text string |
|---|
<filename>alumni/utils.py<gh_stars>0
from typing import Any
import numpy as np
import scipy.sparse.csr
import sklearn.base
import sklearn.tree.tree
def assert_equal(
actual: Any, desired: Any, err_msg: str = "", verbose: bool = True
) -> None:
# recursively call itself when needed (copied from np.testing.ass... |
<gh_stars>0
import time
import os
import gym
import numpy as np
import matplotlib.pyplot as plt
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.results_plotter import load_results, ts2x... |
import os
import soundfile as sf
import sounddevice as sd
from sys import argv
from prompt_yes_no import *
from scipy.io.wavfile import write
DTYPE = "int16"
MONO, STEREO = 1, 2 # number of audio channels
SAMPLE_RATE = 8000 # voice recording
# OUTPUT_FOLDER = os.path.join("D:\\", "Music")
OUTPUT_FOLDER = '.'
def ... |
#!/usr/bin/python
# -*- coding: utf-8 -*-
##########################################################################################
# author: <NAME>
# contact: <EMAIL>
# date: 2021-05-10
# file: mpl_discrete_poisson_pmf_A.py
# tested with python 3.7.6 in conjunction with mpl version 3.4.2
#############################... |
import os
import sys
py_dll_path = os.path.join(sys.exec_prefix, 'Library', 'bin')
os.add_dll_directory(py_dll_path)
import numpy as np
import cv2
from PIL import Image, ImageDraw
from matplotlib import cm
from scipy import ndimage
import torch
import torchvision
from torchvision.models.detection.faster_rcnn... |
#!/usr/bin/env python
# coding: utf-8
# ## Setup
# In[1]:
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
########################################################
# python
import pandas as pd
import numpy as np
import scipy.stats
norm = scipy.stats.norm
import... |
<reponame>m-philipps/pyPESTO
import os
from functools import partial
import numpy as np
import scipy.optimize as so
import pypesto
import pypesto.optimize as optimize
from pypesto.C import AMICI_STATUS, AMICI_T, AMICI_Y, MEAN, WEIGHTED_SIGMA
from pypesto.engine import MultiProcessEngine
from pypesto.ensemble import (... |
"""Transform feature matrices with grouped covariates."""
import logging
import numpy as np
from scipy.interpolate import interp1d
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.utils import check_array, check_random_state, check_scalar
from sklearn.utils import shuffle as util_shuffle
from .u... |
<filename>src/algorithms.py<gh_stars>1-10
import pandas as pd
import numpy as np
import scipy as sp
from sklearn.feature_extraction import DictVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier, export_graphviz, ExtraTreeClassifier
def get_feature_relevance... |
<reponame>fabiosky/dcv-color-primitives<filename>benches/geninput.py
#!/usr/bin/env python3
from array import array
from itertools import product as cartesian_product
from random import Random
from os.path import join, exists, dirname, realpath
from os import environ
from fractions import Fraction as frac
import sys
s... |
from joblib import Parallel, delayed
from scipy import spatial
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import multiprocessing as mp
import copy
import time
tree = None
pred_vectors = None
def ordered_distance(i):
return tree.query(pred_vectors[i],k=1000)[1]
def load(a_tree, a_pred_ve... |
<reponame>cphyc/py_extrema<filename>py_extrema/critical_events.py<gh_stars>0
from scipy.spatial import cKDTree
from py_extrema.extrema import ExtremaFinder
import numpy as np
from tqdm.autonotebook import tqdm
import pandas as pd
from unyt import unyt_array
from .extrema import logger
from .utils import measure_hessia... |
import os
import glob
import matplotlib.image as mpimg
import numpy as np
import cv2
import time
import sys
from skimage.feature import hog
from sklearn.svm import LinearSVC
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.externals import joblib
from sc... |
import numpy as np
import os
import h5py
from scipy.io import loadmat
import random
import json
import cv2
class_name_list_all = [
"02691156_airplane",
"02828884_bench",
"02933112_cabinet",
"02958343_car",
"03001627_chair",
"03211117_display",
"03636649_lamp",
"03691459_speaker",
"04090263_rifle",
"04256520_couch",
"0... |
import binomial
import scipy.stats
import numpy as np
import unittest
import logging
import sys
from timeit import timeit
class TestBinomial(unittest.TestCase):
PRECISION = 5
def test_simple_entry(self):
self.assertEqual(binomial.binomialpmf(1,20,0.1,),scipy.stats.binom.pmf(1,20,0.1))
def test_lis... |
# -*- coding: utf-8 -*-
#
from __future__ import division
import numpy
import sympy
from ..helpers import untangle, fsd, z
from .helpers import volume_unit_ball
class HammerStroud(object):
"""
<NAME> and <NAME>,
Numerical Evaluation of Multiple Integrals II,
Math. Comp. 12 (1958), 272-280,
<http... |
<gh_stars>1-10
"""
markowitzModel.py
Created by <NAME> at 13/09/2020, University of Milano-Bicocca.
