{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# NHS Winter SitRep\n", "\n", "Notebook starting to explore NHS Winter Sitrep Data.\n", "\n", "*The original version of this notebook with working notes on the creation of the data harvester can be found here: [../archive/Winter_SitRep_old.ipynb](../archive/Winter_SitRep_old.ipynb).*\n", "\n", "The [NHS Winter Sitrep data from 2017-18](https://www.england.nhs.uk/statistics/statistical-work-areas/winter-daily-sitreps/winter-daily-sitrep-2017-18-data/) is available as daily data published on a weekly basis in the form of an Excel spreadsheet. A time series spreadsheet is also published weekly that collates data from all the weekly spreadsheets in the 2017-18 Winter collection period.\n", "\n", "The [psychemedia/openHealthDataDoodles/tree/cli_winter_sitrep](https://github.com/psychemedia/openHealthDataDoodles/tree/cli_winter_sitrep) utility is a command line tool that attempts to download the most recent timeseries spreadsheets for *Acute* and *NHS111* sitreps, and extract the data into a simple unnormalised SQLite3 database tables, one for the *Acute* data, one for the *NHS111* data.\n", "\n", "*If you are running this notebook from the orginal repository via Binderhub, the `cli_winter_sitrep` utility should already be installed.*" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Grabbing data for Winter Sitrep: Acute Time series 20 November 2017 to 31 December 2017 (XLSX, 774kB)\n", "Grabbing data for Winter SitRep: NHS111 Time series 20 November 2017 to 31 December 2017 (XLSX, 395kB)\n", "/srv/venv/lib/python3.5/site-packages/pandas/core/generic.py:1534: UserWarning: The spaces in these column names will not be changed. In pandas versions < 0.14, spaces were converted to underscores.\n", " chunksize=chunksize, dtype=dtype)\n" ] } ], "source": [ "# Attempt to collect the most recent data\n", "!nhs_winter_sitrep collect" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import natural_time_periods as ntpd\n", "from dateutil import parser" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Accessing the database" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import sqlite3\n", "conn = sqlite3.connect('nhs_sitrepdb.db')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " name\n", "0 sitrep\n", "1 nhs111" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q=\"SELECT name FROM sqlite_master WHERE type='table';\"\n", "\n", "pd.read_sql_query(q,conn)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DateAreaCodeNamevalueCategoryReport
02017-11-20London Commissioning RegionRF4Barking, Havering And Redbridge University Hos...0A&E closuresA&E closures
12017-11-21London Commissioning RegionRF4Barking, Havering And Redbridge University Hos...0A&E closuresA&E closures
22017-11-22London Commissioning RegionRF4Barking, Havering And Redbridge University Hos...0A&E closuresA&E closures
32017-11-23London Commissioning RegionRF4Barking, Havering And Redbridge University Hos...0A&E closuresA&E closures
42017-11-24London Commissioning RegionRF4Barking, Havering And Redbridge University Hos...0A&E closuresA&E closures
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" ], "text/plain": [ " Date Area Code \\\n", "0 2017-11-20 London Commissioning Region RF4 \n", "1 2017-11-21 London Commissioning Region RF4 \n", "2 2017-11-22 London Commissioning Region RF4 \n", "3 2017-11-23 London Commissioning Region RF4 \n", "4 2017-11-24 London Commissioning Region RF4 \n", "\n", " Name value Category \\\n", "0 Barking, Havering And Redbridge University Hos... 0 A&E closures \n", "1 Barking, Havering And Redbridge University Hos... 0 A&E closures \n", "2 Barking, Havering And Redbridge University Hos... 0 A&E closures \n", "3 Barking, Havering And Redbridge University Hos... 0 A&E closures \n", "4 Barking, Havering And Redbridge University Hos... 0 A&E closures \n", "\n", " Report \n", "0 A&E closures \n", "1 A&E closures \n", "2 A&E closures \n", "3 A&E closures \n", "4 A&E closures " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Test query - sitrep\n", "pd.read_sql_query(\"SELECT * FROM sitrep LIMIT 5;\", conn, parse_dates=['Date'])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ReportCategory
0A&E closuresA&E closures
1A&E divertsA&E diverts
2G&A bedsCore Beds Open
3G&A bedsEscalation Beds Open
4G&A bedsTotal Beds Open
5G&A bedsTotal beds occ'd
6G&A bedsOccupancy rate
7Beds Occ by long stay patients> 7 days
8Beds Occ by long stay patients> 21 days
9D&V, NorovirusBeds closed
10D&V, NorovirusBeds closed unocc
11Adult critical careCC Adult avail
12Adult critical careCC Adult Occ
13Adult critical careOccupancy rate
14Adult critical careCC Adult Open
15Paediatric intensive carePaed Int Care Avail
16Paediatric intensive carePaed Int Care Occ
17Paediatric intensive careOccupancy rate
18Paediatric intensive carePaed Int Care Open
19Neonatal intensive careNeo Int Care Avail
20Neonatal intensive careNeo Int Care Occ
21Neonatal intensive careOccupancy rate
22Neonatal intensive careNeo Int Care Open
23Ambulance Arrivals and DelaysArriving by ambulance
24Ambulance Arrivals and DelaysDelay 30-60 mins
25Ambulance Arrivals and DelaysDelay >60 mins
\n", "
" ], "text/plain": [ " Report Category\n", "0 A&E closures A&E closures\n", "1 A&E diverts A&E diverts\n", "2 G&A beds Core Beds Open\n", "3 G&A beds Escalation Beds Open\n", "4 G&A beds Total Beds Open\n", "5 G&A beds Total beds occ'd\n", "6 G&A beds Occupancy rate\n", "7 Beds Occ by long stay patients > 7 days\n", "8 Beds Occ by long stay patients > 21 days\n", "9 D&V, Norovirus Beds closed\n", "10 D&V, Norovirus Beds closed unocc\n", "11 Adult critical care CC Adult avail\n", "12 Adult critical care CC Adult Occ\n", "13 Adult critical care Occupancy rate\n", "14 Adult critical care CC Adult Open\n", "15 Paediatric intensive care Paed Int Care Avail\n", "16 Paediatric intensive care Paed Int Care Occ\n", "17 Paediatric intensive care Occupancy rate\n", "18 Paediatric intensive care Paed Int Care Open\n", "19 Neonatal intensive care Neo Int Care Avail\n", "20 Neonatal intensive care Neo Int Care Occ\n", "21 Neonatal intensive care Occupancy rate\n", "22 Neonatal intensive care Neo Int Care Open\n", "23 Ambulance Arrivals and Delays Arriving by ambulance\n", "24 Ambulance Arrivals and Delays Delay 30-60 mins\n", "25 Ambulance Arrivals and Delays Delay >60 mins" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_sql_query(\"SELECT DISTINCT Report, Category FROM sitrep;\", conn)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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CategoryDateRegionCodeNHS 111 area nameReportvalue
0Calls answered within 60 Seconds2017-11-20North111AA1North East England NHS 111Answered in 602400.0
1Calls answered within 60 Seconds2017-11-21North111AA1North East England NHS 111Answered in 602135.0
2Calls answered within 60 Seconds2017-11-22North111AA1North East England NHS 111Answered in 602163.0
3Calls answered within 60 Seconds2017-11-23North111AA1North East England NHS 111Answered in 601899.0
4Calls answered within 60 Seconds2017-11-24North111AA1North East England NHS 111Answered in 601957.0
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" ], "text/plain": [ " Category Date Region Code \\\n", "0 Calls answered within 60 Seconds 2017-11-20 North 111AA1 \n", "1 Calls answered within 60 Seconds 2017-11-21 North 111AA1 \n", "2 Calls answered within 60 Seconds 2017-11-22 North 111AA1 \n", "3 Calls answered within 60 Seconds 2017-11-23 North 111AA1 \n", "4 Calls answered within 60 Seconds 2017-11-24 North 111AA1 \n", "\n", " NHS 111 area name Report value \n", "0 North East England NHS 111 Answered in 60 2400.0 \n", "1 North East England NHS 111 Answered in 60 2135.0 \n", "2 North East England NHS 111 Answered in 60 2163.0 \n", "3 North East England NHS 111 Answered in 60 1899.0 \n", "4 North East England NHS 111 Answered in 60 1957.0 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Test query - nhs111\n", "pd.read_sql_query(\"SELECT * FROM nhs111 LIMIT 5;\", conn, parse_dates=['Date'])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ReportCategory
0Answered in 60Calls answered within 60 Seconds
1Answered in 60Calls answered
2AbandonedCalls abandoned after at least 30 seconds waiting
3AbandonedCalls offered
4TriageCalls where person triaged
5Clinical AdvisorCalls transferred to or answered by a clinical...
6Clinical InputCalls to a CAS clinician
7Call BackCalls back within 10 minutes
8Call BackCalls where person offered call back
9DispositionsAmbulance dispatches
10DispositionsRecommended to attend A&E
11DispositionsRecommended to attend primary and community care
12DispositionsRecommended to contact primary care
13DispositionsRecommended to speak to primary care
14DispositionsRecommended to dental
15DispositionsRecommended to pharmacy
16DispositionsRecommended to attend other service
17DispositionsNot recommended to attend other service
18DispositionsGiven health information
19DispositionsRecommended home Care
20DispositionsRecommended non clinical
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" ], "text/plain": [ " Report Category\n", "0 Answered in 60 Calls answered within 60 Seconds\n", "1 Answered in 60 Calls answered\n", "2 Abandoned Calls abandoned after at least 30 seconds waiting\n", "3 Abandoned Calls offered\n", "4 Triage Calls where person triaged\n", "5 Clinical Advisor Calls transferred to or answered by a clinical...\n", "6 Clinical Input Calls to a CAS clinician\n", "7 Call Back Calls back within 10 minutes\n", "8 Call Back Calls where person offered call back\n", "9 Dispositions Ambulance dispatches\n", "10 Dispositions Recommended to attend A&E\n", "11 Dispositions Recommended to attend primary and community care\n", "12 Dispositions Recommended to contact primary care\n", "13 Dispositions Recommended to speak to primary care\n", "14 Dispositions Recommended to dental\n", "15 Dispositions Recommended to pharmacy\n", "16 Dispositions Recommended to attend other service\n", "17 Dispositions Not recommended to attend other service\n", "18 Dispositions Given health information\n", "19 Dispositions Recommended home Care\n", "20 Dispositions Recommended non clinical" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_sql_query(\"SELECT DISTINCT Report, Category FROM nhs111;\", conn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utils\n", "\n", "Helpful quueries wrapped in functions." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def lookupTrust(conn,trust, typ='sitrep'):\n", " typ = typ.lower()\n", " name='NHS 111 area name' if typ=='nhs111' else 'Name'\n", " area='Region' if typ=='nhs111' else 'Area'\n", " q='''SELECT DISTINCT \"{name}\",{area}, code FROM {typ} WHERE LOWER(\"{name}\") LIKE \"%{trust}%\";'''.format(typ=typ,\n", " name=name, \n", " trust=trust.lower(),\n", " area=area)\n", " return pd.read_sql_query(q, conn)\n", "\n", "\n", "def lookupTrustCode(conn,trust, typ='sitrep'):\n", " df=lookupTrust(conn,trust, typ=typ)\n", " name='NHS 111 area name' if typ.lower()=='nhs111' else 'Name'\n", " if len(df) > 1: return df\n", " return df[name].iloc[0],df['Code'].iloc[0]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NameAreaCode
0Isle Of Wight NHS TrustSouth Of England Commissioning RegionR1F
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" ], "text/plain": [ " Name Area Code\n", "0 Isle Of Wight NHS Trust South Of England Commissioning Region R1F" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lookupTrust(conn,'Wight')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NHS 111 area nameRegionCode
0Isle Of Wight NHS 111South111AA6
\n", "
" ], "text/plain": [ " NHS 111 area name Region Code\n", "0 Isle Of Wight NHS 111 South 111AA6" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lookupTrust(conn,'Wight', 'NHS111')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('Isle Of Wight NHS 111', '111AA6')" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lookupTrustCode(conn,'Wight', 'NHS111')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('Isle Of Wight NHS Trust', 'R1F')" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lookupTrustCode(conn,'Wight', 'sitrep')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NHS 111 area nameRegionCode
0Inner North West London NHS 111London111AA7
1Hillingdon London NHS 111London111AA9
2South West London NHS 111London111AG5
3North West London NHS 111London111AD4
4North Central London NHS 111London111AD5
5Outer North East London NHS 111London111AD6
6South East London NHS 111London111AD7
7East London & City NHS 111London111AD8
8LONDON REGIONLondonNone
\n", "
" ], "text/plain": [ " NHS 111 area name Region Code\n", "0 Inner North West London NHS 111 London 111AA7\n", "1 Hillingdon London NHS 111 London 111AA9\n", "2 South West London NHS 111 London 111AG5\n", "3 North West London NHS 111 London 111AD4\n", "4 North Central London NHS 111 London 111AD5\n", "5 Outer North East London NHS 111 London 111AD6\n", "6 South East London NHS 111 London 111AD7\n", "7 East London & City NHS 111 London 111AD8\n", "8 LONDON REGION London None" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lookupTrustCode(conn,'London', 'NHS111')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "def lookupFromTrustCode(conn,code, typ='sitrep'):\n", " typ = typ.lower()\n", " name='NHS 111 area name' if typ=='nhs111' else 'Name'\n", " area='Region' if typ=='nhs111' else 'Area'\n", " q='''SELECT DISTINCT \"{name}\",{area}, code FROM {typ} WHERE LOWER(Code) LIKE \"%{code}%\";'''.format(typ=typ,\n", " name=name, \n", " code=code.lower(),\n", " area=area)\n", " return pd.read_sql_query(q, conn).iloc[0].to_dict()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Area': 'South Of England Commissioning Region',\n", " 'Code': 'R1F',\n", " 'Name': 'Isle Of Wight NHS Trust'}" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lookupFromTrustCode(conn,'R1F')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### `inflect`\n", "The [`inflect` package](https://github.com/pwdyson/inflect.py) provides a variety of functions for generating text." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "import inflect\n", "P = inflect.engine()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Test plot" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def timeSeriesPlot(df,time='Date',val='value',title=''):\n", " df.set_index('Date')['value'].plot(title=title)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "q='''\n", "SELECT * FROM sitrep WHERE Category = \"Total beds occ'd\" AND Code=\"R1F\";\n", "'''\n", "\n", "df = pd.read_sql_query(q, conn, parse_dates=['Date'])\n", "\n", "timeSeriesPlot(df,title=\"R1F Total beds occ'd \")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Time limited queries" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "def getperiod_dates(nl_period):\n", " if nl_period is None: return ''\n", " period = ''.join(nl_period.lower().split()) if nl_period is not None else nl_period\n", " if period in ['lastmonth', 'lastweek']: \n", " if period == 'lastmonth': fromdate,todate=(ntpd.last_month(iso=True))\n", " elif period == 'lastweek': fromdate,todate=(ntpd.last_week(iso=True))\n", " return fromdate, todate\n", " return ''\n", "\n", "def getperiod_sql_clause(nl_period, col='Date'):\n", " period = getperiod_dates(nl_period)\n", " if not period: return period\n", " else: (fromdate, todate)=period\n", " q=' AND date({col}) BETWEEN date(\"{fromdate}\") AND date(\"{todate}\") '.format(col=col, fromdate=fromdate,\n", " todate=todate)\n", " return q " ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(('2017-12-25', '2017-12-31'),\n", " ' AND date(Date) BETWEEN date(\"2017-12-01\") AND date(\"2017-12-31\") ')" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "getperiod_dates('last week'), getperiod_sql_clause('last month')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "def period_text(period):\n", " p = getperiod_dates(period)\n", " if p: return '({} to {})'.format(parser.parse(p[0]).strftime('%A %d %B %Y'),\n", " parser.parse(p[1]).strftime('%A %d %B %Y'))\n", " return ''" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'(Monday 25 December 2017 to Sunday 31 December 2017)'" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "period_text('last week')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "def timeSeriesSelectPlot(code, category, report=None, title=None, period=None):\n", " q=''' SELECT * FROM sitrep WHERE Category = \"{category}\" AND Code=\"{code}\"'''.format(category=category,code=code)\n", " #We may have to disambiguate category values\n", " if report is not None:\n", " q=q+' AND Report=\"{report}\"'.format(report=report)\n", " \n", " #Add time limit\n", " q = q+ getperiod_sql_clause(period, col='Date')\n", " \n", " df = pd.read_sql_query(q, conn, parse_dates=['Date'])\n", " if title is None:\n", " title = '{} - '.format(report) if report is not None else ''\n", " title = '{}{} for {}'.format(title, category,code)\n", " \n", " timeSeriesPlot(df,title=title)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "timeSeriesSelectPlot('R1F',\"Total beds occ'd\")" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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Ts2nbqjlTfjqKEcepdnxDUqIXkUZTVub84YO1PP3heganduK58cPp1Unj8Q1NiV5EGsWeg8XcOT2bD1cXcM3wVB7ReHyjUaIXkQa3bts+JkxaTJ7G42NCiV5EGtS7X2zl7hnZtG3VgqkTRnG67uXa6JToRaRBlJU5T36wlmc+XM/gtM48N26YxuNjRIleROrdnoPF3DHtc+auKWRMZioPj9Z4fCwp0YtIvfrWePyVpzJuZLrG42NMiV6kCsWlZSz8aicZ3drRR1/Jj8q7X+Rz94ylGo+PM0r0IhXk7DjAtEV5vJy1ie37D9PM4NyTunPdiHTOOymFFs2jujFbk1Ja5jw5ey3Pzl3PkLTOPDduOD07tYl1WBJSohch6L3PXrmNqQtz+WTddpoZnH9yd64alsrKLXuZnpXHhxOz6NmxDWNOT+Pa09PUyw9Fjsdfm5nGw1cOpHULjcfHkxpvDm5macBEoAfgwPPu/pSZPUJwC8EyoAD4kbtvCW89+BRwKVAUrl9S3TV0c3CJlYq9916d2nBtmMgjZ4gUl5YxZ1UBUxfm8rd1hQCc2z+F60f2bdK9/LXb9jFhYhabdx/kgcsHcoPG4xtVtDcHjybR9wJ6ufsSM+sALAauBDa5+95wn9uAAe5+k5ldCvyCINGPBJ5y95HVXUOJXhrTkZIyPlhVsffeg+tHpnFO/+40b1Z9osrbWcSMrDymL8qjYN9henRszbWZaVw7Ir1J9fLfWZ7P3S8v5ZhWLXhu3DAyNR7f6KJN9NHcSjAfyA+X95nZKqCPu6+M2K0dQW8fgl7+xPA+sfPNrLOZ9QrPIxIz3/Te89i+/wi9O7Xhzu/2Z8zpqbWa353W9RjuvugkbrugHx+uLmDKglyembueZ+au59z+KVw3Ip3zT+6etL380jLnidlr+OPcDRqPTxC1GqM3swxgKLAgfP4ocCOwBzgv3K0PkBdx2KZw3bcSvZlNACYApKen1zpwkWiU996nLMjl0/W1771Xp2XzZlw8sCcXD+z5rV7+hEmLv+7ljzk9jdQux9Rji2JrT1Ext0//nI/WFDL29DQeGq3x+ERQ49DN1zuatQc+Bh5191kVtt0LtHH3B8zsTeCx8KbimNkc4F/cvcqxGQ3dSH3L2XGAqQvzeGXxN733sSPSuSazdr332iopLQt6+Qtz+XjtN2P5ydDLX7N1HxMmZbFl90EevGIg14/QeHys1dvQTXiylsBMYHLFJB+aDLwNPABsBtIitqWG60Qa1JGSb2bORPbebxiZztn9U+rUe49Wi+bNuGhgTy4a2JNNu4qYvig5evnl4/HtWrdg6k9HaTw+wUTzYawBLwI73f2OiPX93H1duPwL4Bx3v9rM/hG4lW8+jH3a3UdUdw316KUuNm4Pxt7Le+99Orfl2tPTGJOZFhdjx+W9/KkLc/ko7OWf0z+F6xOgl19a5vz+/TX86aMNDE0PxuN7dIz9z1QC9Tnr5jvAJ8BygqmUAPcB/wycFK7LAW5y983hC8OzwCUE0yv/qbphG1Cil9qr2Htv3sw4/+TuXD+i8XrvR2PTriJmLMpjelYe2/YGM3bGZAbTOeOtl7+nqJjbpn3Ox2s1Hh+v6i3RNwYleolWvPfeo1VVL/+6EelcEAe9/Mjx+IeuOJXrR2rCRDxSopekUd57n7Iwh8/W76B5M+OCk7tz3ch0zu4Xv733aG3aVcSMrE1MX5TLtr2H6d6h9dcvXmldG7+X//byfO4Jx+OfGzeM4X01Hh+vlOgl4W3cfoCpi3J5JWsTOw4Evfexp6dxTYL13qNVUlrG3DWFTFmQ83Uv/+x+KVw/MhjLb9nAvfzSMud376/hzxqPTxhK9JKQjpSU8f7KrUxdmJuUvfdobd59kOmL8pixKI+tew/RvcM3Y/kN0cvfXXSE26Zl87e1hVw3Io0Hr9B4fCJQopeEUlnv/boRQe+9KfcqS0rL+GhNIVMW5vLRmgKcoJd/3Yh0Ljilfnr5q7fuZcLExeTv0Xh8olGil7hXVe/9+pHpnNWEeu/Raohe/lvL8vnlKxqPT1RK9BK31Huvm/Je/tSFucw9yl5+5Hj8sPTO/Fnj8QlJiV7iSnnvfcqCXP6+Iei9f/eU4GYe6r0fvc27Dwbz8mvRy//2eHw6D14xQOPxCUqJXuLCV9sPMG1hLq8s/nbvfUxmGt3Vg6w3lfXyz+oXfPs2spev8fjkokQvMXOkpIz3VgRj7+W99wtP6cF1I9M568RuNFPvvUFt2X3w60qa+XsOkdKhNWMyU0ntcgwP/3UlHdq04M/jhjO8b5dYhyp1pEQvMfPj/1nEh6sLSO3SlutGpHPN8FT13mOgpLSMj9cWMmVB0Msvcxjetwt/vmGYfh9Jol6rV4pEa+22fXy4uoCfn3sC91x0knrvMdSieTMuOKUHF5zSgy27D7J88x7OO6k7rVrEbxE1aRhK9FKvJs3LoVWLZvzkrOOV5ONI785t6d2EbnMo36aXdqk3+w4VM2vJJi47rRdd27WKdTgiElKil3rz2uebOXCklPFn9I11KCISQYle6oW7M3FeDqf16cSQtM6xDkdEIijRS72Y/+VO1hXsZ/wZfXUfUZE4U2OiN7M0M5trZivNbIWZ3R6uf9zMVpvZMjN71cw6h+szzOygmWWHj+cauhESe5Pmb6RT25ZcPqh3rEMRkQqi6dGXAHe7+wBgFHCLmQ0AZgOnuvsgYC1wb8QxG9x9SPi4qd6jlriybe8h3luxjTGZqbRtpa/Si8SbGhO9u+e7+5JweR+wCujj7u+7e0m423wgteHClHg2ZUEuZe6MG6UPYUXiUa3G6M0sAxgKLKiw6cfAOxHPjzOzz83sYzM7q04RSlwrLi1j6sJczumfQt9j28U6HBGpRNRfmDKz9sBM4A533xux/l8Jhncmh6vygXR332Fmw4HXzGxg5DHhcROACQDp6SqslKjeW7GVgn2H+T/qzYvErah69GbWkiDJT3b3WRHrfwRcBtzgYdEcdz/s7jvC5cXABqB/xXO6+/PununumSkpKXVuiMTGpHk5pHZpy7kndY91KCJShWhm3RjwArDK3Z+IWH8J8CvgCncvilifYmbNw+XjgX7Al/UduMTemq37WPDVTsaN6qt68iJxLJqhmzOB8cByM8sO190HPA20BmaH86bnhzNszgYeNrNioAy4yd131nvkEnOT5m+kVYtmjMlMi3UoIlKNGhO9u38KVNZde7uK/WcSDPNIEtt3qJhXl2zmskGqayMS7/TNWDkqr4Z1bW48IyPWoYhIDZTopdbK69oMSlVdG5FEoEQvtTbvyx2sL9jPeE2pFEkISvRSa5Pm5dD5mJZcPlh1bUQSgRK91MrWPYd4f+U2xmSm0aal6tqIJAIleqmVKQvDujYjNWwjkiiU6CVqR0qCujbn9k8h/dhjYh2OiERJiV6i9t6KrRTuO6wplSIJRoleojZpXg5pXdtydn/VJhJJJEr0EpXVW/eycONOxo1UXRuRRKNEL1GZNC+H1qprI5KQlOilRnsPFfPq55u5fHBvuqiujUjCUaKXGs1avImiI6X6JqxIglKil2q5O5Pm5zA4tRODVddGJCEp0Uu15m3YwYbCA4zXlEqRhKVEL9WaGNa1uWxQr1iHIiJHSYleqpS/5yCzV23jWtW1EUlo0dwzNs3M5prZSjNbYWa3h+sfN7PVZrbMzF41s84Rx9xrZuvNbI2ZXdyQDZCGM3VBWNdGH8KKJLRoevQlwN3uPgAYBdxiZgOA2cCp7j4IWAvcCxBuGwsMBC4B/lR+s3BJHEdKypiyMI/zTupOWlfVtRFJZDUmenfPd/cl4fI+YBXQx93fd/eScLf5QGq4PBqY5u6H3f0rYD0wov5Dl4b07oqtbN9/mPFnqDcvkuhqNUZvZhnAUGBBhU0/Bt4Jl/sAeRHbNoXrKp5rgpllmVlWYWFhbcKQRjBp3kbSux7DOf1U10Yk0UWd6M2sPTATuMPd90as/1eC4Z3Jtbmwuz/v7pnunpmSomQST1bl72XRxl2MG5VOM9W1EUl4LaLZycxaEiT5ye4+K2L9j4DLgAvc3cPVm4HIgiip4TpJEJPmq66NSDKJZtaNAS8Aq9z9iYj1lwC/Aq5w96KIQ94AxppZazM7DugHLKzfsKWh7D1UzGufb+aKwb3pfIzq2ogkg2h69GcC44HlZpYdrrsPeBpoDcwOXguY7+43ufsKM5sBrCQY0rnF3UvrP3RpCDPL69roQ1iRpFFjonf3T4HKBmrfruaYR4FH6xCXxMDXdW3SOjMoVXVtRJKFvhkrX/v7hh18WXiAG/UFKZGkokQvX5s4byNd27XiH1XXRiSpKNELAFt2H2T2ym2MUV0bkaSjRC8ATF2YiwM3jEyPdSgiUs+U6IUjJWVMXZjH+aprI5KUlOiFd77IV10bkSSmRC9MmpdD32OP4WzVtRFJSkr0TdzKLXvJytnFuJF9VddGJEkp0Tdx5XVtrslMrXlnEUlISvRN2J6DQV2b0UNU10YkmSnRN2EzF2/iYHEpN56REetQRKQBKdE3Ue7OS/NzGJLWmVP7dIp1OCLSgJTom6jP1u/gy+0HuFFTKkWSnhJ9E1Ve1+bS01TXRiTZKdE3QZt3H+SDVdu49nTVtRFpCpTom6CpC4K6NtePUF0bkaYgmlsJppnZXDNbaWYrzOz2cP014fMyM8uM2D/DzA6aWXb4eK4hGyC1c7iklGmLcrngZNW1EWkqormVYAlwt7svMbMOwGIzmw18AVwF/Gclx2xw9yH1GKfUk3e/2Mr2/UcYrymVIk1GNLcSzAfyw+V9ZrYK6OPuswHC+8VKgpg4L4eMY4/hrBO7xToUEWkktRqjN7MMYCiwoIZdjzOzz83sYzM7q4pzTTCzLDPLKiwsrE0YcpRWbNnD4pxdjBulujYiTUnUid7M2gMzgTvcfW81u+YD6e4+FLgLmGJmHSvu5O7Pu3umu2empKhqYmN4aX4ObVo245rhabEORUQaUVSJ3sxaEiT5ye4+q7p93f2wu+8IlxcDG4D+dQ1U6iaoa7OF0YP70OmYlrEOR0QaUTSzbgx4AVjl7k9EsX+KmTUPl48H+gFf1jVQqZtXwro2urmISNMTzaybM4HxwHIzyw7X3Qe0Bp4BUoC3zCzb3S8GzgYeNrNioAy4yd131n/oEq2ysqCuzdB01bURaYqimXXzKVDVJ3evVrL/TIJhHokTn23YzlfbD/DktYNjHYqIxIC+GdsETJyXw7GqayPSZCnRJ7nNuw8yJ6xr07qF6tqINEVK9Elu8vwcAK4fqbo2Ik2VEn0SO1xSyvRFeZx/cg9Su6iujUhTpUSfxN5ZvpUdB47o5iIiTZwSfRKbOG8jx3Vrx3dU10akSVOiT1JfbN7DktzdqmsjIkr0yaq8rs3Vw1JjHYqIxJgSfRLaU1TMa9mbuXKI6tqIiBJ9Unp5cR6HistU10ZEACX6pFNe12Z43y4M7K26NiKiRJ90Pl2/nY07ihg/Sr15EQko0SeZ8ro23zutZ6xDEZE4oUSfRDbtKuLD1dsYO0J1bUTkG0r0SWTyglwArh+pYRsR+YYSfZIor2tzwSk96NO5bazDEZE4Es2tBNPMbK6ZrTSzFWZ2e7j+mvB5mZllVjjmXjNbb2ZrzOzihgpevvH28nx2qq6NiFQimlsJlgB3u/sSM+sALDaz2cAXwFXAf0bubGYDgLHAQKA38IGZ9Xf30voNXSJNnJfD8d3aceYJqmsjIt9WY4/e3fPdfUm4vA9YBfRx91XuvqaSQ0YD09z9sLt/BawHRtRn0PJtX2zew+eqayMiVajVGL2ZZQBDgQXV7NYHyIt4vilcJw1k0rwc2rZszg+Gq66NiPxvUSd6M2tPcNPvO9x9b10vbGYTzCzLzLIKCwvreroma09RMa8v3cyVQ3vTqa3q2ojI/xZVojezlgRJfrK7z6ph981AWsTz1HDdt7j78+6e6e6ZKSkp0cYrFXxd12ZURqxDEZE4Fc2sGwNeAFa5+xNRnPMNYKyZtTaz44B+wMK6hSmVKStzJs3PIbNvFwb07hjrcEQkTkUz6+ZMYDyw3Myyw3X3Aa2BZ4AU4C0zy3b3i919hZnNAFYSzNi5RTNuGsYn67eTs6OIuy4iGI0SAAAMu0lEQVTsH+tQRCSO1Zjo3f1ToKqpHK9WccyjwKN1iEuiMGneRrq1b8Ulp6qujYhUTd+MTVB5O4uYs7qAsaenq66NiFRLiT5BTV6QiwHXj0yPdSgiEueU6BPQoeJSZmTlceGAHvRWXRsRqYESfQIqr2ujKZUiEg0l+gQ0cV4Ox6e048wTj411KCKSAJToE8zyTXvIztvN+FF9Cb7iICJSPSX6BDNp/kbVtRGRWlGiTyC7i47wevYWrhzah45tVNdGRKKjRJ9AXs7axOGSMt1cRERqRYk+QZSVOS8tyOH0jC6c0kt1bUQkekr0CeJv6wrJ2VHE+DMyYh2KiCQYJfoEMWleDt3at+aSgaprIyK1o0SfAPJ2FvHhmgKuG5FGqxb6lYlI7ShrJICXFuTQzEx1bUTkqCjRx7lDxaXMWJTHhaf0oFcn1bURkdpToo9zby3LZ1dRMeM1pVJEjpISfZybOD+HE1La8Q8nqK6NiBydaO4Zm2Zmc81spZmtMLPbw/VdzWy2ma0L/+0Srj/XzPaYWXb4uL+hG5Gslm3azVLVtRGROoqmR18C3O3uA4BRwC1mNgD4NTDH3fsBc8Ln5T5x9yHh4+F6j7qJmDQvh2NaNecq1bURkTqoMdG7e767LwmX9wGrgD7AaODFcLcXgSsbKsimaNeBI7yxVHVtRKTuajVGb2YZwFBgAdDD3fPDTVuBHhG7nmFmS83sHTMbWMW5JphZlpllFRYW1j7yJPfy4jzVtRGRehF1ojez9sBM4A533xu5zd0d8PDpEqCvuw8GngFeq+x87v68u2e6e2ZKSspRBZ+sysqcl+bnMiKjKyf3VF0bEambqBK9mbUkSPKT3X1WuHqbmfUKt/cCCgDcfa+77w+X3wZamlm3eo88iX28tpDcnUWaUiki9SKaWTcGvACscvcnIja9AfwwXP4h8Hq4f8/wGMxsRHiNHfUZdLKbND+HlA6tuVh1bUSkHrSIYp8zgfHAcjPLDtfdBzwGzDCzfwZygDHhtquBm82sBDgIjA2HdiQKeTuLmLumgF+cd6Lq2ohIvagx0bv7p0BVk7gvqGT/Z4Fn6xhXk/XS/PK6Nhq2EZH6oS5jHDlUXMr0rDwuGtCDnp3axDocEUkSSvRx5M1l+exWXRsRqWdK9HFk0ryNnNi9PWccr7o2IlJ/ovkwNm4V7jvMX5duiXUY9WLPwWKWbtrDQ1cMVF0bEalXCZ3ot+w+yMNvrox1GPWma7tWXDWsT6zDEJEkk9CJfmDvjiy9/6JYh1Fv2rRqRusWzWMdhogkmYRO9C2aN6PTMfqYQUSkOsqSIiJJToleRCTJKdGLiCQ5JXoRkSSnRC8ikuSU6EVEkpwSvYhIkrN4KBVvZoUENe2PVidgTz2FE0vJ0g5QW+JRsrQDkqst6UDuUR7b191rvBdrXCT6ujKz5919QqzjqKtkaQeoLfEoWdoBSdeWwmiSdV0ky9DNX2MdQD1JlnaA2hKPkqUdkFxt2d3QF0iKHr2ISKIysyx3z2zIayRLj15EJFE939AXUI9eRCTJJVSP3szSzGyuma00sxVmdnu4/kEz22xm2eHj0ljHWpOq2hJu+4WZrQ7X/0cs46xJNb+T6RG/j41mlh3rWGtSTVuGmNn8sC1ZZjYi1rHWpJq2DDazeWa23Mz+amYdYx1rTcysjZktNLOlYVseCtcfZ2YLzGx9+PfWKtaxxi13T5gH0AsYFi53ANYCA4AHgXtiHV89teU84AOgdbite6xjPZp2VNjn98D9sY61Dr+T94HvhesvBT6Kdax1aMsi4Jxw/Y+BR2IdaxRtMaB9uNwSWACMAmYAY8P1zwE3xzrWeH0kVI/e3fPdfUm4vA9YBSTkLZmqacvNwGPufjjcVhC7KGtW0+/EgvsijgGmxibC6FXTFgfKe76dgLi/f2U1bekP/C3cbTbwg9hEGD0P7A+ftgwfDpwPvBKufxG4MgbhRa2adya3hu9K3My6NcS1EyrRRzKzDGAowas7wK1mtszM/tvMusQssKNQoS39gbPCt6Qfm9npsYytNir5nQCcBWxz93WxiOloVWjLHcDjZpYH/A64N3aR1V6FtqwARoebrgHSYhNV7ZhZ83D4r4DgBWoDsNvdS8JdNhH/nb7DwPnuPhgYAlxiZqOAz4DvUrcvjVYrIRO9mbUHZgJ3uPte4M/ACQQ/vHyCoYKEUElbWgBdCd6a/hKYYQlwt/BK2lHuOhKgNx+pkrbcDNzp7mnAncALsYyvNippy4+Bn5vZYoIhnSOxjC9a7l7q7kOAVGAEcHKMQ6q1qt6ZuPvn7r6xIa+dcInezFoS/OFOdvdZAO6+LfxDKAP+i+APIe5V1haCnsms8I9iIVAGNMjbufpSRTswsxbAVcD0WMVWW1W05YdA+fLLJPDfl7uvdveL3H04wQvwhljGWFvuvhuYC5wBdA7/xiB4Adgcs8CiVPGdibsvqOmY+pBQiT7s2b4ArHL3JyLW94rY7fvAF40dW21V1RbgNYIPZDGz/kArYHvjRxidatoBwdvR1e6+qfEjq71q2rIFOCdcPh+I+2Goav6vdA//bQb8G8GHmHHNzFLMrHO43Ba4kOAzh7nA1eFuPwRej02E0av4zsTMTm2M6ybUPHoz+w7wCbCcoKcLcB/B8MAQgg9oNgI/c/f8WMQYrWra8gHw3wTtOUIwm+jDmAQZhara4e5vm9n/APPdPe6TCVT7O9kLPEUwrHYI+Lm7L45JkFGqpi39gFvC57OAez3Ok4CZDSL4sLU5Qed0hrs/bGbHA9MIhjo/B8aVT2JIBGZ2P1Dk7r8Ln28EMt293jt2CZXoRUQSlZmlAMXuvjt8Z/I+8H/d/c1w+0YaKNEn1NCNiEgC6wXMNbNlBN9nmO3ub5rZbWa2iWA4Z5mZ/aW+L6wevYhIklOPXkQkybWoeZfYMrNSgg+UWgIlwETgyXAqpYiI1CDuEz1wMJyOVD41bArB19EfiGlUIiIJIqGGbsK6LxMIyh1Y+OWDx81sUVj+4Gfl+5rZv4QV+paa2WOxi1pEJLYSoUf/Le7+pZk1B7oT1OzY4+6nm1lr4DMze5/g69GjgZHuXmRmXWMYsohITCVcoq/gImCQmZV/O64TwRdCvgv8P3cvAnD3nTGKT0Qk5hIu0YffhislqBVhwC/c/b0K+1wci9hEROJRQo3Rh98sew54Nvza9nvAzWHxJsysv5m1Iyhj+k9mdky4XkM3ItJkJUKPvm1Y7a18euUkoLxI01+ADGBJWMSpELjS3d81syFAlpkdAd4mqPMhItLk6JuxIiJJLqGGbkREpPaU6EVEklxcJnozSzOzuWa2MryJ7u3h+q5mNtvM1oX/dgnXn2xm88zssJndE3Gek8wsO+Kx18zuiFW7RERiIS7H6MM7RvVy9yVm1gFYTHCH9x8BO939MTP7NdDF3f8lLI3QN9xnV3kh/wrnbE5wq7GR7t5gN+EVEYk3cdmjd/d8d18SLu8juG1YH4Jvu74Y7vYiQWLH3QvcfRFQXM1pLwA2KMmLSFMTl4k+kpllAEOBBUCPiFsEbgV61OJUYwluhiwi0qTEdaI3s/YEd7G/w933Rm4LvzAV1biTmbUCrgBervcgRUTiXNwm+vDbrjOBye4+K1y9LRy/Lx/HL4jydN8Dlrj7tvqPVEQkvsVlog+/5foCsMrdn4jY9Abww3D5h8DrUZ7yOjRsIyJNVLzOuvkO8AnBnaXK7yR1H8E4/QwgHcgBxrj7TjPrCWQR3JCkDNgPDHD3vWHtm1zgeHff07gtERGJvbhM9CIiUn/icuhGRETqjxK9iEiSU6IXEUlySvQiIklOiV5EJMkp0UuTZGalYUXTFWa21MzuNrNq/z+YWYaZXd9YMYrUFyV6aaoOuvsQdx8IXEjw7ekHajgmA1Cil4SjefTSJJnZfndvH/H8eGAR0I2g5PUkoF24+VZ3/7uZzQdOAb4iqJ76NPAYcC7QGviju/9nozVCJEpK9NIkVUz04brdwEnAPqDM3Q+ZWT9gqrtnmtm5wD3uflm4/wSgu7v/xsxaA58B17j7V43aGJEatIh1ACJxqCXwrJkNAUqB/lXsdxEwyMyuDp93AvoR9PhF4oYSvQhfD92UElREfQDYBgwm+BzrUFWHAb9w9/caJUiRo6QPY6XJM7MU4Dng2fA+B52AfHcvA8YDzcNd9wEdIg59D7g5LKmNmfUPi+iJxBX16KWpamtm2QTDNCUEH76Wl8T+EzDTzG4E3gUOhOuXAaVmthT4H+Apgpk4S8LS2oWEt7cUiSf6MFZEJMlp6EZEJMkp0YuIJDklehGRJKdELyKS5JToRUSSnBK9iEiSU6IXEUlySvQiIknu/wPBnlP4CzqebwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "timeSeriesSelectPlot('R1F',\"Total beds occ'd\", period='last week')" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "timeSeriesSelectPlot('R1F',\"Occupancy rate\",report=\"G&A beds\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using the data to verify previously published reports\n", "\n", "For example, via @carlbaker, I see this:\n", "\n", "![](https://pbs.twimg.com/media/DSr_wHqW0AMxwzE.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's get a count of the delayed ambulances, by Trust, within that period:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
NameSUM(value)
0East Kent Hospitals University NHS Foundation ...585
1Worcestershire Acute Hospitals NHS Trust413
2East Lancashire Hospitals NHS Trust391
3Lancashire Teaching Hospitals NHS Foundation T...390
4United Lincolnshire Hospitals NHS Trust373
\n", "
" ], "text/plain": [ " Name SUM(value)\n", "0 East Kent Hospitals University NHS Foundation ... 585\n", "1 Worcestershire Acute Hospitals NHS Trust 413\n", "2 East Lancashire Hospitals NHS Trust 391\n", "3 Lancashire Teaching Hospitals NHS Foundation T... 390\n", "4 United Lincolnshire Hospitals NHS Trust 373" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q='''\n", "SELECT Name, SUM(value) FROM sitrep \n", "WHERE date(Date) BETWEEN date('2017-12-25') AND date('2017-12-31') \n", "AND (Category = 'Delay >60 mins' OR Category='Delay 30-60 mins') AND value NOT NULL GROUP BY Name ORDER BY SUM(value) DESC\n", ";\n", "'''\n", "\n", "pd.read_sql_query(q, conn).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also find the total number of arrivals for each Trust in the same period:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
NameSUM(value)
0Barts Health NHS Trust1783
1Pennine Acute Hospitals NHS Trust1699
2Heart Of England NHS Foundation Trust1535
3Leeds Teaching Hospitals NHS Trust1429
4Frimley Health NHS Foundation Trust1402
\n", "
" ], "text/plain": [ " Name SUM(value)\n", "0 Barts Health NHS Trust 1783\n", "1 Pennine Acute Hospitals NHS Trust 1699\n", "2 Heart Of England NHS Foundation Trust 1535\n", "3 Leeds Teaching Hospitals NHS Trust 1429\n", "4 Frimley Health NHS Foundation Trust 1402" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q2='''\n", "SELECT Name, SUM(value) FROM sitrep \n", "WHERE date(Date) BETWEEN date('2017-12-25') AND date('2017-12-31') \n", "AND Category = 'Arriving by ambulance' AND value NOT NULL GROUP BY Name ORDER BY SUM(value) DESC\n", ";\n", "'''\n", "\n", "pd.read_sql_query(q2, conn).head()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can combine those and find the percentage of total arrivials to each Trust that were delayed, ordering from most delayed." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NameDelayedTotalpc
