text string |
|---|
import pickle
import functools
from functools import partial
import itertools
import imp
import multiprocessing
from multiprocessing import Pool
import re
import os
from pathlib import Path
import numpy as np
import scipy
import pandas as pd
import bruno_util
from bruno_util import pandas_util
from bruno_util import... |
<gh_stars>0
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
"""
Created on Mon Apr 29 09:56:21 2019
@author: jacob
"""
import ftplib
import glob
import subprocess as sp
import csv
import numpy as np
import netCDF4 as nc4
import pygrib as pg
import matplotlib.pyplot as plt
plt.switch_backend('agg')
import scipy
impor... |
<reponame>guidov/cmtm
import numpy as np
import scipy
import matcompat
# if available import pylab (from matlibplot)
try:
import matplotlib.pylab as plt
except ImportError:
pass
#%function [cu]=cohbias(v,cb);
#%
#%Corrects for the bias inherent to coherence estimates. Note the Matlab
#%function cohere.m ret... |
<gh_stars>1-10
###########
# plot_rc.py: load a dynamic spectrum, bin to an appropriate resolution, and
# then make a scatterplot of RCP vs. LCP to identify r_c from the slope
###########
import dynspec.plot
import dynspec.pipeline_utils
reload(dynspec.plot)
reload(dynspec.pipeline_utils)
from dynspec.p... |
# -*- coding: utf-8 -*-
"""
Created on Sun Nov 22 20:04:52 2020
@author: takashi-154
"""
import os
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import tifffile as tiff
import astropy.io.fits as iofits
from scipy import optimize
from skimage import exposure
class DynamicBa... |
import numpy as np
from PDE_find import Diff, Diff2, FiniteDiff
import Data_generator as Data_generator
import scipy.io as scio
from requests import get
from inspect import isfunction
import math
import pdb
import random
import matplotlib.pyplot as plt
import torch.nn as nn
import torch
from torch.nn import... |
<reponame>PavanKB/arvato_customer_segmentation<gh_stars>0
import pandas as pd
import numpy as np
from collections import Counter
from scipy.stats import chi2_contingency
def calc_proportion(x):
"""
Calculates the proportion for each unique value in list
:params x: A list of values
"""
p = Counter(... |
#!/usr/bin/env python2
# -*- coding: UTF-8 -*-
# File: noisered.py
# Date: Fri Dec 27 04:23:28 2013 +0800
import sys
from scipy.io import wavfile
import os
from random import Random
from silence import remove_silence
from utils import monophonic
NOISE_WAV = "/tmp/noise.wav"
NOISE_MODEL = "/tmp/noise.prof"
THRES = 0... |
<filename>Hindmarsh-Rose-Codes/hindmarsh_rose.py
# -*- coding: utf-8 -*-
"""
Created on Fri May 7 11:23:09 2021
@author: <NAME>
Hindmarsh Rose model
"""
