text string |
|---|
<filename>code_testing/test_WDM_splice.py
import numpy as np
from numpy.testing import assert_allclose,assert_raises,assert_almost_equal
import sys
from scipy.constants import c, pi
sys.path.append('src')
from scipy.integrate import solve_ivp
from functions import *
def specific_variables(N):
n2 = 2.5e-20
alpha... |
<reponame>nam8/Barry
import sys
import os
import pandas as pd
from scipy.interpolate import interp1d
from scipy.stats import norm
import numpy as np
sys.path.append("..")
from barry.cosmology.camb_generator import getCambGenerator
from barry.postprocessing import BAOExtractor, PureBAOExtractor
from barry.config import... |
import urllib.request
from bs4 import BeautifulSoup
from statistics import mode
states_dict = {
"Alabama" : "https://en.wikipedia.org/wiki/COVID-19_pandemic_in_Alabama",
"Alaska" : "https://en.wikipedia.org/wiki/COVID-19_pandemic_in_Alaska",
"Arizona" : "https://en.wikipedia.org/wiki/COVID-19_pandemic_in_A... |
"""
The evaluation module for VA-JCR/VA-JCM models. Only works in Python >= 3.5.
Some code is forked from https://github.com/ECHO960/PKU-MMD/blob/master/evaluate.py related to the paper:
<NAME>, <NAME>, <NAME>, <NAME>, and <NAME>, "PKU-MMD: A large scale benchmark for continuous multi-modal human
action unders... |
<reponame>EsmeeHuijten/DESHIMAmodel
import matplotlib.pyplot as plt
plt.rcParams['animation.ffmpeg_path'] = 'C:/FFmpeg/bin/ffmpeg.exe'
from mpl_toolkits.axes_grid1 import make_axes_locatable
from scipy import interpolate, optimize
import os
import math
import numpy as np
import matplotlib.animation as animation
from ma... |
<reponame>samtx/pyapprox<filename>pyapprox/cvar_regression.py
import numpy as np
from scipy import sparse
from functools import partial
from scipy import integrate
def value_at_risk(samples,alpha,weights=None,samples_sorted=False):
"""
Compute the value at risk of a variable Y using a set of samples.
Para... |
import torch
import numpy as np
import scipy.special as sc
from scipy.optimize import brentq
import ctypes
def generate_so3_lebedev(n=26, n_gamma=8):
"""
@param: (n, n_gamma) grid
@return: np.ndarray (n x n_gamma, 3)
"""
LIB = ctypes.CDLL("./liblebedevlaikov.so")
LDNS = [6, 14, 26, ... |
<gh_stars>1-10
import os
import numpy as np
import pandas as pd
from PIL import Image
import random
import scipy.misc
from sklearn.model_selection import train_test_split
from tensorflow.contrib.learn.python.learn.datasets.base import Datasets
from tensorflow.contrib.learn.python.learn.datasets.mnist import DataSet,... |
import numpy as np;
from sklearn.base import BaseEstimator, TransformerMixin
from scipy.sparse import issparse
class SumarizeTransformer(BaseEstimator):
def __init__(self, agg = None):
self.agg = agg
def transform(self, X, y=None):
if self.agg == 'min':
return X.min(axis=1,keepd... |
import math, numpy
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
global dm, m0
dm = -(75.5368+175.5873)*9 #rate of fuel consumption kg/s
m0 = 486.931*(10.0**3.0) #mass at 100m/s
m01 = 548.759*(10.0**3.0) #mass at 0m/s
mwet = 424.6*(10.0**3.0) #mass of first stage fuel tank with fuel
mdr... |
from sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer
from sklearn.pipeline import Pipeline
from src.data.DBConnection import DBConnection
from src.data.make_dataset import preprocess_pipeline
import time
from numpy import round
from pathlib import Path
import scipy
import pickle
import log... |
"""The ``templates`` module allows for fast creation of a few select sample types and diffraction geometries without having to
worry about any of the "under the hood" scripting.
