text string |
|---|
<filename>lcls_tools/math_tools/fit_gaussian.py
#!/usr/local/lcls/package/python/current/bin/python
################################################################################
# Modified version of <NAME>'s read_xcor_data script
# Reads in a matlab file of xcor data and fits gaussians to it
# Data must have column... |
import argparse
from copy import copy
try:
import ujson as json
except ModuleNotFoundError:
import json
import kaggle_environments
import numpy as np
import os
from pathlib import Path
import pandas as pd
from scipy import stats
import tqdm
from typing import *
from hungry_geese.utils import STATE_TYPE, read_j... |
<gh_stars>0
#
# Solution class
#
import numpy as np
import scipy.integrate
from pkmodel import Model, Protocol
class Solution:
"""A Pharmokinetic (PK) model solution
Parameters
----------
model: an object in the Model Class
protocol: an object in the Protocol Class
T: The system is solved f... |
<reponame>ChaShaoAn/Super-Resolution
# -*- coding: utf-8 -*-
import numpy as np
import cv2
import torch
from utils import utils_image as util
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
"""
# -------------------... |
# Author : <NAME>
# Date : December 7th, 2017
# Purpose : Implement the Diebold-Mariano Test (DM test) to compare
# forecast accuracy
# Input : 1) actual_lst: the list of actual values
# 2) pred1_lst : the first list of predicted values
# 3) pred2_lst : the second list of pred... |
<reponame>Abhinav43/LibMultiLabel
import torch
import torch.nn as nn
from torch.nn.init import xavier_uniform_
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from ..networks.base import BaseModel
import torch.nn.functional as F
import torch_geometric.transforms as T
from scipy import sparse
... |
import os
import re
from operator import itemgetter, sub
from sys import argv
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm
from matplotlib.ticker import MaxNLocator
from matplotlib.widgets import Slider, Button
from scipy.spatial import distance
def get_system(file):
"""... |
import cv2
from ketisdk.utils.proc_utils import ProcUtils
import os
import math
from scipy import optimize
import matplotlib.pyplot as plt
import numpy as np
class ArrayUtils():
def crop_oriented_rect_polar(self, im, center, angle, rx, ry):
xc, yc = center
# top, left, right, bottom = xc-rx, yc-ry,... |
"""
decoding_analys.py
This script contains functions for decoding analysis.
Authors: <NAME>
Date: January, 2021
Note: this code uses python 3.7.
"""
import itertools
import logging
import numpy as np
import pandas as pd
import scipy.stats as scist
from util import logger_util, gen_util, logreg_util, math_util, ... |
from sympy import Add, Mul, collect, collect_const
from devito.passes.clusters.utils import dse_pass
from devito.symbolics import estimate_cost, retrieve_scalars
from devito.tools import ReducerMap
__all__ = ['factorize']
MIN_COST_FACTORIZE = 100
"""
Minimum operation count of an expression so that aggressive facto... |
################################################################################
"""
`electricpy.sim` - Simulation Module.
>>> from electricpy import sim
"""
################################################################################
from warnings import warn as _warn
import matplotlib.pyplot as _plt
# Import... |
<filename>pyfermod/stats/stats_chisquare.py<gh_stars>10-100
"""
Performs a chi-square test that a sample with the observed
counts of categorical data comes from a population with the
given expected counts or relative frequencies of that data.