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import odeint
def hindmarsh_rose_model(X, t, current, r, x1, a, b, c, ... |
<reponame>ICRC-BME/epycom<gh_stars>0
# -*- coding: utf-8 -*-
# Copyright (c) St. Anne's University Hospital in Brno. International Clinical
# Research Center, Biomedical Engineering;
# Institute of Scientific Instruments of the CAS, v. v. i., Medical signals -
# Computational neuroscience. All Rights Reserved.
# Distri... |
import math, string, itertools, fractions, heapq, collections, re, array, bisect, copy, functools, random
import sys
from collections import deque, defaultdict, Counter; from heapq import heappush, heappop
from itertools import permutations, combinations, product, accumulate, groupby
from bisect import bisect_left, bis... |
<filename>pyrsss/ionex/read_ionex.py
"""
IONEX Parser & Interpolator v1.1
written by
<NAME>
University of Illinois at Urbana-Champaign
<EMAIL>
January 23, 2016
Developed in accordance with the format specifications described in
"IONEX: The IONosphere Map EXchange Format Version 1"
written by
<NAME>, <NAME>
Astronom... |
<filename>src/benchmarking/utils.py
# Copyright 2020 ChainLab
#
# 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 ... |
<reponame>jonvw28/SLICUAV
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 22 15:07:00 2019
@author: jon
structure to compute features for a given tree crown clusterwise
Inputs:
tag_id - a unique numeric ID for labelling each cluster that is analysed
tag - the species three letter tag for this species (eg MAC)
rgb_im... |
<gh_stars>0
import numpy as np
def lotka(t, x, theta):
y = np.empty(x.shape)
y[0] = x[0] * (theta[0] - theta[1] * x[1])
y[1] = -x[1] * (theta[2] - theta[3] * x[0])
return y
def lotka_old(t, x):
y = np.empty(x.shape)
y[0] = x[0] - x[0]*x[1]
y[1] = -x[1] + x[0]*x[1]
return y
def lotk... |
from abc import abstractmethod
from math import pi
from better_abc import ABC
from scipy.special import gamma
from utils.integrands import sanitize_variable
from utils.integrands.abstract import Integrand, KnownIntegrand, RegulatedKnownIntegrand
class VolumeIntegrand(Integrand, ABC):
"""Abstract class to define... |
<reponame>airibarne/Kats
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Base I/O code for time series data in Kats
This is a base implementation to load datasets for test and evaluation/b... |
import logging
logging.disable(logging.CRITICAL)
import numpy as np
import scipy as sp
import scipy.sparse.linalg as spLA
import copy
import time as timer
import torch
import torch.nn as nn
from torch.autograd import Variable
import copy
# samplers
import mjrl.samplers.trajectory_sampler as trajectory_sampler
import m... |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
import os
from sklearn.cluster import MeanShift
from scipy.stats import norm
from tqdm import tqdm
def plot_distribution(series, path, title='', xlabel=None):
'''Plot a histogram of a series with mean, std and m... |
<filename>cpab/cpa1d/CpaSpace.py
#!/usr/bin/env python
"""
Created on Sun Mar 9 21:43:46 2014
Author: <NAME>
Email: <EMAIL>
"""
import numpy as np
#from cpab.essentials import *
from scipy import sparse
from of.utils import *
from of.gpu import CpuGpuArray
from cpab.cpaNd import CpaSpace as CpaSpaceNd
from cpab.cpaN... |
from mutation import *
from evolocity_graph import *
import evolocity as evo
np.random.seed(1)
random.seed(1)
def parse_args():
import argparse
parser = argparse.ArgumentParser(description='Coronavirus sequence analysis')
parser.add_argument('model_name', type=str,
help='Type of la... |
<reponame>peterdonnelly1/u24_lymphocyte
import pickle
import sys
import os
import urllib
import gzip
import cPickle
import time
import lasagne
import theano
import numpy as np
import theano.tensor as T
from lasagne import layers
from lasagne.updates import nesterov_momentum
from nolearn.lasagne import NeuralNet
from n... |
""" Module for calculating DCT type 4 using FFT and pre/post-twiddling
.. warning::
These core transforms will produce aliasing when used without overlap.
Please use :py:mod:`mdct` unless you know what this means.
"""
from __future__ import division
import numpy
import scipy
__all__ = [
'mdct', 'imdct',... |
<reponame>viniciusriosfuck/vertical
# cd C:\Users\Fuck\Documents\python\quarentena_vertical
# python SEIR_IJ_v7.py
import numpy as np
import pandas as pd
def get_input_data():
'''Provides the inputs for the simulation'''
from collections import namedtuple
Demograph_Parameters = namedtuple('Demograph_Par... |
<filename>code/Trianing_tensorflow.py
import pandas as pd
import numpy as np
import swifter
from collections import Counter
import statistics
from statistics import mode
import pickle
import time
import math
import matplotlib.pyplot as plt
from sklearn.metrics import roc_auc_score, roc_curve
from keras.callbacks impo... |
<reponame>geojames/py_streamNormalcoords
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#------------------------------------------------------------------------------
__author__ = '<NAME>'
__contact__ = '<EMAIL>'
__copyright__ = '(c) <NAME> 2019'
__license__ = 'MIT'
__date__ = '3 December 2019'
__version__ = '2... |
<gh_stars>100-1000
import torch
import random
import typing
from scipy.special import comb
class EpisodeSampler(torch.utils.data.BatchSampler):
"""Sample data to form a classification task
Args:
data_source (Dataset): dataset to sample from
"""
def __init__(self, sampler: torch.utils.data.Sa... |
<filename>datasets/jacquard.py
from __future__ import print_function
from __future__ import absolute_import
# --------------------------------------------------------
# Visual Detection: State-of-the-Art
# Copyright: <NAME>
# Licensed under The MIT License [see LICENSE for details]
# Written by <NAME>
# ---------------... |
"""Interpolation image and mask by linear function.
