text string |
|---|
"""
Plot optimal predictions for variational annealing estimations.
Created by <NAME> at 20:00 06-22-2018
This work is licensed under the
Creative Commons Attribution-NonCommercial-ShareAlike 4.0
International License.
To view a copy of this license, visit
http://creativecommons.org/licenses/by-nc-sa/4.0/.
"""
i... |
<gh_stars>100-1000
# See in the Dark (SID) dataset
import torch
import os
import glob
import rawpy
import numpy as np
import random
from os.path import join
import data.torchdata as torchdata
import util.process as process
from util.util import loadmat
import h5py
import exifread
import pickle
import PIL.Image as Image... |
import numpy as np
import cv2
import json, codecs
from random import *
from random import randint
import scipy.io as sio
import os
import os.path as osp
import matplotlib as mpl
if os.environ.get('DISPLAY','') == '':
print('no display found. Using non-interactive Agg backend')
mpl.use('Agg')
import matplotlib.p... |
<filename>apl/acquisitions.py
from typing import Any
import scipy as sp
import numpy as np
from numpy.typing import ArrayLike
class Acquisition:
def __call__(self, *args: Any, **kwds: Any) -> ArrayLike:
raise NotImplementedError
class ExpectedImprovement(Acquisition):
def __init__(self, xi: float) -... |
# classifier - classification algorithms for Bregman toolkit
__version__ = '1.0'
__author__ = '<NAME>'
__copyright__ = "Copyright (C) 2010 <NAME>, Dartmouth College, All Rights Reserved"
__license__ = "GPL Version 2.0 or Higher"
__email__ = '<EMAIL>'
import numpy as N
import scipy.linalg
from random import random
c... |
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import numpy as np
import sys
sys.path.append('/home/groups/ZuckermanLab/copperma/cell/celltraj')
import celltraj
import h5py
import pickle
import os
import subprocess
import time
sys.path.append('/home/groups/ZuckermanLab/copperma/msmWE/Bayesian... |
<gh_stars>1-10
from Multiple_GAN_codes.Basic_structure import *
from keras.datasets import mnist
import time
from utils import *
from scipy.misc import imsave as ims
from ops import *
from utils import *
from Utlis2 import *
import random as random
from glob import glob
import os,gzip
import keras as keras
... |
from .interpolation import PolynomialInterpolation
from .abstract import Point, Vector, Circle
from .curves import Trajectory
from pygame.locals import *
from . import colors
import numpy as np
import pygame
import pickle
import cmath
import math
import cv2
import sys
import os
class Fourier:
def transform(pts, ... |
<reponame>s-akanksha/DialoGraph_ICLR21
# -*- coding: utf-8 -*-
'''
Created on : Wednesday 01 Apr, 2020 : 01:49:35
Last Modified : Monday 29 Jun, 2020 : 06:24:46
@author : <NAME>
Institute : Carnegie Mellon University
'''
import os, sys, pdb, numpy as np, random, argparse, codecs, pickle, time, json, csv, co... |
import glob
import os.path
import cv2
import numpy as np
import collections
import matplotlib
import scipy.spatial.distance
import itertools
import matplotlib.pyplot as plt
import matplotlib.animation as animation
IMAGE_DIR = '../input/train/'
MSEC_PER_FRAME = 200
MSEC_REPEAT_DELAY= 2000
ADD_MASK_OUTLIN... |
<gh_stars>0
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Copyright 2020-2022 <NAME>. All Rights Reserved.
See Licence file for details.