"""
import numpy as np
import pygalmesh
from scipy.spatial.transform import Rotation
from xrd_simulator.detector import Detector
from xrd_sim... |
"""
Data visualization toolbox.
"""
from matplotlib import style as mpstyle
from matplotlib.pyplot import figure
from pandas import DataFrame
from scipy.cluster.hierarchy import dendrogram
AXES = (("frame_on", False),)
FIGURE = (
("clear", True),
("dpi", 100),
("edgecolor", None),
("facecolor", None),
... |
<filename>advanced_python/assignments/proj3/data_project.py<gh_stars>0
import numpy as np
import pandas as pd
#import matplotlib
import matplotlib.pyplot as plt
import datetime
import pandas_datareader.data as web
import math
import scipy.optimize as sco
#download data from Yahoo Finance and stock growth vi... |
<reponame>karanchawla/ai_for_robotics
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sun Apr 2 10:00 2017
@author: <NAME> (<EMAIL>)
"""
import numpy as np
import matplotlib.pyplot as plt
from scipy.sparse import linalg as sla
from enum import Enum
import copy
import pylab
import matplotlib.pyplot as pl... |
<filename>scripts/solve_matrix_equation.py
from envtest import my_mat_solve
from sympy.matrices import Matrix, MatrixSymbol
# Call function to solve the linear equation A*x=b symbolically
A = Matrix([[2, 1, 3], [4, 7, 1], [2, 6, 8]])
b = Matrix(MatrixSymbol('b', 3, 1))
x = my_mat_solve(A, b)
print(x) |
<gh_stars>1-10
import numpy as np
import matplotlib.pyplot as plt
import sys
import scipy as sp
from pathlib import Path
from scipy.signal import convolve2d
import cProfile
import pstats
from time import strftime
from .find_peaks import find_peak_indices
# from skimage.feature import peak_local_max
np.set_printoptions... |
<gh_stars>1-10
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
from scipy.interpolate import UnivariateSpline
def balance(gamma, interm, r, sa, x):
return r[x], UnivariateSpline(r, interm, k=3, s=0).integral(0., r[x]), gamma[x]*sa(r[x])
|
#! /usr/bin/env python3
import sympy
class _symbolic_object:
_tokens = []
def __init__(self, token):
if len(token) == 0:
raise ValueError("Empty string is not a valid token.")
if token in self.__class__._tokens:
raise ValueError("The token \"" + token + "\" is... |
"""General functions for mathematical and numerical operations.
Functions
---------
- spline - Create a general spline interpolation function.
- cumtrapz_loglog - Perform a cumulative integral in log-log space.
- extend - Extend the given array by extraplation.
- sa... |
"""
Methods for estimating beta from pairwise comparisons
"""
import numpy as np
from sklearn.linear_model import LogisticRegression as logistic_reg
from scipy.optimize import minimize
def averaging(X, XC, yn):
"""
Estimate covariance from X, beta from XC and yn.
"""
N, d = X.shape
# Estimated mea... |
<filename>mpys/mps.py
"""MPS class."""
import numpy as np
from scipy.linalg import qr, rq
from mpys.mps_ops import contract
class Mps(object):
"""Class for matrix product states (MPS).
Attributes:
L (int): length of the MPS.
d (int): physical dimension.
D (int): maximum bond dimensi... |
"""
MATLAB® file utilies (:mod:`scipy.io.matlab`)
=============================================
.. currentmodule:: scipy.io.matlab
This submodule is meant to provide lower-level file utilies related to reading
and writing MATLAB files.
