#!/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_reader import get_OCTSpectralRawFrame

from oct_converter.readers import FDS

from struct import unpack

def compression_blosc(input_data,decomp_data,name,mode):

CR=[]

CL=[]

cBW=[]

dBW=[]

psnr=[]

for bound in np.logspace(start=-7, stop=-1, num=6):

compressor = libpressio.PressioCompressor.from_config({

# configure which compressor to use

"compressor_id": "sz",

# configure the set of metrics to be gathered

"early_config": {

"pressio:metric": "composite",

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

},

# configure SZ

"compressor_config": {

"pressio:abs": bound,

}})

comp_data = compressor.encode(input_data)

decomp_data = compressor.decode(comp_data, decomp_data)

metrics = compressor.get_metrics()

CR.append(metrics['size:compression_ratio'])

CL.append(bound)

cBW.append((metrics['size:uncompressed_size']/1048576)/metrics['time:compress'])

dBW.append((metrics['size:uncompressed_size']/1048576)/metrics['time:decompress'])

psnr.append(metrics['error_stat:ssim'])

#print(f"metrics={json.dumps(metrics, indent=4)}")

json_object = json.dumps(metrics, indent=4)

if(mode == "CR"):

plt.plot(CL,CR, label=name)

elif(mode == "cBW"):

plt.plot(CL,cBW, label=name)

elif(mode == "dBW"):

plt.plot(CL,dBW, label=name)

elif(mode == "psnr"):

plt.plot(CL,psnr, label=name)

srcpath='/scratch1/mfaykus/biofilm/ThorLabs/automated-oct-scans-acquisition-master/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)

#input_path = Path(__file__).parent / "/scratch/mfaykus/BioFilm/06.21.22/20220621_095224161_06.21.22 Control Processed Stack_processed.raw"

input_path=l[0]

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

size = (2048, 1000, 250)

voxel_size=(10/size[0],10/size[1],2.23/size[2])

input_data=np.fromfile(input_path,dtype=np.int16).reshape(size) #load it

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

#input_data = np.fromfile(input_path, dtype=np.uint8)

input_data = np.ascontiguousarray(input_data)

#input_data = input_data.astype(float)

#print(type(input_data))

#print(input_data.dtype)

#input_data = input_data.reshape(100,500,500)

decomp_data = input_data.copy()

#print(input_data)

#print(input_data.shape)

#print(size)

#exit(0)

CR=[]

CL=[]

cBW=[]

dBW=[]

i=0

for bound in np.logspace(start=-7, stop=-1, num=6):

compressor = libpressio.PressioCompressor.from_config({

# configure which compressor to use

"compressor_id": "sz",

# configure the set of metrics to be gathered

"early_config": {

"pressio:metric": "composite",

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

},

# configure SZ

"compressor_config": {

"pressio:abs": bound,

}})

#print(compressor.codec_id)

#print(compressor.get_config())

#print(compressor.get_compile_config())

# run compressor to determine metrics

comp_data = compressor.encode(input_data)

decomp_data = compressor.decode(comp_data, decomp_data)

metrics = compressor.get_metrics()

print(f"bound={bound:1.0e}, metrics={json.dumps(metrics, indent=4)}")

json_object = json.dumps(metrics, indent=4)

with open("sample.json", "w") as outfile:

json.dump(json_object, outfile)

print(bound)

if(i ==3):

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')

i = i + 1

plt.xlabel("Bound")

plt.ylabel("Compression Ratio")

compression_blosc(input_data,decomp_data,"sz","CR")

plt.legend(['sz'])

plt.tight_layout()

plt.savefig("CR_sz.png")

plt.close()

#plt.yscale("log")

plt.xlabel("Bound")

plt.ylabel("Compression Bandwidth: MB/s")

compression_blosc(input_data,decomp_data,"sz","cBW")

plt.legend(['sz'])

plt.tight_layout()

plt.savefig("cBW_sz.png")

plt.close()

plt.xlabel("Bound")

plt.ylabel("Decompression Bandwidth: MB/s")

compression_blosc(input_data,decomp_data,"sz","dBW")

plt.legend(['sz'])

plt.tight_layout()

plt.savefig("dBW_sz.png")

plt.close()

plt.xlabel("Bound")

plt.ylabel("PSNR")

compression_blosc(input_data,decomp_data,"sz","psnr")

plt.legend(['sz'])

plt.tight_layout()

plt.savefig("PSNR_sz.png")

plt.close()