fixed more PEP8 things

HEXRD · Nov 9, 2023 · 694e3f5 · 694e3f5
1 parent 4f16526
commit 694e3f5
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Showing 2 changed files with 101 additions and 86 deletions.
diff --git a/scripts/preprocess_NF_imgs.py b/scripts/preprocess_NF_imgs.py
@@ -18,7 +18,7 @@
 try:
     import matplotlib.pyplot as plt
     matplot = True
-except(ImportError):
+except (ImportError):
     logging.warning(f'no matplotlib, debug plotting disabled')
     matplot = False
 
@@ -29,16 +29,17 @@ def load_instrument(yml):
     return instrument.HEDMInstrument(instrument_config=icfg)
 
 
-#%%
+# %%
 
 parser = argparse.ArgumentParser(description='Preprocess NF image stack')
 
-parser.add_argument('input_file', type=str, help='Input File for NF reconstruction')
+parser.add_argument('input_file', type=str,
+                    help='Input File for NF reconstruction')
 
 args = parser.parse_args()
 fname = args.input_file
 
-#%%
+# %%
 cfg = nf_config.open(fname)[0]
 
 analysis_name = cfg.analysis_name
@@ -49,7 +50,7 @@ def load_instrument(yml):
 main_dir = cfg.main_dir
 output_dir = cfg.output_dir
 
-#full_stem = os.path.join(img_folder, img_stem)
+# full_stem = os.path.join(img_folder, img_stem)
 
 det_fname = cfg.input_files.det_file
 
@@ -79,17 +80,17 @@ def load_instrument(yml):
     print('Invalid image processing method')
 
 
-#==============================================================================
+# ==============================================================================
 # %% NEAR FIELD - MAKE MEDIAN DARK
-#==============================================================================
+# ==============================================================================
 
 dark = nfutil.gen_nf_dark(img_folder, img_nums, num_for_dark, nrows,
                           ncols, dark_type='median',
                           num_digits=num_digits, stem=img_stem)
 
-#==============================================================================
+# ==============================================================================
 # %% NEAR FIELD - LOAD IMAGE DATA AND PROCESS
-#==============================================================================
+# ==============================================================================
 
 image_stack = nfutil.gen_nf_cleaned_image_stack(img_folder, img_nums, dark,
                                                 nrows, ncols,

diff --git a/scripts/preprocess_tomo_mask_yaml.py b/scripts/preprocess_tomo_mask_yaml.py
@@ -1,4 +1,4 @@
-#%% Necessary Dependencies
+# %% Necessary Dependencies
 
 import numpy as np
 import logging
@@ -9,10 +9,10 @@
 
 try:
     import matplotlib.pyplot as plt
-    matplot=True
-except(ImportError):
+    matplot = True
+except (ImportError):
     logging.warning(f'no matplotlib, debug plotting disabled')
-    matplot=False
+    matplot = False
 
 from hexrd.grainmap import nfutil_yaml
 from hexrd.grainmap import tomoutil
@@ -25,16 +25,18 @@ def load_instrument(yml):
         icfg = yaml.load(f, Loader=yaml.FullLoader)
     return instrument.HEDMInstrument(instrument_config=icfg)
 
-#%%
+
+# %%
 parser = argparse.ArgumentParser(description='Preprocess tomo image stack')
 
-parser.add_argument('input_file', type=str, help='Input File for NF reconstruction')
+parser.add_argument('input_file', type=str,
+                    help='Input File for NF reconstruction')
 
 args = parser.parse_args()
 fname = args.input_file
 # fname = '/Users/lim37/Documents/AFRL/NF_config_v1.yml'
 
-#%%
+# %%
 cfg = nf_config.open(fname)[0]
 
 output_stem = cfg.analysis_name
@@ -48,159 +50,171 @@ def load_instrument(yml):
 img_stem = tomo.img_stem
 num_digits = tomo.num_digits
 
-#Locations of tomography bright field images
+# Locations of tomography bright field images
 tbf_data_folder = tomo.bright.folder
 tbf_img_start = tomo.bright.img_start
 tbf_num_imgs = tomo.bright.num_imgs
 
