lint

SIRF_data_preparation/Siemens_mMR_ACR/VOI_prep.py

@@ -1,91 +1,95 @@
-#%% file to prepare VOIs from the original ACR NEMA VOIs supplied by Pawel Markiewicz
-#%%
-import numpy as np
-import matplotlib.pyplot as plt
+# %% file to prepare VOIs from the original ACR NEMA VOIs supplied by Pawel Markiewicz
+# %%
 import os
-import sys
+
+import matplotlib.pyplot as plt
+import numpy as np
+
 import sirf.STIR as STIR
-from sirf.Utilities import examples_data_path
-from scipy.ndimage import binary_erosion
-from pathlib import Path
+import SIRF_data_preparation.data_QC as data_QC
 from SIRF_data_preparation.data_utilities import the_data_path, the_orgdata_path
 from SIRF_data_preparation.dataset_settings import get_settings
-import SIRF_data_preparation.data_QC as data_QC
-#%%
+
+# %%
 scanID = 'Siemens_mMR_ACR'
 org_VOI_path = the_orgdata_path(scanID, 'output', 'sampling_masks')
 data_path = the_data_path(scanID)
 output_path = the_data_path(scanID, 'PETRIC')
 os.makedirs(output_path, exist_ok=True)
 settings = get_settings(scanID)
 slices = settings.slices
-#%%
+# %%
 OSEM_image = STIR.ImageData(os.path.join(data_path, 'OSEM_image.hv'))
 cmax = OSEM_image.max()
-#%% read in original VOIs
+# %% read in original VOIs
 orgVOIs = STIR.ImageData(os.path.join(org_VOI_path, 'acr-all-sampling-0-2mm_dipy.nii'))
 data_QC.plot_image(orgVOIs, **slices)
-#%%
+# %%
 orgVOIs_arr = orgVOIs.as_array()
 print(np.unique(orgVOIs_arr))
-#%% output
+
+
+# %% output
 # [  0,  10-29,  40-81,  90- 101, 300- 317]
-#%%
+# %%
 def plotMask(mask):
     plt.figure()
     plt.subplot(141)
-    plt.imshow(mask[:,109,:])
+    plt.imshow(mask[:, 109, :])
     plt.subplot(142)
-    plt.imshow(mask[85,:,:])
+    plt.imshow(mask[85, :, :])
     plt.subplot(143)
-    plt.imshow(mask[99,:,:])
+    plt.imshow(mask[99, :, :])
     plt.subplot(144)
-    plt.imshow(mask[40,:,:])
-#%% background mask: slices in uniform part (idx 300-317), taking slightly eroded mask
-mask = (orgVOIs_arr>=300) * (orgVOIs_arr<316)
+    plt.imshow(mask[40, :, :])
+
+
+# %% background mask: slices in uniform part (idx 300-317), taking slightly eroded mask
+mask = (orgVOIs_arr >= 300) * (orgVOIs_arr < 316)
 plotMask(mask)
-#%%
+# %%
 background_mask = OSEM_image.clone()
 background_mask.fill(mask)
-#%% 6 cylinder masks
-mask = (orgVOIs_arr>=10) * (orgVOIs_arr<101)
+# %% 6 cylinder masks
+mask = (orgVOIs_arr >= 10) * (orgVOIs_arr < 101)
 plotMask(mask)
-#%% cold cylinder mask
-mask = (orgVOIs_arr>=90) * (orgVOIs_arr<100)
+# %% cold cylinder mask
+mask = (orgVOIs_arr >= 90) * (orgVOIs_arr < 100)
 plotMask(mask)
 cold_cyl_mask = OSEM_image.clone()
 cold_cyl_mask.fill(mask)
-#%% faint hot cylinder mask
-mask = (orgVOIs_arr>=20) * (orgVOIs_arr<29)
+# %% faint hot cylinder mask
+mask = (orgVOIs_arr >= 20) * (orgVOIs_arr < 29)
 plotMask(mask)
 hot_cyl_mask = OSEM_image.clone()
 hot_cyl_mask.fill(mask)
-#%% Jasczcak part, not available so derive from "background mask"
-mask = (orgVOIs_arr>=300) * (orgVOIs_arr<316)
+# %% Jasczcak part, not available so derive from "background mask"
+mask = (orgVOIs_arr >= 300) * (orgVOIs_arr < 316)
 # get single cylinder from a slice in the background
-oneslice = mask[85,:,:].copy()
-mask[35:45,:,:] = oneslice[:,:]
-mask[46:127,:,:] = False
+oneslice = mask[85, :, :].copy()
+mask[35:45, :, :] = oneslice[:, :]
+mask[46:127, :, :] = False
 plotMask(mask)
 rods_mask = OSEM_image.clone()
 rods_mask.fill(mask)
-#%% whole phantom
-mask = OSEM_image.as_array() > cmax/20
+# %% whole phantom
+mask = OSEM_image.as_array() > cmax / 20
 plotMask(mask)
 whole_object_mask = OSEM_image.clone()
 whole_object_mask.fill(mask)
-#%%
+# %%
 plotMask(OSEM_image.as_array())
-#%% write PETRIC VOIs
+# %% write PETRIC VOIs
 whole_object_mask.write(os.path.join(output_path, 'VOI_whole_object.hv'))
 background_mask.write(os.path.join(output_path, 'VOI_background.hv'))
 cold_cyl_mask.write(os.path.join(output_path, 'VOI_cold_cylinder.hv'))
 hot_cyl_mask.write(os.path.join(output_path, 'VOI_hot_cylinder.hv'))
 rods_mask.write(os.path.join(output_path, 'VOI_rods.hv'))
 