0The Queen Elizabeth Hospital, King's Lynn, NHS...23941857.177033
1Lancashire Teaching Hospitals NHS Foundation T...39072453.867403
2East Lancashire Hospitals NHS Trust39189043.932584
3East Kent Hospitals University NHS Foundation ...585135043.333333
4Sherwood Forest Hospitals NHS Foundation Trust27463743.014129
5Portsmouth Hospitals NHS Trust32175342.629482
6Mid Essex Hospital Services NHS Trust24359241.047297
7Kettering General Hospital NHS Foundation Trust24061838.834951
8Worcestershire Acute Hospitals NHS Trust413110337.443336
9The Dudley Group NHS Foundation Trust34495136.172450
\n", "
" ], "text/plain": [ " Name Delayed Total \\\n", "0 The Queen Elizabeth Hospital, King's Lynn, NHS... 239 418 \n", "1 Lancashire Teaching Hospitals NHS Foundation T... 390 724 \n", "2 East Lancashire Hospitals NHS Trust 391 890 \n", "3 East Kent Hospitals University NHS Foundation ... 585 1350 \n", "4 Sherwood Forest Hospitals NHS Foundation Trust 274 637 \n", "5 Portsmouth Hospitals NHS Trust 321 753 \n", "6 Mid Essex Hospital Services NHS Trust 243 592 \n", "7 Kettering General Hospital NHS Foundation Trust 240 618 \n", "8 Worcestershire Acute Hospitals NHS Trust 413 1103 \n", "9 The Dudley Group NHS Foundation Trust 344 951 \n", "\n", " pc \n", "0 57.177033 \n", "1 53.867403 \n", "2 43.932584 \n", "3 43.333333 \n", "4 43.014129 \n", "5 42.629482 \n", "6 41.047297 \n", "7 38.834951 \n", "8 37.443336 \n", "9 36.172450 " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q3='''\n", "SELECT total.Name, Delayed, Total, 100.0*Delayed/Total AS pc FROM (SELECT Name, SUM(value) AS Delayed FROM sitrep \n", "WHERE date(Date) BETWEEN date('{fromdate}') AND date('{todate}') \n", "AND (Category = 'Delay >60 mins' OR Category='Delay 30-60 mins') \n", "AND value NOT NULL GROUP BY Name) delayed JOIN (SELECT Name, SUM(value) AS Total FROM sitrep \n", "WHERE date(Date) BETWEEN date('{fromdate}') AND date('{todate}') \n", "AND Category = 'Arriving by ambulance' AND value NOT NULL GROUP BY Name) total on total.Name = delayed.Name \n", "ORDER BY pc DESC\n", "'''.format(fromdate='2017-12-25', todate='2017-12-31')\n", "tmp = pd.read_sql_query(q3, conn)\n", "tmp.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's compare:\n", "\n", "![](https://pbs.twimg.com/media/DSr_wHqW0AMxwzE.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That display may not be so useful though, because the population sizes differ. Something along the lines of a [funnel plot](https://blog.ouseful.info/2011/10/31/power-tools-for-aspiring-data-journalists-r/) can be used to check rates where rate distrbutions are statistically normal, which may not be the case here. \n", "\n", "For now let's stick with a scatter plot withouth any distribution guide lines to see if there are any outliers:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pd.read_sql_query(q3, conn).plot(kind='scatter', x='Total',y='pc');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we're looking for stories, outliers are a good place to start. For example trusts where there is a high rate of delays (*y-axis in chart above*), or perhaps a large number of delays.\n", "\n", "Let's size the scatter plot by the number of delayed visits to see if any points jump out." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pd.read_sql_query(q3, conn).plot(kind='scatter', x='Total',y='pc', s=tmp['Delayed']/10);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also look for trusts whre there is a large number of delays more directly. In this case, a high *y-axis* value is bad...:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pd.read_sql_query(q3, conn).plot(kind='scatter', x='Total',y='Delayed');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, we can add a bit more subtlety to the chart by sizing the nodes, in this case, by the delay rate:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Size by percent...\n", "tmp=pd.read_sql_query(q3, conn)\n", "tmp.plot(kind='scatter', x='Total',y='Delayed', s=tmp['pc']);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's have a go at adding some sort of distribution guide. Anything above the line is what we're looking for...:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "tmp.plot(kind='scatter', x='Total',y='pc', s=tmp['Delayed']/10, ylim=(0,100));\n", "\n", "#Not sure if I'm doing this right? Normal distribution model is wrong for a start?\n", "import numpy as np\n", "import weightedstats as ws\n", "\n", "tmp=tmp\n", "number=tmp['Total']\n", "p=tmp['pc']/100\n", "\n", "p_se = np.sqrt( (p*(1-p)) / (number) )\n", "\n", "p_fem = ws.weighted_mean(p, weights=p_se)\n", "dff = pd.DataFrame({'Total':np.arange(1, max(tmp['Total']), 20)})\n", "dff['number_ul95'] = 100 * ( p_fem + 1.96 * np.sqrt((p_fem*(1-p_fem)) / dff['Total']) )\n", "dff['number_ul999'] = 100 * ( p_fem + 3.29 * np.sqrt((p_fem*(1-p_fem)) / dff['Total']) )\n", "plt.plot(dff['Total'],dff['number_ul95'] )\n", "plt.plot(dff['Total'],dff['number_ul999'] );\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's another table we could try to recreate, again via @carlbaker: *Bed Occupancy*:\n", "\n", "![](https://pbs.twimg.com/media/DSsDvgMW4AATHKK.png)\n", "\n", "But how do we calculate this? The average of the occupancy rates over the week?\n", "\n", "Which data sheet(s) does the data come from and how is it analysed to generate that table?\n", "\n", "For example, what if we naively just try to take the average of the rates for each trust over the week?" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Nameav
0Weston Area Health NHS Trust96.274174
1James Paget University Hospitals NHS Foundatio...96.191210
2Harrogate And District NHS Foundation Trust95.764972
3University Hospitals Birmingham NHS Foundation...95.652122
4St Helens And Knowsley Hospital Services NHS T...95.351082
5King's College Hospital NHS Foundation Trust94.762360
6North Middlesex University Hospital NHS Trust94.109607
7Nottingham University Hospitals NHS Trust93.903388
8Countess Of Chester Hospital NHS Foundation Trust93.614342
9North Cumbria University Hospitals NHS Trust93.088257
\n", "
" ], "text/plain": [ " Name av\n", "0 Weston Area Health NHS Trust 96.274174\n", "1 James Paget University Hospitals NHS Foundatio... 96.191210\n", "2 Harrogate And District NHS Foundation Trust 95.764972\n", "3 University Hospitals Birmingham NHS Foundation... 95.652122\n", "4 St Helens And Knowsley Hospital Services NHS T... 95.351082\n", "5 King's College Hospital NHS Foundation Trust 94.762360\n", "6 North Middlesex University Hospital NHS Trust 94.109607\n", "7 Nottingham University Hospitals NHS Trust 93.903388\n", "8 Countess Of Chester Hospital NHS Foundation Trust 93.614342\n", "9 North Cumbria University Hospitals NHS Trust 93.088257" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q4='''\n", "SELECT Name, 100*AVG(value) AS av FROM sitrep \n", "WHERE date(Date) BETWEEN date('{fromdate}') AND date('{todate}') \n", "AND Category = \"Occupancy rate\" GROUP BY Name\n", "ORDER BY av DESC\n", "'''.format(fromdate='2017-12-25', todate='2017-12-31')\n", "pd.read_sql_query(q4, conn).head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is why I think research reports need to show their working or at least have the working available..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Isle of Wight Report\n", "\n", "Sample graphical reports for Isle of Wight." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "code='R1F'" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "typ='G&A beds'\n", "\n", "q2='''\n", "SELECT Date, Name, Category, value FROM sitrep WHERE Report='{typ}' AND Category='Occupancy rate'\n", "AND Code = '{code}'\n", "'''.format(typ=typ,code=code)\n", "tmp = pd.read_sql_query(q2, conn, parse_dates=['Date'])\n", "tmp_p = tmp[['Date','Category','value']].pivot_table(index='Date',columns='Category')\n", "tmp_p.columns = tmp_p.columns.get_level_values(1)\n", "tmp_p.plot(title=typ).legend(loc='center left', bbox_to_anchor=(1, 0.5));" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": 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6TJCJgYKFUfDb7Tl4fHvOaA9jQvvoWAUUAR54fEXSgMemhPlgXlwg3vuxHBodVeCbaLo0ehwva8LChCC7z4kL9kKXVo8LrV02j8upacHUiP7bMO+dH4uGdg12nKpxeLyEjEUULIyw8oYOlDd2oryxE8rW7tEezoRVqFQjM9rfYp6CJfdeEYt6dQ/+e+bCMI+MjLRjZY3Q6AxYkOhYsADAZnGmpg4Nqpq6evMVzM2ZEoCUUB+8c7hswNkJQsYDChZG2MFiVe/3x8toKWI4NHdoUK/uQaLcy+5zrogPRGKIN945dH5cVuCraKStetYcKFTBzUWEWQp/u8+JNzWUshEsmJIbMywEC4wx3HdFLErq23GgSNXvdkLGGwoWRtiBQhUi/d3h7SahvIVhUmTs/pco97H7HMYY7p4fgwKlGodLGoZraMNif2E9Frz4A85daB3toYxJB4pUmBMb0K9plC1+nq4I8HS1uSMip6oFAJAW3j9YAIBrMkIRKnOjIk1kQqBgYQT16PQ4UtqIRYnBmB3jj2Pnm0Z7SBNSoSlYCPF26LwbpoUhyFs67l7cvzIunVAhoP4qGjtQ1tCBBQ7kK5hMCfayHSzUtCI2yBM+bi4Wb3cRi3DnXAWOnm9Ebg0FcmR8o2BhBP1c1owurR4LEoJwWWwAyho6KG9hGBQq1fBxkyDER+rQeVKJGHfOVeBQcQPya9uGaXTOpdEZsC9PaIZFf0v9HTQuASxIHLi+Ql+mhlLWlqXOVrf2FmOy5pbZUfCSSvA29SAh49y4DxaOlDZg4Yv7B1WB7/uCOlz96iG09+iGYWT9HSiqh6tYhDlTAnBZbAAA0FLEMChUqpEk97HaVdCW9bOj4O4ixuYfR7ZIU1OHBlf9/QCOlDq2BHLsfCPauoW/XwoW+jtQpEKUvwcUAfZtmTQXF+SF1i4tGtr7F1eqb+uGsq37kvoKlvi4ueCmWZHYmVOLCy22d1YQMpaN62ChXt2NX39yGuWNnYPKYv/4WCXyatuw+2ztMIyuvwNFKmTF+MPDVYLkUB/4UN6C03HOUVinRoIDyY3mfD1csSJdjj25Sru7DjrDjlM1KK5vx78Plzt03u5cJTxdxQj3dUdtGwUL5kzLfgsSggYVOMaHWO8RkVNtPbmxr7suV2BdVhTEIsfHQMhYMW6DBb2B4/99chrtPVooAjywO9exN3x1txaHioVPcZ9nVw/HEC9xoaULRXXtvWunYhFDVkwABQtOpmzrhrpb53C+grnlqXK0detwtHTkfjemv8H9hfVQqXvsOkdv4Pg2T4lFScGI8vdAHc0sXCK7vBmdGv2g8hUAs4ZSFio55lS3QMSA1LCBg4UIPw88vTINIT5ugxoHIWPBuA0WXv2uGEfPN+LpG9JwS1YUcmvaHFqK2F+ogkZvwKLEIPxU1jTsW88urp1efOG6LNYf5Y2dqB2g8MtEtumHUvxU5rxEzwKl4zsh+roiIQgermLsOad01rBsOnehFfm1bbh9TjT0Bo4vT9tXyOdEeRMa2jVYniaHXOaG2nEULOw4VTPsNS0OFKl6l/0GQ+7jBi+pxOL2yZyaViSEeMPd1f4dFoSMZ3YFC4yxcsbYWcbYacbYCeN1/oyxbxljxcZ/bfd9daLDxQ14/ftirJ4RgbUzI7E8TSjnu9eBF/c9ubUI8pbi6ZVpYAzYfnJ4K60dKFIhVObWu38bQG/ewvFJuiviQJEKf9tTgD/uyHVabYMi5eB2QphzcxFjUWIwvjlXB/0IFNTZll0NV7EIj1yVgKmRvvj8RLVdz8eec0q4SkRYlBgMucwN9erucVEA6Pj5Rjzy2Wm8uq9oWB/nQJEKs2L84CmVDOp8xhimBHmiuE/3Sc45cqpbkT5AciMhE4kjMwuLOOfTOOczjZcfB/Ad5zwewHfGy8Ouvq0bG7eeQlyQF55emQoAiA7wRHKoD/bk2hcsdGv12F+gwrLUEET4eWBeXCC2Z1cP2wutVm/A4eKGfmunkzlvQac34NldeXAVi1BYp8bBYufUNiisU0Pu4waZh+XtbPZaliZHQ3sPsiuanTIuazQ6A748fQFXpYTA18MVazIjUFinxrkLtndjcM6xN1eJK+KD4CmVQO7jBq2eo3GMdzpsaO/Brz89BQMHKps6oRumvJDa1i4UKNWDXoIwsbR9sqalC00dGmRE2k5uJGQiGcoyxA0A3jd+/z6AlUMfjm06vQG//vQUOnr0eGP9DHi4XvzEsDxVjuzKZtTbkeR1sEiFLq0ey1NDAQBrMiNQ09KFY8NUUfF0VQvUPbp+L1yTOW/hsxPVKKprx4trMxDsLcU7TtpaVqhUI0E++FkFk8VJwXAVi+wOQAdrf2E9mjo0WJMZAQC4PiMMrmIRtg2QR5NT3YoLrd29s2pymbAeXjeGkxwNBo6Ht55Gc6cWd85VQKvnqBmmHQK9y352tKS2JT7YG3VtPWjrvtiRtDe5kWYWyCRib7DAAXzDGMtmjN1nvC6Ec27KKlQCCLF0ImPsPsbYCcbYCZVqaGVPX/uuGMfON+HplWmI7zPNvCJdDs6BvcY957bsyVXC18MFs2OF8q/LUuXwlkoGfIEerAOFKohFDHPj+rfHnYx5C+puLf7+bSFmKfxw/dQw3Hm5c2ob6A0cxfXtSAwZ3E4Ic15SCebHB2LvOeWwln/ell2NIG8p5scLfxsyDxdclRqCHadr0KOz3rFwzzklJCKGJcnCm6HcmDw3lvMW3vihBIeKG/Cn61JxbYYQqJ9XDU+u0IEiFeQ+bkgY4t9CnIWyzznVrXARMySFDj0oJWS8sDdYmMc5nwFgBYCHGGNXmN/IhVdTi6+onPO3OOczOeczg4IGPyV4qFiF1/eXYE1mRO+nMHPxwV6IDfTE3gE+CWp0BuzLr8OS5BC4iIUf381FjGunhmH3WeWw1Fw4UKRCZpQfZO79p8YnY97CGz+UoqFdgz9ckwLGGNZnRcPDVTzkwjXljR3Q6AxDSm40tzxNjpqWrt4eAM7W0N6D/QX1uHF6OCTii/8V12ZGoKVTi/0F9RbP45xjT64Sc6YEwNdDaI0capxZUI7RmYVj5xvx92+LcMO0MNySFYmYQE8AwPlhqDqp0xtwyMKy32D07ogwCxbO1rQgSe4DqYSSG8nkYVewwDmvMf5bD+ALAFkA6hhjoQBg/NfyK5sT1LV1Y+OnpxEf7IWnb0izeAxjDMvT5Dh6vhHNNtZtjxqL2CxPlV9y/ZrMCHRp9fg6x7k1F1TqHpytabXa8W6y5S1UNXXi3cNluHF6OKYa13xlHi74xcxIfHXmwpAKCzkjudHckuQQiEVs2JYidpyqgc7A+wW/8+ODEOIjxecnLM90FdW1o6yho3cJAgACvKQQixiUY3CGqqG9B7/+5BQUAZ54dlU6GGPw93SFzN0F5y1sSxyq01UtUHfrHOoyaU2knztcxaLeYMFgEJIb7amvQMhEMmCwwBjzZIx5m74HsBRALoD/ArjDeNgdAL4cjgFyzvHIZ6fRqdHjX+tm2NyqtDxNDr2BY1++9aWIPcYiNvPiL10SmBHli9hAT6cvRRwqNq2dWn7hMuUtHJ0kwcILewshYsCjyxIvuf6Xl8dAb+B470j5oO+7sE4Nxi5+GhwqP09XXBbrjz25zl+K4JxjW3Y1pkb69ltSE4sYVk2PwA9FKtSr+wdPu3NrwRhwVUrIJeeEeEuhbLWvRsNI0RvzFFq7tPjX+hnwMu5MYIwhJtBzWPpZHCgSlv0ut7Ds5yiJWISYQM/eYKGiqRPqbh0FC2TSsWdmIQTAYcbYGQA/AdjFOd8D4HkAVzHGigEsMV52OsYYNi5JwEtrp/Z7Ue0rPVyGcF93q58EzYvY9O1AxxjD6swI/FTehHInvoAdKFIh0MsVKaHWp8bnTAlARWPnhC8Hm13RjK/OXMB982MR5ut+yW1RAR5YnibHluMV6BjkUlChUg1FgKdT974vTwvF+YYOFNtoKDQY5y60oUCptrikBgBrMsOFmgun+tci2JOrxMxoPwR7X1rkJ0TmNuYSHN/Yb8xTuD4VyX3+D8QGeQ5LzsKBIhWmR/paXPYbjLgQr97CTDnVQqfJgco8EzLRDBgscM7Pc86nGr9SOefPGq9v5JxfyTmP55wv4ZwP26L7LIU/rjEmRNnCGMOyVDkOFTdYzD0wFbFZkWb5vm6cEQ7GgP+cdM7sgt7AcbBIhSvigyCyUer1MmOi5fFh2o0xFnDO8cyuPAR7S3H/gikWj7lnfizaunX47ETVoB6jsE495IS2vpalhIAxYPdZ5y5FmGorXJ8RZvH2uGBvTIv0xbbsS2sulDd0oECpxnILf8NyH7cxlSh7tLQRr+wrwsppYbh5VmS/22MDPaFs6x50cGhJQ3sPcqpbh7xl0lxckBeqmjrRrdUjp7oVbi6iS+qlEDIZjNsKjtYsT5NDozdYTA4zFbFZaGUtM1TmLtRcOFnjlJoLuTWtaO7UDrh2miz3gczdBcdKx2aSY0unBg99fHJI29y+yqnFqcoWPLos0WqRnBlRfpgZ7Yd3D5c5vP++W6tHeUOH0/IVTIJ93JAZ5efUao5CbYUaXJUaYrMehKnmQm7NxV0ipnEsS+2/+Uguc0Nd29hYhmjp1ODXn56CIvBinkJfsUHCG64zlyIOG+t1LBxEl0lr4oK9YODCOHOqW5AaJrskIZWQyWDC/cVnRvsh0EvabymibxEba9bOjBRqLjghh+BAkQqMAfMGWDsViRiyYvyHrc7DUH2VU4tdZ2ux9efBfeLv1urxt90FSAn1weoZlqfdTe6ZH4vq5i7sPTfwFlhzJfXtMPChlXm2ZnmaHPm1bU4rCf59QR2aO7VWlyBMrpsaBleJCNuyLz7ve3KVyIiQIcKvfxdFuY8b2nt0UJvVBBgtr35XjMb2Hrx283Sr/99MOyKcGSwcL2uCj5sEqWHO+zsw5cAUKoXAjSo3kslowgULYhHD0tQQ7C+sR7f24j51UxGbFWlyG2cDS1NC4O3mnJoLB4pUyAiXIcBLOuCxl8WO3bwF03bUgbalWvPvH8tQ09KFP1yTPGDnvatSQqAI8MBbh847lFRYVGfqCeH86eFlxp0zztoVsS27GsHeUswfIIiUubtgWaocX565gB6dHrWtXThd1dI7nr7GSmGm86p2fHi0AjfNikKajTdWRYBx+6QT8xZOVjRjepSfzWU/R8UEekLEhHLyXVo9JTeSSWnCBQsAsCJNjk6NvreKGyC08pWIGK5Mtj096eYixnVTw/B1bu2QPqG1dmpxqrIZC+ycDnVG3kJDew++cXLzo5ZODY6eb0SIjxSFdWqHt7o1tPfgjf2lWJIcYrEoVV9iEcPd82JwpqoFJxwotVyoVMNVLOp9A3KmSH8PpIX7OGUpQqXuwf5CFVbNCLdrKnuNsebCd/n1vcHacisB71gpzPTX3QVwcxHjkasSbB7nbmytXdbgnOTRtm4tiurVyIx2bpsaNxcxovw98F2+sLRJyY1kMpqQwcJlsQGQubv0vrgLRWxqLyliY8uazAh0aw34+uzgay4cLmmAgVvfMtmXM/IWNv9Yhvs+zHbqbo5v84RmSn8x1rdw9A3z42OV6NDo8MTVSXafsyYzEr4eLnj7oP1Fmgrr1JgS7DVsa8nLU+U4Vdky5ATCL0/XQG/gWDPAcozJvLhAhPhIsS27GrtzlUgI8cKUIMuzJ6EyYYfJUGpVDNWR0gZ8m1eHDYumIMh74Bm1mEBPpxVmOl3ZAs7h9GABEJYiNHoDvKQSxAY6PyAlZKybkMGCi1iEJckh2JdXB43OgMI6NcobO61+IutreqQvYoOGVnPhQFE9ZO4umGrnlKUz8hYKaoWpeGcm4+09p0S4rzuWpoRgaoTMoaUIg4Fj28kqzJ0SYPUNzhJ3VzFuuywa3+bX2b2eXahUO6XMszWm3QffOJhLYY5zjs9PWK6tYI1YxHDjjAgcKFLh5/KmfsXEzAX7CG/OoxUs6A0cz+zMR7ivO355eYxd58QGeaJM1eGUOhbZFc0QMfQW+3KmKca8hbRwH6cucRAyXkzIYAEQpmrbunU4dr4Re3KV/YrY2MIYw5rMCPxc3jyoT+mccxwoUmFefKBDn3SHmrdQaFy3d9baenuPDgeLG7B+k7/nAAAgAElEQVQ8TW6skBmKM9Wtdu+K+Lm8CVVNXQMm8lly25xouIhEePfwwLMLrV1a1LZ2D0tyo0lcsBfigr2G9Nyeu9CGwjo11jr4fKyeEQG9gcPAhW6Y1ri5iOHv6epQyWeDgaPJSZ0qt5+sRl5tG367IqlfHRNrYgI9oe7RoaF96GM4WdmMRLlPb+EnZ4ozBru0BEEmqwkbLMyPD4SHqxh7zimxJ1eJWdH+/YrY2HLj9AiIRQyP/yfnko5zA+Gc41/7S1DX1oNFDm7fMuUtDGYnRnuPDtXNXQjwdMXpqqFPlwPA/oJ6aHSG3hkZ07/2zi5sy66Gl1RiNSHPlmBvN6ycHoZt2dUDvpkVD2Nyo7nlqXIcL2tEY/vgtidu+akSrhIRrrNSW8GauGAvZEb7ISbQ02ZxLwAI8XFzaGZh+8lqzH3+uyH/