From 18a09b28e19e81d0a64039e14f3dd849a12966d9 Mon Sep 17 00:00:00 2001
From: Vincenzo Marciano <50915433+SanBast@users.noreply.github.com>
Date: Wed, 21 Sep 2022 09:00:25 +0200
Subject: [PATCH] Add files via upload
---
10s_windowing.ipynb | 330 +++++++++
first_test.ipynb | 1705 +++++++++++++++++++++++++++++++++++++++++++
2 files changed, 2035 insertions(+)
create mode 100644 10s_windowing.ipynb
create mode 100644 first_test.ipynb
diff --git a/10s_windowing.ipynb b/10s_windowing.ipynb
new file mode 100644
index 0000000..05a6cbc
--- /dev/null
+++ b/10s_windowing.ipynb
@@ -0,0 +1,330 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib\n",
+ "import seaborn as sns\n",
+ "from pylab import rcParams\n",
+ "import os\n",
+ "import gzip\n",
+ "from tqdm import tqdm\n",
+ "import pandas as pd\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "%config InlineBackend.figure_format='retina'\n",
+ "\n",
+ "sns.set(style='whitegrid', palette='muted', font_scale=1.2)\n",
+ "\n",
+ "HAPPY_COLORS_PALETTE = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#ADFF02\", \"#8F00FF\"]\n",
+ "\n",
+ "sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))\n",
+ "rcParams['figure.figsize'] = 20, 10"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "full_df = pd.read_csv('full_df.csv')\n",
+ "\n",
+ "outdoor_df = full_df[full_df['IndoorProb']!=100]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\marci\\AppData\\Local\\Temp\\ipykernel_4064\\544610941.py:1: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " outdoor_df['series_id'] = np.arange(len(outdoor_df)) // 10 + 1\n"
+ ]
+ }
+ ],
+ "source": [
+ "outdoor_df['series_id'] = np.arange(len(outdoor_df)) // 10 + 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\marci\\AppData\\Local\\Temp\\ipykernel_4064\\3215045333.py:1: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " outdoor_df['Timestamp'] = pd.to_datetime(outdoor_df['Timestamp'], unit='s')\n"
+ ]
+ }
+ ],
+ "source": [
+ "outdoor_df['Timestamp'] = pd.to_datetime(outdoor_df['Timestamp'], unit='s')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "19040895"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(outdoor_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Unnamed: 0 | \n",
+ " Patient | \n",
+ " Cohort | \n",
+ " Day | \n",
+ " StepPerSec | \n",
+ " Timestamp | \n",
+ " IndoorProb | \n",
+ " series_id | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 29017 | \n",
+ " 29017 | \n",
+ " 1000 | \n",
+ " HA | \n",
+ " Day1 | \n",
+ " 0.875 | \n",
+ " 2020-08-13 07:03:37 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 29018 | \n",
+ " 29018 | \n",
+ " 1000 | \n",
+ " HA | \n",
+ " Day1 | \n",
+ " 0.875 | \n",
+ " 2020-08-13 07:03:38 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 29019 | \n",
+ " 29019 | \n",
+ " 1000 | \n",
+ " HA | \n",
+ " Day1 | \n",
+ " 0.875 | \n",
+ " 2020-08-13 07:03:39 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 29020 | \n",
+ " 29020 | \n",
+ " 1000 | \n",
+ " HA | \n",
+ " Day1 | \n",
+ " 0.875 | \n",
+ " 2020-08-13 07:03:40 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 29021 | \n",
+ " 29021 | \n",
+ " 1000 | \n",
+ " HA | \n",
+ " Day1 | \n",
+ " 0.875 | \n",
+ " 2020-08-13 07:03:41 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Unnamed: 0 Patient Cohort Day StepPerSec Timestamp \\\n",
+ "29017 29017 1000 HA Day1 0.875 2020-08-13 07:03:37 \n",
+ "29018 29018 1000 HA Day1 0.875 2020-08-13 07:03:38 \n",
+ "29019 29019 1000 HA Day1 0.875 2020-08-13 07:03:39 \n",
+ "29020 29020 1000 HA Day1 0.875 2020-08-13 07:03:40 \n",
+ "29021 29021 1000 HA Day1 0.875 2020-08-13 07:03:41 \n",
+ "\n",
+ " IndoorProb series_id \n",
+ "29017 0 1 \n",
+ "29018 0 1 \n",
+ "29019 0 1 \n",
+ "29020 0 1 \n",
+ "29021 0 1 "
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "outdoor_df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 1843185/1843185 [14:16<00:00, 2151.82it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "rows = []\n",
+ "for _,group in tqdm(outdoor_df[outdoor_df['StepPerSec'] < 1].groupby(['series_id', 'Patient']), position=0, leave=True):\n",
+ " #if group.StepPerSec.count()==10:\n",
+ " rows.append([\n",
+ " group['Patient'].unique()[0], \n",
+ " group['Cohort'].unique()[0], \n",
+ " group['StepPerSec'].count(),\n",
+ " group['Timestamp'].iloc[0],\n",
+ " group['Timestamp'].iloc[-1],\n",
+ " group['StepPerSec'].mean(),\n",
+ " group['StepPerSec'].std()\n",
+ " ])\n",
+ " #print(row)\n",
