | [((3851, 3863), 'tensorflow.Session', 'tf.Session', ([], {}), '()\n', (3861, 3863), True, 'import tensorflow as tf\n'), ((3994, 4193), 'ReinforcementLearning.Modules.Models.Models.DDPG_Global_And_Local_Models_v1', 'DDPG_Global_And_Local_Models_v1', ([], {'is_global_model': '(True)', 'a_dim': 'self.action_space', 's_dim... |
from __future__ import annotations
import numpy as np
import pytest
import xarray as xr
from scipy.stats import genpareto, norm, uniform
from xclim.core.options import set_options
from xclim.core.units import convert_units_to
from xclim.sdba.adjustment import (
LOCI,
DetrendedQuantileMapping,
EmpiricalQua... | [
"xclim.sdba.processing.uniform_noise_like",
"xarray.testing.assert_equal",
"scipy.stats.norm.rvs",
"xclim.sdba.base.Grouper",
"xclim.core.units.convert_units_to",
"xclim.sdba.adjustment.LOCI.from_dataset",
"numpy.arange",
"pytest.mark.parametrize",
"numpy.testing.assert_array_almost_equal",
"xclim... | [((796, 866), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""group,dec"""', "(['time', 2], ['time.month', 1])"], {}), "('group,dec', (['time', 2], ['time.month', 1]))\n", (819, 866), False, 'import pytest\n'), ((2214, 2299), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""kind,name"""', "[(ADDI... |
from abc import ABC, abstractmethod
from typing import List, Optional
from gym import Space, spaces
import numpy as np
from stable_baselines3.common import noise
from yacht import Mode, utils
from yacht.config import Config
from yacht.config.proto.environment_pb2 import EnvironmentConfig
from yacht.data.datasets impo... | [
"yacht.environments.action_noises.build_action_noise",
"yacht.utils.build_from_kwargs",
"stable_baselines3.common.noise.VectorizedActionNoise",
"yacht.environments.action_noises.apply_action_noise",
"numpy.clip",
"numpy.min",
"numpy.where",
"numpy.array",
"gym.spaces.Box",
"numpy.max"
] | [((4742, 4829), 'yacht.utils.build_from_kwargs', 'utils.build_from_kwargs', (['action_schema_class', 'action_schema_kwargs'], {'to_numpy': '(False)'}), '(action_schema_class, action_schema_kwargs, to_numpy\n =False)\n', (4765, 4829), False, 'from yacht import Mode, utils\n'), ((1702, 1743), 'numpy.array', 'np.array'... |
import os
import numpy as np
IMG_EXTENSIONS = ['.h5', ]
def is_image_file(filename):
"""Check whether this file has specified extensions (".h5") or not.
Args:
filename (str): Image filename
Returns:
bool: Whether this file's extension is in IMG_EXTENSIONS or not.
"""
return any(... | [
"os.path.expanduser",
"os.path.isdir",
"os.walk",
"numpy.random.random",
"os.path.join",
"os.listdir"
] | [((827, 850), 'os.path.expanduser', 'os.path.expanduser', (['dir'], {}), '(dir)\n', (845, 850), False, 'import os\n'), ((876, 891), 'os.listdir', 'os.listdir', (['dir'], {}), '(dir)\n', (886, 891), False, 'import os\n'), ((906, 931), 'os.path.join', 'os.path.join', (['dir', 'target'], {}), '(dir, target)\n', (918, 931)... |
import numpy as np
import bokeh.plotting as bpl
class plotter:
'''
This object is meant to emulate a matplotlib.pyplot.figure object, but with
the bokeh html plotting library.
'''
def __init__(self, rootpath=None):
'''
ARGUMENTS:
rootpath - String of a path to a directo... | [
"bokeh.plotting.figure",
"bokeh.plotting.output_file",
"bokeh.plotting.show",
"numpy.array",
"numpy.linspace",
"bokeh.plotting.save"
] | [((4267, 4289), 'numpy.linspace', 'np.linspace', (['(0)', '(1)', '(100)'], {}), '(0, 1, 100)\n', (4278, 4289), True, 'import numpy as np\n'), ((3620, 3638), 'bokeh.plotting.show', 'bpl.show', (['self.fig'], {}), '(self.fig)\n', (3628, 3638), True, 'import bokeh.plotting as bpl\n'), ((4138, 4156), 'bokeh.plotting.save',... |
import numpy as np
import tensorflow as tf
from onnx_tf.common import sys_config
class PadMixin(object):
@classmethod
def get_padding_as_op(cls, x, pads):
num_dim = int(len(pads) / 2)
tf_pads = np.transpose(np.array(pads).reshape([2, num_dim]))
if sys_config.device == 'MCU':
# make sure to ... | [
"tensorflow.pad",
"numpy.array"
] | [((650, 668), 'tensorflow.pad', 'tf.pad', (['x', 'padding'], {}), '(x, padding)\n', (656, 668), True, 'import tensorflow as tf\n'), ((223, 237), 'numpy.array', 'np.array', (['pads'], {}), '(pads)\n', (231, 237), True, 'import numpy as np\n'), ((538, 555), 'numpy.array', 'np.array', (['tf_pads'], {}), '(tf_pads)\n', (54... |
import time
import numpy as np
from ..misc import NumpyRNGContext
def func(i):
"""An identity function that jitters its execution time by a
pseudo-random amount.