.. autosummary::
:toctree: generated/
matfile_version - Get the MATLAB fil... |
<filename>fnc_kfold_our_model.py
import sys
import numpy as np
import nltk
nltk.download('wordnet')
from sklearn.ensemble import GradientBoostingClassifier
from feature_engineering import refuting_features, polarity_features, hand_features, gen_or_load_feats
from libraries import sequence_padding
from feature_engineeri... |
<reponame>LCS2-IIITD/collusive-retweeters-ASONAM-2018
"""
Title: Retweet Us, We Will Retweet You: Spotting Collusive Retweeters
Involved in Blackmarket Services. (ASONAM 2018)
Authors: <NAME>, <NAME>, <NAME>, <NAME>
"""
import sys
from sklearn import svm
from sklearn.model_selection import train_test_split, Stra... |
<gh_stars>0
from __future__ import print_function
import time
import itertools
import collections
import logging
from six.moves import cPickle
import numpy as np
from scipy import optimize
import theano
import theano.tensor as tt
import theano.compile.sharedvalue as ts
import numerical.numpytheano as nt
import numeric... |
<reponame>zxhyJack/image-enhancement
import cv2
import copy
import time
import numpy as np
from scipy.ndimage.filters import generic_filter, uniform_filter
def window_stdev(X, window_size):
r, c, l = X.shape
X += np.random.rand(r, c, l) * 1e-6
c1 = uniform_filter(X, window_size, mode="reflect")
c2 = u... |
# CR(-2) is particularly computationally convenient
from math import fsum, inf
class Estimator:
# NB: This works better you use the true wmin and wmax
# which is _not_ the empirical minimum and maximum
# but rather the actual smallest and largest possible values
def __init__(self, wmin=0, wmax... |
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 5 13:17:47 2018
@author: JHodges
"""
import numpy as np
import matplotlib
matplotlib.rcParams['ps.useafm'] = True
matplotlib.rcParams['pdf.use14corefonts'] = True
#matplotlib.rcParams['text.usetex'] = True
import matplotlib.pyplot as plt
from PIL import Image, ImageDraw... |
<reponame>tbj128/mian
import numpy as np
import scipy
from scipy.sparse import csr_matrix
import os
from skbio.diversity import alpha_diversity
from mian.core.data_io import DataIO
otu = scipy.sparse.load_npz("/Users/boyanjin/Documents/Personal/workspace/mian/mian/data/18/b54c2ded-31db-4ae6-9d05-9e1550370f03/table.s... |
#!/usr/bin/python
import glob,sys,time
from argparse import (ArgumentParser, FileType)
import logging, os, sys, re, collections, operator, math, shutil, datetime
from collections import Counter
import pandas as pd
import copy, statistics
from multiprocessing import Pool
from projectX.constants import SAMPLE_FIELDS,SAMP... |
<gh_stars>100-1000
"""
Utility functions and classes
"""
from __future__ import print_function, division
import numpy as np
import scipy.sparse as sp
from itertools import chain
from random import uniform
def rand_convex(n):
rand = np.matrix([uniform(0.0, 1.0) for i in range(n)])
return rand / np.sum(rand)
... |
<gh_stars>0
from scipy import spatial
import numpy as np
import json
import caas
def run(workingPath, imagePathFilename, result_image):
rgb_values = result_image["rgb"]
cd = caas.color_definitions
color_scheme_result = {}
distances = np.empty([0])
color_schemes = list(cd.keys())
for col... |
<filename>src/spectrogram_converter.py
import numpy, scipy, matplotlib.pyplot as plt
import librosa, librosa.display
x, sr = librosa.load('audio/drum_sound.wav')
plt.figure(figsize=(5, 5))
librosa.display.waveplot(x, sr, alpha=0.8)
# plt.suptitle('Drum (Time Domain)')
# plt.ylabel('Amplitude')
# plt.xlabel('Time (Se... |
<reponame>ttuanho/MATH_2859<gh_stars>0
# By
# ████████╗██╗ ██╗ █████╗ ███╗ ██╗ ██╗ ██╗ ██████╗
# ╚══██╔══╝██║ ██║██╔══██╗████╗ ██║ ██║ ██║██╔═══██╗
# ██║ ██║ ██║███████║██╔██╗ ██║ ███████║██║ ██║
# ██║ ██║ ██║██╔══██║██║╚██╗██║ ██╔══██║██║ ██║
# ██║ ╚██████╔╝██║ ██║██║ ╚███... |
import csv
import random as random
import numpy as np
import matplotlib.pyplot as plt
from scipy.special import expit, logit
# Variables a usar
archivos_fonts = [
"data/font1.csv",
"data/font2.csv",
"data/font3.csv"
]
archivo_respuestas = [
"data/respuestas1.csv",
"data/respuestas2.csv",
"data/... |
<reponame>rodluger/exoaurora<gh_stars>1-10
#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
search.py
---------
Searching the HARPS data for the OI emission signal.
