Edit state vars to connect image viewer to plotting view

src/NiChart_Viewer/src/pages/home.py

@@ -35,6 +35,11 @@
     # st.session_state.in_csv_MUSE = f'{st.session_state.dir_root}/test/test_input/test2_rois/Study1/Study1_DLMUSE.csv'
     # st.session_state.in_csv_Demog = f'{st.session_state.dir_root}/test/test_input/test2_rois/Study1/Study1_Demog.csv'
 
+    # FIXME: Set these variables when the images are loaded or computed
+    st.session_state.dir_t1img = st.session_state.dir_root + '/test/test_input/test3_nifti+roi'
+    st.session_state.dir_dlmuse = st.session_state.dir_root + '/test/test_input/test3_nifti+roi'
+    st.session_state.suffix_t1img = '_T1.nii.gz'
+    st.session_state.suffix_dlmuse = '_T1_DLMUSE.nii.gz'
 
     # Path to out folder
     st.session_state.out_dir = ''

src/NiChart_Viewer/src/pages/view_img.py

@@ -1,3 +1,4 @@
+import os
 import pandas as pd
 import streamlit as st
 from pandas.api.types import (
@@ -18,11 +19,16 @@
 #     st.session_state.pid = 1
 #     st.session_state.instantiated = True
 
+# Parameters for viewer
 VIEWS = ["axial", "sagittal", "coronal"]
 VIEW_AXES = [0, 1, 2]
 VIEW_OTHER_AXES = [(1,2), (0,2), (0,1)]
+MASK_COLOR = (0, 255, 0)  # RGB format
 
 def reorient_nifti(nii_in, ref_orient = 'LPS'):
+    '''
+    Initial img is reoriented to a standard orientation
+    '''
 
     # Find transform from current (approximate) orientation to
     # target, in nibabel orientation matrix and affine forms
@@ -40,6 +46,7 @@ def crop_image(img, mask):
     '''
     Crop img to the foreground of the mask
     '''
+
     # Detect bounding box
     nz = np.nonzero(mask)
     mn = np.min(nz, axis=1)
@@ -74,7 +81,9 @@ def crop_image(img, mask):
 def detect_mask_bounds(mask):
     '''
     Detect the mask start, end and center in each view
+    Used later to set the slider in the image viewer
     '''
+
     mask_bounds = np.zeros([3,3]).astype(int)
     for i, axis in enumerate(VIEW_AXES):
         mask_bounds[i,0] = 0
@@ -91,6 +100,7 @@ def show_nifti(img, view, sel_axis_bounds):
     '''
     Displays the nifti img
     '''
+
     # Create a slider to select the slice index
     slice_index = st.slider(f"{view}", 0, sel_axis_bounds[1] - 1,
                             value=sel_axis_bounds[2], key = f'slider_{view}')
@@ -105,6 +115,10 @@ def show_nifti(img, view, sel_axis_bounds):
 
 @st.cache_data
 def prep_images(f_img, f_mask, sel_roi_ind):
+    '''
+    Read images from files and create 3D matrices for display
+    '''
+
     # Read nifti
     nii_img = nib.load(f_img)
     nii_mask = nib.load(f_mask)
@@ -127,83 +141,87 @@ def prep_images(f_img, f_mask, sel_roi_ind):
     img = np.stack((img,)*3, axis=-1)
 
     img_masked = img.copy()
-    img_masked[mask == 1] = mask_color
+    img_masked[mask == 1] = MASK_COLOR
 
     # Scale values
     img = img / img.max()
     img_masked = img_masked / img_masked.max()
 
     return img, mask, img_masked
 
-# # Config page
-# st.set_page_config(page_title="DataFrame Demo", page_icon="📊", layout='wide')
-
-# FIXME: Input data is hardcoded here for now
-# fname = "../examples/test_input/vTest1/Study1/StudyTest1_DLMUSE_All.csv"
-fname = "../examples/test_input3/ROIS_tmp.csv"
-df = pd.read_csv(fname)
-
-f1 = "../examples/test_input3/IXI002-Guys-0828_T1.nii.gz"
-f2 = "../examples/test_input3/IXI002-Guys-0828_T1_DLMUSE.nii.gz"
-
-sel_roi = 'Ventricles'
-mask_color = (0, 255, 0)  # RGB format
 
-# Select roi index
+# FIXME: This will be read from file
 dict_roi = {'Ventricles':51, 'Hippocampus_R':100, 'Hippocampus_L':48}
-sel_roi_ind = dict_roi[sel_roi]
 
-# Process image and mask to prepare final 3d matrix to display
-img, mask, img_masked = prep_images(f1, f2, sel_roi_ind)
+
+# Read dataframe with subject mrids
+df = pd.read_csv(st.session_state.fname_subj_list)
 
 # Page controls in side bar
 with st.sidebar:
 
-    # Show selected id (while providing the user the option to select it from the list of all MRIDs)
-    # - get the selected id from the session_state
-    # - create a selectbox with all MRIDs
-    # -- initialize it with the selected id if it's set
-    # -- initialize it with the first id if not
-    sel_id = st.session_state.sel_id
-    if sel_id == '':
-        sel_ind = 0
-        sel_type = '(auto)'
-    else:
-        sel_ind = df.MRID.tolist().index(sel_id)
-        sel_type = '(user)'
-    sel_id = st.selectbox("Select Subject", df.MRID.tolist(), key=f"selbox_mrid", index = sel_ind)
+    with st.container(border=True):
 
