def GeneratePercentiles(sols):
n_time_points = len(sols[0]['t'])
y_plot = np.zeros((len(categories.keys()), len(sols), n_time_points))
for k, sol in enumerate(sols):
sol['y'] = np.asarray(sol['y'])
for name in categories.keys():
y_plot[categories[name]['index'],
k, :] = sol['y_plot'][categories[name]['index']]
y_L95, y_U95, y_LQ, y_UQ, y_median = [
np.zeros((len(categories.keys()), n_time_points)) for i in range(5)
]
for name in categories.keys():
y_L95[categories[name]['index'], :] = np.asarray([
np.percentile(y_plot[categories[name]['index'], :, i], 2.5)
for i in range(n_time_points)
])
y_LQ[categories[name]['index'], :] = np.asarray([
np.percentile(y_plot[categories[name]['index'], :, i], 25)
for i in range(n_time_points)
])
y_UQ[categories[name]['index'], :] = np.asarray([
np.percentile(y_plot[categories[name]['index'], :, i], 75)
for i in range(n_time_points)
])
y_U95[categories[name]['index'], :] = np.asarray([
np.percentile(y_plot[categories[name]['index'], :, i], 97.5)
for i in range(n_time_points)
])
y_median[categories[name]['index'], :] = np.asarray([
statistics.median(y_plot[categories[name]['index'], :, i])
for i in range(n_time_points)
])
return [y_U95, y_UQ, y_LQ, y_L95, y_median]
def stacked_bar_plot(sols,cats_to_plot,population_plot,population_frame):
# population_plot = params.population
font_size = 13
lines_to_plot = []
ii = -1
for sol in sols:
ii += 1
for name in categories.keys():
if name == cats_to_plot:
sol['y'] = np.asarray(sol['y'])
xx = sol['t']
y_sum = np.zeros(len(xx))
xx = [xx[i] for i in range(1,len(xx),2)]
for i in range(population_frame.shape[0]): # age_cats
y_plot = 100*sol['y'][categories[name]['index']+ i*params.number_compartments,:]
y_sum = y_sum + y_plot
legend_name = categories[name]['longname'] + ': ' + np.asarray(population_frame.Age)[i] # first one says e.g. infected
y_plot = [y_plot[i] for i in range(1,len(y_plot),2)]
line = {'x': xx, 'y': y_plot,
'hovertemplate': '%{y:.2f}%, ' + '%{text} <br>',# +
# 'Time: %{x:.1f} days<extra></extra>',
'text': [population_format(i*population_plot/100) for i in y_plot],
# 'marker_line_width': 0,
# 'marker_line_color': 'black',
'type': 'bar',
'name': legend_name}
lines_to_plot.append(line)
ymax = max(y_sum)
# ymax = 0
# for line in lines_to_plot:
# ymax = max(ymax,max(line['y']))
yax = dict(range= [0,min(1.1*ymax,100)])
##
lines_to_plot.append(
dict(
type='scatter',
x = [0,sol['t'][-1]],
y = [ 0, population_plot],
yaxis="y2",
opacity=0,
hoverinfo = 'skip',
showlegend=False
))
yy2 = [0]
for i in range(8):
yy2.append(10**(i-5))
yy2.append(2*10**(i-5))
yy2.append(5*10**(i-5))
yy = [i for i in yy2]
pop_vec_lin = np.linspace(0,yy2[1],11)
for i in range(len(yy)-1):
if yax['range'][1]>yy[i] and yax['range'][1] <= yy[i+1]:
pop_vec_lin = np.linspace(0,yy2[i+1],11)
vec = [i*(population_plot) for i in pop_vec_lin]
log_bottom = -8
log_range = [log_bottom,np.log10(yax['range'][1])]
pop_vec_log_intermediate = np.linspace(log_range[0],ceil(np.log10(pop_vec_lin[-1])), 1+ ceil(np.log10(pop_vec_lin[-1])-log_range[0]) )
pop_log_vec = [10**(i) for i in pop_vec_log_intermediate]
vec2 = [i*(population_plot) for i in pop_log_vec]
layout = go.Layout(
template="simple_white",
font = dict(size= font_size), #'12em'),
margin=dict(t=5, b=5, l=10, r=10,pad=15),
hovermode='x',
xaxis= dict(
title='Days',
automargin=True,
hoverformat='.0f',
