peterm790/Working_With_The_HE2AT_Database.ipynb

## Working_With_The_HE2AT_Database.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9bca5a38-8e2d-4ab3-a178-a8cd10bcaf87",
   "metadata": {},
   "source": [
    "# We have a database\n",
    "\n",
    "Hello, HE2AT team! We now have a database! For For now it is a very minimal example with only one RP1 study available ZAF055. We will soon add MWI198. The database has been created with SQLAlchemy and it is a SQLite database. there are a number of ways to access the database. In python you could us SQLAlchemy itself or you could use DuckDB. This notebook will outline some examples using DuckDB. The same SQL commands could also be use in R."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cb487e4d-b3d7-4fd1-b3a9-a86161cc9e15",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR 1: PROJ: proj_create_from_database: Open of /share/apps/anaconda3-2022.05/envs/pangeo/share/proj failed\n"
     ]
    }
   ],
   "source": [
    "import fsspec\n",
    "from pathlib import Path\n",
    "import shutil\n",
    "\n",
    "import duckdb\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import geopandas as gpd\n",
    "import intake\n",
    "import xarray as xr\n",
    "import dask\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "\n",
    "fs = fsspec.filesystem(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7756be3b-2fbb-4bfa-b601-8004728aaf42",
   "metadata": {},
   "source": [
    "## Structure\n",
    "\n",
    "First let's consider the structure of the database. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "64649003-55d0-406e-8c92-773943cbb530",
   "metadata": {},
   "outputs": [],
   "source": [
    "# copy the latest layout from our database definition's directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "14e570ab-7c5c-4862-b6ea-52d2582b0b05",
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = shutil.copy(Path(Path.home(),'heat_center/data/health_open/database/definition/my_data_model_diagram.svg'), 'my_data_model_diagram.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "007e12b4-afea-4b1a-958a-cc0107ebd315",
   "metadata": {},
   "source": [
    "![SVG Image](my_data_model_diagram.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec3319d2-5b9e-4abe-802e-d46bb2b44674",
   "metadata": {},
   "source": [
    "# Access Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "251948b5-d3bf-4a57-b028-2b202d6e6916",
   "metadata": {},
   "source": [
    "## Using DuckDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "434273e8-6840-47c5-9b58-e65c67b5d19f",
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = duckdb.connect(f\"{Path(Path.home(),'heat_center/data/health_open/database/data/RP1.db')}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37cd910c-97de-4397-b0fe-c64682b76c3a",
   "metadata": {},
   "source": [
    "Let's see which studies are present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dfc241ee-1b8b-4bc4-ab27-a4d793e90e49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('MWI198',), ('ZAF055',)}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "        SELECT s.name\n",
    "        FROM study s\n",
    "        INNER JOIN patient p ON s.name = p.study_id;\n",
    "        \"\"\"\n",
    "\n",
    "set(conn.execute(query).fetchall())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86b9489c-aaef-4a01-9daf-ed43e44facd8",
   "metadata": {},
   "source": [
    "and how many patients does this include:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bb06fe64-9d82-4275-9bf4-0ef62369136f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5483"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "        SELECT p.patient_id\n",
    "        FROM patient p\n",
    "        \"\"\"\n",
    "\n",
    "len(conn.execute(query).fetchall())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce77e85c-73f6-4a1d-9652-3f0b623d065d",
   "metadata": {},
   "source": [
    "and what data is available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c767bda4-9171-4ffc-8f61-449b96ab0161",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Location',\n",
       " 'Race',\n",
       " 'Age-Years',\n",
       " 'HIV Status',\n",
       " 'Highly Active Antiretroviral Therapy',\n",
       " 'Birth Date',\n",
       " 'Preterm Birth',\n",
       " 'Birth Weight',\n",