'''
from __future__ import division, print_function, absolute_import, unicode_literals
from pool import Pool
import matplotlib as mpl; mpl.use('Agg')
m... |
# -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>
# <codecell>
from __future__ import division
from pandas import *
import os, os.path
import sys
import numpy as np
sys.path.append('/home/will/HIVReportGen/AnalysisCode/')
sys.path.append('/home/will/PySeqUtils/')
# <codecell>
store = HDFStore('/home/will/HIVRepor... |
<filename>Chapter11/c11_17_ModifiedVaR_one_day.py
"""
Name : c11_17_modifed_VaR_one_day.py
Book : Python for Finance (2nd ed.)
Publisher: Packt Publishing Ltd.
Author : <NAME>
Date : 6/6/2017
email : <EMAIL>
<EMAIL>
"""
import numpy as np
import pandas as pd
from scipy.stats ... |
from scipy import stats, linalg
from geosoup.common import Handler, Opt, np
import warnings
__all__ = ['Distance',
'Mahalanobis',
'Euclidean']
class Distance(object):
"""
Parent class for all the distance type methods
"""
def __init__(self,
sam... |
#!/usr/bin/env python
"""
Taken from https://github.com/AndrewRook/astro/tree/master/voronoi
"""
import sys
import numpy as np
import ConfigParser
import time
import os
import astropy.io.fits as pyfits
from sklearn.neighbors import BallTree
import scipy.ndimage
#import bottleneck as bn
def writefits(image,filename,hea... |
from pixell import enmap, curvedsky, fft as enfft, sharp, wcsutils
from enlib import array_ops, bench
from soapack import interfaces as sints
from optweight import alm_c_utils
import numpy as np
from scipy.interpolate import interp1d, RectBivariateSpline
from scipy import ndimage
import numba
import healpy as hp
from ... |
#!/usr/bin/env python
"""
This script is used to develop SpecViewer only. It should not be used for
production.
It creates a JSON file from a dictionary to be used in the QAP SpecViewer
development.
"""
import json
import os
import numpy as np
from scipy.ndimage import gaussian_filter1d
def main():
# Create fak... |
import sys
import os
import platform
# Use OpenBLAS with 1 thread only as it seems to be using too many
# on the CIs apparently.
os.environ["OPENBLAS_NUM_THREADS"] = "1"
import scipy
import scipy.cluster._hierarchy
import scipy.cluster._vq
import scipy.fft
import scipy.integrate._dop
import scipy.integrate._odepack
i... |
<reponame>AroosaIjaz/Mypennylane
# Copyright 2018 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 r... |
<filename>imports.py
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.4.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
impo... |
import os
import math
import time
import datetime
from functools import reduce
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import scipy.misc as misc
from scipy import signal
import skimage.color as sc
import torch
import torch.optim as optim
import torch.optim.lr_schedu... |
<reponame>hugo19941994/movie-pepper-back<filename>recommender.py
#!/usr/bin/env python
"""
Sisrec.py
Movies recomendation engine
"""
from typing import List # noqa: F401
from scipy.spatial.distance import cosine
from concurrent.futures import ProcessPoolExecutor
import sys
import math
import json
# Importance of ea... |
<reponame>soravux/jambokoko
# coding: utf-8
# In[1]:
import os, sys, urllib, gzip, glob, time
import pickle # cPickle as pickle
sys.setrecursionlimit(10000)
# In[2]:
import matplotlib.pyplot as plt
#get_ipython().magic(u'matplotlib inline')
import numpy as np
from scipy.misc import imread, imsave
from IPython.dis... |
<gh_stars>0
import cmath
print('Welcome to the Quadratic Solver App.')