+ "missing_df = pd.DataFrame(np.array(rows),columns=['Subject', 'Cohort', 'Duration', 'start_timestamp', 'end_timestamp', 'mean', 'std'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "missing_df.to_csv('walking_missing.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "ModuleNotFoundError",
+ "evalue": "No module named 'openpyxl'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn [10], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mExcelWriter\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfile_for_missing_steps.xlsx\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m writer:\n\u001b[0;32m 2\u001b[0m missing_df\u001b[38;5;241m.\u001b[39mto_excel(writer, sheet_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m10s_steps\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+ "File \u001b[1;32mc:\\Users\\marci\\miniconda3\\lib\\site-packages\\pandas\\io\\excel\\_openpyxl.py:49\u001b[0m, in \u001b[0;36mOpenpyxlWriter.__init__\u001b[1;34m(self, path, engine, date_format, datetime_format, mode, storage_options, if_sheet_exists, engine_kwargs, **kwargs)\u001b[0m\n\u001b[0;32m 36\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\n\u001b[0;32m 37\u001b[0m \u001b[39mself\u001b[39m,\n\u001b[0;32m 38\u001b[0m path,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 47\u001b[0m ):\n\u001b[0;32m 48\u001b[0m \u001b[39m# Use the openpyxl module as the Excel writer.\u001b[39;00m\n\u001b[1;32m---> 49\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mopenpyxl\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mworkbook\u001b[39;00m \u001b[39mimport\u001b[39;00m Workbook\n\u001b[0;32m 51\u001b[0m engine_kwargs \u001b[39m=\u001b[39m combine_kwargs(engine_kwargs, kwargs)\n\u001b[0;32m 53\u001b[0m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39m\u001b[39m__init__\u001b[39m(\n\u001b[0;32m 54\u001b[0m path,\n\u001b[0;32m 55\u001b[0m mode\u001b[39m=\u001b[39mmode,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 58\u001b[0m engine_kwargs\u001b[39m=\u001b[39mengine_kwargs,\n\u001b[0;32m 59\u001b[0m )\n",
+ "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'openpyxl'"
+ ]
+ }
+ ],
+ "source": [
+ "with pd.ExcelWriter('file_for_missing_steps.xlsx') as writer:\n",
+ " missing_df.to_excel(writer, sheet_name='10s_steps')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.9.12 ('base')",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.12"
+ },
+ "orig_nbformat": 4,
+ "vscode": {
+ "interpreter": {
+ "hash": "9324f6f91069ef608944cf59327718832b88647e83e66beddcee769fe0e7a057"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/first_test.ipynb b/first_test.ipynb
new file mode 100644
index 0000000..acbd4ae
--- /dev/null
+++ b/first_test.ipynb
@@ -0,0 +1,1705 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib\n",
+ "import seaborn as sns\n",
+ "from pylab import rcParams"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "%config InlineBackend.figure_format='retina'\n",
+ "\n",
+ "sns.set(style='whitegrid', palette='muted', font_scale=1.2)\n",
+ "\n",
+ "HAPPY_COLORS_PALETTE = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#ADFF02\", \"#8F00FF\"]\n",
+ "\n",
+ "sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))\n",
+ "rcParams['figure.figsize'] = 20, 10"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import gzip\n",
+ "import shutil\n",
+ "\n",
+ "folder = 'mobilised-contextual-factors-v1'\n",
+ "\n",
+ "def extractFiles(path, verbose=False):\n",
+ " for p in os.listdir(folder):\n",
+ " i=0\n",
+ " subfold = os.path.join(folder, p)\n",
+ " for f in os.listdir(subfold):\n",
+ " i+=1\n",
+ " if f.endswith('.gz'):\n",
+ " filename = os.path.join(subfold, f)\n",
+ " extr_filename = filename.split('.gz')[0]\n",
+ " with gzip.open(filename, 'rb') as f_in:\n",
+ " with open(extr_filename, 'wb') as f_out:\n",
+ " shutil.copyfileobj(f_in, f_out)\n",
+ " if verbose:\n",
+ " print(f'Extracted {i} files for patient {p}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "info_df = pd.read_csv('CF_RWS_missingfiles.xlsx - Sheet1.csv')\n",
+ "info_df.drop(columns='Unique ID ', inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " ID | \n",
+ " Cohort | \n",
+ " D1_RWS | \n",
+ " D2_RWS | \n",
+ " D3_RWS | \n",
+ " D4_RWS | \n",
+ " D5_RWS | \n",
+ " D6_RWS | \n",
+ " D7_RWS | \n",
+ " FL_RWS | \n",
+ " Mean | \n",
+ " SD | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 1000 | \n",
+ " HA | \n",
+ " 1.435969103 | \n",
+ " 1.208401082 | \n",
+ " 1.47810092 | \n",
+ " 1.266655896 | \n",
+ " 1.091846016 | \n",
+ " 1.19178053 | \n",
+ " 1.303706372 | \n",
+ " 0.52494135 | \n",
+ " 1.152204595 | \n",
