jkrumbiegel

using Makie
using MakieLayout
using NIfTI


nipath = "T1.nii.gz"
nidata = niread(nipath)
data = Float32.(nidata) ./ maximum(nidata)

function axhline!(la::LAxis, y::Node; kwargs...)

bessarabov

git log --author="Linus Torvalds" --date=iso | perl -nalE 'if (/^Date:\s+[\d-]{10}\s(\d{2})/) { say $1+0 }' | sort | uniq -c|perl -MList::Util=max -nalE '$h{$F[1]} = $F[0]; }{ $m = max values %h; foreach (0..23) { $h{$_} = 0 if not exists $h{$_} } foreach (sort {$a <=> $b } keys %h) { say sprintf "%02d - %4d %s", $_, $h{$_}, "*"x ($h{$_} / $m * 50); }'

maleadt

# experimentation with batched tridiagonal solvers on the GPU for Oceananigans.jl
#
# - reference serial CPU implementation
# - batched GPU implementation using cuSPARSE (fastest)
# - batched GPU implementation based on the serial CPU implementation (slow but flexible)
# - parallel GPU implementation (potentially fast and flexible)
#
# see `test_batched` and `bench_batched`

using CUDAdrv

briochemc

# Here I store more data as I go, 
# which I could use to plot figures comparing methods
# the convergence speed of different methods

# I use the Rosenbrock classic example and
# Optim.jl as an iterative process which will
# call f multiple times to find its minimum
# and I plot the convergence vs time
using Optim, Cassette

christophernhill

darothen

from itertools import product

import numpy as np

INV_SQRT_3 = 1.0 / np.sqrt(3.0)
ASIN_INV_SQRT_3 = np.arcsin(INV_SQRT_3)


def gaussian_bell(xs, ys, xc=0., yc=0., xsigma=1., ysigma=1.):
    """ Compute a 2D Gaussian with asymmetric standard deviations and