akochepasov

Fetch only a known branch

git fetch --depth=5 origin feature/BSD-745-db-simulation

Switch-rename existing branch

git switch feature/BSD-745-db-simulation
git switch -c BSD-745 --track origin/feature/BSD-745-db-simulation

Branch from current

git checkout -b feature/BSD-713

akochepasov

Creating a Fork

Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your repo or just head straight to the command line:

# Clone your fork to your local machine
git clone [email protected]:USERNAME/FORKED-PROJECT.git

akochepasov

# Based on oneAPI description
# Both from C order
n, m = A.shape
ldm, lda = n, 3
ku, kl = 1, 1
B = np.zeros((lda, ldm))
for j in range(n):
    k = ku - j
    for i in range(max(0, j-ku), min(m, j + kl + 1)):
        B[(k + i), j] = A[i, j]

akochepasov

def diagonal_form(a, upper = 1, lower= 1):
    """
    a is a numpy square matrix
    this function converts a square matrix to diagonal ordered form
    returned matrix in ab shape which can be used directly for scipy.linalg.solve_banded
    """
    n = a.shape[1]
    assert(np.all(a.shape ==(n,n)))
    
    ab = np.zeros((2*n-1, n))

akochepasov

Install WSL2 in Win10

Install Docker

https://docs.microsoft.com/en-us/windows/wsl/tutorials/wsl-containers#install-docker-desktop

Install CentOS through docker image (use tag centos:7)

https://docs.microsoft.com/en-us/windows/wsl/use-custom-distro
(set as default linux: wsl --set-default CentOS-7)

Install Dev tools from RH SCL

akochepasov

%%time
from itertools import combinations as _combu

import numpy as np
from scipy.optimize import linprog

def inversions(X):
    # build inequalities
    A = [v2 - v1 for v2, v1 in _combu(X, 2)]

akochepasov

/********   Header Files ********/
#include <iostream>
#include <sstream>
#include <stdio.h>
#include <string.h>

#include <string>

#include <vector>
#include <queue>

akochepasov

# Creating heatmap (slower)
dbins    = np.linspace(0.0, 1.0, 51)
m = np.zeros((len(dbins), len(x_axis)))
m2 = np.zeros((len(dbins), len(x_axis)))

for r in res:
    rd = np.digitize(r, dbins)
    for i, d in enumerate(rd):
        m[d, i] += 1

akochepasov

#!/usr/bin/python2


# Copyright (C) 2016 Sixten Bergman
# License WTFPL
#
# This program is free software. It comes without any warranty, to the extent
# permitted by applicable law. 
# You can redistribute it and/or modify it under the terms of the Do What The
# Fuck You Want To Public License, Version 2, as published by Sam Hocevar. See

akochepasov

function [maxtab, mintab]=peakdet(v, delta, x)
%PEAKDET Detect peaks in a vector
%        [MAXTAB, MINTAB] = PEAKDET(V, DELTA) finds the local
%        maxima and minima ("peaks") in the vector V.
%        MAXTAB and MINTAB consists of two columns. Column 1
%        contains indices in V, and column 2 the found values.
%      
%        With [MAXTAB, MINTAB] = PEAKDET(V, DELTA, X) the indices
%        in MAXTAB and MINTAB are replaced with the corresponding
%        X-values.