FIXME: This function should be defined in test_console.py, but Astropy's
`python setup.py test` interacts strangely with Python's `multip... | [
"numpy.random.uniform"
] | [((543, 569), 'numpy.random.uniform', 'np.random.uniform', (['(0)', '(0.01)'], {}), '(0, 0.01)\n', (560, 569), True, 'import numpy as np\n')] |
import unittest
import numpy as np
from pysster.One_Hot_Encoder import One_Hot_Encoder
class Test_One_Hot_Encoder(unittest.TestCase):
def setUp(self):
self.one = One_Hot_Encoder("ACGT")
self.reference_seq = "GATTACA"
self.reference_one_hot = np.array([[0,0,1,0],
... | [
"numpy.array_equal",
"pysster.One_Hot_Encoder.One_Hot_Encoder",
"numpy.array"
] | [((191, 214), 'pysster.One_Hot_Encoder.One_Hot_Encoder', 'One_Hot_Encoder', (['"""ACGT"""'], {}), "('ACGT')\n", (206, 214), False, 'from pysster.One_Hot_Encoder import One_Hot_Encoder\n'), ((289, 417), 'numpy.array', 'np.array', (['[[0, 0, 1, 0], [1, 0, 0, 0], [0, 0, 0, 1], [0, 0, 0, 1], [1, 0, 0, 0], [0, \n 1, 0, 0... |
"""
Copyright 2021 <NAME>
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
https://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, softw... | [
"pandas.read_excel",
"pandas.DataFrame",
"pandas.ExcelWriter",
"numpy.array"
] | [((677, 705), 'pandas.ExcelWriter', 'pd.ExcelWriter', (['"""out_1.xlsx"""'], {}), "('out_1.xlsx')\n", (691, 705), True, 'import pandas as pd\n'), ((744, 817), 'pandas.read_excel', 'pd.read_excel', (['"""测点-测压阀-第1批.xlsx"""'], {'sheet_name': 'name', 'header': 'None', 'dtype': 'str'}), "('测点-测压阀-第1批.xlsx', sheet_name=name... |
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 28 09:28:15 2017
@author: tkoller
"""
import warnings
import casadi as cas
import numpy as np
from casadi import MX, mtimes, vertcat
from casadi import reshape as cas_reshape
from .gp_reachability_casadi import lin_ellipsoid_safety_distance
from .uncertainty_propagation... | [
"casadi.MX.eye",
"casadi.nlpsol",
"casadi.reshape",
"numpy.copy",
"numpy.random.randn",
"numpy.zeros",
"numpy.hstack",
"numpy.shape",
"casadi.vertcat",
"casadi.mtimes",
"casadi.MX.sym",
"casadi.Function",
"numpy.array",
"numpy.dot",
"numpy.eye",
"warnings.warn",
"numpy.vstack"
] | [((3535, 3560), 'numpy.shape', 'np.shape', (['self.h_mat_safe'], {}), '(self.h_mat_safe)\n', (3543, 3560), True, 'import numpy as np\n'), ((3816, 3832), 'numpy.eye', 'np.eye', (['self.n_s'], {}), '(self.n_s)\n', (3822, 3832), True, 'import numpy as np\n'), ((3850, 3880), 'numpy.zeros', 'np.zeros', (['(self.n_s, self.n_... |
import scipy.io.wavfile as wav
import noisereduce as nr
import sounddevice as sd
import numpy as np
import pyaudio
import matplotlib.pyplot as plt
import soundfile as sf
import time
def denoiser(file):
data, fs = sf.read(file, dtype='int16')
noise, fs = sf.read('./tmp/static.wav', dtype='int16')
data = lis... | [
"soundfile.read",
"numpy.array",
"noisereduce.reduce_noise",
"scipy.io.wavfile.write"
] | [((218, 246), 'soundfile.read', 'sf.read', (['file'], {'dtype': '"""int16"""'}), "(file, dtype='int16')\n", (225, 246), True, 'import soundfile as sf\n'), ((263, 305), 'soundfile.read', 'sf.read', (['"""./tmp/static.wav"""'], {'dtype': '"""int16"""'}), "('./tmp/static.wav', dtype='int16')\n", (270, 305), True, 'import ... |
'''
===============================================================================
-- Author: <NAME>, <NAME>
-- Create date: 04/11/2020
-- Description: This codes is for aligning templates bade on ORB.
ORB (Oriented FAST and Rotated BRIEF) ORB is a FAST keypoint
detector an... | [
"cv2.warpPerspective",
"cv2.drawMatches",
"cv2.waitKey",
"cv2.cvtColor",
"cv2.addWeighted",
"numpy.hstack",
"cv2.imread",
"cv2.ORB_create",
"cv2.DescriptorMatcher_create",
"imutils.resize",
"cv2.imshow",
"cv2.findHomography"
] | [((1998, 2021), 'cv2.imread', 'cv2.imread', (['sys.argv[1]'], {}), '(sys.argv[1])\n', (2008, 2021), False, 'import cv2\n'), ((2032, 2055), 'cv2.imread', 'cv2.imread', (['sys.argv[2]'], {}), '(sys.argv[2])\n', (2042, 2055), False, 'import cv2\n'), ((2226, 2260), 'imutils.resize', 'imutils.resize', (['aligned'], {'width'... |
# %%
"""
Created on Sat Mar 20 09:43:09 2021
@author: j
this program read GOES files (cloud mask or a specific channel) and produce an estimate of Cloud Fraction outside and inside an SST contour (the value set for is the 26.5 contour). A good part of this code have been readadapted from personal communication with t... | [
"GOES.create_gridmap",
"matplotlib.pyplot.title",
"numpy.isnan",
"matplotlib.pyplot.contour",
"numpy.arange",
"pyresample.geometry.SwathDefinition",
"numpy.meshgrid",
"matplotlib.rcParams.update",
"matplotlib.pyplot.colorbar",
"matplotlib.dates.DateFormatter",
"datetime.timedelta",
"GOES.open_... | [((1584, 1671), 'GOES.locate_files', 'GOES.locate_files', (['path', '"""OR_ABI-L2-ACMF*.nc"""', '"""20200202-040000"""', '"""20200204-040400"""'], {}), "(path, 'OR_ABI-L2-ACMF*.nc', '20200202-040000',\n '20200204-040400')\n", (1601, 1671), False, 'import GOES\n'), ((1761, 1889), 'xarray.open_dataset', 'xr.open_datas... |
#!/usr/bin/env python
# coding: utf-8
# # Principal Component Analysis
#
# ## 1. Libraries
# In[1]:
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import display
from sklearn.decomposition import PCA
# ## 2. Data
# In[2]:
# Generate initial points (Gaussian, centered around 0,0)
np.r... | [
"numpy.radians",
"numpy.random.seed",
"numpy.sin",
"sklearn.decomposition.PCA",
"numpy.random.normal",
"numpy.cos",
"matplotlib.pyplot.subplots",
"numpy.vstack"
] | [((316, 336), 'numpy.random.seed', 'np.random.seed', (['(3347)'], {}), '(3347)\n', (330, 336), True, 'import numpy as np\n'), ((493, 509), 'numpy.radians', 'np.radians', (['(60.0)'], {}), '(60.0)\n', (503, 509), True, 'import numpy as np\n'), ((667, 686), 'sklearn.decomposition.PCA', 'PCA', ([], {'n_components': '(2)'}... |
import numpy as np
"""
A "service" represents a third-party integration into the API server.
In this example, the third-party is the NumPy library.
When the GraphQL endpoint receives a request, it forwards it to a service which
is responsible for producing the result.