+ " 0.3011331211 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 1001 | \n",
+ " HA | \n",
+ " 0.7617805546 | \n",
+ " 1.19912152 | \n",
+ " 0.9624181844 | \n",
+ " 1.338421753 | \n",
+ " 0.9198683369 | \n",
+ " 1.188084293 | \n",
+ " 0.5 | \n",
+ " - | \n",
+ " 1.017985681 | \n",
+ " 0.2891685504 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 1002 | \n",
+ " HA | \n",
+ " 1.265350375 | \n",
+ " 1.442073541 | \n",
+ " 1.219132102 | \n",
+ " 1.177058133 | \n",
+ " 1.279356812 | \n",
+ " 1.435563794 | \n",
+ " 1.396013597 | \n",
+ " - | \n",
+ " 1.32486633 | \n",
+ " 0.1073837562 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 1003 | \n",
+ " PD | \n",
+ " 0.69954984 | \n",
+ " 0.664800558 | \n",
+ " 0.817476946 | \n",
+ " 0.994005528 | \n",
+ " 0.810221351 | \n",
+ " 1.02773144 | \n",
+ " 1.336634741 | \n",
+ " 1.234558728 | \n",
+ " 0.9481223915 | \n",
+ " 0.2290128237 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 1004 | \n",
+ " PD | \n",
+ " 0.844786013 | \n",
+ " 0.8920834942 | \n",
+ " 1.053314499 | \n",
+ " 0.9711816153 | \n",
+ " 0.7733374742 | \n",
+ " 1.097224889 | \n",
+ " 1.097224889 | \n",
+ " - | \n",
+ " 0.9613075535 | \n",
+ " 0.1285896639 | \n",
+ "
\n",
+ " \n",
+ " 5 | \n",
+ " 1005 | \n",
+ " PD | \n",
+ " 1.267099798 | \n",
+ " 0.577708104 | \n",
+ " 0.692487668 | \n",
+ " 1.15232039 | \n",
+ " 1.04157882 | \n",
+ " 1.245193767 | \n",
+ " 0.857921834 | \n",
+ " 1.069256386 | \n",
+ " 0.9879458459 | \n",
+ " 0.2544200958 | \n",
+ "
\n",
+ " \n",
+ " 6 | \n",
+ " 1006 | \n",
+ " COPD | \n",
+ " 0.5794542551 | \n",
+ " 0.925525475 | \n",
+ " 1.016206426 | \n",
+ " 1.074272686 | \n",
+ " 0.828173891 | \n",
+ " 0.747681114 | \n",
+ " 1.00362861 | \n",
+ " 0.730722234 | \n",
+ " 0.8632080864 | \n",
+ " 0.1707967679 | \n",
+ "
\n",
+ " \n",
+ " 7 | \n",
+ " 1007 | \n",
+ " COPD | \n",
+ " 1.280669881 | \n",
+ " 1.028317112 | \n",
+ " 1.441277606 | \n",
+ " 0.8890439578 | \n",
+ " 1.244073181 | \n",
+ " 1.25669323 | \n",
+ " - | \n",
+ " - | \n",
+ " 1.190012495 | \n",
+ " 0.197711534 | \n",
+ "
\n",
+ " \n",
+ " 8 | \n",
+ " 1008 | \n",
+ " HA | \n",
+ " 1.43906575 | \n",
+ " 1.838087746 | \n",
+ " 1.751026051 | \n",
+ " 1.751026051 | \n",
+ " 1.719487051 | \n",
+ " 1.662955206 | \n",
+ " 1.629731588 | \n",
+ " - | \n",
+ " 1.684482778 | \n",
+ " 0.1273881511 | \n",
+ "
\n",
+ " \n",
+ " 9 | \n",
+ " 1009 | \n",
+ " PD | \n",
+ " 1.02140015 | \n",
+ " 1.455552837 | \n",
+ " 1.059055115 | \n",
+ " 0 | \n",
+ " 1.495886345 | \n",
+ " - | \n",
+ " - | \n",
+ " - | \n",
+ " 1.257973611 | \n",
+ " 0.2524385006 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " ID Cohort D1_RWS D2_RWS D3_RWS D4_RWS \\\n",
+ "0 1000 HA 1.435969103 1.208401082 1.47810092 1.266655896 \n",
+ "1 1001 HA 0.7617805546 1.19912152 0.9624181844 1.338421753 \n",
+ "2 1002 HA 1.265350375 1.442073541 1.219132102 1.177058133 \n",
+ "3 1003 PD 0.69954984 0.664800558 0.817476946 0.994005528 \n",
+ "4 1004 PD 0.844786013 0.8920834942 1.053314499 0.9711816153 \n",
+ "5 1005 PD 1.267099798 0.577708104 0.692487668 1.15232039 \n",
+ "6 1006 COPD 0.5794542551 0.925525475 1.016206426 1.074272686 \n",
+ "7 1007 COPD 1.280669881 1.028317112 1.441277606 0.8890439578 \n",
+ "8 1008 HA 1.43906575 1.838087746 1.751026051 1.751026051 \n",
+ "9 1009 PD 1.02140015 1.455552837 1.059055115 0 \n",
+ "\n",
+ " D5_RWS D6_RWS D7_RWS FL_RWS Mean \\\n",
+ "0 1.091846016 1.19178053 1.303706372 0.52494135 1.152204595 \n",
+ "1 0.9198683369 1.188084293 0.5 - 1.017985681 \n",
+ "2 1.279356812 1.435563794 1.396013597 - 1.32486633 \n",
+ "3 0.810221351 1.02773144 1.336634741 1.234558728 0.9481223915 \n",
+ "4 0.7733374742 1.097224889 1.097224889 - 0.9613075535 \n",
+ "5 1.04157882 1.245193767 0.857921834 1.069256386 0.9879458459 \n",
+ "6 0.828173891 0.747681114 1.00362861 0.730722234 0.8632080864 \n",
+ "7 1.244073181 1.25669323 - - 1.190012495 \n",
+ "8 1.719487051 1.662955206 1.629731588 - 1.684482778 \n",
+ "9 1.495886345 - - - 1.257973611 \n",
+ "\n",
+ " SD \n",
+ "0 0.3011331211 \n",
+ "1 0.2891685504 \n",
+ "2 0.1073837562 \n",
+ "3 0.2290128237 \n",
+ "4 0.1285896639 \n",
+ "5 0.2544200958 \n",
+ "6 0.1707967679 \n",
+ "7 0.197711534 \n",
+ "8 0.1273881511 \n",
+ "9 0.2524385006 "
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "info_df.head(10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "info_df.replace('-', np.nan, inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " ID | \n",
+ " Cohort | \n",
+ " D1_RWS | \n",
+ " D2_RWS | \n",
+ " D3_RWS | \n",
+ " D4_RWS | \n",
+ " D5_RWS | \n",
+ " D6_RWS | \n",
