Upload aphantasia_drawing_dataset.py

aphantasia_drawing_dataset.py

@@ -1,13 +1,13 @@
 # -*- coding: utf-8 -*-
 """aphantasia_drawing_dataset.ipynb
 
-Automatically generated by Colaboratory.
+Automatically generated by Colab.
 
 Original file is located at
-    https://colab.research.google.com/drive/1FHMQJWfjSzSrtEpARqh7IKVrfF7LwyRA
+    https://colab.research.google.com/drive/1DYVroeFqoNK7DDiw_3OIczPeqEME-rbh
 """
 
-
+!pip install -q datasets
 
 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
 #
@@ -26,9 +26,10 @@ Original file is located at
 """TODO: Add a description here."""
 
 
-#import csv
+
 import base64
 from PIL import Image
+import pandas as pd
 import numpy as np
 import io
 import json
@@ -49,7 +50,6 @@ month={Sep}
 }
 """
 
-
 _DESCRIPTION = """\
 This dataset comes from the Brain Bridge Lab from the University of Chicago.
 It is from an online memory drawing experiment with 61 individuals with aphantasia
@@ -65,10 +65,7 @@ updated September 27, 2023.
 _HOMEPAGE = "https://osf.io/cahyd/"
 
 
-
-
-
-url = "https://drive.google.com/file/d/1aRhQlKPDk29yYPkx2kPhqaMwec5QZ4JE/view?usp=sharing"
+url = "https://drive.google.com/file/d/1v1oaZog5j5dD_vIElOEWLCZUrXvJ3jzx/view?usp=drive_link"
 
 def _get_drive_url(url):
         base_url = 'https://drive.google.com/uc?id='
@@ -78,6 +75,7 @@ def _get_drive_url(url):
 _URL = {"train": _get_drive_url(url)}
 
 
+
 class AphantasiaDrawingDataset(datasets.GeneratorBasedBuilder):
     """TODO: Short description of my dataset."""
 
@@ -141,67 +139,72 @@ class AphantasiaDrawingDataset(datasets.GeneratorBasedBuilder):
     def _generate_examples(self, filepath):
       """This function returns the examples in the raw (text) form."""
       logging.info("generating examples from = %s", filepath)
+      def byt_to_image(image_bytes):
+        if image_bytes is not None:
+          image_buffer = io.BytesIO(image_bytes)
+          image = Image.open(image_buffer)
+          return image
+        return None
 