-#%%
+# %%
 VOIs = (whole_object_mask, background_mask, cold_cyl_mask, hot_cyl_mask, rods_mask)
-#%%
-[ data_QC.plot_image(VOI, **slices) for VOI in VOIs]
-#%%
-data_QC.VOI_checks(['VOI_whole_object', 'VOI_background', 'VOI_cold_cylinder', 'VOI_hot_cylinder', 'VOI_rods'], OSEM_image, srcdir=output_path, **slices)
+# %%
+[data_QC.plot_image(VOI, **slices) for VOI in VOIs]
+# %%
+data_QC.VOI_checks(['VOI_whole_object', 'VOI_background', 'VOI_cold_cylinder', 'VOI_hot_cylinder', 'VOI_rods'],
+                   OSEM_image, srcdir=output_path, **slices)

SIRF_data_preparation/Siemens_mMR_ACR/download.py

@@ -6,9 +6,10 @@
 
 from zenodo_get import zenodo_get
 
+from sirf.Utilities import examples_data_path
+
 # %%
 from SIRF_data_preparation.data_utilities import the_data_path
-from sirf.Utilities import examples_data_path
 
 sirf_data_path = os.path.join(examples_data_path('PET'), 'mMR')
 

SIRF_data_preparation/Siemens_mMR_ACR/register_mumap.py

@@ -2,7 +2,8 @@
 
 import sirf.Reg as Reg
 import sirf.STIR as STIR
-from SIRF_data_preparation.data_utilities import the_data_path, the_orgdata_path
+from SIRF_data_preparation.data_utilities import the_orgdata_path
+
 # %% set paths filenames
 scanID = 'Siemens_mMR_ACR'
 intermediate_data_path = the_orgdata_path(scanID, 'processing')

SIRF_data_preparation/Siemens_mMR_NEMA_IQ/Siemens_mMR_NEMA_VOIs.py

@@ -5,9 +5,7 @@
 from pathlib import Path
 
 import matplotlib.pyplot as plt
-import numpy
-from data_QC import VOI_mean, plot_image
-from scipy import ndimage
+from data_QC import VOI_mean
 from scipy.ndimage import binary_erosion
 
 import sirf.STIR as STIR
@@ -78,9 +76,9 @@
 plt.figure()
 data_QC.plot_image(reference_image, **slices, alpha=allVOIs)
 
-#%%
-VOI_checks(['VOI_whole_object', 'VOI_sphere5'], OSEM_image, srcdir=os.path.join(datadir, 'PETRIC'), **slices)
-#%%
-[ VOI_mean(OSEM_image, VOI) for VOI in VOIs]
-#%%
-[ VOI_mean(reference_image, VOI) for VOI in VOIs]
+# %%
+data_QC.VOI_checks(['VOI_whole_object', 'VOI_sphere5'], OSEM_image, srcdir=os.path.join(datadir, 'PETRIC'), **slices)
+# %%
+[VOI_mean(OSEM_image, VOI) for VOI in VOIs]
+# %%
+[VOI_mean(reference_image, VOI) for VOI in VOIs]

SIRF_data_preparation/create_initial_images.py

@@ -7,7 +7,8 @@
   <data_path>  path to data files
 
 Options:
-  -t <template_image>, --template_image=<template_image>  filename (relative to <data_path>) of image to use for data sizes [default: PETRIC/VOI_whole_phantom.hv]
+  -t <template_image>, --template_image=<template_image>  filename (relative to <data_path>) of image to use
+                                                          for data sizes [default: PETRIC/VOI_whole_phantom.hv]
   -s <xy-size>, --xy-size=<xy-size>  force xy-size (do not use when using VOIs as init) [default: -1]
   -S <subsets>, --subsets=<subsets>  number of subsets [default: 2]
   -i <subiterations>, --subiterations=<subiterations>     number of sub-iterations [default: 14]

SIRF_data_preparation/data_utilities.py

@@ -29,6 +29,7 @@ def the_data_path(*folders):
     '''
     return os.path.join(DATA_PATH, *folders)
 
+
 def the_orgdata_path(*folders):
     '''
     Returns the path to original data (for downloads/processing)
@@ -37,6 +38,7 @@ def the_orgdata_path(*folders):
     '''
     return os.path.join(ORG_DATA_PATH, *folders)
 
+
 def fix_siemens_norm_EOL(in_filename, out_filename):
     with open(in_filename, mode="rb") as f:
         data = bytearray(f.read())

SIRF_data_preparation/dataset_settings.py

@@ -14,7 +14,7 @@ def get_settings(scanID: str):
         slices = {'transverse_slice': 72, 'coronal_slice': 109} # , 'sagittal_slice': 89}
         num_subsets = 7
     elif scanID == 'Siemens_mMR_ACR':
-        slices = { 'transverse_slice': 99}
+        slices = {'transverse_slice': 99}
         num_subsets = 7
     elif scanID == 'NeuroLF_Hoffman_Dataset':
         slices = {'transverse_slice': 72}