(<EMAIL>)
All rights reserved.
This file is part of the EcoFin-Library (https://github.com/LucaCamerani/EcoFin-Library),
and is released under the "BSD Open Source License".
"""
from collections import namedtuple
imp... |
"""
predict labels for birdsong syllables,
using already-trained models specified in config file
"""
import os
import sys
import glob
# from dependencies
import yaml
import numpy as np
from sklearn.externals import joblib
from scipy.io import wavfile
# from hvc
import hvc.featureextract
from .parseconfig import par... |
<reponame>zangobot/secml
"""
.. module:: CFunction
:synopsis: Wrapper to manage a function and its gradient
.. moduleauthor:: <NAME> <<EMAIL>>
.. moduleauthor:: <NAME> <<EMAIL>>
"""
from scipy import optimize as sc_opt
from secml.core import CCreator
from secml.array import CArray
from secml.core.constants import... |
#!/usr/bin/env python3
from fractions import Fraction
from functools import reduce
import operator
def product(fracs):
t = reduce(operator.mul, fracs, 1)
return t.numerator, t.denominator
if __name__ == '__main__':
n = int(input())
l = [Fraction(*map(int, input().split())) for _ in range(n)]
pr... |
import numpy as np
import os
import scipy.io as spio
import pydicom
import sys
def names_of_slices(folder_path,patient):
path = slices_path + patient
all_slices_names = []
all_slices=[]
for file in os.listdir(path):
all_slices.append([pydicom.dcmread(path + '/' + file)])
all_slice... |
<filename>HELENA3.py
#!/usr/bin/env python3
#################################
# Point of Contact #
# #
# Dr. <NAME> #
# University of Michigan #
# Electrical Engineering #
# & Computer Science Dept. #
# 1301 Beal Ave, Ann Arbor, #
# MI 48109-2122 USA #
# <EMAIL> #
# ... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Author: <NAME>
# Description : FFT Baseline Correction
import sys, os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button, SpanSelector
from matplotlib import gridspec
import scipy.fftpack
... |
<reponame>or-tal-robotics/dynamic_map_matcher
#!/usr/bin/env python
import rospy
import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors import NearestNeighbors # for KNN algorithm
from scipy.optimize import differential_evolution
import copy
import pandas as pd
import rosbag
import rospkg
ground_tr... |
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import IsolationForest
import os
import pandas as pd
import scipy
from scipy import stats
import sys
rng = np.random.RandomState(42)
# # Generate train data
# X = 0.3 * rng.randn(100, 2)
# X_train = np.r_[X + 2, X - 2]
# # Generate some regular ... |
<reponame>microsoft/nanotune
import logging
from functools import partial
from typing import List, Optional, Union, Dict, Tuple, Sequence, Any
import numpy.typing as npt
import numpy as np
import scipy as sc
import itertools
import json
import logging
import copy
from numpy.linalg import inv
from numpy.linalg import m... |
<filename>scr/sound.py
from pylab import*
from scipy.io import wavfile
if len(sys.argv) < 2:
print("Plays a wave file.\n\nUsage: %s filename.wav" % sys.argv[0])
sys.exit(-1)
file_name = sys.argv[1]