method:
resampleImageToFixedSize
resampleMaskToFixedSize
resampleImageToFixedSpacing
resampleMaskToFixedSpacing
resampleImageToFixedScale
resampleMaskToFixedScale
"""
import numpy as np
from scipy.ndimage.interpolation import zoom
class DataResampler(object):
d... |
<filename>Dryads/load.py
import sys
import os
import warnings
import numbers
#
from collections import *
#
import numpy as np
import scipy
#
from astropy.io import fits
import astropy.wcs as wcs
import astropy.units as u
import astropy.constants as c
import FITS_tools as fits_tools
#
import matplotlib.pyplot as plt... |
import pycuda.driver as drv
import numpy as np
from scipy.sparse import csr_matrix
from km3net.kernels import Match3BSparse
import km3net.util as util
from .context import create_plot, skip_if_no_cuda_device
def test_Match3BSparse():
skip_if_no_cuda_device()
N = 500
window_width = 150
x,y,z,ct = uti... |
<filename>data_preparation/processing/longread/assign_RI_persistence_metric.py
#!/usr/bin/env python3
"""
assign_RI_persistence_metric.py
Python 3 code for assigning read-transcript maps with intron persistence values
SAMPLE RUN:
time python intronomer-paper/benchmarking_data/assign_RI_persistence_metric.py
-m HX1_fi... |
"""
Test the pipeline module.
"""
from distutils.version import LooseVersion
from tempfile import mkdtemp
import shutil
import time
import pytest
import numpy as np
from scipy import sparse
from sklearn.externals.six.moves import zip
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import as... |
<gh_stars>1-10
#!/usr/bin/env python
import gflags
import httplib2
import logging
import math
import numpy
import pprint
import sys
from apiclient.discovery import build
from apiclient.errors import HttpError
#from oauth2client.client import OAuth2WebServerFlow
from oauth2client.client import AccessTokenRefreshError... |
<gh_stars>1-10
# fixtures.py
# Copyright (c) 2013-2019 <NAME>
# See LICENSE for details
# pylint: disable=C0111,C0411,E0611,R0903,R0914,W0621
# Standard library imports
from __future__ import print_function
import math
import os
import shutil
import warnings
# PyPI imports
from imageio import imread
import numpy as n... |
<reponame>cscorley/triage
from __future__ import print_function, division
import csv
import src
import src.main
import src.utils
import logging
import sys
import scipy.stats
import os.path
def fake(*args, **kwargs):
print('Fake called with', str(args), str(kwargs))
sys.exit(1)
# fake out the create_model so... |
<gh_stars>0
# We can pick up a certain MP3 music from our local computer or from the dataset
# Do feature extraction and statistical computation
# Put feature into model and give the predicted genre of the music
import os
from keras.models import load_model
from scipy import stats
import utils
import numpy as np... |
<filename>tests/test_align.py
import pytest
import numpy as np
import scipy.ndimage as ndi
from autolamella.align import (
_calculate_beam_shift,
_normalize_image,
_simple_register_translation,
)
import autolamella.data
from autolamella.data.mocktypes import MockAdornedImage
@pytest.mark.parametrize(
... |
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from __future__ import unicode_literals
import abc
import sys
import numpy as np
import pandas as pd
from sklearn import linear_model, preprocessing, cluster
import scipy.linalg as slin
import scipy.sparse.... |
<reponame>wtsia/gosl
import numpy as np
import scipy.linalg as la
from auxiliary import *
a = np.array([
[+1-1j, +2, +1, +1, -1, +0+0j],
[+2+0j, +2, +1, +0, +0, +0+1j],
[+3+1j, +1, +3, +1, +2, -1+0j],
[+1+0j, +0, +1, -1, +0, +0+1j],
],dtype=complex)