print('A quadratic equation is of the form: ax ^ 2 + bx + c = 0')
print('Your solution can be real or complex numbers.')
print('A complex number has two parts: a + bj')
print("Where 'a' is the real portion and 'bj' is the imaginary portion." )
# ... |
import os
import pickle
from time import time
from datetime import datetime, timedelta
# Configure logger first before importing any sub-module that depend on the logger being already configured.
import logging.config
logging.config.fileConfig("logging.ini")
logger = logging.getLogger(__name__)
import num... |
from tqdm import tqdm
import inspect
import pandas as pd
from scipy.sparse import issparse, SparseEfficiencyWarning
from .moments import moments, strat_mom
from .velocity import fit_linreg, velocity, ss_estimation
from .estimation_kinetic import *
from .utils_kinetic import *
from .utils import (
update_dict,
... |
# -*- coding: utf-8 -*-
"""
Container for the primary EMUS routines.
"""
import numpy as np
from scipy.special import logsumexp
try:
import usample.linalg as lm
import usample.autocorrelation as autocorrelation
from .usutils import unpackNbrs
except ImportError:
import linalg as lm
import autocorrel... |
import sympy as sym
from metric import Metric
from coordinate_system_implementation_generator import JavaCoordinateSystemCreator
xi = sym.symbols('xi', real=True, positive=True)
eta = sym.symbols('eta', real=True)
phi = sym.symbols('phi', real=True, positive=True)
R = sym.symbols('R', real=True, positive=True, con... |
"""
Code to test the nearest neighbor search algorithm.
RESULT: the algorithm appears to work perfectly, finding the exact
points requested (with nudge=0). With nudge != 0 the error is equal
to the nudge I added to the search points.
"""
import os, sys
sys.path.append(os.path.abspath('../../LiveOcean/alpha'))
impor... |
<reponame>sasasagagaga/Code-examples
import numpy as np
from sklearn.tree import DecisionTreeRegressor
from scipy.optimize import minimize_scalar
from sklearn.metrics import mean_squared_error
rmse = lambda x, y: np.sqrt(mean_squared_error(x, y))
def bootstrap(X, y):
idx = np.random.randint(0, X.shape[0], X.sha... |
# -*- coding: utf-8 -*-
import numpy as np
from scipy import sparse
from . import Graph # prevent circular import in Python < 3.5
class Path(Graph):
r"""Path graph.
A signal on the path graph is akin to a 1-dimensional signal in classical
signal processing.
On the path graph, the graph Fourier tr... |
<filename>lib/ys_optimize.py
# Python Module for import Date : 2016-04-08
# vim: set fileencoding=utf-8 ff=unix tw=78 ai syn=python : per Python PEP 0263
'''