"""
import numpy as np
import scipy.stats as stats
import sys
from Selkealgo import Sellke_algo
#This bit generates synthetic datasets that are needed to generate estimates
#fo... |
<reponame>SBhojani/zunzun<filename>zunzun/LongRunningProcess/StatisticalDistributions.py
import inspect, time, math, random, multiprocessing, io
import os, sys
import numpy, scipy, scipy.stats, pyeq3
from . import StatusMonitoredLongRunningProcessPage
import zunzun.forms
from . import ReportsAndGraphs
from . import p... |
<gh_stars>0
""" This script loads the raw app_events dataset and creates the dense and
sparse features"""
import os
import sys
from os import path
import numpy as np
import pandas as pd
from scipy import sparse, io
from scipy.sparse import csr_matrix, hstack
from dotenv import load_dotenv, find_dotenv
from sklearn... |
import os
import itertools
os.chdir('c:/mypysces/kraken')
## os.chdir('/home/bgoli/mypysces/kraken')
## import pysces
from . import *
## from Kraken import *
# investigate using vstack with type/range checking
def concatenateArrays(array_list):
output = None
for arr in range(len(array_list)):
if ... |
from collections import OrderedDict
import os, sys
import astropy.io.fits as fits
from astropy.nddata import NDData
import numpy as np
import matplotlib.pyplot as plt
import scipy.interpolate as sciint
import logging
_log = logging.getLogger('webbpsf')
from . import conf
_DISABLE_FILE_LOGGING_VALUE = 'none'
_Stre... |
from PAPyA.Methods.file_reader import FileReader
import pandas as pd
import scipy.stats as ss
class getRanks(FileReader):
def __init__(self, config_path: str, log_path: str, size: str, sd=None):
super().__init__(config_path, log_path, size, sd)
def getRanks(self):
load = FileReader(self.co... |
from dataclasses import dataclass, field
import sympy as sp
from sympy import Matrix as Mat
# import numpy as np
from typing import List, Optional, TYPE_CHECKING, Tuple
from .base import SimpleForceBase3D
# from .visual import LineAnimation
from .utils import norm, get_name
if TYPE_CHECKING:
# from .variable_list... |
<filename>dataset.py
#!/usr/bin/env python3
import megengine as mge
from megengine.data.dataset import Dataset
import os
import json
from pathlib import Path
from typing import Iterator, Sequence
from tqdm import tqdm
import cv2
import numpy as np
import pickle as pkl
from skimage import img_as_float32 as img_as_flo... |
from dowhy.do_sampler import DoSampler
from statsmodels.nonparametric.kernel_density import KDEMultivariateConditional, KDEMultivariate, EstimatorSettings
import numpy as np
from scipy.interpolate import interp1d, LinearNDInterpolator
class KernelDensitySampler(DoSampler):
def __init__(self, *args, **kwargs):
... |
<gh_stars>10-100
import datetime
import numpy as np
import pandas as pd
import pytest
from scipy.stats import describe, entropy
import fairlens.metrics.statistics as fls
def test_distribution_mean_continuous():
sr = pd.Series(np.random.randn(50))
assert fls.compute_distribution_mean(sr, x_type="continuous")... |
# <NAME>
# <EMAIL>
import numpy as np
from scipy.integrate import quad
class AXEhelper_computeTraceNWavelength:
'''
AXEhelper_computeTraceNWavelength (using Python 3) computes trace and wavelength in aXe definition.
This class can be easily used following AXEhelper_computeSIP.
Given obj = AXEhelper_co... |
<filename>beluga/numeric/data_classes/Trajectory.py
import numpy as np
import scipy.interpolate
import beluga
class Trajectory(object):
r"""
Class containing information for a trajectory.