My current thoughts are that services should no... | [
"numpy.matmul"
] | [((866, 890), 'numpy.matmul', 'np.matmul', (['first', 'second'], {}), '(first, second)\n', (875, 890), True, 'import numpy as np\n')] |
import os, sys
import numpy as np
import paddle
# paddle.enable_static()
import paddle.fluid as fluid
from paddle import ParamAttr
# import paddle.nn as nn
# import paddle.nn.functional as F
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
SEED = 666
input_shape =... | [
"numpy.load",
"numpy.random.seed",
"numpy.sum",
"paddle.concat",
"paddle.unsqueeze",
"paddle.nn.GRUCell",
"torch.cat",
"numpy.mean",
"paddle.mm",
"paddle.zeros",
"numpy.random.rand",
"paddle.nn.functional.softmax",
"paddle.fluid.dygraph.guard",
"numpy.max",
"torch.Tensor",
"torch.nn.Li... | [((7329, 7349), 'numpy.random.seed', 'np.random.seed', (['SEED'], {}), '(SEED)\n', (7343, 7349), True, 'import numpy as np\n'), ((8167, 8187), 'numpy.random.seed', 'np.random.seed', (['SEED'], {}), '(SEED)\n', (8181, 8187), True, 'import numpy as np\n'), ((8291, 8306), 'torch.Tensor', 'torch.Tensor', (['x'], {}), '(x)\... |
"""
Iterating through all sequences in a data directory, computing data stats for
each sequence (#instances, #activities, ...), cleaning stats and saving them
"""
import os
import copy
from tqdm import tqdm
import json
import argparse
import numpy as np
import pandas as pd
def get_args():
""" Reading command lin... | [
"pandas.DataFrame",
"json.dump",
"copy.deepcopy",
"tqdm.tqdm",
"json.load",
"argparse.ArgumentParser",
"pandas.DataFrame.from_dict",
"os.path.basename",
"os.getcwd",
"os.path.isdir",
"numpy.std",
"numpy.min",
"numpy.mean",
"numpy.max",
"os.path.join",
"os.listdir",
"numpy.unique"
] | [((350, 394), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '__doc__'}), '(description=__doc__)\n', (373, 394), False, 'import argparse\n'), ((8231, 8246), 'tqdm.tqdm', 'tqdm', (['seq_paths'], {}), '(seq_paths)\n', (8235, 8246), False, 'from tqdm import tqdm\n'), ((1841, 1879), 'numpy.uniqu... |
import numpy as np
def read_example_spectroscopy(sn):
spectra_file = 'example_data/'+sn+'.spectra'
JD_spectra = np.atleast_1d(np.genfromtxt(spectra_file, usecols=0))
spectra = np.atleast_1d(np.genfromtxt(spectra_file, usecols=1, dtype=str))
wl_spectra, f_spectra = [], []
for spectrum in spectra:... | [
"numpy.genfromtxt"
] | [((136, 174), 'numpy.genfromtxt', 'np.genfromtxt', (['spectra_file'], {'usecols': '(0)'}), '(spectra_file, usecols=0)\n', (149, 174), True, 'import numpy as np\n'), ((207, 256), 'numpy.genfromtxt', 'np.genfromtxt', (['spectra_file'], {'usecols': '(1)', 'dtype': 'str'}), '(spectra_file, usecols=1, dtype=str)\n', (220, 2... |
import sys
import json
import torch
import time
import gym
import numpy as np
import pandas as pd
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
import matplotlib.pyplot as plt
import functools
from env.BitcoinTradingEnv import BitcoinTradingEnv
f... | [
"env.indicators.prepare_indicators",
"matplotlib.pyplot.show",
"numpy.sum",
"matplotlib.pyplot.plot",
"torch.load",
"REINFORCE.update_policy",
"time.time",
"numpy.mean",
"torch.cuda.is_available",
"REINFORCE.PolicyNetwork",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel",
"env.Bitcoin... | [((455, 533), 'env.indicators.prepare_indicators', 'prepare_indicators', (['"""data/bitstampUSD_1-min_data_2012-01-01_to_2019-08-12.csv"""'], {}), "('data/bitstampUSD_1-min_data_2012-01-01_to_2019-08-12.csv')\n", (473, 533), False, 'from env.indicators import prepare_indicators\n'), ((590, 701), 'env.BitcoinTradingEnv.... |
from zipfile import ZipFile, ZIP_DEFLATED
from io import BytesIO
import torch
import time
import numpy as np
import math
import yaml
import shutil
import sys
import random
import re
import os
import transformer
import pickle
from pathlib import Path
import json
import argparse
from transformers import get_linear_schedu... | [
"os.mkdir",
"pickle.dump",
"transformer.Transformer",
"numpy.random.seed",
"argparse.ArgumentParser",
"wandb.finish",
"os.remove",
"torch.cat",
"pathlib.Path",
"torchtext.vocab.Vocab",
"shutil.rmtree",
"torch.no_grad",
"torch.utils.data.DataLoader",
"random.Random",
"os.path.exists",
"... | [((1998, 2011), 'wandb.login', 'wandb.login', ([], {}), '()\n', (2009, 2011), False, 'import wandb\n'), ((2652, 2777), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Train the model on dataset"""', 'formatter_class': 'argparse.ArgumentDefaultsHelpFormatter'}), "(description='Train the mo... |
"""Utilities for real-time data augmentation on image data.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
from keras_preprocessing.image.numpy_array_iterator import NumpyArrayIterator
from keras_preprocessing.image.utils i... | [
"numpy.random.randint",
"keras_preprocessing.image.utils.array_to_img",
"numpy.array",
"os.path.join"
] | [((512, 554), 'numpy.array', 'np.array', (['[self.x[j] for j in index_array]'], {}), '([self.x[j] for j in index_array])\n', (520, 554), True, 'import numpy as np\n'), ((750, 792), 'numpy.array', 'np.array', (['[self.y[j] for j in index_array]'], {}), '([self.y[j] for j in index_array])\n', (758, 792), True, 'import nu... |
from xml.dom.minidom import parse
import matplotlib.pyplot as plt
from PIL import Image
import pandas as pd
import os
import numpy as np
import random
import imageio
def readxml(xml_path,image_dir):
"""
str:xml file path
->
List:[filename,path,size,objectinfo]
"""
tree=parse(xml_... | [
"pandas.DataFrame",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.imshow",
"imageio.imread",
"matplotlib.pyplot.axis",
"PIL.Image.open",
"matplotlib.pyplot.Rectangle",
"random.random",
"xml.dom.minidom.parse",
"matplotlib.pyplot.figure",
"numpy.array",
"os.listdir",
"imageio.mimsave"
] | [((310, 325), 'xml.dom.minidom.parse', 'parse', (['xml_path'], {}), '(xml_path)\n', (315, 325), False, 'from xml.dom.minidom import parse\n'), ((2090, 2112), 'PIL.Image.open', 'Image.open', (['image_path'], {}), '(image_path)\n', (2100, 2112), False, 'from PIL import Image\n'), ((2122, 2154), 'matplotlib.pyplot.figure'... |