# sampFreq, snd = wavfile.read('../Downloads/TT_soundtrack_-_k13.wav')
# sampFreq, snd = wavfile.read('../Downl... |
from optimism.JaxConfig import *
import optimism.EquationSolver as EquationSolver
from optimism.Objective import Objective
from optimism.Objective import param_index_update
from optimism.SparseCholesky import SparseCholesky
import numpy as onp
from scipy.sparse import csc_matrix
from scipy.sparse import diags as sparse... |
# This files contains your custom actions which can be used to run
# custom Python code.
#
# See this guide on how to implement these action:
# https://rasa.com/docs/rasa/core/actions/#custom-actions/
# This is a simple example for a custom action which utters "Hello World!"
import re
import io
import ast
import req... |
import numpy as np
import utils
import math
import scipy
from scipy import optimize
import random
from scipy.special import xlogy
class RegLogisticRegressor:
def __init__(self):
self.theta = None
def sigmoid(self, x):
return (1 / (1 + np.exp(-x)))
def train(self,X,y,reg=1e-5,num_iters=1... |
<gh_stars>1-10
import abc
import random
from typing import List, Optional, Union
import numpy as np
import torch
from qiskit import QuantumCircuit
from qiskit.opflow import PauliOp
from qiskit.quantum_info import Pauli, DensityMatrix, Statevector
from scipy.linalg import expm
from utils.np_utils import normalized_mat... |
<gh_stars>10-100
import logging
import numpy as np
import pandas as pd
import scipy.io
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
logger = logging.getLogger(__name__)
def get_train(label=0, scale=False, v=0, *args):
"""Get training dataset for Thyroid data... |
from utils import *
import numpy
import matplotlib.pyplot as plt
import os, os.path
from scipy.constants import pi, hbar, e
vf = 1.1e6
# Use C/m^2
def delta_phi_gr(sigma):
fac = hbar * vf / e * numpy.sqrt(pi * numpy.abs(sigma) / e)
return fac * numpy.sign(sigma)
quantities = ("V", "c_p", "c_n", "zflux_cp", "... |
import bpy
import bpy_extras
import math
import random
import cv2
import bmesh
import numpy as np
from scipy.optimize import minimize, minimize_scalar
from mathutils import Euler, Vector
from abc import ABC, abstractmethod
from blvcw.crystal_well_simulation_utils import get_random_euler, get_normal_distributed_values... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Software License Agreement (Lesser GPL)
#
# Copyright (C) 2009-2012 <NAME>
#
# ikfast is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of ... |
# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
"""Informativeness model.
Loads a language model and computes various entropy-based informativeness
measures.
"""
from functools import lru_cache
import logging
import numpy as np
import scipy
from gensim.models import Word2Vec
__all__ = ('Informativeness')
logger = logging.getLogger(__name__)
class Informati... |
# Written by Dr <NAME>, Marda Science LLC
# for the USGS Coastal Change Hazards Program
#
# MIT License
#
# Copyright (c) 2020, Marda Science LLC
#
# 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 Soft... |
"""
SGA.galex
=========
Code to generate GALEX custom coadds / mosaics.
"""
import os, pdb
import numpy as np
from astrometry.util.util import Tan
from astrometry.util.fits import fits_table
import SGA.misc
def _ra_ranges_overlap(ralo, rahi, ra1, ra2):
import numpy as np
x1 = np.cos(np.deg2rad(ralo))
y... |
<filename>training.py
# -*- coding: utf-8 -*-
"""
Created on Sun Mar 4 08:38:40 2018
@author: Nasif
"""
from sklearn import datasets
from sklearn.svm import SVC
from scipy import misc
from PIL import Image
import PIL.ImageOps
digits = datasets.load_digits()
features = digits.data
labels = digits.t... |
<filename>dataset.py
import os
import csv
import pickle
import numpy as np
from scipy.sparse import csr_matrix
from tqdm import tqdm
"""
Just a reminder :)
features_prefixes = ['real_permission', 'feature', 'api_call',
'call', 'permission', 'provider', 'activity',
'i... |
import os, sys
import numpy as np
from scipy.sparse import csr_matrix, coo_matrix
def texts_nwd_csr(list_texts):
'''
Make a csr n_wd matrix from a list of texts.
each text is a list of tokens.
provide dict_w_iw == mapping of words to indices i_w=0,...,V-1
'''
## unqiue words and alphabeticall... |
import pandas as pd
import numpy as np
import json
from tqdm import tqdm
from scipy.optimize import minimize
from utils import get_next_gw, time_decay
from ranked_probability_score import ranked_probability_score, match_outcome
class Bradley_Terry:
""" Model game outcomes using logistic distribution """
de... |
import numpy as np
import sys
from sympy import *
from matplotlib import pyplot as plt
def biseccion(func,rango, tol, iterMax):
#Funcion de prueba: "E**x - x - 2", [0,2], 10**(-10), 100
x = Symbol('x') #Inicializa "x" como símbolo
f = sympify(func) #Se traduce el string "func" a una función... |
<gh_stars>1000+
# -*- coding: utf-8 -*-
"""Copyright 2015 <NAME>.