),
yaxis= dict(mirror= True,
title='Percentage of Total Population',
range= yax['range'],
automargin=True,
type = 'linear'
),
barmode = 'stack',
updatemenus = [dict(
buttons=list([
dict(
args=[{"yaxis": {'title': 'Percentage of Total Population', 'type': 'linear', 'range': yax['range'], 'automargin': True},
"yaxis2": {'title': 'Population','type': 'linear', 'overlaying': 'y1', 'range': yax['range'], 'ticktext': [population_format(0.01*vec[i]) for i in range(len(pop_vec_lin))], 'tickvals': [i for i in pop_vec_lin],'automargin': True,'side':'right'}
}], # tickformat
label="Linear",
method="relayout"
),
dict(
args=[{"yaxis": {'title': 'Percentage of Total Population', 'type': 'log', 'range': log_range,'automargin': True},
"yaxis2": {'title': 'Population','type': 'log', 'overlaying': 'y1', 'range': log_range, 'ticktext': [population_format(0.01*vec2[i]) for i in range(len(pop_log_vec))], 'tickvals': [i for i in pop_log_vec],'automargin': True,'side':'right'}
}], # 'tickformat': yax_form_log,
label="Logarithmic",
method="relayout"
)
]),
x= 0.5,
xanchor="right",
pad={"r": 5, "t": 30, "b": 10, "l": 5},
active=0,
y=-0.13,
showactive=True,
direction='up',
yanchor="top"
)],
legend = dict(
font=dict(size=font_size*(20/24)),
x = 0.5,
y = 1.03,
xanchor= 'center',
yanchor= 'bottom'
),
legend_orientation = 'h',
legend_title = '<b> Key </b>',
yaxis2 = dict(
title = 'Population',
overlaying='y1',
range = yax['range'],
side='right',
ticktext = [population_format(0.01*vec[i]) for i in range(len(pop_vec_lin))],
tickvals = [i for i in pop_vec_lin],
automargin=True
)
)
return {'data': lines_to_plot, 'layout': layout}
def age_structure_plot(sols,cats_to_plot,population_plot,population_frame): # ,confidence_range=None
# population_plot = params.population
font_size = 13
lines_to_plot = []
ii = -1
for sol in sols:
ii += 1
for name in categories.keys():
if name == cats_to_plot:
sol['y'] = np.asarray(sol['y'])
xx = sol['t']
for i in range(population_frame.shape[0]): # # age_categories
y_plot = 100*sol['y'][categories[name]['index']+ i*params.number_compartments,:]
legend_name = categories[name]['longname'] + ': ' + np.asarray(population_frame.Age)[i] # first one says e.g. infected
line = {'x': xx, 'y': y_plot,
'hovertemplate': '%{y:.2f}%, ' + '%{text} <br>',# +
# 'Time: %{x:.1f} days<extra></extra>',
'text': [population_format(i*population_plot/100) for i in y_plot],
'opacity': 0.5,
'name': legend_name}
lines_to_plot.append(line)
ymax = 0
for line in lines_to_plot:
ymax = max(ymax,max(line['y']))
yax = dict(range= [0,min(1.1*ymax,100)])
##
lines_to_plot.append(
dict(
type='scatter',
x = [0,sol['t'][-1]],
y = [ 0, population_plot],
yaxis="y2",
opacity=0,
hoverinfo = 'skip',
showlegend=False
))
shapes=[]
annots=[]
# if control_time[0]!=control_time[1] and not no_control:
# shapes.append(dict(
# # filled Blue Control Rectangle
# type="rect",
# x0= control_time[0],
# y0=0,
# x1= control_time[1],
# y1= yax['range'][1],
# line=dict(
# color="LightSkyBlue",
# width=0,
# ),
# fillcolor="LightSkyBlue",
# opacity= 0.15
# ))
# annots.append(dict(
# x = 0.5*(control_time[0] + control_time[1]),
# y = 0.5,
# text="<b>Control<br>" + "<b> In <br>" + "<b> Place",
# textangle=0,
# font=dict(
# size= font_size*(30/24),
# color="blue"
# ),
# showarrow=False,
# opacity=0.4,
# xshift= 0,
# xref = 'x',
# yref = 'paper',
# ))
yy2 = [0]
