def run_rolling_average(x, y, ct, g_id, avgsize, curdate, ylabel):
rname = ct.replace('_', ' ')
fdata = f'gpdata/dat/{ct}-{g_id}.dat'
fgplot = f'gpdata/{ct}-{g_id}.gp'
fsvg = f'svg/{ct}-{g_id}.svg'
freport = f'report/{ct}-{g_id}.html'
dump_xy_dat(fdata, x, y)
dump_svg(fgplot,
fsvg,
f'7-Rolling average of cases for {rname} on {curdate}',
f'Total {ylabel}',
f'{avgsize}-rolling Average of {ylabel}',
fdata,
2,
f"{avgsize}-rolling average",
opt='xrange [0<*:]\nset yrange [0<*:]',
point=False,
logx=True,
logy=True)
with open(freport, 'w') as f:
table_info = f'<tr><td>Success on a {avgsize}-rolling average</td></tr>'
table_stat = f'<tr><th>Day</th><th>{avgsize}-average of {ylabel}</th></tr>'
for i, j in zip(x, y):
table_stat += f'<tr><td>{i}</td><td>{j}</td></tr>'
f.write(
param_page(rname, table_info, table_stat, '<tr><td>None</td></tr>',
fsvg))
return
def run_model_sigmoid(x, y, ct, g_id, ylabel, data_consolidated,
model_consolidated, curdate, text):
rname = ct.replace('_', ' ')
fdata = f'gpdata/dat/{ct}-{g_id}.dat'
fgplot = f'gpdata/{ct}-{g_id}.gp'
fsvg = f'svg/{ct}-{g_id}.svg'
freport = f'report/{ct}-{g_id}.html'
result, model = find_fit_sigmoid(x, y)
if result.success == False:
data_consolidated.append('n.a.')
data_consolidated.append('n.a.')
dump_xy_dat(fdata, x, y)
dump_svg(fgplot,
fsvg,
f'{text} for {rname} on {curdate}',
'Days from the first infected',
f'{ylabel}',
fdata,
2,
f"{rname} data",
opt='colorsequence podo',
txt1='NO FIT AVAILABLE FOR THE CURRENT DATA',
point=True)
#loggingg.info(f'run_model_sigmoid: cannot fit sigmoid for {ct}')
else:
chisqr = result.chisqr
center = int(round(result.params['step_center'].value))
data_consolidated.append(center)
data_consolidated.append(chisqr)
model_consolidated.append(result)
dump_xyz_dat(fdata, x, y, result.best_fit)
dump_svg2D(fgplot,
fsvg,
f'{text} for {rname} on {curdate}',
'Days from the first infected',
f'{ylabel}',
fdata,
2,
3,
f"{rname} data",
f'{text}',
opt='yrange [0<*:]',
txt1=f'Mid = {center} days',
txt2=f'�² = {chisqr:9.2}')
dump_report(freport, result, x, y, 7, fsvg, model, ct, ylabel)
return
def run_data(x, y, ct, g_id, ylabel, curdate, text, txt1=''):
rname = ct.replace('_', ' ')
fdata = f'gpdata/dat/{ct}-{g_id}.dat'
fgplot = f'gpdata/{ct}-{g_id}.gp'
fsvg = f'svg/{ct}-{g_id}.svg'
dump_xy_dat(fdata, x, y)
dump_svg(fgplot,
fsvg,
f'{text} for {rname} on {curdate}',
'Days from the first infected',
f'{ylabel}',
fdata,
2,
f"{rname} data",
opt='colorsequence podo',
txt1=txt1,
point=True)
def run_socnet_model(x, y, ct, g_id, cur, tag, ylabel, data_consolidated,
model_consolidated, curdate, text):
import fitrs3
from scipy.stats import chisquare
rname = ct.replace('_', ' ')
fdata = f'gpdata/dat/{ct}-{g_id}.dat'
fgplot = f'gpdata/{ct}-{g_id}.gp'
fsvg = f'svg/{ct}-{g_id}.svg'
freport = f'report/{ct}-{g_id}.html'
file1 = f'scnlog/{ct}-p1.dat'
file2 = f'scnlog/{ct}-p2.dat'
partition = len(y) // 4
forecast = fitrs3.previsaoredeslp(y, 7, 200, 400, 100, file1, file2,
partition, y[-1] + 50, y[-1] * 20, 4, 6,
0.2, 0.7, 0, 101)
if forecast is None:
data_consolidated.append('n.a.')
data_consolidated.append('n.a.')
dump_xy_dat(fdata, x, y)
dump_svg(fgplot,
fsvg,
f'{text} for {rname} on {curdate}',
'Days from the first infected',
f'{ylabel}',
fdata,
2,
f"{rname} data",
opt='colorsequence podo',
txt1='NO FIT AVAILABLE FOR THE CURRENT DATA',
point=True)
else:
chisqr = chisquare(y, f_exp=forecast[:len(y)])[0]
data_consolidated.append(chisqr)
model_consolidated.append('socnet-fitrs3')
nx = x if forecast is None else np.arange(len(forecast))
dump_xyz_dat(fdata, nx, y, forecast)
dump_svg2D(fgplot,
fsvg,
f'{text} for {rname} on {curdate}',
'Days from the first infected',
f'{ylabel}',
fdata,
2,
3,
f"{rname} data",
f'{text}',
opt='yrange [0<*:]',
txt1=f'SOCNET',
txt2=f'�² = {chisqr:9.2}')
if forecast is not None:
with open(freport, 'w') as f:
table_info = f'<tr><td>Success status</td><td>Forecast calculated with socnet-fitrs3</td></tr>'
table_info += f'<tr><td>Abort status</td><td>n.a.</td></tr>'
table_info += f'<tr><td>Fit message</td><td>n.a.</td></tr>'
table_stat = '<tr> <td>n.a</td></tr>'
table_obs = f'<tr><th>Days from the first infected</th><th>{ylabel}</th><th>Model {ylabel}</th></tr>'
if forecast != None:
for i, j, k in itertools.zip_longest(nx,
y,
forecast,
fillvalue='nan'):
table_obs += f'<tr><td>{i}</td><td>{j}</td><td>{k:.0f}</td></tr>'
else:
for i, j in itertools.zip_longest(x, y, fillvalue='nan'):
table_obs += f'<tr><td>{i}</td><td>{j}</td><td>n.a.</td></tr>'
f.write(param_page(rname, table_info, table_stat, table_obs, fsvg))
return
def run_edo_model(ct,
g_id,
cur,
tag,
ylabel,
data_consolidated,
model_consolidated,
curdate,
text,
paramp=None,
forecast_days=14):
rname = ct.replace('_', ' ')
fdata = f'gpdata/dat/{ct}-{g_id}.dat'
fdatc = f'gpdata/dat/{ct}-{g_id+10}.dat'
fgplot = f'gpdata/{ct}-{g_id}.gp'
fsvg = f'svg/{ct}-{g_id}.svg'
fsvg1 = f'svg/{ct}-{g_id+10}.svg'
fsvg2 = f'svg/{ct}-{g_id+11}.svg'
freport = f'report/{ct}-{g_id}.html'
x, y, model, forecast = fit_edo_shape(ct,
cur,
tag,
paramp=paramp,
forecast_days=forecast_days)
if model.success == False:
data_consolidated.append('n.a.')
model_consolidated.append(None)
dump_xy_dat(fdata, x, y)
dump_svg(fgplot,
fsvg,
f'{text} for {rname} on {curdate}',
'Days from the first infected',
f'{ylabel}',
fdata,
2,
f"{rname} data",
opt='colorsequence podo',
txt1='NO FIT AVAILABLE FOR THE CURRENT DATA',
point=True)
else:
chisqr = model.chisqr
param = model.params
data_consolidated.append(chisqr)
model_consolidated.append(model)
if tag == 'accCombined':
x = x[:len(x) // 2]
y = y[:len(x) // 2]
nx = x if forecast is None else np.arange(len(forecast) // 2)
else:
nx = x if forecast is None else np.arange(len(forecast))
dump_xyz_dat(fdata, nx, y, forecast)
dump_svg2D(fgplot,
fsvg,
f'{text} for {rname} on {curdate}',
'Days from the first infected',
f'{ylabel}',
fdata,
2,
3,
f"{rname} data",
f'{text}',
opt='yrange [0<*:]',
txt1=f'SIMDRQME',
txt2=f'�² = {chisqr:9.2}')
S, Q, E, A, I, D, R, M = edo.eval_edo(param, 250)
pop = param['pop'].value
Sr = [x / pop for x in S]
Qr = [x / pop for x in Q]
Er = [x / pop for x in E]
Ar = [x / pop for x in A]
Ir = [x / pop for x in I]
Dr = [x / pop for x in D]
Rr = [x / pop for x in R]
Mr = [x / pop for x in M]
svg.dump_8_dat(fdatc, Sr, Qr, Er, Ar, Ir, Dr, Rr, Mr)
svg.dump_svg8D(fsvg1, fsvg2, fdatc,
f'SIMDRQME Compartment Model for {rname} on {curdate}',
'days', 'perc. of pop.',
['S', 'Q', 'E', 'A', 'I', 'D', 'R', 'M'])
if model != None:
with open(freport, 'w') as f:
table_info = f'<tr><td>Success status</td><td>{model.success}</td></tr>'
table_info += f'<tr><td>Abort status</td><td>{model.aborted}</td></tr>'
table_info += f'<tr><td>Fit message</td><td>{model.message}</td></tr>'
table_stat = '<tr> <td>' + model._repr_html_() + '</td></tr>'
table_obs = f'<tr><th>Days from the first infected</th><th>{ylabel}</th><th>Model {ylabel}</th></tr>'
if forecast != None:
for i, j, k in itertools.zip_longest(nx,
y,
forecast,
fillvalue='nan'):
table_obs += f'<tr><td>{i}</td><td>{j}</td><td>{k:.0f}</td></tr>'
else:
for i, j in itertools.zip_longest(x, y, fillvalue='nan'):
table_obs += f'<tr><td>{i}</td><td>{j}</td><td>n.a.</td></tr>'
f.write(
param_page1(rname, table_info, table_stat, table_obs, fsvg,
fsvg1, fsvg2))
return
def copy_edo_model(ct,
g_id,
cur,
tag,
ylabel,
data_consolidated,
model_consolidated,
curdate,
text,
paramp=None,
forecast_days=14):
rname = ct.replace('_', ' ')
fdata = f'gpdata/dat/{ct}-{g_id}.dat'
fgplot = f'gpdata/{ct}-{g_id}.gp'
fsvg = f'svg/{ct}-{g_id}.svg'
freport = f'report/{ct}-{g_id}.html'
x, y, model, forecast = copy_edo_shape(ct, cur, tag, model_consolidated[0],
paramp, forecast_days)
if model.success == False:
data_consolidated.append('n.a.')
model_consolidated.append(None)
dump_xy_dat(fdata, x, y)
dump_svg(fgplot,
fsvg,
f'{text} for {rname} on {curdate}',
'Days from the first infected',
f'{ylabel}',
fdata,
2,
f"{rname} data",
opt='colorsequence podo',
txt1='NO FIT AVAILABLE FOR THE CURRENT DATA',
point=True)
else:
chisqr = model.chisqr
data_consolidated.append(chisqr)
model_consolidated.append(model)
nx = x if forecast is None else np.arange(len(forecast))
dump_xyz_dat(fdata, nx, y, forecast)
dump_svg2D(fgplot,
fsvg,
f'{text} for {rname} on {curdate}',
'Days from the first infected',
f'{ylabel}',
fdata,
2,
3,
f"{rname} data",
f'{text}',
opt='yrange [0<*:]',
txt1=f'EDO',
txt2=f'�² = {chisqr:9.2}')
if model != None:
with open(freport, 'w') as f:
table_info = f'<tr><td>Success status</td><td>{model.success}</td></tr>'
table_info += f'<tr><td>Abort status</td><td>{model.aborted}</td></tr>'
table_info += f'<tr><td>Fit message</td><td>{model.message}</td></tr>'
table_stat = '<tr> <td>' + model._repr_html_() + '</td></tr>'
table_obs = f'<tr><th>Days from the first infected</th><th>{ylabel}</th><th>Model {ylabel}</th></tr>'
if forecast != None:
for i, j, k in itertools.zip_longest(nx,
y,
forecast,
fillvalue='nan'):
table_obs += f'<tr><td>{i}</td><td>{j}</td><td>{k:.0f}</td></tr>'
else:
for i, j in itertools.zip_longest(x, y, fillvalue='nan'):
table_obs += f'<tr><td>{i}</td><td>{j}</td><td>n.a.</td></tr>'
f.write(param_page(rname, table_info, table_stat, table_obs, fsvg))
return