+ " D7_RWS | \n",
+ " FL_RWS | \n",
+ " Mean | \n",
+ " SD | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 1000 | \n",
+ " HA | \n",
+ " 1.435969103 | \n",
+ " 1.208401082 | \n",
+ " 1.47810092 | \n",
+ " 1.266655896 | \n",
+ " 1.091846016 | \n",
+ " 1.19178053 | \n",
+ " 1.303706372 | \n",
+ " 0.52494135 | \n",
+ " 1.152204595 | \n",
+ " 0.3011331211 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 1001 | \n",
+ " HA | \n",
+ " 0.7617805546 | \n",
+ " 1.19912152 | \n",
+ " 0.9624181844 | \n",
+ " 1.338421753 | \n",
+ " 0.9198683369 | \n",
+ " 1.188084293 | \n",
+ " 0.5 | \n",
+ " NaN | \n",
+ " 1.017985681 | \n",
+ " 0.2891685504 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 1002 | \n",
+ " HA | \n",
+ " 1.265350375 | \n",
+ " 1.442073541 | \n",
+ " 1.219132102 | \n",
+ " 1.177058133 | \n",
+ " 1.279356812 | \n",
+ " 1.435563794 | \n",
+ " 1.396013597 | \n",
+ " NaN | \n",
+ " 1.32486633 | \n",
+ " 0.1073837562 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 1003 | \n",
+ " PD | \n",
+ " 0.69954984 | \n",
+ " 0.664800558 | \n",
+ " 0.817476946 | \n",
+ " 0.994005528 | \n",
+ " 0.810221351 | \n",
+ " 1.02773144 | \n",
+ " 1.336634741 | \n",
+ " 1.234558728 | \n",
+ " 0.9481223915 | \n",
+ " 0.2290128237 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 1004 | \n",
+ " PD | \n",
+ " 0.844786013 | \n",
+ " 0.8920834942 | \n",
+ " 1.053314499 | \n",
+ " 0.9711816153 | \n",
+ " 0.7733374742 | \n",
+ " 1.097224889 | \n",
+ " 1.097224889 | \n",
+ " NaN | \n",
+ " 0.9613075535 | \n",
+ " 0.1285896639 | \n",
+ "
\n",
+ " \n",
+ " 5 | \n",
+ " 1005 | \n",
+ " PD | \n",
+ " 1.267099798 | \n",
+ " 0.577708104 | \n",
+ " 0.692487668 | \n",
+ " 1.15232039 | \n",
+ " 1.04157882 | \n",
+ " 1.245193767 | \n",
+ " 0.857921834 | \n",
+ " 1.069256386 | \n",
+ " 0.9879458459 | \n",
+ " 0.2544200958 | \n",
+ "
\n",
+ " \n",
+ " 6 | \n",
+ " 1006 | \n",
+ " COPD | \n",
+ " 0.5794542551 | \n",
+ " 0.925525475 | \n",
+ " 1.016206426 | \n",
+ " 1.074272686 | \n",
+ " 0.828173891 | \n",
+ " 0.747681114 | \n",
+ " 1.00362861 | \n",
+ " 0.730722234 | \n",
+ " 0.8632080864 | \n",
+ " 0.1707967679 | \n",
+ "
\n",
+ " \n",
+ " 7 | \n",
+ " 1007 | \n",
+ " COPD | \n",
+ " 1.280669881 | \n",
+ " 1.028317112 | \n",
+ " 1.441277606 | \n",
+ " 0.8890439578 | \n",
+ " 1.244073181 | \n",
+ " 1.25669323 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " 1.190012495 | \n",
+ " 0.197711534 | \n",
+ "
\n",
+ " \n",
+ " 8 | \n",
+ " 1008 | \n",
+ " HA | \n",
+ " 1.43906575 | \n",
+ " 1.838087746 | \n",
+ " 1.751026051 | \n",
+ " 1.751026051 | \n",
+ " 1.719487051 | \n",
+ " 1.662955206 | \n",
+ " 1.629731588 | \n",
+ " NaN | \n",
+ " 1.684482778 | \n",
+ " 0.1273881511 | \n",
+ "
\n",
+ " \n",
+ " 9 | \n",
+ " 1009 | \n",
+ " PD | \n",
+ " 1.02140015 | \n",
+ " 1.455552837 | \n",
+ " 1.059055115 | \n",
+ " 0 | \n",
+ " 1.495886345 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " 1.257973611 | \n",
+ " 0.2524385006 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " ID Cohort D1_RWS D2_RWS D3_RWS D4_RWS \\\n",
+ "0 1000 HA 1.435969103 1.208401082 1.47810092 1.266655896 \n",
+ "1 1001 HA 0.7617805546 1.19912152 0.9624181844 1.338421753 \n",
+ "2 1002 HA 1.265350375 1.442073541 1.219132102 1.177058133 \n",
+ "3 1003 PD 0.69954984 0.664800558 0.817476946 0.994005528 \n",
+ "4 1004 PD 0.844786013 0.8920834942 1.053314499 0.9711816153 \n",
+ "5 1005 PD 1.267099798 0.577708104 0.692487668 1.15232039 \n",
+ "6 1006 COPD 0.5794542551 0.925525475 1.016206426 1.074272686 \n",
+ "7 1007 COPD 1.280669881 1.028317112 1.441277606 0.8890439578 \n",
+ "8 1008 HA 1.43906575 1.838087746 1.751026051 1.751026051 \n",
+ "9 1009 PD 1.02140015 1.455552837 1.059055115 0 \n",
+ "\n",
+ " D5_RWS D6_RWS D7_RWS FL_RWS Mean \\\n",
+ "0 1.091846016 1.19178053 1.303706372 0.52494135 1.152204595 \n",
+ "1 0.9198683369 1.188084293 0.5 NaN 1.017985681 \n",
+ "2 1.279356812 1.435563794 1.396013597 NaN 1.32486633 \n",
+ "3 0.810221351 1.02773144 1.336634741 1.234558728 0.9481223915 \n",
+ "4 0.7733374742 1.097224889 1.097224889 NaN 0.9613075535 \n",
+ "5 1.04157882 1.245193767 0.857921834 1.069256386 0.9879458459 \n",
+ "6 0.828173891 0.747681114 1.00362861 0.730722234 0.8632080864 \n",
+ "7 1.244073181 1.25669323 NaN NaN 1.190012495 \n",
+ "8 1.719487051 1.662955206 1.629731588 NaN 1.684482778 \n",
+ "9 1.495886345 NaN NaN NaN 1.257973611 \n",
+ "\n",
+ " SD \n",
+ "0 0.3011331211 \n",
+ "1 0.2891685504 \n",
+ "2 0.1073837562 \n",
+ "3 0.2290128237 \n",
+ "4 0.1285896639 \n",
+ "5 0.2544200958 \n",