tmp = np.array([
[+1, -1, +0, -1],
[-1, -1, ... |
<gh_stars>1-10
# SPDX-License-Identifier: Apache-2.0
# Copyright 2019 Blue Cheetah Analog Design Inc.
#
# 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/LI... |
<gh_stars>0
"""
xpath mapping from xml file, with convertion functions
"""
from datetime import datetime
import numpy as np
from scipy.interpolate import RectBivariateSpline, interp1d
from shapely.geometry import box
import pandas as pd
import xarray as xr
import warnings
import geopandas as gpd
from shapely.geometry ... |
<reponame>gavin-parker/Thesis
import numpy as np
import os
from scipy import ndimage
import tensorflow as tf
class ReflectanceDataset:
normals = []
inputs = []
ground_truths = []
def __init__(self):
self.inputs = tf.train.match_filenames_once("{}/radiance/*.png".format(self.train_... |
<gh_stars>1-10
from collections import defaultdict
import keras.backend as K
import pickle
import pathlib
import pandas as pd
import scipy.special
import scipy.stats
from keras.models import Model
import gc
import palmnet.hunt
from palmnet.core.faustizer import Faustizer
from palmnet.core.layer_replacer_faust import L... |
# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
import numpy as np
import pandas as pd
import scipy
from scipy import stats
import matplotlib.pyplot as plt
import pandas
sentiment_singularity_csv = '8GPU_rnn_translator_singularity.csv'
sentiment_native_csv = '8GPU_rnn_translator_nativ... |
from scipy import misc
import numpy as np
fonts = ['AcmeFont', 'AgencyFB', 'Algerian', 'ArialBlack', 'Broadway', 'Calibri', 'Itaianate']
alpha = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H',
'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P',
'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X',
'Y', 'Z']
for font in font... |
<gh_stars>1000+
# 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 requ... |
import numpy as np
import math
from sympy.physics.quantum.cg import CG
from scipy.special import sph_harm
def radial_function(x, J, alpha=1, beta=0.5):
return np.exp(alpha * np.linalg.norm(x) + beta + J * 0.001)
def get_Q_lk_transpose(k, l, J, m):
Q_lk_transpose = np.zeros((2 * l + 1, 2 * k + 1))
for i ... |
<gh_stars>0
# Copyright (c) 2015-2016, the authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import sys
import numpy as np
from scipy.linalg import LinAlgError
from GPy import Model,likelihoods
from GPy.core.parameterization.variational import VariationalPosterior, NormalPosterior... |
"""
This module contains the functions needed to use the spherical harmonic techniques
"""
import numpy as np
import scipy.sparse
from scipy.special import lpmv, spherical_jn, spherical_yn
from numba import jit
#@jit(nopython=True)
def sub2indSH(m,n):
"""
i = sub2indSH(m,n)
Convert Spherical Harmonic (m,n) indi... |
<reponame>anabastos/faceteer
import spacy
import json
from collections import Counter
import numpy as np
from scipy import stats
import xlwt
nlp = spacy.load('pt')
age_list = []
gender_list = []
city_list = []
religion_list = []
with open('../data.json') as json_file:
data = json.load(json_file)
text = ''
... |
<filename>spectral_work.py
import gmi_misc
import gmi_config
gmi_config.read_config()
import matplotlib.pyplot as plt
import pyshtools
import numpy as np
from scipy.interpolate import griddata
MINN = 1
#FOLDER = 'result_2degx2deg_100000_apriori_wolfgang_ocean_cont_CRUST'
#try:
