Merge pull request #7 from AlexBowring/resubmission_two

Resubmission two
NISOx-BDI · Nov 20, 2018 · c8811f6 · c8811f6
2 parents bd5d8e7 + a71bd79
commit c8811f6
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Showing 29 changed files with 292 additions and 241 deletions.
diff --git a/figures/ds001_notebook.ipynb b/figures/ds001_notebook.ipynb
diff --git a/figures/ds109_notebook.ipynb b/figures/ds109_notebook.ipynb
diff --git a/figures/ds120_notebook.ipynb b/figures/ds120_notebook.ipynb
diff --git a/figures/img/Fig_ds001_BA_AFNI_FSL.png b/figures/img/Fig_ds001_BA_AFNI_FSL.png
diff --git a/figures/img/Fig_ds001_BA_AFNI_SPM.png b/figures/img/Fig_ds001_BA_AFNI_SPM.png
diff --git a/figures/img/Fig_ds001_BA_FSL_SPM.png b/figures/img/Fig_ds001_BA_FSL_SPM.png
diff --git a/figures/img/Fig_ds001_bold_BA_AFNI_SPM.png b/figures/img/Fig_ds001_bold_BA_AFNI_SPM.png
diff --git a/figures/img/Fig_ds001_bold_BA_FSL_SPM.png b/figures/img/Fig_ds001_bold_BA_FSL_SPM.png
diff --git a/figures/img/Fig_ds001_bold_BOLD_AFNI_FSL.png b/figures/img/Fig_ds001_bold_BOLD_AFNI_FSL.png
diff --git a/figures/img/Fig_ds001_perm_BA_AFNI_FSL.png b/figures/img/Fig_ds001_perm_BA_AFNI_FSL.png
diff --git a/figures/img/Fig_ds001_perm_BA_AFNI_SPM.png b/figures/img/Fig_ds001_perm_BA_AFNI_SPM.png
diff --git a/figures/img/Fig_ds001_perm_BA_FSL_SPM.png b/figures/img/Fig_ds001_perm_BA_FSL_SPM.png
diff --git a/figures/img/Fig_ds001_permintra_BA_AFNI.png b/figures/img/Fig_ds001_permintra_BA_AFNI.png
diff --git a/figures/img/Fig_ds001_permintra_BA_FSL.png b/figures/img/Fig_ds001_permintra_BA_FSL.png
diff --git a/figures/img/Fig_ds001_permintra_BA_SPM.png b/figures/img/Fig_ds001_permintra_BA_SPM.png
diff --git a/figures/img/Fig_ds109_BA_AFNI_FSL.png b/figures/img/Fig_ds109_BA_AFNI_FSL.png
diff --git a/figures/img/Fig_ds109_BA_AFNI_SPM.png b/figures/img/Fig_ds109_BA_AFNI_SPM.png
diff --git a/figures/img/Fig_ds109_BA_FSL_SPM.png b/figures/img/Fig_ds109_BA_FSL_SPM.png
diff --git a/figures/img/Fig_ds109_bold_BA_AFNI_SPM.png b/figures/img/Fig_ds109_bold_BA_AFNI_SPM.png
diff --git a/figures/img/Fig_ds109_bold_BA_FSL_SPM.png b/figures/img/Fig_ds109_bold_BA_FSL_SPM.png
diff --git a/figures/img/Fig_ds109_bold_BOLD_AFNI_FSL.png b/figures/img/Fig_ds109_bold_BOLD_AFNI_FSL.png
diff --git a/figures/img/Fig_ds109_perm_BA_AFNI_FSL.png b/figures/img/Fig_ds109_perm_BA_AFNI_FSL.png
diff --git a/figures/img/Fig_ds109_perm_BA_AFNI_SPM.png b/figures/img/Fig_ds109_perm_BA_AFNI_SPM.png
diff --git a/figures/img/Fig_ds109_perm_BA_FSL_SPM.png b/figures/img/Fig_ds109_perm_BA_FSL_SPM.png
diff --git a/figures/img/Fig_ds109_permintra_BA_AFNI.png b/figures/img/Fig_ds109_permintra_BA_AFNI.png
diff --git a/figures/img/Fig_ds109_permintra_BA_FSL.png b/figures/img/Fig_ds109_permintra_BA_FSL.png
diff --git a/figures/img/Fig_ds109_permintra_BA_SPM.png b/figures/img/Fig_ds109_permintra_BA_SPM.png
diff --git a/figures/img/Fig_ds120_BA_AFNI_SPM.png b/figures/img/Fig_ds120_BA_AFNI_SPM.png
diff --git a/figures/lib/bland_altman.py b/figures/lib/bland_altman.py
@@ -40,6 +40,26 @@ def mask_using_nan(data_img):
     return(data_img_nan)
 