.. math::
\gamma(t) : I \subset \mathbb{R} \rightarrow B
"""
def __new__(cls, *args, **kwargs):
... |
<reponame>CSUBioGroup/GLOBE-release
from fbpca import pca
from sklearn.preprocessing import normalize
import numpy as np
import scipy.sparse as sps
from annoy import AnnoyIndex
from sklearn.neighbors import NearestNeighbors
from sklearn.metrics.pairwise import rbf_kernel, euclidean_distances
KNN = 20
APPROX = True
d... |
# -*- coding: utf-8 -*-
import os
import numpy as np
from keras.models import Model
from keras.layers import Input, Lambda
from keras.preprocessing.image import ImageDataGenerator
from base_model import BaseModel
import keras.backend as K
from keras.optimizers import Adam
from scipy.special import comb
import sys
sys... |
<gh_stars>1-10
from sympy import *
from ga import Ga
from mv import MV
from printer import Format, xpdf, Fmt
Format()
ew,ex,ey,ez = MV.setup('e_w e_x e_y e_z',metric=[1,1,1,1])
a = MV('a','vector')
a.set_coef(1,0,0)
b = ex+ey+ez
c = MV('c','vector')
print 'a =',a
print 'b =',b
print 'c =',c
print a.reflect_in_blade(... |
<filename>test_voice_cloning.py
import sys
import os
#import IPython
#from IPython.display import Audio
inputs = [
"fǎmíngdùn zhènqū wèi měiguó kānsàsīzhōu sītǎfúdé xiàn xiáxià de zhènqū|000-zh|000-zh|zh",
]
tacotron_dir = "Multilingual_Text_to_Speech"
wavernn_dir = "WaveRNN"
tacotron_chpt = "newGENERA... |
"""Tests for square-free decomposition algorithms and related tools. """
from sympy.polys.sqfreetools import (
dup_sqf_p, dmp_sqf_p,
dup_sqf_norm, dmp_sqf_norm,
dup_sqf_part, dmp_sqf_part,
dup_sqf_list, dup_sqf_list_include,
dmp_sqf_list, dmp_sqf_list_include,
dup_gff_list, dmp_gff_list)
from ... |
#!/usr/bin/env python3
import sys
import numpy as np
import scipy.signal
from midas.node import BaseNode
from midas import utilities as mu
# EEG processing node
class EEGNode(BaseNode):
def __init__(self, *args):
""" Initialize EEG node. """
super().__init__(*args)
self.metric_functions.... |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, mean_squared_error
import seaborn as sns
from scipy import stats
import math
def clean_data(df):
"""
... |
#!/usr/bin/env python
# coding: utf-8
## convert jupyter notebook to sript
# jupyter nbconvert --to script notebookname.ipynb
## import package
from sklearn.linear_model import LinearRegression
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os.path
from scipy import stats
import sys
... |
import numpy as np
from scipy.stats import bernoulli
import scipy
# Distributions of random matrices as input for the group testing problem
def Diag(N=100, p=0.1):
""" Returns a diagonal matrix: test anyone with probability p """
r = bernoulli.rvs(p, size=int(N))
return np.diag(r)
def Ber(N=100, T=30, p=0.2):
... |
<reponame>igemsoftware2021/iGEM_ParisBettencourt21<filename>minicell_bioproduction_model_v2.py
### imports ###
from os import path
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from scipy.integrate import odeint
import random as rd
######### implementation ###########
### functions
... |
<gh_stars>0
import numpy as np
import netket as nk
import sys
import scipy.optimize as spo
import netket.custom.utils as utls
from quspin.operators import hamiltonian # operators
from quspin.basis import spin_basis_general # spin basis constructor
import scipy as sp
from netket.operator import local_values as _lo... |
<reponame>UBC-MDS/BlackBox_Python<gh_stars>0
import pandas as pd
import numpy as np
import pandas as pd
from scipy.optimize import minimize
def getMLE(distribution,data):
"""
compute the log likelihood of data given the distribution
Args:
distribution: type of distribution of the data. for example (bin... |
from pytorch_forecasting.models import TemporalFusionTransformer
from pytorch_forecasting.utils import to_list
from pytorch_forecasting.data.encoders import EncoderNormalizer, GroupNormalizer, MultiNormalizer, NaNLabelEncoder
import matplotlib.pyplot as plt
from typing import Any, Callable, Dict, Iterable, List, Tuple... |
from glob import glob
import ana
from matplotlib import cm
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import seaborn as sns
import pandas as pd
import numpy as np
import scipy.stats
def m456(ax, nr=456):
files = glob('data/MF... |
import numpy as np
from scipy.io import wavfile
import os
raw_gen_samples_path = "./djenerated_samples_raw"
wav_gen_samples_path = "./djenerated_samples_wav"
sr = 44100
for filename in os.listdir(raw_gen_samples_path):
if filename.endswith(".npy"):
gen_song_samples = np.load(os.path.join(raw_gen_samples_p... |
import statistics
rd = open("tr-word-list.txt","r")
wr = open("tr-long-words.txt","w")
ws = rd.readlines()
rd.close()
lengths = []
for w in ws:
lengths.append(len(w))
mean = statistics.mean(lengths)
sd = statistics.stdev(lengths)
treshold = round(mean + 2*sd)
print(mean,sd,treshold)
long_words=[]
for w in ws:
i... |
<gh_stars>0
"""This module contains functions that calculate the variation
of concentration or MR signal with time according to a tracer kinetic model.