+ "6 0.1707967679 \n",
+ "7 0.197711534 \n",
+ "8 0.1273881511 \n",
+ "9 0.2524385006 "
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "info_df.head(10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " ID | \n",
+ " Cohort | \n",
+ " D1_RWS | \n",
+ " D2_RWS | \n",
+ " D3_RWS | \n",
+ " D4_RWS | \n",
+ " D5_RWS | \n",
+ " D6_RWS | \n",
+ " D7_RWS | \n",
+ " FL_RWS | \n",
+ " Mean | \n",
+ " SD | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 55 | \n",
+ " 3011 | \n",
+ " MS | \n",
+ " NaN | \n",
+ " 0.939241775 | \n",
+ " 1.299488989 | \n",
+ " 0.961851214 | \n",
+ " 1.187980886 | \n",
+ " 1.214225988 | \n",
+ " 1.1568706 | \n",
+ " 1.203426462 | \n",
+ " 1.137583702 | \n",
+ " 0.1251033395 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " ID Cohort D1_RWS D2_RWS D3_RWS D4_RWS D5_RWS \\\n",
+ "55 3011 MS NaN 0.939241775 1.299488989 0.961851214 1.187980886 \n",
+ "\n",
+ " D6_RWS D7_RWS FL_RWS Mean SD \n",
+ "55 1.214225988 1.1568706 1.203426462 1.137583702 0.1251033395 "
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "info_df.loc[info_df['ID']==3011]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#nans_l = info_df.isnull().sum(axis=1)\n",
+ "#type(nans_l)\n",
+ "#idx_val = nans_l.where(nans_l<2).dropna().index.to_list()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#val_df = info_df[info_df.index.isin(idx_val)]\n",
+ "#val_df.head(10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#print([el for el in info_df.ID.values if el not in val_df.ID.values])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#val_df.dropna(axis=1, inplace=True)\n",
+ "#val_df = val_df[(val_df.T !='0').all()].reset_index()\n",
+ "#val_df.head(len(val_df))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010,\n",
+ " 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021,\n",
+ " 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 2000, 2001, 2002,\n",
+ " 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,\n",
+ " 3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009, 3010,\n",
+ " 3011, 3013, 3014, 4002, 4005, 4006, 4011, 4013, 4019, 5000, 5003,\n",
+ " 5005, 5008, 5009, 5010, 5012, 5019], dtype=int64)"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "info_df.ID.values"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "IDEA:\n",
+ "* select only those patients who have both indoor and outdoor experiments\n",
+ "* normalize/standardize the steps per second based on the patient\n",
+ "* plot the norm steps distribution for each pathology in the cohort\n",
+ "* looking for boundaries/threshold to discern the step behaviour indoor/outdoor for each cohort"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here I store the subjects' ID that haven't enough data to working on (non valid IDS). \n",
+ "The same list is then used to filter out those patients that have either all indoor or outdoor actions and an high amount of 0.5 step per second (?)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Processing subject: 1000\n",
+ "Processing subject: 1001\n",
+ "Processing subject: 1002\n",
+ "Processing subject: 1003\n",
+ "Processing subject: 1004\n",
+ "Processing subject: 1005\n",
+ "Processing subject: 1006\n",
+ "Processing subject: 1007\n",
+ "Processing subject: 1008\n",
+ "Processing subject: 1009\n",
+ "Processing subject: 1010\n",
+ "Processing subject: 1011\n",
+ "Processing subject: 1012\n",
+ "Processing subject: 1013\n",
+ "Processing subject: 1014\n",
+ "Processing subject: 1015\n",
+ "Processing subject: 1016\n",
+ "Processing subject: 1017\n",
+ "Processing subject: 1018\n",
+ "Processing subject: 1019\n",
+ "Processing subject: 1020\n",
+ "Processing subject: 1021\n",
+ "Processing subject: 1022\n",
+ "Processing subject: 1023\n",
+ "Processing subject: 1024\n",
+ "Processing subject: 1025\n",
+ "Processing subject: 1026\n",
+ "Processing subject: 1027\n",
+ "Processing subject: 1028\n",
+ "Processing subject: 1029\n",
+ "Processing subject: 2000\n",
+ "Processing subject: 2001\n",
+ "Processing subject: 2002\n",
+ "Processing subject: 2003\n",
+ "Processing subject: 2004\n",
+ "Processing subject: 2005\n",
+ "Processing subject: 2006\n",
+ "Processing subject: 2007\n",
+ "Processing subject: 2008\n",
+ "Processing subject: 2009\n",
+ "Processing subject: 2010\n",
+ "Processing subject: 2011\n",
+ "Processing subject: 2012\n",
+ "Processing subject: 2013\n",
+ "Processing subject: 3000\n",
+ "Processing subject: 3001\n",
+ "Processing subject: 3002\n",
+ "Processing subject: 3003\n",
+ "Processing subject: 3004\n",