vXT06PDS3kJMj/LFdXbURDExbZ8catlnvYHjVGULMqOH5808xbi7YkaU85c4CBkPJmyw4OYixqKkYPz39AUUKNU2P5FZIpe54cU1GThR3oxf/N9Ru16AdXoD/rAjFy99IxSiuWGaY28KvXkLgwgWTLsB7rsiFsDgdy6Y23NOiUAvae8LZEygJ5Lk3nZ9uu7o0WHX2Vpckx56SStxR9wzPxbdWgM+OlZh87gCY0+IBCfXWOhreZocBg6b5cStaWzvwfbsaqyaFm6ztoI1m26dgQ/vzhqwMVKozM2hmYUz1S3o1hrwn5M1Do/J3JsHz6Ne3dPbHMxesU7aPllcr0Z7j27Y3sxTw2T46lfzLNa3IGQymLDBAiB8EjRVchzMf/IbZ0Tg33fOQlVTJ25848feT7CWdGn0eOCjk/j4eCUeWDAFf//FtN6ulvYSiRhmx/jj2CA6UJqaKK1IC0V8sNeQ8xa6tXrsL6jHstSQS7Y7LkuV4+eKJqjUtj9d785VolOjx5qZji9BmCSEeGNhYhA+OFp+yTbYvorq1PCSShDep4S0s6WG+SDCz3o5cVs+OlaJHp0B98y3by2/r2BvN4u1FfpydGbB1POgb6VIR9S2duGtg6W4NiPU4eTCMF93uEpEQ05yzDbunBmO5EaT9AjZkLtYEjJeTehgYVFSMFwlIqtFbOxxRUIQtt4/B1oDx+pNR3Dcwqf+pg4N1r1zDN8V1OHP16fi8RVJg06Cuiw2AJVNjuctFCjV8HAVI8LPHSvS5PiprGnQ0+UAcKi4AZ0afb+k0OVpcnAu7JKwZVt2FRQBHpg5xBfv++bHoqFdgy9PW//kW6gUyjwP9ws5Ywwr0uQ4XNLg0HPbrdXjw2PlWJQYZHdi42CFytzQ2KFBj856cGWupL4dMncXlDV04GSl/VtVzb24pxAGDvx2uf07XkzEIgZFgMeQay1kVzQj0MsVUf6D+39OCLFtQgcLXlIJ/rY6Hb+/OnlI95MWLsN/HpyLQG8pbnv3J+wya2Nd2diJ1ZuOIO9CGzatn4E75iqG9FimT0anq1ocOq+oTo34EG+IRAzLjNPlA72h27I7txYydxdcFhtwyfVJcm8oAjywO9f6ttKqpk4cO9+ENZkRQ34DnzMlACmhPnj7UJnFEtyccxTWqZEoH943YZObZkXBwIUKhfbacaoGDe0a3Ds/dhhHJjDVWqi3o+xzS6cGDe0a3DlXAQ9X8aB2/+RUt+A/p2pw97wYRA7yjVrYPjm0nIVTlS2YHuVHn/wJGSYTOlgAgFXTIzC7zxveYET6e2D7A3ORHiHDrz45iXcPlyGnugU3bvoRTR0afHzPbIvNfRyVFOoNFzFDTnWrQ+cV1V3cOpgS6oMofw/sHmTeglZvwL68OixJDum3lMKYEIwcLW1Ea6flxM/tJ6vBGLDKzloCtjDGcO8VMSipb8cBsyJbJip1D1o6tU7vCWFNXLAX1mVF4ePjlTaXpUwMBo63D51HSqgP5kwZ+t/hQExVHO3JWzAtQUyL9MWKtFDsPFOLLo19MxKAsTnYznwEeLpiw0LLzcHsERvkhcrGTof7gZg0tvegrKFjWJcgCJnsJnyw4Ex+nq74+J7ZWJoSgqd35mH1piOQSsTY/uAczFT4O+UxpBIxkuQ+va1w7dHQ3oOGdk3v1kFhm6McR0ob0NrleBXKY+cb0dats1qXYkVaKHQGbjHRz2Dg2H6yGpdPCXRaDsG1GWGQ+7jh7UP9t1H2JjeO0MwCAGxcEg8PVzGe+zp/wGN/KKpHqaoD910ROyKfek3Bgj1VHE3BQlywF9ZkRkDdo8M3efYHmHvPKfFTeRMeWZoAb7fBt4OOCfSEzsBR1Ty4HRknK4X/KxQsEDJ8KFhwkJuLGG+sz8S982MwM9ofX2yYi7hg575RpUfIcLam1e7Ol6bkRvNP18tS5dDqucXumwPZnauEh6sY8+Mtl2fOCJchVOZmMYnypyHUVrDGRSzCXZcrcKS0Ebk1l8649PaEGKGZBQAI8JLifxbHYX+h6pKS4pa8fbAMoTI3u1qsO0Nvfwg7gwU3FxHCfd0xO8YfEX7udi9F9Oj0eO7rAiSEeOGmmf3bTztiSpBpR8TgliJOVjZDImLU4ImQYUTBwiCIRQy/vyYFn9x3GYJ97K/dYK+pETKou3Uot7PLoenTtfm6/fRIX4T4SG3mFliiN3B8c64Oi5KCrRbWEYkYlqXKcbBIhQ7jbhOTz08MvraCLTdnRcHTVYx3+swuFCrVCPSS2tWsy5numKtApL87nt2VD72VoC63phVHzzfirssVDu+MGSxvqQQermK7ZhaK69sRG+gFkYhBJGJYPSMCh0sa7Equ/eBIBSqbOvH7a1KGXGI7JtBUa2FwSY7ZFc1IDZfZXQiKEOI4ChbGoPRwobDM2Rr78haK6tTw93RFoNfFvhemN/QDRSp0anQ2zr5UdkUzGtp7bJYVBoRdET06A34ovPjJuqNHh925tbg2IxTurs594Za5u+DmrCjszKm95M1MSG4c3mJMlkglYjyxIhmFdWqrrbvfPnQeXlIJbs6KGrFxMcYg93Gzq/NkSX17b/tlQOiJwjnwxSnbNReaOjR47ftiLEwMsrv3iS3+nq7w9XAZ1PZJrd6AnOoWZFKxJEKGFQULY1BCiBekEpHdSY6FdZa3Di5Pk6Nba8CBQttT5eb25CrhKhFhUZLt6pOzFP4I8HS9ZCni67O1Qm0FJy5BmLvrcgU4gPeOlAMQ8iOExM7hK/Nsy4o0OWYp/PD3bwv7dSi90NKFnTm1uGlWJHyGsJ4/GHI7CjN1anSoaelCvFmwEOnvgcti/fH5iSqbNRde3VeETo1+yLuMzMUEeg6qimN+bRu6tQbKVyBkmFGwMAZJxCKkhtmX5GgwcBQp1Uiy0BchS+EPPw8Xuws0cc6x95wSV8QHDlhfXyxiWJoagu/z63oLJm3LrkZMoOewvXBH+Hng6vRQfHK8EupuLSqbOtGtNYzKzAIgfIr/wzUpaGjXYNMPpZfctvnHMgBCgDPS5HYUZjJN+ZvPLABCx8/yxs7eIkd9ldSr8dHxStySFenUmhGxgV6DquJoGueMYSrzTAgRULAwRmVE+CK3ps3qerhJTUsXOjR6i6WOJWIRlqbI8X1+vV1Fes7WCE2i7M03WJYqR4dGjx9LGlDZ2InjZc6prWDLvfNjoO7RYevPVb2Ns4a7zLMtUyN9sWp6ON45XIbq5k4AgLpbi09/qsLV6aGDLgY2FHKZsAxhK0HWfCeEuRVpcps1F577ugAeLmJsXJLgvAFD6D5Z19bTLwdmINkVzQiTufW25yaEDA8KFsaojAgZurT63hd1a4oGaKK0PE0OdY8OR0oG7jexJ1cJsYjZ3Z1z7pRAeLtJsDtXebG2wvRwu84drIwIX2TF+GPzj+XIu9AGYHSDBQB4bFkiGIC/7SkEAGz9uQrqHh3uHWRp56GSy9ygM3A0dFgvzFRcr4ZYxBAd4HnJ9Z5SCa5OD8XOnP41Fw4Vq/B9QT0eWhyHQCcnlA62R8SpyhbMoCUIQoYdBQtjlKkV7kBLEQM1UZobFwBvqWTAfgacc+zJVWJObAB8PVxtHmviKhFhSXIIvs2rw7bsasyLC0TYMPdnAIQS0DUtXfjgaDki/d3hOQwtiR0R5uuO+66IxVdnLuCnsiZs/rEcs2P8R62dsamKY12r9WChpL4d0QEecJX0fwlYkxmB9h4d9potX+kNHM/uykekvzvuHGKVUktijNsnHUlyrG3tQk1LF3WCJGQEULAwRsUGesLTVTxgkmNRnRrhvu5Wi+JIJWIsTg7GN3lKmxXyiuvbcb6hw2ohJmuWpcrR2qVFTYtzayvYsjgpGLFBnmgewcqNA3lgwRQEeUtx7wcnUNPSNSKlna25WJjJ+hbIkvr2S5IbzWUp/BHpf2nNhc9OVKFAqcbjy5OHZYuiIsATjDnWqvpkBRVjImSkULAwRolEDGnhMuQMsH3S1ETJluWpcjR3avFTueVulip1D17/vgSMAUvtXIIwWZAQBHcXMbylEixNcW5tBWtEIoa75wlT/CPVE2IgnlIJHluaiNYuLWKDPLF4gN0kw6m3MJOVHREanQEVjZ398hVMTDUXfixtQE1LF9p7dHj5m0JkRvvh6vTh+R27uYgRJnN3aBniZGUz3FxESAkbnd0whEwmds/fMsbEAE4AqOGcX8sYiwHwKYAAANkAbuOca4ZnmJPT1EhfvHekHBqdweJ0sVZvQKmqHQsTbb8xLUgMgpuLCHtzlZg75WJVxgJlG949VIYvT1+A1mDAHXMUDheZcncV4/8tiYebROT02gq2rJ4RgWPnm7DCCf04nGV1ZgR+Lm/C8jT5oLuOOkOgpxQSEbNamKmisQM6A7caLADC8/uPfcX44mQ1urR6NLRr8M4ds4Y1eTU2yNOhwkzZFc3ICPcdsYJXhExmjiz2/j8A+QBMYfzfALzCOf+UMfZ/AO4GsMnJ45vU0sNl0OgMKKpTI81CKdvyhg5o9XzArYMerhIsSAjCnnNKPHldKg4Wq/DuoTIcLmmAu4sYN82KxF2XKxAbNLgtiA8sGHwTocFycxHj9Vumj/jj2iIWMby4dupoDwMiEUOIj/VaC707IYKsz8pE+ntgTmwAPj5eicYODVZOC8O0yOHNwYgN9MT2kzXgnA8YlHRr9Th3oRV3zxu95R5CJhO7QnLGWASAawC8Y7zMACwGsM14yPsAVg7HACezqb1JjpaXInrLPNtRlGhFWijq2npwxQv7cdfmn1Fcr8Zvlifi6BOL8fTKtEEHCmRsCvGRWq21YAoWpgR7WrzdZE1mBGpbu8EAPLY8ydlD7Ccm0BPtPTqo1AO31z5b0wqtnlO+AiEjxN75u38A+A0AU4ZcAIAWzrlpU3Q1AIt75hhj9zHGTjDGTqhU9lcSJECkvzt8PVys7ogoqhO2v8UG2X7RB4DFycHw83CBn6cL/nHTNBz6zWJsWBhn984HMr6Eytytzyyo2hHu6w4PV9sTiyvS5Qj0kuKhRXFO6yBqiylgtWdHxEljMabpUVSMiZCRMOAyBGPsWgD1nPNsxthCRx+Ac/4WgLcAYObMmfa1USQAhAqB6eEyqzMLhUo1FAEedmWn+7i54OffL4FYxEakVTIZXSE+bthfWG9xSr9vTwhrPFwlOPrEYkhGKP8ixlhr4byqA5fFBtg8NruiGYoAD6fXeyCEWGbPzMLlAK5njJVDSGhcDOBVAL6MMVOwEQHAdvcZMigZETIU1ql7SyqbK6yzXObZGolYRIHCJBEqc0OnRg91n4qIBgNHqcq+YAEQ2oOP1N9MuK87XCWiAVtVc85xsrKZijERMoIGDBY4509wziM45woANwP4nnO+HsB+AGuMh90B4MthG+UklhHhC72BI6+27ZLrOzU6VDZ1jnr1QjI2hRi3T/bNW6hp6UK31mB3sDCSRCKGmADPAbdPVjV1oaFdQ8WYCBlBQ9lz9FsAjzDGSiDkMLzrnCERcxkRwi6Is32WIkrq28G59TLPZHIzVXHsGyyYkhutFWQabUL3SdvBQnalUC+EkhsJGTkO1cnlnP8A4Afj9+cBZDl/SMSc3McNgV5SnOmT5Ni7E8KBZQgyeYRamVmw1kBqrIgN8sS+/Dpo9Qar9ROyK5rhJZXQrBohI4iqmYxxjDFMjZD1m1koUqrh5iJClP/IdzUkY1+wj5D413dHREl9OwK9XMfsLpiYQE/oDBxVTZ0Wb2/r1uJQcQOmRfpCPIqFrwiZbChYGAfSI2QoUbWj3SxZrbBOjfhgb3rBJBZJJWIEeLr2q+JYXK/GlDFcU8O0fdJS3kJdWzd+8X9HUdPchTuGoZkVIcQ6ChbGgakRvuAcOGfWJ0LoCUHTsMS6EB+3S/pDcM7t3jY5WmLNtk+aK65T48Y3jqCqqRP/vnOW3W3UCSHOMbq9fYld0k1JjjWtmB0bgOYODerVPZTcSGwKlbnhgtnMgqq9B23dujGb3AgAfp6u8PNwuaQw009lTbjn/Z8hdRFj6/1zLJY+n4yys7ODJRLJOwDSQB/8yNAYAOTqdLp7MjMz6y0dQMHCOBDoJUW4rzvOGPMWCusouZEMLETmhlNVFxNjLyY3ju0ZKWFHhDDWr8/WYuPW04jwc8f7d2UhknJ0ekkkknfkcnlyUFBQs0gkooJ3ZNAMBgNTqVQpSqXyHQDXWzqGotFxIj1chrPGHRFFpmCBliGIDaE+bmjq0PQW9Cod4zshTGKDvFDW0IHNP5bhoS0nkR4uw/YH5lKg0F9aUFBQGwUKZKhEIhEPCgpqhTBLZfmYERwPGYKMSBnKGzvR2qlFoVINHzcJQnyo1C2xzlSYqb5NaMxUXN8OL+nY/7uJCfREvboHf/4qD0tTQvDxPbPh5zk2d2+MMhEFCsRZjH9LVmMCChbGiYxwoWHO2ZpWFCqFMs9UupnY0ltrwZjkaEpuHOt/N6achNvnROON9Zl29T4hhAwvChbGiXTjC+iZ6hYU1qmRQMmNZACmKo61rV0A7G8gNdoWJAThwGML8efrU2lr8Djw4Ycf+jLGMk+dOuXmyHkLFiyIa2hosBoJlpeXuyxfvjx26CMEHnnkkbAnn3xyVLbQDOaxs7KyEg8ePDim1t0oWBgnZB4uUAR44JtzSqi7dZSvQAYkN84s1LV1o7VLi3p1z7gIFgAgOsBzzM+AEMGnn37qP2PGjPYPPvjA39LtWq32kssGgwF6vR4HDhwoCQwM7N8hz0ihUGj37Nlz3snDJYNEwcI4kh7h27sjgnZCkIF4u7nA01WM2tbuizshxnBBJjL+tLa2in7++WevzZs3l3/xxRe9wcLOnTu9MzMzExcvXhwXHx+fVlhY6KpQKNJWrVqlSEhISC0tLXUNDw9Pr62tlWzYsCH8r3/9a5DpXNMn8cLCQtf4+PhUAHjttdcCli5dOmX+/Pnx0dHRaQ888ECE6fhXXnklUKFQpKWnpyfffPPN0bfffnuUpbHm5OR4TJs2LSk6Ojrt5ZdfDgSAVatWKT788ENf0zHXX399zEcffeRrfl5ra6tozpw5CSkpKckJCQkpptsLCwtdY2JiUlevXq1QKBRp119/fcyOHTu8Z8yYkRQdHZ22f/9+D1uPvXPnTu9FixbFmY65/fbbo1577bV+vdnXr18flZaWlhwXF5f68MMPh5muDw8PT3/44YfDTOMyzey0traK1qxZo0hISEhJSEhIee+993wB4D//+Y/PtGnTklJSUpJXrFgR29ra6tD7P22dHEemRsjw1ZkLAGgnBLGPXCYUZhovOyHI4Dy27UxkkVLt1GnrBLl354trplbZOmbLli2+CxcubM3IyOjx8/PTHTp0yGP+/PmdAJCXl+dx6tSpc0lJSZrCwkLXyspK6bvvvlt25ZVXlpvfx/r165s2btwY9cQTT6gA4Msvv/Tbu3dvkV6vv2RqKS8vz+PMmTN57u7uhri4uLRHH320TiKR4KWXXgo9efJknq+vr2Hu3LkJqampXZbGmp+f756dnZ2vVqvF06dPT1m9enXrPffc0/DKK6+E3HbbbS2NjY3i7Oxsr+3bt5eZn+fh4WHYtWtXib+/v6G2tlYye/bspHXr1rUAQFVVldvWrVvPZ2ZmlmdkZCR//PHHASdOnCjYsmWL77PPPhu6aNGiUmuPbe/v4e9//3tNSEiIXqfTYe7cuYnHjx93nz17dhcABAYG6vLy8vKff/75oOeffz5k69atFY8//nioj4+PvqioKA8AVCqVuLa2VvLcc8+FHjx4sMjHx8fw+9//Xv7000+HvPTSS7X2joNmFsYRU96C3McNMg+XUR4NGQ/kMjdhZkHVDleJiLYfEqf67LPP/G+55ZZmAFi9enXThx9+2Du7kJGR0ZGUlKQxXQ4NDdVceeWV/ep4X3755V2NjY2S8vJyl6NHj7rLZDJ9XFyctu9x8+bNawsICNB7eHjwuLi47tLSUumhQ4c8Z8+erQ4JCdFLpVK+atWqZmtjXbFiRYuXlxcPDQ3VzZkzp+3QoUOe11xzTXt5ebnbhQsXJO+++67/Nddc0+ziculrq8FgYBs3boxISEhIWbRoUUJ9fb1rdXW1BADCw8N7srKyusRiMRISEroWL17cJhKJMGPGjM7q6mqprce29zl+//33/VNSUpJTUlJSiouL3c6cOdObG7Ju3bpmAMjKyuqsqqqSAsDBgwd9Hn744d7CSkFBQfoffvjBs7S01C0rKyspKSkp5dNPPw2orKx0aIsRzSyMI2nhMjAGJMhpVoHYR+7jjqOlDSipb0dsoCclDE5QA80ADIe6ujrxsWPHvAsLC91/9atfQa/XM8YYNxgM1YDwidz8+L6XzV1//fXNH330kZ9SqXS58cYbmywd4+rq2rtNVCwWc61W69Afc98cGNPlm266qfHtt9/23759u//mzZvL+5735ptv+jc2NkrOnj2bL5VKeXh4eHpXV5eo75hEIhHc3Ny4cXwwnxmx9NguLi7cYLj4lPT09PT7eQoKClz/+c9/hmRnZ+cHBQXpV69ereju7u79kG96PIlEwnU6ndXng3OOefPmtX311Vdl1o4ZCM0sjCOeUglunR2NG6eHj/ZQyDghl0lRp+5BoVJNSxDEqT788EO/VatWNV24cOFsTU3NWaVSmRMREaHZu3evw39ot956a9P27dv9d+7c6XfbbbdZnR3oa968eR3Hjx/3VqlUYq1Wiy+//NLP2rG7d+/27ezsZEqlUnzs2DHvefPmdQDAAw880PDmm2+GAEBmZmZ33/NaW1vFgYGBWqlUyr/66ivvCxcuOFz0w9JjT5kypaekpMS9q6uLNTQ0iA8fPtwvEa25uVns7u5u8Pf311dVVUl++OGHAWudL1iwoO2VV14JNl1WqVTihQsXdpw4ccIrNzdXCgBtbW2inJwchwqu0MzCOPP0SqsFtgjpR+7jBr2Bo6alC2tnRgx8AiF2+vzzz/0fe+wxpfl1N9xwQ/NHH33UuzRhr5kzZ3Z3dHSIQkJCNNHR0f2WIKyJiYnRPvzww7UzZ85Mlslkuri4uG6ZTGZxh0VycnLn3LlzE5ubmyWPPvporUKh0AJAZGSkbsqUKd3XXXddi6Xz7rnnnqYVK1bEJSQkpGRkZHTGxMT0CygGYu2xr7vuuuakpKTUiIiIntTU1H592efMmdOVlpbWOWXKlLTQ0FBNZmZm+0CP9de//rX2rrvuioqPj08ViUT8d7/73YU77rij5c033yy/+eabYzUaDQOAp556qiYjI6PH3p+BcT5yBcBmzpzJT5w4MWKPR8hk921eHe79QPg/9691M3BNRugoj4gMBmMsm3M+0/y6M2fOlE+dOrVhtMY0VrS2topkMplBq9Vi2bJlcXfeeWfD7bffbvGN3xK1Wi1KSUlJOX36dH5AQIDVrZyTwZkzZwKnTp2qsHQbLUMQMoGZCjMBtBOCTEyPPfZYWFJSUkpCQkJqVFRUz6233mp3oLBjxw7vxMTE1Hvvvbd+sgcKA6FlCEImMFNhJhEDFIG0E4JMPG+99Vb1YM9duXKleuXKlWedOZ6JioIFQiawAE9XuIgZIvw8IJVQjwVCyODQMgQhE5hIxBDm646EEFqCIIQMHs0sEDLBvX7LdMjcqYgXIWTwKFggZILLiPAd+CBCCLGBliEIIYQMilgszkxKSkqJi4tLTUxMTHnqqadC9HrbmwrMG0QNRVFRkWtKSkqy6fFfeOGF3mZUhw4d8khISEiJiopKu/POOyPNKyWaa2hoEC9fvjw2JiYmNTY2NnXfvn2egFCdcu7cufHR0dFpc+fOjVepVINO+Nm4cWPYjh07xn3Z3QGDBcaYG2PsJ8bYGcbYOcbYn43XxzDGjjPGShhjWxljDle1IoQQMn5JpVJDQUFBXklJybnvv/++6Ntvv5U9+uijYQOfOXRRUVHa7OzsgoKCgrzs7Oz8V199VV5eXu4CABs2bIjetGlTRXl5ee758+fdtm3bZrFN73333Re5dOnStrKysnN5eXl506ZN6waAp556KnThwoXqioqK3IULF6qffPJJ+WDH+Y9//OPCypUr1YM9f6ywZ2ahB8BizvlUANMALGeMXQbgbwBe4ZzHAWgGcPfwDZMQQshYFh4ernvnnXfKN2/eHGwwGKDT6XD//fdHpKWlJSckJKS8+OKLgX3PKSwsdM3MzEw0NkpK/vbbbz0B+1pHu7m5cXd3dw4AXV1dzDR7UFFR4dLe3i668sorO0QiEdavX9+4Y8eOfmWgGxsbxcePH/feuHFjg+n+AgMD9QCwZ88e3/vvv78RAO6///7G3bt39zv/tddeC1iyZMmUuXPnxoeHh6c/99xzQX/6059CkpOTU6ZOnZpUV1cnBoDVq1crNm/e7Gd8jiy2ld61a5dXUlJSSlJSUkpycnJKc3PzmJv1HzBngQslHk0lJl2MXxzAYgDrjNe/D+BPADY5f4iEEEJs2vFQJOrznFtIIzilEyv/5VCDqpSUFI1er0dNTY1k69atvjKZTBzCOpoAACAASURBVJ+bm5vf1dXFZs2alXTddde1mTdVCgsL0x06dKjIw8ODnz17VnrLLbfE5ubm5tvTOhoASkpKXK6++ur4qqoq6ZNPPlmtUCi0Bw8e9AgNDe0tGR0dHa2pra3tl+FbWFjo6u/vr1u7dq0iLy/PIyMjo+Ptt9+u8vHxMTQ2NkpMZacjIyO1jY2NFt8ri4qK3M+cOZPX1dUlSkxMTPvjH/9Yk5+fn3f33XdHvvnmmwFPPvlkfd9zLLWVfvnll+WvvfZaxdKlSztaW1tFtppujRa7ohfGmJgxdhpAPYBvAZQCaOGc64yHVAOw2N2IMXYfY+wEY+yESqVyxpgJIYSMcfv27fP57LPPApKSklKmT5+e3NzcLMnLy3MzP0aj0bB169YpEhISUtauXTultLTUDQDsaR0NAHFxcdqioqK8/Pz83C1btgRWVVXZnbSv0+lYfn6+x0MPPaTKz8/P8/DwMPzxj3/st9wgEon6dY00mTt3rtrPz88QFham8/Ly0q9du7YFANLT0zvLy8stNmqy1Fb6sssua3/00Ucjn3nmmeCGhgaxpZ91tNn1xHLO9QCmMcZ8AXwBIMneB+CcvwXgLUDoDTGYQRJCCLHBwRmA4ZKXl+cqFosRHh6u45yzl19+uXL16tVt5scUFhb25rc9++yzIcHBwdrt27eXGQwGuLu7Z5puG6h1tDmFQqFNSkrq2rdvn/fixYvbzWcSKioqXENDQ7UlJSUu1157bTwA/PKXv1TdfPPNzSEhIZrFixd3GB+v+fnnn5cDQEBAgK6iosIlOjpaW1FR4eLv76+z9LjWWlSLRCJYaxltqa30c889p1y5cmXrl19+KZs/f37Srl27iqdPn+5ww6rh5NC6COe8BcB+AHMA+DLGTMFGBIAaJ4+NEELIOHHhwgXJvffeG33XXXfVi0QiXHXVVa2bNm0K6unpYQCQk5MjbWtru+Q9p7W1VRwaGqoVi8V44403Asx3UgzUOrq0tNSlvb2dAUIb5p9//tkrNTW1Ozo6Wuvl5WX47rvvPA0GAz7++OOAG264oSUuLk5bUFCQV1BQkPeb3/xGFRUVpZPL5ZozZ85IAeCbb77xSUxM7AaAZcuWtbz55psBAPDmm28GLF++3O5+E4Nx7tw5aVZWVtezzz6rzMjI6MjNzXUb+KyRNeDMAmMsCICWc97CGHMHcBWE5Mb9ANYA+BTAHQC+HM6BEkIIGVt6enpESUlJKTqdjonFYn7TTTc1PvXUU3UA8PDDDzeUl5dL09PTkznnzN/fX/v111+Xmp+/cePG+tWrV0/59NNPAxYvXtzq7u7eu1Y/UOvonJwc99/+9rcRjDFwzvGrX/1KmZWV1QUA//rXvyruvvvumO7ubrZo0aK2tWvXtlq6j9dff71y/fr1sRqNhkVFRfV88skn5QDw5z//uXbVqlVToqOjA8PDwzVffPFFqaXzneWFF14IPnLkiA9jjCcmJnatWbPG4nhH04AtqhljGRASGMUQZiI+45z/hTEWCyFQ8AdwCsCtnHObvbGpRTUhhDhuMraoptbRI89Wi2p7dkPkAJhu4frzALKGPDpCCCHEzI4dO7w3bNigeOCBB+ooUBgbqNwzIYSQMYVaR489Y67wAyGEEELGFgoWCCGEEGITBQuEEEIIsYmCBUIIIYTYRMECIYSQQRnNFtX26O7uZrfccku0QqFIi4mJSX3vvfd8AaHx1DXXXBMbFRWVlpGRkWReVdJRL7zwQtA///nPAOeNemyi3RCEEEIGxdSiGgBqamoka9eujW1raxO/8sorF4b7sbu7u5lGo2E+Pj5Wmy498cQToUFBQdry8vJcvV6P+vp6CQC8+uqrgTKZTFdZWZn71ltv+T3yyCMRu3btOj+YcfzmN7+ZFE2PaGaBEELIkI10i2qVSiVOTk5OXbduXfSBAwcsdtz85JNPAp955hklAIjFYoSGhuoAYOfOnb6//OUvGwHgrrvuaj5y5Ii3qcW1yc6dO71nzZqVeOWVV06JiIhI37BhQ/imTZv809PTkxMSElLOnTsnBYBHHnkk7MknnwwBgKysrMQHH3wwPD09PVmhUKTt2bPHCwBOnDjhlp6enpyUlJSSkJCQcvbsWYtNpsYymlkghJBx7o8//jGypLnEqS2q4/ziOp++/Okx26I6MjJSV1JSkvvhhx/6/u53vwtvampyWb9+fcO9997bGBISom9oaBADwpv5kSNHvKOjo3veeuutysjISF1dXZ1rTEyMBgBcXFzg5eWlr6urk5iCCZOCggL33Nzcc8HBwbro6Oh0qVTacPbs2fynn346+OWXXw7+97//3e/50el07OzZs/lbt26V/eUvfwlbvnx50euvvx60YcOGugcffLCpu7ub6XQW+1KNaTSzQAghxOlGokW1u7s7v++++5p//PHH4v/+978l33//vU9UVNTU8vJyF61Wy+rq6lwuv/zyjry8vP/f3n2HR1Wm/x9/3xB6ACmhVxGFKKCICCouCvYCFlCQorsW2LWsX9217rr71a/6W8uqKCJWmiiKCDYQURdcQSkiSAepoSSAIQQIJJnn98dzkACZSc9M8PO6rlyZOW3uk8w55z5PO8vOPPPMPXfccUfTguxDu3bt9jRv3jyzSpUqrlmzZvsvueSSXQAdOnTYt2HDhlzbOfTp0+cXgLPOOmvPpk2bKgJ07dp1zzPPPNPwoYcearBq1aqK8fHxZe4JzCpZEBEp4wpaAlBSovGI6qSkpLiRI0fWeeedd+o0bNjwwMiRI39u0qRJZvDI6NCgQYN+ARgwYMDOsWPH1gWoX7/+gbVr11Zs1apVZmZmJunp6eXr169/1O1+pUqVwj6COjs7O69HUP+6zJAhQ3Z269Ztz6RJk2pefvnlrYcNG7b+yiuv3J2vP2qMUMmCiIgUWWk/onrHjh3le/bs2eqcc845KSMjw6ZOnbrq66+/Xj148ODUuLg4ypUrR48ePXZ98skn1QE+/fTTGq1bt94HcNlll6W+8cYbdQDefPPNWl27dt1drlzJXQ6XLl1asW3btvsffvjh5Isuuih14cKFVUrsw0qIShZERKRQovmIaoA777wz+fLLLw97oX/22Wc39e/fv+W9995bvk6dOlmjR49eB3DXXXdtv+aaa1o2a9bslJo1a2a/++67JfoI6rFjx9aeMGFCnbi4OJeQkJD56KOPbinJzysJeT6iujjpEdUiIgWnR1TryZOlIdIjqlUNISIiMeXDDz+sftJJJ518yy23JCtRiA2qhhARkZiiR1THHpUsiIiUTaFQKJRri3yRggq+S2FHw1SyICJSNv2UkpJSUwmDFFUoFLKUlJSawE/hllE1hIhIGZSVlXXz1q1bX9u6desp6MZPiiYE/JSVlXVzuAWULIiIlEGnn356MnBltOOQ3wZloyIiIhKRkgURERGJSMmCiIiIRKRkQURERCLKM1kws6Zm9pWZLTWzJWZ2VzC9tplNN7NVwe9aJR+uiIiIlLb8lCxkAfc45xKBLsCfzCwRuB+Y4ZxrDcwI3ouIiMgxJs9kwTm3xTm3IHi9G1gGNAZ6AaOCxUYBvUsqSBEREYmeArVZMLMWwGnAd0B959zBx2xuBeqHWedWM5tnZvNSUlKKEKqIiIhEQ76TBTOLByYCf3bOpeWc5/xzrnN91rVzbqRzrpNzrlNCQkKRghUREZHSl69kwcwq4BOFcc65D4LJ28ysYTC/IZBcMiGKiIhINOWnN4QBrwPLnHPP5pg1BRgcvB4MTC7+8ERERCTa8vNsiLOBgcBiM1sYTHsQeBKYYGZ/ANYDfUsmRBEREYmmPJMF59w3QLhHoPYo3nBEREQk1mgERxEREYlIyYKIiIhEpGRBREREIlKyICIiIhEpWRAREZGIlCyIiIhIREoWREREJCIlCyIiIhKRkgURERGJSMmCiIiIRKRkQURERCJSsiAiIiIRKVkQERGRiJQsiIiISERKFkRERCQiJQsiIiISkZIFERERiUjJgoiIiESkZEFEREQiUrIgIiIiESlZEBERkYiULIiIiEhEShZEREQkIiULIiIiElGeyYKZvWFmyWb2U45ptc1supmtCn7XKtkwRUREJFryU7LwFnDxEdPuB2Y451oDM4L3IiIicgzKM1lwzs0Edh4xuRcwKng9CuhdzHGJiIhIjChsm4X6zrktweutQP1wC5rZrWY2z8zmpaSkFPLjREREJFqK3MDROecAF2H+SOdcJ+dcp4SEhKJ+nIiIiJSywiYL28ysIUDwO7n4QhIREZFYUthkYQowOHg9GJhcPOGIiIhIrMlP18nxwGzgJDPbZGZ/AJ4ELjCzVUDP4L2IiIgcg+LyWsA51y/MrB7FHIuIiIjEII3gKCIiIhEpWRAREZGIlCyIiIhIREoWREREJCIlCyIiIhKRkgURERGJSMmCiAhAegps+THaUYjEJCULIiIAk26FV3vApvnRjkQk5ihZEBHZlQRrvoJQJrx3I+z7JdoRicQUJQtSdmUdiHYEZUN2JriwD4YVgB/HAw6ueR12b4EP/6S/mUgOShakbNr8AzzVCr5/NdqRxLZ9v8DwLvD2dRAKRTua2OQcLHwbmp8N7a6FC/4XVnwCc16OdmQiMUPJgpQ9Gbt8UfH+NJjxKOzZEe2IYpNzMPl22LEGVk2Db56JdkSxaeP3sHMNnNrfv+8yFNpcDtP/rvYLIoGykyxkZ0U7AokFBy+AqRvhiufhQDr85/9FO6rYNOdlWP4xXPgYtOsLXz0O676JdlSxZ+E4qFAVEnv592bQ60Wo0dAnpXt3RjU8kVgQ+8mCc/DJPb6lsuoQ5ftXYdkU6PkInH6j/5n7GqSsjHZkRbNjDbx9PSQtKJ7tbZrv74xPuhS6/gku/zfUbgXv/wHSk4vnM44FB/bCkkk+UahU/dD0KrXg2rd8+4XJar8gEvvJghlUbwg/TYT5b0Y7GommpAXw+UPQ+iLoeoef1v0BqFgNpv8turEVxca58FpPWPkZfPznorct2PeLvyOu3hB6veSPoUrx0OctyEiFD26BUHZxRF72Lf/EV2edesPR85qcDhc+Cis+hTnDSz82kRgS+8kCwDn/A616wGf3w5ZF0Y5GomFfqr8AVkuAq0ZAueCrG58A3e6BlVN917eyZsVnMOoKqFwTznvYDwq06J3Cb88535J/9xbo8yZUrX1oXoNT4JJ/wc9fw6xnixz6MWHhODiumW/cmJszh+RovzCvdGMTiSHmSrF4rVOnTm7evEIecHu2w4hzfN3irV9D5RrFGdpRskPZTF4zmY71OtKiZovi23DWAV+M7hwkXglxlYpv29Gy6gt/d9+8a8ls3zmYMNBfWG/8FJqdefj8zAx4qbMvRr5tJpQrXzJxFLd5b/gqtoYdoP97ULUOvN4T0jbDHfP937SgZr8E0x6Ei56Arn88er5zMOk2WPweDJoCLbuF39aeHb5Er3VPqH18wWOJdakb4bl28Lv74LwHwi+3LxVe6eb/du2vC79cQhtfnRFXsdhDNbP5zrlOxb5hkXwqO8kCwPpv4a3LILE3XPuGL14tAfuz9/PArAeYvn461StWZ9j5wzi9/ulF2+jenTDvdfj+NUjf6qfF14czboFOv4dqdYoeeGlzDr75N8z456EkLuGk4v+c716Bz/4KFzwKZ9+Z+zJLJvmShytegNMHF38Mxck5+Or/YOZT0PpCXz1wMDHYMAfeuMhXr3S/v2Db3TTPr9v6Irh+XPjjY386jOzui9+HfAPx9Q6fn7LSF7v/+A5k7YM6rf3/tlJ8weKJdTOfgi8fgzsXQu2WkZdNmu/blOwN1/PGgQtBfAM481Y4/abDS3WKSMmCRFvZShYAZj0DM/4XLnsWzvhD8QSWw679u7jzyztZkLyA29rfxrR109icvpknuj3BhS0uLPgGt6/yJ96F4/2Jt9X5vsEZ5qev/gLiKkOH66HLH0vmYlsSQtn+Aj73NWh7pU/k4uvBzTOgYtXi+5ykBfD6hXBCD7h+/KHqhyM55y+UO9fCnQsOb6wWS7IzYcqd8OPb0HEQXPZvKB93+DITBsOqz33pQo1G+dvu3p3wyu/86yEzfQO9SLYtgVfPh2ZdYMAHYOVg7X9g9nDfzbJ8JehwHTQ9E6bcAe36wFWvlFiCXuqcg2EdoXojuOmTom8vFII1X8LsF+HnryCuiu+K2eWPUPeEIm9eyYJEW9lLFkIheLsPrJ0FN0/3RbjFZHP6ZoZ+MZSNuzfy+DmPc3HLi0nNSOWOL+/gx5Qfua/zfdzQNmgIlZEGKcvDb2zvTl/MfPDE276vP3HUTzx8ueRlwV3cu5C9H064wCcTx3cv9hPzhrQNNIxvSIVyFY6euT/dN36r2STvDWXug4k3+255Z90JPf/pT5Bjr4HTBvhuZ8VhXyq8cq5PTIbMyvtObdM8eK0HdLsXekRo8BgKwbbFkLW/cHFVqu6LnAv6/8lI86Ufa2b4koPf3Zf7Nn5ZBy+e4S/QvfNuWOdCIZLe6UPj1f/Bfj/NN8zLjwWjfSJwyrX+u7ztJ98m5GBpV3yCX+4///IlIVcO8wnOwd3JymDFLysIdw6pWL4ibWq3oZwVsGmUc7B1MWRlFGy9g6ol5F1ScLAEp/fLh8ZXKC7blvhjetEEyD4AJ17sj+kW3Qp9TCtZkGgre8kCBO0XukGFynDrf4ql/cKKnSsY+sVQMrIyeP785zmjwRm/zsvIyuD+WfczY8MMbmx1FXen7afcwrG+j38kuZ14w0lP8cnF3FdhTwrUO9nXObfrU6R2DdmhbL7a+BWjl47mh+QfOKPBGTx33nPUqJjjb7Zzrb/Q7/z5UFe75mflfmLbu9OPBrhpLlz8JHQZcmjel4/5ot2rXvElJUWxZ7v/nC0L4abPoGnn/K038WZY9hHcPg+Oa3r4vAN7fePB2cNhx6qixdfoNOh6u6+jLp9L8pXTrk2+KmX+KP+dueK5wy66uZr+d/jvC774v9GpYRfLzNzLo+/3YtKBrfQ/rh1/vWIM5fPbZsM5mDTE/00Oft9OudYfVzmFsmHs1f4Ce/MMaHAKW/dsZegXQ1mdujriR1zQ/AKe6PYElcrn8zucdcB3VVw8IX/L58bKwaVPRy55nHIHLJ4I964sueqV9GSY+7ovfdu73f/tmhTueq9kQaKtbCYLAOtnB+0XroRr3yzSXfjszbO5++u7qVahGiN6jqB1rdaHL+Ac2Rtm8+Ssh3gnezuX7NnLY/W6U7HdteEvFOUq+CLcI0+8ecnMgJ/e9xe05CVQrR50PtiuoW6+N7Mncw+TVk1i7LKxJKUn0Ti+Md2bdufdFe/SokYLXu75Mg2qNfDDJo/r44vHO/SDRe/Cvp3Q8FR/MTy596F9/GUdjL0WUjfANa8eGsTmoOwsGN0LNi8oWvuFnT/75CVtM1zzGrS9Iv/rpm6EFzv5da55zU/bvdWPzzDvjUP71vkWqN6gkPGthe9GwI7VUKMxdL7Vt5M4sug/ab5vcLjkQ/8+8UpfEtO4Y96fkbELXjgNEtrCjR/n+v3em57MPROv5Bv2cHqlBObvT6Fns5480e0JKsfl83uXnQnJS6FB+8jHUHqyb2BcqTqr+r7O0Jn3kp6ZzgOdH6Buldy/l4u2L2L4wuF0rNeRF85/gZqVauaxz2nw7gBfHXLuX3wVSWF894qvxul2L5z/8NH7dWAvPH2i/3/ko+SmyDIzfG+dxF4qWZAyq+wmC+C7f834J1z2DJxxc6E28dGaj/j7f/9Oi5o5LqAHZWfBssn+hJ80H1f5ON446SyeS/uJzg068+/z/n34HXpxcs53cZv9Eqye7ts1tL/OV2XUaxN2tS3pW3h7+du8v/J90jPTOa3eaQxMHMh5Tc8jrlzcr4lRfIV4Xm49mNaf3Odb4Q9431/cj7z7rt7IN9hqfLof0Cf7APQb70secrN7q7+oVK0Lt3xZ8PYLSfNhXF9w2dDv3aN7PuTHjEdh1tO+iHntTFj8PoSyoM1lvtSkWdeiV/GEQv6CNPtFWDcLKlSD027wicPBqqUNs6FSDV+KcOZtvoteQcx9zfeWuG4ctL38sFnbty/n9o+uZ5ll8XCjnvS58DlGLxnNU/Oe4rR6pzHs/GF5X5wLau0s5k7ow10NGlC5Sm1e7vkyJ9WOnBB+tvYzHvrmIZpWb8qIniNoGN8w9wXTtsC4oDrkymFFqxrIzoJP7vbVLB36w5UvHJ7UL5rgx5q48RNocU7hP6cUKVmQaCvTycKG1HWMnTKQz7N2kBlX2RfXF7B+NO1A2tFF8/tS/Ynm+5Gwa6Mf+a7LUH8Cq1iNj3/+mL/99280qNqAm065iStaXUGVuCrFtl9HSVmRo3V6hu+Tz+EXu8UVyjG6WiWmV64AZlzQ9DwGtvsD7RPaH7W55TuX88fPbiLjQBrPZ1bnjH4fHn2XHQr5xpezX/R3egA1m8KAiXmXGKz5EsZc7S+evV7K/36u/BzeG4yrVpcfL32c0Zu/Yl3aOnq36s3Vra8mvmI+i4v374YXOsKe5OAiPsBfrOu0yn8suVixcwVjlo5h0fZFXNLyEq476TpqV67tx/6YMzxISjL9wsc184ndaQMK39gyOwtGnO0TtD9+92uXvPXrZzJkxp/Ybo6n2t5E9y73/LrK1HVTeXDWgzSp3oQRPUfQKD6fDSTzYeraqTw48z6aHtjPiFP+RMODA2PlYe7Wudz15V1UiavC8J7Dj04wkpf7RGHfL9B3tG/MWlTO+bYWXz/ux2jpO+rQ/2F0r6Ah7MLwDWZz2LpnK+OXj+fjnz8mo7DtKIBxl44rdDdsJQsSbUVKFszsYuB5oDzwmnPuyUjLF0ey4Jxj/rb5jF46mq83fk15K0/PcjWo/csGv0Dt432R6pHdwcJIqJrAoMRBVCxf8VDx8g9Be4QW3fydaOuLjjqpzN06l2fmPcOSHUuoWakmfU/sS782/UiomkfbhKLYs93HlrYZgGzn+PLANkbvXcvCrFSqWxzXhKrSf+MyGpar7C9UXYYc3kfeOZj5FJtnPcnQpi3YWA4e7/Y4F7e4OPznbl0Myz72QyvXCHNneKQv/w9m/gt6j4BT++W9/IIxZH10F180as3o+s1Y/MtyalSsQYsaLVi0fRHVKlTj6tZXc0PbG2gc3zjv7a37r4+7w3V59wyIIORCfJP0DWOWjmHOljlUiatC29ptWZC8gIrlKnJFqysY0HYAJ9Q6wZeqLHoXarXwA/kUx3gPq76Acdf8Om7Cop/e4fa5jwHwYue/0f7ko/v9H7w4V46rzPCew2lTO3xJVH6NWjKKp+c97asUkndSc+P3v7ZfyI+Vv6xk6BdD2ZO5h+fOe44uDYMqhvXfwvjrfcnZDe8Va4NlABaMgY/u8nH2f88nXs+1891S8+iaumT7EkYvHc3n6z4nRIjfNfkdDavl8/ufi1va3xK2yiYvShYk2gqdLJhZeWAlcAGwCZgL9HPOLQ23TlGShcxQJtPWTWPM0jEs3bGU4yodR9+T+nL9Sdf7C3TqRvg+aES2Pw2advENtvI6aTvnG27NeckP/Wrl4ZRr/Lp5nLicc/yQ/AOjl47myw1fUr5ceS5teSkDEwcWywk6nPQD6UxaPYlxy8aRlJ5Ek/gmDEgcQO8TelOtQrXgTvdlP/BOzuL3Jp3hk/+BBaOgQz92Xfgod868lwXJC/hLp78w6OQ8Gt0VRCjb38ElzYdbvgpfdeIcaV89ygcLRzKuTgJbyaJ5jeYMbDuQK1pdQdUKVVmyYwljlo5h2tpphAjRo1kPBiUO4tR64Rv+FdW+rH18tOYjxi4by9pda6lXtR792/Tn2hOvpWalmvy862fGLR3HlDVTyMjO4OxGZzMocRBdG3XFirt74ZirIWkeX3f9A3/5+T0SnPFyj+E0bx5+QKXVv6xmyBdDSM9M59nuz3JWozDVRnkIuRBPzX2KscvGHmqsuC8taL8QH4y/kL+Sk4ONItelreOxsx/jsowsmHiLL4UZMBFqNS9UjHlaNR0mDPJtflqe6xPuuxbl+nnZoWy+3vQ1o5eMZkHygoInqSVEyYJEW1GSha7AP5xzFwXvHwBwzj0Rbp3CJgtjlo7hrSVvkbw3mZY1WzIwcSCXH3957kX/+3f7k8GclyF1PRzXPGgoFeYEvn2Fb+RXpZZvRHjGLfm/e85hY9pGxi0fxwerPmBf1j461utYIieXzFAm3yR9Q3pmOh3rdWRQ4iC6N+2eewv43Vt9vffc133DvmoJvqdFt3vg/L+B2WEDUJ3T+BxqVSr8XfhRsvbBiqlQLs5/di4OZO1lVmg3e8uVo3P9Tgw8eTDnNjk31+52W/ds5Z3l7zBh5QR2H9hNu7rtaFGjRfHFG8h22Xy7+VtS96eSWCeRQYmDuLDFhbl2OU3NSOW9le8xfvl4UvalcMJxJ9C2dtviDSgjlQOrpjG9ahUSXRwvXjGBOnVPzHO1bXu2MXTGUNamruWC5hcQVy4uz3WOlJSexILkBQxoO4C/nPGXQ/+Xdd/4YaobneYHbcqnNJfFn/ctZ252Gj337KVKlTp+FMn89pYorL07fPuVrP2+1LHV+Uct4nD8mPIjG3dvpFG1RtzQ9oaCVX+VICULEm1FSRauBS52zt0cvB8InOmcu/2I5W4FbgVo1qzZ6evXry/wZ/3j23+QlJ7EoMRBnN347Pz12w5l+5KC70f61vvhVDkOOg72PQGKYTChtANpfLDyAyavmcy+rH1F3l5u2ie0Z1DiIE6pm78i4F8bLf4wzrcj6PT7w2Znh7IZ9sMwpq6bWvzBZmX47pYRvmcdaxzPwO5P0LZuYthlctqbuZfJayYzadUk0g6kFVekh2lTuw0DEwfSsV7HfJUUZGZnMnXdVN5Z8Q479oUb5a8IMnbRgco8ctV7VK2a/6Ls3Qd288i3j7B0R9gCv4jKW3n6tenHgMQBR8+c+xp8O6zAT2Q8gOPxyOvvHwAACnNJREFUqjCnciXfuLa0BnoKZfmkoXJNX+2RiwbVGtC/TX/Ob3Z+oZKrkqJkQaKtxJOFnApbspAVyoqpA1dEpDQpWZBoK8pTJ5OAnKPeNAmmFTslCiIiItFTlGRhLtDazFqaWUXgemBK8YQlIiIisaLQt+zOuSwzux2Yhu86+YZzbkmxRSYiIiIxoUjl+865T4FPiykWERERiUFFqYYQERGR3wAlCyIiIhKRkgURERGJSMmCiIiIRFSqT500s13AqgiL1AR2FfO8srhuScbUDAg3pGVZ3J9j7f9zLMVUlHVjMaairFuU47IZ/lxdgk+pE8mDc67UfoCRhZ1f2Hllcd0SjinlGNufY+3/c8zEpP0p0HYjHZdh5+lHP6X1U9rVEB8VYX5h55XFdUsyptQofG4s/o2Lsq5iKvl1YzGmoqxblOMy0jyRUlGq1RASfWY2z2mMeZGYEum41DErsUANHH97RkY7ABE5SqTjUsesRJ1KFkRERCSimCtZMLOmZvaVmS01syVmdlcwvbaZTTezVcHvWtGONT8i7M+7ZrYw+FlnZgujHWt+mdnFZrbCzFab2f1HzHvBzNKjFVthmNkbZpZsZj/lmNYn+H+FzKxMFQGH2Z9TzWxO8H2bZ2adoxljfkU4fv5hZkk5jqFLox2ryLEs5koWzKwh0NA5t8DMqgPzgd7AjcBO59yTwQWqlnPuviiGmi/h9sc5tzTHMs8Au5xz/xutOPPLzMoDK4ELgE34p4/2c84tDS6qdwFXOefioxhmgZjZuUA6MNo5d0owrS0QAl4B7nXOzYtiiAUSZn8+B/7tnPssuLD+1TnXPYph5kuE80FfIN0593RUAxT5jYi5kgXn3Bbn3ILg9W5gGdAY6AWMChYbhT9hxLwI+wOAmRn+xDc+OhEWWGdgtXPuZ+fcAeAdoFeQRDwF/DWq0RWCc24msPOIacuccyuiFFKR5LY/gANqBK9rAptLNahCyuv4EZHSEXPJQk5m1gI4DfgOqO+c2xLM2grUj1JYhXbE/hzUDdjmnIs0WFUsaQxszPF+UzDtdmBKjv+RxJY/A0+Z2UbgaeCBKMdTYLkcP7eb2aKg2qVMVEuKlFUxmyyYWTwwEfizcy4t5zzn605iq/4kDxH2px9lp1QhnKpAH2BYtAORsIYCdzvnmgJ3A69HOZ4CyeX4eRloBZwKbAGeiWJ4Ise8mEwWzKwC/sQwzjn3QTB5W1B/ebAeMzla8RVUmP3BzOKAq4F3oxVbISQBTXO8bwKsAU4AVpvZOqCqma2OQmwS3mDg4HfvPXx1UpmQ2/HjnNvmnMt2zoWAVylD+yNSFsVcshDU4b8OLHPOPZtj1hT8CY/g9+TSjq0wIuwPQE9guXNuU+lHVmhzgdZm1tLMKgLXAx865xo451o451oAe51zJ0Q1SjnSZuB3wevzifyMlpgR7vg5eOMQuAr46ch1RaT4xGJviHOAWcBifGt0gAfx9ZQT8A9VWQ/0dc4d2Ygr5oTbH+fcp2b2FjDHOTciWvEVRtCa/jmgPPCGc+7/jpifXsZ6Q4wHugN1gW3AI/gGgsOABPxwuwudcxdFK8aCCLM/K4DngTggA/ijc25+tGLMrwjng374KggHrANuU3sZkZITc8mCiIiIxJaYq4YQERGR2KJkQURERCJSsiAiIiIRKVkQERGRiJQsiIiISERKFkRERCQiJQsiIiISkZIFERERiUjJgoiIiESkZEFEREQiUrIgIiIiESlZEBERkYiULIiIiEhEShZEREQkIiULIiIiEpGSBREREYlIycIxxMyyzWyhmS0xsx/N7B4z0/9YJEaYWXq0YxApjLhoByDFap9z7lQAM6sHvA3UAB6JalQiIlKm6a7zGOWcSwZuBW43r7yZPWVmc81skZnddnBZM7vPzBYHpRFPRi9qkWOfmcWb2QwzWxAcd72C6S3MbJmZvRqUDn5uZlWiHa8IqGThmOac+9nMygP1gF7ALufcGWZWCfivmX0OtAnmnemc22tmtaMYsshvQQZwlXMuzczqAnPMbEowrzXQzzl3i5lNAK4BxkYrUJGDlCz8dlwItDeza4P3NfEnpp7Am865vQDOuZ1Rik/kt8KAx83sXCAENAbqB/PWOucWBq/nAy1KPzyRoylZOIaZ2fFANpCMP0Hd4ZybdsQyF0UjNpHfsBuABOB051ymma0DKgfz9udYLhtQNYTEBLVZOEaZWQIwAnjROeeAacBQM6sQzD/RzKoB04GbzKxqMF3VECIlqyaQHCQK5wHNox2QSF5UsnBsqWJmC4EKQBYwBng2mPcavkhzgZkZkAL0ds5NNbNTgXlmdgD4FHiw1CMXOcaZWRy+5GAc8JGZLQbmAcujGphIPpi/6RQRkZJkZh2AV51znaMdi0hBqRpCRKSEmdkQYDzwcLRjESkMlSyIiIhIRCpZEBEpZmbW1My+MrOlwQBLdwXTa5vZdDNbFfyuFUxvY2azzWy/md17xLbuDrbxk5mNN7PKuX2mSElSsiAiUvyygHucc4lAF+BPZpYI3A/McM61BmYE7wF2AncCT+fciJk1DqZ3cs6dApQHri+dXRA5RMmCiEgxc85tcc4tCF7vBpbhB1/qBYwKFhsF9A6WSXbOzQUyc9lcHL6nUxxQFdhcwuGLHEXJgohICTKzFsBpwHdAfefclmDWVg6N3Jgr51wSvrRhA7AFP2T75yUWrEgYShZEREqImcUDE4E/O+fScs4LBkuL2MI8aNPQC2gJNAKqmdmAEgpXJCwlCyIiJSAYLXUiMM4590EweZuZNQzmN8QPxR5JT/zzIlKcc5nAB8BZJRWzSDhKFkREilkwSurrwDLn3LM5Zk0BBgevBwOT89jUBqCLmVUNttkD3/5BpFRpnAURkWJmZucAs4DF+CdLgh9G/TtgAtAMWA/0dc7tNLMG+KGfawTLpwOJwWOs/wlch+9h8QNws3Mu5wOnREqckgURERGJSNUQIiIiEpGSBREREYlIyYKIiIhEpGRBREREIlKyICIiIhEpWZBjlpllm9nC4Il9P5rZPWYW8TtvZi3MrH9pxSgiUhYoWZBj2T7n3KnOuZOBC4BLgEfyWKcFoGRBRCQHjbMgxywzS3fOxed4fzwwF6gLNAfGANWC2bc75741szlAW2At/qmALwBPAt2BSsBLzrlXSm0nRERigJIFOWYdmSwE01KBk4DdQMg5l2FmrYHxzrlOZtYduNc5d3mw/K1APefcY2ZWCfgv0Mc5t7ZUd0ZEJIrioh2ASJRUAF40s1OBbODEMMtdCLQ3s2uD9zWB1viSBxGR3wQlC/KbEVRDZOOf9PcIsA3ogG+7kxFuNeAO59y0UglSRCQGqYGj/CaYWQIwAnjR+bq3msAW51wIGAiUDxbdDVTPseo0YGjwuGHM7EQzq4aIyG+IShbkWFbFzBbiqxyy8A0aDz4ueDgw0cwGAVOBPcH0RUC2mf0IvAU8j+8hsSB4RHAK0Lu0dkBEJBaogaOIiIhEpGoIERERiUjJgoiIiESkZEFEREQiUrIgIiIiESlZEBERkYiULIiIiEhEShZEREQkIiULIiIiEtH/B8nA/htmYsflAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "typ='Ambulance Arrivals and Delays'\n", "\n", "q3='''\n", "SELECT Date, Name, Category, value FROM sitrep WHERE Report='{typ}'\n", "AND Code = '{code}'\n", "'''.format(typ=typ,code=code)\n", "tmp = pd.read_sql_query(q3, conn, parse_dates=['Date'])\n", "tmp_p = tmp[['Date','Category','value']].pivot_table(index='Date',columns='Category')\n", "tmp_p.columns = tmp_p.columns.get_level_values(1)\n", "tmp_p.plot(title=typ).legend(loc='center left', bbox_to_anchor=(1, 0.5));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Are there correlations between ambulance delays and bed availability, I wonder?" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "q4='''\n", "SELECT Date, Name, Category, value FROM sitrep WHERE Report='Adult critical care'\n", "AND Code = '{code}'\n", "'''.format(code=code)\n", "tmp = pd.read_sql_query(q4, conn, parse_dates=['Date'])\n", "tmp_p = tmp[['Date','Category','value']].pivot_table(index='Date',columns='Category')\n", "tmp_p.columns = tmp_p.columns.get_level_values(1)\n", "tmp_p.plot().legend(loc='center left', bbox_to_anchor=(1, 0.5));" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "startDate = '2017-12-25'\n", "endDate = '2017-12-31'" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "q5='''\n", "SELECT Date, Name, Category, value FROM sitrep WHERE Report='Ambulance Arrivals and Delays'\n", "AND date(Date) BETWEEN date('{fromdate}') AND date('{todate}')\n", "AND Code = '{code}'\n", "'''.format(fromdate='2017-12-25', todate='2017-12-31',code=code)\n", "tmp = pd.read_sql_query(q5, conn, parse_dates=['Date'])\n", "tmp_p = tmp[['Date','Category','value']].pivot_table(index='Date',columns='Category')\n", "tmp_p.plot(kind='bar').legend(loc='center left', bbox_to_anchor=(1, 0.5));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example report: https://onthewight.com/50-patients-stuck-outside-st-marys-in-ambulances-for-up-to-an-hour-in-december/\n", "\n", "> Ambulances were forced to wait up to an hour at A&E 52 times on the Isle of Wight last month, with emergency patients stranded inside the vehicles waiting to be admitted.\n", "\n", "> The NHS has released statistics concerning Isle Of Wight NHS Trust as part of a special series which highlights the winter pressures facing the health service.\n", "\n", " >The figures show that in December, 52 of the Trust’s patients had to spend between half an hour and an hour waiting in an ambulance at hospital, before they could be transferred to the emergency department.\n", "\n", ">Some for more than an hour 13 were stuck in ambulances for more than 60 minutes.\n", "\n", "> NHS England’s target time is up to 15 minutes.\n", "\n", "> The waits, known as handover delays, can be due to ambulance queues or slow processing at hospitals, and can have the knock-on effect of delaying paramedics being despatched to future emergencies.\n", "\n", "> In total 5.4% of all patients arriving by ambulances at hospital were delayed by between 30 and 60 minutes.\n", "\n", "Let's have a go at trying to match those numbers..." ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DateNameCategorySUM(value)
02017-12-31Isle Of Wight NHS TrustArriving by ambulance1455
12017-12-31Isle Of Wight NHS TrustDelay 30-60 mins59
22017-12-31Isle Of Wight NHS TrustDelay >60 mins13
\n", "
" ], "text/plain": [ " Date Name Category SUM(value)\n", "0 2017-12-31 Isle Of Wight NHS Trust Arriving by ambulance 1455\n", "1 2017-12-31 Isle Of Wight NHS Trust Delay 30-60 mins 59\n", "2 2017-12-31 Isle Of Wight NHS Trust Delay >60 mins 13" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q_month='''\n", "SELECT Date, Name, Category, SUM(value) FROM sitrep WHERE Report='Ambulance Arrivals and Delays'\n", "AND date(Date) BETWEEN date('{fromdate}') AND date('{todate}')\n", "AND Code = '{code}'\n", "GROUP BY Category\n", "'''.format(fromdate='2017-12-01', todate='2017-12-31',code=code)\n", "pd.read_sql_query(q_month, conn, parse_dates=['Date'])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I can match the *13 over an hour* but not the others?" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
NameDelayedTotalpc
0Isle Of Wight NHS Trust7214554.948454
\n", "