       " 'Gestational Age at Birth (weeks)',\n",
       " 'Apgar Score',\n",
       " 'Sex',\n",
       " 'Noncompliance with antiretroviral medication regimen (finding)',\n",
       " 'Alcohol Abuse',\n",
       " 'Death Indicator',\n",
       " 'Perinatal Mortality ',\n",
       " 'CD4 Expressing Cell Count',\n",
       " 'HIV Viral Load Measurement',\n",
       " 'Body Weight',\n",
       " 'Height',\n",
       " 'Body Mass Index',\n",
       " 'Systolic Blood Pressure',\n",
       " 'Diastolic Blood Pressure',\n",
       " 'Delivery Procedure',\n",
       " 'Adverse Event',\n",
       " 'Smoking Status',\n",
       " 'Number of Fetuses',\n",
       " 'Hospitalization',\n",
       " 'Gastroenteritis',\n",
       " 'Pneumonia ',\n",
       " 'Upper Respiratory Tract Infection',\n",
       " 'Employment Status',\n",
       " 'Syphilis',\n",
       " 'Hypertension']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_var_names = \"\"\"\n",
    "SELECT DISTINCT c.variable_name\n",
    "FROM codebook c\n",
    "JOIN variable_data v ON c.variable_name = v.variable_id\n",
    "\"\"\"\n",
    "var_names_data = conn.execute(query_var_names).fetchall()\n",
    "[x[0] for x in var_names_data]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3a1214d-e0b9-42b8-839b-295ed019f355",
   "metadata": {},
   "source": [
    "Birth Weight is the easiest to check the quality of our data, let's get the mean "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7a905273-aa3b-4463-9ee4-ea6465fc962c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the variable_id\n",
    "variable_id = 'Birth Weight'\n",
    "\n",
    "# Construct the SQL query to get data_type and fetch integer_value or float_value accordingly\n",
    "query = f\"\"\"\n",
    "SELECT * FROM variable_data WHERE variable_id = '{variable_id}'\n",
    "\"\"\"\n",
    "\n",
    "# Execute the SQL query and fetch a pandas df\n",
    "df = conn.execute(query).fetchdf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f3062611-81f3-4c7c-9f8f-e0626bc59a1b",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>variable_id</th>\n",
       "      <th>date</th>\n",
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       "      <th>float_source_value</th>\n",
       "      <th>source_encoding</th>\n",
       "      <th>transformation_instruction</th>\n",
       "      <th>data_type</th>\n",
       "      <th>string_value</th>\n",
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       "      <th>float_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24015</td>\n",
       "      <td>1eb15e7a348a47e03e08063728ff846a56f91aa0c40dc7...</td>\n",
       "      <td>Birth Weight</td>\n",
       "      <td>2015-07-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Mother</td>\n",
       "      <td>ZAF055</td>\n",
       "      <td>ZAF055</td>\n",
       "      <td>WeightatBirth</td>\n",
       "      <td>float</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>2015-10-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Mother</td>\n",
       "      <td>ZAF055</td>\n",
       "      <td>ZAF055</td>\n",
       "      <td>WeightatBirth</td>\n",
       "      <td>float</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1</td>\n",
       "      <td>Mother</td>\n",
       "      <td>ZAF055</td>\n",
       "      <td>ZAF055</td>\n",
       "      <td>WeightatBirth</td>\n",
       "      <td>float</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x * 1000</td>\n",
       "      <td>int</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3100</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>d92022ad8c0bb829d452b8db1760ec1aee10a6a1803ea4...</td>\n",
       "      <td>Birth Weight</td>\n",
       "      <td>2016-02-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Mother</td>\n",
       "      <td>ZAF055</td>\n",
       "      <td>ZAF055</td>\n",
       "      <td>WeightatBirth</td>\n",
       "      <td>float</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.7</td>\n",
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       "      <td>2016-02-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Mother</td>\n",
       "      <td>ZAF055</td>\n",
       "      <td>ZAF055</td>\n",
       "      <td>WeightatBirth</td>\n",
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       "      <td>2.9</td>\n",