import random
import math
import numpy as np
class Node:
""" Node of a graph """
def __init__(self, state, parent=None, action=None, path_cost=0):
self.state = state
self.parent = parent
self.action = action
self.path_cost = path_cost
self.depth = parent.depth + 1 if p... | [
"numpy.sum",
"math.radians",
"random.shuffle",
"random.choice",
"math.sin",
"numpy.fliplr",
"math.cos",
"numpy.argwhere"
] | [((4396, 4444), 'random.choice', 'random.choice', (['[x for x in state.A1 if x != old]'], {}), '([x for x in state.A1 if x != old])\n', (4409, 4444), False, 'import random\n'), ((5290, 5318), 'random.shuffle', 'random.shuffle', (['self.initial'], {}), '(self.initial)\n', (5304, 5318), False, 'import random\n'), ((5555,... |
"""GLM-PCA, supporting missing values
We seek to fit the model
x_{ij} ~ Poisson(s_i μ_{ij})
where ln μ_{ij} = (LF)_{ij}. GLM-PCA fits this model using Fisher scoring
updates (Newton-Raphson updates, using the Fisher information instead of the
Hessian) to maximize the log likelihood (Townes 2019). To handle missing d... | [
"numpy.outer",
"numpy.square",
"numpy.isfinite",
"numpy.array",
"numpy.exp",
"numpy.random.normal"
] | [((1092, 1107), 'numpy.exp', 'np.exp', (['(l @ f.T)'], {}), '(l @ f.T)\n', (1098, 1107), True, 'import numpy as np\n'), ((2186, 2201), 'numpy.exp', 'np.exp', (['(l @ f.T)'], {}), '(l @ f.T)\n', (2192, 2201), True, 'import numpy as np\n'), ((3229, 3261), 'numpy.random.normal', 'np.random.normal', ([], {'size': '(n, rank... |
import numpy as np
from bactoml.df_pipeline import DFLambdaFunction, DFFeatureUnion, SampleWisePipeline, DFInPlaceLambda
from bactoml.decision_tree_classifier import HistogramTransform, DTClassifier
from bactoml.fcdataset import FCDataSet
from bactoml.graph_model import GraphModel
from FlowCytometryTools import Poly... | [
"numpy.divide",
"sklearn.preprocessing.StandardScaler",
"FlowCytometryTools.ThresholdGate",
"bactoml.decision_tree_classifier.HistogramTransform",
"bactoml.decision_tree_classifier.DTClassifier",
"bactoml.df_pipeline.DFLambdaFunction",
"bactoml.df_pipeline.SampleWisePipeline",
"numpy.linspace",
"skl... | [((2532, 2568), 'sklearn.pipeline.Pipeline', 'Pipeline', (['[tlog_step, tcc_gate_step]'], {}), '([tlog_step, tcc_gate_step])\n', (2540, 2568), False, 'from sklearn.pipeline import Pipeline\n'), ((2577, 2622), 'sklearn.pipeline.Pipeline', 'Pipeline', (['[hna_gate_step, event_counter_step]'], {}), '([hna_gate_step, event... |
# Tests for utils.py in the cmbpix package
def test_patches_works():
# Test that patches works for known cases, as well as for self-consistency
from cmbpix import patches
import numpy as np
# Index 0 @ NSIDE=1 contains only these 4 indices @ NSIDE=2
patch0nside1to2 = np.array([0,4,5,13])
patch0nside1to4 = np.ar... | [
"healpy.nside2npix",
"numpy.array",
"cmbpix.patches"
] | [((275, 298), 'numpy.array', 'np.array', (['[0, 4, 5, 13]'], {}), '([0, 4, 5, 13])\n', (283, 298), True, 'import numpy as np\n'), ((315, 327), 'numpy.array', 'np.array', (['[]'], {}), '([])\n', (323, 327), True, 'import numpy as np\n'), ((345, 361), 'cmbpix.patches', 'patches', (['(0)', '(1)', '(2)'], {}), '(0, 1, 2)\n... |
# encoding: utf-8
import os.path as op
import numpy as np
import pandas as pd
import numpy.testing as npt
import random
import taj
## py.test taj -s # -s pour afficher le résultat de la console
from taj import data_utils
data_path = op.join(taj.__path__[0], 'data')
'''
def test_balance_data():
print()
insta... | [
"numpy.asarray",
"taj.data_utils.load_data",
"os.path.join",
"numpy.testing.assert_equal"
] | [((236, 268), 'os.path.join', 'op.join', (['taj.__path__[0]', '"""data"""'], {}), "(taj.__path__[0], 'data')\n", (243, 268), True, 'import os.path as op\n'), ((1321, 1504), 'numpy.asarray', 'np.asarray', (['[[1.0, 2.0, 0.0, 7.0, 0.0], [1.0, 3.0, 1.0, 5.0, 0.0], [7.1, 4.5, 2.1, 0.8,\n 0.0], [1.0, 0.0, 4.0, 5.0, 0.0],... |
# -*- coding: utf-8 -*-
import pytest
import tempfile
import os
import numpy as np
@pytest.fixture(scope="function")
def txt_values():
values = [23.0, 24.0, 25e-2, -26, -27.0, -28e3]
b_string = b''
for v in values:
b_string += '{}\n'.format(v).encode()
fp = tempfile.NamedTemporaryFile()
f... | [
"tempfile.NamedTemporaryFile",
"numpy.asarray",
"os.path.dirname",
"pytest.fixture",
"numpy.reshape"
] | [((86, 118), 'pytest.fixture', 'pytest.fixture', ([], {'scope': '"""function"""'}), "(scope='function')\n", (100, 118), False, 'import pytest\n'), ((392, 424), 'pytest.fixture', 'pytest.fixture', ([], {'scope': '"""function"""'}), "(scope='function')\n", (406, 424), False, 'import pytest\n'), ((547, 577), 'pytest.fixtu... |
from xicam.plugins import OperationPlugin
import numpy as np
from astropy.modeling import fitting
from astropy.modeling import Fittable1DModel
from typing import Tuple
from enum import Enum
from xicam.plugins import manager as pluginmanager
from pyqtgraph.parametertree import Parameter
class AstropyQSpectraFit(Operat... | [
"xicam.plugins.manager.get_plugins_of_type",
"numpy.logical_and"
] | [((1551, 1599), 'numpy.logical_and', 'np.logical_and', (['(domain_min <= q)', '(q <= domain_max)'], {}), '(domain_min <= q, q <= domain_max)\n', (1565, 1599), True, 'import numpy as np\n'), ((672, 730), 'xicam.plugins.manager.get_plugins_of_type', 'pluginmanager.get_plugins_of_type', (['"""Fittable1DModelPlugin"""'], {... |
# coding=utf-8
# Copyright 2021 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | [
"numpy.minimum",
"kws_streaming.layers.compat.tf.signal.linear_to_mel_weight_matrix",
"kws_streaming.layers.mel_table.SpectrogramToMelMatrix",
"kws_streaming.layers.compat.tf.constant",
"kws_streaming.layers.compat.tf.matmul"