FilterPy library.
http://github.com/rlabbe/filterpy
Documentation at:
https://filterpy.readthedocs.org
Supporting book at:
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
This is licensed under an MIT license. See the readme.MD file
f... |
<reponame>tomjaguarpaw/knossos-ksc
"""
Correctness test for blas_combined.kso
Requires:
- pytest
- scipy
- ksc
"""
# fmt: off
import pytest
import numpy as np
import scipy.linalg
from rlo import utils
from ksc.utils import translate_and_import # pylint:disable=no-name-in-module
def make_random_normal(n, v, sz):
... |
# Copyright (c) 2019 <NAME>
# Universidad Carlos III de Madrid
#
# The Bayesian CPD computation is based on
# the original code by <NAME> (2006).
#
# INFINITE HIERARCHICAL CHANGE-POINT DETECTION
import numpy as np
from scipy.stats import norm
import random
import matplotlib.pyplot as plt
class infiniteHierCPD():
... |
<filename>visualize.py
# Copied from https://github.com/emansim/baselines-mansimov/blob/master/baselines/a2c/visualize_atari.py
# and https://github.com/emansim/baselines-mansimov/blob/master/baselines/a2c/load.py
# Thanks to the author and OpenAI team!
import glob
import os
import pandas as pd
import matplotlib
mat... |
<filename>src/train/bork_nlg_model.py<gh_stars>0
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import numpy as np
import scipy
import math
import os
import tensorflow as tf
import matplotlib.pyplot as plt
import seaborn as sns
def load_sts_dataset(filename):
# Loads a subset of the STS dat... |
"""
[summary]
[extended_summary]
"""
# region [Imports]
# * Standard Library Imports ------------------------------------------------------------------------------------------------------------------------------------>
import gc
import os
import re
import sys
import json
import lzma
import time
import queue
import ... |
<gh_stars>0
"""
PySC2_A3C_AtariNetNew.py
A script for training and running an A3C agent on the PySC2 environment, with reference to DeepMind's paper:
[1] Vinyals, Oriol, et al. "Starcraft II: A new challenge for reinforcement learning." arXiv preprint arXiv:1708.04782 (2017).
Advantage estimation uses generalized advan... |
<filename>tests/test_envs.py
import unittest
from gym import spaces
from functools import reduce
import numpy as np
import numpy.testing as npt
from scipy import stats
from cognibench.models import decision_making
from cognibench.envs import BanditEnv, ClassicalConditioningEnv
from cognibench.simulation import simulate... |
import Image,numpy,math, pylab, scipy
import mpl_toolkits.mplot3d.axes3d as p3
from mayavi import mlab
import matplotlib.pyplot as plt
import gaussfitter
from scipy import ndimage
class Image_obj:
def __init__ (self, image=None,array=None,surf=None):
if image != None:
self.im = image
... |
import numpy as np
from menpofit.aam import HolisticAAM
from menpo.feature import igo
from menpofit.aam import LucasKanadeAAMFitter
from menpofit.fitter import align_shape_with_bounding_box
from menpo.shape import PointCloud
from menpofit.sdm import RegularizedSDM
from menpo.feature import hellinger_vector_128_dsift
im... |
"""
dtwhaclustering.plot_linear_trend
----------------------------------
DTW HAC analysis support
:author: <NAME>
:date: 2021/06
:copyright: Copyright 2021 Institute of Earth Sciences, Academia Sinica.
"""
import pandas as pd
import numpy as np
from scipy.interpolate import griddata
import xarray as xr
import pygmt
f... |
from argparse import ArgumentParser
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from fyne import heston
from scipy.integrate import solve_ivp
from statsmodels.api import OLS
import settings
_EPS = 1.e-12
def optimal_controls(time, inventory_bounds, price_risk_aversion,
... |
<gh_stars>0
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Calculate HOG features for an image"""
import os
import Image
import numpy
from scipy.misc import toimage
def main(filename):
"""
Orchestrate the HOG feature calculation
Parameters
----------
filename : str
"""
bins = 8
g... |
#python example to infer document vectors from trained doc2vec model
import gensim.models as g
import codecs
import sys
import pandas as pd
import pandas_datareader as pdr
from pandas_datareader import data, wb
import time
import math
import os
from datetime import datetime
from datetime import date
from datetime impor... |
<gh_stars>0
"""
Module that contains the command line app.
