tGD_pstXRay.py

import sys
import numpy as np
import pandas as pd
import tGD_aux as aux
from glob import glob
import compress_pickle as pkl
import MoNeT_MGDrivE as monet
from datetime import datetime
import matplotlib.pyplot as plt
# import re


(thi, tho, QNT) = (.5, .5, '50')
(USR, DRV, AOI) = (sys.argv[1], sys.argv[2], sys.argv[3])
# (USR, DRV, AOI) = ('dsk', 'tGD', 'HLT')
X_RAN = [0, 5*365/3]
EXPS = ('000', )

for exp in EXPS:
    # Select path ------------------------------------------------------------
    (PT_ROT, PT_IMG, PT_DTA, PT_PRE, PT_OUT, PT_MTR) = aux.selectPath(USR, DRV, exp)
    PT_IMG = PT_IMG+'xRay/'
    monet.makeFolder(PT_IMG)
    tS = datetime.now()
    aux.printExperimentHead(PT_ROT, PT_IMG, PT_MTR, tS, 'X-Ray '+AOI)
    # Load file --------------------------------------------------------------
    fNames = glob(PT_OUT+'*{}*.npy'.format(AOI))
    xpNumS = str(len(fNames)).zfill(4)
    for (i, fName) in enumerate(fNames):
        xpNumCS = str(i + 1).zfill(4)
        print('* Exporting {}/{}'.format(xpNumCS, xpNumS), end='\r')
        # Name formatting -> tuple--------------------------------------------
        repsRatios = np.load(fName)
        fList = fName.split('/')[-1].split('-')[0].split('_')[1:]
        fList.append(fName.split('/')[-1].split('-')[1].split('_')[1])
        fKeys = tuple(list(map(int, fList)))
        # fList = re.split(r'[a-zA-Z_/.-]+', fName)[9:16]
        # AOI = re.split(r'[0-9_./-]+', fName)[18]
        # Select cmap --------------------------------------------------------
        (scalers, HD_DEP, IND_RAN, palette) = aux.selectDepVars('TTI', AOI)
        cmap = palette.reversed()
        # load TTI and TTO ---------------------------------------------------
        ttiR = pkl.load(PT_MTR+'{}_TTI_{}_mlr.bz'.format(AOI, QNT))
        tti = ttiR[fKeys][int(thi*100)]
        ttoR = pkl.load(PT_MTR+'{}_TTO_{}_mlr.bz'.format(AOI, QNT))
        tto = ttoR[fKeys][int(tho*100)]
        # load Summary.csv TTI and TTO ----------------------------------------
        summ_ttiR = pd.read_csv(PT_MTR+'{}_TTI_{}_qnt.csv'.format(AOI, QNT))
        summ_tti = ttiR[fKeys][int(thi*100)]
        summ_ttoR = pd.read_csv(PT_MTR+'{}_TTO_{}_qnt.csv'.format(AOI, QNT))
        summ_tto = ttoR[fKeys][int(tho*100)]
        # Plotting-------------------------------------------------------------
        (fig, ax) = plt.subplots(nrows=1, ncols=1)
        ax.imshow(repsRatios, cmap=cmap)
        # add TTI-------------------------------------------------------------
        [plt.axvline(i, color='#f8f7ff', alpha=.65, lw=0.175, ls='-.') for i in tti]
        # add TTO-------------------------------------------------------------
        [plt.axvline(j, color='cyan', alpha=.75, lw=0.2, ls='dotted') for j in tto]
        # TTO and TTI from Summary.csv
        [plt.axvline(i, color='#3DFE70', alpha=.9, lw=0.3) for i in summ_tti]
        [plt.axvline(j, color='#3DFE70', alpha=.9, lw=0.3) for j in summ_tto]
        # Save the figure------------------------------------------------------
        outName = fName.split('/')[-1].split('.')[0][:-4]
        plt.xlim(X_RAN)
        ax.axes.xaxis.set_ticklabels([])
        ax.axes.yaxis.set_ticklabels([])
        ax.axes.xaxis.set_visible(False)
        ax.axes.yaxis.set_visible(False)
        ax.xaxis.set_tick_params(size=0)
        ax.yaxis.set_tick_params(size=0)
        plt.savefig(PT_IMG+outName + '.png', bbox_inches='tight', pad_inches=0.01, dpi=500)
        plt.close("all")