"""
from __future__ import print_function
import numpy
import pyferret
impo... |
<reponame>candacelax/1-stage-wseg
"""Convert .mat segmentation mask of SBD to .png
See: https://github.com/visinf/1-stage-wseg/issues/9
"""
import os
import sys
import glob
import argparse
from PIL import Image
from scipy.io import loadmat
# load tqdm optionally
try:
from tqdm import tqdm
except ImportError:
... |
<filename>utils.py
import math
import os
import random
from collections import deque
import numpy as np
import scipy.linalg as sp_la
import gym
import torch
import torch.nn as nn
import torch.nn.functional as F
from skimage.util.shape import view_as_windows
from torch import distributions as pyd
class eval_mode(obj... |
<reponame>nii-yamagishilab/NELE-GAN<filename>train_nele.py
# coding=utf-8
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from joblib import Parallel, delayed
import shutil
import scipy.io
import librosa
import os
import time
import numpy as np
import numpy.matlib
import random
import subpro... |
import os
import shutil
import signal
import subprocess
import sys
from random import randint
from statistics import mean
from time import sleep
from client import Client
from src.config import INIT_PORT, HOP_SERVER_PORT, HYPERCUBE_SIZE
from src.utils import reset_hops, get_hops, NODES, log
from openpyxl import load_w... |
# from bitarray import bitarray
# import random
import math
import statistics
# import copy
import numpy as np
# import logging
import collections
from numpy import linalg as LA
def get_covariance_matrix(data):
arr = np.array(data)
return np.cov(arr, bias=False)
# return np.cov(arr, bias=True)
# covMat... |
<reponame>lamsoa729/FoXlink<filename>foxlink/me_zrl_odes.py
#!/usr/bin/env python
"""@package docstring
File: me_zrl_odes.py
Author: <NAME>
Email: <EMAIL>
Description: Class that contains the all ODEs relevant to solving the moment
expansion formalism of the Fokker-Planck equation for bound crosslinking motors.
"""
fr... |
print('Importing packages...')
import pandas as pd
import matplotlib.pyplot as plt
import datetime as dt
import seaborn as sns
import numpy as np
import matplotlib.dates as mdates
import datetime
#sns.set(color_codes=True)
import matplotlib as mpl
mpl.rcParams['pdf.fonttype'] = 42
import statistics as st
sns.set_style(... |
import pandas as pd
data=pd.read_csv("C:/Users/user/Documents/API_NY.GDP.PCAP.CD_DS2_en_csv_v2_1068945.csv") #your raw data obtained from world bank
import pandas as pd
import matplotlib.pyplot as plt
fulldataonly=data.dropna()
listofcountry=fulldataonly['Country Name']
listofcountry=list(listofcountry)
def findco... |
import numpy as np
from scipy.special import expit
import matplotlib.pyplot as plt
from scipy.stats import norm
from scipy.stats import multivariate_normal
##################################################################################################################################
################################... |
<filename>morfeus/sasa.py
"""Solvent accessible surface area code."""
import functools
import typing
from typing import Any, Dict, Iterable, List, Optional, Union
import numpy as np
import scipy.spatial
from morfeus.data import atomic_symbols, jmol_colors
from morfeus.geometry import Atom, Sphere
from morfeus.io imp... |
from __future__ import annotations
import heapq
import random
import uuid
from fractions import Fraction
from functools import reduce
from typing import Any, Mapping, Optional, Sequence, Union
import aiger
import aiger_bv as BV
import aiger_discrete
import attr
import funcy as fn
from aiger_discrete import FiniteFunc... |
# Some methods in this file ported to Pytorch from https://github.com/Ashish77IITM/W-Net/
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from scipy.stats import norm
from torch import Tensor
# The weight matrix w is a measure of the weight between each pixel and
# every other pi... |
import os
import time
import warnings
import multiprocessing as mp
from typing import List
import pandas as pd
import numpy as np
import scipy
import scipy.stats as stats
import matplotlib.pyplot as plt
from dateutil.relativedelta import relativedelta
from datetime import datetime
from tqdm import tqdm
from pvrpm.cor... |
<filename>Exp3C-metusalem2012_Reservoir50.py
from wikipedia2vec import Wikipedia2Vec
import numpy as np
import matplotlib.pyplot as plt
import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
import csv
import scipy
from scipy import stats
from easyesn.optimizers import GradientOptimizer
... |
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
from loadData import gauss, loadAndPrepareInput
from scipy import signal
from generateTestData import loadTestData
from plot import plotProjections2D, plotError2D
from scipy.optimize import curve_fit
def growthRate(X, x, bins, y, angle, convFu... |
<filename>pearl/stop/roi.py<gh_stars>1-10
from __future__ import division, print_function
# def prepare_mcf_data(in_mcf_data):
# np.hstack(np.vstack([np.zeros(in_mcf_data.shape[0]), np.diff(in_mcf_data)])
# return
def fit_FIR_roi(experiment,
h5_file,
in_files,
... |
<reponame>spcl/daceml<filename>daceml/autodiff/backward_pass_generator.py
"""Automatic Differentiation of SDFGStates.