" ], "text/plain": [ " Name Delayed Total pc\n", "0 Isle Of Wight NHS Trust 72 1455 4.948454" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q_pc='''\n", "SELECT total.Name, Delayed, Total, 100.0*Delayed/Total AS pc FROM (SELECT Name, SUM(value) AS Delayed FROM sitrep \n", "WHERE date(Date) BETWEEN date('{fromdate}') AND date('{todate}') \n", "AND (Category = 'Delay >60 mins' OR Category='Delay 30-60 mins') \n", "AND Code = '{code}'\n", "AND value NOT NULL GROUP BY Name) delayed JOIN (SELECT Name, SUM(value) AS Total FROM sitrep \n", "WHERE date(Date) BETWEEN date('{fromdate}') AND date('{todate}') \n", "AND Category = 'Arriving by ambulance' \n", "AND Code = '{code}'\n", "AND value NOT NULL GROUP BY Name) total on total.Name = delayed.Name \n", "ORDER BY pc DESC\n", "'''.format(fromdate='2017-12-01', todate='2017-12-31',code=code)\n", "pd.read_sql_query(q_pc, conn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simple Report Generation\n", "\n", "We can generate simple text reports from the data.\n", "\n", "For example, something of the form *Last week, of M ambulance arrivals overall, N patients at LOCATION Y waited over thirty minutes, of which Z waited over one hour.*" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "def query_ambulance_arrivals(conn, code, period = 'last week'):\n", " q_simple='''\n", " SELECT Date, Name, Category, SUM(value) as Total FROM sitrep WHERE Report='Ambulance Arrivals and Delays'\n", " {period}\n", " AND Code = '{code}'\n", " GROUP BY Category\n", " '''.format(period=getperiod_sql_clause(period), code=code)\n", "\n", " return pd.read_sql_query(q_simple, conn).set_index('Category').to_dict(orient='index')" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Arriving by ambulance': {'Date': '2017-12-31 00:00:00',\n", " 'Name': 'Isle Of Wight NHS Trust',\n", " 'Total': 357},\n", " 'Delay 30-60 mins': {'Date': '2017-12-31 00:00:00',\n", " 'Name': 'Isle Of Wight NHS Trust',\n", " 'Total': 18},\n", " 'Delay >60 mins': {'Date': '2017-12-31 00:00:00',\n", " 'Name': 'Isle Of Wight NHS Trust',\n", " 'Total': 3}}" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "locationQuery = 'Wight'\n", "\n", "trust,code = lookupTrustCode(conn,locationQuery, 'sitrep')\n", "query_ambulance_arrivals(conn, code,'last week')" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "def _report_ambulance_arrivals(repdict, resp):\n", " repdict['overall'] = P.number_to_words(resp['Arriving by ambulance']['Total'])\n", " repdict['over60'] = P.number_to_words(resp['Delay >60 mins']['Total'])\n", " repdict['over30'] = P.number_to_words(resp['Delay 30-60 mins']['Total'])\n", "\n", " txt = '''\\\n", "{nl_period}, of {overall} {location} ambulance arrivals overall, {over30} incurred a handover delay between thirty minutes and an hour \\\n", "and {over60} had a delay of over an hour.'''.format(**repdict)\n", " return txt\n", "\n", "def report_ambulance_arrivals(conn, locationQuery, period='last week'):\n", " trust,code = lookupTrustCode(conn,locationQuery, 'sitrep')\n", " resp = query_ambulance_arrivals(conn, code, period)\n", " repdict={}\n", " repdict['location'] = trust\n", " repdict['nl_period']=period.capitalize()\n", " return _report_ambulance_arrivals(repdict, resp)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Last week, of three hundred and fifty-seven Isle Of Wight NHS Trust ambulance arrivals overall, eighteen incurred a handover delay between thirty minutes and an hour and three had a delay of over an hour.\n" ] } ], "source": [ "txt = report_ambulance_arrivals(conn, locationQuery, 'last week')\n", "print(txt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### More Example Reports" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DateNameCategoryvalue
02017-12-01Isle Of Wight NHS TrustDelay 30-60 mins0
12017-12-02Isle Of Wight NHS TrustDelay 30-60 mins2
22017-12-03Isle Of Wight NHS TrustDelay 30-60 mins1
32017-12-04Isle Of Wight NHS TrustDelay 30-60 mins3
42017-12-05Isle Of Wight NHS TrustDelay 30-60 mins0
52017-12-06Isle Of Wight NHS TrustDelay 30-60 mins0
62017-12-07Isle Of Wight NHS TrustDelay 30-60 mins2
72017-12-08Isle Of Wight NHS TrustDelay 30-60 mins5
82017-12-09Isle Of Wight NHS TrustDelay 30-60 mins1
92017-12-10Isle Of Wight NHS TrustDelay 30-60 mins2
102017-12-11Isle Of Wight NHS TrustDelay 30-60 mins2
112017-12-12Isle Of Wight NHS TrustDelay 30-60 mins2
122017-12-13Isle Of Wight NHS TrustDelay 30-60 mins1
132017-12-14Isle Of Wight NHS TrustDelay 30-60 mins2
142017-12-15Isle Of Wight NHS TrustDelay 30-60 mins4
152017-12-16Isle Of Wight NHS TrustDelay 30-60 mins0
162017-12-17Isle Of Wight NHS TrustDelay 30-60 mins2
172017-12-18Isle Of Wight NHS TrustDelay 30-60 mins5
182017-12-19Isle Of Wight NHS TrustDelay 30-60 mins2
192017-12-20Isle Of Wight NHS TrustDelay 30-60 mins3
202017-12-21Isle Of Wight NHS TrustDelay 30-60 mins0
212017-12-22Isle Of Wight NHS TrustDelay 30-60 mins0
222017-12-23Isle Of Wight NHS TrustDelay 30-60 mins0
232017-12-24Isle Of Wight NHS TrustDelay 30-60 mins2
242017-12-25Isle Of Wight NHS TrustDelay 30-60 mins2
252017-12-26Isle Of Wight NHS TrustDelay 30-60 mins2
262017-12-27Isle Of Wight NHS TrustDelay 30-60 mins0
272017-12-28Isle Of Wight NHS TrustDelay 30-60 mins6
282017-12-29Isle Of Wight NHS TrustDelay 30-60 mins3
292017-12-30Isle Of Wight NHS TrustDelay 30-60 mins3
302017-12-31Isle Of Wight NHS TrustDelay 30-60 mins2
\n", "
" ], "text/plain": [ " Date Name Category value\n", "0 2017-12-01 Isle Of Wight NHS Trust Delay 30-60 mins 0\n", "1 2017-12-02 Isle Of Wight NHS Trust Delay 30-60 mins 2\n", "2 2017-12-03 Isle Of Wight NHS Trust Delay 30-60 mins 1\n", "3 2017-12-04 Isle Of Wight NHS Trust Delay 30-60 mins 3\n", "4 2017-12-05 Isle Of Wight NHS Trust Delay 30-60 mins 0\n", "5 2017-12-06 Isle Of Wight NHS Trust Delay 30-60 mins 0\n", "6 2017-12-07 Isle Of Wight NHS Trust Delay 30-60 mins 2\n", "7 2017-12-08 Isle Of Wight NHS Trust Delay 30-60 mins 5\n", "8 2017-12-09 Isle Of Wight NHS Trust Delay 30-60 mins 1\n", "9 2017-12-10 Isle Of Wight NHS Trust Delay 30-60 mins 2\n", "10 2017-12-11 Isle Of Wight NHS Trust Delay 30-60 mins 2\n", "11 2017-12-12 Isle Of Wight NHS Trust Delay 30-60 mins 2\n", "12 2017-12-13 Isle Of Wight NHS Trust Delay 30-60 mins 1\n", "13 2017-12-14 Isle Of Wight NHS Trust Delay 30-60 mins 2\n", "14 2017-12-15 Isle Of Wight NHS Trust Delay 30-60 mins 4\n", "15 2017-12-16 Isle Of Wight NHS Trust Delay 30-60 mins 0\n", "16 2017-12-17 Isle Of Wight NHS Trust Delay 30-60 mins 2\n", "17 2017-12-18 Isle Of Wight NHS Trust Delay 30-60 mins 5\n", "18 2017-12-19 Isle Of Wight NHS Trust Delay 30-60 mins 2\n", "19 2017-12-20 Isle Of Wight NHS Trust Delay 30-60 mins 3\n", "20 2017-12-21 Isle Of Wight NHS Trust Delay 30-60 mins 0\n", "21 2017-12-22 Isle Of Wight NHS Trust Delay 30-60 mins 0\n", "22 2017-12-23 Isle Of Wight NHS Trust Delay 30-60 mins 0\n", "23 2017-12-24 Isle Of Wight NHS Trust Delay 30-60 mins 2\n", "24 2017-12-25 Isle Of Wight NHS Trust Delay 30-60 mins 2\n", "25 2017-12-26 Isle Of Wight NHS Trust Delay 30-60 mins 2\n", "26 2017-12-27 Isle Of Wight NHS Trust Delay 30-60 mins 0\n", "27 2017-12-28 Isle Of Wight NHS Trust Delay 30-60 mins 6\n", "28 2017-12-29 Isle Of Wight NHS Trust Delay 30-60 mins 3\n", "29 2017-12-30 Isle Of Wight NHS Trust Delay 30-60 mins 3\n", "30 2017-12-31 Isle Of Wight NHS Trust Delay 30-60 mins 2" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q_month='''\n", "SELECT Date, Name, Category, value FROM sitrep WHERE Report='Ambulance Arrivals and Delays'\n", "AND date(Date) BETWEEN date('{fromdate}') AND date('{todate}')\n", "AND Code = '{code}' \n", "AND Category = 'Delay 30-60 mins'\n", "ORDER BY Category,Date\n", "'''.format(fromdate='2017-12-01', todate='2017-12-31',code=code)\n", "pd.read_sql_query(q_month, conn, parse_dates=['Date'])" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "image/png": 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isUqN4BgXTLgveEj+v1DAQAYTBQw9UHO1Gee/ykd5bhNsnC0wfkEgAkc7j8gv3fryFhz6MBVtKh3iV4bBL8L41ML2Nh2+fucSFLWtWPRM9KA1qZLeK8msx8EPUjFqpg/uujfohsu0KtuR9kMZ0k+XQaPWwTPEDkKRAMVpdQi72wOTlkp+F8GXTqtHWU4jilLrUJRWh9bmdvD4DF4h9vCPcoZ/hFPnrcIcx6GutAWFKbUoSq1FfbkKAODoaQ3/KCcERDpD7GCO81/nI/tcJcSO5pj8QAh85Y5DdnwUMJDBRAHDLTTXteLnbwuRd7Ea5tbGhwmF3e0x4u+FVyk0OPxhGupKlZh4fzDkk72MtzKm1+GetZHwDRu6L0DSMz98kYPscxW495louAVcG8uhbGhDyvESZP1YAZ3WgIAoZ4yK94Gbv+3141lkDoj/Y/fjWUayKxercPqLXGg1eghFfPjKHREQ5QwfuSNEPThuRa362kO8ChQAh87uhlEzvBFzj/+Qd9lRwEAGEwUMN9Cm0iLpaDHSO5IFRU3zxqh43x59yYwUWo0ex/+diaLUOjh5W6OutAWTlkoQHuc11FUjPdDeqsPuV3+BQMjHkhfGQFHXisvflyDvQjUAQDLWFaNm+sLB3eo362b9WIHT/82Fg7sl7nk88qbjWUay9lYd/t+LP0HsYI6xCcauw9u5s0Td3I7i9DrUlSghu9tz2Dx1kwIGMpgoYOiC4ziknSrDxcNF0LTqEDreHbHz/K9LR3wnMRg4nP8yH6knSxExxQt3L5EMdZVIL5TmNODAv1Jg52qJpmo1BGY8hE30ROR0726DANN4FoGIj7k3GM8y0l04WIiLh4uxeENMrwaHjjQUMJDBRAFDF2k/lOHs3ivwljlgwqKg300/flO1GrYuv31sMhn+fvxfHnJ/rkL4FC9ExHn1KpFR53iWFi1mPiaHf0TfsnAON+rmdnzx4k/wCXPArFXhQ12dAUUBAxlMFDB0qMhrxLfvpcBH7og5q8OH9S2ShJiY/v/2NdhTKTQ4sjUNtSXG8SwRU7z7s3pD4uy+K0g/XY4HXoqFvdtvu2TuJBQwkME0skfv9ZOWxjYc254BG2cLTF8ho2CBjBiMsdtqGbKyFWHBU6PhF+GEs3vzcHbvFRgMQ/8joq+a61qRcaYcoePd7vhggZDBNqIDhrYWLc7tz4NOq+/zNnRaPY5+nA6d1oDZq8PvqIGNhPSEUMTHrD+FI3K6N9J+KMPRj9LQVK0e6mr1yYVDRWCMYczc3qdHJ4Tc2oi+OpZk1yPlRCmqCpsx58/hPXo6W1ccxyFx9xXUXFVi9urwG44oJ+T3gMdjmLg4GLZOFvhxXx6K03+GR7AdZBM9EDjKGYIhvn2wJ+rLW5D7SxVGTfe5YwcqEzKURnTAIBnjBh6PhxOfZWH/W8mY+3hEr5ohMxLLkXO+EjFz/BAQ5TyANSVkZAiP80LAKGfk/lyFrB8rcOKzLJzZI0BIrCtCJ3rA2Xv43k3x87eFMDMXYPQs36GuCiF3pBEdMABAULQLrO1FOPJRGr58Kxlz/hwOj2D7bteryG/Cj/vy4BfuiFhqviSkk5WtCKPjfTFqpg8qrjQh61wFss5VIj2xHM4+YsgmeiB4jOuw6r6rzG9CcVodxi0IgLlVz+8UIYT03B1zl4SithWHP0yForYVU5eHImSs202XbWlsw75/JMHMnI/7NsRAZElfMITcSptKiysXqpH1YwXqy1sgEPIQFOOC8QuDYGnTu67A/sZxnPGx5jWtePDV8QOeUno4obskyGAa0YMeu7J1tsC9z0TDPcgWJz7LwoVDRbhRMKTXGnB0WwZ0Gj3mrI6gYIGQHjC3EiJiiheWbByDxRtiIBnnhrykGhz9OB16nWFI63Y1ox6V+QqMmev/uwoWCBlsd0zAABi/1Ob9XxSk49xw8VARTn6eDb322pcZx3FI3JOLmuJmTH9EBgcPGuRISG8wxuDqZ4Mpy6SY9nAoqgoVOLsvb8jqwxk4/PxNAWycLRA6wX3I6kHI78Hw6YTsJ3wBD1MfDoWNswUuHCyCsqENs1eHw9xKiMyzFcg+1zHIcRQNciTkdgTHuKKuVIlL35XApWNsw2C7crEa9eUqzFwZBv7vIPsmIUPpjvwfxhjDmHv8MX2FDFVFCnz5VjJyf6nC2b1X4Ct3pHu0CeknY+cHwlvmgMQ9uagqVAzqvvU6Ay4cLISTtzWCol0Gdd+E/B7dkQGDSchYN8x/YhRaW9px4rMsiB3MMeNRGXj0JEdC+gWPxzBzZRis7UQ4ti0dKoVm0PadebYCzXVtGL8gkJ7OSsgguKMDBgDwCLbD4mdjIIl1xZw1NMiRkP5mbiXE7NUR0LTq8N32jEEZBNnepkPSkSJ4SuzgLXMY8P0RQn4HAQMA2LlaYsajYfQkR0IGiJOXNaYuD0VlgQI/DsIgyLRTpWhVajFuYSBlWSVkkHQbMDDGvBljPzDGshhjmYyxJzrmOzDGjjPG8jr+2nfMZ4yxzYyxfMZYGmNs9EAfBCFk6AXHuGLUDB9knClH1rmKAdtPa0s7Ln1fgoAoZ7j52w7Yfggh1+vJXRI6AE9zHHeJMSYGkMwYOw7gEQAnOY57kzG2AcAGAM8BmA0guGMaC+Cjjr+EkDvcuIWBqCtTInF3Lhw8rHp0QTfoDSjJbEDOT5VQNrR1u3ybSgudRo+x8wP6o8qEkB7qNmDgOK4SQGXHayVjLBuAJ4D5AOI6FvsPgNMwBgzzAezkjE9N+pkxZscYc+/YDiHkDmYcBCnH/968iGPbMnDfX2NgZSu64bLNda3IPl+J7POVUDVpYCEWwtlHDODWXQzm1mYYNdOXuhgJGWS9eg4DY8wPwCgAvwBw7RIEVAFw7XjtCaC0y2plHfMoYCDkd8DcWojZq8Px5T+T8d2ODMz/yyjwBcbeT73WgMLUWmT9WIGynEYwBviEOWLSEgl8IxzpWQqEDGM9DhgYY9YAvgTwF47jmrsONOI4jmOM9SopBWNsFYBVAODj49ObVQkhw5yTlxhTl4fi+08zce5/eQib7InsHyuR+0sV2lRaiB3METvPH9Lx7hA7UCpqQkaCHgUMjDEhjMHCLo7jvuqYXW3qamCMuQOo6ZhfDsC7y+peHfOuw3HcdgDbAWPyqT7WnxAyTAWPcUVNiRIpx0uQnlgOHp8hIMoZsgke8JLa07MTCBlhug0YmLEp4VMA2RzHvdvlrQMAHgbwZsffb7vMX8sY2wPjYEcFjV8g5Pdp/IIAMAZYiM0gHecGC/HQZrYkhPRdT1oYJgB4CEA6YyylY97zMAYK+xhjKwFcBXB/x3tHAMwBkA9ADWBFv9aYEDJi8Pg83HVv0FBXgxDSD3pyl8SPuPmw5Wk3WJ4D8Pht1osQQgghwwgNSSaEEEJItyhgIIQQQki3KGAghBBCSLcoYCCEEEJItyhgIIQQQki3KGAghBBCSLcoYCCEEEJItyhgIIQQQki3KGAghBBCSLcoYCCEEEJItyhgIIQQQki3KGAghBBCSLcoYCCEEEJItyhgIIQQQki3KGAghBBCSLcoYCCEEEJItwRDXQFCCCH9Jzk52UUgEHwCQA76UUh6xwAgQ6fTPRYdHV3z6zcpYCCEkDuIQCD4xM3NLdTZ2bmRx+NxQ10fMnIYDAZWW1srq6qq+gRAwq/fp+iTEELuLHJnZ+dmChZIb/F4PM7Z2VkBY+vUb98f5PoQQggZWDwKFkhfdXx2bhgbUMBACCGkX5WUlAjmzp0b4O3tLQ8LCwudPHlyUFpamqi/9xMbGxvi5+cnl0qlsoCAgLBNmzY59Wb9Q4cOiadMmRLU0+ULCgqE06ZNC/T19ZV7e3vLV6xY4d3W1sZ6X/ORiQIGQggh/cZgMCAhISFo0qRJytLS0ozMzMzsN998s7yiokLYk/W1Wm2v9rdz587CnJycrJ9++innlVde8RqoC7jBYMCCBQuCEhISmq5evZpRVFSUoVKpeE888YTnQOxvOKKAgRBCSL85dOiQWCAQcM8++2ytad748eNbZ82a1WIwGPCnP/3JKzg4OEwikch27Nhhb1onOjo6ZOrUqUHBwcFyANi6datDeHh4qFQqlf3hD3/w1el0t9xvc3Mz38LCwiAQCDgA+Oqrr2yioqKkMpksdPbs2QEKhYIHAPv377fx9/cPk8lkofv377czrX/48GFrqVQqk0qlstDQUFljY+N118eDBw+KRSKR4YknnqgHAIFAgI8//rh07969Tkqlkrd582bHadOmBcbGxob4+vrKn376aXfTujc7FktLy1H/93//5xkSEiKLjIyUlpaWDusbEShgIIQQ0m/S0tIsIiMj1Td6b+fOnXbp6ekW2dnZmSdPnrzy0ksveV29elUIAFlZWZZbt24tKS4uzrh06ZL5/v37HZKSknJycnKyeDwe9/HHHzveaJvLly8PkEgksvDwcPn69esrBAIBKisrBW+88Yb7mTNnrmRlZWWPHj1a/eqrr7qq1Wq2du1avwMHDuRnZGRk19TUdLZ6vPPOO26bN2++mpOTk/Xzzz/nWFtbG7ruJz09/TfH5eDgYHB3d2/PysoSdRy71YEDB/IzMzMzDxw44HDmzBnLWx1La2srb/z48S25ublZ48ePb/nggw+cb+/sD6xhHc0QQgjpu2f2p3pfqVJa9uc2JW5i9duLI0v7su7Zs2fF999/f4NAIIC3t7du7NixLT/++KOlra2tISIiQiWVStsB4NixY+KMjAzLyMjIUABoa2vjubi43LCJYefOnYWTJk1SV1RUCMaPHy+dP39+c3JyskVBQYF5bGysFAC0Wi2Ljo5uSUlJMffy8tKEh4drAGDZsmX1n3zyiTMAjBs3rmX9+vXe999/f8MDDzzQGBgYaLjR/m5l4sSJzW5ubnoAuOeeexpPnz5tLRAIuJsdi1Ao5JYuXaoAgOjoaNWJEydservPwUQBAyGEkH4THh7e+s0339j3dj1LS8vOCzTHcey+++6r//DDD8t7ur6Hh4dOLperz5w5Y2VpaWmYOHFi88GDB4u6LnP+/HmLm63/xhtvVC1YsEDx7bff2t59993Sw4cP540aNarN9L5cLv/NcTU0NPAqKyvNZDKZ5pdffrFk7PrhE4yxWx6LQCDgeDye6TV0Ot2wHkBJAQMhhNyh+toScDvmzZunfPHFF9mmTZuc1q9fXwcAv/zyi0VjYyN/0qRJyh07djivXbu2vqamRnDhwgXrzZs3l6alpV13IZ81a1bzvffeG/T8889Xe3p66qqrq/kKhYIvkUjab7ZfpVLJy8zMtNywYUNVYGBg+9NPP+2TkZEhksvlmubmZl5xcbEwKiqqrby83CwzM1MUFham2bNnj4Np/czMTFFsbGxrbGxsa3JysmVGRoZ514AhISFBuXHjRt6WLVsc165dW6/T6bBmzRrv++67r04sFhsA4Mcff7Sprq7mW1lZGY4cOWL3ySefFFtZWRl6eyzDFQUMhBBC+g2Px8OBAwcK1qxZ4/3++++7iUQizsvLS/PBBx+Uzpw5s+X8+fPWoaGhYYwx7uWXXy7z8fHRpaWlXbeN6Ojoto0bN5ZPmzZNYjAYIBQKuc2bN5fc6CK7fPnyAHNzc0N7eztbunRp3d13360GgG3bthUvXbo0oL29nQHA3/72t/KIiAjNBx98cHXu3LlBFhYWhrFjx7a0tLTwAeCtt95yOX/+vA1jjAsJCWldvHix4tfH9c033+SvWrXK9+2333Y3GAyYOnWqYvPmzZ0tBxEREaqEhITAqqoqs8WLF9dPmjRJDQA9PZbhjnHc0D/fIyYmhktKShrqahBCyIjCGEvmOC6m67zU1NTiyMjIuqGq0+/V5s2bHZOSkqx27txZMtR1uV2pqalOkZGRfr+eT3dJEEIIIaRb1CVBCCGE3KZ169bVA6gf6noMpG5bGBhj/2aM1TDGMrrM+ztjrJwxltIxzeny3l8ZY/mMsVzGWPxAVZwQQgghg6cnXRKfA5h1g/nvcRwX1TEdAQDGmAzAUgBhHetsZYzx+6uyhBBCCBka3QYMHMedAdDQw+3NB7CH4zgNx3FFAPIBxN5G/QghhBAyDNzOoMe1jLG0ji4L08MsPAF0ve+3rGMeIYQpJ0ooAAAgAElEQVQQQkawvgYMHwEIBBAFoBLAO73dAGNsFWMsiTGWVFtb2/0KhBBCRgQ+nx9tSuQklUplzz//vFt/bdvT0zO8srLylgP2N2zYcN3+Ro0aJe2PfS9atMjP09MzXCqVyvz9/cO6JpjqidzcXLPg4OCwni5fX1/PX7hwoZ+Pj4/c29tbvnDhQr/6+voh6+bvU8DAcVw1x3F6juMMAHbgWrdDOQDvLot6dcy70Ta2cxwXw3FcjLPzsM63QQghpBdEIpEhJycnyzS98cYbVYO5/82bN193Ib98+XJOf237tddeK8vJycnKzMzM2rt3r1NOTo5Zf23715YtW+br7+/fXlJSklFaWprh5+fX/uCDD/oO1P6606eAgTHW9R9jIQDTHRQHACxljIkYY/4AggFcuL0qEkIIuROsWbPGMzAwMEwikchWrVrlBQClpaWCGTNmBIaEhMhCQkJkx48ftwKA6dOnB4aFhYUGBQWFbdq0yelG27vRMmvWrPHUaDQ8qVQqS0hI8AeMaaQB4FbptWNjY0NmzZoV4O/vH5aQkOBvMNw695RareYBgOmx0GfPnrUcM2ZMSFhYWOjEiRODTVk4z549a2k6tnfffdfFtH5SUpK5KeW1RCKRpaeni7puPyMjQ5Senm711ltvVZjmvf322xVpaWlWmZmZokOHDoljYmJC4uLigvz8/OR/+MMffPR6PYCbp/b29PQMf/LJJz1kMlmoRCKRXb582byn/3ZAz26r3A3gJwAhjLEyxthKAG8xxtIZY2kApgB4EgA4jssEsA9AFoBjAB7nOE7fmwoRQggZ2UwXbNO0Y8cO+6qqKv6RI0fs8/LyMq9cuZL1xhtvVALA6tWrfe6++25lbm5uVmZmZtbo0aPbAGDXrl3FmZmZ2SkpKVnbtm1zraqq+k1T/I2W2bp1a7mphePAgQPXJZ+6VXrt7Oxsiw8//LA0Pz8/s6SkRHT8+HHrGx3bxo0bvaRSqczHxydiwYIFDZ6enjqNRsPWrVvn8+233xZkZmZmP/zww3Xr16/3BICVK1f6/etf/yrJzc3N6rqdDz74wHnNmjXVOTk5WWlpadn+/v7XPSo6NTXVXCaTqQWCa70vAoEAMplMnZKSYg4A6enpVlu3bi3Jz8/PKC4uFu3cudP+Zqm9TdtwcnLSZWVlZT/66KO1b775pit6odsHN3Ec98ANZn96i+VfB/B6bypBCCFkAHzzuDdqsvo1vTVcZGos+PCWSa1MF+yu87RaLUQikWHJkiV+c+fObVqyZIkCAM6fPy/ev39/EWC8IDo6OuoB4J///Kfr4cOH7QCgqqpKmJmZae7m5qbqus2eLNPVrdJrh4eHqwIDA7UAEBYWpi4oKLhhV8Nrr71WtmLFikaFQsG7++67JcePH7eys7PT5+XlWUydOlUCGFsynJ2dtXV1dXylUsmfPXt2CwA8+uij9adOnbIFgPHjx6s2bdrkXlZWZrZ06dJGU8rt3ggPD1fJZLJ2ALj//vsbzp49a21ubm64UWpv0zp/+MMfGgEgNjZWfeDAgV5lFaVHQxNCCBlwQqEQKSkp2YsXL248dOiQXVxcXPDNlj106JA4MTFRnJSUlJObm5sVGhra2trayuvtMr0hEok6Eyvx+fxuU03b2toaJkyYoExMTLTmOI4FBQW1msZsXLlyJevcuXN5t1p/9erVDd9++22+hYWFYe7cucEHDhwQd30/MjKyLSsry9LUzQAAer0eWVlZlpGRkW2AMX12Vx3ptDFx4sRmU10KCgoy9+3bd9W0jLm5OQcYU2v3Np02PRqaEELuVN20BAwmhULBa2lp4S1ZskQxffr0lsDAwHAAmDBhgvLtt992fumll2p0Oh0UCgW/qamJb2trqxeLxYbLly+bp6