"""
import MathsTools as tools
import ExceptionHandling as exceptionHandler
import numpy as np
from scipy.optimize import fsolve
from joblib import Parallel, de... |
import os
import glob
import numpy as np
from tensorpack import RNGDataFlow
from record_breakout import Recorder
import glob
from scipy import misc
import gym
from cv2 import resize
FRAME_HISTORY = 4
GAMMA = 0.99
TRAIN_TEST_SPLIT = 0.8
# Timon will pass me the key
GAME_NAMES = {
'MontezumaRevenge-v0': 'revenge',
... |
<reponame>martok/py-symcircuit
import re
from typing import List, Dict, Set, Tuple, Union, Optional, Iterable
from sympy import StrPrinter, Eq, Symbol, symbols, Expr, Limit, cse
from sympy import Tuple as TTuple
from sympy.core.assumptions import _assume_defined
from sympy.printing.pycode import pycode
from sympy.solv... |
<reponame>weecology/NEON_crown_maps
#Interactive
#Allometry
import geopandas
import pandas as pd
import glob
import re
import numpy as np
from dask import delayed
import dask.dataframe as dd
from check_site import get_site, get_year
from sklearn.linear_model import LinearRegression
from scipy.optimize import curve_f... |
<filename>scripts/pyvision/__init__.py
# PyVision License
#
# Copyright (c) 2006-2011 <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. Redistributions of source code must retain th... |
import plots
from sympy import *
x,y = symbols('x,y')
myPlot = plots.MyStandardPlot()
myPlot.slopefield(-x*y,[x,-2,2],[y,-2,2],samples=25)
myPlot.ygraph(2*exp(-x*x/2),[x,-2,2],samples=100,color=1)
myPlot.ygraph(-2*x/1.5*exp(-x*x/2),[x,-2,2],samples=100,color=2)
myPlot.ygraph(2*exp(-x*x/2-x-1.5),[x,-2,2],samples=100,c... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 5 10:09:21 2019
@author: <NAME>
"""
import rospy
import numpy as np
from rospy_tutorials.msg import Floats # for landmarks array
from rospy.numpy_msg import numpy_msg # for landmarks array
import matplotlib.pyplot as plt
from sklearn.neighbors imp... |
<reponame>shun60s/Vocal-Tube-Noise-K-Model
#coding:utf-8
# load wav file (16bit mono) as source
import os
import numpy as np
from scipy.io.wavfile import read as wavread
from matplotlib import pyplot as plt
# Check version
# Python 3.6.4 on win32 (Windows 10)
# numpy 1.14.0
# matplotlib 2.1.1
# sc... |
from __future__ import print_function, division
import sys
import os
import rhalphalib as rl
import numpy as np
import scipy.stats
import pickle
import ROOT
rl.util.install_roofit_helpers()
rl.ParametericSample.PreferRooParametricHist = False
def expo_sample(norm, scale, obs):
cdf = scipy.stats.expon.cdf(scale=sc... |
<reponame>jmaggio14/physops<gh_stars>0
import numpy as np
import physops
import scipy
class Wavefront(np.ndarray):
def __new__(cls,definition=None,title="wavefront",wavelength=None,size=(1000,1000),x_range=None,y_range=None,definition_kwargs={}):
if x_range == None:
x_range = np.linspace(-size[... |
<gh_stars>0
from django.shortcuts import render
from django.http import HttpResponse
from django.conf import settings
from django.views.decorators.csrf import csrf_exempt
from django.http import JsonResponse
import numpy as np
import pandas as pd
from scipy import stats
import json, os, math, datetime
import copy
from... |
# coding: utf-8
# In[1]:
import numpy as np
import imutils
import time
import timeit
import dlib
import cv2
import matplotlib.pyplot as plt
from scipy.spatial import distance as dist
from imutils.video import VideoStream
from imutils import face_utils
from threading import Thread
from threading import Timer
from ch... |