This module exposes the add_backward_pass method that can be used to add a backward pass to an
SDFGState.
"""
import collections
import copy
import logging
import numbers
from typing import List, ... |
"""
Model predictive control sample code without modeling tool (cvxpy)
author: <NAME>
"""
import cvxpy
import numpy as np
import matplotlib.pyplot as plt
import cvxopt
from cvxopt import matrix
import scipy.linalg
DEBUG_ = False
def use_modeling_tool(A, B, N, Q, R, P, x0, umax=None, umin=None, xmin=None, xmax=No... |
<reponame>hechth/CoreMS
__author__ = "<NAME>"
__date__ = "Jun 24, 2019"
from IsoSpecPy import IsoSpecPy
from numpy import isnan, power, exp, nextafter
from pandas import DataFrame
from scipy.stats import pearsonr, spearmanr, kendalltau
from corems.encapsulation.constant import Atoms
from corems.encapsulation.constant... |
"""
file: fitters.py
brief:
author: <NAME>
date: December 23, 2020
"""
import numpy as np
from scipy.optimize import curve_fit
def crystal_ball(data, mu, amplitude, alpha, n, sigma):
"""
The Crystal Ball function is defined here: https://en.wikipedia.org/wiki/Crystal_Ball_function
It's really a probabilit... |
# uncompyle6 version 3.7.4
# Python bytecode 3.5 (3350)
# Decompiled from: Python 3.8.5 (default, Jan 27 2021, 15:41:15)
# [GCC 9.3.0]
# Embedded file name: /home/docker/CSN/bin/gpu_ccsn.py
# Compiled at: 2021-03-31 16:15:26
# Size of source mod 2**32: 19699 bytes
"""
python version of csn algorithm
https://github.com... |
<filename>src/aves/features/geometry.py<gh_stars>0
# source: https://stackoverflow.com/questions/34803197/fast-b-spline-algorithm-with-numpy-scipy
import scipy.interpolate as si
import numpy as np
def bspline(cv, n=100, degree=3, periodic=False):
""" Calculate n samples on a bspline
cv : Array ov con... |
<gh_stars>1-10
import concurrent.futures
from copy import deepcopy, copy
from functools import partial
import json
import math
import os
from os.path import join
from time import time, sleep
from pathos.multiprocessing import ProcessPool, ThreadPool
from threading import Lock
from cloudvolume import Storage
from clou... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on 21/10/2018
@author: <NAME>
"""
import numpy as np
import scipy.sparse as sps
def train_test_holdout(URM_all, train_perc = 0.8):
numInteractions = URM_all.nnz
URM_all = URM_all.tocoo()
shape = URM_all.shape
train_mask = np.random.choi... |
import numpy as np
import os
import pickle
import seaborn
import matplotlib.pyplot as plt
from scipy import stats
from operator import itemgetter
import pandas as pd
import real_data_analyses as rda
from joblib import Parallel, delayed
from importlib import reload
#load subject information
exec(open('/home/lingee/wrk... |
# -*- coding: utf-8 -*-
from __future__ import print_function, division
# QuSpin modules
# numpy modules
import numpy as _np # generic math functions
# _scipy modules
import scipy as _scipy
import scipy.sparse as _sp
from scipy.sparse.linalg import expm_multiply as _expm_multiply