_______________| ys_optimize.py : Convex optimization given noisy data.
We smooth some of the rough edges among the "scipy.optimi... |
<reponame>00sapo/ASMD<gh_stars>1-10
import csv
import os
import re
from copy import deepcopy
from functools import wraps
import numpy as np
import pretty_midi
import scipy.io
from . import utils
def convert(exts, no_dot=True, remove_player=False):
"""
This function is designed to be used as decorators for f... |
<reponame>Atzingen/DynamicMouseAuthentication
import gc
import random
import copy
import os
import traceback
import numpy
import pandas as pd
from tqdm.notebook import tqdm as tqdmn
import math
import statistics
import xgboost as xgb
import scipy
import matplotlib.pyplot as plt
MEDIUM_SIZE = 20
BIGGER_SIZE = 25
plt.... |
<reponame>always-newbie161/pyprobml
# K-means clustering for semisupervised learning
# Code is from chapter 9 of
# https://github.com/ageron/handson-ml2
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib as mpl
import itertools
from scipy import linalg
#color_iter = it... |
<filename>models/ASIS/test.py
import argparse
import math
import h5py
import numpy as np
import tensorflow as tf
import socket
from scipy import stats
from IPython import embed
import os
import sys
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(os.path.dirname(BASE_DIR))
... |
<reponame>msgoff/sympy
from sympy.ntheory.generate import Sieve, sieve
from sympy.ntheory.primetest import (
mr,
is_lucas_prp,
is_square,
is_strong_lucas_prp,
is_extra_strong_lucas_prp,
isprime,
is_euler_pseudoprime,
)
from sympy.testing.pytest import slow
def test_euler_pseudoprimes():
... |
<reponame>davidmccandlish/vcregression
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("a", help="alphabet size", type=int)
parser.add_argument("l", help="sequence length", type=int)
parser.add_argument("-name", help="name of output folder")
parser.add_argument("-data", help="path to input data",... |
import numpy
import sympy
from sympy.diffgeom import Manifold, Patch
from pystein import geodesic, metric, coords
from pystein.utilities import tensor_pow as tpow
class TestGeodesic:
def test_numerical(self):
M = Manifold('M', dim=2)
P = Patch('origin', M)
rho, phi, a = sympy.symbols('rho phi a', nonnegativ... |
<gh_stars>1-10
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import scipy.optimize as opt
import cmath
import sys
import warnings
from statistics import jackknifeMean
from statistics import jackknifeCreutz
from statistics import autocorrTime
warnings.simplefilter(action='ig... |
<filename>datasets/nyu_hand.py
# -*- coding: utf-8 -*-
import os
import numpy as np
import sys
import struct
from torch.utils.data import Dataset
import scipy.io as scio
def pixel2world(x, y, z, img_width, img_height, fx, fy):
w_x = (x - img_width / 2) * z / fx
w_y = (img_height / 2 - y) * z / fy
w_z = z
... |
<gh_stars>1-10
import numpy as np
import easyvvuq as uq
import os
import fabsim3_cmd_api as fab
import matplotlib.pyplot as plt
from scipy import stats
def get_kde(X, Npoints = 100):
kernel = stats.gaussian_kde(X)
x = np.linspace(np.min(X), np.max(X), Npoints)
pde = kernel.evaluate(x)
return x, pde
#... |
# Authors: <NAME> <<EMAIL>>
# <NAME> <<EMAIL>>
#
# License: BSD (3-clause)
import os
import copy
import numpy as np
from scipy import linalg
from .fiff.constants import FIFF
from .fiff.tag import find_tag
from .fiff.tree import dir_tree_find
from .fiff.proj import read_proj
from .fiff.channels import _read_b... |
<reponame>Vinicius-Tanigawa/Undergraduate-Research-Project<gh_stars>0
## @ingroup Methods-Power-Fuel_Cell-Discharge
# larminie.py
#
# Created : Apr 2015, <NAME>
# Modified: Feb 2016, <NAME>
# ----------------------------------------------------------------------
# Imports
# ----------------------------------------... |
<filename>coba/benchmarks.py<gh_stars>0
"""The benchmarks module contains core benchmark functionality and protocols.
This module contains the abstract interface expected for Benchmark implementations. This
module also contains several Benchmark implementations and Result data transfer class.
"""
import math
import ... |
from collections import Sequence
import numpy as np
from scipy.sparse.base import spmatrix
from ..externals.six import string_types
def is_multilabel(y):
if hasattr(y, '__array__'):
y = np.asarray(y)
if not (hasattr(y, "shape") and y.ndim == 2 and y.shape[1] > 1):
return False
def type_of_... |
<reponame>Luke-Ludwig/DRAGONS<gh_stars>1-10
# Copyright(c) 2019-2020 Association of Universities for Research in Astronomy, Inc.
"""
tracing.py
This module contains functions used to locate peaks in 1D data and trace them
in the orthogonal direction in a 2D image.