+ "Processing subject: 3005\n",
+ "Processing subject: 3006\n",
+ "Processing subject: 3007\n",
+ "Processing subject: 3008\n",
+ "Processing subject: 3009\n",
+ "Processing subject: 3010\n",
+ "Processing subject: 3011\n",
+ "Processing subject: 3013\n",
+ "Processing subject: 3014\n",
+ "Processing subject: 4002\n",
+ "Processing subject: 4005\n",
+ "Processing subject: 4011\n",
+ "Processing subject: 4013\n",
+ "Processing subject: 4019\n",
+ "Processing subject: 5000\n",
+ "Processing subject: 5003\n",
+ "Processing subject: 5005\n",
+ "Processing subject: 5008\n",
+ "Processing subject: 5009\n",
+ "Processing subject: 5010\n",
+ "Processing subject: 5012\n",
+ "Processing subject: 5019\n",
+ "(402, 86400, 2) (388, 86400, 4)\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
+ "\n",
+ "scl = StandardScaler()\n",
+ "norm = MinMaxScaler()\n",
+ "#df = pd.DataFrame()\n",
+ "#ctx_array = np.empty([86400,])\n",
+ "#s_array = np.empty([86400,]\n",
+ "ctx_l, ctx_subj_i = [], []\n",
+ "s_l, s_subj_i = [], []\n",
+ "\n",
+ "non_valid_ids = []\n",
+ "\n",
+ "for p in os.listdir(folder):\n",
+ " #if int(p) in val_df['ID'].values:\n",
+ " try:\n",
+ " cohort = info_df[info_df['ID']==int(p)]['Cohort'].values[0]\n",
+ " print('Processing subject: ', p)\n",
+ " subfold = os.path.join(folder, p)\n",
+ " for f in os.listdir(subfold):\n",
+ " if 'Day' in f:\n",
+ " if f.endswith('.json') and 'step' in f:\n",
+ " steps_file = pd.read_json(os.path.join(subfold, f))\n",
+ " #steps_arr = np.expand_dims(np.array(steps_file['data'][0]['steps'], dtype=float), 1)\n",
+ " #scl_steps = scl.fit_transform(steps_arr)\n",
+ " #norm_steps = norm.fit_transform(scl_steps)\n",
+ " s_l.append([[p, cohort, f.split('-')[3], float(el)] for el in steps_file['data'][0]['steps']])\n",
+ " elif f.endswith('.json') and 'Context' in f:\n",
+ " json_ctx_file = pd.read_json(os.path.join(subfold, f))\n",
+ " ctx_l.append([[k, json_ctx_file['data'][0]['contextValues'][k][0]] for k in json_ctx_file['data'][0]['contextValues']])\n",
+ " \n",
+ " except:\n",
+ " continue\n",
+ " #else:\n",
+ " # non_valid_ids.append(p)\n",
+ "\n",
+ "ctx_array = np.array(ctx_l)\n",
+ "s_array = np.array(s_l)\n",
+ "print(ctx_array.shape, s_array.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "del ctx_l, s_l"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ctx_array = np.reshape(ctx_array, (ctx_array.shape[0]*ctx_array.shape[1], 2))\n",
+ "s_array = np.reshape(s_array, (s_array.shape[0]*s_array.shape[1], 4))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
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+ " \n",
+ " \n",
+ " | \n",
+ " Timestamp | \n",
+ " IndoorProb | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 1597273200 | \n",
+ " 100 | \n",
+ "
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+ " \n",
+ " 1 | \n",
+ " 1597273201 | \n",
+ " 100 | \n",
+ "
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+ " \n",
+ " 2 | \n",
+ " 1597273202 | \n",
+ " 100 | \n",
+ "
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+ " \n",
+ " 3 | \n",
+ " 1597273203 | \n",
+ " 100 | \n",
+ "
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+ " \n",
+ " 4 | \n",
+ " 1597273204 | \n",
+ " 100 | \n",
+ "
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+ " \n",
+ "
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+ "
"
+ ],
+ "text/plain": [
+ " Timestamp IndoorProb\n",
+ "0 1597273200 100\n",
+ "1 1597273201 100\n",
+ "2 1597273202 100\n",
+ "3 1597273203 100\n",
+ "4 1597273204 100"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ctx_df = pd.DataFrame(ctx_array, columns=['Timestamp', 'IndoorProb'])\n",
+ "step_df = pd.DataFrame(s_array, columns=['Patient', 'Cohort', 'Day', 'StepPerSec'])\n",
+ "\n",
+ "ctx_df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "del ctx_array, s_array"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 100\n",
+ "1 100\n",
+ "2 100\n",
+ "3 100\n",
+ "4 100\n",
+ " ... \n",
+ "34732795 50\n",
+ "34732796 50\n",
+ "34732797 50\n",
+ "34732798 50\n",
+ "34732799 50\n",
+ "Name: IndoorProb, Length: 34732800, dtype: int16"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ctx_df['IndoorProb'].astype(dtype=np.int16)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "full_df = pd.concat([step_df, ctx_df], axis=1)\n",
+ "del step_df,ctx_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Delete the uncertain 50 value for indoor/outdoor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "full_df.dropna(inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Int64Index: 33523200 entries, 0 to 33523199\n",
