#!/usr/bin/python3
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##\author Kevin Watts
##\brief Make CSV files smaller for use in spreadsheet software

PKG = 'diagnostic_analysis'
import roslib
roslib.load_manifest(PKG)

import csv, os, sys

##\brief Makes sparse CSV by skipping every nth value
##\param csv_file str : CSV filename
##\param skip int : Write every nth row to sparse CSV
##\return Path of output file
def make_sparse_skip(csv_file, skip):
    output_file = csv_file[:-4] + '_sparse.csv'

    input_reader = csv.reader(open(csv_file, newline=''), delimiter=',')

    f = open(output_file, 'w')
    output_writer = csv.writer(f)

    skip_count = skip
    for row in input_reader:
        if skip_count == skip:
            output_writer.writerow(row)
            skip_count = 0
            
        skip_count = skip_count + 1

    return output_file

##\brief Makes sparse CSV with the given number of rows
##\param csv_file str : CSV filename
##\param length int : Desired number of rows in CSV
##\return Path of output file
def make_sparse_length(csv_file, length):
    output_file = csv_file[:-4] + '_sprs_len.csv'

    input_reader = csv.reader(open(csv_file, newline=''), delimiter=',')

    f = open(output_file, 'w')
    output_writer = csv.writer(f)

    # Calculate skip count for file
    orig_len = len(open(csv_file, 'r').read().split('\n'))
    skip = max(int(orig_len / length), 1)

    skip_count = skip
    for row in input_reader:
        if skip_count >= skip:
            output_writer.writerow(row)
            skip_count = 0
            
        skip_count = skip_count + 1

    return output_file