amWv16e7daRiAQcBqNhnUNAgCgp+m1e0Kr1SI5Odl6zZo11REREW0NDQ2CEydOWE2fPl2l0WhYenq6KCYmpk0sFuu/++476/j4+JbPP/+8M512VlaWWWhoqCYsLKympKTELCUlxSIhIUFpel8ul2vCwsLUzz33nPumTZsqAeC5555zl8vlarlcrikuLjZLT0+3ysnJMQsODm7fv3+/w2OPPVYbFxenulFq74iIiF63YPwatTAQQgjpV78ew7BmzRrPpqYm/qxZs4IlEols/PjxIa+++mopAHz00UcliYmJYolEIpPL5bLLly+bL1q0SKHT6VhAQEDYM8884xkZGfmbboZbLbNs2bLa0NDQzkGPJg899FBTWFhYa2hoaFhcXJzElF67N8dmGsMglUrDQkND1cuXL28yNzfn9uzZU7BhwwavkJAQWVhYmCwxMdEaAD799NPidevW+UilUhnHcZ2/6L/44gsHiUQSJpVKZdnZ2RZ/+tOffpOHYteuXcV5eXnm3t7ecm9vb3leXp75rl27ik3vy+Vy1erVq30CAwPlPj4+moceeqjJw8NDZ0rtLZFIZDExMdL09PReDW68GUpvTQghIxSlt/79OnTokPidd95x/eGHH/L7e9uU3poQQgghfUYBAyGEEDLCzJ07VzkQrQu3QgEDIYQQQrpFAQMhhBBCukUBAyGEEEK6RQEDIYQQQrpFAQMhhJB+VVBQIJw2bVqgr6+v3NvbW75ixQrvtra2Xj1V8E5y6NAhsSmp1khGAQMhhJB+YzAYsGDBgqCEhISmq1evZhQVFWWoVCreE0884TnUdRtIWq32pu+dOnVKfPbs2RsmsxpJKGAghBDSbw4ePCgWiUSGJ554oh4wJpT6+OOPS/fu3eukVCp5Op0Oq1at6kwx/frrr7sAQGJiouWoUaOkISEhsvDw8NDGxkbe5s2bHZcvX+5j2vaUKVOCDh06JAaMKatXrlzpHRQUFDZ+/HhJRUWFAADeeecdJ7lcHhoSEiKLj48PVCqVPABYtGiR3yOPPOI9atQoqZeXV/hnn33WmXjphRdecJNIJLKQkBDZmjVrPDMzM0UymSzU9H56evp1ZZPY2NiQRx991Fsul4e+9tprrv/9739tIyIipKGhobK77rpLUlpaKsjNzTXbuXOn88cff+wqlUplx44ds66oqBDEx8cHyuXyULlcHvr999+PiNYHyiVBCCGk36Snp1tERkaqu85zcHAwuLu7t2dlZYlOnz5tXVJSYpaVlZUpFApRXV3Nb2trY8uWLQvctWtXweTJk9UNDQ08a2trw63209rayouJiVF9+umnpevXr3ffsGGDx86dO0uWLVvW+PTTT9cBwLp16zw2b97s9MILL9QAQHV1tTApKSknJSXFfOHChUErVqxo3Ldvn82RI0fskpOTc8RisaG6uprv6uqqF4vF+vPnz1vcddddrdu2bXNatmzZbx7dDADt7e0sIyMjGwBqa2v5S5cuzeHxeHj33XedXnnlFbcdO3aULV++vNba2lr/yiuvVAPAvHnz/J966qnq+Pj4lry8PLP4+PjgwsLCzP44/wOJAgZCCLlDvXjuRe/8xvx+TW8dZB+kfnXCq31OanXq1Cmb1atX1wqFQgCAq6ur/sKFCxYuLi7ayZMnqwFjgNHddng8Hh577LEGwJg2+t577w0CgOTkZIuXXnrJU6lU8lUqFX/y5MkK0zoJCQlNfD4f0dHRbfX19UIAOH78uM2DDz5YJxaLDab6AMAjjzxSt2PHDqfY2NjSb7/91v7ixYvZN6rHAw880GB6XVRUZLZgwQKv2tpaYXt7O8/b2/uGCZ/OnTtnk5eX15nwqqWlha9QKHi2trbdHvdQoi4JQggh/UYul7empqZeF6Q0NDTwKisrzWQyWa8yJgoEAs5guHYN1Wg0N71mmVI9r1q1yn/Lli0lV65cyXruuecquq5jSu0MAN3lUXr44Ycbf/jhB9s9e/bYhYeHq93c3PQ3Ws4UaADA2rVrfdasWVNz5cqVrC1btly9WX05jsOlS5eyTSmoa2pq0oZ7sABQCwMhhNyxbqcloK8SEhKUGzdu5G3ZssVx7dq19TqdDmvWrPG+77776sRisWHatGnN27Ztc5o7d26zqUsiIiKiraamRpiYmGg5efJkdWNjI8/a2toQGBjYvmPHDku9Xo+ioiJhWlpaZ1+/wWDAZ599Zr9q1arGzz//3DE2NlYJAGq1mufj46PVaDRsz549Du7u7jcfjQggPj6++fXXX/dYtWpVQ9cuCUtLS27y5MmKp556ymfLli3FPTl2pVLJ9/Hx0QLA559/7miaLxaL9c3NzXxTeeLEic3/+Mc/XF599dVqADB1ffTqRA8BamEghBDSb3g8Hr755pv8r776yt7X11fu7+8vF4lEhs2bN5cDwJNPPlnr5eXVLpVKw0JCQmSffvqpg7m5Obdr166CdevW+YSEhMji4uIkarWaN2PGjBZvb29NUFBQ2J///GcfmUzWOTbCwsLCcOHCBavg4OCwM2fOiP/xj39UAsCGDRsqYmNjQ2NiYqTBwcFt3dV38eLFzbNnz26KiooKlUqlsldffdXN9N7y5csbGGO49957m3ty7C+88ELFAw88EBgWFhbq6OjYmTZ70aJFTYcPH7YzDXrcvn176aVLl6wkEoksMDAwbMuWLc69OcdDhdJbE0LICPV7Tm9taWk5Sq1WXx7Ifbz00kuuCoWC//7771cM5H6Gm5ult6YuCUIIIeRXZsyYEXj16lVRYmLilaGuy3BBAQMhhJARZ6BbF44fP14wkNsfiWgMAyGEEEK6RQEDIYQQQrpFAQMhhBBCukUBAyGEEEK6RQEDIYSQflNVVcWXSqUyqVQqc3JyinRxcYkwlW+U4rq6upr/1ltvdfscAq1WC7FYHHWj+Xw+P9q0j7CwsNCTJ0/2KpnTunXrPF555RWXni5/9OhRa7lcHurv7x/m7+8f9t577zn1Zn8jFd0lQQghpN+4ubnpc3JysgDgqaee8uiadOlGamtrBf/+97+dn3322dq+7tPS0rJzn3v27LHduHGj57Rp0wbkdsji4mLho48+6v/ll1/m33XXXa0VFRWCadOmSby9vdsXL17cowc8jVTUwkAIIWRQbNy40TU4ODgsODg4zJTWev369Z7FxcXmUqlUtmbNGs+GhgbeuHHjJDKZLFQikch2795t25t9KBQKvq2tbedTFp9//nm38PDwUIlEIlu/fr27af769evd/fz85NHR0SEFBQXmpvkvv/yyS2BgYJhEIpHNnz/f/9fbf/vtt12WLVtWZ3qUs4eHh+61114re/vtt90AYP78+f7Lli3zCQsLC/Xz85Pv27fPBjC2hDz22GNeprq8++67TgDwzTffiMePHy+ZOXNmoJ+fn3zhwoV+vTqpg4haGAghhAy4U6dOWf3vf/9zvHz5cpZWq2XR0dGhM2bMUG7atKl88eLF5qYWAo1Gw44cOZLv4OBgKC8vF9x1113SBx54QHGrbavVar5UKpVpNBpWV1cnPHbsWC4A7N2717akpMQsNTU1m+M4xMXFBR8/ftxKIBBwBw8etM/IyMjUaDS8iIgI2dixY1sAYMuWLW6lpaXp5ubmXF1dHf/X+8rOzrZYtWrVda0hEyZMUHXNPllRUWGWlpaWnZGRIYqPjw+ZN29e+vvvv+/k4uKiS09Pz25tbWWjR48OnTdvXjMAZGZmWqalpWV6eXlpo6KiQk+ePGk1bdo01e2f9f5FAQMhhNyhKp5/wVuTl9ev6a1FwcFqjzde73VSq8TEROt58+Y1WltbcwC42bNnN506dcp67ty51zXjcxyHdevWeV24cMGax+OhqqrKrLKyUuDk5KS7yaav65L47rvvrFesWOF/5cqVrO+++87mhx9+sJXJZDLAmJgqOzvbvK6uTmCqi7W1tX7GjBlNpm0FBwe33Xvvvf7z5s1rWrZsWdPN9nkrixYtauTz+YiMjNS4u7u3Z2RkiE6cOGGTn59v8dVXXzkAxkRVWVlZIgCIiopS+fn5aQFALperCwoKzIZjwEBdEoQQQoaNrVu3OjY3N/MzMzOzcnJysuzs7HRqtfo3gyVvJj4+vqWmpsasurqaz3Ec1q9fX2lKI11SUpKxbt26+lutf+bMmSurV6+uvXjxotXo0aNDdbrr4xSpVNqalJR0XRB2/vx5q+Dg4M5sk4yx65I0McbAcRzef//9q6a6lJeXp8+fP18JAGZmZp2prXk8HqfT6Xp8vIOp2xYGxti/AcwFUMNxnLxjngOAvQD8ABQDuJ/juEZmTEj+PoA5ANQAHuE47tLAVJ0QQsit9KUlYKDExcUp16xZ4/f3v/+9Sq/Xs2PHjtnt3r270NbWVq9SqTp/vCoUCr6zs7NOKBTi66+/tqmpqRH2Zj9JSUnmjDHO2dlZP2vWrOY333zTfeXKlQ02NjaGgoICoaWlJTdlyhTlmjVr/F555ZWqtrY2duLECbtVq1ZV63Q6FBYWmiUkJChnzpzZ4unpGaFUKnn29vadF/Snn366ZuLEidL77ruvaezYsa2VlZWCF1980fNvf/tbuWmZL7/80uHPf/5zQ0ZGhqiystJMLpdrZsyY0bx161aXOXPmKIVCIVJTU0WBgYHt/XN2B0dPuiQ+B7AFwM4u8zYAOMlx3JuMsQ0d5ecAzAYQ3DGNBfBRx19CCCG/Y1OmTFEvWrSoftSoUTIAePTRR2tjY2NbASA8PFwtkUhk06dPV7zwwgvVs2fPDpJIJLKoqCiVr6+vprttm8YwmMrbtm0r4vF4WLJkiSI7O9s8JiZGCgBWVlaGPXv2FE6ZMkV9zz33NMpksjBHR0dtVFSUCgC0Wi1bunRpQEtLC4/jOLZmzZqqrsECAAQGBmq3b99evHLlSj9ToPP4449X33///Z1dK+7u7u3h4eGhKpWKv3nz5mJzc3Nu/fr1tSUlJWYymSwMABwdHbVHjx7Nv/0zO3h6lN6aMeYH4FCXFoZcAHEcx1UyxtwBnOY4LoQxtq3j9e5fL3er7VN6azLScRwHYwMbIYPn95zeeriaP3++/+LFixsfeuihPo1/GA5ult66r2MYXLsEAVUAXDteewLo2gRW1jHv1nJzgc8/N77WaoG4OOCLL4xltdpY3rvXWFYojOWvvjKW6+qM5YMHO2pTZSwfO2Ysl5YayydOGMuFhcZyYuK1fcfFAefPG8sZGcbyxYvGckqKsZySYixfvGgsZ2QYy+fPG8u5ucZyYqKxXFhoLJ84YSyXdpyWY8eM5aoqY/ngQWO5ruP/91dfGcuKjkHBe/cay2q1sfzFF8ayVmssf/65sWyyYwcwffq18tatwOzZ18rvvw8kJFwrb9oELFp0rfzmm8DSpdfKr74KPPjgtfJLLwErVlwr//Wv0P9xJa40XsGx4mNIWT4d5xeOwtqTa7H25Fr8sCgSPyyK7CyfmR+Ok/dfez9t4Tgon157bXsrVhj3YfLgg8Y6mCxdaqyjyaJFxmMwSUgwHqPJ7NnGc2AyfbrxHJnExfXps1errkXi5S9RGh2Id/8+BWN2jcHcj2KQGe6Mj/+5AG9ffBtfn9qMpvFRKP/6P1Br1T3+7Bku/II2XRsMly/RZ6+bzx5WrbpWXr8eePzxa+W//MU4mTz+uHEZk1WrjNswGSGfPQDXvvcIGUS3fZcEx3Hcrwd49ARjbBWAVQAQIRLdbjVGPI1eA07XBvPuFx0yKq0KSlU1NM1X8WXSuyhUFGJS7lHwGprw8oELAID1tVVw4CxQo64BAKi1xnFApnKrrhXtPD5q1DXgwCGvMR8XcwuRdVqE5WHLETk0h3ZLOoMOOXUZyCr8CdFNhdj10yv4n1IPO6UO76lrYOC8cJ/kPpjVNkDI+3+oUlXjcO4+2NY0w6uxDNt/+jt+bt6EMIUFXqovxoFf3kR6y3Y4XK3Fqrp0fPrDX3C51AJexU14sroQmw7/AZnZlri7Vow3NGrYDfUJIP2qVdeKzLpMeKoqcTJrF6zybLHQb+5QV4v0k2+//bZoqOswUKhLYoCptWqUt5SjrrXuuqm2tRb1rfWoba1FXWsdlO1KMDD42vgi1CEUUkcppA5ShDqEwt7cfkiPobylHJ9lfIav875Gu8E4RkfIE8LXxhcBtgHwt/VHgG0AAuwC4GvjCwuBRTdbvKZKVYXdObvxvyv/g7JdiUjnSCyXLcdUn6kQ8Hoezyo0CqTXpaNcWQ57c3s4WTh1TpbC7u8q0xl0qFHXoLylvHOqaKlASXMJshuyodEbu1FdLF0Q6RzZOckcZTDjm/1mexzHob6tvnM75S3lKFOWoaKlAlqDFiK+qHMy45td91fEF0HIF+JQwSEUKAoQ5xWH52Kfg5fYq8fngwwPHMehvKUcqbWpndOVhivQccaR9742vngo9CEskS7p0/apS4IMhJt1SfQ1YHgbQH2XQY8OHMc9yxi7B8BaGO+SGAtgM8dxsd1t/04JGDiOQ4myBGm1aUitTUVabRquNF6BntNft5yFwOK6C5pp0hl0yGnIQU5DDipV12IsV0tXSB2uBRBeYi+odWoo25Vobm9Gs6YZynalcdIqO8s6ToeJnhMx2382PK277xn6tcKmQnya8SkOFx4GYwwJgQmI84pDgF0APK09e3VB745aq8Y3+d/gi+wvUKoshae1J5aFLsPCoIWwNrO+blkDZ0BhU+F1X8KFisKbbttSYPmbcy02E18XIFSrqju/xAGAgcHF0gWe1p6QOcoQ6RKJKOcouFm59dsxd0er1+KL7C/wUepHMHAG/DH8j1ghX3HDAIUMDzqDDtn12UiqTkJKTQpSa1NR32a8i89CYIFwp/DOYDPCOeK2fwxQwEAGQp8DBsbYbgBxAJwAVAP4G4BvAOwD4APgKoy3VTZ03Fa5BcAsGG+rXMFxXLeRwEgNGFraW5BRn4HUmlSk1aUhrTYNTRrjOBcroRXCncIR4RyBYLvgzguVs6UzLAWW3Q6Qa2prQk5jDnLqc5DdkI2chhwUNxfDwBluuo6FwAJioRhiMzFsRDbQ6DXIqs8CAEQ5R2FOwBzM9J0JRwvHW+47sz4Tn6R9gpMlJyHii7BYshgPhz08KBdLvUGP02WnsTNzJy7VXIK10BqLghdhjNsYZNZnIrU2Fem16VBqlQAAW5EtIpwijF/CLpHwt/FHk6bputabG00t2hY4WTjB09oTHtYe8LL2goe1BzytPeFp7Ql3K3cI+b26m2vAVKmq8NbFt3D86nH4iH3w/NjnMcFzwlBX647AcRyaNE1QaBRwsXTpUWtUVwbOgNyGXFyouoCLVReRXJ2MFm0LAGPrQaRzpPHz6RKJILugfg2yAQoYyMC4rRaGgTYSAobm9mbkNuQiu9548c5uyEZBUwE4GM9foG0gIpwjOn85BNgGgM/7zVNFb0urrhV5jXmoUFVALBTDxswGYrOOAMHM5oYXuDJlGY4VH8PhwsPIb8oHn/Exzn0c5gTMwVTvqZ2/3jmOQ3J1Mnak78D5ivMQC8V4IPQBLAtdBgdzh349jp7KqMvAzqyd+L74e+g5PXiMhyC7oOu6BHxtfPt0d4LeoO/3f5+Bdr78PN648AauNl/FDN8ZeHbMszcN4gycAaXKUmQ3ZBs/tw3ZaGprwmSvyZjtPxt+tn6DW/kh1NzejHKlsWuorKWss4vINLXqOp+3Awdzh84g0hQ8msoe1h4Q8oTIb8rHxaqLuFB5AUnVSWhuN95N52vji1i3WMS6xSLGLQZOFgOfwJACBjIQKGDohbrWOmNQUJ+N7IZsZNdno6ylrPN9FwsXSB2lkDvKEekcCbmzHDZmNkNY45650ngFR4uO4mjRUZS3lEPEF2GS1ySMdRuLw0WHcbnmMhzMHfCQ7CEsDVn6m66AoVKlqkKpshQyRxmshL3KWnvHade34/PMz7E9bTt4jIfVkauxNGQpSpQlncFsTkMOchtzodIanywrYAIE2gXCQmCB1NpUcOAgc5Rhjv8czPKbBVcr1272OvJoDVp8cOkD7M/bD2W78rr3rIRW1wUDntaesBXZolpd3TnOpLylHBWqCugM1z/lz0Jg0RlgeFp7ItYtFmPcxiDWLXZIzuNwDBiqqqr4cXFxIQBQV1cn5PF4nIODgw4AUlJSss3Nza+76FRXV/P/85//OHSXrVKr1cLBwSFKqVSmdJ2fkZEhWrx4caDp0dC9dbvr94Wnp2d4eXl5+q/nr1u3zsPJyUn30ksv1QxWXW7kZgED5ZLooNVr8XX+1/gs47PrggNvsTdCHUOxSLKocxzBYPxyGAgSewkk9hKsG7UOqbWpOFp0FMeKj+H41eNws3LDX2P/ioXBC3s1aHEwuFm5DerYgeHMjG+GVRGrcE/APXjzwpt4L/k9vJf8Xuf7FgILhNiHYF7APIQ6hkLqIEWQXVDnuIcqVRW+K/4OR4qOYFPSJryT9A5i3GIwx38OZvjOgK2oV4kBh6UqVRWeSXwGKbUpmOU3C3In+XUtBjZmNj1qldIb9Khtre0cuFrWUoaG1gbIHGWIdY/t07ig34OhSG9NBseIDhgUGgUuVF3AJK9JEPH7dmum1qDFoYJD2Ja2DeUt5YhwjsAD0gc6v2zFZuJ+rvXQY4whyiUKUS5ReGbMMyhoKkCAbcCw6bMn3fO09sQHUz/AmbIzSKlJgcReghCHEPiIfW7Z1eJm5YaHwx7Gw2EPo1hRjKNFR3Gk6Ahe/ullvP7L65joMRHx/vGQOcrgLfaGkDeyPhPnK85jw5kN0Og1eHvS25jlP6vP2+Lz+J3BarRrdD/W8vdr48aNrnv37nUCgEceeaT2hRdeqOma3nrq1KmK1157rXLOnDlBzc3NfJ1Ox15++eXy7rJV6nQ6Nnfu3ICsrCwLqVTaum/fviJra2suMTHRcv369d5qtZrn6Oio27VrV5G3t7cuMTHR8o9//KMfj8fDpEmTOp/QeOHCBYtHH33UT6fTMYPBgG+++SZfJpNd9/jmrVu3OvzrX/9y4ziOxcfHN23ZsqUcAPbs2WP7yiuveOj1eubk5KQ9d+5cXmNjI2LzUtcAABVjSURBVG/lypU+GRkZlgDw4osvVjz00ENN9vb2nU1X69evd9+/f7+jo6Oj1s3NTevk5NTSn+e8P43ogOFUySm8dP4lWAutMdVnKu7xvwex7rE9GlikN+hxuOgwPk79GKXKUoQ5hmHjuI2Y4DHhd/XEPgFPgBCHkKGuBumjSV6TMMlrUp/W9bP1w5+j/ozVkauR3ZCNI4VHcLT4KE6XnQZg7MrwtvE23jLb5fZZf1v/Xg8OHGh6gx7b0rbh49SPEWgXiHfj3oW/rf9QV4t0MZDprQsKCsy3bdtWPG3aNNXChQv93n33Xednnnmm9i9/+YvPkSNH8t3d3XUfffSRw7PPPuu5e/fuqytXrvTfunVr8cyZM1UrV670Nm3n/fffd37iiSeq/vjHPza2trb+psu+oKBA+Prrr3smJSVlOzg46CdOnCjZvXu37YQJE1RPPvmkT2JiYq5EImmvrq7mA8Czzz7r4eTkpLty5UqWwWBAfX09HwAyMjKyAeCHH36wvFma7eFoRAcM8wLnwc3KDUeLjuLE1RM4UHAADuYOiPeLxxz/OYh0jvzNxV9v0OO74u/wUepHKG4uhtRBig+mfoDJXpN/V4ECISaMMcgcZZA5yvBUzFPIbshGYVMhChWFKGwqREFTAU6Xnr7u9mA3KzcE2gZiQdACzPSbCR4busS3DW0N2HBmA36q/AkJgQnYOG7jsOtWGyond2Z7N5S39Gt05+BprZ62PHRYpbf29PRsN6WDfuihhxq2b9/udPnyZWV+fr75lClTJABgMBjw/9u79yiryjPP49+nqriD2CAWhMhNIKi0Eq1OhBjRaIi4TEzS0caxRxczGe0MZJQVO0m3s3JZPZckOpqkk7FFO5EkxkwmYrRHE29Di7HbNKCoeItGUEEuFWkRxFKoeuaPc8CSAXcJVbVPUd/PWmfVOXu/Z+/n7FV1zq/ed5/9jhw5csf69esbWlpa6mbNmvUawNy5c19+4IEHhgDMmDFj2xVXXDHq+eef7ztnzpxXpk6d+rZ5LO6///5BM2bM2Dpq1KidAOeee+7L991335AdO3bE9OnTt06ePPlNgMbGxlaApUuXHnLrrbc+C1BXV8eIESPe9h37JUuWDNnXNNu1qEcHhoa6Bqa/ZzrT3zOdy0+8nN+s/Q13rL6Dxc8s5qanbmL04NHMHj+b2eNnM/HQidzz/D1c88g1PPvKs0w8dCJXn3I1HxnzkVLf7KRaUhd1HDP8GI4Zfszblu9o3cGLW1/kuS3PsXrLap7b8hyPNj/KXy79S6599FrmTZtXyt/Sw5se5rL7LuOVllf42vSv8elJnzb493Dtp7fu06cPjY2NxxZNb72v6aQnT578+ooVK55uv279+vX7/NybN2/e5pkzZ752yy23DD3zzDMnXXvttWtmz55ds//xd7ceHRja61ffj9PGnsZpY09j25vbWPLiEm5ffTs/XPVDrn/seob2G8qWN7YwYegErph5BbPGlvtfkdST9Knvw4RDK1fz3KV9b92Cf1zAlGFTmDdtXrf01mUmP3riR3x7xbcZNXgUPznzJxw1/Kgu3WdPtD89AV2lK6e3XrduXb/77rtv4MyZM7ffeOONw2bMmLHt+OOPb9m4cWPfJUuWDDz11FO3t7S0xKpVq/o1NTW19O/fv+2ee+4ZdPrpp7+2aNGi3d8bf+KJJ/pOnTr1jalTp25avXp1v4cffnhA+8Dw4Q9/+LXLL7/8iA0bNtQPHz689Re/+MWwBQsWbPzQhz702pe+9KUjfve73/XdNSTR2NjYOnPmzFevuuqqwxcuXLh215BE+16GfU2z3ZnHvTMdNIGhvcF9B/PxIz/Ox4/8OJtbNnPXmrtYtmEZp445ldnjZve4799Ltai+rr5yMbBxs/jV6l9xzSPX8Pn/+3mOGX4M86bN46TRJ3U4OGQmm1s2777o0TtpbWvluw9/l3tfuJfTxpzG33zobw7Kk5MPNl05vfWECRNarrzyysa5c+cOnDJlyusLFixoHjBgQP7sZz/7/SWXXHLE1q1b69va2mL+/PkbmpqaWq677rrVF1988bi6ujpmzpy5e0jkhhtuGL548eJhDQ0N2djY+Oa3vvWtl9rv58gjj9xx+eWXrzv55JPfl5kxa9asV+bMmbMF4Oqrr37hE5/4xMTMpLGxccfSpUuf+eY3v/nS3Llzx06aNOmYurq6/MpXvrLu/PPP330+xr6m2a5VXodBUqfY8xtHx404jnnT5nHiqBN3B4fWtlZe2vbS24Y2dt3fdQGkjmiIBi494VIuOPqCXj0EUYvXYVDP53UYJHWpPnV9+NSkT3HWhLP45e9/ycJHF3LR3Rdx/OHHM2LgCFZvWc2aLWt2T2AGMLz/cCYcOoEzxp3BhEMndPg6ELuuKSKp+xgYJHWqPvV9OGfyOZx95Nnc/MzNLHp8EZu2b2LCoROY8Z4Zu7+aOX7o+IPiQlFSb2FgkNQl+tb35bwp53HelPPKLkVSJ/BrApJ0cGlra2vrvSd26IBUf3f2Oi2ygUGSDi6rmpubhxoa9G61tbVFc3PzUGDV3tY7JCFJB5GdO3d+dsOGDddv2LBhKv5TqHenDVi1c+fOz+5tpYFBkg4iJ5xwwibgE2XXoYOP6VOSJBUyMEiSpEIGBkmSVMjAIEmSChkYJElSIQODJEkqZGCQJEmFDAySJKmQgUGSJBUyMEiSpEIGBkmSVMjAIEmSChkYJElSIQODJEkqZGCQJEmFGg7kyRGxBtgKtAI7M7MpIoYB/wsYB6wBzs3Mfz2wMiVJUpk6o4fh1MyclplN1cdfBu7NzEnAvdXHkiSpB+uKIYmzgUXV+4uAT3bBPiRJUjc60MCQwF0RsSIiLqoua8zM9dX7G4DGvT0xIi6KiOURsby5ufkAy5AkSV3pgM5hAE7KzHURcThwd0Q81X5lZmZE5N6emJkLgYUATU1Ne20jSZJqwwH1MGTmuurPTcAtwAeAjRExCqD6c9OBFilJksq134EhIgZFxJBd94FZwCrgNuDCarMLgVsPtEhJklSuAxmSaARuiYhd2/lpZv46IpYBP4+Ifw88D5x74GVKkqQy7XdgyMzngOP2svxl4LQDKUqSJNUWr/QoSZIKGRgkSVIhA4MkSSpkYJAkSYUMDJIkqZCBQZIkFTIwSJKkQgYGSZJUyMAgSZIKGRgkSVIhA4MkSSpkYJAkSYUMDJIkqZCBQZIkFTIwSJKkQgYGSZJUyMAgSZIKGRgkSVIhA4MkSSpkYJAkSYUMDJIkqZCBQZIkFTIwSJKkQgYGSZJUyMAgSZIKGRgkSVIhA4MkSSpkYJAkSYUMDJIkqVBD2QWoF8qE5qfg6TugrgGO/TMYMrLsqiRJ76DLAkNEnAF8B6gHrs/Mb3TVvtQDtLXC2mXw1P+Bp26Hzc+9te6er8PkM+D4C2Di6VBvjpWkWtMl78wRUQ98H/gosBZYFhG3ZeYTXbE/1agdLbB6aSUkPP0reG0T1PWB8SfD9PnwvjNhx3Z46Eew8qfw9O0wZBRMOx/e/+cwbHzZr2DfWl6Ftf8CLzxYua1dDkMaYcwMGHMijJkOh02CiLIrlaROEZnZ+RuNmA58LTM/Vn38VwCZ+d/31r6pqSmXL1/e6XV0mtYd8Pq/wvaXK7fX/gCvb658+A0c/tZt0HDoNxTqetGpIa07K8di17HZ/jK81gxrfgPP3A1vboO+g2HSR2HKWZWf/YfuZTs74Hd3VsLDs3dDtsGEUyq9DlPOgoZ+3f3K3u7V9fDCP1cDwj/BxscrNUY9jDoWRjfB1vWV9dv/UHnOwOFwxImVADF2Bow8Fhr6lvs6dFCJiBWZ2VR2HeoduqrvdzTwYrvHa4EPdvpe1q6Af1nYudvMNnjj1bd/ALZs6fjzox4GDmsXJIZBn4HAQfCf5rs5NoMOhz/+TOXDfvzJxR/49X3gqLMqty1rKz0OD/0YfvHvYMAwOPLUSkDrbjtb4KWH4ZXnK4/7DIT3/gmc/MVKEHhvE/Qb8lb7THj595VQ8cKDlZDx9O2VdQ0DKu0PGd39r0O1a/IsmPqnZVchFSptsDgiLgIuAhgzZsz+bWT7y5U35M7Wf2jlw/7QsW//4G/fmzBwGLTtrPQ2bN/jP+zdt83wh2dgx+udX2NZ+h9SPTZjYOBh+z42g0fuf0/L0PfCzC/Chy+D1f8IKxZVuvzLUFftQfjgX1QCwsg/roSbfYmAwyZWbsdfUFm2dSO8+OBbAaIrfmfVcx1+VNkVSB3ikIQk9VAOSag7ddVg+zJgUkSMj4i+wBzgti7alyRJ6mJdMiSRmTsjYj5wJ5WvVf4gMx/vin1JkqSu12XnMGTmHcAdXbV9SZLUfXrR9/8kSdL+MjBIkqRCBgZJklTIwCBJkgoZGCRJUqEuuXDTuy4iohl4fo/FQ4GOXJN5DPBCB3fV0W32tnZl7rvW25W571pvV+a+a71dV2xzb+91YzNzRAfrkQ5MZtbkDVjYwXbNXbDNXtWuJ9Tosam9dj2hxoPs2HT4vc6bt6641fKQxD90sN0rXbDN3tauzH3Xersy913r7crcd62364ptvpv3OqnT1cSQxIGIiOXptdQlHeR8r1PZarmHoaM6eX5rSapJvtepVD2+h0GSJHW9g6GHoVtExBkR8XREPBsRX95j3XcjYltZtZUtIn4QEZsiYlW7ZedExOMR0RYRvbYbdR/HZlpEPBgRKyNieUR8oMwayxARR0TEkoh4ovp7ckl1+dciYl312KyMiDPLrlVShYGhAyKiHvg+MBs4GjgvIo6urmsC/qjE8mrBDcAZeyxbBXwaWNrt1dSWG/j/j823gK9n5jTgK9XHvc1O4AuZeTRwIjBv198UcHVmTqvenMBOqhEGho75APBsZj6XmW8CPwPOrgaJK4AvllpdyTJzKbB5j2VPZubTJZVUM/Z2bIAEDqneHwq81K1F1YDMXJ+ZD1XvbwWeBEaXW5Wkd2Jg6JjRwIvtHq+tLpsP3JaZ60upSj3VpcAVEfEicCXwVyXXU6qIGAe8H/htddH8iHi0OpzT23vvpJphYNh/A4FzgL8tuxD1OJ8DFmTmEcAC4O9Lrqc0ETEYuBm4NDNfBa4BjgSmAeuB/1FieZLaMTB0zDrgiHaP3wv8HpgIPBsRa4CBEfFsCbWp57kQWFy9/7+pDHn1OhHRh0pYuDEzFwNk5sbMbM3MNuA6eumxkWqRgaFjlgGTImJ8RPQF5gC/zMyRmTkuM8cB2zNzYqlVqqd4CZhZvf8R4JkSaylFRASVnpUnM/OqdstHtWv2KSonz0qqAV6HoYOqX+/6NlAP/CAz/+se67dl5uBSiitZRNwEnAIcBmwEvkrlRL+/BUZQuaTtysz8WFk1lmUfx+Zp4DtAA9AC/MfMXFFWjWWIiJOA+4HHgLbq4r8GzqMyHJHAGuBizxGSaoOBQZIkFXJIQpIkFTIwSJKkQgYGSZJUyMAgSZIKGRgkSVIhA4MkSSpkYJAkSYUMDJIkqZCBQZIkFTIwSJKkQgYGSZJUyMAgSZIKGRgkSVIhA4MkSSpkYJAkSYVqPjBERGtErIyIxyPikYj4QkTUfN2StD8iYlvZNUh701B2AR3wemZOA4iIw4GfAocAXy21KkmSepEe9Z96Zm4CLgLmR0V9RFwREcsi4tGIuHhX24j4UkQ8Vu2V+EZ5VUvSuxMRgyPi3oh4qPo+dnZ1+biIeDIirqv2ut4VEQPKrle9Q0/oYXibzHwuIuqBw4GzgS2Z+ScR0Q94ICLuAqZU130wM7dHxLASS5akd6sF+FRmvhoRhwEPRsRt1XWTgPMy8z9ExM+BPwV+Ulah6j16XGDYwyzg2Ij4TPXxUCp/TKcDP8zM7QCZubmk+iRpfwTw3yLiZKANGA00VtetzsyV1fsrgHHdX556ox4XGCJiAtAKbKLyR/X5zLxzjzYfK6M2Seok5wMjgBMyc0dErAH6V9e90a5dK+CQhLpFjzqHISJGAH8HfC8zE7gT+FxE9KmunxwRg4C7gbkRMbC63CEJST3JUGBTNSycCowtuyCpJ/QwDIiIlUAfYCfwY+Cq6rrrqXTHPRQRATQDn8zMX0fENGB5RLwJ3AH8dbdXLknvQkQ0UOlBuBH4h4h4DFgOPFVqYRIQlX/UJUlli4jjgOsy8wNl1yLtqUcNSUjSwSoi/gK4CfjPZdci7Y09DJIkqZA9DJIkqZCBQZJKEBFHRMSSiHiietXGS6rLh0XE3RHxTPXnH1WXT4mIf46INyLisj22taC6jVURcVNE9N/bPqUDYWCQpHLsBL6QmUcDJwLzIuJo4MvAvZk5Cbi3+hhgM/CfgCvbbyQiRleXN2XmVKAemNM9L0G9iYFBkkqQmesz86Hq/a3Ak1Su6Hg2sKjabBHwyWqbTZm5DNixl801UPkKegMwEHipi8tXL2RgkKSSRcQ44P3Ab4HGzFxfXbWBty4JvVeZuY5Kr8MLwHoq8+vc1WXFqtcyMEhSiSJiMHAzcGlmvtp+XfWKtu/4VbbqOQ5nA+OB9wCDIuLPu6hc9WIGBkkqSfWy9jcDN2bm4urijRExqrp+FJV5c97J6VQmpGrOzB3AYmBGV9Ws3svAIEklqF7O/u+BJzPzqnarbgMurN6/ELi1YFMvACdGxMDqNk+jcj6E1Km8cJMklSAiTgLuBx6jMoU1VOa8+S3wc2AM8DxwbmZujoiRVOaVOKTafhtwdGa+GhFfB/6MyjcvHgY+m5ntZ7WUDpiBQZIkFXJIQpIkFTIwSJKkQgYGSZJUyMAgSZIKGRgkSVIhA4N6pYhojYiV1Rn+HomIL0TEO/49RMS4iPg33VWjJNUSA4N6q9czc1pmHgN8FJgNfLXgOeMAA4OkXsnrMKhXiohtmTm43eMJwDLgMGAs8GNgUHX1/Mz8p4h4EDgKWE1lFsHvAt8ATgH6Ad/PzGu77UVIUjcyMKhX2jMwVJe9ArwP2Aq0ZWZLREwCbsrMpog4BbgsM8+qtr8IODwz/0tE9AMeAM7JzNXd+mIkqRs0lF2AVIP6AN+LiGlAKzB5H+1mAcdGxGeqj4cCk6j0QEjSQcXAILF7SKKVysyAXwU2AsdROc+nZV9PAz6fmXd2S5GSVCJPelSvFxEjgL8DvpeVMbqhwPrMbAP+LVBfbboVGNLuqXcCn6tOUUxETI6IQUjSQcgeBvVWAyJiJZXhh51UTnLcNcXw/wRujogLgF8Dr1WXPwq0RsQjwA3Ad6h8c+Kh6rTCzcAnu+sFSFJ38qRHSZJUyCEJSZJUyMAgSZIKGRgkSVIhA4MkSSpkYJAkSYUMDJIkqZCBQZIkFTIwSJKkQv8PMGeiEZpRhnEAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Beds