-#Locations of tomography dark field images
+# Locations of tomography dark field images
 tdf_data_folder = tomo.dark.folder
-tdf_img_start = tomo.dark.img_start#for this rate, this is the 6th file in the folder
+# for this rate, this is the 6th file in the folder
+tdf_img_start = tomo.dark.img_start
 tdf_num_imgs = tomo.dark.num_imgs
 
-#Locations of tomography images
+# Locations of tomography images
 tomo_data_folder = tomo.images.folder
-tomo_img_start = tomo.images.img_start#for this rate, this is the 6th file in the folder
+# for this rate, this is the 6th file in the folder
+tomo_img_start = tomo.images.img_start
 tomo_num_imgs = tomo.images.num_imgs
 
 tomo_mask_file = cfg.NF_reconstruction.tomography.mask_data_file
 
-#==============================================================================
+# ==============================================================================
 # %% USER OPTIONS -CAN BE EDITED
-#==============================================================================
+# ==============================================================================
 
 
-ome_range_deg = tomo.ome_range #degrees
+ome_range_deg = tomo.ome_range  # degrees
 
 
-#tomography options
-recon_thresh = tomo.processing.recon_thresh#usually varies between 0.0001 and 0.0005
+# tomography options
+# usually varies between 0.0001 and 0.0005
+recon_thresh = tomo.processing.recon_thresh
 noise_obj_size = tomo.processing.noise_obj_size
 min_hole_size = tomo.processing.min_hole_size
 erosion_iter = tomo.processing.erosion_iter
 dilation_iter = tomo.processing.dilation_iter
 
 
-project_single_layer = tomo.reconstruction.project_single_layer #projects the center layers through the volume
-cross_sectional_dim = tomo.reconstruction.cross_sectional_dim #cross sectional to reconstruct (should be at least 20%-30% over sample width)
-voxel_spacing = tomo.reconstruction.voxel_spacing#in mm
+# projects the center layers through the volume
+project_single_layer = tomo.reconstruction.project_single_layer
+# cross sectional to reconstruct (should be at least 20%-30% over sample width)
+cross_sectional_dim = tomo.reconstruction.cross_sectional_dim
+voxel_spacing = tomo.reconstruction.voxel_spacing  # in mm
 v_bnds = tomo.reconstruction.v_bnds
 
 
-#==============================================================================
+# ==============================================================================
 # %% LOAD INSTRUMENT DATA
-#==============================================================================
+# ==============================================================================
 
-instr=load_instrument(det_file)
+instr = load_instrument(det_file)
 
 panel = next(iter(instr.detectors.values()))
 
-nrows=panel.rows
-ncols=panel.cols
-pixel_size=panel.pixel_size_row
+nrows = panel.rows
+ncols = panel.cols
+pixel_size = panel.pixel_size_row
 
-rot_axis_pos=panel.tvec[0] #should match t_vec_d[0] from nf_detector_parameter_file
-vert_beam_center=panel.tvec[1]
+# should match t_vec_d[0] from nf_detector_parameter_file
+rot_axis_pos = panel.tvec[0]
+vert_beam_center = panel.tvec[1]
 
 
 # need to do a few calculations because not every row will be reconstructed
 # depending on sampling
-vert_points=np.arange(v_bnds[0]+voxel_spacing/2.,v_bnds[1],voxel_spacing)
+vert_points = np.arange(v_bnds[0]+voxel_spacing/2., v_bnds[1], voxel_spacing)
 
-center_layer_row=nrows/2.+vert_beam_center/pixel_size
+center_layer_row = nrows/2.+vert_beam_center/pixel_size
 
-rows_to_recon=np.round(center_layer_row-vert_points/pixel_size).astype(int)
+rows_to_recon = np.round(center_layer_row-vert_points/pixel_size).astype(int)
 
-center_layer_row=int(center_layer_row)
+center_layer_row = int(center_layer_row)
 
