import argparse
import numpy as np 
import matplotlib.pyplot as plt
# import matplotlib.cm as cm

parser = argparse.ArgumentParser(
    description='Calculate the froc metrics and store in froc_metrics.yml')
parser.add_argument('-experiment',
    help='Title of experiment')                
parser.add_argument('--series', '-s', 
    metavar='[series_name]', required=True, nargs='+',
    help='List of series to include')
args = parser.parse_args()

########## constants #################
SERIES = args.series
series_ = '_'.join(args.series)
EXPERIMENT = args.experiment
SALIENCY_DIR = f'./../train_output/{EXPERIMENT}_{series_}/saliency.npy'
IMAGES_DIR = f'./../train_output/{EXPERIMENT}_{series_}/images_list.npy'
SEGMENTATION_DIR = f'./../train_output/{EXPERIMENT}_{series_}/segmentations.npy'

########## load saliency map ############
heatmap = np.load(SALIENCY_DIR)
heatmap = np.squeeze(heatmap)

######### load images and segmentations ###########
images_list = np.load(IMAGES_DIR)
images_list = np.squeeze(images_list)
segmentations = np.load(SEGMENTATION_DIR)
######## take average ##########
# len(heatmap) is smaller then maximum number of images
# if len(heatmap) < 100:
    # heatmap = np.mean(abs(heatmap),axis=0)
heatmap = abs(heatmap)

fig, axes = plt.subplots(2,len(SERIES))
print(np.shape(axes))
print(np.shape(heatmap))
print(np.shape(images_list))
max_value = np.amax(heatmap)
min_value = np.amin(heatmap)

for indx in range(len(SERIES)):
    print(indx)
    axes[0,indx].imshow(images_list[:,:,12,indx],cmap='gray')
    im = axes[1,indx].imshow(np.squeeze(heatmap[:,:,12,indx]),vmin=min_value, vmax=max_value)
    axes[0,indx].set_title(SERIES[indx])
    axes[0,indx].set_axis_off()
    axes[1,indx].set_axis_off()

cbar = fig.colorbar(im, ax=axes.ravel().tolist(), shrink=0.5, orientation='horizontal')
cbar.set_ticks([min_value,max_value])
cbar.set_ticklabels(['less important', 'important'])
fig.suptitle('Saliency map', fontsize=16)
plt.savefig(f'./../train_output/{EXPERIMENT}_{series_}/saliency_map.png', dpi=300)