# multi-processing modules
from multip... |
import scipy.io.wavfile as wav
import matplotlib.pyplot as plt
import numpy as np
rate, signal = wav.read('./Zhonghua.wav')
sigSize = np.size(signal)
time = np.linspace(0, sigSize, sigSize) / rate
normal = signal / 2**15
sample = normal[20000:20512]
plt.subplot(2, 1, 1)
plt.subplots_adjust(hspace=0.5)
plt.plot(time, n... |
import os
from os import path
from scipy.io import loadmat
from torchvision.datasets.utils import download_url
sop_dir = path.join('datasets', 'Stanford_Online_Products')
train_file = 'train.txt'
test_file = 'test.txt'
def generate_sop_train_test(sop_dir, train_file, test_file):
original_train_file = path.join(s... |
#!/usr/bin/env python3
from collections import defaultdict
from warnings import warn
import numpy as np
from pandas import DataFrame
from scipy.linalg import eig
from pgmpy.factors.discrete import State
from pgmpy.utils import sample_discrete
from pgmpy.extern import six
from pgmpy.extern.six.moves import range, zip
... |
from __future__ import print_function
import numpy as np
from scipy.ndimage import map_coordinates
def extract_line_slice(cube, x, y, order=3, respect_nan=True):
"""
Given an array with shape (z, y, x), extract a (z, n) slice by
interpolating at n (x, y) points.
All units are in *pixels*.
.. n... |
# author jiang
# -*- coding:utf-8-*-
import torch
import torchvision.transforms as transforms
import numpy as np
import dlib
from dlib import rectangle
from utils.ddfa import ToTensorGjz, NormalizeGjz, str2bool
import scipy.io as sio
from utils.inference import parse_roi_box_from_landmark, crop_img, predict_68pts, pars... |
<filename>jp.atcoder/abc012/abc012_4/25518652.py
import sys
import typing
import numpy as np
import scipy
from scipy import sparse
def solve(
n: np.ndarray,
a: np.ndarray,
b: np.ndarray,
t: np.ndarray,
) -> typing.NoReturn:
g = sparse.csr_matrix(
(t, (a, b)),
shape... |
"""
ndt.py
File containing class definitions of NDT approximation for Consensus NDT SLAM
Also contains helper NDT functions
Author: <NAME>
Date created: 15th April 2019
Last modified: 13th November 2019
"""
import numpy as np
import pptk
import utils
import transforms3d
from scipy.optimize import check_grad
from scipy.... |
<gh_stars>0
import numpy as np
import scipy, os
from scipy.signal import butter,lfilter
from scipy.ndimage.filters import gaussian_filter1d
import matplotlib.pyplot as plt
from matplotlib.pyplot import mlab
import xml.etree.ElementTree
samplingRate=30000.
#===============================================================... |
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 1 09:52:46 2018
@author: Jackie
"""
import numpy as np
from skimage.morphology import label, binary_dilation,binary_erosion,remove_small_holes
from scipy.ndimage import generate_binary_structure
from load import mask_12m_no, mask_12m
from lib import delta
... |
from netaddr import IPAddress, IPNetwork
import os, sys
sys.path.insert(0, os.path.abspath('..'))