-#==============================================================================
+# ==============================================================================
 # %% TOMO PROCESSING - GENERATE DARK AND BRIGHT FIELD
-#==============================================================================
-tdf=tomoutil.gen_median_image(tdf_data_folder,tdf_img_start,tdf_num_imgs,nrows,ncols,stem=img_stem,num_digits=num_digits)
-tbf=tomoutil.gen_median_image(tbf_data_folder,tbf_img_start,tbf_num_imgs,nrows,ncols,stem=img_stem,num_digits=num_digits)
+# ==============================================================================
+tdf = tomoutil.gen_median_image(tdf_data_folder, tdf_img_start,
+                                tdf_num_imgs, nrows, ncols, stem=img_stem, num_digits=num_digits)
+tbf = tomoutil.gen_median_image(tbf_data_folder, tbf_img_start,
+                                tbf_num_imgs, nrows, ncols, stem=img_stem, num_digits=num_digits)
 
-#==============================================================================
+# ==============================================================================
 # %% TOMO PROCESSING - BUILD RADIOGRAPHS
-#==============================================================================
-
-rad_stack=tomoutil.gen_attenuation_rads(tomo_data_folder,tbf,tomo_img_start,tomo_num_imgs,nrows,ncols,stem=stem,num_digits=num_digits,tdf=tdf)
+# ==============================================================================
 
+rad_stack = tomoutil.gen_attenuation_rads(
+    tomo_data_folder, tbf, tomo_img_start, tomo_num_imgs, nrows, ncols, stem=stem, num_digits=num_digits, tdf=tdf)
 
 
-#==============================================================================
+# ==============================================================================
 # %% TOMO PROCESSING - INVERT SINOGRAM
-#==============================================================================
+# ==============================================================================
 # center = 0.0
 
-test_fbp=tomoutil.tomo_reconstruct_layer(rad_stack,cross_sectional_dim,layer_row=center_layer_row,\
-                                                   start_tomo_ang=ome_range_deg[0][0],end_tomo_ang=ome_range_deg[0][1],\
-                                                   tomo_num_imgs=tomo_num_imgs, center=rot_axis_pos,pixel_size=pixel_size)
+test_fbp = tomoutil.tomo_reconstruct_layer(rad_stack, cross_sectional_dim, layer_row=center_layer_row,
+                                           start_tomo_ang=ome_range_deg[0][0], end_tomo_ang=ome_range_deg[0][1],
+                                           tomo_num_imgs=tomo_num_imgs, center=rot_axis_pos, pixel_size=pixel_size)
 
-test_binary_recon=tomoutil.threshold_and_clean_tomo_layer(test_fbp,recon_thresh, \
-                                                          noise_obj_size,min_hole_size, erosion_iter=erosion_iter, \
-                                                          dilation_iter=dilation_iter)
+test_binary_recon = tomoutil.threshold_and_clean_tomo_layer(test_fbp, recon_thresh,
+                                                            noise_obj_size, min_hole_size, erosion_iter=erosion_iter,
+                                                            dilation_iter=dilation_iter)
 
-tomo_mask_center=tomoutil.crop_and_rebin_tomo_layer(test_binary_recon,recon_thresh,voxel_spacing,pixel_size,cross_sectional_dim)
+tomo_mask_center = tomoutil.crop_and_rebin_tomo_layer(
+    test_binary_recon, recon_thresh, voxel_spacing, pixel_size, cross_sectional_dim)
 
 
-#==============================================================================
+# ==============================================================================
 # %% TOMO PROCESSING - VIEW RAW FILTERED BACK PROJECTION
-#==============================================================================
+# ==============================================================================
 
 if matplot:
     plt.figure(1)
-    plt.imshow(test_fbp,vmin=recon_thresh,vmax=recon_thresh*2)
+    plt.imshow(test_fbp, vmin=recon_thresh, vmax=recon_thresh*2)
     plt.title('Check Thresholding')
-    #Use this image to view the raw reconstruction, estimate threshold levels. and
-    #figure out if the rotation axis position needs to be corrected
-
+    # Use this image to view the raw reconstruction, estimate threshold levels. and
+    # figure out if the rotation axis position needs to be corrected
 
     plt.figure(2)
-    plt.imshow(tomo_mask_center,interpolation='none')
+    plt.imshow(tomo_mask_center, interpolation='none')
     plt.title('Check Center Mask')
 