] | [((4545, 4582), 'kws_streaming.layers.compat.tf.matmul', 'tf.matmul', (['fft_mag', 'mel_weight_matrix'], {}), '(fft_mag, mel_weight_matrix)\n', (4554, 4582), False, 'from kws_streaming.layers.compat import tf\n'), ((5728, 5789), 'numpy.minimum', 'np.minimum', (['(non_zero_ind + 1)', 'self.mel_weight_matrix.shape[0]'], ... |
# Lint as: python3
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless ... | [
"jax.numpy.transpose",
"jax.numpy.sum",
"jax.numpy.squeeze",
"jax.nn.log_softmax",
"lingvo.jax.base_layer.MaybeShard",
"jax.numpy.arange",
"jax.numpy.expand_dims",
"jax.numpy.argmax",
"jax.numpy.asarray",
"lingvo.jax.layers.linears.FeedForwardLayer.Params",
"numpy.mod",
"jax.numpy.matmul",
"... | [((6142, 6168), 'jax.nn.log_softmax', 'jax.nn.log_softmax', (['logits'], {}), '(logits)\n', (6160, 6168), False, 'import jax\n'), ((6696, 6718), 'jax.numpy.sum', 'jnp.sum', (['class_weights'], {}), '(class_weights)\n', (6703, 6718), True, 'from jax import numpy as jnp\n'), ((7892, 7932), 'jax.numpy.transpose', 'jnp.tra... |
import torch
import os
import json
import numpy as np
from nemo.collections.asr.parts.features import WaveformFeaturizer
from nemo.collections.asr.modules import AudioToMelSpectrogramPreprocessor
from nemo.utils.nemo_logging import Logger
logger = Logger()
class MeanStdDevProcessor(object):
def __init__(self) -... | [
"nemo.utils.nemo_logging.Logger",
"nemo.collections.asr.parts.features.WaveformFeaturizer",
"numpy.std",
"torch.squeeze",
"numpy.mean",
"torch.unsqueeze",
"torch.tensor",
"os.path.join",
"numpy.concatenate",
"nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor"
] | [((249, 257), 'nemo.utils.nemo_logging.Logger', 'Logger', ([], {}), '()\n', (255, 257), False, 'from nemo.utils.nemo_logging import Logger\n'), ((1744, 1790), 'os.path.join', 'os.path.join', (['"""data/arabic/train"""', '"""cmvn/mean"""'], {}), "('data/arabic/train', 'cmvn/mean')\n", (1756, 1790), False, 'import os\n')... |
import io
import numpy as np
import sys
from gym.envs.toy_text import discrete
from enum import Enum, unique
@unique
class Action(Enum):
UP = 0
RIGHT = 1
DOWN = 2
LEFT = 3
UPRIGHT = 4
DOWNRIGHT = 5
DOWNLEFT = 6
UPLEFT = 7
UP = Action.UP.value
RIGHT = Action.RIGHT.value
DOWN = Action.DO... | [
"io.StringIO",
"numpy.nditer",
"numpy.zeros",
"numpy.ones",
"numpy.arange",
"numpy.prod"
] | [((1591, 1605), 'numpy.prod', 'np.prod', (['shape'], {}), '(shape)\n', (1598, 1605), True, 'import numpy as np\n'), ((1774, 1812), 'numpy.nditer', 'np.nditer', (['grid'], {'flags': "['multi_index']"}), "(grid, flags=['multi_index'])\n", (1783, 1812), True, 'import numpy as np\n'), ((4830, 4868), 'numpy.nditer', 'np.ndi... |
# Standard library
# Third-party
from astropy.constants import c as speed_of_light
from astropy.nddata import StdDevUncertainty
import astropy.units as u
import numpy as np
from scipy.interpolate import InterpolatedUnivariateSpline
from specutils import Spectrum1D
# This project
from .utils import WVLNU, wavelength_c... | [
"numpy.vander",
"numpy.stack",
"scipy.interpolate.InterpolatedUnivariateSpline",
"numpy.linalg.lstsq",
"numpy.zeros",
"numpy.linalg.cond",
"numpy.linalg.inv",
"numpy.arange",
"numpy.diag",
"numpy.linalg.solve",
"astropy.nddata.StdDevUncertainty",
"numpy.unique",
"numpy.sqrt"
] | [((409, 463), 'numpy.sqrt', 'np.sqrt', (['((speed_of_light + dv) / (speed_of_light - dv))'], {}), '((speed_of_light + dv) / (speed_of_light - dv))\n', (416, 463), True, 'import numpy as np\n'), ((982, 1034), 'scipy.interpolate.InterpolatedUnivariateSpline', 'InterpolatedUnivariateSpline', (['wvln', 'flux'], {'k': '(3)'... |
import numpy as np
import rdflib
import six
import bald
def valid_array_reference(parray, carray, broadcast_shape=None):
"""
Returns boolean.
Validates bald array broadcasting rules between one parent array and
one child array.
Args:
* parray - a numpy array: the parent of a bald array r... | [
"bald.HttpCache",
"numpy.broadcast",
"numpy.zeros"
] | [((1217, 1245), 'numpy.broadcast', 'np.broadcast', (['parray', 'carray'], {}), '(parray, carray)\n', (1229, 1245), True, 'import numpy as np\n'), ((1994, 2010), 'bald.HttpCache', 'bald.HttpCache', ([], {}), '()\n', (2008, 2010), False, 'import bald\n'), ((6630, 6662), 'numpy.zeros', 'np.zeros', (['self.array.bald__shap... |
from tqdm import tqdm
import numpy as np
from numba import njit
from gensim.parsing.preprocessing import stem
from pattern3.text.en.inflect import singularize, pluralize
import pickle
from copy import deepcopy
class CleanGlove:
def __init__(self, glove_path, codenames_path, stopwords_path, threshold=0.5, limit=in... | [
"pattern3.text.en.inflect.singularize",
"copy.deepcopy",
"pattern3.text.en.inflect.pluralize",
"tqdm.tqdm",
"pickle.dump",
"numba.njit",
"numpy.array",
"gensim.parsing.preprocessing.stem",
"numpy.sqrt"
] | [((1306, 1325), 'numba.njit', 'njit', ([], {'fastmath': '(True)'}), '(fastmath=True)\n', (1310, 1325), False, 'from numba import njit\n'), ((1663, 1678), 'numpy.sqrt', 'np.sqrt', (['u_norm'], {}), '(u_norm)\n', (1670, 1678), True, 'import numpy as np\n'), ((1696, 1711), 'numpy.sqrt', 'np.sqrt', (['v_norm'], {}), '(v_no... |
import numpy as np
import re
from tqdm import tqdm
import pandas as pd
import random
from SimilarFPAnalyzer import SimilarFPAnalyzer
from LiPolymerDataBase import MAX_SMILES
from DAWrapper import find_best_bit_DA,find_best_bit_BQ
from Fingerprint import get_Tanimoto
class Dummy:
def __init__(self):
pass
... | [
"numpy.isin",
"pandas.DataFrame.from_dict",
"numpy.random.randn",
"random.choices",
"re.match",
"random.random",
"Fingerprint.get_Tanimoto",
"numpy.where",
"numpy.array",
"SimilarFPAnalyzer.SimilarFPAnalyzer",
"DAWrapper.find_best_bit_BQ"
] | [((1472, 1508), 'SimilarFPAnalyzer.SimilarFPAnalyzer', 'SimilarFPAnalyzer', (['target_param', 'dum'], {}), '(target_param, dum)\n', (1489, 1508), False, 'from SimilarFPAnalyzer import SimilarFPAnalyzer\n'), ((1760, 1786), 'numpy.array', 'np.array', (['self.column_list'], {}), '(self.column_list)\n', (1768, 1786), True,... |
"""This module sets up and runs iceberg drift simulations and optimizations.