import os
import numpy as np
import matplotlib as mpl
mpl.use("Agg")
import matplotlib.pyplot as plt
from itertools import product
from scipy import interpolate
import fsps
from mangadap.proc.templatelibrary import TemplateLibrary
from mangadap.proc.ppxffit import PPXFFit
from mangadap.proc.stellarcontinuummodel imp... |
<gh_stars>0
import random
import itertools as it
import cvxopt
from cvxopt import matrix, solvers
from fractions import Fraction
from copy import deepcopy
from collections import defaultdict
import numpy as np;
from numpy import unique
import pandas as pd;
from numpy import vstack
import re
from IPython.display import ... |
from scipy.spatial.transform.rotation import Rotation
from alitra import Euler, Quaternion
def euler_to_quaternion(
euler: Euler, sequence: str = "ZYX", degrees: bool = False
) -> Quaternion:
"""
Transform a quaternion into Euler angles.
:param euler: An Euler object.
:param sequence: Rotation se... |
#!/usr/bin/env python3
# numpy and scipy
import numpy as np
import scipy.fftpack
import scipy.misc
from scipy.special import erf
from scipy import signal
from scipy.ndimage.filters import gaussian_filter
# galsim and lmfit
import galsim
import lmfit
# astropy
# TODO: replace with afw equivalents
from astropy.convolu... |
<filename>Loan-Approval-Analysis/code.py
# --------------
# Import packages
import numpy as np
import pandas as pd
from scipy.stats import mode
# code starts here
bank = pd.read_csv(path)
categorical_var = bank.select_dtypes(include='object')
print(categorical_var)
numerical_var = bank.select_dtypes(include='num... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
MIT License
Copyright (c) 2022 Jongrae.K
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 r... |
"""Project Euler problem 5"""
from collections import Counter
import fractions
def calculate(end_number):
"""Returns the smallest positive number that is evenly divisible
by all of the numbers from 1 to the specified number"""
factors_by_number = []
for divisor in range(2, end_number + 1):
cou... |
<filename>tests/test_api.py
# -*- coding: utf-8 -*-
import unittest
import os
from mathdeck import check, load
from sympy import symbols, expand
class TestApi(unittest.TestCase):
pass
if __name__ == '__main__':
unittest.main()
|
import numpy as np
from scipy.spatial.distance import (
correlation,
cosine,
pdist,
cdist
)
from scipy import stats
# cos類似度
def cos_similarity(item1, item2):
return 1 - cosine(item1, item2)
# scipyによるPearsonの(積率)相関係数
def distance_correlation(x, y):
return 1 - correlation(x, y)
# ピアソンの積率相関... |
import numpy as np
import healpy as hp
from scipy.special import factorial, comb
from .. import utils
from .. import units as u
from .template import Model
class CMBMap(Model):
def __init__(
self, nside, map_IQU=None, map_I=None, map_Q=None, map_U=None, map_dist=None
):
super().__init__(nside... |
<filename>Othala/ThirdParty/AirPLS.py
"""
Othala.ThirdParty.AirPLS.py
airPLS.py Copyright 2014 <NAME> - <EMAIL>
Baseline correction using adaptive iteratively reweighted penalized least squares
This program is a translation in python of the R source code of
airPLS version 2.0 by <NAME> and <NAME> -
https://... |
import ee
import copy
import json
import backoff
import requests
import numpy as np
from io import StringIO
import geopandas as gpd
from affine import Affine
from rasterio import features
from pyproj import Transformer
from collections.abc import Iterable
from scipy import interpolate, ndimage
from google.auth.transpo... |
'''