 
+def squeeze_four_d_image(data_img):
+    # Set masking using NaN's
+    data_orig = data_img.get_data()
+    data_three_d = data_orig[:,:,:,0]
+    data_img_three_d = nib.Nifti1Image(data_three_d, data_img.get_affine())
+
+    return(data_img_three_d)
+
+# Getting white matter and csf images to mask out from BA plots
+fsldir = os.environ['FSLDIR']
+white_matter = os.path.join(fsldir, 'data', 'standard', 'tissuepriors', 'avg152T1_white.img')
+white_matter_img = nib.load(white_matter)
+white_matter_img = mask_using_nan(white_matter_img)
+white_matter_img = squeeze_four_d_image(white_matter_img)
+
+csf = os.path.join(fsldir, 'data', 'standard', 'tissuepriors', 'avg152T1_csf.img')
+csf_img = nib.load(csf)
+csf_img = mask_using_nan(csf_img)
+csf_img = squeeze_four_d_image(csf_img)
+
 def bland_altman_values(data1_file, data2_file, reslice_on_2=True,
                         *args, **kwargs):
 
@@ -54,22 +74,45 @@ def bland_altman_values(data1_file, data2_file, reslice_on_2=True,
     if reslice_on_2:
         # Resample data1 on data2 using nearest nneighbours
         data1_resl_img = resample_from_to(data1_img, data2_img, order=0)
-
+        white_matter_resl_img = resample_from_to(white_matter_img, data2_img, order=0)
+        csf_resl_img = resample_from_to(csf_img, data2_img, order=0)
+
         # Load data from images
         data1 = data1_resl_img.get_data()
         data2 = data2_img.get_data()
+        white_matter = white_matter_resl_img.get_data()
+        csf = csf_resl_img.get_data()
     else:
         # Resample data2 on data1 using nearest nneighbours
         data2_resl_img = resample_from_to(data2_img, data1_img, order=0)
+        white_matter_resl_img = resample_from_to(white_matter_img, data1_img, order=0)
+        csf_resl_img = resample_from_to(csf_img, data1_img, order=0)
 
         # Load data from images
         data1 = data1_img.get_data()
         data2 = data2_resl_img.get_data()
-
+        white_matter = white_matter_resl_img.get_data()
+        csf = csf_resl_img.get_data()
+
+    # Masking white matter and csf images for a threshold of 0.5
+    white_matter_mask = white_matter >= 0.5
+    white_matter_mask = white_matter_mask*1 
+    csf_mask = csf >= 0.5
+    csf_mask = csf_mask*1
+
     # Vectorise input data
     data1 = np.reshape(data1, -1)
     data2 = np.reshape(data2, -1)
-
+    white_matter_mask = np.reshape(white_matter_mask, -1)
+    csf_mask = np.reshape(csf_mask, -1)
+
+    not_white_matter_csf_indices = np.logical_not(
+        np.logical_or(
+            white_matter_mask == 1, csf_mask == 1))
+
+    data1 = data1[not_white_matter_csf_indices]
+    data2 = data2[not_white_matter_csf_indices] 
+
     in_mask_indices = np.logical_not(
         np.logical_or(
             np.logical_or(np.isnan(data1), np.absolute(data1) == 0),
@@ -351,21 +394,44 @@ def scatter_values(data1_file, data2_file, reslice_on_2=True,
     if reslice_on_2:
         # Resample data1 on data2 using nearest nneighbours
         data1_resl_img = resample_from_to(data1_img, data2_img, order=0)
+        white_matter_resl_img = resample_from_to(white_matter_img, data2_img, order=0)
+        csf_resl_img = resample_from_to(csf_img, data2_img, order=0)
 
         # Load data from images
         data1 = data1_resl_img.get_data()
         data2 = data2_img.get_data()
+        white_matter = white_matter_resl_img.get_data()
+        csf = csf_resl_img.get_data()
     else:
         # Resample data2 on data1 using nearest nneighbours
         data2_resl_img = resample_from_to(data2_img, data1_img, order=0)
+        white_matter_resl_img = resample_from_to(white_matter_img, data1_img, order=0)
+        csf_resl_img = resample_from_to(csf_img, data1_img, order=0)
 
         # Load data from images
         data1 = data1_img.get_data()
         data2 = data2_resl_img.get_data()
+        white_matter = white_matter_resl_img.get_data()
+        csf = csf_resl_img.get_data()
+
+    # Masking white matter and csf images for a threshold of 0.5
+    white_matter_mask = white_matter >= 0.5
+    white_matter_mask = white_matter_mask*1 
+    csf_mask = csf >= 0.5
+    csf_mask = csf_mask*1
 
     # Vectorise input data
     data1 = np.reshape(data1, -1)
     data2 = np.reshape(data2, -1)
+    white_matter_mask = np.reshape(white_matter_mask, -1)
+    csf_mask = np.reshape(csf_mask, -1)
+
+    not_white_matter_csf_indices = np.logical_not(
+        np.logical_or(
+            white_matter_mask == 1, csf_mask == 1))
+
+    data1 = data1[not_white_matter_csf_indices]
+    data2 = data2[not_white_matter_csf_indices]
 
     in_mask_indices = np.logical_not(
         np.logical_or(