-#==============================================================================
+# ==============================================================================
 # %% PROCESS REMAINING LAYERS
-#==============================================================================
+# ==============================================================================
 
-full_mask=np.zeros([len(rows_to_recon),tomo_mask_center.shape[0],tomo_mask_center.shape[1]])
+full_mask = np.zeros(
+    [len(rows_to_recon), tomo_mask_center.shape[0], tomo_mask_center.shape[1]])
 
 
 for ii in np.arange(len(rows_to_recon)):
     print('Layer: ' + str(ii) + ' of ' + str(len(rows_to_recon)))
 
-    if project_single_layer: #not recommended option
-        full_mask[ii]=tomo_mask_center
+    if project_single_layer:  # not recommended option
+        full_mask[ii] = tomo_mask_center
 
     else:
-        reconstruction_fbp=tomoutil.tomo_reconstruct_layer(rad_stack,cross_sectional_dim,layer_row=rows_to_recon[ii],\
-                                                       start_tomo_ang=ome_range_deg[0][0],end_tomo_ang=ome_range_deg[0][1],\
-                                                       tomo_num_imgs=tomo_num_imgs, center=rot_axis_pos,pixel_size=pixel_size)
+        reconstruction_fbp = tomoutil.tomo_reconstruct_layer(rad_stack, cross_sectional_dim, layer_row=rows_to_recon[ii],
+                                                             start_tomo_ang=ome_range_deg[0][
+                                                                 0], end_tomo_ang=ome_range_deg[0][1],
+                                                             tomo_num_imgs=tomo_num_imgs, center=rot_axis_pos, pixel_size=pixel_size)
 
-        binary_recon=tomoutil.threshold_and_clean_tomo_layer(reconstruction_fbp,recon_thresh, \
-                                                             noise_obj_size,min_hole_size,erosion_iter=erosion_iter, \
-                                                             dilation_iter=dilation_iter)
+        binary_recon = tomoutil.threshold_and_clean_tomo_layer(reconstruction_fbp, recon_thresh,
+                                                               noise_obj_size, min_hole_size, erosion_iter=erosion_iter,
+                                                               dilation_iter=dilation_iter)
 
-        tomo_mask=tomoutil.crop_and_rebin_tomo_layer(binary_recon,recon_thresh,voxel_spacing,pixel_size,cross_sectional_dim)
+        tomo_mask = tomoutil.crop_and_rebin_tomo_layer(
+            binary_recon, recon_thresh, voxel_spacing, pixel_size, cross_sectional_dim)
 
-        full_mask[ii]=tomo_mask
+        full_mask[ii] = tomo_mask
 
-#==============================================================================
+# ==============================================================================
 # %%  TOMO PROCESSING - VIEW LAST TOMO_MASK FOR SAMPLE BOUNDS
-#==============================================================================
+# ==============================================================================
 
 if matplot:
     plt.figure(3)
-    plt.imshow(tomo_mask,interpolation='none')
+    plt.imshow(tomo_mask, interpolation='none')
     plt.title('Check Center Mask')
 
-#==============================================================================
+# ==============================================================================
 # %%  TOMO PROCESSING - CONSTRUCT DATA GRID
-#==============================================================================
+# ==============================================================================
 
-test_crds, n_crds, Xs, Ys, Zs = nfutil.gen_nf_test_grid_tomo(full_mask.shape[2], full_mask.shape[1], v_bnds, voxel_spacing)
+test_crds, n_crds, Xs, Ys, Zs = nfutil.gen_nf_test_grid_tomo(
+    full_mask.shape[2], full_mask.shape[1], v_bnds, voxel_spacing)
 
-#%%
+# %%
 
 np.savez(os.path.join(data_folder, tomo_mask_file, mask=full_mask, Xs=Xs, Ys=Ys,
-        Zs=Zs, voxel_spacing=voxel_spacing))
+                      Zs=Zs, voxel_spacing=voxel_spacing))