# raw_grid_inp = gmi_misc.read_g... |
<reponame>AdityaAS/cycada
import os.path
from .cityscapes import remap_labels_to_train_ids
import numpy as np
import scipy.io
import torch
import torch.utils.data as data
from PIL import Image
from .data_loader import register_data_params, register_dataset_obj
from .data_loader import DatasetParams
from .cityscapes i... |
# This file is part of QuTiP: Quantum Toolbox in Python.
#
# Copyright (c) 2011 and later, <NAME> and <NAME>.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# 1. Redistribut... |
# -*- coding: utf-8 -*-
from ebisu import *
from ebisu.alternate import *
import unittest
import numpy as np
def relerr(dirt, gold):
return abs(dirt - gold) / abs(gold)
def maxrelerr(dirts, golds):
return max(map(relerr, dirts, golds))
def klDivBeta(a, b, a2, b2):
"""Kullback-Leibler divergence between two... |
<filename>code/teststar3_dataall.py<gh_stars>0
# this is to test a field to get stellar parameters but for the data not for a field
import pyfits
import glob
import pickle
from scipy import interpolate
from scipy import ndimage
import numpy as np
from glob import glob
testfile = "normed_data.pickle"
def test_fi... |
import cv2
import torch
from torch.autograd import Variable
import torch.utils.data
import numpy as np
import scipy.io as scio
import os
from PIL import Image
import model as model
import anchor as anchor
from tqdm import tqdm
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
fx = 475.065948
fy = 475.065857
u0 = 315.944855
v0... |
from sympy import (Symbol, symbols, factorial, factorial2, binomial,
rf, ff, gamma, polygamma, EulerGamma, O, pi, nan,
oo, zoo, simplify, expand_func)
from sympy.functions.combinatorial.factorials import subfactorial
from sympy.utilities.pytest import XFAIL, raises
def test_rf_ev... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import unicode_literals, print_function
import sys
import numpy as np
import scipy.optimize
import matplotlib.pyplot as plt
import cv2
import ellipse
DEBUG_IMAGES = []
def debug_show(name, src):
global DEBUG_IMAGES
filename = 'debug{:02d}_{}.pn... |
#!/usr/bin/env python
import datetime
import numpy as np
import os
import pause
import pickle
import rospy as ros
from scipy.interpolate import interp1d
import sys
import time
from PyQt5 import QtCore, QtWidgets, QtGui
import qdarkstyle
import matplotlib
matplotlib.use('Qt5Agg')
import matplotlib.pyplot as plt
from ... |
import json
import time
from typing import List, Tuple
from pymemcache.client.base import Client
import numpy
import requests
from PIL import Image
import mss.tools
from light_scene import LightScene
client = Client('localhost')
sct = mss.mss()
def get_dominant_color(im) -> Tuple:
import numpy as np
import ... |
""" Decimal """
from decimal import Decimal as D, getcontext, FloatOperation
from decimal import ROUND_DOWN
getcontext().prec = 50
getcontext().traps[FloatOperation] = True
#getcontext().rounding = ROUND_05UP
a = D('3.14') # Correct
#a = D(3.14) # Wrong!
print(a ** D('0.123'))
print(a % D('1.5'))
print(D(2).sqrt())
p... |
<filename>poleSeeker.py
#pl.ylabel(r'$x_{n}$') - TEX stuff
from numpy import inf,pi,cos,sin
from sympy import fourier_transform,plot,exp,symbols,Heaviside
from sympy.abc import t,s
import numpy as np
from matplotlib import pyplot as pl
from pylab import stem,linspace
import re
def poleSeeker(fxn):
pattern0 = 'u\[-... |
<filename>framework/Optimizers/FiniteDifference.py
# Copyright 2017 Battelle Energy Alliance, LLC
#
# 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.... |
import numpy as np
import scipy as sp
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
database = sb.load_dataset("tips")
print(database)
graph=sb.FacetGrid(database,col="sex",hue="smoker")
graph.map(plt.scatter,"total_bill","tip")
plt.show()
|
<reponame>antonioalfa22/EFE-WebClassifier
from scipy import stats
def getInitialWordsFrequency(filename):
file = open(filename, 'r', encoding="utf-8")
lines = file.readlines()
file.close()
wordsList = []
for i in range(0, 5000):
wordsList.append(lines[i].split("\t")[0])
return wordsLis... |
"""" This implementation is largely based on and adapted from:
https://github.com/sskhandle/Iterative-Classification """
import numpy as np
import scipy.sparse as sp
def get_class(kls):
parts = kls.split('.')
module = ".".join(parts[:-1])
md = __import__(module)
for comp in parts[1:]:
md = g... |
<gh_stars>0
from alignments.MegaAlignment import MegaAlignment
from regression.CloneFrequencyComputer_cnv1 import CloneFrequencyComputer_cnv1
from scipy import stats
import numpy
class Calculation:
def compute_estimated_SNVfrequency1(self, CloFre, CloSeq):
eSNVfre={}
c=0
snv_num=len(... |
"""
Shuffle spikes with different parameters and see how the proportion of syllables having significant PCC changes
"""
from pyfinch.database.load import DBInfo, ProjectLoader
from util.draw import *
def create_db():
from pyfinch.database.load import ProjectLoader
db = ProjectLoader().load_db()
with open... |
# -*- coding: utf-8 -*-
"""
Unit tests for the spectral module.
:copyright: Copyright 2014-2020 by the Elephant team, see `doc/authors.rst`.
:license: Modified BSD, see LICENSE.txt for details.
