Delete dataprocessingcopy.py

dataprocessingcopy.py

@@ -1,203 +0,0 @@
-# -*- 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')