#!/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

from matplotlib_scalebar.scalebar import ScaleBar

import seaborn as sns

import pandas as pd

import rawpy

import imageio

import bitstring

#from OCT_reader import get_OCTSpectralRawFrame

from oct_converter.readers import FDS

from struct import unpack

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="202_zfp_input_data.raw"

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

size = (250, 2048, 1000)

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

input_path=l[6]

print(input_path)

#exit(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])

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

#decomp_data=np.flip(decomp_data, axis=2)

#decomp_data = input_data.copy()

crop=int(500/4) #find here the best position to crop the image and avoid spurious signals such as the Zspacer

bckHeight=int(100/4) #find here the position of the line above which the background intensity is calculated

fig, ax = plt.subplots(2,figsize=(20,10))

sl=1 #select the slice to be displayed (first)

im=input_data[sl,:,:]

ax[0].imshow(im.swapaxes(1,0), cmap='gray')

#ax[0].set_aspect(voxel_size[2]/voxel_size[0]) #set here the aspect ratio,

# you can calculate it from the z spacing and x,y spacing parameters in the .srm files

#ax[0].axhline(y=bckHeight, color='r', linestyle='-', label='background height')

#ax[0].axhline(y=crop, color='g', linestyle='-', linewidth=3,label='crop')

scalebar = ScaleBar(voxel_size[0]/1000,'mm') # set here the scale 1 pixel = 11 micron

ax[0].axis('off')

plt.gca().add_artist(scalebar)

ax[0].set_title('input');

sl=-1

im=decomp_data[sl,:,:]

ax[1].imshow(im.swapaxes(1,0), cmap='gray')

#ax[1].set_aspect(voxel_size[2]/voxel_size[0]) #set here the aspect ratio,

# you can calculate it from the z spacing and x,y spacing parameters in the .srm files

#ax[1].axhline(y=bckHeight, color='r', linestyle='-', label='background height')

#ax[1].axhline(y=crop, color='g', linestyle='-', linewidth=3,label='crop')

ax[1].legend(loc=2)

scalebar = ScaleBar(voxel_size[0]/1000,'mm') # set here the scale 1 pixel = 11 micron

ax[1].axis('off')

plt.gca().add_artist(scalebar)

ax[1].set_title('Decomp');

fig.savefig('Ascan.png') #save figure