"""
import unittest
import numpy as np
import scipy.signal as spsig
import quantities as pq
import neo.core as n
from numpy... |
# -*- coding: utf-8 -*-
import numpy as np
import scipy.optimize as opt
import numpy.linalg as la
import numpy.ma as ma
import warnings
from .wst import WST
from .wst_operator import WSTOp
from .rwst import RWST
from .rwst_models import RWSTModel1
class RWSTOp:
"""
Reduced Wavelet Scattering Transform (RWST... |
<reponame>skforest/intro_ds
# -*- coding: UTF-8 -*-
"""
此脚本用于展示利用谱聚类进行图片分割
"""
import os
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
from sklearn.feature_extraction import image
from spectral import spectral_clustering
def read_img(path):
"""
读取图片，并将其转换为邻接矩阵
"""
# 对于彩色照片，只... |
# <NAME> (Rechazo de Banda)
import numpy as np
from PIL import Image, ImageOps
import matplotlib.pyplot as plt
from scipy import ndimage
def imageToArray(filename: str, rgb: bool = False):
img = Image.open(filename)
if not rgb:
img = ImageOps.grayscale(img)
img_array = np.array(img)
return img... |
<gh_stars>0
from anlffr.helper import biosemi2mne as bs
import mne
import numpy as np
from scipy import io
import os
import fnmatch
import pylab as pl
# Adding Files and locations
froot = 'D:/DATA/Saccade/'
subj = 'BM'
fpath = froot + subj + '/'
print 'Running Subject', subj
bdfs = fnmatch.filter(os.listdir(fpath... |
<reponame>xida2020/scikit-fem
from unittest import TestCase
from pathlib import Path
import numpy as np
import pytest
from scipy.spatial import Delaunay
from numpy.testing import assert_array_equal, assert_almost_equal
from skfem.mesh import (Mesh, MeshHex, MeshLine, MeshQuad, MeshTet, MeshTri,
... |
from scipy.optimize import fsolve
from matplotlib import cm, rcParams
from shapely import geometry
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import math, csv, os
""" ToDo : check if this is equivalent to the G-function for weak coupling """
c = ['#aa3863', '#d97020', '#ef9f07', '#449775',... |
"""Functions for downloading and reading MNIST data."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import scipy.io as sio
import gzip
import os
import glob
from six.moves import urllib
import numpy
import cPickle
def extract_test_data(work_directory, o... |
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
import os
import numpy as np
import random
from scipy import misc
#import tensorflow.contrib as tf_contrib
# Xavier : tf_contrib.layers.xavier_initializer()
# He : tf_contrib.layers.variance_scaling_initializer()
# Normal : tf.random_normal_initializ... |
#!/usr/bin/env python
"""Discrete synthesis from a dummy abstraction with mixed switching.
This is an example to demonstrate how the output of a discretization algorithm
that abstracts a switched system might look like,
where the mode of the system depends on a combination of
environment and system controlled variable... |
<reponame>yohm/GWSN_metamodeling
#%%
from pyexpat import model
import sys,math
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from keras.models import load_model
from icecream.icecream import ic
#%%
mpl.rcParams.update(mpl.rcParamsDefault)
#%%
plt.rcParams['font.family'] ='sans-serif'
plt.... |
<gh_stars>1-10
from fawkes.models import NetworkPoisson
from scipy.stats import gaussian_kde
import matplotlib.pyplot as plt
import numpy as np
import h5py as h5
import pandas as pd
import os
import sys
def import_data(path, name, dates):
mkt_open = 34200
mkt_close = 57600
msg_columns = ['sec', 'nano', 'ty... |
<filename>test_pymc3_w_ext.py
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('seaborn-darkgrid')
from dust_extinction.dust_extinction import GCC09_MWAvg, FM90
import theano
import theano.tensor as tt
def fm90func(c1, c2, c3, c4, xo, gamma, x, hs):
return c1 + c2*x + c3*(x**2/((x**2 - xo**2)**2 ... |
# -*- coding: utf-8 -*-
"""
==========
Statistics
==========
Demonstrates the different statistics you can use with ChainConsumer.
