My commit

dataprocessingcopy.py

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+# -*- coding: utf-8 -*-
+"""dataprocessing.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/10At7vh21OGTlE-Myv1NhAHi7l7NwBocQ
+"""
+
+import pandas as pd
+import numpy as np
+import os
+from zipfile import ZipFile
+import re
+import json
+import base64
+
+from google.colab import drive
+drive.mount('/content/drive')
+
+path = "/content/drive/MyDrive/Duke/huggingface_project/data"
+
+df = pd.read_excel(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.columns] = df[df.columns].replace([np.nan,pd.NA, "nan","na","NA","n/a","N/A","N/a"], None)
+
+data = {}
+for ind, row in df.iterrows():
+  data[row["sub_id"]] = {
+      "subject_id": int(row["sub_id"]),
+      "treatment": row["treatment"],
+      "demographics": dict(df.iloc[ind][1:-1])
+  }
+  data[row["sub_id"]]["demographics"]["art_ability"] = int(data[row["sub_id"]]["demographics"]["art_ability"])
+  data[row["sub_id"]]["demographics"]["vviq_score"] = int(data[row["sub_id"]]["demographics"]["vviq_score"])
+  data[row["sub_id"]]["demographics"]["osiq_score"] = int(data[row["sub_id"]]["demographics"]["osiq_score"])
+
+stored_images = {}
+with ZipFile(path + "/Images.zip", "r") as zip:
+  for image_file in zip.namelist():
+    with zip.open(image_file, 'r') as fil:
+      im = fil.read()
+      im_encoded = base64.b64encode(im).decode("utf-8")
+      stored_images[image_file.removesuffix(".jpg")] = im_encoded
+
+def get_sub_files(subject, file_list):
+  pattern = re.compile("^.*" + subject + "-[a-z]{3}\d-(kitchen|livingroom|bedroom).*")
+  sub_files = [f for f in file_list if pattern.match(f)]
+  sub = {
+    "kitchen": {
+        "perception": "",
+        "memory": ""
+    },
+    "livingroom": {
+        "perception": "",
+        "memory": ""
+    },
+    "bedroom": {
+        "perception": "",
+        "memory": ""
+    },
+  }
+
+  for fil in sub_files:
+    if "kitchen" in fil:
+      if "pic" in fil:
+        sub["kitchen"]["perception"] = fil
+      else:
+        sub["kitchen"]["memory"] = fil
+    elif "livingroom" in fil:
+      if "pic" in fil:
+        sub["livingroom"]["perception"] = fil
+      else:
+        sub["livingroom"]["memory"] = fil
+    else:
+      if "pic" in fil:
+        sub["bedroom"]["perception"] = fil
+      else:
+        sub["bedroom"]["memory"] = fil
+  return sub
+
+with ZipFile(path + "/Aphantasia-Drawings.zip", "r") as zip:
+  files = zip.namelist()
+  aphan_subs = list({f.split("/")[0] for f in files})
+  aphantasia_drawing_dataset = {}
+  for s in aphan_subs:
+    if int(s[3:]) in data.keys():
+      data[int(s[3:])]["drawings"] = get_sub_files(s, files)
+    else:
+      data[int(s[3:])] = {"drawings": get_sub_files(s,files)}
+
+with ZipFile(path + "/Control-Drawings.zip", "r") as zip:
+  files = zip.namelist()
+  cntrl_subs = list({f.split("/")[0] for f in files})
+  full_control = {}
+  for s in cntrl_subs:
+    if int(s[3:]) in data.keys():
+      data[int(s[3:])]["drawings"] = get_sub_files(s, files)
+    else:
+      data[int(s[3:])] = {"drawings": get_sub_files(s,files)}
+
+stored_images["kitchen"] = stored_images.pop('high_sun_ajwbpqrwvknlvpeh')
+stored_images["bedroom"] = stored_images.pop('low_sun_acqsqjhtcbxeomux')
+stored_images["livingroom"] = stored_images.pop('low_sun_byqgoskwpvsbllvy')
+
+def extract_images(subject, treatment):
+  images_bytes = {
+      "kitchen": {
+          "perception": "",
+          "memory": ""
+      },
+      "livingroom": {
+          "perception": "",
+          "memory": ""
+      },
+      "bedroom": {
+          "perception": "",
+          "memory": ""
+      }
+  }
+  for room in ["kitchen", "livingroom", "bedroom"]:
+    paths = data[subject]["drawings"].get(room).values()
+    paths = [p for p in paths if p != ""]
+    if treatment == "aphantasia":
+      with ZipFile(path + "/Aphantasia-Drawings.zip", "r") as zip:
+        for filename in paths:
+          with zip.open(filename, 'r') as fil:
+            im = fil.read()
+            im_encoded = base64.b64encode(im).decode("utf-8")
+            if "mem" in filename:
+              images_bytes[room]["memory"] = im_encoded
+            else:
+              images_bytes[room]["perception"] = im_encoded
+    else:
+      with ZipFile(path + "/Control-Drawings.zip", "r") as zip:
+        for filename in paths:
+          with zip.open(filename, 'r') as fil:
+            im = fil.read()
+            im_encoded = base64.b64encode(im).decode("utf-8")
+            if "mem" in filename:
+              images_bytes[room]["memory"] = im_encoded
+            else:
+              images_bytes[room]["perception"] = im_encoded
+
+  return images_bytes
+
+missing = []
+for i in data.keys():
+  if "drawings" in data[i] and "treatment" in data[i]:
+    data[i]["drawings"] = extract_images(i,data[i]["treatment"])
+  else:
+    missing.append(i)
+
+for num in missing:
+  data.pop(num, None)
+
+for sub in data.keys():
+  data[sub]["image"] = stored_images
+
+subject_data_path = path + "/clean_data.json"
+
+#with open(subject_data_path, "w", encoding="utf-8") as sub_data:
+  #json.dump(data, sub_data, indent=2)
+
+type(data)
+
+da = pd.DataFrame(data)
+
+
+
+
+
+flattened_data = []
+
+for key, value in data.items():
+    flattened_subject = pd.json_normalize(value, sep='_')
+    flattened_data.append(flattened_subject)
+
+
+da = pd.concat(flattened_data, ignore_index=True)
+
+# Save the DataFrame to a Parquet file
+da.to_parquet(path + 'data.parquet')