script python qui permet de convertir les donnees xml de l'application health...

script python qui permet de convertir les donnees xml de l'application health vers un format csv pour faciliter l'exploitation

Utils/xmltocsv.py

+#!/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