#!/usr/bin/env python
# -*- coding: utf-8 -*-

# Import the code needed to manage files.
import os, glob

#...for parsing the arguments.
import argparse

#...for the logging.
import logging as lg

# Import the JSON library.
import json

#...for processing the datasets.
from cernatschool.dataset import Dataset

#...for kluster finding.
from cernatschool.kluster import KlusterFinder

from helpers import getKlusterPropertiesJson

from visualisation import makeKlusterImage

if __name__ == "__main__":

    print("*")
    print("*==================================*")
    print("* CERN@school - cluster processing *")
    print("*==================================*")

    # Get the datafile path from the command line
    parser = argparse.ArgumentParser()
    parser.add_argument("inputPath",  help="Path to the input dataset.")
    parser.add_argument("outputPath", help="The path for the output files.")
    parser.add_argument("-v", "--verbose", help="Increase output verbosity", action="store_true")
    args = parser.parse_args()

    ## The path to the data file.
    datapath = args.inputPath

#    ## The basename of the data file.
#    filename = os.path.basename(datapath)
#
#    basename = filename.split(".")[0]

    ## The output path.
    outputpath = args.outputPath

    # Set the logging level to DEBUG.
    if args.verbose:
        level=lg.DEBUG
    else:
        level=lg.INFO

    # Configure the logging.
    lg.basicConfig(filename='log_process-frames.txt', filemode='w', level=level)

    print("*")
    print("* Input path          : '%s'" % (datapath))
#    print("*--> Base name        @ '%s'" % (basename))
#    print("*")
#    print("* Output file         : '%s'" % (outputpath))
    print("*")

# NO! We are just dealing with the raw frames here, right?
# The metadata comes from the file itself. Hmm...
#    ## The dataset wrapper.
#    ds = Dataset(datapath)
#
#    ## The frames from the dataset (geospatial info. doesn't matter).
#    frames = ds.getFrames((0.0, 0.0, 0.0))

    ## The number of klusters found in all.
    n_klusters = 0

    ## The number of non-gammas.
    n_non_gammas = 0

    klusters = []

    # Loop over the frames found in the input path.
    lg.info("* Found datafiles:")
    for frame in sorted(glob.glob(datapath + "/*.txt")):
        lg.info("*--> '%s'" % (frame))

        # Read the contents of the frame (assuming x y C format).
        f = open(frame, "r")
        lines = f.readlines()
        f.close()

        ## The pixel dictionary for the frame.
        pixels = {}

        # Loop over the lines and extract the pixel information.
        for l in lines:
            vals = l.strip().split("\t")
            pixels[256 * int(vals[1]) + int(vals[0])] = int(vals[2])

        # Find the clusters.
        kf = KlusterFinder(pixels, 256, 256, False)

        # Count the clusters.
        n_klusters   += kf.getNumberOfKlusters()
        n_non_gammas += kf.getNumberOfNonGammas()

        ## The base of the cluster ID (from the frame name).
        frameid = os.path.basename(frame).split(".")[0]

        lg.info("*  | Frame ID = '%s'." % (frameid))

        # Loop over the clusters.
        for i, kl in enumerate(kf.getListOfKlusters()):

            ## The kluster ID.
            klusterid = frameid + "_k%05d" % (i)

            lg.info("*  |--> Kluster ID: '%s'." % (klusterid))

            if not kl.isGamma():
                klusters.append(getKlusterPropertiesJson(klusterid, kl))

                makeKlusterImage(klusterid, kl, outputpath)

                # TMP
                #break

        # TMP
        #break

        lg.info("*  |")

    lg.info("*")
    lg.info("* Number of klusters found   : %d" % (n_klusters))
    lg.info("* Number of non-gammas found : %d" % (n_non_gammas))
    lg.info("*")

    with open(outputpath + "/klusters.json", "w") as jf:
        json.dump(klusters, jf)