import numpy as np
import pickle

IMAGE_WIDTH = 3
IMAGE_HEIGHT = 3
FILE_COUNT = 3

def gen_sample(count, width, height):
    with open('data/raw_data.pickle', 'wb') as f:
        for i in range(count):
            data = np.arange(9, dtype=np.float64) + i * 10
            data = data.reshape(height, width)
            pickle.dump(data, f)
            print i, data

def preprocess(count, height):
    outfiles = []
    for i in range(height):
        outfilename = 'data/row_%03d.dat' % i
        outfiles.append(open(outfilename, 'wb'))


    with open('data/raw_data.pickle', 'rb') as f:
        for i in range(count):
            data = pickle.load(f)
            for j in range(height):
                data[j].tofile(outfiles[j])

    for i in range(height):
        outfiles[i].close()

def process(width, height):
    result_rows = []

    for i in range(height):
        outfilename = 'data/row_%03d.dat' % i
        data = np.fromfile(outfilename, dtype=np.float64).reshape(-1, width)
        median_row = np.median(data, axis=0)
        result_rows.append(median_row)
        print i, data

    return np.vstack(result_rows)

gen_sample(FILE_COUNT, IMAGE_WIDTH, IMAGE_HEIGHT)
preprocess(FILE_COUNT, IMAGE_HEIGHT)
result = process(IMAGE_WIDTH, IMAGE_HEIGHT)
print "Result", result