Revise Theodora's Data Normalisation

maldi_theodora_normalisation.py

import uMAIA
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import warnings
import os

# Define the path to save the data
workdir = '/beegfs/scratch/bruening_scratch/pklemm/2024-01-theodora/uMAIA/data_maldi'

# List of acquisitions
acquisitions = [
    '20231120_Hypothalamus_Brain3_slide10_421x352_Att35_7um',
    '20231210_Hypothalamus_Brain10_slide4_417x375_7um_Att35',
    '20231211_Hypothalamus_Brain8_slide8_506x272_7um_Att35',
    '20231212_Hypothalamus_Brain5_slide3_460x364_7um_Att35'
]

# Define the path to save the data
PATH_SAVE = os.path.join(workdir, 'theodora_maldi.zarr')

# Define the list of columns and rows
cols_list = [421, 417, 506, 460]
rows_list = [352, 375, 272, 364]

# Create a list of NumPy arrays filled with boolean True values
masks = [np.full((rows_list[i], cols_list[i]), True, dtype=bool) for i, m in enumerate(acquisitions)]

# Loop over the acquisitions
for i, m in enumerate(acquisitions):
    # Construct the full path for the current mask's .npy file
    full_path = os.path.join(workdir, m, 'mask.npy')
    
    # Ensure the subdirectory exists, create if necessary
    os.makedirs(os.path.dirname(full_path), exist_ok=True)
    
    # Save the mask array to the .npy file
    print(full_path)
    np.save(full_path, masks[i])

# Load known masks into a list
mask_list = [np.load(f'data/{m}/mask.npy') for m in acquisitions]

small_num  = 0.0002

# Read images and masks
x, masks, masks_list = uMAIA.ut.tools.read_images_masks(acquisitions,
                                        path_images=PATH_SAVE, mask_list=mask_list,
                                        gaussian_smoothing=True, gaussian_sigma=0.4,
                                        log_transform=True, epsilon=small_num
                                       )

# Define the path to save the parameters
PATH_SAVE = os.path.join(workdir, 'parameters_maldi')

# Initialize the state
init_state = uMAIA.norm.initialize(x, masks,  subsample=True)

# Normalize the data
svi_result = uMAIA.norm.normalize(x, 
                              masks,
                              init_state=init_state,
                              subsample=True,
                              optimizer=None,
                              num_steps=5000,
                              seed=42)

# Check if the directory exists, if not, create it
if not os.path.isdir(PATH_SAVE):
    os.mkdir(PATH_SAVE)

# Save the result
uMAIA.ut.tools.save_svi(svi_result, PATH_SAVE)

# Transform the data
x_MAIA = uMAIA.norm.transform(x, masks, PATH_SAVE)

# Save the normalized data to a zarr file
path_theodora_maldi = os.path.join(workdir, 'theodora_maldi.zarr')
path_theodora_maldi_nprmalised = os.path.join(workdir, 'theodora_maldi_normalised.zarr')

uMAIA.ut.tools.to_zarr_normalised(path_theodora_maldi, path_theodora_maldi_nprmalised, acquisitions, x_MAIA, mask_list, small_num)