for i in range(8):
yy2.append(10**(i-5))
yy2.append(2*10**(i-5))
yy2.append(5*10**(i-5))
yy = [i for i in yy2]
pop_vec_lin = np.linspace(0,yy2[1],11)
for i in range(len(yy)-1):
if yax['range'][1]>yy[i] and yax['range'][1] <= yy[i+1]:
pop_vec_lin = np.linspace(0,yy2[i+1],11)
vec = [i*(population_plot) for i in pop_vec_lin]
log_bottom = -8
log_range = [log_bottom,np.log10(yax['range'][1])]
pop_vec_log_intermediate = np.linspace(log_range[0],ceil(np.log10(pop_vec_lin[-1])), 1+ ceil(np.log10(pop_vec_lin[-1])-log_range[0]) )
pop_log_vec = [10**(i) for i in pop_vec_log_intermediate]
vec2 = [i*(population_plot) for i in pop_log_vec]
layout = go.Layout(
template="simple_white",
shapes=shapes,
annotations=annots,
font = dict(size= font_size), #'12em'),
margin=dict(t=5, b=5, l=10, r=10,pad=15),
hovermode='x',
xaxis= dict(
title='Days',
automargin=True,
hoverformat='.0f',
),
yaxis= dict(mirror= True,
title='Percentage of Total Population',
range= yax['range'],
automargin=True,
type = 'linear'
),
updatemenus = [dict(
buttons=list([
dict(
args=[{"yaxis": {'title': 'Percentage of Total Population', 'type': 'linear', 'range': yax['range'], 'automargin': True},
"yaxis2": {'title': 'Population','type': 'linear', 'overlaying': 'y1', 'range': yax['range'], 'ticktext': [population_format(0.01*vec[i]) for i in range(len(pop_vec_lin))], 'tickvals': [i for i in pop_vec_lin],'automargin': True,'side':'right'}
}], # tickformat
label="Linear",
method="relayout"
),
dict(
args=[{"yaxis": {'title': 'Percentage of Total Population', 'type': 'log', 'range': log_range,'automargin': True},
"yaxis2": {'title': 'Population','type': 'log', 'overlaying': 'y1', 'range': log_range, 'ticktext': [population_format(0.01*vec2[i]) for i in range(len(pop_log_vec))], 'tickvals': [i for i in pop_log_vec],'automargin': True,'side':'right'}
}], # 'tickformat': yax_form_log,
label="Logarithmic",
method="relayout"
)
]),
x= 0.5,
xanchor="right",
pad={"r": 5, "t": 30, "b": 10, "l": 5},
active=0,
y=-0.13,
showactive=True,
direction='up',
yanchor="top"
)],
legend = dict(
font=dict(size=font_size*(20/24)),
x = 0.5,
y = 1.03,
xanchor= 'center',
yanchor= 'bottom'
),
legend_orientation = 'h',
legend_title = '<b> Key </b>',
yaxis2 = dict(
title = 'Population',
overlaying='y1',
range = yax['range'],
side='right',
ticktext = [population_format(0.01*vec[i]) for i in range(len(pop_vec_lin))],
tickvals = [i for i in pop_vec_lin],
automargin=True
)
)
return {'data': lines_to_plot, 'layout': layout}
def uncertainty_plot(sols,cats_to_plot,population_plot,population_frame,confidence_range=None):
if len(cats_to_plot)==0:
cats_to_plot=['I']
font_size = 13
lines_to_plot = []
percentiles = ['97.5','75','25','2.5']
labels = ['1','75-97.5 percentile','25-75 percentile','2.5-25 percentile']
showledge = [False,True,True,True]
xx = sols[0]['t']
for name in categories.keys():
if name == cats_to_plot:
ii = 0
for yy in confidence_range[:-1]:
# print(yy.shape)
if ii == 0:
fill = None
else:
fill = 'tonexty'
if ii==2:
opac = '0.5)'
else:
opac = '0.2)'
ii = ii+1
yy = np.asarray(yy)
y_plot = 100*yy[categories[name]['index'],:]
line = {'x': xx, 'y': y_plot,
'hovertemplate': '%{y:.2f}%, %{text}, ' + percentiles[ii-1] + ' percentile<extra></extra>',
# 'Time: %{x:.1f} days<extra></extra>',
'text': [population_format(i*population_plot/100) for i in y_plot],
