#!/usr/bin/env python

"""
# aim
make image file for input into pyFAI for initial detector 

# protocol
take scan of LaB6
vanile settings for crystallina 8M are 100 scan positions, 10 shots per position
run this on output

# usage
python make-tiff.py -j <jugfrau-name> -s <path to scan file> -n <name of output file>

# output
creates a .npy file that can be loaded directly into pyFAI
"""

# modules
from matplotlib import pyplot as plt
import numpy as np
from sfdata import SFScanInfo
from tqdm import tqdm
import argparse

def convert_image( path_to_json, jungfrau, name ):

    # opens scan
    scan = SFScanInfo( path_to_json )

    # step through scan and average files from each positions
    mean_image = []
    for step in tqdm( enumerate(scan) ):
        # step is a SFDataFiles object 
        subset = step[1]
        mean = np.mean( subset[ jungfrau ].data, axis=0 )
        mean_image.append(mean)

    # sum averaged imaged
    sum_image = np.sum( mean_image, axis=0 )

    # output to file
    np.save( "{0}.npy".format( name ), sum_image )

    # create plot of summed, averaged scan
    fig, ax = plt.subplots()
    ax.imshow(sum_image, vmin=0, vmax=1000)
    plt.show()

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "-j",
        "--jungfrau",
        help="name of the jungfrau used",
        type=str,
        default="JF17T16V01"
    )
    parser.add_argument(
        "-s",
        "--scan",
        help="path to json scan file",
        type=str,
        default="/sf/cristallina/data/p20590/raw/run0003/meta/scan.json"
    )
    parser.add_argument(
        "-n",
        "--name",
        help="name of output file",
        type=str,
        default="sum_mean_scan"
    )
    args = parser.parse_args()

    convert_image( args.scan, args.jungfrau, args.name )