-    # st.sidebar.warning('Selected subject: ' + mrid)
-    st.warning(f'Selected {sel_type}: {sel_id}')
+        # Selection of MRID
+        sel_id = st.session_state.sel_id
+        if sel_id == '':
+            sel_ind = 0
+            sel_type = '(auto)'
+        else:
+            sel_ind = df.MRID.tolist().index(sel_id)
+            sel_type = '(user)'
+        sel_id = st.selectbox("Select Subject", df.MRID.tolist(), key=f"selbox_mrid", index = sel_ind)
 
-    st.write('---')
+        # st.sidebar.warning('Selected subject: ' + mrid)
+        st.warning(f'Selected {sel_type}: {sel_id}')
 
-    ## FIXME: read list of rois from dictionary
-    ##        show the roi selected in the plot
-    sel_roi = st.selectbox("Select ROI", list(dict_roi.keys()), key=f"selbox_rois", index = 0)
+        ## FIXME: read list of rois from dictionary
+        ##        show the roi selected in the plot
+        sel_roi = st.selectbox("Select ROI", list(dict_roi.keys()), key=f"selbox_rois", index = 0)
 
-    st.write('---')
+    with st.container(border=True):
 
-    # Create a list of checkbox options
-    #list_orient = st.multiselect("Select viewing planes:", VIEWS, VIEWS[0])
-    list_orient = st.multiselect("Select viewing planes:", VIEWS, VIEWS)
+        # Create a list of checkbox options
+        #list_orient = st.multiselect("Select viewing planes:", VIEWS, VIEWS[0])
+        list_orient = st.multiselect("Select viewing planes:", VIEWS, VIEWS)
 
-    # View hide overlay
-    is_show_overlay = st.checkbox('Show overlay', True)
+        # View hide overlay
+        is_show_overlay = st.checkbox('Show overlay', True)
 
-    # Print the selected options (optional)
-    if list_orient:
-        st.write("Selected options:", list_orient)
+        # Print the selected options (optional)
+        if list_orient:
+            st.write("Selected options:", list_orient)
 
-# Detect mask bounds and center in each view
-mask_bounds = detect_mask_bounds(mask)
+sel_roi = st.session_state.sel_roi
 
-# Show images
-blocks = st.columns(len(list_orient))
-for i, tmp_orient in enumerate(list_orient):
-    with blocks[i]:
-        if is_show_overlay == False:
-            show_nifti(img, tmp_orient, mask_bounds[i,:])
-        else:
-            show_nifti(img_masked, tmp_orient, mask_bounds[i,:])
+# Select roi index
+sel_roi_ind = dict_roi[sel_roi]
+
+
+# File names for img and mask
+f_img = os.path.join(st.session_state.dir_t1img, sel_id + st.session_state.suffix_t1img)
+f_mask = os.path.join(st.session_state.dir_dlmuse, sel_id + st.session_state.suffix_dlmuse)
+
+if os.path.exists(f_img) & os.path.exists(f_mask):
+
+    # Process image and mask to prepare final 3d matrix to display
+    img, mask, img_masked = prep_images(f_img, f_mask, sel_roi_ind)
+
+    # Detect mask bounds and center in each view
+    mask_bounds = detect_mask_bounds(mask)
+
+    # Show images
+    blocks = st.columns(len(list_orient))
+    for i, tmp_orient in enumerate(list_orient):
+        with blocks[i]:
+            if is_show_overlay == False:
+                show_nifti(img, tmp_orient, mask_bounds[i,:])
+            else:
+                show_nifti(img_masked, tmp_orient, mask_bounds[i,:])
+
+else:
+    if not os.path.exists(f_img):
+        st.sidebar.warning(f'Image not found: {f_img}')
+    else:
+        st.sidebar.warning(f'Mask not found: {f_mask}')
 

src/NiChart_Viewer/src/pages/view_plot_data.py

@@ -92,6 +92,8 @@ def display_plot(pid):
 
                 x_var = st.selectbox("X Var", df_filt.columns, key=f"x_var_{pid}", index = x_ind)
                 y_var = st.selectbox("Y Var", df_filt.columns, key=f"y_var_{pid}", index = y_ind)
+                st.session_state.sel_roi = y_var
+
                 hue_var = st.selectbox("Hue Var", df_filt.columns, key=f"hue_var_{pid}", index = hue_ind)
                 trend_type = st.selectbox("Trend Line", st.session_state.trend_types, key=f"trend_type_{pid}", index = trend_index)
 
@@ -219,6 +221,7 @@ def filter_dataframe(df: pd.DataFrame, pid) -> pd.DataFrame:
         spare_csv = st.sidebar.text_input("Enter the name of the ROI csv file:",
                                           value = st.session_state.in_csv_sMRI,
                                           label_visibility="collapsed")
+        st.session_state.fname_subj_list = spare_csv
 
 if os.path.exists(spare_csv):
 
@@ -260,6 +263,8 @@ def filter_dataframe(df: pd.DataFrame, pid) -> pd.DataFrame:
                                                             index = def_ind_x)
                 st.session_state.default_y_var = st.selectbox("Default Y Var", df.columns, key=f"y_var_init",
                                                             index = def_ind_y)
+                st.session_state.sel_roi = st.session_state.default_y_var
+
                 st.session_state.default_hue_var = st.selectbox("Default Hue Var", df.columns, key=f"hue_var_init",
                                                                 index = def_ind_hue)
                 trend_index = st.session_state.trend_types.index(st.session_state.default_trend_type)