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      "text/plain": [
       "      id                                         patient_id   variable_id  \\\n",
       "0  24015  1eb15e7a348a47e03e08063728ff846a56f91aa0c40dc7...  Birth Weight   \n",
       "1  24016  5b5aa4da0d67c22b4b22dffd498a8b7d88d0c718382d74...  Birth Weight   \n",
       "2  24017  b4ec5af3168edc57458fb52634c0736572cdcb5fb9ef54...  Birth Weight   \n",
       "3  24018  d92022ad8c0bb829d452b8db1760ec1aee10a6a1803ea4...  Birth Weight   \n",
       "4  24019  1bf498e659d280b7b82f2cd3a32f194c5cadd23ba79d5f...  Birth Weight   \n",
       "\n",
       "         date time  transformed relation   study   table source_variable_id  \\\n",
       "0  2015-07-23  NaN            1   Mother  ZAF055  ZAF055      WeightatBirth   \n",
       "1  2015-10-15  NaN            1   Mother  ZAF055  ZAF055      WeightatBirth   \n",
       "2  2015-08-25  NaN            1   Mother  ZAF055  ZAF055      WeightatBirth   \n",
       "3  2016-02-27  NaN            1   Mother  ZAF055  ZAF055      WeightatBirth   \n",
       "4  2016-02-25  NaN            1   Mother  ZAF055  ZAF055      WeightatBirth   \n",
       "\n",
       "  source_value_type string_source_value  integer_source_value  \\\n",
       "0             float                 NaN                   NaN   \n",
       "1             float                 NaN                   NaN   \n",
       "2             float                 NaN                   NaN   \n",
       "3             float                 NaN                   NaN   \n",
       "4             float                 NaN                   NaN   \n",
       "\n",
       "   float_source_value source_encoding transformation_instruction data_type  \\\n",
       "0                 3.2             NaN                   x * 1000       int   \n",
       "1                 3.2             NaN                   x * 1000       int   \n",
       "2                 3.1             NaN                   x * 1000       int   \n",
       "3                 3.7             NaN                   x * 1000       int   \n",
       "4                 2.9             NaN                   x * 1000       int   \n",
       "\n",
       "  string_value  integer_value  float_value  \n",
       "0          NaN           3200          NaN  \n",
       "1          NaN           3200          NaN  \n",
       "2          NaN           3100          NaN  \n",
       "3          NaN           3700          NaN  \n",
       "4          NaN           2900          NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ad543a18-aae9-409b-add9-e1c2f2f62372",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2918.515205724508"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['integer_value'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cbcfc18-f3e5-472c-b554-77789e51ed27",
   "metadata": {},
   "source": [
    "and max? obvs a mistake"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2f583450-2cd4-4cd9-b0c6-fbd12d0ae6d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5200"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['integer_value'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d7edf797-e039-4ef5-adc8-38dff6867521",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['integer_value'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29a07a0c-03d9-4bdb-a7c6-78f3f0f6820e",
   "metadata": {},
   "source": [
    "Maybe even a plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e1f61e63-c0d6-4e4e-8579-e09d592ecfe1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['integer_value'].plot.hist() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72746030-d728-4125-b0d0-bd12780cd5eb",
   "metadata": {},
   "source": [
    "which studies do these come from?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7d4c3f0e-a122-4e0d-bd97-0d499e34837e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ZAF055'}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df.study) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfc543cf-bc68-4ba2-a2a9-f6a331b79fd7",
   "metadata": {},
   "source": [
    "Just the one"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9941b9fc-0ecb-4192-a35e-c16f51fe2dea",
   "metadata": {},
   "source": [
