#!/usr/bin/env python

from pathlib import Path

import json

import libpressio

import numpy as np

import sys

import os

import numpy as np

from ipywidgets import widgets,interact,IntProgress

import matplotlib.pyplot as plt

from skimage import morphology

from skimage.morphology import closing, square, reconstruction

from skimage import filters

#from OctCorrection import *

#from ImageProcessing import *

from mpl_toolkits.mplot3d import Axes3D

import os

import glob

import re

import pickle

from PIL import Image

import pandas as pd

#import cv2

from tifffile import imsave, imread

from numba import jit

import matplotlib.pyplot as plt

import seaborn as sns

import pandas as pd

import rawpy

import imageio

from oct_converter.readers import FDS

from struct import unpack

#srcpath='/scratch1/mfaykus/automated-oct-scans-acquisition/sample_scans/';

srcpath='/scratch/mfaykus/BioFilm/'

pathexp='/scratch1/mfaykus/biofilm/ThorLabs/automated-oct-scans-acquisition-master/sample_scans/output/';

if not os.path.exists(pathexp):

os.makedirs(pathexp)

l=glob.glob(srcpath+'*/*.raw')

l.sort()

print(l)

print(len(l))

compressors = ['zstd','blosclz','lz4','lz4hc','zlib']

#compressors = ['zstd']

df = pd.DataFrame({

'filename':[],

'compressor':[],

'CL':[],

'cBW':[],

'dBW':[],

'CR':[]})

i = 0

while i < len(l):

input_path=l[i]

print(input_path)

size = (250, 2048, 1000)

#size = (int(900/4),int(900/4),int(1024/4))

input_data=np.fromfile(input_path,dtype=np.uint8).reshape(size) #load it

#print(type(input_data))

input_data=np.flip(input_data, axis=2)

#input_data = input_data.astype('int32')

input_data = np.ascontiguousarray(input_data)

diff_data = input_data.copy()

decomp_data = input_data.copy()

d=1

while d < 250:

diff_data[d] = abs(input_data[d] - input_data[d-1])

d=d+1

input_data = diff_data

for comp in compressors:

j = 0

while j < 9:

compressor = libpressio.PressioCompressor.from_config({

# configure which compressor to use

"compressor_id": "blosc",

# configure the set of metrics to be gathered

"early_config": {

"blosc:compressor": comp,

"blosc:metric": "composite",

"composite:plugins": ["time", "size", "error_stat", "external"]

},

"compressor_config": {

"blosc:clevel": j,

}})

comp_data = compressor.encode(input_data)

decomp_data = compressor.decode(comp_data, decomp_data)

metrics = compressor.get_metrics()

#print(metrics)

df.loc[len(df)] = [l[i], comp, j , (metrics['size:uncompressed_size']/1048576)/metrics['time:compress'], (metrics['size:uncompressed_size']/1048576)/metrics['time:decompress'], metrics['size:compression_ratio']]

save = 0

if save == 1:

save_out = np.ndarray.tofile(comp_data,'202_zfp_comp_data.raw')

save_out2 = np.ndarray.tofile(decomp_data,'202_zfp_decomp_data.raw')

save_out3 = np.ndarray.tofile(input_data,'202_zfp_input_data.raw')

j = j + 1

i = i + 1

if(i==4 or i==5):

i=6

print(df)

df.to_csv('df_bioFilm_Ldiff_abs_int.csv')

#line_plot = sns.lineplot(data=df, x="CL", y="CR", hue = "compressor", errorbar=('sd',2),err_style='bars')

#fig = line_plot.get_figure()

#fig.savefig('lineplot_CR.png')