#!/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 matplotlib.ticker import StrMethodFormatter

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

import textwrap

def wrap_labels(ax, width, break_long_words=False):

labels = []

for label in ax.get_xticklabels():

text = label.get_text()

labels.append(textwrap.fill(text, width=width,

break_long_words=break_long_words))

ax.set_xticklabels(labels, rotation=0)

def app_minimum_throughput_single(df, labels, xlab, ylab, ylim, title):

fig, axs = plt.subplots(nrows=1,ncols=3,figsize=(5,8))

plt.close(1)

#seaborn.set(font_scale = 2)

for ax, graph_df,tol,axis in zip(axs, [df0, df10, df20],[0,10,20], [1,2,3]):

graph_df.set_index('strategy').plot(kind="bar", stacked=True, color=['skyblue', 'pink'])

ax.set(xlabel=xlab, ylabel=ylab)

ax.set_title(title, weight='bold', fontsize=18)

if(tol != 0):

ylim=15

ax.set_ylim(0, ylim)

#ax.legend(title='Timer', loc='upper left', bbox_to_anchor=(1,1))

ax.margins(y=0.3) # make room for the labels

for bar in ax.patches:

# Using Matplotlib's annotate function and

# passing the coordinates where the annotation shall be done

ax.annotate(format(bar.get_height(), '.3f'),

(bar.get_x() + bar.get_width() / 2,

bar.get_height() / 2), ha='center', va='center',

size=10, xytext=(0, 5), rotation=30, weight='bold',

textcoords='offset points')

# for bars in plot.containers:

# plot.bar_label(bars, fmt='%.3f', label_type='center')

#plt.tight_layout()

ax.set_xticklabels(labels, fontsize=14)

wrap_labels(ax, 10)

plt.tight_layout()

plt.show()

#plot.figure.savefig(outfile) (edited)

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

df = pd.read_csv('data/bioFilm_tiff_lossy_switch.csv')

#df = pd.read_csv('data/bioFilm_tiff_lossy_threads.csv')

diff0 = pd.read_csv('data/bioFilm_tiff_lossy_switch.csv')

#app_minimum_throughput_single(df, labels, xlab, ylab, ylim, title)

#name = "/scratch/mfaykus/BioFilm/10.20.22/ATLC3_Trial1/10.20.2022.ATLC30000.tif"

#name = "/scratch/mfaykus/BioFilm/11.17.22/ATLC8b/ATLC8b.2days.11.17.220001.tif"

#name = "/scratch/mfaykus/BioFilm/3.10.23/ATLC3/ATLC3.3.10.23_0002.tif"

#name = "/scratch/mfaykus/BioFilm/3.10.23/HB/HB.3.10.230003.tif"

#name = "/scratch/mfaykus/BioFilm/3.15.23/HB2.3.15.23/HB2.3.15.230004.tif"

#name = "/scratch/mfaykus/BioFilm/3.29.23/ATLC3/Static/ATLC3_Static_3_29_23_day70005.tif"

#name = "/scratch/mfaykus/BioFilm/3.29.23/ATLC3/Transfer/ATLC3_Transfer_3_29_23_day7_0006.tif"

#name = "/scratch/mfaykus/BioFilm/3.29.23/HB2/Static/HB2_Static_3_29_23_Day7_0007.tif"

#name = "/scratch/mfaykus/BioFilm/9.30.22/ATCL8b.Day3.Trial1/ATCL8b.day3.Trial10008.tif"

#name = "/scratch/mfaykus/BioFilm/9.30.22/Control/Control.Trial10009.tif"

#name = "/scratch/mfaykus/BioFilm/9.30.22/H2.Day3.Trial1/Common Trial0010.tif"

#name = "/scratch/mfaykus/BioFilm/9.30.22/H2.Day3.Trial2/H2.day3.Trial20011.tif"

#print(df.loc[df['filename'] == name,["CR"]])

difference = ((df.loc[:,["CR"]] - diff0.loc[:,["CR"]])/diff0.loc[:,["CR"]])*100

#difference = ((df.loc[df['filename'] == name,["CR"]] - diff0.loc[diff0['filename'] == name,["CR"]])/diff0.loc[diff0['filename'] == name,["CR"]])*100

print("difference")

print(difference.mean())

print(df.groupby(['compressor'])['CR'].max())

print(df.groupby(['diff'])['CR'].max())

print(df.groupby(['compressor'])['ssim'].max())

print(df.groupby(['diff'])['ssim'].max())

print(df.groupby(['diff'])['CR'])

#pd.set_option("display.max_rows", 10000)

#pd.set_option("display.expand_frame_repr", True)

#pd.set_option('display.width', 10000)

#print(df.loc[(df['CR'] <= 1 ),["diff"]])

#print(df.loc[(df['CR'] <= 1 ),["filename"]])

#line_plot = sns.lineplot(data=df, x="bound", y="CR", hue = "diff")

#line_plot = sns.lineplot(data=df.query("compressor =='zfp'"), x="bound", y="cBW", hue = "diff")

#line_plot.set(xlabel='bound', ylabel='Compression Ratio')

#line_plot.legend([],[], frameon=False)

#line_plot.plot(legend=False)

#line_plot.set(xlabel='bound', ylabel='Compression Ratio')

xlab = "Error Bound"

ylab = "Compression Ratio"

title = "SZ: Pre-processing Compression Ratio (CR)"

#bar_plot = sns.barplot(data=df, x="bound", y="CR", hue = "diff")

bar_plot = sns.barplot(data=df.query("compressor =='sz'"), x="bound", y="CR", hue = "diff")

#bar_plot.set_xticklabels(bar_plot.get_xticklabels(), fontsize=4.5)

#line_plot.axhline(0, color = 'r')

#plt.ylim(0, 75)

bar_plot.set_xticklabels(["1E-7", "1E-6","1E-5","1E-4","1E-3","1E-2","1E-1"])

#plt.yscale('log')

#plt.xscale('log')

plt.xlabel('Error Bound')

plt.ylabel('CR')

#plt.ylabel('Decompression Bandwidth MB/s')

#line_plot.set(xscale="log", yscale="log")

#line_plot.ticklabel_format(useOffset=False, style='plain')

#plt.xlabel('Error Bound')

#plt.ylabel('ssim')

#plt.ylim(0, 300)

#line_plot.set(title='sz: CR')

bar_plot.set(title='SZ: Pre-processing Compression Ratio (CR)')

#fig = line_plot.get_figure()

#bar_plot.figure.subplots_adjust(left = 0.16)

fig = bar_plot.get_figure()

#fig = plt.figure(figsize = (50, 50))

#heat_plot = sns.heatmap(data=df.loc[:,["CR"]], yticklabels=df.loc[:,["compressors"]])

#fig = heat_plot.get_figure()

fig.savefig('poster/tiff_base_decoding_time.png')