diff --git a/image_processing/build_dataset.py b/image_processing/build_dataset.py
index 3e6155f841b4419831db3aff898885ea878ac76f..1c1b6584f4a7eb14477b6f3af77bf3fc1c5a27a7 100644
--- a/image_processing/build_dataset.py
+++ b/image_processing/build_dataset.py
@@ -1,3 +1,4 @@
+import argparse
import glob
import os
import pandas as pd
@@ -154,12 +155,13 @@ def create_antibio_dataset(path='../data/label_raw/230804_strain_peptides_antibi
return df
-def create_dataset():
+def create_dataset(bin_mz=1,tolerance=0.005,noise=1000,apex='apex',suffix='-d200'):
"""
Create images from raw .mzML files and sort it in their corresponding class directory
:return: None
"""
- label = create_antibio_dataset(suffix='-d200')
+ label = create_antibio_dataset(suffix=suffix)
+ tolerance_str=str(tolerance)[2::]
for path in glob.glob("../data/raw_data/**.wiff"):
print(path)
species = None
@@ -173,52 +175,44 @@ def create_dataset():
name = label[label['path_aer'] == path.split("/")[-1]]['sample_name'].values[0]
analyse = 'AER'
if species is not None: #save image in species specific dir
- directory_path_png = '../data/processed_data_wiff_clean_005_10000_apex/png_image/{}'.format(species)
- directory_path_npy = '../data/processed_data_wiff_clean_005_10000_apex/npy_image/{}'.format(species)
+ directory_path_png = '../data/processed_data_wiff_clean_{}_{}_{}_{}/png_image/{}'.format(tolerance_str,noise,apex,bin_mz,species)
+ directory_path_npy = '../data/processed_data_wiff_clean_{}_{}_{}_{}/npy_image/{}'.format(tolerance_str,noise,apex,bin_mz,species)
if not os.path.isdir(directory_path_png):
os.makedirs(directory_path_png)
if not os.path.isdir(directory_path_npy):
os.makedirs(directory_path_npy)
if not os.path.isfile(directory_path_png + "/" + name + '_' + analyse + '.png'):
- try :
- mat = build_image_ms1_wiff_charge_filtered_apex_only(path, bin_mz=1,tolerance=0.005,noise=10000)
- mpimg.imsave(directory_path_png + "/" + name + '_' + analyse + '.png', mat)
- np.save(directory_path_npy + "/" + name + '_' + analyse + '.npy', mat)
- print('image create')
- except:
- print(name +' couldnt be laoded')
-
- #reiterate for other kind of raw file
- label = create_antibio_dataset(suffix='_100vW_100SPD')
- for path in glob.glob("../data/raw_data/**.wiff"):
- print(path)
- species = None
- if path.split("/")[-1] in label['path_ana'].values:
- species = label[label['path_ana'] == path.split("/")[-1]]['species'].values[0]
- name = label[label['path_ana'] == path.split("/")[-1]]['sample_name'].values[0]
- analyse = 'ANA'
- elif path.split("/")[-1] in label['path_aer'].values:
- species = label[label['path_aer'] == path.split("/")[-1]]['species'].values[0]
- name = label[label['path_aer'] == path.split("/")[-1]]['sample_name'].values[0]
- analyse = 'AER'
- if species is not None:
- directory_path_png = '../data/processed_data_wiff_clean_005_10000_apex/png_image/{}'.format(species)
- directory_path_npy = '../data/processed_data_wiff_clean_005_10000_apex/npy_image/{}'.format(species)
- if not os.path.isdir(directory_path_png):
- os.makedirs(directory_path_png)
- if not os.path.isdir(directory_path_npy):
- os.makedirs(directory_path_npy)
- if not os.path.isfile(directory_path_png + "/" + name + '_' + analyse + '.png'):
- try :
- mat = build_image_ms1_wiff_charge_filtered_apex_only(path, bin_mz=1,tolerance=0.005,noise=10000)
- mpimg.imsave(directory_path_png + "/" + name + '_' + analyse + '.png', mat)
- np.save(directory_path_npy + "/" + name + '_' + analyse + '.npy', mat)
- print('image create')
- except:
- print(name +' couldnt be laoded')
-
+ if apex == 'apex':
+ try :
+ mat = build_image_ms1_wiff_charge_filtered_apex_only(path, bin_mz=bin_mz,tolerance=tolerance,noise=noise)
+ mpimg.imsave(directory_path_png + "/" + name + '_' + analyse + '.png', mat)
+ np.save(directory_path_npy + "/" + name + '_' + analyse + '.npy', mat)
+ print('image create')
+ except:
+ print(name +' couldnt be loaded')
+ else :
+ try :
+ mat = build_image_ms1_wiff_charge_filtered(path, bin_mz=bin_mz,tolerance=tolerance,noise=noise)
+ mpimg.imsave(directory_path_png + "/" + name + '_' + analyse + '.png', mat)
+ np.save(directory_path_npy + "/" + name + '_' + analyse + '.npy', mat)
+ print('image create')
+ except:
+ print(name +' couldnt be loaded')
+
+def load_args_dataset():
+ parser = argparse.ArgumentParser()
+
+ parser.add_argument('--noise', type=float, default=1000.)
