ov = {'x': [], 'y': []}
date_range = brondata.getDateRange(metenisweten)
lastDays = arguments.lastDays()
if (lastDays > 0):
date_range = date_range[-lastDays:]
for d in date_range:
datum = d.strftime("%Y-%m-%d")
if datum not in metenisweten:
continue
value = metenisweten[datum]['mobiliteit']['rijden']
if value:
rijden['x'].append(dateCache.parse(datum))
rijden['y'].append(value)
if 'OV' in metenisweten[datum]['mobiliteit']:
value = metenisweten[datum]['mobiliteit']['OV']
if value:
ov['x'].append(dateCache.parse(datum))
ov['y'].append(value)
value = metenisweten[datum]['mobiliteit']['lopen']
if value:
lopen['x'].append(dateCache.parse(datum))
lopen['y'].append(value)
rijden['y'] = brondata.double_savgol(rijden['y'], 1, 13, 1)
#ov['y'] = brondata.smooth(ov['y'])
totaaltests = {'x': [], 'y': [], 'total': 0}
positief_percentage = {'x': [], 'y': []}
date_range = brondata.getDateRange(metenisweten)
lastDays = arguments.lastDays()
if (lastDays > 0):
date_range = date_range[-lastDays:]
for d in date_range:
datum = d.strftime("%Y-%m-%d")
# --------------------------------- Normale grafieken (exclusief data van vandaag want dat is altijd incompleet)
if datum in metenisweten and dateCache.parse(datum).date() <= (
dateCache.today()):
# positief['x'].append(dateCache.parse(datum))
# positief['y'].append(metenisweten[datum]['positief'])
if 'rivm_totaal_tests' in metenisweten[datum] and metenisweten[datum][
'rivm_totaal_tests']:
totaaltests['x'].append(dateCache.parse(datum))
totaaltests['y'].append(metenisweten[datum]['rivm_totaal_tests'])
totaaltests['total'] += int(
metenisweten[datum]['rivm_totaal_tests'])
elif 'rivm_totaal_personen_getest' in metenisweten[
datum] and metenisweten[datum]['rivm_totaal_personen_getest']:
totaaltests['x'].append(dateCache.parse(datum))
totaaltests['y'].append(
metenisweten[datum]['rivm_totaal_personen_getest'])
'yellow',
'limegreen',
),
baseline='zero')
totaal_prikken = decimalstring(vaccins_delta['totaal'][-1])
ax1.plot(vaccins_delta['x'],
vaccins_delta['totaal'],
color='black',
label='Totaal per dag (nu: %s)' % totaal_prikken)
graphname = 'vaccins'
for event in events:
if graphname in event \
and dateCache.parse(event[graphname][0]) > date_range[0]\
and (len(event[graphname]) <= 2 or len(date_range) <= event[graphname][2]):
anotate(ax1, vaccins_delta['x'], vaccins_delta['totaal'],
event['date'], event['event'], event[graphname][0],
event[graphname][1])
# Put vertical line at current day
plt.text(x=dateCache.today(),
y=0,
s=datetime.now().strftime("%d"),
color="white",
fontsize=8,
ha="center",
va="center",
bbox=dict(boxstyle='round,pad=0.1',
facecolor='red',
ic_voorspeld = {'x': [], 'y': [], 'avgsize': 3}
besmettingsgraad = {'x': [], 'y': []}
date_range = brondata.getDateRange(metenisweten)
lastDays = arguments.lastDays()
if (lastDays > 0):
date_range = date_range[-lastDays:]
for d in date_range:
datum = d.strftime("%Y-%m-%d")
# ------------ Totaal positief en laatste meetdatum
if datum in metenisweten and dateCache.parse(
datum).date() <= dateCache.today():
totaal_positief = metenisweten[datum]['totaal_positief']
if metenisweten[datum]['rivm-datum']:
filedate = metenisweten[datum]['rivm-datum']
# --------------- Opname en IC data van vandaag en gisteren zijn niet compleet, niet tonen
if datum in metenisweten and dateCache.parse(datum).date() <= (
dateCache.today() - datetime.timedelta(days=3)):
if 'nu_op_ic' in metenisweten[datum] and metenisweten[datum][
'nu_op_ic']:
ic['x'].append(dateCache.parse(datum))
ic['y'].append(metenisweten[datum]['nu_op_ic'])
if len(ic['y']) > 1:
ic['rc'].append(ic['y'][-1] - ic['y'][-2])
# The heatmap data is already in metenisweten, we can optimze. Complete this:
# metenisweten = brondata.readjson('../cache/daily-stats.json')
# print("Converting records to x/y data")
# for date_statistics in metenisweten:
# if date_statistics > startdate:
# for age in metenisweten[date_statistics]['besmettingleeftijd']:
# datax = (date_statistics - startdate).days
# datay = metenisweten[date_statistics]['besmettingleeftijd']
datelog = []
with open('../cache/COVID-19_casus_landelijk.json', 'r') as json_file:
data = json.load(json_file)
print("Converting records to x/y data")
for record in data:
date_statistics = dateCache.parse(record['Date_statistics'])
if date_statistics >= startdate:
datelog.append(date_statistics)
try:
datax = (date_statistics - startdate).days
datay = int(record['Agegroup'].split('-')[0].split('+')[0])+5
filedate = record['Date_file']
x.append(datax)
y.append(datay)
# if datax not in weightsmap:
# weightsmap[datax] = 1
# else:
# weightsmap[datax] = weightsmap[datax] + 1
except ValueError:
#!/usr/bin/env python3 # from matplotlib import pyplot as plt from modules.brondata import dateCache datestr ='2021-06-19' print(datestr) print(dateCache.parse(datestr))
lastDays = arguments.lastDays()
if (lastDays>0):
date_range = date_range[-lastDays:]
print('Generating Rt graph...')