    "Or a catagorical dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cec96d2d-8f5d-4c81-9a0b-4428ba807f6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Natural': 1491, 'C-section': 162})"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "data = conn.execute(\"SELECT * FROM variable_data WHERE variable_id = 'Delivery Procedure'\").fetchall()\n",
    "Counter([x[-3] for x in data]) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "438029ad-2ade-47c0-afbe-c5f109ed5618",
   "metadata": {},
   "source": [
    "Apgar score is perhaps the easiest to relate to climate data so let's do a dry run of this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "94379b86-8ddc-467d-a8ef-ce11acf2882d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the variable_id\n",
    "variable_id = 'Apgar Score'\n",
    "\n",
    "# Construct the SQL query to get data_type and fetch integer_value or float_value accordingly\n",
    "query = f\"\"\"\n",
    "SELECT * FROM variable_data WHERE variable_id = '{variable_id}'\n",
    "\"\"\"\n",
    "\n",
    "# Execute the SQL query and fetch a pandas df\n",
    "df = conn.execute(query).fetchdf()\n",
    "\n",
    "df = df.drop(columns = 'id').drop_duplicates()  # this is dropping rows with all cols identical, not sure why duplicates are present "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "07b7fa9e-74e4-487b-b9a2-a5269cc00a67",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['patient_id','integer_value']]\n",
    "df = df.rename(columns = {'integer_value':'apgar_score'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e6c37fd-1489-451f-88c9-285ef587021f",
   "metadata": {},
   "source": [
    "There is no date explicitely mapped but we know this is measured at birth "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "eea3f5d7-c2ac-49aa-8c11-f5055e7b6a4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the variable_id\n",
    "variable_id = 'Birth Date'\n",
    "\n",
    "# Construct the SQL query to get data_type and fetch integer_value or float_value accordingly\n",
    "query = f\"\"\"\n",
    "SELECT * FROM variable_data WHERE variable_id = '{variable_id}'\n",
    "\"\"\"\n",
    "\n",
    "# Execute the SQL query and fetch a pandas df\n",
    "birth_date_df = conn.execute(query).fetchdf()\n",
    "\n",
    "birth_date_df = birth_date_df.drop(columns = 'id').drop_duplicates() # not sure why duplicates??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6e69664d-0d5e-4327-a7ca-e3ee6326b825",
   "metadata": {},
   "outputs": [],
   "source": [
    "date_of_birth = []\n",
    "for index, row in df.iterrows():\n",
    "    try:\n",
    "        date_of_birth.append(birth_date_df[birth_date_df.patient_id == row.patient_id].string_value.item())\n",
    "    except:\n",
    "        date_of_birth.append(np.nan)\n",
    "\n",
    "\n",
    "df['date_of_birth'] = date_of_birth \n",
    "df = df.dropna(how = 'any') # no point keeping data with no date, although could still use as baseline ?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "919c76e9-681e-471b-88b7-e88b436e2365",
   "metadata": {},
   "source": [
    "Now we need location data \n",
    ":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5645787f-7c61-4647-acc2-30c1ca8fb7e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the variable_id\n",
    "variable_id = 'Location'\n",
    "\n",
    "# Construct the SQL query to get data_type and fetch integer_value or float_value accordingly\n",
    "query = f\"\"\"\n",
    "SELECT * FROM variable_data WHERE variable_id = '{variable_id}'\n",
    "\"\"\"\n",
    "\n",
    "# Execute the SQL query and fetch a pandas df\n",
    "location_df = conn.execute(query).fetchdf()\n",
    "location_df = location_df.drop(columns = 'id').drop_duplicates() # not sure why duplicates??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e2302835-71a7-4eaa-bd9c-9e3f20122e0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "location_df = location_df[location_df.study == 'ZAF055'] # not actually necessary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b9f203e4-e90b-47ca-95de-ac711baa5d25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DeliveryPlace', 'cliniccode'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(location_df.source_variable_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afec88f9-54de-4929-a9ab-9d002a4d20b3",
   "metadata": {},