from Utils.Constants import Constants
from geolite2 import geolite2
from math import sin, cos, atan2, radians, sqrt, degrees
from statistics import mean, variance
from numpy import array, percentile
from scipy.stats impor... |
<filename>read_data.py
import os
from utils import load_vertical_tagged_data
import torch
from torch.nn.utils.rnn import pad_sequence
import statistics as stat
class Dataset():
"""
"""
def __init__(self,
data_dir='./data/datasets/conll04',
data_name='conll04',
... |
import numpy as np
import pytest
import xarray as xr
from scipy.stats import lognorm, norm
from xclim.indices import stats
class TestFA(object):
def setup(self):
self.nx, self.ny = 2, 3
x = np.arange(0, self.nx)
y = np.arange(0, self.ny)
cx = xr.IndexVariable("x", x)
cy =... |
<gh_stars>1-10
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
from scipy.misc import imsave
from scipy import io as sio
from skimage.measure import compare_ssim as ssim
from skimage.measure import compare_psnr as psnr
import os
import shutil
from PIL impor... |
# @version: 1.0 date: 19/10/2016 by <NAME>
# @author: <EMAIL>, <EMAIL>, <EMAIL>
# @copyright: EPFL-IC-LCAV 2015
from unittest import TestCase
import numpy as np
from scipy.signal import fftconvolve
import pyroomacoustics as pra
class TestSTFT(TestCase):
def test_stft_nowindow(self):
frames = 100
... |
<gh_stars>0
import pandas as pd
import numpy as np
from pliers.utils import attempt_to_import, verify_dependencies
import matplotlib.pyplot as plt
from scipy.spatial.distance import mahalanobis
from numpy.linalg import LinAlgError
sns = attempt_to_import('seaborn')
def correlation_matrix(df):
'''
Returns a ... |
<reponame>lkilcher/dolfyn-light<gh_stars>0
import numpy as np
from scipy.signal import medfilt
from ..tools import misc as tbx
def find_surface(apd, thresh=10, nfilt=1001):
"""
Find the surface, from the echo data of the *apd* adcp object.
*thresh* specifies the threshold used in detecting the surface.
... |
<reponame>EliasVansteenkiste/edge_detection_framework
import numpy as np
from PIL import Image
import os
import scipy
from sklearn.metrics import fbeta_score
import pathfinder
rng = np.random.RandomState(37145)
# def read_mat(dataset, idx, plot=False):
# path = pathfinder.DATA_PATH + '/' + dataset + '/' + str... |
<reponame>TheFloe1995/correct-pose
import os
from abc import ABC, abstractmethod
import torch
import numpy as np
from torch import distributions
import scipy.stats
# This crazy matrix defines the probabilities for each joint (row) to be confused with another joint
# (col) given that a confusion happened.
# TODO: Outso... |
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 12 11:04:21 2018
@author: DIhnatov
"""
""" Implementation of Residual Network
In theory, very deep networks can represent very complex functions; but in practice, they are hard to train. Residual Networks, introduced by He et al., allow you to train much deeper networks ... |
<reponame>Borlaff/EuclidVisibleInstrument<filename>sandbox/peakFindingCentroiding.py
import time
import numpy as np
import matplotlib.pyplot as plt
import scipy.ndimage
import matplotlib.patches
plt.figure(figsize=(10,10))
ax1 = plt.subplot(221)
ax2 = plt.subplot(222)
ax3 = plt.subplot(223)
ax4 = plt.subplot(224)
siz... |
import os
import sys
import numpy as np
from scipy import spatial as ss
import pdb
import cv2
from utils import hungarian,read_pred_and_gt,AverageMeter,AverageCategoryMeter
gt_file = 'val_gt_loc.txt'
exp_name = './RAZ_results'
pred_file = 'Raz_loc_val_0.5.txt'
img_path = ori_data = '/media/D/DataSet/NWPU-ori/im... |
<reponame>bruce-edelman/kombine
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Unit tests for nose.
"""
from __future__ import division
import numpy as np
from scipy.stats import multivariate_normal
from .clustered_kde import KDE
from .clustered_kde import ClusteredKDE
from .clustered_kde import optimized_kde
fro... |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# In[2]:
df=pd.read_csv("./Dataset/Mall_Customers.csv")
# In[3]:
df.head()
# In[4]:
df.info()
# In[5]:
df.describe()
# In[6]:
df.rename(columns={'Annual Inco... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Compare one dataset to another at a variety of p-value cutoffs.