       with open(filepath, "r") as subjects_file:
-        subjects_data = json.load(subjects_file)
-        idx = 0
-        for sub in subjects_data:
-          for room in subjects_data[sub]["drawings"].keys():
-            if subjects_data[sub]["drawings"][room]["perception"] != "":
-              img_byt = base64.b64decode(subjects_data[sub]["drawings"][room]["perception"])
-              img = Image.open(io.BytesIO(img_byt))
-              subjects_data[sub]["drawings"][room]["perception"] = img
-            else:
-              subjects_data[sub]["drawings"][room]["perception"] = None
-
-            if subjects_data[sub]["drawings"][room]["memory"] != "":
-              img_byt = base64.b64decode(subjects_data[sub]["drawings"][room]["memory"])
-              img = Image.open(io.BytesIO(img_byt))
-              subjects_data[sub]["drawings"][room]["memory"] = img
-            else:
-              subjects_data[sub]["drawings"][room]["memory"] = None
-
-          for room in subjects_data[sub]["image"].keys():
-            img_byt = base64.b64decode(subjects_data[sub]["image"][room])
-            img = Image.open(io.BytesIO(img_byt))
-            subjects_data[sub]["image"][room] = img.resize((500,500))
-          idx += 1
-          age = int(subjects_data[sub]["demographics"]["age"]) if subjects_data[sub]["demographics"]["age"] else np.nan
-          yield idx-1, {
-              "subject_id": subjects_data[sub]["subject_id"],
-              "treatment": subjects_data[sub]["treatment"],
+        subjects_data = pd.read_parquet(subjects_file)
+        #idx = 0
+        for sub_row in subjects_data.iterrows():
+          # for room in subjects_data[sub]["drawings"].keys():
+          #   if subjects_data[sub]["drawings"][room]["perception"] != "":
+          #     img_byt = base64.b64decode(subjects_data[sub]["drawings"][room]["perception"])
+          #     img = Image.open(io.BytesIO(img_byt))
+          #     subjects_data[sub]["drawings"][room]["perception"] = img
+          #   else:
+          #     subjects_data[sub]["drawings"][room]["perception"] = None
+
+          #   if subjects_data[sub]["drawings"][room]["memory"] != "":
+          #     img_byt = base64.b64decode(subjects_data[sub]["drawings"][room]["memory"])
+          #     img = Image.open(io.BytesIO(img_byt))
+          #     subjects_data[sub]["drawings"][room]["memory"] = img
+          #   else:
+          #     subjects_data[sub]["drawings"][room]["memory"] = None
+
+          # for room in subjects_data[sub]["image"].keys():
+          #   img_byt = base64.b64decode(subjects_data[sub]["image"][room])
+          #   img = Image.open(io.BytesIO(img_byt))
+          #   subjects_data[sub]["image"][room] = img.resize((500,500))
+          # idx += 1
+          age = int(sub_row["demographics.age"]) if sub_row["demographics.age"] else np.nan
+          yield {
+              "subject_id": sub_row["subject_id"],
+              "treatment": sub_row["treatment"],
               "demographics": {
-                  "country": subjects_data[sub]["demographics"]["country"],
-                  "age": subjects_data[sub]["demographics"]["age"],
-                  "gender": subjects_data[sub]["demographics"]["gender"],
-                  "occupation": subjects_data[sub]["demographics"]["occupation"],
-                  "art_ability": subjects_data[sub]["demographics"]["art_ability"],
-                  "art_experience": subjects_data[sub]["demographics"]["art_experience"],
-                  "device": subjects_data[sub]["demographics"]["device"],
-                  "input": subjects_data[sub]["demographics"]["input"],
-                  "difficult": subjects_data[sub]["demographics"]["difficult"],
-                  "diff_explanation": subjects_data[sub]["demographics"]["diff_explanation"],
-                  "vviq_score": subjects_data[sub]["demographics"]["vviq_score"],
-                  "osiq_score": subjects_data[sub]["demographics"]["osiq_score"]
-              },
+                  "country": sub_row["demographics.country"],
+                  "age": age, #sub_row["demographics.age"],
+                  "gender": sub_row["demographics.gender"],
+                  "occupation": sub_row["demographics.occupation"],
+                  "art_ability": sub_row["demographics.art_ability"],
+                  "art_experience": sub_row["demographics.art_experience"],
+                  "device": sub_row["demographics.device"],
+                  "input": sub_row["demographics.input"],
+                  "difficult": sub_row["demographics.difficult"],
+                  "diff_explanation": sub_row["demographics.diff_explanation"],
+                  "vviq_score": sub_row["demographics.vviq_score"],
+                  "osiq_score": sub_row["demographics.osiq_score"]
+                  },
               "drawings": {
                   "kitchen": {
-                      "perception": subjects_data[sub]["drawings"]["kitchen"]["perception"],
-                      "memory": subjects_data[sub]["drawings"]["kitchen"]["memory"]
+                      "perception": byt_to_image(sub_row["drawings.kitchen.perception"]),
+                      "memory": byt_to_image(sub_row["drawings.kitchen.memory"])
                       },
                   "livingroom": {
-                      "perception": subjects_data[sub]["drawings"]["livingroom"]["perception"],
-                      "memory": subjects_data[sub]["drawings"]["livingroom"]["memory"]
+                      "perception": byt_to_image(sub_row["drawings.livingroom.perception"]),
+                      "memory": byt_to_image(sub_row["drawings.livingroom.memory"])
                       },
                   "bedroom": {
-                      "perception": subjects_data[sub]["drawings"]["bedroom"]["perception"],
-                      "memory": subjects_data[sub]["drawings"]["bedroom"]["memory"]
+                      "perception": byt_to_image(sub_row["drawings.bedroom.perception"]),
+                      "memory": byt_to_image(sub_row["drawings.bedroom.memory"])
                       }
-              },
+                  },
               "image": {
-                  "kitchen": subjects_data[sub]["image"]["kitchen"],
-                  "livingroom": subjects_data[sub]["image"]["livingroom"],
-                  "bedroom": subjects_data[sub]["image"]["bedroom"]
-              }
-            }
-
+                  "kitchen": byt_to_image(sub_row["image.kitchen"]),
+                  "livingroom": byt_to_image(sub_row["image.livingroom"]),
+                  "bedroom": byt_to_image(sub_row["image.bedroom"])
+                  }
+              }