Merge from Haji's fork into main fork

.gitignore

@@ -76,6 +76,7 @@ target/
 
 # Jupyter Notebook
 .ipynb_checkpoints
+./visualization/notebooks/.ipynb_checkpoints/*
 
 # IPython
 profile_default/
@@ -135,3 +136,6 @@ docker/local_cache/*
 # Mac OS files
 .DS_Store
 ._.DS_Store
+
+.snakemake
+.idea

Snakefile

@@ -0,0 +1,87 @@
+import os
+import sys
+import numpy as np
+import pandas as pd
+import matplotlib as mpl
+configfile:
+    "config.json"
+os.environ['HCPANNOT_LOAD_PATH'] = config['CACHE_DIR']
+
+# The cache directories:
+from visualization import *
+RATERS = ['BrendaQiu', 'bogengsong', 'JiyeongHa', 'lindazelinzhao', 'nourahboujaber', 'jennifertepan']
+ROIS = ['hV4', 'VO1', 'VO2']
+HEMIS = ['lh','rh']
+
+
+rule get_subj_ids_for_rater:
+    output:
+        subj_ids = os.path.join(config['CACHE_DIR'], 'subj_ids', "rater-{rater}_hemi-{hemi}_roi-{roi}.txt")
+    params:
+        trace_save_path = config['OUTPUT_DIR']
+    run:
+        subj_ids = viscontours.get_trace_list_drawn_by_rater(params.trace_save_path, wildcards.hemi, wildcards.roi, wildcards.rater)
+        with open(output.subj_ids,'w') as fp:
+            for item in subj_ids:
+                # write each item on a new line
+                fp.write("%s," % item)
+
+rule save_trace_cache:
+    output:
+        os.path.join(config['PROC_DIR'],'fsaverage', "{rater}", "{sid}", "{hemi}.roi-{roi}_space-fsaverage_npoints-{n_points}.mgz")
+    params:
+        data_dir = config['DATA_DIR'],
+        proc_dir = config['PROC_DIR']
+    run:
+        x, y = viscontours.make_fsaverage_coords(rater=wildcards.rater,
+                                                 hemi=wildcards.hemi,
+                                                 roi=wildcards.roi,
+                                                 subject=int(wildcards.sid),
+                                                 n_points=int(wildcards.n_points),
+                                                 data_dir=params.data_dir,
+                                                 proc_dir=params.proc_dir,
+                                                 save_path=output[0])
+
+
+def get_subj_ids(wildcards, all=False):
+    if all is False:
+        subj_ids = viscontours.get_trace_list_drawn_by_rater(config['TRACE_DIR'],
+                                                             wildcards.hemi,
+                                                             wildcards.roi,
+                                                             wildcards.rater)
+    else:
+        import neuropythy as ny
+        subj_ids = ny.data['hcp_lines'].subject_list
+    return subj_ids
+
+
+rule all:
+    input:
+        lambda wildcards: expand(os.path.join(config['PROC_DIR'],'fsaverage', "{rater}", "{sid}", "{hemi}.roi-{roi}_space-fsaverage_npoints-{n_points}.mgz"), rater=['JiyeongHa', 'BrendaQiu'], hemi=HEMIS, roi=ROIS, sid=get_subj_ids(wildcards, all=True), n_points=[500])
+
+
+def convert_txt_to_list(wildcards):
+    text_file = os.path.join(config['PATH_SAVE_DIR'], f"rater-{wildcards.rater}_hemi-{wildcards.hemi}_roi-{wildcards.roi}.txt")
+    with open(text_file,'r') as fp:
+        fd_list = fp.read().split(',')
+    return fd_list[:-1]
+
+def get_trace_file_names(wildcards, all):
+    ids_list = get_subj_ids(wildcards, all)
+    all_files = []
+    for sid in ids_list:
+        f = os.path.join(config['PATH_SAVE_DIR'], f"contour-path_space-fsaverage_rater-{wildcards.rater}_sid-{sid}_hemi-{wildcards.hemi}_roi-{wildcards.roi}_npoints-{wildcards.n_points}.mgz")
+        all_files.append(f)
+    return all_files
+
+
+rule save_hcp_subject_list:
+    input:
+        all_files = lambda wildcards: get_trace_file_names(wildcards, all=True)
+    output:
+        os.path.join(config['OUTPUT_DIR'], "subject_list", "hcp_subject_list.txt")
+    run:
+        import neuropythy as ny
+        test = ny.data['hcp_lines'].subject_list
+        with open(output[0],'w') as f:
+            f.write('\n'.join(str(i) for i in test))

config.json

@@ -0,0 +1,6 @@
+{
+  "CACHE_DIR": "/Volumes/server/Projects/annot-vc/hcpannot-cache",
+  "DATA_DIR": "/Volumes/server/Projects/annot-vc/data_branch/save",
+  "PROC_DIR": "/Volumes/server/Projects/annot-vc/derivatives/proc",
+  "OUTPUT_DIR": "/Volumes/server/Projects/annot-vc/derivatives"
+}

docker/roi-tool/jupyter_notebook_config.py

@@ -149,7 +149,7 @@
 #  - originate from pages served by this server (validated with XSRF cookie and
 #  token), or - authenticate with a token
 #  
-#  Some anonymous compute resources still desire the ability to run code,
+#  Some anonymous compute resources still desire the ability to run visualization,
 #  completely without authentication. These services can disable all
 #  authentication and security checks, with the full knowledge of what that
 #  implies.

hcpannot/__init__.py

@@ -1,5 +1,5 @@
 '''
-The hcpannot package contains code for annotating the HCP 7 Tesla Retinotopy
+The hcpannot package contains visualization for annotating the HCP 7 Tesla Retinotopy
 Dataset (DOI: 10.1167/18.13.23).
 
 These tools are intended to be run using a Docker container; for information

hcpannot/io.py

@@ -10,7 +10,7 @@
 import os, json
 from collections.abc import Mapping
 from pathlib import Path
-
+import pandas
 import numpy as np
 import neuropythy as ny
 
@@ -929,23 +929,26 @@ def load_report(region, rater, sid, h, reports_path=None):
     except Exception as e:
         pass
     return data
+
 def load_allreports(region,
                     reports_path=None,
                     include_mean=True,
-                    sids=subject_list):
+                    sids=None): 
     """Loads all reports for a region and returns a dataframe of them.
 
     This runs `load_report` over all raters, subjects, and hemispheres and
     returns a dataframe of all the reports. If a report file is not found,
     then the row is left with NaNs indicating missing data.
     """
+    from .config import raters_by_region 
+
     if include_mean:
         if include_mean == True:
             include_mean = 'mean'
         include_mean = [include_mean]
     else:
         include_mean = []
-    raters = (region_raters[region] + include_mean)
+    raters = (raters_by_region[region] + include_mean)
     return pandas.DataFrame(
         [load_report(region, rater, sid, h, reports_path=reports_path)
          for rater in raters