turtleizzy

import pickle
import _pickle as cPickle
import marshal
import numpy as np
import time
import io

# ---------------------------------------------------------------------------------
# from fastnumpyio
# https://github.com/divideconcept/fastnumpyio/blob/main/fastnumpyio.py

turtleizzy

ITK vs SimpleITK - Which should you use if you start a new project in Python?

Basic information

Both ITK (python package) and SimpleITK (python package) are bindings of ITK C++ library providing a number of verified utilities for medical image processing, especially 3D volumetric image processing.

ITK python package provides both C++ style bindings ( functions and classes in UpperCamelCase ) and pythonic bindings ( in snake_case ). C++ style bindings keeps the original pipeline structure and template programming style in ITK and the pythoic bindings provides somewhat procedural interface. For example, in C++ style bindings, you need to specify the datatype when instantiating filters, an example is as following. Refer to the tutorial for more information.

InputType = itk.Image[itk.F,3]
OutputType = itk.Image[itk.F,3]

turtleizzy

#!/usr/bin/env python

"""Dump a range of Embedded Controller memory via ACPI. Requires "acpi_call" kernel module to be loaded. By HorstBaerbel (https://github.com/HorstBaerbel) in 2018."""

import re
import sys
import time
import argparse

output = '\033[0m'

turtleizzy

Based on docker images and launchpad package versions.

Ubuntu version	Codename	gcc	libstdc++	ABI
14.04	trusty	4.8.3	6.0.19	GLIBCXX_3.4.19, CXXABI_1.3.7
16.04	xenial	5.3.1	6.0.21	GLIBCXX_3.4.21, CXXABI_1.3.9
18.04	bionic	7.3.0	6.0.25	GLIBCXX_3.4.25, CXXABI_1.3.11
20.04	focal	9.3.0	6.0.28	GLIBCXX_3.4.28, CXXABI_1.3.12
22.04	jammy	11.2.0	6.0.30	GLIBCXX_3.4.29, CXXABI_1.3.13

turtleizzy

import numpy as np

numbers = np.array([67000, 48720, 222600, 27560, 2500000, 2120, 53446, 53000, 53000, 53000, 45077, 7977, 100000, 6382, 30605, 64782, 33485, 3697, 71056, 2515])
targetSum = 301925

arr = [np.zeros([targetSum+1], dtype=np.int), np.zeros([targetSum+1], dtype=np.int)]
curArr = 1
for i in range(len(numbers)):
    curArr = 1 - curArr
    if (i==0):