"""
###############################################################################
# By default, ChainConsumer uses maximum likelihood statistics. Thus you do not
# need to explicitly ... |
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
try:
import Tango
import pylab as pb
except:
pass
import numpy as np
from base_plots import gpplot, x_frame1D, x_frame2D
from ...models.gp_coregionalized_regression import GPCoregionalizedRegres... |
<gh_stars>1-10
from time import sleep, time_ns
from scipy.spatial import distance
from serial import Serial
from shared.byte_coders import *
from random import choices
from string import ascii_lowercase
from numpy import mean, std, sqrt
from scipy import stats
class Measurer():
"""An object that performs the measure... |
<gh_stars>1-10
from __future__ import print_function
import numpy as np
from scipy.spatial import cKDTree
from scipy.spatial.distance import cdist
import networkx as nx
__all__ = ['closest_points', 'optimal_permutation']
def closest_points(from_pts, to_pts):
return cKDTree(to_pts).query(from_pts)[1]
def opti... |
import sys
import optparse
import argparse
from scipy.io import wavfile
import numpy as np
import utils
from optparse import OptionParser
if __name__ == '__main__':
parser = OptionParser()
parser.add_option("-s", type="float", dest="start_time", default=1)
parser.add_option("-f", type="float", dest="en... |
<filename>src/common/training/batcher.py<gh_stars>10-100
import torch
import numpy as np
from scipy.sparse import coo_matrix
from torch.autograd import Variable
from common.training.options import gpu
class Batcher():
def __init__(self,data,size):
self.data = data
self.size = size
self.... |
'''The functions of the lambdata package
'''
# Prints the contingency table and chi2 stats
# given two columns of a DataFrame
import pandas as pd
import numpy as np
from scipy import stats
def cont_chi2(col1, col2):
contingency = pd.crosstab(col1, col2)
print(contingency)
chi2, p_value, dof, expected = ... |
#!/usr/bin/env python
"""
Test the creation of redundant features (see sklearn source)
The structure of X is columns of [informative, redundant, nuisance] features
"""
import numpy as np
from scipy import stats
from sklearn.preprocessing import MinMaxScaler
from sympy import symbols
from synthetic_data.synthetic_dat... |
import scipy.io
mat = scipy.io.loadmat('/media/ghost/8c5dd22b-3c0c-41d2-9807-c4094164ca3e/ghost/down/nyu_depth_v2_labeled(1).mat')
mat.items() |
# Copyright 2018-2020 Xanadu Quantum Technologies Inc.
# 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 applicable law or... |
"""This module contains the alpha-s calculation."""
import warnings
import scipy
from ..core.adsorbate import Adsorbate
from ..core.isotherm import Isotherm
from ..graphing.calcgraph import plot_tp
from ..utilities.exceptions import ParameterError
from ..utilities.math_utilities import find_linear_sections
from .are... |
<gh_stars>10-100
from __future__ import print_function, division, absolute_import
from warnings import warn
import numpy as np
import numpy.linalg as npl
from scipy.stats import rankdata
from . import subsample_columns, grouped_path, default_lagrange_sequence
from ..affine import astransform, power_L
from ..smooth i... |
<filename>omnidata_annotator/scripts/create_segmentation_2d_images.py
"""
Name: create_segmentation_2d_images.py
Desc: Creates and saves 2D segmentation images for each point. We use normalized cuts for
segmenting images into perceptually similar groups.
Requires (to be run):
- generate_points.py
... |
<gh_stars>1-10
import xarray as xr
import rioxarray
import pandas as pd
import numpy as np
import faiss
from scipy.interpolate import griddata
from sklearn.neighbors import NearestNeighbors
from datetime import timedelta, date
# Script to join monthly ERA .grib data with daily MODIS .tiff data
# We join in both time (... |
"""
(C) Copyright 2021 IBM Corp.
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 applicable law or agreed to in writing, software
dist... |
#!/usr/local/sci/bin/python
# PYTHON3
#
# Author: <NAME>
# Created: 22 April 2015
# Last update: 2 Apr 2019
# Location: /data/local/hadkw/HADCRUH2/UPDATE2014/PROGS/PYTHON/ # this will probably change
# GitHub: https://github.com/Kate-Willett/Climate_Explorer/tree/master/PYTHON/
# -----------------------
# CODE PURPOSE... |
__author__ = '<NAME> and <NAME>'
from scipy import size, zeros, ndarray, array
from numpy.random import randn
from pybrain.structure.evolvables.evolvable import Evolvable
class ParameterContainer(Evolvable):
""" A common interface implemented by all classes which
contains data that can change during executi... |
<reponame>LiuNingtao/Python-
import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import scipy
from PIL import Image
from scipy import ndimage
import tensorflow as tf
from tensorflow.python.framework import ops
from cnn_utils import *
np.random.seed(1)
X_train_orig, Y_train_orig, X_test_orig, Y_... |
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