Why does this file exist, and why not put this in __main__?
You might be tempted to import things from __main__ later, but that will cause
problems: the code will get executed twice:
- When you run `python -mTime_Frequency_Analysis` python will execute... |
<filename>steps/MTP_Steps.py
#import packages
import datetime
import quandl
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy as spy
from behave import given, when, then
################################################################################
quandl.ApiConfig.api_key = 'API ke... |
#
# BSD 3-Clause License
#
# Copyright (c) 2019, Analog Devices, Inc.
# 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 the above copyright notice, ... |
<reponame>edunnsigouin/ds21grl
"""
Calculates anomalous monthly mean climatology xy file for a given surface
(or vertically integrated) variable in a given aquaplanet simulation.
Anomalies are defined relative to the control simulation.
"""
import numpy as np
import xarray as xr
from scipy ... |
from logs import logDecorator as lD
import jsonref, pprint
import matplotlib
matplotlib.use('Qt5Agg')
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.set(style="dark")
sns.set_palette(sns.diverging_palette(240, 120, l=60, n=3, center="dark"))
from scipy import stats
from scipy.stats import... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 24 15:54:31 2020
@author: heiko
"""
import numpy as np
import scipy.optimize as opt
from pyrsa.rdm import compare
def fit_mock(model, data, method='cosine', pattern_sample=None,
pattern_descriptor=None):
""" formally acceptable f... |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
sbpy Photometry Module
created on June 23, 2017
"""
__all__ = ['ref2mag', 'mag2ref', 'spline',
'DiskIntegratedModelClass', 'LinearPhaseFunc', 'HG', 'HG12', 'HG1G2',
'DiskFunctionModel', 'LommelSeeliger', 'Lambert', 'LunarLambert... |
<filename>scripts/pagerank_power_method_sparse.py<gh_stars>1-10
# Implements the power method without using any matrix multiplications, i.e. Monte Carlo approximation to the sum implied by v = Mv
# Author : <NAME>, <NAME>
# This function is the Python implementation of https://github.com/probml/pmtk3/blob/master/demos... |
<reponame>nsevilla/descqa<gh_stars>0
from __future__ import print_function, division, unicode_literals, absolute_import
import os
import re
import fnmatch
from itertools import cycle
from collections import defaultdict, OrderedDict
import numpy as np
import numexpr as ne
from scipy.stats import norm
import time
from .... |
import random
import numpy as np
import skimage.io as sio
import skimage.color as sc
import skimage.transform as st
import torch
from scipy.signal import convolve2d
from skimage.util import view_as_windows
import torch
from torchvision import transforms
def random_patch_select(img,ih,iw,ip):
ix = random.randran... |
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 24 16:52:00 2017
@author: Paul
"""
import numpy as np
from skimage import measure
from collections import defaultdict
import PySkelFrac.classes as c
from scipy import ndimage
import copy
import time
import cv2
def NewAssociatedContours(AllContours,AllArc... |
import ops.utils
import networkx as nx
import pandas as pd
import numpy as np
import scipy.spatial.kdtree
from collections import Counter
from scipy.spatial.distance import cdist
from scipy.interpolate import UnivariateSpline
from statsmodels.stats.multitest import multipletests
def format_stats_wide(df_stats):
... |
import logging
import os
from pathlib import Path
import click
import pandas as pd
from scipy import stats
from tqdm import tqdm
logging.basicConfig(level=logging.INFO)
CORRECT_NER_ENTAILS = "Entails"
CORRECT_NER_NOT_ENTAILS = "Not Entails/Error"
CORRECT_NER_VALS = [CORRECT_NER_ENTAILS, CORRECT_NER_NOT_ENTAILS]
AGG... |
<reponame>DhruvThunderBolt/ControllingChaosInTheDuffingOscillator
import numpy as np
from scipy.integrate import odeint
'''import scipy.integrate as integrate'''
import matplotlib.pyplot as plt
import matplotlib
import math
import sympy
import statistics
import sys
import operator
import collections
import time
import ... |
<reponame>Womac/pyroomacoustics
"""
Adaptive Filter in STFT Domain Example
======================================
In this example, we will run adaptive filters for system
identification, but in the frequeny domain.