   "source": [
    "We could subset by births only at clinic but will keep simple, presumably how is close to clinic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9b6d2d1d-9d73-408c-b0f7-0ab366335ce7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Clinic', 'Home', 'Hospital'}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(location_df[location_df.source_variable_id == 'DeliveryPlace'].string_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "558cebf7-177f-4821-8eca-bdb2d18e172a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Clinic': 276, 'Hospital': 273, 'Home': 4})"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = conn.execute(\"SELECT * FROM variable_data WHERE source_variable_id = 'DeliveryPlace'\").fetchall()\n",
    "Counter([x[-3] for x in data]) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "458b9fec-2e7b-45b8-a85a-0fd467d5472f",
   "metadata": {},
   "source": [
    "very few at home anyway, unclear if hospital location is different to clinicode "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ba57476d-ed19-474e-8bab-a9d7fce3f753",
   "metadata": {},
   "outputs": [],
   "source": [
    "location_df = location_df[location_df.source_variable_id == 'cliniccode'] # not actually necessary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "baad8001-135f-4c75-9177-7f0f89048480",
   "metadata": {},
   "outputs": [],
   "source": [
    "location = []\n",
    "for index, row in df.iterrows():\n",
    "    try:\n",
    "        location.append(location_df[location_df.patient_id == row.patient_id].string_value.item())\n",
    "    except:\n",
    "        location.append(np.nan)\n",
    "\n",
    "\n",
    "df['location'] = location \n",
    "df = df.dropna(how = 'any') # no point keeping data with no location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "54bdf4bc-70e3-4834-9b8f-96bc3a9211aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Embalenhle',\n",
       " 'Empumelelweni',\n",
       " 'Grootvlei',\n",
       " 'KwaMhlanga',\n",
       " 'Lebohang',\n",
       " 'Paulina M',\n",
       " 'Phola',\n",
       " 'Siphosesimbi',\n",
       " 'Siyathemba',\n",
       " 'Thembalethu',\n",
       " 'Vlaklaagte',\n",
       " 'Winifred Maboa'}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df.location)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38a014ce-a8b7-4f6c-b914-b407be3ffefb",
   "metadata": {},
   "source": [
    "We now need to link these names to a geoegraphic location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a4c0ef6f-f63f-47f4-a08d-e387749402dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_geojson =  Path(Path.home(), f'heat_center/data/health_open/RP1/ZAF055/geolocation/location.geojson')\n",
    "gdf = gpd.read_file(input_geojson)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5cde6cfe-7d75-4c00-b08d-6d3e26ade9ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "geometry = []\n",
    "lat = []\n",
    "lon = []\n",
    "for index, row in df.iterrows():\n",
    "    geometry.append(gdf[gdf.Name == row.location].geometry.item())\n",
    "    lat.append(gdf[gdf.Name == row.location].Lat.item())\n",
    "    lon.append(gdf[gdf.Name == row.location].Lon.item())\n",
    "    \n",
    "df['geometry'] = geometry\n",
    "df['lat'] = lat\n",
    "df['lon'] = lon"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "339126f7-1cc4-4f8c-bfb4-c6b4fdabd061",
   "metadata": {},
   "source": [
    "Let's fetch some climate data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ad4028e9-a85a-4fd9-bbed-68d6f5295cb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_mfdataset(Path(Path.home(),f'heat_center/data/climate/reanalysis/rechunk/ERA5-Land/ERA5-Land_tas.zarr'), engine = 'zarr')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7e27180-4c6e-43e6-9d5e-4048859f056a",
   "metadata": {},
   "source": [
    "and get a sense of scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "21159358-4fef-494b-bc4a-b69c3121249c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[28.505088, -26.943274, 31.121585, -25.157834]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(gdf.total_bounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "320f5f9c-d234-4185-9ed9-416758901231",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 1, figsize=(20, 8), subplot_kw=dict(projection=ccrs.PlateCarree()))\n",
