From 6372fdcf6780b45e58b7ec640e20e3b5bb5668bc Mon Sep 17 00:00:00 2001
From: AZIZI ANIS p2312052 <anis.azizi@etu.univ-lyon1.fr>
Date: Tue, 10 Dec 2024 09:10:26 +0000
Subject: [PATCH] script python qui permet de convertir les donnees xml de
l'application health vers un format csv pour faciliter l'exploitation
---
Utils/xmltocsv.py | 154 ++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 154 insertions(+)
create mode 100644 Utils/xmltocsv.py
diff --git a/Utils/xmltocsv.py b/Utils/xmltocsv.py
new file mode 100644
index 0000000..a9861b6
--- /dev/null
+++ b/Utils/xmltocsv.py
@@ -0,0 +1,154 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Simple Apple Health XML to CSV
+==============================
+:File: convert.py
+:Description: Convert Apple Health "export.xml" file into a csv
+:Version: 0.0.2
+:Created: 2019-10-04
+:Updated: 2023-10-29
+:Authors: Jason Meno (jam)
+:Dependencies: An export.xml file from Apple Health
+:License: BSD-2-Clause
+"""
+
+# %% Imports
+import os
+import pandas as pd
+import xml.etree.ElementTree as ET
+import datetime as dt
+import sys
+
+
+# %% Function Definitions
+
+def preprocess_to_temp_file(file_path):
+ """
+ The export.xml file is where all your data is, but Apple Health Export has
+ two main problems that make it difficult to parse:
+ 1. The DTD markup syntax is exported incorrectly by Apple Health for some data types.
+ 2. The invisible character \x0b (sometimes rendered as U+000b) likes to destroy trees. Think of the trees!
+
+ Knowing this, we can save the trees and pre-processes the XML data to avoid destruction and ParseErrors.
+ """
+
+ print("Pre-processing and writing to temporary file...", end="")
+ sys.stdout.flush()
+
+ temp_file_path = "temp_preprocessed_export.xml"
+ with open(file_path, 'r') as infile, open(temp_file_path, 'w') as outfile:
+ skip_dtd = False
+ for line in infile:
+ if '<!DOCTYPE' in line:
+ skip_dtd = True
+ if not skip_dtd:
+ line = strip_invisible_character(line)
+ outfile.write(line)
+ if ']>' in line:
+ skip_dtd = False
+
+ print("done!")
+ return temp_file_path
+
+def strip_invisible_character(line):
+ return line.replace("\x0b", "")
+
+
+def xml_to_csv(file_path):
+ """Loops through the element tree, retrieving all objects, and then
+ combining them together into a dataframe
+ """
+
+ print("Converting XML File to CSV...", end="")
+ sys.stdout.flush()
+
+ attribute_list = []
+
+ for event, elem in ET.iterparse(file_path, events=('end',)):
+ if event == 'end':
+ child_attrib = elem.attrib
+ for metadata_entry in list(elem):
+ metadata_values = list(metadata_entry.attrib.values())
+ if len(metadata_values) == 2:
+ metadata_dict = {metadata_values[0]: metadata_values[1]}
+ child_attrib.update(metadata_dict)
+ attribute_list.append(child_attrib)
+
+ # Clear the element from memory to avoid excessive memory consumption
+ elem.clear()
+
+ health_df = pd.DataFrame(attribute_list)
+
+ # Every health data type and some columns have a long identifer
+ # Removing these for readability
+ health_df.type = health_df.type.str.replace('HKQuantityTypeIdentifier', "")
+ health_df.type = health_df.type.str.replace('HKCategoryTypeIdentifier', "")
+ health_df.columns = \
+ health_df.columns.str.replace("HKCharacteristicTypeIdentifier", "")
+
+ # Reorder some of the columns for easier visual data review
+ original_cols = list(health_df)
+ shifted_cols = ['type',
+ 'sourceName',
+ 'value',
+ 'unit',
+ 'startDate',
+ 'endDate',
+ 'creationDate']
+
+ # Add loop specific column ordering if metadata entries exist
+ if 'com.loopkit.InsulinKit.MetadataKeyProgrammedTempBasalRate' in original_cols:
+ shifted_cols.append(
+ 'com.loopkit.InsulinKit.MetadataKeyProgrammedTempBasalRate')
+
+ if 'com.loopkit.InsulinKit.MetadataKeyScheduledBasalRate' in original_cols:
+ shifted_cols.append(
+ 'com.loopkit.InsulinKit.MetadataKeyScheduledBasalRate')
+
+ if 'com.loudnate.CarbKit.HKMetadataKey.AbsorptionTimeMinutes' in original_cols:
+ shifted_cols.append(
+ 'com.loudnate.CarbKit.HKMetadataKey.AbsorptionTimeMinutes')
+
+ remaining_cols = list(set(original_cols) - set(shifted_cols))
+ reordered_cols = shifted_cols + remaining_cols
+ health_df = health_df.reindex(labels=reordered_cols, axis='columns')
+
+ # Sort by newest data first
+ health_df.sort_values(by='startDate', ascending=False, inplace=True)
+
+ print("done!")
+
+ return health_df
+
+
+def save_to_csv(health_df):
+ print("Saving CSV file...", end="")
+ sys.stdout.flush()
+
+ today = dt.datetime.now().strftime('%Y-%m-%d')
+ health_df.to_csv("apple_health_export_" + today + ".csv", index=False)
+ print("done!")
+
+ return
+
+def remove_temp_file(temp_file_path):
+ print("Removing temporary file...", end="")
+ os.remove(temp_file_path)
+ print("done!")
+
+ return
+
+def main():
+ file_path = "apple_health_export/export.xml"
+ temp_file_path = preprocess_to_temp_file(file_path)
+ health_df = xml_to_csv(temp_file_path)
+ save_to_csv(health_df)
+ remove_temp_file(temp_file_path)
+
+ return
+
+
+# %%
+if __name__ == '__main__':
+ main()
\ No newline at end of file
--
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