Functions in this module:
estimate_peak_width: estim... |
<reponame>anekimken/DABEST-python<gh_stars>0
# #! /usr/bin/env python
# Load Libraries
import pytest
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.use('Agg')
import numpy as np
import scipy as sp
import pandas as pd
import seaborn as sns
from .._api import load
from .utils import create_dummy_dataset,... |
import numpy as np
from scipy.stats import rankdata
from sklearn.cluster import KMeans
from skactiveml.base import SingleAnnotatorPoolQueryStrategy
from skactiveml.utils import (
is_labeled,
check_type,
simple_batch,
MISSING_LABEL,
)
from skactiveml.utils._selection import combine_ranking
class Repre... |
<filename>qutip/rhs_generate.py
# 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 ar... |
<filename>sphericalharmonics/sphharmhard.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 6 13:16:55 2018
@author: dietz
"""
from cmath import exp
from math import sin, cos
import numba
@numba.njit(numba.complex128(numba.int64, numba.int64, numba.float64, numba.float64), nogil=True)
def sph_... |
import os
import copy
import numpy as np
from astropy.io import fits
import astropy.units as u
import astropy.constants as const
from specutils import Spectrum1D
from astropy.table import Table
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt
from spectres import spectres
from paintbox.utils impo... |
<filename>CLEVER/collect_gradients.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
collect_gradients.py
Front end for collecting maximum gradient norm samples
Copyright (C) 2017-2018, IBM Corp.
Copyright (C) 2017, <NAME> <<EMAIL>>
and <NAME> <<EMAIL>>
This program is licenced under the Apache ... |
#!/usr/bin/env python
import numpy as np
import scipy.optimize as opt
import sys,cPickle
# import scipy.weave
__doc__ = """Definitions for the threshold nonlinearity
Copyright (C) 2014 <NAME>
This code reproduces the analyses in the paper
<NAME> (2014): Quantifying the effect of inter-trial dependence on perc... |
<filename>wise/wiseutils.py
import logging
import datetime
import numpy as np
import wds
import matcher
import features as wfeatures
from libwise import plotutils, nputils, imgutils
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from mpl_toolkits.axisartist.grid_finder ... |
<reponame>lanl/scico
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# This file is part of the SCICO package. Details of the copyright
# and user license can be found in the 'LICENSE.txt' file distributed
# with the package.
r"""
ℓ1 Total Variation (ADMM)
=========================
This example demonstrates impulse noi... |
"""
========================================
Saving and loading coordinates with asdf
========================================
In this example we are going to look at saving and loading collections of
coordinates with `asdf <https://asdf.readthedocs.io/en/latest/>`__.
asdf is a modern file format designed to meet the... |
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
if __name__ == '__main__':
# # 1.重点新闻筛选
# # 读取已手动去除离群点的数据源,
# data = np.loadtxt(r"../data/data.csv", delimiter=",", usecols=5, dtype="i4")
#
# # 绘制原始数据的频率直方图
# hist, bins = np.histogram(data, 300, normed=True)
# bins... |
import h5py
import os
import glob
import re
import numpy as np
from . import peano
import warnings
from scipy.integrate import quad
base_path = os.environ['EAGLE_BASE_PATH']
release = os.environ['EAGLE_ACCESS_TYPE']
class Snapshot:
""" Basic SnapShot superclass which finds the relevant files and gets relevant inf... |
<reponame>Lynn-015/Test_01
import numpy as np
from scipy.sparse import kron,identity
from mpo import MPO,op2mpo
class TestMPO(object):
def __init__(self):
self.op=np.array([[0.5,0.],[0.,-0.5]])
for i in range(5):
self.op=kron(self.op,identity(2))
self.mpo=op2mpo(self.op,2,6)
def shape(self):
for i in ra... |
<filename>compute_scores.py
#computes scores.
