Upload data_processing.py

data_processing.py

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+# -*- coding: utf-8 -*-
+"""data_processing.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Oz1QL0mD9g3lVBgtmqHa-QiwwIJ2JaX5
+"""
+
+import pandas as pd
+import numpy as np
+import os
+from zipfile import ZipFile
+import re
+import json
+import io
+from PIL import Image, ImageFile
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+from google.colab import drive
+drive.mount('/content/drive')
+
+path = "/content/drive/MyDrive/Duke/aphantasia_drawing_project/"
+
+data_path = os.path.join(path,"data",'drawing_experiment')
+
+
+
+df = pd.read_excel(data_path+"/questionnaire-data.xlsx", header=2)
+
+df["vviq_score"] = np.sum(df.filter(like = "vviq"), axis = 1)
+df["osiq_score"] = np.sum(df.filter(like = "osiq"), axis = 1)
+df["treatment"] = np.where(df.vviq_score > 40, "control", "aphantasia")
+
+df = df.rename(columns={
+    "Sub ID": "sub_id",
+    df.columns[5]: "art_ability",
+    df.columns[6]: "art_experience",
+    df.columns[9]: "difficult",
+    df.columns[10]: "diff_explanation"
+})
+
+df.columns = df.columns.str.lower()
+
+df = df.drop(df.filter(like="unnamed").columns, axis = 1)
+df = df.drop(df.filter(regex="(vviq|osiq)\d+").columns, axis = 1)
+
+df[df.columns[df.dtypes == "object"]] = df[df.columns[df.dtypes == "object"]].astype("string")
+
+df = df.replace([np.nan,pd.NA, "nan","na","NA","n/a","N/A","N/a"], None)
+
+df.set_index('sub_id', inplace=True)
+
+
+
+actual_image_path = os.path.join(data_path,"Stimuli","Images")
+
+
+
+actual_images = {}
+for image_file in os.listdir(actual_image_path):
+  img_path = os.path.join(actual_image_path, image_file)
+  actual_images[image_file.removesuffix(".jpg")] = Image.open(img_path)
+
+
+
+key_map = {
+    'high_sun_ajwbpqrwvknlvpeh': 'kitchen',
+    'low_sun_acqsqjhtcbxeomux': 'bedroom',
+    "low_sun_byqgoskwpvsbllvy":"livingroom"
+    }
+
+for old_key, new_key in key_map.items():
+  actual_images[new_key] = actual_images.pop(old_key)
+
+aphantasia_drawings_path = os.path.join(data_path,"Drawings","Aphantasia")
+control_drawings_path = os.path.join(data_path,"Drawings","Control")
+
+
+
+directories = {
+    "Aphantasia": aphantasia_drawings_path,
+    "Control": control_drawings_path
+}
+
+
+
+aphantasia_subs = {i: "Aphantasia" for i in os.listdir(directories["Aphantasia"]) if "sub" in i}
+control_subs = {i: "Control" for i in os.listdir(directories["Control"]) if "sub" in i}
+sub_treatment_key = {**aphantasia_subs, **control_subs}
+
+
+
+def get_sub_files(sub):
+  treatment_group = sub_treatment_key[sub]
+  directory = directories[treatment_group]
+  pattern = re.compile("^.*" + sub + "-[a-z]{3}\d-(kitchen|livingroom|bedroom).*")
+  sub_files = os.listdir(os.path.join(directory, sub))
+  files_needed = {'mem1',"mem2",'mem3','pic1','pic2','pic3'}
+
+  sub_key = {}
+  for f in sub_files:
+    if pattern.match(f):
+      main_path = os.path.join(directory, sub, f)
+      draw_type = f.split("-")[1]
+      label = f.split("-")[2].removesuffix(".jpg")
+      alt_path = os.path.join(directory, sub, "-".join([sub, draw_type]) + ".jpg")
+      try:
+        img = Image.open(main_path)
+      except:
+        img = Image.open(alt_path)