"""
from __future__ import division, print_function
import numpy as np
from scipy.signal import fftcon... |
import scipy as sc
import scipy.stats as stats
import scipy.linalg as linalg
import math as m
def sample_normal(mean,covar,nsamples=1):
"""sample_normal: Sample a d-dimensional Gaussian distribution with
mean and covar.
Input:
mean - the mean of the Gaussian
covar - the covariance of ... |
"""Recursive nearest agglomeration (ReNA):
fastclustering for approximation of structured signals
Author:
<NAME>, <NAME>, <NAME> and <NAME>
"""
import numpy as np
from sklearn.utils.validation import check_is_fitted
from sklearn.externals.joblib import Parallel, delayed, Memory
from sklearn.externals import s... |
from typing import List, Tuple, cast
import numpy as np
import scipy.linalg as spLinalg
from ..misc.utils import isType
from .classification import isMatrixDiagDominant
def svdAndReconstruction(A: np.ndarray, singularValues: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""
Generates ... |
import matplotlib.pyplot as plt
import numpy as np
from numpy import arange, pi, real, interp, floor, log, exp, sqrt
from numpy.fft import fft
from scipy.interpolate import interp1d
plt.style.use('seaborn')
def CallPriceHestonFFT(s_0,k,r,tau,z):
# This function computes the Heston price of european call options ... |
# external imports
import numpy as np
import matplotlib.pyplot as plt
from scipy.linalg import block_diag
# internal inputs
from pympc.dynamics.discrete_time_systems import AffineSystem, PieceWiseAffineSystem
from pympc.optimization.parametric_programs import MultiParametricQuadraticProgram, MultiParametricMixedIntege... |
<reponame>TOPDyn/TOPDyn
from time import time
import numpy as np
from scipy.sparse import csc_matrix, csr_matrix
from scipy.sparse.linalg import spsolve
from scipy import spatial
def shapeH8(rrx, ssx, ttx):
""" Linear Shape Functions and Derivatives.
Args:
rrx (:obj:`float`): Local coordinate of the e... |
# %% [markdown]
# # Bayesian Linear Regression
# In this post I talk about reformulating linear regression in a Bayesian framework.
# This gives us the notion of epistemic uncertainty which allows us to generate probabilistic model predictions.
# I formulate a model class which can perform linear regression via Bayes r... |
"""Evaluating DL models on M4 timeseries
"""
from darts import TimeSeries, SeasonalityMode
from darts.models import Theta, FourTheta
from darts.utils.statistics import check_seasonality, remove_from_series, extract_trend_and_seasonality
from darts.utils import _build_tqdm_iterator
from scipy.stats import boxcox, box... |
# gate_factory.py
"""Contains factory functions for creating quantum gates."""
from cmath import exp
from math import sqrt
from numpy import array, eye, ones, zeros
from quantum.quantum_gate import QuantumGate, tensor_power
def x_gate():
"""Factory method for Pauli-X gate (i.e. "not gate")"""
return Quantum... |
<filename>backend/backend/files/graph_utils.py
import os
import re
import sys
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import scipy
import zipfile
import seaborn as sns
def histogram(correlation_matrix,folder_path,bin_size = 0.10,img_format = 'png'):
"""!
\brie... |
# hspace_widget.py
#
# This file is part of scqubits: a Python package for superconducting qubits,
# arXiv:2107.08552 (2021). https://arxiv.org/abs/2107.08552
#
# Copyright (c) 2019 and later, <NAME> and <NAME>
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# ... |
import glob as glb
import sys
import numpy as np
from sklearn.decomposition import KernelPCA as pca_f
import os
import matplotlib as mpl
mpl.use("WebAgg")
import matplotlib.pyplot as plt
from scipy.spatial.distance import pdist
import scipy.cluster.hierarchy as sch
import time
import copy
def genome_label(bed_file_li... |
import sys
sys.path.insert(0, "..")