#from features import *
import sys
import math
from datetime import datetime
import calendar
import numpy as np
import pylab
import matplotlib.pyplot as plt
import matplotlib
import pandas as pd
import pandas.io.sql as pd_sql
from scipy import sparse
import sqlite3 as s... |
<filename>VAD.py
# -*- coding: utf-8 -*-
import struct
import os
import pandas as pd
import wave
import numpy as np
import json
import zipfile
import matplotlib.pyplot as plt
import pickle
import scipy.signal as signal
# 将record.wav输入信号 采样 切割 转化成若干processedi.pcm文件
# 通过高门限的一定是需要截取的音，但是只用高门限，会漏掉开始的清音
# 如果先用低门限，噪音可能不会被滤... |
from numpy import arctan, zeros, pi, real as re, imag as im, linspace,eye, prod, newaxis
from numpy import array as arr, exp, log, arange, diag, kron, savetxt, cumsum, argmax
from numpy.linalg import det, norm, solve
from scipy.optimize import fsolve
import matplotlib.pyplot as plt
from copy import deepcopy
from ... |
<gh_stars>0
from __future__ import print_function, division, absolute_import
import sys
import math
import time
import numpy as np
import theano
from matplotlib import pyplot as plt
try:
import seaborn
except:
pass
# ===========================================================================
# Progress bar
... |
import itertools
import os
import pathlib
from scipy.optimize import fsolve
import BOPTools.SplitFeatures
import FreeCAD
import Part as FCPart
import Points
import gmsh
import meshio
# import Draft
import numpy as np
from Draft import make_fillet
from FreeCAD import Base
import DraftVecUtils
from OCC.C... |
#!/usr/bin/env python
#
# Copyright (C) 2017 - Massachusetts Institute of Technology (MIT)
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option... |
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 26 11:21:14 2015
@author: noore
"""
import numpy as np
import scipy
class LINALG(object):
@staticmethod
def svd(A):
# numpy.linalg.svd returns U, s, V such that
# A = U * s * V
# however, matlab and octave return U, S, V such that
... |
<filename>covid19model/py/etl.py<gh_stars>0
### ETL script for generating input tables to model
### main point: ETL JHU covid-19 case and mortality data
# todo: refactor
import os
import numpy as np
import pandas as pd
import warnings
from scipy.stats import gamma
warnings.simplefilter(
action="ignore", category... |
<filename>SeriesAnalysis/Stationarity/AugmentedDickeyFuller.py
import pandas as pd
import numpy as np
import yfinance as yf
from sklearn.linear_model import LinearRegression
import statsmodels
import statsmodels.api as sm
import statsmodels.tsa.stattools as ts
import datetime
import scipy.stats
import math
import op... |
import numpy as np
from scipy.special import logsumexp, kl_div
import torch
import torch.nn as nn
EPS = float(np.finfo(np.float32).eps)
__all__ = ['BeliefPropagation', 'BeliefPropagationTorch']
class BeliefPropagation(object):
def __init__(self, J, b, msg_node, msg_adj):
"""
Belief Propagation for Binary... |
import itertools
import math
import numpy as np
import pytest
from hypothesis import given, strategies as st
from scipy.sparse import coo_matrix
from tmtoolkit import bow
@given(dtm=st.lists(st.integers(0, 10), min_size=2, max_size=2).flatmap(
lambda size: st.lists(st.lists(st.integers(0, 10),
... |
<reponame>alexbarcelo/dislib<gh_stars>10-100
import unittest
import numpy as np
from scipy.sparse import csr_matrix
from sklearn.cluster import KMeans as SKMeans
from sklearn.datasets import make_blobs
import dislib as ds
from dislib.cluster import KMeans
class KMeansTest(unittest.TestCase):
def test_init_param... |
<gh_stars>0
"""
This is companion code to Project 4 for CSEP576au21
(https://courses.cs.washington.edu/courses/csep576/21au/)
Instructor: <NAME>
"""
# ======================================================================
# Copyright 2021 <NAME> https://corvidim.net/ablavsky/
#
# Permission is hereby granted, free of... |
# default package
from logging import getLogger
from typing import Dict
# third party
import numpy as np
import scipy.signal
import scipy.stats
# logger
logger = getLogger(__name__)
def calc_all(data: np.ndarray, fs: float) -> Dict:
features = {
"Mean": np.mean(data),
"Std": np.std(data),
... |
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