+      sub_key[draw_type] = {
+          "label": label,
+          "drawing": img
+      }
+
+  unknown_drawings = files_needed - sub_key.keys()
+  if unknown_drawings:
+    for unk in unknown_drawings:
+      path = os.path.join(directory, sub, "-".join([sub, unk]) + ".jpg")
+      try:
+        img = Image.open(path)
+      except:
+        img = "No Image"
+      sub_key[unk] = {
+          "label": "unknown",
+          "drawing": img
+      }
+  return sub_key
+
+
+
+
+
+subject_data = {}
+for sub in iter(sub_treatment_key):
+  subject_data[sub] = get_sub_files(sub)
+
+def is_image_blank(image):
+  if image.mode != 'RGB':
+    image = image.convert('RGB')
+  pixels = list(image.getdata())
+  return all(pixel == (255, 255, 255) for pixel in pixels)
+
+for sub in iter(subject_data):
+  dat = subject_data[sub]
+  for key in dat.keys():
+    if is_image_blank(dat[key]["drawing"]):
+      dat[key]["label"] = "blank"
+
+
+
+subs_missing_labels = {}
+for sub in iter(subject_data):
+  dat = subject_data[sub]
+  for key in dat.keys():
+    if "unknown" in dat[key]["label"]:
+      if sub not in subs_missing_labels:
+        subs_missing_labels[sub] = []
+      subs_missing_labels[sub].append(key)
+
+subs_missing_labels
+
+subject_data["sub8"]["pic3"]["label"] = "livingroom"
+subject_data["sub6"]["pic3"]["label"] = "bedroom"
+subject_data["sub6"]["pic1"]["label"] = "kitchen"
+
+
+
+def clean_sub_dat(sub):
+  id = int(sub[3:])
+  treatment_group = sub_treatment_key[sub]
+  if id in df.index:
+    demographics_dict = df.loc[id].to_dict()
+  else:
+    demographics_dict = {}
+  demographics_dict.pop("treatment",None)
+  drawings = {
+    "bedroom": {},
+    "kitchen": {},
+    "livingroom": {}
+    }
+  for draw_type, draw_data in subject_data[sub].items():
+    t = "memory" if draw_type[:-1] == "mem" else "perception"
+    for d in drawings.keys():
+      if draw_data["label"] == d:
+        drawings[d][t] = draw_data["drawing"]
+
+  return {
+      "subject_id": id,
+      "treatment": treatment_group,
+      "demographics": demographics_dict,
+      "drawings": drawings,
+      "image": actual_images
+  }
+
+
+
+full_data = []
+for s in subject_data.keys():
+  full_data.append(clean_sub_dat(s))
+
+"""160,161,162 removed, they dont have images"""
+
+
+
+full_df = pd.json_normalize(full_data)
+
+
+
+def image_to_byt(img, size=(224, 224)):
+  if  pd.isna(img):
+    return None
+  img_resized = img.resize(size)
+  img_byte_arr = io.BytesIO()
+  img_resized.save(img_byte_arr, format='PNG')
+  return img_byte_arr.getvalue()
+
+drawing_columns = [col for col in full_df.columns if "drawings" in col or "image" in col]
+
+
+
+for col in drawing_columns:
+  full_df[col] = full_df[col].apply(image_to_byt)
+
+def safe_convert_to_int(value):
+  try:
+    return int(value)
+  except (ValueError, TypeError):
+    return -99
+
+col_to_process = [
+    "demographics.age",
+    "demographics.art_ability",
+    "demographics.vviq_score",
+    "demographics.osiq_score"
+]
+
+for col in col_to_process:
+    full_df[col] = full_df[col].apply(safe_convert_to_int)
+
+full_data_path = os.path.join(path, "data","aphantasia_data.parquet")
+
+full_df.to_parquet(full_data_path, index=False)
+