    "\n",
    "axs.add_feature(cfeature.COASTLINE)\n",
    "axs.set_extent([10, 40, -35, -5], crs=ccrs.PlateCarree())\n",
    "ds.isel(time = -1).tas.plot(ax = axs)\n",
    "gdf.plot(ax=axs, marker='o', color='red', markersize=10)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "330be770-c763-489e-8835-092a5245a6f4",
   "metadata": {},
   "source": [
    "Nic proposed a while back to create a temperature profile throughout a pregnancy as the integration of the temperature time series experienced during the pregnancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3cf0c05f-afb0-4ae3-bdaa-2d4b5cf19b5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pmarsh/.local/lib/python3.10/site-packages/xarray/core/indexing.py:1593: PerformanceWarning: Slicing with an out-of-order index is generating 74 times more chunks\n",
      "  return self.array[key]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6min 40s, sys: 49.1 s, total: 7min 29s\n",
      "Wall time: 5min 14s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ds = ds.sel(latitude = slice(-25,-27), longitude = slice(28,32))\n",
    "ds = ds.resample(time='D').max(dim='time')\n",
    "climatology = ds.groupby('time.dayofyear').mean('time')\n",
    "ds = ds.groupby('time.dayofyear') - climatology\n",
    "ds = ds.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "7a62ca59-b096-4a3f-ae45-bc22531356d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 769 ms, sys: 20 ms, total: 789 ms\n",
      "Wall time: 778 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "temp_profiles = []\n",
    "for index, row in df.iterrows():\n",
    "    da = ds.sel(latitude = row.lat, longitude = row.lon, method = 'nearest')\n",
    "    end = pd.to_datetime(row.date_of_birth) + pd.Timedelta(hours = 12)\n",
    "    start = end - pd.Timedelta(weeks = 40)\n",
    "    da = da.sel(time = slice(start, end))\n",
    "    temp_profiles.append(da.tas.mean(dim = 'time').item())\n",
    "    \n",
    "df['temp_profiles'] = temp_profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "3be42833-ff78-42f2-85df-1c6bb9b899f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "percentile_90 = df['temp_profiles'].quantile(0.9)\n",
    "subset_top_90 = df[df['temp_profiles'] >= percentile_90]\n",
    "rest = df[df['temp_profiles'] < percentile_90]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "9d687a78-976a-4e8d-90c2-654e63b3cbd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.744186046511627"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_top_90.apgar_score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "063c1305-87d8-43a7-99c5-35e00868c39b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.818897637795276"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest.apgar_score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "f285a351-bb3f-4f97-a014-6de319553af2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5cfc882b60>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.hist(rest['apgar_score'], bins=5, color='green', label='Rest')\n",
    "plt.hist(subset_top_90['apgar_score'], bins=5, color='red', label='Top 90th Percentile')\n",
    "\n",
    "plt.xlabel('Apgar Score')\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b0a3e45-930c-463c-9409-fb852db2d3b9",
   "metadata": {},
   "source": [
    "This has only been ZAF055 but to confirm data for MWI198 does exist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "81910858-9a31-47d1-a52e-b7010c3a424d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construct the SQL query to get data_type and fetch integer_value or float_value accordingly\n",
    "query = f\"\"\"\n",
    "SELECT * FROM variable_data WHERE study = 'MWI198'\n",
    "\"\"\"\n",
    "\n",
    "# Execute the SQL query and fetch a pandas df\n",
    "df = conn.execute(query).fetchdf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1c12fc79-b397-4a8e-8d72-2edf986fa1b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Adverse Event',\n",
       " 'Birth Date',\n",
       " 'Body Weight',\n",
       " 'Death Indicator',\n",
       " 'Employment Status',\n",
       " 'Gastroenteritis',\n",
       " 'Height',\n",
       " 'Highly Active Antiretroviral Therapy',\n",
       " 'Hospitalization',\n",
       " 'Hypertension',\n",
       " 'Location',\n",