Rt_avg = { 'x':[], 'y':[]}
Rt_low = { 'x':[], 'y':[]}
Rt_up = { 'x':[], 'y':[]}
for d in date_range:
datum = d.strftime("%Y-%m-%d")
if datum in metenisweten and metenisweten[datum]['Rt_avg'] is not None:
Rt_avg['x'].append(dateCache.parse(datum))
Rt_avg['y'].append(float(metenisweten[datum]['Rt_avg']))
if datum in metenisweten and metenisweten[datum]['Rt_up'] is not None:
Rt_up['x'].append(dateCache.parse(datum))
Rt_up['y'].append(float(metenisweten[datum]['Rt_up']))
if datum in metenisweten and metenisweten[datum]['Rt_low'] is not None:
Rt_low['x'].append(dateCache.parse(datum))
Rt_low['y'].append(float(metenisweten[datum]['Rt_low']))
plt.figure(figsize=(10,3))
plt.subplots_adjust(bottom=0.2, left=0.09, right=0.91)
plt.grid(which='both', axis='both', linestyle='-.',
color='gray', linewidth=1, alpha=0.3)
plt.plot(Rt_avg['x'], Rt_avg['y'], color='black', label='Rt_avg')
plt.plot(Rt_up['x'], Rt_up['y'], color='gray', linestyle='--', alpha=0.3)
outputdir = '../docs'
metenisweten = brondata.readjson('../cache/daily-stats.json')
dates = []
gemiddeldeleeftijdarray = []
# Assumes last records are newest
for date in metenisweten:
dates.append(date)
totaal_positief = metenisweten[date]['totaal_positief']
totaal_positief_datum = date
if date in metenisweten \
and dateCache.parse(date).date() <= (dateCache.today() - datetime.timedelta(days=3))\
and 'nu_op_ic' in metenisweten[date] and metenisweten[date]['nu_op_ic']:
# print(str(date)+' '+str(metenisweten[date]['nu_op_ic']))
nu_op_ic = metenisweten[date]['nu_op_ic']
nu_op_ic_datum = date
if date in metenisweten \
and dateCache.parse(date).date() <= (dateCache.today() - datetime.timedelta(days=3))\
and 'nu_opgenomen' in metenisweten[date] and metenisweten[date]['nu_opgenomen']:
# print(str(date)+' '+str(metenisweten[date]['nu_opgenomen']))
nu_opgenomen = metenisweten[date]['nu_opgenomen']
nu_opgenomen_datum = date
if metenisweten[date]['rivm_schatting_besmettelijk']['value']:
d = record['Date_of_statistics_week_start']
c = record['Variant_code']
if d not in varianten_map:
varianten_map[d] = {}
if c not in varianten_map[d]:
varianten_map[d][c] = {}
varianten_map[d][c]['cases'] = record['Variant_cases']
varianten_map[d][c]['size'] = record['Sample_size']
# Take al variant percentages and multiply them with the actual number of sick people on that day
for key in varianten_map:
varianten_totaal['x'].append(dateCache.parse(key))
# For each variant, determine the number of sick people (variant percentage times estimate)
geschat_ziek = metenisweten[key]['rolf_besmettelijk']
totaal_percentage = 0
for variantcode in variantcodes.keys():
if variantcode == 'onbekend':
continue
if variantcode in varianten_map[key]:
percentage = varianten_map[key][variantcode][
'cases'] / varianten_map[key][variantcode]['size']
else:
percentage = 0
totaal_percentage += percentage
varianten_totaal[variantcode].append(percentage * geschat_ziek)