"""
import numpy as np
import xarray as xr
from scipy.optimize import minimize
from icedef import iceberg, metocean, drift, tools, timesteppers, plot
from logging import getLogger, FileHandler, DEBUG, Formatter
from time import gmtime, strf... | [
"scipy.optimize.minimize",
"icedef.tools.dx_to_dlon",
"icedef.metocean.Atmosphere",
"time.gmtime",
"icedef.metocean.Ocean",
"numpy.zeros",
"xarray.Dataset",
"logging.Formatter",
"icedef.tools.dy_to_dlat",
"numpy.mean",
"icedef.iceberg.quickstart",
"numpy.timedelta64",
"xarray.DataArray",
"... | [((9300, 9336), 'numpy.zeros', 'np.zeros', (['nt'], {'dtype': '"""datetime64[ns]"""'}), "(nt, dtype='datetime64[ns]')\n", (9308, 9336), True, 'import numpy as np\n'), ((13851, 13863), 'xarray.Dataset', 'xr.Dataset', ([], {}), '()\n', (13861, 13863), True, 'import xarray as xr\n'), ((558, 617), 'logging.FileHandler.__in... |
import pickle
import sklearn.metrics as skm
import numpy as np
from sklearn.linear_model import Ridge
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix, \
explained_variance_score, mean_squared_error, r2_score, mean_absolute_error, median_absolute_error, roc_auc... | [
"sklearn.linear_model.Ridge",
"sklearn.metrics.accuracy_score",
"sklearn.metrics.r2_score",
"sklearn.metrics.recall_score",
"sklearn.metrics.mean_absolute_error",
"sklearn.metrics.roc_auc_score",
"sklearn.metrics.median_absolute_error",
"sklearn.metrics.f1_score",
"pickle.load",
"sklearn.model_sel... | [((754, 813), 'sklearn.model_selection.StratifiedKFold', 'StratifiedKFold', ([], {'n_splits': '(10)', 'shuffle': '(True)', 'random_state': '(42)'}), '(n_splits=10, shuffle=True, random_state=42)\n', (769, 813), False, 'from sklearn.model_selection import StratifiedKFold\n'), ((668, 682), 'pickle.load', 'pickle.load', (... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
"""
Empirical XMM background model.
Written by <NAME>, adapted by <NAME> and <NAME>
(C) 2013-2016
For example usage, see examples/sherpa/background/xmm/fit.py
"""
import os
import logging
import numpy
if "MAKESPHINXDOC" not in os.en... | [
"bxa.sherpa.background.xmm.get_embedded_file",
"numpy.savetxt",
"os.path.exists",
"logging.info",
"numpy.array",
"numpy.loadtxt"
] | [((1265, 1299), 'numpy.array', 'numpy.array', (['([1.0] * urmf.detchans)'], {}), '([1.0] * urmf.detchans)\n', (1276, 1299), False, 'import numpy\n'), ((1390, 1442), 'numpy.array', 'numpy.array', (['([1] * urmf.detchans)'], {'dtype': 'numpy.uint32'}), '([1] * urmf.detchans, dtype=numpy.uint32)\n', (1401, 1442), False, '... |
import vectorbt as vbt
import numpy as np
import pandas as pd
from numba import njit
from datetime import datetime
import pytest
from vectorbt.generic import nb as generic_nb
from vectorbt.generic.enums import range_dt
from tests.utils import record_arrays_close
seed = 42
day_dt = np.timedelta64(86400000000000)
ma... | [
"vectorbt.RAND.run",
"pandas.Series.vbt.signals.generate_random",
"vectorbt.STX.run",
"pandas.DataFrame.vbt.signals.generate_random_both",
"numpy.empty",
"pandas.Series.vbt.signals.generate",
"vectorbt.generic.nb.bshift_1d_nb",
"pandas.Int64Index",
"pandas.DataFrame.vbt.signals.generate_both",
"nu... | [((286, 316), 'numpy.timedelta64', 'np.timedelta64', (['(86400000000000)'], {}), '(86400000000000)\n', (300, 316), True, 'import numpy as np\n'), ((653, 707), 'pandas.Series', 'pd.Series', (['[1.0, 2.0, 3.0, 2.0, 1.0]'], {'index': 'mask.index'}), '([1.0, 2.0, 3.0, 2.0, 1.0], index=mask.index)\n', (662, 707), True, 'imp... |
"""
This video filter tries to remove pixels that are made artificially brighter
by stray X-rays.
"""
import numpy as np
import scipy.ndimage
from . Filter import Filter
class HXRFilter(Filter):
def __init__(self, threshold=0.9):
"""
Constructor.
threshold: Relative amount by ... | [
"numpy.abs",
"numpy.copy",
"numpy.geterr",
"numpy.seterr",
"numpy.zeros",
"numpy.interp"
] | [((662, 682), 'numpy.zeros', 'np.zeros', (['data.shape'], {}), '(data.shape)\n', (670, 682), True, 'import numpy as np\n'), ((901, 912), 'numpy.geterr', 'np.geterr', ([], {}), '()\n', (910, 912), True, 'import numpy as np\n'), ((921, 947), 'numpy.seterr', 'np.seterr', ([], {'divide': '"""ignore"""'}), "(divide='ignore'... |
"""
Beta hedging
Author: <NAME> and <NAME>
This algorithm computes beta to the S&P 500 and attempts to maintain
a hedge for market neutrality. More information on beta hedging can
be found in the beta hedging lecture as part of the Quantopian
Lecture Series.