+ "Data columns (total 6 columns):\n",
+ " # Column Dtype \n",
+ "--- ------ ----- \n",
+ " 0 Patient object\n",
+ " 1 Cohort object\n",
+ " 2 Day object\n",
+ " 3 StepPerSec object\n",
+ " 4 Timestamp object\n",
+ " 5 IndoorProb object\n",
+ "dtypes: object(6)\n",
+ "memory usage: 1.7+ GB\n"
+ ]
+ }
+ ],
+ "source": [
+ "full_df.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "full_df = full_df[full_df['IndoorProb']!=50]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#full_df.drop(columns='Patient', inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "full_df['StepPerSec'] = full_df['StepPerSec'].astype('float32')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "full_df.to_csv('full_df.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_nonzero = full_df[full_df['StepPerSec'] > 0.].reset_index(drop=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['Patient', 'Cohort', 'Day', 'StepPerSec', 'IndoorProb'], dtype='object')"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_nonzero.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 HA\n",
+ "1 HA\n",
+ "2 HA\n",
+ "3 HA\n",
+ "4 HA\n",
+ " ... \n",
+ "1151069 CHF\n",
+ "1151070 CHF\n",
+ "1151071 CHF\n",
+ "1151072 CHF\n",
+ "1151073 CHF\n",
+ "Name: Cohort, Length: 1151074, dtype: object"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_nonzero.Cohort"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "HA 460483\n",
+ "MS 241799\n",
+ "PD 211459\n",
+ "COPD 152310\n",
+ "CHF 68649\n",
+ "PFF 16374\n",
+ "Name: Cohort, dtype: int64"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_nonzero['Cohort'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1.3181818723678589"
+ ]
+ },
+ "execution_count": 65,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#c_df = df_nonzero.groupby(['Cohort','IndoorProb'])['StepPerSec']\n",
+ "Q1 = df_nonzero['StepPerSec'].quantile(0.25)\n",
+ "Q3 = df_nonzero['StepPerSec'].quantile(0.75)\n",
+ "IQR = Q3 - Q1\n",
+ "IQR"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "3.795454680919647"
+ ]
+ },
+ "execution_count": 66,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Q3 + 1.5 * IQR"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Patient | \n",
+ " Cohort | \n",
+ " Day | \n",
+ " StepPerSec | \n",
+ " IndoorProb | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 174652 | \n",
+ " 1004 | \n",
+ " PD | \n",
+ " Day4 | \n",
+ " 4.0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 174653 | \n",
+ " 1004 | \n",
+ " PD | \n",
+ " Day4 | \n",
+ " 4.0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 548458 | \n",
+ " 1018 | \n",
+ " HA | \n",
+ " Day5 | \n",
+ " 19.0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 589728 | \n",
+ " 1023 | \n",
+ " COPD | \n",
+ " Day3 | \n",
+ " 5.0 | \n",
+ " 100 | \n",
+ "
\n",
+ " \n",
+ " 589729 | \n",
+ " 1023 | \n",
+ " COPD | \n",
+ " Day3 | \n",
+ " 5.0 | \n",
+ " 100 | \n",
+ "
\n",
+ " \n",
+ " 589730 | \n",
+ " 1023 | \n",
+ " COPD | \n",
+ " Day3 | \n",
+ " 5.0 | \n",
+ " 100 | \n",
+ "
\n",
+ " \n",
+ " 589731 | \n",
+ " 1023 | \n",
+ " COPD | \n",
+ " Day3 | \n",
+ " 5.0 | \n",
+ " 100 | \n",
+ "
\n",
+ " \n",
+ " 841687 | \n",
+ " 3000 | \n",
+ " PD | \n",
+ " Day7 | \n",
+ " 19.0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 841688 | \n",
+ " 3000 | \n",
+ " PD | \n",
+ " Day7 | \n",
+ " 19.0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 984274 | \n",
+ " 3013 | \n",
+ " MS | \n",
+ " Day3 | \n",
+ " 9.0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 993399 | \n",
+ " 3013 | \n",
+ " MS | \n",
+ " Day4 | \n",
+ " 12.0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 1004994 | \n",
+ " 3013 | \n",
+ " MS | \n",
+ " Day5 | \n",
+ " 9.5 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 1004995 | \n",
+ " 3013 | \n",
+ " MS | \n",
+ " Day5 | \n",
+ " 9.5 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 1140838 | \n",
+ " 5019 | \n",
+ " CHF | \n",
+ " Day1 | \n",
+ " 10.0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Patient Cohort Day StepPerSec IndoorProb\n",
+ "174652 1004 PD Day4 4.0 0\n",
+ "174653 1004 PD Day4 4.0 0\n",
+ "548458 1018 HA Day5 19.0 0\n",
+ "589728 1023 COPD Day3 5.0 100\n",
+ "589729 1023 COPD Day3 5.0 100\n",
+ "589730 1023 COPD Day3 5.0 100\n",
+ "589731 1023 COPD Day3 5.0 100\n",
+ "841687 3000 PD Day7 19.0 0\n",
+ "841688 3000 PD Day7 19.0 0\n",
+ "984274 3013 MS Day3 9.0 0\n",
+ "993399 3013 MS Day4 12.0 0\n",
+ "1004994 3013 MS Day5 9.5 0\n",
+ "1004995 3013 MS Day5 9.5 0\n",
