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# https://stackoverflow.com/questions/38754668/plane-fitting-in-a-3d-point-cloud

def PCA(data, correlation = False, sort = True):
""" Applies Principal Component Analysis to the data

Parameters
----------        
data: array
    The array containing the data. The array must have NxM dimensions, where each
    of the N rows represents a different individual record and each of the M columns

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# https://stackoverflow.com/questions/15753701/how-can-i-pass-a-list-as-a-command-line-argument-with-argparse

import argparse

parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input',type = str, default = "sample.json")
parser.add_argument( "-l","--list", nargs="*", type=int, default=[0,1,2])

args = parser.parse_args()

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def read_data_from_file( filename ):
    with open(filename, 'r') as json_file:
        data = json.load(json_file)
    return data

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# https://stackoverflow.com/questions/48609781/distance-between-one-point-and-rest-of-the-points-in-an-array/48610147

from scipy.spatial import distance

X = [(35.0456, -85.2672)]
coords = [(35.1174, -89.9711),
          (35.9728, -83.9422),
          (36.1667, -86.7833)]
distance.cdist(X, coords, 'euclidean')
array([[ 4.70444794,  1.6171966 ,  1.88558331]])

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import os

files = os.listdir()
# files is a string list

for filename in files:
    print(filename)

 
# https://askubuntu.com/questions/443830/delete-all-files-whose-filenames-contain-a-particular-string

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# plot 3d points

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure(figsize=(4,4))
ax = fig.add_subplot(111, projection='3d')

# points np array

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with open('filename') as f:
  lines = f.readlines()

    
with open('write','w') as f:
  f.write('Hello\n')

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#https://stackoverflow.com/questions/34372480/rotate-point-about-another-point-in-degrees-python

import numpy as np

def rotate(p, origin=(0, 0), degrees=0):
    angle = np.deg2rad(degrees)
    R = np.array([[np.cos(angle), -np.sin(angle)],
                  [np.sin(angle),  np.cos(angle)]])
    # atleast_2d View inputs as arrays with at least two dimensions.
    o = np.atleast_2d(origin)

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#!/usr/bin/env python3

# https://stackoverflow.com/questions/32892932/create-the-oriented-bounding-box-obb-with-python-and-numpy
def oobb(pts):
    '''
    given: 
    	pts, 2d or 3d
    return:
    	corners, center of the oobb
    '''

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// A simple quickref for Eigen. Add anything that's missing.
// Main author: Keir Mierle

#include <Eigen/Dense>

Matrix<double, 3, 3> A;               // Fixed rows and cols. Same as Matrix3d.
Matrix<double, 3, Dynamic> B;         // Fixed rows, dynamic cols.
Matrix<double, Dynamic, Dynamic> C;   // Full dynamic. Same as MatrixXd.
Matrix<double, 3, 3, RowMajor> E;     // Row major; default is column-major.
Matrix3f P, Q, R;                     // 3x3 float matrix.