teoliphant

# The kernel function is 
#   roughtly equivalent to new[:] = np.interp(xnew, xvals, yvals)
#   But this is broadcast so that it can be run many, many times 
#   quickly.

# Call using ynew = interp1d(xnew, xdata, ydata)
#   ynew.shape will be xnew.shape
#   Also, ydata.shape[-1] must be xdata.shape[-1]
#   and if ydata or xdata have ndim greater than 1, the initial dimensions
#   must be xnew.shape:

teoliphant

My 11 year-old son is learning about regression in his 9th grade math class.   
For them regression is two tables of data and a calculator button.  
The graphing calculators also provide a button to find the best "median-fit" line and
the students were asked to find it as well as the regression line.   The regression line can
easily be found with numpy.polyfit(x, y, 1).   

I did not know of a function to calculate the best "median-fit" line.  I had to review a few
online videos to learn exactly what a best "median-fit" line is and found the 3-median method 
for determining the best "median-fit" line.   It's sometimes called the median median fit. 

teoliphant

import numpy as np

def array_for_sliding_window(x, wshape):
    """Build a sliding-window representation of x.

    The last dimension(s) of the output array contain the data of
    the specific window.  The number of dimensions in the output is 
    twice that of the input.

    Parameters

teoliphant

teoliphant

teoliphant

from numpy.random import rand
from numpy import r_, ix_, uint8, roll
import matplotlib.pyplot as plt
import time

size = 200
GRID = (rand(size,size) > 0.75).astype(uint8)
# Rotate indices because the world is round
indx = r_[0:size]
up = roll(indx, -1)