       " 'Number of Fetuses',\n",
       " 'Pneumonia ',\n",
       " 'Syphilis',\n",
       " 'Upper Respiratory Tract Infection'}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df.variable_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "9e5b9e30-b734-4ff2-8a93-66a3d1369fad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'no': 25583, 'yes': 420})"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "data = conn.execute(\"SELECT * FROM variable_data WHERE variable_id = 'Gastroenteritis'\").fetchall()\n",
    "Counter([x[-3] for x in data]) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b298d36c-503d-4288-b75d-e59238ca9b89",
   "metadata": {},
   "source": [
    "## SQLAlchemy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6bbb63f-b4c0-44b2-9f43-710646f997c3",
   "metadata": {},
   "source": [
    "The database can alse be accesd via the SQLAlchemy ORM. I prefer sql but your mileage may vary...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "13eb5fa7-2d69-4083-856e-177e045c3f07",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "from sqlalchemy.orm import sessionmaker\n",
    "import sys\n",
    "sys.path.insert(1, '/terra/projects/heat_center/data/health_open/database/definition')\n",
    "from HE2AT_data_model_definition import Base, Codebook, VariableData, Study, Patient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "15cada2e-9b1c-4e10-83e0-73387f0b068c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the database engine and session\n",
    "engine = create_engine(f\"sqlite:///{(Path(Path.home(),'heat_center/data/health_open/database/data/RP1.db'))}\")\n",
    "Session = sessionmaker(bind=engine)\n",
    "session = Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d8ba6c2e-1e0d-489d-8e72-1afc078e3874",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get all available variable names from codebook that have associated data in variable_data\n",
    "filtered_var_names = session.query(Codebook.variable_name.distinct()) \\\n",
    "    .join(VariableData, Codebook.variable_name == VariableData.variable_id) \\\n",
    "    .all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "82e60b1d-3602-4bc5-aed1-0a5949934289",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Birth Date',),\n",
       " ('Age-Years',),\n",
       " ('Race',),\n",
       " ('Sex',),\n",
       " ('Height',),\n",
       " ('Body Weight',),\n",
       " ('Body Mass Index',),\n",
       " ('Location',),\n",
       " ('Smoking Status',),\n",
       " ('Alcohol Abuse',),\n",
       " ('Employment Status',),\n",
       " ('Hypertension',),\n",
       " ('Delivery Procedure',),\n",
       " ('Number of Fetuses',),\n",
       " ('Preterm Birth',),\n",
       " ('Gestational Age at Birth (weeks)',),\n",
       " ('Birth Weight',),\n",
       " ('Apgar Score',),\n",
       " ('Death Indicator',),\n",
       " ('Perinatal Mortality ',),\n",
       " ('Hospitalization',),\n",
       " ('Gastroenteritis',),\n",
       " ('Pneumonia ',),\n",
       " ('Syphilis',),\n",
       " ('HIV Status',),\n",
       " ('HIV Viral Load Measurement',),\n",
       " ('CD4 Expressing Cell Count',),\n",
       " ('Highly Active Antiretroviral Therapy',),\n",
       " ('Noncompliance with antiretroviral medication regimen (finding)',),\n",
       " ('Upper Respiratory Tract Infection',),\n",
       " ('Adverse Event',),\n",
       " ('Systolic Blood Pressure',),\n",
       " ('Diastolic Blood Pressure',)]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_var_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7bfb9e64-26a1-4c67-b059-33f980651e9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('ZAF055',),\n",
       " ('MWI198',),\n",
       " ('MWI088',),\n",
       " ('MWI026',),\n",
       " ('MWI021',),\n",
       " ('MWI094',),\n",
       " ('ZAF124',),\n",
       " ('MWI524',),\n",
       " ('ZAF357',),\n",
       " ('ZAF014',),\n",
       " ('MUL140',),\n",
       " ('MUL159',),\n",
       " ('MWI049',),\n",
       " ('KEN539',),\n",
       " ('ZAF192',),\n",
       " ('KEN086',),\n",
       " ('TZA556',),\n",
       " ('ZAF155',),\n",
       " ('MWI097',),\n",
       " ('MWI532',),\n",
       " ('ZAF358',),\n",
       " ('MUL006',)]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.query(Study.name).all()"
   ]
  }
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