import cv2
import numpy as np
from astropy.io import fits
import mwTools

# Define the field to process
fields = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])

for field in fields:
    print('Processing field ' + str(field))
    
    # Load the fits files
    print('Loading fits files...')
    redData = fits.open('mw' + str(field) + '-red.fits')[0].data[::-1, :]
    blueData = fits.open('mw' + str(field) + '-blue.fits')[0].data[::-1, :]

    # Obtain the zero coverage mask
    print('Calculating the zero coverage mask...')
    zeroCoverageMask = np.logical_or(redData == 0, blueData == 0)

    # Shift a bit the images to remove most of the negative values
    print('Shifting data values...')
    redData = redData + 0.005
    blueData = blueData + 0.005
    redData[zeroCoverageMask] = 0.0
    blueData[zeroCoverageMask] = 0.0
        
    # Remove the negative values
    redData[redData < 0] = 0
    blueData[blueData < 0] = 0
        
    # Create the green dataset
    print('Creating green dataset...')
    greenData = (redData + blueData)/2.0
    
    # Calculate the bgr colors
    print('Calculating bgr colors...')
    exponent = 0.5
    minValue = 0.00 + 0.1 * mwTools.gaussianDistribution(field, 7 , 1.8) 
    maxValue = 0.85 + 1.8 * mwTools.gaussianDistribution(field, 7 , 1.8)
    print('Singal range: (' + str(round(maxValue,2)) + ', ' + str(round(minValue, 2)) + ')')
    redData = mwTools.getColorIntensity(redData, minValue, maxValue, exponent)
    blueData = mwTools.getColorIntensity(blueData, minValue, maxValue, exponent)
    greenData = mwTools.getColorIntensity(greenData, minValue, maxValue, exponent)
    bgrData=cv2.merge([blueData, greenData, redData])
    
    # Save the image
    scaleFactor = 0.1
    #bgrData = cv2.resize(bgrData, None, fx=scaleFactor, fy=scaleFactor, interpolation=cv2.INTER_CUBIC)
    cv2.imwrite("field"+str(field) +".jpg", bgrData)