load lsun dataset as numpy array
usage:
import lsun
(test_x, test_y) = load_lsun_test()
'''
from PIL import Image
from scipy.ndimage import filters
import os
import tensorflow as tf
import numpy as np
TEST_X_PATH = '/home/cwx17/data/sun'
TRAIN_X_ARR_PATH = '/home/cwx17/data/sun/train.npy'
TEST_X_ARR_PA... |
<reponame>qxdnfsy/PEN-Net-Keras-Img_Inpainting<filename>core/data_loader.py
import os
import scipy.misc
from core.utils import *
class data_loader:
def __init__(self, dataset_path,batch_size):
self.dataset_path = dataset_path + "/"
self.batch_size=batch_size
def Load_data(self, batch_size=-1,... |
from __future__ import division, print_function
import numpy as np
import pandas as pd
from scipy.stats import skew
from sklearn.preprocessing import RobustScaler
from sklearn.linear_model import LassoCV,RidgeCV
from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor, GradientBoostingRegressor
fro... |
import scipy.io as sio
import numpy as np
FIT = [
'Gaussian',
'Asymmetric',
'Super',
'RMS',
'RMS cut peak',
'RMS cut area',
'RMS floor'
]
ACCL = 'accelerator'
STAT = 'status'
CTRL = 'ctrlPV'
READ = 'readPV'
BEAM = 'beam'
PROF = 'profPV'
TS = 'ts'
CONFIG = 'config'
# Disclaimer: It is up ... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2011 <NAME> <<EMAIL>>
# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
"""
Automated tests for similarity algorithms (the similarities package).
"""
import logging
import unittest
import math
import os
import numpy
import scipy... |
import pquality as pq
import networkx as nx
from cdlib.utils import convert_graph_formats
from collections import namedtuple
import numpy as np
import scipy
from cdlib.evaluation.internal.link_modularity import cal_modularity
import Eva
__all__ = ["FitnessResult", "link_modularity", "normalized_cut", "internal_edge_de... |
<filename>engine.py
import pandas as pd
import numpy as np
import pickle
import matplotlib.pyplot as plt
from scipy import stats
import tensorflow as tf
import seaborn as sns
from pylab import rcParams
from sklearn.model_selection import train_test_split
from keras.models import Model, load_model
from keras.layers impo... |
import tensorflow as tf
import numpy as np
from tqdm import tqdm
from scipy.sparse import vstack, hstack
class AutoRec(object):
def __init__(self,
input_dim,
embed_dim,
batch_size,
lamb=0.01,
learning_rate=1e-4,
... |
<reponame>TUD-RST/pytrajectory
"""
This example of the inverted pendulum demonstrates the basic usage of
PyTrajectory as well as its visualisation capabilities.
"""
# import all we need for solving the problem
from pytrajectory import TransitionProblem
import numpy as np
from sympy import cos, sin
from numpy import pi... |
import numpy as np
import astropy.units as u
from spectral_cube import SpectralCube
from scipy.optimize import curve_fit
import os
from cube_analysis.spectra_shifter import fourier_shift, cube_shifter
from cube_analysis.tests.utils import generate_hdu
def test_shifter(shape=(100, 100, 100), sigma=8., amp=1.):
'... |
"""
@author sanjeethr, oligoglot
Implements SGDClassifier using FeatureUnions for Sentiment Classification of text
It also has code to experiment with hyper tuning parameters of the classifier
"""
from __future__ import print_function
import numpy as np
import pickle
import json
from pprint import pprint
from time im... |
<reponame>truongc2/data-describe
from scipy.stats import f_oneway, levene
def varying(group, alpha=0.01):
"""Identifies varying box plots (i.e. with different means) using the one-way ANOVA test.