+ "1140838 5019 CHF Day1 10.0 0"
+ ]
+ },
+ "execution_count": 71,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "outlier_df1 = df_nonzero[df_nonzero['StepPerSec']>(Q3 + 1.5 * IQR)]\n",
+ "outlier_df2 = df_nonzero[df_nonzero['StepPerSec']>4]\n",
+ "outlier_df1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 91,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "clean_df1 = df_nonzero[~((df_nonzero['StepPerSec'] > (Q3 + 1.5 * IQR)))]\n",
+ "clean_df2 = df_nonzero[~((df_nonzero['StepPerSec'] > 4))]\n",
+ "clean_df3 = df_nonzero[~((df_nonzero['StepPerSec'] > (Q3 + 1.5 * IQR)) | (df_nonzero['StepPerSec'] == 0.5))]\n",
+ "clean_df4 = df_nonzero[~((df_nonzero['StepPerSec'] > 4) | (df_nonzero['StepPerSec'] == 0.5) )]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 92,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_stat_df(df):\n",
+ " max_df = df.groupby(['Cohort', 'IndoorProb'], as_index=False)['StepPerSec'].max().rename(columns={'StepPerSec':'MaxStepPerSec'})\n",
+ " min_df = df.groupby(['Cohort','IndoorProb'], as_index=False)['StepPerSec'].min().rename(columns={'StepPerSec':'MinStepPerSec'})\n",
+ " std_df = df.groupby(['Cohort', 'IndoorProb'], as_index=False)['StepPerSec'].std().rename(columns={'StepPerSec':'StdStepPerSec'})\n",
+ " mean_df = df.groupby(['Cohort', 'IndoorProb'], as_index=False)['StepPerSec'].mean().rename(columns={'StepPerSec':'MeanStepPerSec'})\n",
+ " q1_df = df.groupby(['Cohort', 'IndoorProb'], as_index=False)['StepPerSec'].quantile(0.25).rename(columns={'StepPerSec':'Q1(0.25)'})\n",
+ " q2_df = df.groupby(['Cohort', 'IndoorProb'], as_index=False)['StepPerSec'].quantile(0.5).rename(columns={'StepPerSec':'Q2(0.5)'})\n",
+ " q3_df = df.groupby(['Cohort', 'IndoorProb'], as_index=False)['StepPerSec'].quantile(0.75).rename(columns={'StepPerSec':'Q3(0.75)'})\n",
+ " q4_df = df.groupby(['Cohort', 'IndoorProb'], as_index=False)['StepPerSec'].quantile(0.95).rename(columns={'StepPerSec':'Q4(0.95)'})\n",
+ "\n",
+ " res_df = pd.concat([max_df, min_df, std_df, mean_df, q1_df, q2_df, q3_df, q4_df], axis=1)\n",
+ " #stat_df = pd.concat([max_df, min_df, std_df, mean_df], axis=1)\n",
+ " res_df = res_df.loc[:,~res_df.columns.duplicated()].copy()\n",
+ "\n",
+ " ids_cohort = {'Cohort':[], 'IndoorProb':[], 'IDs': []}\n",
+ " for k,group in df.groupby(['Cohort', 'IndoorProb']):\n",
+ " ids_cohort['Cohort'].append(k[0])\n",
+ " ids_cohort['IndoorProb'].append(k[1])\n",
+ " ids_cohort['IDs'].append(group['Patient'].unique())\n",
+ "\n",
+ " id_df = pd.DataFrame(ids_cohort)\n",
+ " res_df = res_df.join(id_df.set_index(['Cohort', 'IndoorProb']), on=['Cohort', 'IndoorProb'])\n",
+ " \n",
+ " return res_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 93,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "stat_df = create_stat_df(df_nonzero)\n",
+ "clean_stat_df1 = create_stat_df(clean_df1)\n",
+ "clean_stat_df2 = create_stat_df(clean_df2)\n",
+ "clean_stat_df3 = create_stat_df(clean_df3)\n",
+ "clean_stat_df4 = create_stat_df(clean_df4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 94,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with pd.ExcelWriter('stats_steps_cohort.xlsx') as writer: # doctest: +SKIP\n",
+ " stat_df.to_excel(writer, sheet_name='Raw Dataset')\n",
+ " clean_stat_df1.to_excel(writer, sheet_name='Cleaned Dataset 1')\n",
+ " clean_stat_df2.to_excel(writer, sheet_name='Cleaned Dataset 2')\n",
+ " clean_stat_df3.to_excel(writer, sheet_name='Cleaned Dataset 3')\n",
+ " clean_stat_df4.to_excel(writer, sheet_name='Cleaned Dataset 4')\n",
+ " outlier_df1.to_excel(writer, sheet_name='Outlier Dataset 1')\n",
+ " outlier_df2.to_excel(writer, sheet_name='Outlier Dataset 2')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 117,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_time_series(df, PATH):\n",
+ " for k,group in df.groupby(['Cohort', 'IndoorProb'], as_index=False)['StepPerSec']:\n",
+ " #print(f'Patients belonging to {k[0]}: {group}')\n",
+ " plt.close()\n",
+ " group.reset_index(inplace=True)\n",
+ " #sns.histplot(data=group, x='StepPerSec', kde=True)\n",
+ " plt.plot(group['StepPerSec'])\n",
+ " plt.title(k)\n",
+ " plt.tight_layout()\n",
+ " #sns.catplot(x=\"IndoorProb\", y=\"StepPerSec\",\n",
+ " #kind=\"violin\", palette=\"Set2\", data=group, inner='quartile', scale='count')\n",
+ " #plt.show()\n",
+ " plt.savefig(f'{PATH}/{k[0]}_{k[1]}.png')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 118,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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