+ parser.add_argument('--tolerance', type=float, default=0.005)
+ parser.add_argument('--mz_bin', type=float, default=1.)
+ parser.add_argument('--apex', type=str, default='apex')
+ args = parser.parse_args()
+
+ return args
if __name__ =='__main__' :
- create_dataset()
+ args = load_args_dataset()
+ create_dataset(noise=args.noise, tolerance=args.tolerance, bin_mz=args.mz_bin, apex=args.apex, suffix='-d200')
+ create_dataset(noise=args.noise, tolerance=args.tolerance, bin_mz=args.mz_bin, apex=args.apex, suffix='_100vW_100SPD')
#KLEAER-12-AER-d200.wiff problème
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diff --git a/image_processing/image_comparison.py b/image_processing/image_comparison.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc8dfb65ec726bb226723eaf4d68cfb51bbe1f78
--- /dev/null
+++ b/image_processing/image_comparison.py
@@ -0,0 +1,42 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+import matplotlib.colors
+
+def compare_image(img_path,ref_path):
+ img = np.load(img_path)
+ img = np.log(img+1)
+ img = img/img.max()
+
+ ref = np.load(ref_path)
+
+ am1 = np.ones_like(img) # Find data to colour special
+ am1 = np.ma.masked_where(ref==0, am1) # Mask the data we are not colouring
+
+
+ # Colourmaps for each special colour to place. The left-hand colour (black) is
+ # not used because all black pixels are masked. The right-hand colour (red or
+ # green) is used because it represents the highest z-value of the mask matrices
+ cm1 = mpl.colors.ListedColormap(['black', 'green'])
+ cm2 = mpl.colors.ListedColormap(['black', 'red'])
+
+ fig = plt.figure() # Make a new figure
+ ax = fig.add_subplot(111) # Add subplot to that figure, get ax
+
+ # Plot the original data. We'll overlay the specially-coloured data
+ ax.imshow(img, aspect='auto', cmap='inferno', vmin=0, vmax=1,interpolation='nearest')
+
+ # Plot the first mask. Values we wanted to colour (`a<0.3`) are masked, so they
+ # do not show up. The values that do show up are coloured using the `cm1` colour
+ # map. Since the range is constrained to `vmin=0, vmax=1` and a value of
+ # `cm2==True` corresponds to a 1, the top value of `cm1` is applied to all such
+ # pixels, thereby colouring them red.
+ ax.imshow(am1, aspect='auto', cmap=cm1, vmin=0, vmax=1,interpolation='nearest',alpha=0.4);
+
+ plt.savefig('test.png')
+
+if __name__ == '__main__':
+ # img_path = '../data/processed_data_wiff_clean_005_10000_apex/npy_image/all data/Citrobacter portucalensis/CITPOR1_ANA.npy'
+ img_path = '../data/processed_data_wiff/npy_image/all data/Citrobacter portucalensis/CITPOR1_ANA.npy'
+ ref_path = '../image_ref/img_ref_aligned/Citrobacter portucalensis.npy'
+ compare_image(img_path,ref_path)
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