last month\n", "fromdate,todate=ntpd.last_month(iso=True)\n", "\n", "q_month_beds='''\n", "SELECT Date, Name, Category, value FROM sitrep WHERE Report='G&A beds'\n", "AND date(Date) BETWEEN date('{fromdate}') AND date('{todate}')\n", "AND Code = '{code}'\n", "'''.format(fromdate=fromdate, todate=todate,code=code)\n", "tmp_b = pd.read_sql_query(q_month_beds, conn, parse_dates=['Date'])\n", "\n", "\n", "tmp_b = tmp_b[['Date','Category','value']].pivot_table(index='Date',columns='Category')\n", "\n", "#tmp_b[('value','Occupancy rate')] = 100*tmp_b[('value','Occupancy rate')]\n", "tmp_b.columns = tmp_b.columns.get_level_values(1)\n", "tmp_b['Occupancy rate'] = 100*tmp_b['Occupancy rate']\n", "\n", "\n", "tmp_b.plot().legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", "plt.axhline(y=100, color='r', linestyle=':');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Days when G&A escalation beds were open:" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Date
02017-12-04
12017-12-11
22017-12-12
32017-12-17
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" ], "text/plain": [ " Date\n", "0 2017-12-04\n", "1 2017-12-11\n", "2 2017-12-12\n", "3 2017-12-17" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q_esc_beds_open_on='''\n", "SELECT Date FROM sitrep \n", "WHERE date(Date) BETWEEN date('{fromdate}') AND date('{todate}') \n", "AND Category ='Escalation Beds Open' AND value > 0.0\n", "AND Code = '{code}'\n", "'''.format(fromdate='2017-12-01', todate='2017-12-31',code=code)\n", "\n", "pd.read_sql_query(q_esc_beds_open_on, conn, parse_dates=['Date'])" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DateCategoryvalue
02017-12-02Core Beds Open246.000000
12017-12-02Escalation Beds Open0.000000
22017-12-02Total Beds Open246.000000
32017-12-02Total beds occ'd208.000000
42017-12-02Occupancy rate0.845528
52017-12-03Core Beds Open246.000000
62017-12-03Escalation Beds Open0.000000
72017-12-03Total Beds Open246.000000
82017-12-03Total beds occ'd224.000000
92017-12-03Occupancy rate0.910569
102017-12-04Core Beds Open246.000000
112017-12-04Escalation Beds Open1.000000
122017-12-04Total Beds Open247.000000
132017-12-04Total beds occ'd231.000000
142017-12-04Occupancy rate0.935223
152017-12-05Core Beds Open246.000000
162017-12-05Escalation Beds Open0.000000
172017-12-05Total Beds Open246.000000
182017-12-05Total beds occ'd237.000000
192017-12-05Occupancy rate0.963415
202017-12-17Core Beds Open246.000000
212017-12-17Escalation Beds Open3.000000
222017-12-17Total Beds Open249.000000
232017-12-17Total beds occ'd249.000000
242017-12-17Occupancy rate1.000000
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" ], "text/plain": [ " Date Category value\n", "0 2017-12-02 Core Beds Open 246.000000\n", "1 2017-12-02 Escalation Beds Open 0.000000\n", "2 2017-12-02 Total Beds Open 246.000000\n", "3 2017-12-02 Total beds occ'd 208.000000\n", "4 2017-12-02 Occupancy rate 0.845528\n", "5 2017-12-03 Core Beds Open 246.000000\n", "6 2017-12-03 Escalation Beds Open 0.000000\n", "7 2017-12-03 Total Beds Open 246.000000\n", "8 2017-12-03 Total beds occ'd 224.000000\n", "9 2017-12-03 Occupancy rate 0.910569\n", "10 2017-12-04 Core Beds Open 246.000000\n", "11 2017-12-04 Escalation Beds Open 1.000000\n", "12 2017-12-04 Total Beds Open 247.000000\n", "13 2017-12-04 Total beds occ'd 231.000000\n", "14 2017-12-04 Occupancy rate 0.935223\n", "15 2017-12-05 Core Beds Open 246.000000\n", "16 2017-12-05 Escalation Beds Open 0.000000\n", "17 2017-12-05 Total Beds Open 246.000000\n", "18 2017-12-05 Total beds occ'd 237.000000\n", "19 2017-12-05 Occupancy rate 0.963415\n", "20 2017-12-17 Core Beds Open 246.000000\n", "21 2017-12-17 Escalation Beds Open 3.000000\n", "22 2017-12-17 Total Beds Open 249.000000\n", "23 2017-12-17 Total beds occ'd 249.000000\n", "24 2017-12-17 Occupancy rate 1.000000" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q_peak_occ_beds = '''\n", "SELECT Date, Category, value FROM sitrep WHERE Date IN (SELECT Date FROM sitrep \n", "WHERE date(Date) BETWEEN date('{fromdate}') AND date('{todate}') \n", "AND Category = 'Occupancy rate'\n", "AND Code = '{code}'\n", "ORDER BY value DESC LIMIT 5 ) \n", "AND Report = 'G&A beds' AND Code = '{code}'\n", "'''.format(fromdate='2017-12-01', todate='2017-12-31',code=code)\n", "\n", "pd.read_sql_query(q_peak_occ_beds, conn, parse_dates=['Date'])" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DateCategoryvalue
02017-12-17Occupancy rate1
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" ], "text/plain": [ " Date Category value\n", "0 2017-12-17 Occupancy rate 1" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q_fully_occupied = '''\n", "SELECT Date, Category, value FROM sitrep \n", "WHERE date(Date) BETWEEN date('{fromdate}') AND date('{todate}') \n", "AND Category = 'Occupancy rate'\n", "AND Code = '{code}'\n", "AND value = 1\n", "AND Report = 'G&A beds' \n", "AND Code = '{code}'\n", "'''.format(fromdate='2017-12-01', todate='2017-12-31',code=code)\n", "\n", "pd.read_sql_query(q_fully_occupied, conn, parse_dates=['Date'])" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DateNameCategoryvalue
02017-12-04Isle Of Wight NHS TrustCore Beds Open246.000000
12017-12-04Isle Of Wight NHS TrustEscalation Beds Open1.000000
22017-12-04Isle Of Wight NHS TrustTotal Beds Open247.000000
32017-12-04Isle Of Wight NHS TrustTotal beds occ'd231.000000
42017-12-04Isle Of Wight NHS TrustOccupancy rate0.935223
52017-12-11Isle Of Wight NHS TrustCore Beds Open246.000000
62017-12-11Isle Of Wight NHS TrustEscalation Beds Open1.000000
72017-12-11Isle Of Wight NHS TrustTotal Beds Open247.000000
82017-12-11Isle Of Wight NHS TrustTotal beds occ'd246.000000
92017-12-11Isle Of Wight NHS TrustOccupancy rate0.995951
102017-12-12Isle Of Wight NHS TrustCore Beds Open241.000000
112017-12-12Isle Of Wight NHS TrustEscalation Beds Open5.000000
122017-12-12Isle Of Wight NHS TrustTotal Beds Open246.000000
132017-12-12Isle Of Wight NHS TrustTotal beds occ'd245.000000
142017-12-12Isle Of Wight NHS TrustOccupancy rate0.995935
152017-12-17Isle Of Wight NHS TrustCore Beds Open246.000000
162017-12-17Isle Of Wight NHS TrustEscalation Beds Open3.000000
172017-12-17Isle Of Wight NHS TrustTotal Beds Open249.000000
182017-12-17Isle Of Wight NHS TrustTotal beds occ'd249.000000
192017-12-17Isle Of Wight NHS TrustOccupancy rate1.000000
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" ], "text/plain": [ " Date Name Category value\n", "0 2017-12-04 Isle Of Wight NHS Trust Core Beds Open 246.000000\n", "1 2017-12-04 Isle Of Wight NHS Trust Escalation Beds Open 1.000000\n", "2 2017-12-04 Isle Of Wight NHS Trust Total Beds Open 247.000000\n", "3 2017-12-04 Isle Of Wight NHS Trust Total beds occ'd 231.000000\n", "4 2017-12-04 Isle Of Wight NHS Trust Occupancy rate 0.935223\n", "5 2017-12-11 Isle Of Wight NHS Trust Core Beds Open 246.000000\n", "6 2017-12-11 Isle Of Wight NHS Trust Escalation Beds Open 1.000000\n", "7 2017-12-11 Isle Of Wight NHS Trust Total Beds Open 247.000000\n", "8 2017-12-11 Isle Of Wight NHS Trust Total beds occ'd 246.000000\n", "9 2017-12-11 Isle Of Wight NHS Trust Occupancy rate 0.995951\n", "10 2017-12-12 Isle Of Wight NHS Trust Core Beds Open 241.000000\n", "11 2017-12-12 Isle Of Wight NHS Trust Escalation Beds Open 5.000000\n", "12 2017-12-12 Isle Of Wight NHS Trust Total Beds Open 246.000000\n", "13 2017-12-12 Isle Of Wight NHS Trust Total beds occ'd 245.000000\n", "14 2017-12-12 Isle Of Wight NHS Trust Occupancy rate 0.995935\n", "15 2017-12-17 Isle Of Wight NHS Trust Core Beds Open 246.000000\n", "16 2017-12-17 Isle Of Wight NHS Trust Escalation Beds Open 3.000000\n", "17 2017-12-17 Isle Of Wight NHS Trust Total Beds Open 249.000000\n", "18 2017-12-17 Isle Of Wight NHS Trust Total beds occ'd 249.000000\n", "19 2017-12-17 Isle Of Wight NHS Trust Occupancy rate 1.000000" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q_month_esc_beds='''\n", "SELECT Date, Name, Category, value FROM sitrep \n", "WHERE Report='G&A beds'\n", "AND Code = '{code}'\n", "AND Date IN \n", " (SELECT Date FROM sitrep \n", " WHERE date(Date) BETWEEN date('{fromdate}') AND date('{todate}') \n", " AND Category ='Escalation Beds Open' AND value > 0.0\n", " AND Code = '{code}'\n", " ) \n", "'''.format(fromdate='2017-12-01', todate='2017-12-31',code=code)\n", "\n", "pd.read_sql_query(q_month_esc_beds, conn, parse_dates=['Date'])" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DateCategoryvalue
02017-12-02CC Adult avail6
12017-12-02CC Adult Occ6
22017-12-02Occupancy rate1
32017-12-03CC Adult avail6
42017-12-03CC Adult Occ6
52017-12-03Occupancy rate1
62017-12-04CC Adult avail7
72017-12-04CC Adult Occ7
82017-12-04Occupancy rate1
92017-12-05CC Adult avail6
102017-12-05CC Adult Occ6
112017-12-05Occupancy rate1
122017-12-17CC Adult Open7
132017-12-17CC Adult Occ7
142017-12-17Occupancy rate1
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ReportCategory
0Answered in 60Calls answered within 60 Seconds
1Answered in 60Calls answered
2AbandonedCalls abandoned after at least 30 seconds waiting
3AbandonedCalls offered
4TriageCalls where person triaged
5Clinical AdvisorCalls transferred to or answered by a clinical...
6Clinical InputCalls to a CAS clinician
7Call BackCalls back within 10 minutes
8Call BackCalls where person offered call back
9DispositionsAmbulance dispatches
10DispositionsRecommended to attend A&E
11DispositionsRecommended to attend primary and community care
12DispositionsRecommended to contact primary care
13DispositionsRecommended to speak to primary care
14DispositionsRecommended to dental
15DispositionsRecommended to pharmacy
16DispositionsRecommended to attend other service
17DispositionsNot recommended to attend other service
18DispositionsGiven health information
19DispositionsRecommended home Care
20DispositionsRecommended non clinical
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ReportCategoryCodeTotal
0DispositionsAmbulance dispatches111AA61058.0
1DispositionsGiven health information111AA690.0
2DispositionsNot recommended to attend other service111AA61031.0
3DispositionsRecommended home Care111AA6288.0
4DispositionsRecommended non clinical111AA6653.0
5DispositionsRecommended to attend A&E111AA6601.0
6DispositionsRecommended to attend other service111AA6250.0
7DispositionsRecommended to attend primary and community care111AA64412.0
8DispositionsRecommended to contact primary care111AA63049.0
9DispositionsRecommended to dental111AA6460.0
10DispositionsRecommended to pharmacy111AA616.0
11DispositionsRecommended to speak to primary care111AA6887.0
\n", "
" ], "text/plain": [ " Report Category Code \\\n", "0 Dispositions Ambulance dispatches 111AA6 \n", "1 Dispositions Given health information 111AA6 \n", "2 Dispositions Not recommended to attend other service 111AA6 \n", "3 Dispositions Recommended home Care 111AA6 \n", "4 Dispositions Recommended non clinical 111AA6 \n", "5 Dispositions Recommended to attend A&E 111AA6 \n", "6 Dispositions Recommended to attend other service 111AA6 \n", "7 Dispositions Recommended to attend primary and community care 111AA6 \n", "8 Dispositions Recommended to contact primary care 111AA6 \n", "9 Dispositions Recommended to dental 111AA6 \n", "10 Dispositions Recommended to pharmacy 111AA6 \n", "11 Dispositions Recommended to speak to primary care 111AA6 \n", "\n", " Total \n", "0 1058.0 \n", "1 90.0 \n", "2 1031.0 \n", "3 288.0 \n", "4 653.0 \n", "5 601.0 \n", "6 250.0 \n", "7 4412.0 \n", "8 3049.0 \n", "9 460.0 \n", "10 16.0 \n", "11 887.0 " ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def disposition_report(conn, code, nl_period=None):\n", " q='''SELECT DISTINCT Report, Category, Code, SUM(value) as Total FROM nhs111 \n", " WHERE Report='Dispositions'\n", " AND Code= '{code}'\n", " {period}\n", " GROUP BY Category;\n", " \n", " '''.format(code=code, period=getperiod_sql_clause(nl_period))\n", " return pd.read_sql_query(q, conn)\n", " \n", "code= '111AA6'\n", "disposition_report(conn, code, 'last month' )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generate a written report\n", "\n", "*Perhaps also offer ability to compare with previous same period?*" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NHS111 dispositions for Isle Of Wight NHS 111 last month (Friday 01 December 2017 to Sunday 31 December 2017):\n", "\t- Ambulance dispatches: 1058\n", "\t- Given health information: 90\n", "\t- Not recommended to attend other service: 1031\n", "\t- Recommended home Care: 288\n", "\t- Recommended non clinical: 653\n", "\t- Recommended to attend A&E: 601\n", "\t- Recommended to attend other service: 250\n", "\t- Recommended to attend primary and community care: 4412\n", "\t- Recommended to contact primary care: 3049\n", "\t- Recommended to dental: 460\n", "\t- Recommended to pharmacy: 16\n", "\t- Recommended to speak to primary care: 887\n" ] } ], "source": [ "def bulleted_list_builder(reps, header=''):\n", " for rep in reps:\n", " header+='\\n\\t- {}'.format(rep)\n", " return header\n", "\n", "def report_disposition(conn,code,nl_period=None, intify=None):\n", " def _reporter(row):\n", " txt='''{Category}: {Total}'''.format(**row)\n", " return txt\n", " df = disposition_report(conn, code, nl_period )\n", " if intify:\n", " df[intify]=df[intify].astype(int)\n", "\n", " return df.apply(_reporter,axis=1)\n", "\n", "period = 'last month'\n", "locationQuery = 'Wight'\n", "\n", "trust,code = lookupTrustCode(conn,locationQuery, 'nhs111')\n", "\n", "\n", "print( bulleted_list_builder( report_disposition(conn,code,period, intify='Total' ),\n", " header = 'NHS111 dispositions for {} {} {}:'.format(trust, period,period_text(period) )) )" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }