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cov19_local.py
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from pylab import *
####################################
# Load of Input Data (Daten laden) #
####################################
def load_RKI(filename, LandkreisID, state_name ='Bavaria'):
'''
Reads file of the RKI database and selects the relevant data for the specific county.
Input
=====
filename : str
path to '*.csv' file downloaded from the RKI.
LandkreisID : str
String with 5 number entries to select the specific county.
state_name : str, default = 'Bavaria'
Name of the state.
return
======
dic : dictionary {'fall': array, 'tod':array, 'gesund': array}
Array of cumulative number of cases
uday : np.array
Individual days of notification to the RKI
umonth : np.array
Individual month of notification to the RKI
region_name : str
Name of specific region
dic_LK : dict={'name', 'ID'}
Alphabetical sorted names of counties and corresponding IDs
state : li[dat_state, num_state, name_state]
List of dates, cumulative case numbers and state name
'''
import numpy as np
daten_RKI = np.loadtxt(filename,
skiprows=1,
delimiter=',',
usecols=(9,3,8,6,7,-3),
dtype={'names': ('lkID', 'lk_name', 'datum', 'fall', 'tod', 'gesund'), 'formats': ( 'S6', 'S40', 'S10', 'i4', 'i4', 'i4')})
# state average
dat_state = np.unique(daten_RKI['datum'])
case_state = []
#tod_state = []
#gesund_state = []
for dat in dat_state:
case_state_dat = daten_RKI['fall'][daten_RKI['datum'] == dat]
#tod_state_dat = daten_RKI['tod'][daten_RKI['datum'] == dat]
#gesund_state_dat = daten_RKI['gesund'][daten_RKI['datum'] == dat]
case_state.append(np.sum(case_state_dat))#[case_state_dat > 0]))
num_state = np.cumsum(np.array(case_state))
# select unique region
indexes = np.unique(daten_RKI['lkID'], return_index=True)[1]
uID = daten_RKI['lkID'][indexes]
u_index_name = daten_RKI['lk_name'][indexes]
# sort and put to dic
sort_index = np.argsort(u_index_name)
dic_LK = {'name': u_index_name[sort_index], 'ID': uID[sort_index]}
# rename umlaut
for i in range(len(dic_LK['name'])):
dic_LK['name'][i] = dic_LK['name'][i].replace('\xc3\xb6', 'oe')
dic_LK['name'][i] = dic_LK['name'][i].replace('\xc3\x83\xc2\xb6', 'ae')
dic_LK['name'][i] = dic_LK['name'][i].replace('\xc3\xa4', 'ae')
dic_LK['name'][i] = dic_LK['name'][i].replace('\xc3\xbcr', 'ue')
dic_LK['name'][i] = dic_LK['name'][i].replace('\xc3\x83\xc2\xbc', 'ue')
dic_LK['name'][i] = dic_LK['name'][i].replace('\xc3\xbc', 'ue')
dic_LK['name'][i] = dic_LK['name'][i].replace('\xc3\x9f', 'ss')
dic_LK['name'][i] = dic_LK['name'][i].replace('\xc3\x83\xc5\xb8', 'ss')
daten_RKI = daten_RKI[daten_RKI['lkID'] == LandkreisID]
region_name = dic_LK['name'][dic_LK['ID'] == LandkreisID][0]
print 'region_name loaded', region_name
# splitting date
year = np.zeros(len(daten_RKI['datum'])).astype('int')
month = np.zeros(len(daten_RKI['datum'])).astype('int')
day = np.zeros(len(daten_RKI['datum'])).astype('int')
dat_sting = daten_RKI['datum']
for i in range(len(dat_sting)):
li = dat_sting[i].split('/')
year[i] = int(li[0])
month[i] = int(li[1])
day[i] = int(li[2])
# sum up unique dates
from astropy.table import Table
RKI_tab = Table([daten_RKI['datum'], year, month, day, daten_RKI['fall'], daten_RKI['tod'], daten_RKI['gesund']],
names=('datum', 'year', 'month', 'day', 'fall', 'tod', 'gesund'))
#print RKI_tab
udate = np.unique(RKI_tab['datum'])
uday = np.zeros(shape=udate.shape)
umonth = np.zeros(shape=udate.shape)
uyear = np.zeros(shape=udate.shape)
ufall = np.zeros(shape=udate.shape)
utod = np.zeros(shape=udate.shape)
ugesund = np.zeros(shape=udate.shape)
#print RKI_tab['datum']
for i in range(len(udate)):
cond = (RKI_tab['datum'] == udate[i])
fall_day = RKI_tab['fall'][cond]
#print fall_day[fall_day < 0]
ufall[i] = np.sum(fall_day)#[fall_day > 0])
tod_day = RKI_tab['tod'][cond]
utod[i] = np.sum(tod_day)#[tod_day > 0])
#print tod_day[tod_day < 0]
gesund_day = RKI_tab['gesund'][cond]
ugesund[i] = np.sum(gesund_day)#[gesund_day > 0])
#print gesund_day[gesund_day < 0]
uday[i] = RKI_tab['day'][cond][0]
umonth[i] = RKI_tab['month'][cond][0]
uyear[i] = RKI_tab['year'][cond][0]
return {'fall': np.cumsum(ufall), 'tod': np.cumsum(utod), 'gesund':np.cumsum(ugesund)}, uday, umonth, region_name, dic_LK, [num_state, dat_state, state_name]#, uyear, udate
##################################################################################################
# Logarithmic Plot of Cumulative Cases (Logarithmische Darstellung der aufsummierten Fallzahlen) #
##################################################################################################
def get_people_of_county(asked_ID, filename = 'data_RKI/12411-001.csv'):
import numpy as np
EW_ID, EW = np.loadtxt(filename, delimiter=';',
usecols=(0, -1), unpack=True,
dtype={'names': ('EW_ID', 'EW'), 'formats': ( 'S10', 'i4')})
ID_str = asked_ID[1:]
return EW[EW_ID == ID_str][0]
def plot_corona(num_dic, day, month, name, ID, geraet_min=None, geraet_max=None, anteil_beatmung=0.05):
'''
Plots cumulative case numbers against time for the specific county. Fits for any dataset
larger than eight a exponential function and estimates the doubling time. For the fit only
the last eight data points are used to get a recent description of the evolution. Estimates the
the doubling time from every fit by taking the fitting constants b of the ``y = a * exp(b * x)``
underlying theory into account::
``DT = ln(2)/b``
Input
=====
num_dic : dictionary {'fall': array, 'tod':array, 'gesund': array}
Array of cumulative number of cases
day : np.array
String with 5 number entries to select the specific county.
name : str
Name of specific region
ID : str
ID of county in Germany e.g. '09182' for LK Miesbach
geraet_min : int, None
Minimum capacity of intensive care
geraet_max : int, default = None
Maximum capacity of intensive care
anteil_beatmung : float, default = 0.05
Approximated fraction of demand for intensive care
return
======
DT : list
Contains name of county, day array and DT array containing the 8 last data points
where fit was possible
name : str
Name of specific region
day : np.array
Array of 8 days before and including the fit day
val_DT : list
list containing the calculated day-depending doubling times
rates : list
list containing rates (mortality rate, recovery rate, still ill rate)
pass_all : dict
dictionary of daily values of new cases, deaths and recovered cases
'''
import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import curve_fit
print '-' * 30
print name
print '-' * 30
num = num_dic['fall'][num_dic['fall'] > 0]
num_tod = num_dic['tod'][num_dic['fall'] > 0]
num_gesund = num_dic['gesund'][num_dic['fall'] > 0]
day = day[num_dic['fall'] > 0]
month = month[num_dic['fall'] > 0]
day_max = 150.
day_ticks = [14, 18, 22, 26, 30, 3, 7, 11, 15, 19, 23, 27, 1, 5, 9, 13, 17, 21, 25, 29, 2, 6, 10, 14, 18, 22, 26, 30, 3, 7, 11, 15, 19, 23, 27]
fig, ax = plt.subplots(4, figsize=(10,22), gridspec_kw={'height_ratios': [3, 1, 1, 1]})
ax[0].set_title(name + ' (#' + ID +')')
ax[0].axis([13, day_max, 0.9, 1e5])
ax[0].set_title('Abb. 1', loc='right', fontsize=8)
ax[1].axis([13, day_max, 0.8, 1e3])
ax[1].set_title('Abb. 2', loc='right', fontsize=8)
ax[2].set_xlim([13, day_max])
ax[2].set_title('Abb. 3', loc='right', fontsize=8)
ax[3].set_xlim([13, day_max])
ax[3].set_title('Abb. 4', loc='right', fontsize=8)
####
# move to March time frame
#########
day_real = np.copy(day)
day[month == 2] = day[month == 2] - 29
day[month == 1] = day[month == 1] - 29 - 31
day[month == 4] = day[month == 4] + 31
day[month == 5] = day[month == 5] + 31 + 30
day[month == 6] = day[month == 6] + 31 + 30 + 31
day[month == 7] = day[month == 7] + 31 + 30 + 31 + 30
#print 'day now', day
#print 'day_real', day_real
#########
# fit
#########
def func(x, a, b):#, c):
#return a * np.exp(b * x)# + c #
#return a * b**(c*x)
return np.log(a) + b * x #log-linear
x = np.arange(10,day_max,0.5)
# fit only when there are more than 6 data points and cases every day.
data_points = range(8, len(day)+1)
DTs = []
Ntot_today = []
Ntot_week = []
R4s = []
col = 30
colapp = []
print 'Tag DTs R4'
for cut in data_points:
#print day[cut-8:cut]
#print num[cut-8:cut]
#########
### changed 8 fit
#########
#popt, pcov = curve_fit(func, day[:cut], num[:cut])
popt, pcov = curve_fit(func, day[cut-8:cut], np.log(num[cut-8:cut]))
#print len(day[cut-8:cut])
#####
# loglog
cond_week = day[cut-8:cut] > day[cut-1] - 7
num_week = num[cut-8:cut][cond_week]
Ntot_today.append(num[cut-8:cut][-1])
Ntot_week.append(num_week[-1] - num_week[0])
########
# Reproduction number for Rtime notification days (4 days strict)
#%%%%%%%%#daytoday = int(day[cut-1])
#%%%%%%%%#dayminus4 = int(daytoday - 5)
#%%%%%%%%#dayminus8 = int(daytoday - 9)
#%%%%%%%%#print daytoday, dayminus4, dayminus8
#%%%%%%%%#
#%%%%%%%%#print num[day == daytoday], num[day == dayminus4], num[day == dayminus8]
#%%%%%%%%#
#%%%%%%%%#R4_row = (num[day == daytoday] - num[day == dayminus4]) / (num[day == dayminus4] - num[day == dayminus8])
#%%%%%%%%#print R4_row
#%%%%%%%%#
#%%%%%%%%#raise Exception('stop')
########
# Reproduction number for Rtime notification days
#print num[cut-8:cut]
if cut-9 < 0:
num_before_int = num[cut-8:cut][0]
else: num_before_int = num[cut-8] - num[cut-9]
num_diff_8 = np.append(num_before_int, np.diff(num[cut-8:cut]))
#print num_diff_8
R4 = np.sum(num_diff_8[4:]) / np.sum(num_diff_8[0:4])
R4s.append(R4)
#print num_diff_8[0:4], num_diff_8[4:]
#########
# doubling time
#########
DT = np.round(np.log(2) / popt[1],2)
#print DT#, popt
DTs.append(DT)
#print popt, np.log(2) / popt[1]
print '%02d'%int(day_real[cut-1]) + '.' + '%02d'%int(month[cut-1]), '%6.2f'%DT#, '%6.2f'%R4
#print("a =", popt[0], "+/-", pcov[0,0]**0.5)
#print("b =", popt[1], "+/-", pcov[1,1]**0.5)
#print("c =", popt[2], "+/-", pcov[2,2]**0.5)
##########
# plot
##########
########
# plot fit
#########
day_label = 'Fit am ' + '%02d'%int(day_real[cut-1]) + '.' + '%02d'%int(month[cut-1]) + '; VZ: ' + '%6.2f'%DT + ' d'
ax[0].semilogy(x, np.exp(func(x, *popt)), '-', color=plt.cm.viridis(int(col)),
label=day_label)
colapp.append(int(col))
col = col + 256 / len(data_points)
########
# plot Beatmungsbedarf
#########
if cut == data_points[-1]:
# Beatmungsampel
bedarf = anteil_beatmung * np.exp(func(x, *popt))
ax[0].semilogy(x, bedarf,
'-', lw=3, color=plt.cm.Reds(200),
label='Ampel (Beatmungsbedarf ca. ' + str(int(anteil_beatmung*100)) + '%)')
ax[0].semilogy(x[(bedarf < geraet_max)],bedarf[(bedarf < geraet_max)],
'-', lw=3, color=plt.cm.jet(180),
label='Ampel (Beatmungsbedarf ca. ' + str(int(anteil_beatmung*100)) + '%)')
ax[0].semilogy(x[bedarf < geraet_min],bedarf[bedarf < geraet_min],
'-', lw=3, color=plt.cm.Greens(200),
label='Ampel (Beatmungsbedarf ca. ' + str(int(anteil_beatmung*100)) + '%)')
# Fehler
ax[0].semilogy(x, 0.05*np.exp(func(x,
popt[0] - pcov[0,0]**0.5,
popt[1] - pcov[1,1]**0.5)),
#popt[2] - pcov[2,2]**0.5),
'--', color='k', alpha=0.5, label='Unsicherheiten')
ax[0].semilogy(x, 0.05*np.exp(func(x,
popt[0] + pcov[0,0]**0.5,
popt[1] + pcov[1,1]**0.5)),
#popt[2] + pcov[2,2]**0.5),
'--', color='k', alpha=0.5)
#print 'day now2', day
####
# Beatmungs Kapazitaet
#########
ax[0].plot([x[0], x[-1]], [geraet_min]*2, 'k:', label="Kapazitaet Beatmungsapparate")
ax[0].plot([x[0], x[-1]], [geraet_max]*2, 'k:')
####
# gemeldete Fallzahlen
#########
ax[0].semilogy(day, num, 'k+', label="COVID19 erkrankt")
ax[0].semilogy(day, num_tod, 'k*', label="davon verstorben")
ax[0].semilogy(day, num_gesund, 'ko', alpha=0.3, label="davon genesen")
#print 'day now3', day
print '+' * 30
print 'Sterberate (%): ', np.round(num_tod[-1] / num[-1] * 100, 2)
print 'Gesunde (%): ', np.round(num_gesund[-1] / num[-1] * 100, 2)
print '+' * 30
#############
# formating
#############
from matplotlib.ticker import ScalarFormatter
for axis in [ax[0].xaxis, ax[0].yaxis]:
axis.set_major_formatter(ScalarFormatter())
ax[0].grid(True, which="both")
ax[0].set_xticks(np.arange(14, day_max, 4))
ax[0].set_xticklabels(day_ticks)
ax[0].text(13, 0.5, 'Mar')
ax[0].text(31, 0.5, 'Apr')
ax[0].text(31+30, 0.5, 'Mai')
ax[0].text(31+30+31, 0.5, 'Juni')
ax[0].text(31+30+31+30, 0.5, 'Juli')
ax[0].annotate('Ausgangssperre', ha='center', xy=(21, ax[0].get_ylim()[0]), xytext=(21, 0.4),
arrowprops=dict(arrowstyle= '-|>', color='grey', lw=2, ls='-'), alpha=0.6)
ax[0].annotate('Ostern', ha='center', xy=(31+12, ax[0].get_ylim()[0]), xytext=(31+12, 0.4),
arrowprops=dict(arrowstyle= '-|>', color='grey', lw=2, ls='-'), alpha=0.6)
ax[0].annotate('Ende Ferien', ha='center', xy=(31+20, ax[0].get_ylim()[0]), xytext=(31+20, 0.4),
arrowprops=dict(arrowstyle= '-|>', color='grey', lw=2, ls='-'), alpha=0.6)
# credit bar
credit = 'Christine Greif\nhttp://www.usm.uni-muenchen.de/~koepferl\nThis work is licensed under CC-BY-SA 4.0\nData: NPGEO-DE; VZ = Verdopplungszeit'
loc_label = ax[0].get_xlim()[1] * 1.12
link = ax[0].text(loc_label, 9e4, credit, fontsize=8, va='top')
link.set_url('http://www.usm.uni-muenchen.de/~koepferl')
# label
ax[0].set_ylabel('Gesamte Fallzahlen')
lgd = ax[0].legend(loc='best', bbox_to_anchor=(1.12, 0.93))
# percent
#########
EW_county = get_people_of_county(asked_ID=ID)
axi = ax[0].twinx()
#print 'lim', ax.get_ylim(), ax.get_ylim()[0] / EW_county * 100, ax.get_ylim()[1] / EW_county * 100, EW_county
axi.set_ylim(ax[0].get_ylim()[0] / EW_county * 100 , ax[0].get_ylim()[1] / EW_county * 100 )
axi.set_yscale('log')
import matplotlib.ticker as mtick
import matplotlib.ticker as ticker
axi.yaxis.set_major_formatter(ScalarFormatter())
axi.yaxis.set_major_formatter(ticker.FuncFormatter(lambda y,pos: ('{{:.{:1d}f}}%'.format(int(np.maximum(-np.log10(y),0)))).format(y)))
#axi.yaxis.set_major_formatter(FormatStrFormatter('%4d'))
#axi.yaxis.set_major_formatter(mtick.PercentFormatter())
axi.set_ylabel('Prozentualer Anteil zur Gesamteinwohnerzahl (' + str(EW_county) + ') im Kreis')
###########
# plot 2
###########
pass_all = {'day': day, 'fall': np.append(num[0], np.diff(num)), 'gesund': np.append(num_gesund[0], np.diff(num_gesund)), 'tod': np.append(num_tod[0], np.diff(num_tod))}
ax[1].set_ylabel('Taeglich gemeldete Fallzahlen')
# gemittelt ueber 7 Tage
all_smooth = np.zeros(shape = day[6:].shape)
tod_smooth = np.zeros(shape = day[6:].shape)
gesund_smooth = np.zeros(shape = day[6:].shape)
for z in range(7):
stop = -6+z
#print z, stop
if stop != 0:
all_smooth = all_smooth + 1/7. * pass_all['fall'][z:stop]
tod_smooth = tod_smooth + 1/7. * pass_all['tod'][z:stop]
gesund_smooth = gesund_smooth + 1/7. * pass_all['gesund'][z:stop]
else:
all_smooth = all_smooth + 1/7. * pass_all['fall'][z:]
tod_smooth = tod_smooth + 1/7. * pass_all['tod'][z:]
gesund_smooth = gesund_smooth + 1/7. * pass_all['gesund'][z:]
#all_smooth = 0.25 * (pass_all['fall'][0:-3] + pass_all['fall'][1:-2] + pass_all['fall'][2:-1] + pass_all['fall'][3:0])
#tod_smooth = 0.25 * (pass_all['tod'][0:-3] + pass_all['tod'][1:-2] + pass_all['tod'][2:-1] + pass_all['tod'][3:0])
#gesund_smooth = 0.25 * (pass_all['gesund'][0:-3] + pass_all['gesund'][1:-2] + pass_all['gesund'][2:-1] + pass_all['gesund'][3:0])
ax[1].semilogy(day[6:], all_smooth, 'r-', label='neu erkrankt (7-Tagesmittel)')
ax[1].semilogy(day[6:], tod_smooth, 'k-', label='verstorben (7-Tagesmittel)')
ax[1].semilogy(day[6:], gesund_smooth, 'k-', alpha=0.3, label='genesen (7-Tagesmittel)')
# box
import matplotlib.patches as patches
for b in range(len(day)):
if b == 0:
label_fall = "neu erkrankt"
label_gesund = 'genesen'
label_tod = 'verstorben'
else:
label_fall = None
label_gesund = None
label_tod = None
ax[1].add_patch(patches.Rectangle((day[b]-0.45,0.),0.9,pass_all['fall'][b],
linewidth=1,edgecolor='k',facecolor=plt.cm.jet(180), label=label_fall))
ax[1].add_patch(patches.Rectangle((day[b]-0.45,0.),0.9,pass_all['gesund'][b],
linewidth=1,edgecolor='k',facecolor='None', label=label_gesund, hatch='////'))
ax[1].add_patch(patches.Rectangle((day[b]-0.45,0.),0.9,pass_all['tod'][b],
linewidth=1,edgecolor='k',facecolor='w', label=label_tod))
#h = ax[1].hist(pass_all['fall'], bins=np.append(day - 0.5, day[-1] + 0.5))
#print h
#print pass_all['fall']
#ax[1].hist(pass_all['tod'], bins=day)
#ax[1].hist(pass_all['gesund'], bins=day)
from matplotlib.ticker import ScalarFormatter
for axis in [ax[1].xaxis, ax[1].yaxis]:
axis.set_major_formatter(ScalarFormatter())
ax[1].set_axisbelow(True)
ax[1].grid(True, which="both")
ax[1].set_xticks(np.arange(14, day_max, 4))
ax[1].set_xticklabels(day_ticks)
tx1 = ax[1].text(13, 0.3, 'Mar')
tx2 = ax[1].text(31, 0.3, 'Apr')
tx3 = ax[1].text(31+30, 0.3, 'Mai')
tx4 = ax[1].text(31+30+31, 0.3, 'Juni')
tx5 = ax[1].text(31+30+31+30, 0.3, 'Juli')
ax[1].annotate('Ausgangssperre', ha='center', xy=(21, ax[1].get_ylim()[0]), xytext=(21, 0.2),
arrowprops=dict(arrowstyle= '-|>', color='grey', lw=2, ls='-'), alpha=0.6)
ax[1].annotate('Ostern', ha='center', xy=(31+12, ax[1].get_ylim()[0]), xytext=(31+12, 0.2),
arrowprops=dict(arrowstyle= '-|>', color='grey', lw=2, ls='-'), alpha=0.6)
ax[1].annotate('Ende Ferien', ha='center', xy=(31+20, ax[1].get_ylim()[0]), xytext=(31+20, 0.2),
arrowprops=dict(arrowstyle= '-|>', color='grey', lw=2, ls='-'), alpha=0.6)
ax[1].legend(loc='best')
###########
# plot 3
###########
ax[2].set_ylabel('Verdopplungszeiten in Tage')
ax[2].plot(ax[2].get_xlim(), [10,10], ':', lw =2, color='grey', label='VZ = 10')
ax[2].plot(day[7:], np.array(DTs), 'k.-')
#from scipy.signal import find_peaks
#peaks, _ = find_peaks(np.array(DTs))
#print 'peaks', peaks
#ax[2].plot(peaks, np.array(DTs)[peaks], "Hb")
#ax[2].plot(day[7:], np.gradient(DTs, day[7:]), 'ko-')
diff = np.diff(DTs)
ax[2].plot(day[7:][1:][diff < 0], np.array(DTs)[1:][diff < 0], '^', color=plt.cm.Reds(200), label='Achtung: VZ faellt (!!!)')
#ax[2].plot(day[7:][1:], diff, 'k^-')
#print 'gradient', gradient
#print 'diff', np.diff(DTs)
ax[2].grid(True, which="both")
ax[2].set_xticks(np.arange(14, day_max, 4))
ax[2].set_xticklabels(day_ticks)
ax[2].legend(loc='best')
offset = ax[2].get_ylim()[0] - (ax[2].get_ylim()[1] - ax[2].get_ylim()[0]) / 5.
tx1 = ax[2].text(13, offset, 'Mar')
tx2 = ax[2].text(31, offset, 'Apr')
tx3 = ax[2].text(31+30, offset, 'Mai')
tx4 = ax[2].text(31+30+31, offset, 'Juni')
tx5 = ax[2].text(31+30+31+30, offset, 'Juli')
ax[2].annotate('Lock-down', ha='center', xy=(21, ax[2].get_ylim()[0]), xytext=(21, offset),
arrowprops=dict(arrowstyle= '-|>', color='grey', lw=2, ls='-'), alpha=0.6)
ax[2].annotate('Ostern', ha='center', xy=(31+12, ax[2].get_ylim()[0]), xytext=(31+12, offset),
arrowprops=dict(arrowstyle= '-|>', color='grey', lw=2, ls='-'), alpha=0.6)
ax[2].annotate('Ende Ferien', ha='center', xy=(31+20, ax[2].get_ylim()[0]), xytext=(31+20, offset),
arrowprops=dict(arrowstyle= '-|>', color='grey', lw=2, ls='-'), alpha=0.6)
###########
# plot 4
###########
########
# Reproduction number for Rtime notification days (4 days interpoliert)
minus8 = np.interp(day-8-1, day, num)
minus4 = np.interp(day-4-1, day, num)
minus_all = np.interp(np.arange(day[0], day[-1]), day, num)
#print day - 8 - 1
#print day
#print num
#print day - 4 - 1
#for i in range(len(np.arange(day[0], day[-1]))):
# print np.arange(day[0], day[-1])[i], minus_all[i]
R_number_interp = (num - minus4) / (minus4 - minus8)
R_number_interp[day-8-1 < day[0]] = np.nan # before 1st case
R_number_interp[day-4-1 < day[0]] = np.nan # before 1st case
#for da in day:
# if da-4 < day[0]:
# new_minus4 = 0
# else: new_minus4 = np.interp(da-4, day, num)
# if da-8 < day[0]:
# new_minus8 = 0
# else:
# new_minus8 = np.interp(da-8, day, num)
ax[3].set_ylabel('Interpolierte Reproduktionszahl')
ax[3].plot(ax[3].get_xlim(), [1,1], ':', lw =2, color='grey', label='R = 1')
ax[3].plot(day, R_number_interp, 'k.-')
ax[3].plot(day[R_number_interp >= 1], R_number_interp[R_number_interp >= 1], '^', color=plt.cm.Reds(200), label='Achtung: R > 1 (!!!)')
ax[3].grid(True, which="both")
ax[3].set_xticks(np.arange(14, day_max, 4))
ax[3].set_xticklabels(day_ticks)
ax[3].legend(loc='best')
offset = ax[3].get_ylim()[0] - (ax[3].get_ylim()[1] - ax[3].get_ylim()[0]) / 5.
tx1 = ax[3].text(13, offset, 'Mar')
tx2 = ax[3].text(31, offset, 'Apr')
tx3 = ax[3].text(31+30, offset, 'Mai')
tx4 = ax[3].text(31+30+31, offset, 'Juni')
tx5 = ax[3].text(31+30+31+30, offset, 'Juli')
ax[3].annotate('Lock-down', ha='center', xy=(21, ax[3].get_ylim()[0]), xytext=(21, offset),
arrowprops=dict(arrowstyle= '-|>', color='grey', lw=2, ls='-'), alpha=0.6)
ax[3].annotate('Ostern', ha='center', xy=(31+12, ax[3].get_ylim()[0]), xytext=(31+12, offset),
arrowprops=dict(arrowstyle= '-|>', color='grey', lw=2, ls='-'), alpha=0.6)
ax[3].annotate('Ende Ferien', ha='center', xy=(31+20, ax[3].get_ylim()[0]), xytext=(31+20, offset),
arrowprops=dict(arrowstyle= '-|>', color='grey', lw=2, ls='-'), alpha=0.6)
ax[3].text(ax[3].get_xlim()[1] * 1.02,
ax[3].get_ylim()[1] * 1.,
'zu Abb. 1: \nBei Kreisen mit sehr kurzen Verdopplungszeiten wird \nder Verlauf nicht/kaum flacher; mit sehr langen \nVerdopplungszeiten (wenigen Neuerkrankten) \nist der Verlauf fast horizontal. \n(Ziel: horizontale Linie).')
ax[3].text(ax[3].get_xlim()[1] * 1.02,
ax[3].get_ylim()[1] * 0.75,
'zu Abb. 2: \nBalkendiagramm der taeglich gemeldeten Fallzahlen. \n(Ziel: keine gelben und weissen Balken.)'
)
ax[3].text(ax[3].get_xlim()[1] * 1.02,
ax[3].get_ylim()[1] * 0.35,
'zu Abb. 3: \nVerdopplungszahl gibt die Zeit an in der sich die \nFallzahlen verdoppeln. Verdopplungszeiten kleiner \nals 10 oder abnehmend sind bedenklich. \n(Ziel: keine verkuerzenden Verdopplungszeiten \nund viel groesser als 10).'
)
ax[3].text(ax[3].get_xlim()[1] * 1.02,
ax[3].get_ylim()[0],
'zu Abb. 4: \nReproduktionszahl gibt die Anzahl der \nWeiteransteckungen durch einen Infizierten an. \nReproduktionszahl groesser als 1 ist bedenklich. \n(Ziel: Reproduktionszahl viel kleiner als 1).'
)
fig.savefig('expert/' + name + '_expert.pdf', dpi=300, overwrite=True, bbox_inches='tight', bbox_extra_artists=(lgd, tx1, tx2, tx3, tx4, tx5))
##################
# save plot ax[0]
##################
ax[1].remove()
ax[2].remove()
ax[3].remove()
#for a in ax:
# print 'ax'
#fig.set_size_inches(10, 8)
#ax[0].set_aspect(aspect=0.5)
#fig.set_gridspec_kw({'height_ratios': [1, 0, 0, ]})
#fig.gridspec_kw({'height_ratios': [1]})
#extent = ax[0].get_window_extent().transformed(fig.dpi_scale_trans.inverted())
# Pad the saved area by 10% in the x-direction and 20% in the y-direction
fig.savefig('plots/' + name + '.pdf', dpi=300, overwrite=True, bbox_extra_artists=(lgd, tx1, tx2, tx3, tx4, tx5), bbox_inches='tight')
rates = {'death_rate': num_tod / num * 100,
'recover_rate': num_gesund / num * 100,
'ill_rate': (num - num_gesund - num_tod) / num * 100,
'day': day}
return [name, day[7:], DTs, Ntot_today, Ntot_week, rates, R4s, pass_all]
#####################################
# Doubeling Time (Verdopplungszeit) #
#####################################
def plot_DT(DT, state, ncol=4, nrow=3):
'''
Plots day-dependent doubling time against time for the selected counties.
input
======
DT : list
Contains name of county, day array and DT array containing the 8 last data-points
where fit was possible
name : str
Name of specific region
day : np.array
Array of 8 days before and including the fit day
val_DT : list
list containing the calculated day-depending doubling times
rates : list
list containing rates (mortality rate, recovery rate, still ill rate)
state : list
output from load_RKI
ncol : int
Number of columns in plot (should not be changed for now.)
nrow : int
Number of rows in plot (should not be changed for now.)
returns
=======
Saves diagram as PDF.
'''
######################################
# DT for state
#######################
####
# move to March time frame
#########
day_max = 150.
day_ticks = [14, 18, 22, 26, 30, 3, 7, 11, 15, 19, 23, 27, 1, 5, 9, 13, 17, 21, 25, 29, 2, 6, 10, 14, 18, 22, 26, 30, 3, 7, 11, 15, 19, 23, 27]
dat_states = state[1]
state_day = []
for i in range(len(dat_states)):
y, m, d = dat_states[i].split('/')
if m == '01': state_day.append(int(d) - 29 - 31)
if m == '02': state_day.append(int(d) - 29)
if m == '03': state_day.append(int(d))
if m == '04': state_day.append(int(d) + 31)
if m == '05': state_day.append(int(d) + 31 + 30)
if m == '06': state_day.append(int(d) + 31 + 30 + 31)
if m == '07': state_day.append(int(d) + 31 + 30 + 31 + 30)
#########
# fit
#########
def func(x, a, b):#, c):
#return a * np.exp(b * x)# + c
#return a * b**(c*x)
return np.log(a) + b * x
x = np.arange(10,day_max,0.5)
# fit only when there are more than 8 data points and cases every day.
data_points = range(8, len(state_day)+1)
state_day = np.array(state_day)
state_num = np.array(state[0])
DTs_state = []
from scipy.optimize import curve_fit
for cut in data_points:
popt, pcov = curve_fit(func, state_day[cut-8:cut], np.log(state_num[cut-8:cut]))
#########
# doubling time
#########
DT_state = np.round(np.log(2) / popt[1],2)
DTs_state.append(DT_state)
fig, axs = plt.subplots(nrow, ncol, figsize=(28,21))
fig2, axs2 = plt.subplots(nrow, ncol, figsize=(28,21))
fig3, axs3 = plt.subplots(nrow, ncol, figsize=(28,21))
fig4, axs4 = plt.subplots(nrow, ncol, figsize=(28,21))
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.1, hspace=0.1)
axs[0,0].set_title('Entwicklung der Verdopplungszeiten auf Kreisebene')
axs[0,1].set_title('Evolution of the Doubeling Time for German Counties')
axs[0,2].set_title('Entwicklung der Verdopplungszeiten auf Kreisebene')
axs[0,3].set_title('Evolution of the Doubling Time for German Counties')
print len(DT.keys())
# sort after Landkreis name not ID
DT_ids = DT.keys()
DT_names = []
for key in DT_ids:
DT_names.append(DT[key][0])
print key, DT[key][0]
DT_ids = np.array(DT_ids)
DT_names = np.array(DT_names)
DT_index = np.argsort(DT_names)
DT_ids = DT_ids[DT_index]
DT_names = DT_names[DT_index]
#print '*' * 10
#print DT_names[95], DT_ids[95]
sorted_keys = DT_ids
from matplotlib.cm import get_cmap
cmap = get_cmap('inferno') # type: matplotlib.colors.ListedColormap
for i in range(len(sorted_keys)):
if i in range(0, 8):
ax = axs[0,0]
ax2 = axs2[0,0]
ax3 = axs3[0,0]
ax4 = axs4[0,0]
line_col = 20 + 30 * (8 - i)
if i in range(8, 16):
ax = axs[0,1]
ax2 = axs2[0,1]
ax3 = axs3[0,1]
ax4 = axs4[0,1]
line_col = 20 + 30 * (16 - i)
if i in range(16,24):
ax = axs[0,2]
ax2 = axs2[0,2]
ax3 = axs3[0,2]
ax4 = axs4[0,2]
line_col = 20 + 30 * (24 - i)
if i in range(24,32):
ax = axs[0,3]
ax2 = axs2[0,3]
ax3 = axs3[0,3]
ax4 = axs4[0,3]
line_col = 20 + 30 * (32 - i)
if i in range(32,40):
ax = axs[1,0]
ax2 = axs2[1,0]
ax3 = axs3[1,0]
ax4 = axs4[1,0]
line_col = 20 + 30 * (40 - i)
if i in range(40,48):
ax = axs[1,1]
ax2 = axs2[1,1]
ax3 = axs3[1,1]
ax4 = axs4[1,1]
line_col = 20 + 30 * (48 - i)
if i in range(48,56):
ax = axs[1,2]
ax2 = axs2[1,2]
ax3 = axs3[1,2]
ax4 = axs4[1,2]
line_col = 20 + 30 * (56 - i)
if i in range(56,64):
ax = axs[1,3]
ax2 = axs2[1,3]
ax3 = axs3[1,3]
ax4 = axs4[1,3]
line_col = 20 + 30 * (64 - i)
if i in range(64,72):
ax = axs[2,0]
ax2 = axs2[2,0]
ax3 = axs3[2,0]
ax4 = axs4[2,0]
line_col = 20 + 30 * (72 - i)
if i in range(72,80):
ax = axs[2,1]
ax2 = axs2[2,1]
ax3 = axs3[2,1]
ax4 = axs4[2,1]
line_col = 20 + 30 * (80 - i)
if i in range(80,88):
ax = axs[2,2]
ax2 = axs2[2,2]
ax3 = axs3[2,2]
ax4 = axs4[2,2]
line_col = 20 + 30 * (88 - i)
if i in range(88,96):
ax = axs[2,3]
ax2 = axs2[2,3]
ax3 = axs3[2,3]
ax4 = axs4[2,3]
line_col = 20 + 30 * (96 - i)
key = sorted_keys[i]
if i in [0, 8 ,16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96]:
ax.semilogy(state_day[7:], DTs_state, '.:k', label= state[2] + ' average')
print '-' * 20
print state[2], DTs_state[-1], int(state_day[7:][-1])
print '-' * 20
ax.semilogy(DT[key][1], DT[key][2], '.-', c = cmap(line_col), label=DT[key][0])
ax2.loglog(DT[key][3], DT[key][4], '.-', c = cmap(line_col), label=DT[key][0])
print DT[key][2][-1], int(DT[key][1][-1]), DT[key][0]
ax3.plot(DT[key][5]['day'], DT[key][5]['death_rate'], '*-', c = cmap(line_col), label=DT[key][0])
ax4.plot(DT[key][1], DT[key][6], '.-', c = cmap(line_col), label=DT[key][0])
#ax3.plot(DT[key][5]['day'], DT[key][5]['recover_rate'], 'o-', c = cmap(line_col), label=DT[key][0])
#ax3.plot(DT[key][5]['day'], DT[key][5]['ill_rate'], 'x-', c = cmap(line_col), label=DT[key][0])
######
# axis
#factor_1 = 100/60.
x_pos = 57
credit2 = 'Christine Greif\nhttp://www.usm.uni-muenchen.de/~koepferl\nThis work is licensed under CC-BY-SA 4.0\nData: NPGEO-DE'
link = axs[2,3].text(x_pos, 0.7, credit2, fontsize=8, va = 'top')
link = axs3[2,3].text(x_pos, -2, credit2, fontsize=8)
link = axs4[2,3].text(x_pos, -1., credit2, fontsize=8)
link = axs2[2,3].text(3.5, 0.5, credit2, fontsize=8, va='top')
link.set_url('http://www.usm.uni-muenchen.de/~koepferl')
axs[0,0].set_ylabel('Verdopplungszeiten (Tage)')
axs[1,0].set_ylabel('Doubling Time DT (days)')
axs[2,0].set_ylabel('Verdopplungszeiten (Tage)')
for ax in axs.reshape(-1):
ax.set_ylim(1.5,500.9)
ax.set_xlim(13,day_max)
from matplotlib.ticker import ScalarFormatter
for axis in [ax.xaxis, ax.yaxis]:
axis.set_major_formatter(ScalarFormatter())
ax.grid(True, which="both")
ax.set_xticks(np.arange(14, day_max, 4))
ax.set_xticklabels(day_ticks)
ax.legend(loc='upper left')
#if ax in [axs[2,0], axs[2,1], axs[2,2], axs[2,3]]:
ax.text(13, 0.8, 'Maerz/March')
ax.text(31, 0.8, 'April')
ax.text(31+30, 0.8, 'Mai/May')
ax.text(31+30+31, 0.8, 'Juni/June')
ax.text(31+30+31+30, 0.8, 'Juli/July')
for ax2 in axs2.reshape(-1):
from matplotlib.ticker import ScalarFormatter
for axis in [ax2.xaxis, ax2.yaxis]:
axis.set_major_formatter(ScalarFormatter())
ax2.grid(True, which="both")
ax2.set_ylim(1.5,2000)
ax2.set_xlim(1.5,10000)
ax2.legend(loc='upper left')
if ax2 in [axs2[2,0], axs2[2,1], axs2[2,2], axs2[2,3]]:
ax2.set_xlabel('Totale Fallzahlen (total number of cases)')
if ax2 in [axs2[0,0], axs2[1,0], axs2[2,0]]:
ax2.set_ylabel('Fallzahlen in der letzten Woche (number of new cases last week)')
for ax3 in axs3.reshape(-1):
ax3.set_ylim(0,20.9)
ax3.set_xlim(13,day_max)
ax3.grid(True, which="both")
ax3.set_xticks(np.arange(14, day_max, 4))
ax3.set_xticklabels(day_ticks)
if ax3 in [axs3[0,0], axs3[1,0], axs3[2,0]]:
ax3.set_ylabel('Sterberaten in %')
ax3.legend(loc='upper left')