Author: <NAME> (Fraser Lab, Stanford University)
License: MIT
Version: 1.0b2
Created: 2018-05-30
Updated: 2018-05-31
See the README at:
https://github.com/TheFraserLab/enrich_pvalues/blob/master/READM... |
<gh_stars>1-10
# -*- coding: utf-8 -*-
"""
Author
------
<NAME>
Email
-----
<EMAIL>
Created on
----------
- Mon Nov 28 15:00:00 2016
Modifications
-------------
-
Aims
----
- utils for calibration
"""
import itertools
import numpy as np
from astropy.io import fits
from joblib import Parallel, delayed
from scipy... |
<filename>scripts/flow_rank.py
#%% [markdown]
# # Flow
#%%
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from graspologic.utils import binarize, get_lcc, is_fully_connected
from scipy.stats import rankdata, spearmanr
import SpringRank as sr
from pkg.data import load_dat... |
#!/usr/bin/env python
# coding: utf-8
# troduction
# --------------
#
# this section, we start designing FIR filters using the windowing design method. This is the most straightforward design method and it will illustrate the concepts we developed in the previous section.
#
# ### Windowing Method
#
# The window ... |
#!/usr/bin/env python
from __future__ import print_function
import roslib; roslib.load_manifest('robot_kf')
import rospy
import math
import numpy as np
import scipy.optimize
from nav_msgs.msg import Odometry
from sensor_msgs.msg import Imu
class CompassCalibrator:
def __init__(self):
self.collecting = Fals... |
<reponame>brianhie/ample
import numpy as np
import os
from scanorama import *
from scipy.sparse import vstack
from sklearn.cluster import KMeans
from sklearn.metrics import roc_auc_score
from sklearn.preprocessing import normalize, LabelEncoder
from experiments import *
from mouse_brain import keep_valid
from process ... |
"""Contains functions pertaining to the design of physical and chemical unit
processes of AguaClara water treatment plants.
"""
from aguaclara.core.units import u
import aguaclara.core.constants as con
import aguaclara.core.utility as ut
import aguaclara.core.pipes as pipe
import numpy as np
from scipy import interpo... |
<reponame>g3-raman/NITRATES
import numpy as np
from astropy.io import fits
from scipy import stats
import os
from scipy import interpolate, optimize
import logging, traceback
import time
import gc
def get_rt_arr(rt_dir, ident='fwd_ray_trace'):
ray_trace_fnames = np.array([fn for fn in os.listdir(rt_dir) if\
... |
# --------------
# 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 = 'number')
#print(numerical_var)