import crnt4sbml
import numpy
import sympy
network = crnt4sbml.CRNT("../sbml_files/simple_biterminal.xml")
signal = "C2"
response = "s11"
iters = 15
d_iters = 1000
bnds = [(2.4, 2.42), (27.5, 28.1), (2.0, 2.15), (48.25, 48.4), (0.5, 1.1), (1.8, 2.1), (17.0, 17.5), (92.4, 92.6), (0.0... |
<filename>tests/test_fjs_names.py<gh_stars>1-10
from fractions import Fraction
from typing import Callable
import pytest
from xenterval.ji import Monzo
from xenterval.interval.name.fjs import FJS, FJSName
def test_fjs_commas() -> None:
commas_str = (
'80/81',
'63/64',
'33/32',
'1053... |
<gh_stars>0
import re
import pandas as pd
from scipy.stats import beta, entropy
from scipy.special import loggamma, digamma
import random
import numpy as np
# from compute_metrics import exp
def exp(x, a=10):
return 1 - (a ** (-1 * x))
def kl(p, q):
return entropy([p, 1-p], [q, 1-q])
def fit_beta(data):
... |
<reponame>MathPhysSim/PER-NAF<gh_stars>1-10
import logging.config
import matplotlib.pyplot as plt
import random
import scipy.optimize as opt
import gym
import numpy as np
# 3rd party modules
import math
from enum import Enum
class simpleEnv(gym.Env):
"""
Define a simple environment.
The environment define... |
import datetime
import pytz
import pandas as pd
import MetaTrader5 as mt5
import matplotlib.pyplot as plt
import numpy as np
import statistics as stats
frame_MIN1 = mt5.TIMEFRAME_M1
frame_M5 = mt5.TIMEFRAME_M5
frame_M10 = mt5.TIME... |
<gh_stars>10-100
import TimestampedUDPData_pb2
import google.protobuf.json_format
import os
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import cv2
import numpy.linalg as la
import scipy.stats as stats
import TimestampedPacketMotionData_pb2
def sortkey(packet : TimestampedP... |
<filename>src/eval_result_stats.py<gh_stars>0
import pandas as pd
import numpy as np
import os, sys
from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error
import numpy as np
import math
import scipy.integrate as integrate
import scipy.special as special
# from varname import nameof
class CpEva... |
import scipy as sp
from scipy.linalg import eig, inv
from ....caching import lru_cache
from .liouvillian import compute_liouvillian
PI = sp.pi
dot = sp.dot
diag = sp.diag
exp = sp.exp
ix_ = sp.ix_
@lru_cache()
def make_calc_observable(time_t1=0.0, b1_offset=0.0, b1_frq=0.0, carrier=0.0,
pp... |
<filename>train_dae_curves.py
# Martens reports the mse even though the optimized quantity is the likelihood.
# Does he use multiply the error by .5?
import theano
import numpy as np
import theano.tensor as T
from optimizer import Model, krylov_descent
from autoencoder import DAE
import scipy.io as sio
class Dataset:... |
<reponame>UKPLab/ijcai2019-relis<filename>summariser/reward_learner/pref_rewarder.py
import numpy as np
import random
from sklearn.linear_model import LogisticRegression
from sklearn import svm
import scipy.stats as stats
from itertools import permutations
from summariser.utils.evaluator import evaluateReward
from sum... |
import compare_performance
import argparse
from random import shuffle
import statistics
__author__ = 'buchholb'
parser = argparse.ArgumentParser()
parser.add_argument('--queryfile',
type=str,
help='One line per (standard) SPARQL query. TS specific transformations are made by t... |
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from numpy import exp, abs, log
from scipy.special import gamma, factorial
from utils import *
def cumulative_return(rt_v):
return exp(np.sum(rt_v))
def plot_cumulative_return_history(concat_results, strategy_lst, figsize=(10,5)):
plt.figu... |
<reponame>FelSiq/statistics-related<gh_stars>1-10
# Cool video explaining this test: https://www.youtube.com/watch?v=CqLGvwi-5Pc&list=PLblh5JKOoLUIzaEkCLIUxQFjPIlapw8nU&index=6
import typing as t
import numpy as np
import scipy.stats
import sklearn.linear_model
def _check_X_y(X, y):
X = np.asfarray(X)
y = np... |
import numpy as np
import pytest
import scipy.sparse as sp
from lib.dataset import normalization as N
def f(adj):
C = len(adj)
aug_adj = adj + np.eye(C)
d_inv_sqrt = 1. / np.sqrt(aug_adj.sum(axis=1))
return d_inv_sqrt[:, None] * (aug_adj) * d_inv_sqrt
@pytest.fixture
def adj_simple():
return np... |
from numpy import zeros,sqrt,linspace,unique
from scipy import linalg
#======================================================================
# python function to calculate element mass and stiffness matrices for
# vertical bending given beam mass per unit span "m" (mass/length) and
# flexural stiffness "EI" (Force l... |
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