Args:
group: The groups from `split_by_category`
alpha: The significance level
Returns:
... |
<reponame>andrewmkiss/PyXRF<filename>pyxrf/core/utils.py<gh_stars>10-100
import numpy as np
import scipy
import time as ttime
import logging
logger = logging.getLogger(__name__)
# =================================================================================
# The following set of functions are separated from t... |
<filename>nse_opinf_poddmd/cwdc_tdp_pout_vout.py
import numpy as np
import scipy.io
import scipy.sparse as sps
import scipy.sparse.linalg as spsla
from scipy.integrate import solve_ivp
# from scipy.integrate import odeint
# import conv_tensor_utils as ctu
import nse_opinf_poddmd.visualization_utils as vu
import sys
imp... |
#!/usr/bin/env python
import sys,os,math
from sklearn import svm
from scipy import interpolate
import numpy as np
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
def sigmoid(x):
return 1/(1+np.exp(-x))
def getCDF(list):
x=[0]
y=[0]
print("[getCDF]Nums of values: %d"%(len(list)))
... |
#!/usr/bin/env python
######################################################
# GUI to vizualize ROMS input/output files
# Sep 2021
# <EMAIL>
######################################################
import os
import wx
import datetime as dt
from matplotlib.backends.backend_wxagg import FigureCanvasWxAgg as FigureCanvas
f... |
import unittest
import parla.comps.sketchers.aware as aware
import numpy as np
import scipy.linalg as la
import parla.comps.sketchers.oblivious as skob
import parla.utils.linalg_wrappers as ulaw
import parla.utils.stats as ustats
import parla.tests.matmakers as matmakers
np.set_printoptions(precision=4, linewidth=100)... |
<filename>pydsm/postpro.py
"""
This module is to postprocess data for use in visualization and in calculating metrics
"""
import click
import collections
import contextlib
import logging
import sys
import numpy as np
import pandas as pd
import scipy.stats as stats
import pyhecdss
from pandas.core.frame import DataFr... |
<gh_stars>1-10
"""
Ensemble of imbalance ratio folded undersampling experiments.
"""
import csv
import helper as h
from scipy import stats
from tqdm import tqdm
import numpy as np
import method as m
from sklearn import svm, base, neighbors, metrics, naive_bayes, tree, neural_network
from imblearn import under_sampling,... |
import numpy as np
import pandas as pd
import re
import warnings
import scipy.optimize as opt
from scipy.stats import norm, f, chi2, ncf, ncx2, binom
from scipy.special import ncfdtrinc, chndtrinc
import matplotlib.pyplot as plt
import seaborn as sns
from poibin import PoiBin
warnings.filterwarnings("ignore")
def... |
<filename>10 Days of Statistics/Day 3 - Cards of the Same Suit.py
"""
Day 3 - Cards of the Same Suit
Author: <NAME>
"""
