nickvazztau

Python version of the MATLAB code in this Stack Overflow post: http://stackoverflow.com/a/18648210/97160

The example shows how to determine the best-fit plane/surface (1st or higher order polynomial) over a set of three-dimensional points.

Implemented in Python + NumPy + SciPy + matplotlib.

How to cache slow functions with `numpy.array` as function parameter on Python

from numpy_lru_cache_decorator import np_cache

@np_cache()
def function(array):
 ...

	import gmsh
	import numpy as np
	from mpi4py import MPI
	import argparse

	rank = MPI.COMM_WORLD.rank


	# http://www.compumag.org/jsite/images/stories/TEAM/problem30a.pdf
	r1 = 0.02

	import time
	from functools import lru_cache


	def lru_with_ttl(*, ttl_seconds, maxsize=128):
	"""
	A decorator to apply LRU in-memory cache to a function with defined maximum(!) TTL in seconds.
	Be design an actual TTL may be shorter then the passed value (in rare randomized cases). But it can't be higher.

	:param ttl_seconds: TTL for a cache record in seconds


	import re
	import logging

	import git
	import mlflow
	import configparser

	def mlflow_log_repo_tags(default_project_name: str, entry_point: str = __file__, path_in_repository: str = __file__):
	""" This code creates special mlflow tags about current repository infos, which is not done by mlflow when starting an MLFlow run from code instead of from `mlflow run` command

	website_embed = '''<!DOCTYPE html>
	<html>
	<iframe src="https://en.wikipedia.org/wiki/Machine_learning" style='width: 700px; height: 450px' sandbox='allow-same-origin allow-scripts'>
	</iframe>
	</html>'''

	with mlflow.start_run(experiment_id=1, run_name="website_embedding") as run:
	with open("output.html", "w") as f:
	f.write(website_embed)
	mlflow.log_artifact("output.html")

	import matplotlib.pyplot as plt
	import numpy as np
	import sklearn.linear_model

	from mpl_toolkits.mplot3d import Axes3D

	X_train = np.random.rand(2000).reshape(1000,2)*60
	y_train = (X_train[:, 0]2)+(X_train[:, 1]2)
	X_test = np.random.rand(200).reshape(100,2)*60
	y_test = (X_test[:, 0]2)+(X_test[:, 1]2)

	#!/usr/local/bin/python3

	import numpy as np
	import scipy.linalg
	from mpl_toolkits.mplot3d import Axes3D
	import matplotlib.pyplot as plt

	# some 3-dim points
	n=20
	xx=np.linspace(-12,5,num=n)

	#!/usr/local/bin/python3

	# Make a 3d point cloud and fit a surface to it


	import numpy as np
	import scipy.linalg
	from mpl_toolkits.mplot3d import Axes3D
	import matplotlib.pyplot as plt

	import asyncio
	from pathlib import Path
	from typing import Optional

	from watchdog.events import FileSystemEvent, FileSystemEventHandler
	from watchdog.observers import Observer


	class _EventHandler(FileSystemEventHandler):
	def __init__(self, queue: asyncio.Queue, loop: asyncio.BaseEventLoop,