# code en... |
<gh_stars>0
import statistics
import unittest
import pandas as pd
import Levenshtein as Lev
from scripts import FilePaths
from scripts.text_processing import StemTokenizer
from scripts.tfidf_wrapper import TFIDF
from tests.utils.DiceScore import Dice
from tests.vvcode.abstracts2pickle import us_vv_patents_pickle_name... |
<filename>examples/acados_python/getting_started/mhe/export_ocp_solver.py
#
# Copyright 2019 <NAME>, <NAME>, <NAME>,
# <NAME>, <NAME>, <NAME>, <NAME>,
# <NAME>, <NAME>, <NAME>, <NAME>,
# <NAME>, <NAME>, <NAME>, <NAME>, <NAME>
#
# This file is part of acados.
#
# The 2-Clause BSD License
#
# Redistribution and use in so... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on 23/04/2019
@author: <NAME>
"""
import numpy as np
import scipy.sparse as sps
from Data_manager.IncrementalSparseMatrix import IncrementalSparseMatrix
def split_train_in_two_percentage_user_wise(URM_train, train_percentage = 0.1, verbose = False):
"""... |
<gh_stars>1-10
import pandas as pd
import numpy as np
from scipy import stats
hists1m = pd.read_table('tables/genes.prevalence.1m.hists.txt', index_col=0)
biome = pd.read_table('cold/biome.txt', index_col=0, squeeze=True)
tables = {}
data = []
for N in [10, 100]:
for b in hists1m.columns:
gp = hists1m[b].v... |
import itertools
import operator
from scipy import sparse
from bblfsh import Node
from pyspark import Row
from pyspark.rdd import PipelinedRDD
from sourced.ml.models import Cooccurrences, OrderedDocumentFrequencies
from sourced.ml.transformers import Transformer
from sourced.ml.utils import bblfsh_roles, EngineConsta... |
import matplotlib
matplotlib.use("Agg")
from astropy.io import fits as pyfits
import numpy as np
from numpy import median,sqrt,array,exp
import scipy
from scipy import signal,special,optimize,interpolate
import scipy.special as sp
import copy
import glob
import os
from pylab import *
import sys
base = '../'
sys.path.a... |
# ~~~
# This file is part of the paper:
#
# "A relaxed localized trust-region reduced basis approach for
# optimization of multiscale problems"
#
# by: <NAME> and <NAME>
#
# https://github.com/TiKeil/Trust-region-TSRBLOD-code
#
# Copyright 2019-2022 all developers. All rights reserved.
... |
import os, sys, pdb, pickle, pathlib, argparse
from profilehooks import profile
from collections import OrderedDict
import copy, time, math, random
import numpy as np
import scipy as sp
from scipy.spatial.distance import cosine
import matplotlib
matplotlib.use('tkagg')
import matplotlib.pyplot as plt
import torch
i... |
import numpy as np
import numba as nb
from scipy.stats import rankdata
from functools import partial
from nptyping import Array
from sklearn.metrics import pairwise_distances
from sklearn.base import BaseEstimator, TransformerMixin
from julia import Julia
jl = Julia(compiled_modules=False)
import os
class MultiSUR... |
#=================================================================================================
##################################################################################################
# TODO:
#
# 1. Resolve: Is the ... |
<reponame>carrier-io/performance_email_notification
# Copyright 2019 getcarrier.io
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless... |
import numpy as np
#from scipy.stats import multivariate_normal
class EllipticalSliceSampler:
"""Elliptical Slice Sampler Class"""
def __init__(self, mean, covariance, log_likelihood_func):
"""Initialize the parameters of the elliptical slice sampler object"""
self.mean = mean
self.cova... |
<filename>GusarevModel.py
import torch.nn as nn
import torch
import numpy as np
import scipy as scp
import matplotlib.pyplot as plt
import os, sys, time, datetime, pathlib, random, math
##############################
# Gusarev Model
##############################
# AutoEncoder
class Autoencoder(nn.Module):
def __i... |
<reponame>computational-medicine/BMED360-2021
import sympy as sym
x, y = sym.symbols('x y')
print(2*x + 3*x - y) # Algebraic computation
print(sym.diff(x**2, x)) # Differentiates x**2 wrt. x
print(sym.integrate(sym.cos(x), x)) # Integrates cos(x) wrt. x
print(sym.sim... |
<reponame>rajatgarg149/Stock-Trading-using-RRL
import numpy as np
import scipy.optimize as opt
from scipy.optimize import *
from rewardFunction import rewardFunction
from costFunction import costFunction
from updateFt import updateFt
from featureNormalize import featureNormalize
import matplotlib.pyplot as plt
retDA... |
import scipy.spatial.distance as spd
import numpy as np
DEFAULT_ALLOCATE_ID = False
def from_triad(tri, allocate_id=None):
assert isinstance(tri, (list, tuple, np.ndarray)) and len(tri) == 3
return Point(tri[0], tri[1], tri[2], allocate_id=allocate_id)
class Point(object):
"""
The class 'Point' rep... |
# %%
import numpy as np
import matplotlib.pyplot as plt
from scipy import fft, signal
from scipy.io.wavfile import read
# %%
def create_constellation(audio, Fs):
# Parameters
window_length_seconds = 0.5
window_length_samples = int(window_length_seconds * Fs)
window_length_samples += window_length_sam... |
<gh_stars>1-10
class MapInterpolation:
"""
Create a MapInterpolation class where the data for drawing an interpolation
map can be downloaded, modified, manually provided.
