diff --git a/datasets/raise/extract_noise.py b/datasets/raise/extract_noise.py
index 264d14a..ad6aad2 100755
--- a/datasets/raise/extract_noise.py
+++ b/datasets/raise/extract_noise.py
@@ -7,19 +7,19 @@ import csv
 from utils import Color, denoise, iterativeMean, escapeFilePath, saveNpArray, getColorMeans, getImageNpArray
 import matplotlib.pyplot as plt
 
-imagesFolderPath = 'rafael/arw'
-imagesFolderPathFileName = escapeFilePath(imagesFolderPath)
+IMAGES_FOLDER_PATH = 'rafael/230424'
+imagesFolderPathFileName = escapeFilePath(IMAGES_FOLDER_PATH)
 # Among:
 # `denoise` possible denoisers and `mean`.
-denoiser = 'mean'
+DENOISER = 'mean'
 
-raiseNotFlatFields = False
+RAISE_NOT_FLAT_FIELDS = False
 # `[Color.RED, Color.GREEN_RIGHT, ...]` or `Color` or `[None]` for not raw images.
-colors = [None]
+COLORS = [None]
 
-imagesFileNames = os.listdir(imagesFolderPath + ('/png' if raiseNotFlatFields else ''))
+imagesFileNames = os.listdir(IMAGES_FOLDER_PATH + ('/png' if RAISE_NOT_FLAT_FIELDS else ''))
 
-if raiseNotFlatFields:
+if RAISE_NOT_FLAT_FIELDS:
     files = {}
 
     with open('RAISE_all.csv') as csvfile:
@@ -28,7 +28,7 @@ if raiseNotFlatFields:
             file = row['File'] + '.png'
             files[file] = row
 
-    imagesFileNames = [imageFileName for imageFileName in tqdm(imagesFileNames, 'Filtering images') if files[imageFileName]['Device'] == 'Nikon D7000' and Image.open(f'{imagesFolderPath}/png/{imageFileName}').size == (4946, 3278)]
+    imagesFileNames = [imageFileName for imageFileName in tqdm(imagesFileNames, 'Filtering images') if files[imageFileName]['Device'] == 'Nikon D7000' and Image.open(f'{IMAGES_FOLDER_PATH}/png/{imageFileName}').size == (4946, 3278)]
 
 # Among:
 # - `None`
@@ -47,11 +47,11 @@ minColor = None
 maxColor = None
 
 def getImageFilePath(imageFileName):
-    if raiseNotFlatFields:
+    if RAISE_NOT_FLAT_FIELDS:
         imageFileName = imageFileName.replace('.png', '.NEF')
-        imageFilePath = f'{imagesFolderPath}/nef/{imageFileName}'
+        imageFilePath = f'{IMAGES_FOLDER_PATH}/nef/{imageFileName}'
     else:
-        imageFilePath = f'{imagesFolderPath}/{imageFileName}'
+        imageFilePath = f'{IMAGES_FOLDER_PATH}/{imageFileName}'
     return imageFilePath
 
 # `color` is the actual color to estimate PRNU with.
@@ -61,17 +61,17 @@ def treatImage(imageFileName, computeExtremes = False, color = None):
     imageNpArray = getImageNpArray(imageFilePath, computeExtremes, color)
     if imageNpArray is None:
         return
-    if denoiser != 'mean':
-        imageDenoisedNpArray = denoise(imageNpArray, denoiser)
+    if DENOISER != 'mean':
+        imageDenoisedNpArray = denoise(imageNpArray, DENOISER)
     else:
         imageDenoisedNpArray = colorMeans[color]
     imageNoiseNpArray = imageNpArray - imageDenoisedNpArray
     estimatedPrnuIterativeMean.add(imageNoiseNpArray)
 
-if (minColor is None or maxColor is None) and denoiser != 'mean':
+if (minColor is None or maxColor is None) and DENOISER != 'mean':
     # Assuming same intensity scale across color channels.
     for imageFileName in tqdm(imagesFileNames, 'Computing extremes of images'):
-        for color in colors:
+        for color in COLORS:
             treatImage(imageFileName, computeExtremes = True, color = color)
 
     # To skip this step next time.
@@ -79,19 +79,19 @@ if (minColor is None or maxColor is None) and denoiser != 'mean':
     print(f'{minColor=}')
     print(f'{maxColor=}')
 
-if denoiser == 'mean':
-    colorMeans = getColorMeans(imagesFileNames, colors)
+if DENOISER == 'mean':
+    colorMeans = getColorMeans(imagesFileNames, COLORS)
     for color in Color:
         colorMeans[color] = colorMeans[color]
         fileName = f'mean_{imagesFolderPathFileName}_{color}'
         # Then use `merge_single_color_channel_images_according_to_bayer_filter.py` to consider all color channels, instead of saving this single color channel as an image.
         saveNpArray(fileName, colorMeans[color])
 
-for color in colors:
+for color in COLORS:
     estimatedPrnuIterativeMean = iterativeMean()
 
     for imageFileName in tqdm(imagesFileNames, f'Denoising images for color {color}'):
         treatImage(imageFileName, color = color)
 
-    npArrayFilePath = f'mean_{imagesFolderPathFileName}_{denoiser}_{color}'
+    npArrayFilePath = f'mean_{imagesFolderPathFileName}_{DENOISER}_{color}'
     saveNpArray(npArrayFilePath, estimatedPrnuIterativeMean.mean)