import itertools
# You draw 2 cars from a standard 52-card deck without replacing them.
from fractions import Fraction
total_experiment_outcomes = 52
suit_number = 4
for_each_suite_nu... |
#!/usr/bin/env python3
# Copyright 2021, Robotec.ai sp. z o.o.
#
# 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 appli... |
from math import sqrt as raiz
from statistics import mean as media
from statistics import variance as var
def dados_pressao_um_valor(nome_documento, dia, mes, ano, valores_sistolica, valores_diastolica):
quantidade_dados = len(valores_sistolica)
media_sistolica = valores_sistolica[0]
desvio_padrao_sistol... |
import glob
import sys
import cPickle
from os.path import join
import numpy as n
import astropy.io.fits as fits
import os
import astropy.cosmology as co
cosmo = co.Planck13
import astropy.units as uu
import matplotlib
#matplotlib.use('pdf')
matplotlib.rcParams['font.size']=12
import matplotlib.pyplot as p
from scipy... |
<filename>services/statistics/src/statistics/models/stats.py<gh_stars>0
# -*- coding: utf-8 -*-
# Copyright 2020 Green Valley Belgium NV
#
# 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
#
# ... |
import argparse
import json
import numpy as np
import xml.etree.ElementTree as ET
from scipy.spatial.transform import Rotation
BLENDER_FILE = "track.dae"
SCALE = 5 # Scaling factor for the x and y axes
NS = {"xmlns": "http://www.collada.org/2005/11/COLLADASchema"}
HYPER_DURATION = -10
COLLECTIONS = {
"desert": [... |
import csv
import networkx as nx
import numpy as np
import scipy.stats as ss
from random import shuffle
import matplotlib.pyplot as plt
import pandas as pd
from itertools import combinations
###########################################################################
def can_nodes_recover(infection_type):
r"""INoDS can... |
from hydroDL import kPath, utils
from hydroDL.app import waterQuality
from hydroDL.master import basins
from hydroDL.data import usgs, gageII, gridMET, ntn
from hydroDL.master import slurm
from hydroDL.post import axplot, figplot
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import scipy
import... |
###########################################################################################
# #
# This sample shows how to evaluate object detections applying the following metrics: #
# * Precision x Recall curve ----> ... |
<filename>utils/PWMInitializer.py
from utils.meme import parseMeme
import numpy as np
import random
import os
from copy import deepcopy
from tensorflow.keras.initializers import Initializer, truncated_normal
import tensorflow as tf
from scipy.stats import truncnorm
def _truncated_normal(mean,
... |
<reponame>johnmgregoire/JCAPDataProcess
import numpy, copy, operator
from scipy import interpolate
from scipy.signal import savgol_filter
if __name__ == "__main__":
import os, sys
sys.path.append(os.path.split(os.path.split(os.path.realpath(__file__))[0])[0])
sys.path.append(os.path.join(os.path.split(os.pa... |
<filename>mne/utils/tests/test_linalg.py<gh_stars>0
"""Test linalg utilities."""
# Authors: <NAME> <<EMAIL>>
#
# License: BSD-3-Clause
import numpy as np
from numpy.testing import assert_allclose, assert_array_equal
from scipy import linalg
import pytest
from mne.utils import _sym_mat_pow, _reg_pinv, requires_version... |
<filename>analysis_figure_code/SuppTable1/SuppTable1.py
import numpy as np
import iris
from scipy import stats
import matplotlib.pyplot as plt
"""
Created on Mon Jan 20 14:06 2020
@author: <NAME>
======================================================================
Purpose: Outputs results for Supplementary Table 1... |
import argparse
import numpy as np
import sys
from collections import Counter
from scipy.sparse import csr_matrix, lil_matrix, vstack
from scipy.stats import gamma
# Read in vocabulary from file.
def get_vocab(vocab_fn, ignore_case):
vocab = []
vocab_index = {}
for i, line in enumerate(open(vocab_fn, mod... |
import numpy as np
import torch
import os
import cv2
import math
import datetime
from scipy.spatial.distance import cdist
from torch.utils.data import Dataset
class SparseDataset(Dataset):
"""Sparse correspondences dataset."""
def __init__(self, train_path, nfeatures):
self.files = []
self.f... |
<reponame>aprilnovak/openmc
from collections.abc import Mapping
from ctypes import c_int, c_int32, c_double, c_char_p, POINTER
from weakref import WeakValueDictionary
import numpy as np
from numpy.ctypeslib import as_array
import scipy.stats
from openmc.data.reaction import REACTION_NAME
from . import _dll, Nuclide
f... |
<reponame>wonambi-python/wonambi
"""Module to select periods of interest, based on number of trials or any of
the axes.
There is some overlap between Select and the Data.__call__(). The main
difference is that Select takes an instance of Data as input and returns
another instance of Data as output, whil Data.__call__(... |
from __future__ import print_function
"""
Classes that provide support functions for minis_methods,
including fitting, smoothing, filtering, and some analysis.
Test run timing:
cb: 0.175 s (with cython version of algorithm); misses overlapping events
aj: 0.028 s, plus gets overlapping events
July 2017
Note: all va... |
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