https://www.quantopian.com/lectures
This algorithm wa... | [
"pandas.DataFrame",
"statsmodels.api.add_constant",
"numpy.isnan",
"statsmodels.api.OLS"
] | [((4169, 4215), 'pandas.DataFrame', 'pd.DataFrame', (['factors'], {'index': "['alpha', 'beta']"}), "(factors, index=['alpha', 'beta'])\n", (4181, 4215), True, 'import pandas as pd\n'), ((4382, 4400), 'statsmodels.api.add_constant', 'sm.add_constant', (['x'], {}), '(x)\n', (4397, 4400), True, 'import statsmodels.api as ... |
'''
This script has two modes:
> Mode 1:
1. a text string which defines the prefix name of the output files (-n, required)
2. a text string which specifies the output directory (-d, optional)
3. a reference file with annotation in bed format (-r, required)
4. a STAR-aligned bam file (-b, required)
5. the un-trimmed fas... | [
"subprocess.Popen",
"ghmm.GaussianMixtureDistribution",
"gzip.open",
"ghmm.SequenceSet",
"argparse.ArgumentParser",
"numpy.median",
"subprocess.check_output",
"time.time",
"ghmm.GaussianDistribution",
"os.path.isfile",
"numpy.mean",
"tarfile.open",
"ghmm.Float",
"math.log",
"datetime.dat... | [((7314, 7320), 'time.time', 'time', ([], {}), '()\n', (7318, 7320), False, 'from time import time\n'), ((13681, 13706), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (13704, 13706), False, 'import sys, subprocess, math, numpy, gzip, ghmm, time, tarfile, concurrent.futures, random, os, argpars... |
# coding=utf-8
# Copyright 2021 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | [
"types.MethodType",
"numpy.zeros",
"numpy.random.RandomState",
"gym.spaces.Box",
"numpy.concatenate"
] | [((1509, 1607), 'gym.spaces.Box', 'gym.spaces.Box', ([], {'shape': '(obs_space.shape[0] + 1,)', 'low': 'obs_space.low[0]', 'high': 'obs_space.high[0]'}), '(shape=(obs_space.shape[0] + 1,), low=obs_space.low[0], high=\n obs_space.high[0])\n', (1523, 1607), False, 'import gym\n'), ((2047, 2077), 'numpy.concatenate', '... |
import numpy as np
"""
data format conversions, esp for nonlattice
"""
def interval_counts_as_rates(obs_t, interval_counts):
interval_counts = np.asarray(interval_counts)
rates = interval_counts / obs_times_to_delta_times(obs_t)
return obs_t, rates
def rates_as_interval_counts(rates, obs_t, round=True)... | [
"numpy.asarray",
"numpy.amax",
"numpy.cumsum",
"numpy.around",
"numpy.diff",
"numpy.linspace",
"numpy.concatenate"
] | [((150, 177), 'numpy.asarray', 'np.asarray', (['interval_counts'], {}), '(interval_counts)\n', (160, 177), True, 'import numpy as np\n'), ((334, 351), 'numpy.asarray', 'np.asarray', (['obs_t'], {}), '(obs_t)\n', (344, 351), True, 'import numpy as np\n'), ((364, 381), 'numpy.asarray', 'np.asarray', (['rates'], {}), '(ra... |
import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Load the movies dataset and also pass header=None since files don't contain any headers
movies_df = pd.read_csv('ml-1m/movies.dat', sep='::', header=None, engine='python')
print(movies_df.head())
# Load the ratings datase... | [
"matplotlib.pyplot.show",
"matplotlib.pyplot.plot",
"pandas.read_csv",
"tensorflow.global_variables_initializer",
"numpy.zeros",
"tensorflow.Session",
"tensorflow.reduce_mean",
"tensorflow.transpose",
"tensorflow.placeholder",
"tensorflow.matmul",
"tensorflow.shape",
"matplotlib.pyplot.ylabel"... | [((198, 269), 'pandas.read_csv', 'pd.read_csv', (['"""ml-1m/movies.dat"""'], {'sep': '"""::"""', 'header': 'None', 'engine': '"""python"""'}), "('ml-1m/movies.dat', sep='::', header=None, engine='python')\n", (209, 269), True, 'import pandas as pd\n'), ((335, 407), 'pandas.read_csv', 'pd.read_csv', (['"""ml-1m/ratings.... |
from datetime import datetime
import numpy as np
import pandas as pd
import sklearn.model_selection
from sklearn.pipeline import Pipeline
import sklearn.base
import tqdm
import traceback
import warnings
warnings.filterwarnings(action='ignore')
from core import mp_utils
from core.utils import get_component_constructor... | [
"tqdm.tqdm",
"warnings.filterwarnings",
"core.mp_utils.run",
"numpy.random.RandomState",
"numpy.isnan",
"numpy.mean",
"numpy.arange",
"traceback.format_exc",
"numpy.random.choice",
"sklearn.pipeline.Pipeline",
"core.utils.get_component_constructor",
"core.mp_utils.init_mp",
"datetime.datetim... | [((204, 244), 'warnings.filterwarnings', 'warnings.filterwarnings', ([], {'action': '"""ignore"""'}), "(action='ignore')\n", (227, 244), False, 'import warnings\n'), ((3346, 3361), 'sklearn.pipeline.Pipeline', 'Pipeline', (['steps'], {}), '(steps)\n', (3354, 3361), False, 'from sklearn.pipeline import Pipeline\n'), ((1... |
"""Time series of responses to creep."""
from math import sqrt
from typing import List, Union, Optional
import numpy as np
from bridge_sim import sim
from bridge_sim.model import Config, ResponseType, Point
from bridge_sim.shrinkage import CementClass, RH, f_cm, notational_size
from bridge_sim.sim.model import Respo... | [
"math.sqrt",
"numpy.power",
"numpy.isnan",
"bridge_sim.shrinkage.notational_size",
"bridge_sim.util.convert_times",
"bridge_sim.util.print_d"
] | [((1183, 1218), 'bridge_sim.shrinkage.notational_size', 'notational_size', ([], {'config': 'config', 'x': 'x'}), '(config=config, x=x)\n', (1198, 1218), False, 'from bridge_sim.shrinkage import CementClass, RH, f_cm, notational_size\n'), ((1468, 1494), 'bridge_sim.util.print_d', 'print_d', (['D', 'f"""t_0 = {t_0}"""'],... |
import warnings
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
from sklearn.metrics import mean_squared_error, r2_score
from utils import load_data
from utils import plot_data
def normalize_by_self():
X_normalize, Y_normalize, Origin_X_normalize, Y_mean, Y_std, Origin_X_mean,... | [
"warnings.filterwarnings",
"utils.load_data.load_and_normalize",
"sklearn.linear_model.LinearRegression",
"numpy.array",
"utils.load_data.load_and_process"
] | [((336, 377), 'utils.load_data.load_and_normalize', 'load_data.load_and_normalize', (['"""data2.txt"""'], {}), "('data2.txt')\n", (364, 377), False, 'from utils import load_data\n'), ((402, 433), 'sklearn.linear_model.LinearRegression', 'linear_model.LinearRegression', ([], {}), '()\n', (431, 433), False, 'from sklearn... |
# Enter your code here. Read input from STDIN. Print output to STDOUT
N = int(input())
X = list(map(int, input().split()))
X.sort()
# Solution without import Package
## mean
X_mean = round(sum(X)/N,1)
## median
if N%2 != 0:
X_median = X[int(N/2-0.5)]
else:
X_median = round((X[int(N/2-1)]+X[int(N/2)])/2, 1)
## ... | [
"numpy.median",
"numpy.mean",
"scipy.stats.mode"
] | [((492, 502), 'numpy.mean', 'np.mean', (['X'], {}), '(X)\n', (499, 502), True, 'import numpy as np\n'), ((514, 526), 'numpy.median', 'np.median', (['X'], {}), '(X)\n', (523, 526), True, 'import numpy as np\n'), ((536, 549), 'scipy.stats.mode', 'stats.mode', (['X'], {}), '(X)\n', (546, 549), False, 'from scipy import st... |
import numpy as np
from scipy import constants
from layer import Layer
'''
TransferMatrix for conductive sheets
x
^
|
eps1,mu1 | eps2, mu2
|
_____________|__________________>z
|
|
|
sigma_e, sigma_m
'''
'''
Convension
(hin) = (M)(hout)
... | [
"numpy.matrix",
"copy.deepcopy",
"numpy.sum",
"numpy.zeros",
"numpy.ones",
"numpy.sin",
"numpy.array",
"numpy.exp",
"numpy.linspace",
"numpy.matmul",
"numpy.cos",
"numpy.arange",
"numpy.real",
"numpy.sqrt"
] | [((3091, 3110), 'numpy.sqrt', 'np.sqrt', (['(mu0 / eps0)'], {}), '(mu0 / eps0)\n', (3098, 3110), True, 'import numpy as np\n'), ((3539, 3573), 'numpy.array', 'np.array', (['[[M11, M12], [M21, M22]]'], {}), '([[M11, M12], [M21, M22]])\n', (3547, 3573), True, 'import numpy as np\n'), ((3701, 3745), 'numpy.array', 'np.arr... |
import unittest
import numpy as np
import signals
import matplotlib.pyplot as plt
import math
class TestSignalFunctions(unittest.TestCase):
def make_test_signal(self, sample_rate):
""" Make a 10s signal with three frequency components
f = [100, 10, 1]/(2pi) """
x = np.arange(0, 10, 1.0 / ... | [
"unittest.main",
"matplotlib.pyplot.subplot",
"signals.getpeaks",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.legend",
"signals.interpolate_points",
"matplotlib.pyplot.ylabel",
"signals.find_periodicities",
"matplotlib.pyplot.figure",
"numpy.sin",
"numpy.arange",
"numpy.vstack",
"numpy.arra... | [((2920, 2935), 'unittest.main', 'unittest.main', ([], {}), '()\n', (2933, 2935), False, 'import unittest\n'), ((352, 368), 'numpy.sin', 'np.sin', (['(x * 10.0)'], {}), '(x * 10.0)\n', (358, 368), True, 'import numpy as np\n'), ((626, 657), 'numpy.arange', 'np.arange', (['(0)', '(math.pi * 8)', '(0.01)'], {}), '(0, mat... |
"""Data file
============
Data channel methods, unless specified should not be called directly.
"""
import numpy as np
from typing import List, Dict, Optional, Tuple, Callable, Set, Union, Any, Type
import nixio as nix
from nixio.exceptions.exceptions import InvalidFile
from bisect import bisect_left
from more_kivy_... | [
"nixio.File.open",
"more_kivy_app.utils.yaml_dumps",
"numpy.empty",
"numpy.asarray",
"numpy.array",
"bisect.bisect_left",
"more_kivy_app.utils.yaml_loads"
] | [((46055, 46084), 'numpy.array', 'np.array', (['[0]'], {'dtype': 'np.uint8'}), '([0], dtype=np.uint8)\n', (46063, 46084), True, 'import numpy as np\n'), ((48267, 48305), 'numpy.array', 'np.array', (['[[-1, -1]]'], {'dtype': 'np.float64'}), '([[-1, -1]], dtype=np.float64)\n', (48275, 48305), True, 'import numpy as np\n'... |
# author: <NAME>
# Python implementation of:
# Fake News in Social Networks
# @article{aymanns2017fake,
# title={Fake News in Social Networks},
# author={Aymanns, Christoph and <NAME> and <NAME>},
# journal={arXiv preprint arXiv:1708.06233},
# year={2017}
# }
# Based on:
# L... | [
"torch.nn.MSELoss",
"matplotlib.pyplot.show",
"argparse.ArgumentParser",
"model.RDQN_multi",
"env.env_multi",
"torch.autograd.Variable",
"agent.agent",
"numpy.zeros",
"random.choice",
"numpy.max",
"io.open",
"matplotlib.pyplot.subplots"
] | [((1439, 1465), 'numpy.zeros', 'np.zeros', (['(args.T, n_test)'], {}), '((args.T, n_test))\n', (1447, 1465), True, 'import numpy as np\n'), ((1932, 1948), 'numpy.zeros', 'np.zeros', (['args.T'], {}), '(args.T)\n', (1940, 1948), True, 'import numpy as np\n'), ((4411, 4436), 'argparse.ArgumentParser', 'argparse.ArgumentP... |
# --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by <NAME>, <NAME>, based on code from <NAME>
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import ... | [
"numpy.random.seed",
"argparse.ArgumentParser",
"model.utils.config.cfg_from_file",
"model.rpn.bbox_transform.clip_boxes",
"pickle.load",
"pprint.pprint",
"numpy.tile",
"test_net.get_data2imdbval_dict",
"os.path.join",
"os.path.exists",
"torch.FloatTensor",
"roi_data_layer.roidb.combined_roidb... | [((2720, 2785), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Train a Fast R-CNN network"""'}), "(description='Train a Fast R-CNN network')\n", (2743, 2785), False, 'import argparse\n'), ((5632, 5666), 'test_net.get_data2imdbval_dict', 'get_data2imdbval_dict', (['args.imgset'], {}), '(a... |
import numpy as np
from spira.core.parameters.variables import *
from spira.core.parameters.processors import ProcessorTypeCast
from spira.core.parameters.restrictions import RestrictType
from spira.core.parameters.initializer import ParameterInitializer
from spira.core.parameters.descriptor import RestrictedParameter
... | [
"spira.core.parameters.descriptor.RestrictedParameter",
"spira.core.parameters.processors.ProcessorTypeCast.__init__",
"spira.core.parameters.processors.ProcessorTypeCast.process",
"numpy.array",
"spira.core.parameters.restrictions.RestrictType",
"numpy.array_equal",
"numpy.ndarray"
] | [((1121, 1981), 'numpy.array', 'np.array', (['[[0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, \n 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1,\n 0, 0, 0, 1, 0, 0, 0, 1, 0, 0], [0, 0, ... |
import tensorflow as tf
from tensorflow import keras
import os
import numpy as np
layerNames = ['conv1', 'conv2', 'conv3', 'conv4', 'conv5', 'fc6', 'fc7', 'fc8']
alexnet_weights_path = os.path.join(os.path.dirname(__file__), 'data/bvlc_alexnet.npy')
class AlexNet(keras.Model):
def __init__(self, input_shape, pa... | [
"numpy.load",
"tensorflow.keras.layers.MaxPooling2D",
"tensorflow.keras.layers.Conv2D",
"tensorflow.keras.layers.BatchNormalization",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.Dropout",
"os.path.dirname",
"tensorflow.pow",
"tensorflow.keras.Sequential",
"tensorflow.keras.layers.Fla... | [((199, 224), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (214, 224), False, 'import os\n'), ((446, 464), 'tensorflow.keras.Sequential', 'keras.Sequential', ([], {}), '()\n', (462, 464), False, 'from tensorflow import keras\n'), ((569, 732), 'tensorflow.keras.layers.Conv2D', 'keras.layers.... |
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