Keyword arguments:
country -- a country or a list of countries for which the shapefile will
be drawn. Alternatively, if ... |
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 20 14:03:18 2020
@author: Nicolai
"""
import sys
sys.path.append("../differential_evolution")
from JADE import JADE
import numpy as np
import scipy as sc
import testFunctions as tf
def memeticJADE(population, function, minError, maxFeval):
'''
implementation of... |
import numpy as np
import scipy.sparse as sp
import torch
import torch.nn as nn
import networkx as nx
import random
import os, argparse
from models import DGI, LogReg
from utils import process
from estimator.estimator import mi_loss, mi_loss_neg
from attacker.attacker import Attacker
parser = argparse.Argume... |
<reponame>nbonacchi/ibllib
'''
Computes properties of single-cells, e.g. the autocorrelation and firing rate.
'''
import numpy as np
from scipy.signal import convolve, gaussian
from brainbox.core import Bunch
from brainbox.population.decode import xcorr
def acorr(spike_times, bin_size=None, window_size=None):
""... |
<reponame>jippo015/Sub-Zero.bundle
from datetime import datetime, date, time, timedelta
from fractions import Fraction
from importlib import import_module
from collections import OrderedDict
from decimal import Decimal
from logging import warning
from json_tricks import NoPandasException, NoNumpyException
class Dupl... |
"""
Created on 09:42 at 01/06/2021
@author: bo
"""
import numpy as np
from scipy.interpolate import interp1d
def find_interp(spectrum, target_wave):
"""Interpolate the spectrum with respect to the fixed wave interval
Args:
spectrum: [N, 2]
target_wave: [N]
"""
if len(spectrum) == 2:
... |
import matplotlib.pyplot as plt
from scipy.stats import norm
plt.style.use('seaborn')
def BachelierCallPrice(x,s):
# This function computes the zero-strike call option price profile
# according to the Bachelier pricing function.
# INPUTS:
# x :[vector] rate
# s :[scalar] smoothing parameter
... |
import numpy as np
import scipy
from .due import Doi, due
due.cite(
Doi("10.1021/ci400534h"), # lgtm[py/procedure-return-value-used]
path="spyrmsd.hungarian",
description="Hungarian method",
)
def cost_mtx(A: np.ndarray, B: np.ndarray):
"""
Compute the cost matrix for atom-atom assignment.
... |
<reponame>Chris7/pyquant
from __future__ import division, unicode_literals, print_function
import base64
import copy
import gzip
import math
import os
import operator
import traceback
import random
import signal
import sys
from collections import defaultdict, OrderedDict
from functools import partial
from multiprocessi... |
# https://gist.githubusercontent.com/anabranch/48c5c0124ba4e162b2e3/raw/6b1acf8391b15ad3a663beb7e685e6835c964036/tfpdf.py
# http://www.cs.duke.edu/courses/spring14/compsci290/assignments/lab02.html
from __future__ import division
from nltk.tokenize import word_tokenize
from scipy.spatial import distance
from nltk.stem.... |
import pandas as pd
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from scipy import sparse
def calculate_similarity(data_items):
"""Calculate the column-wise cosine similarity for a sparse
matrix. Return a new dataframe matrix with similarities.
"""
data_sparse = sparse.csr... |
<reponame>johnnylord/mtmc-testbed
import logging
import numpy as np
from scipy.optimize import linear_sum_assignment
from ...utils.time import timeit
from .centroid import TargetCentroid
logger = logging.getLogger(__name__)
class AdaptiveKmeans:
"""Adaptive Kmeans clustering algorithm to cluster tracked targe... |
<reponame>merz9b/pyfilter<gh_stars>0
import unittest
from pyfilter.timeseries import AffineModel, EulerMaruyma, OrnsteinUhlenbeck, Parameter
from pyfilter.timeseries.statevariable import StateVariable
import scipy.stats as stats
import numpy as np
import torch
from torch.distributions import Normal, Exponential
def f... |
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