'line': {'width': 0, 'color': categories[name]['colour']},
'fillcolor': categories[name]['fill_colour'][:-4] + opac,
'legendgroup': name + 'fill',
'showlegend': showledge[ii-1],
'mode': 'lines',
# 'opacity': 0.1,
'fill': fill,
'name': labels[ii-1]
}
lines_to_plot.append(line)
for name in categories.keys():
if name == cats_to_plot:
y_plot = 100*confidence_range[-1][categories[name]['index'],:]
line = {'x': xx, 'y': y_plot,
'hovertemplate': '%{y:.2f}%, %{text}',
'text': [population_format(i*population_plot/100) for i in y_plot],
'line': {'color': str(categories[name]['colour'])},
'legendgroup': name,
'name': categories[name]['longname'] + '; median'}
lines_to_plot.append(line)
ymax = 0
for line in lines_to_plot:
ymax = max(ymax,max(line['y']))
yax = dict(range= [0,min(1.1*ymax,100)])
##
lines_to_plot.append(
dict(
type='scatter',
x = [0,xx[-1]],
y = [ 0, population_plot],
yaxis="y2",
opacity=0,
hoverinfo = 'skip',
showlegend=False
))
yy2 = [0]
for i in range(8):
yy2.append(10**(i-5))
yy2.append(2*10**(i-5))
yy2.append(5*10**(i-5))
yy = [i for i in yy2]
pop_vec_lin = np.linspace(0,yy2[1],11)
for i in range(len(yy)-1):
if yax['range'][1]>yy[i] and yax['range'][1] <= yy[i+1]:
pop_vec_lin = np.linspace(0,yy2[i+1],11)
vec = [i*(population_plot) for i in pop_vec_lin]
log_bottom = -8
log_range = [log_bottom,np.log10(yax['range'][1])]
pop_vec_log_intermediate = np.linspace(log_range[0],ceil(np.log10(pop_vec_lin[-1])), 1+ ceil(np.log10(pop_vec_lin[-1])-log_range[0]) )
pop_log_vec = [10**(i) for i in pop_vec_log_intermediate]
vec2 = [i*(population_plot) for i in pop_log_vec]
shapes=[]
annots=[]
# if control_time[0]!=control_time[1] and not no_control:
# shapes.append(dict(
# # filled Blue Control Rectangle
# type="rect",
# x0= control_time[0],
# y0=0,
# x1= control_time[1],
# y1= yax['range'][1],
# line=dict(
# color="LightSkyBlue",
# width=0,
# ),
# fillcolor="LightSkyBlue",
# opacity= 0.15
# ))
# annots.append(dict(
# x = 0.5*(control_time[0] + control_time[1]),
# y = 0.5,
# text="<b>Control<br>" + "<b> In <br>" + "<b> Place",
# textangle=0,
# font=dict(
# size= font_size*(30/24),
# color="blue"
# ),
# showarrow=False,
# opacity=0.4,
# xshift= 0,
# xref = 'x',
# yref = 'paper',
# ))
layout = go.Layout(
template="simple_white",
shapes=shapes,
annotations=annots,
font = dict(size= font_size), #'12em'),
margin=dict(t=5, b=5, l=10, r=10,pad=15),
hovermode='x',
xaxis= dict(
title='Days',
automargin=True,
hoverformat='.0f',
),
yaxis= dict(mirror= True,
title='Percentage of Total Population',
range= yax['range'],
automargin=True,
type = 'linear'
),
updatemenus = [dict(
buttons=list([
dict(
args=[{"yaxis": {'title': 'Percentage of Total Population', 'type': 'linear', 'range': yax['range'], 'automargin': True},
"yaxis2": {'title': 'Population','type': 'linear', 'overlaying': 'y1', 'range': yax['range'], 'ticktext': [population_format(0.01*vec[i]) for i in range(len(pop_vec_lin))], 'tickvals': [i for i in pop_vec_lin],'automargin': True,'side':'right'}
}], # tickformat
label="Linear",
method="relayout"
),
dict(
args=[{"yaxis": {'title': 'Percentage of Total Population', 'type': 'log', 'range': log_range,'automargin': True},
"yaxis2": {'title': 'Population','type': 'log', 'overlaying': 'y1', 'range': log_range, 'ticktext': [population_format(0.01*vec2[i]) for i in range(len(pop_log_vec))], 'tickvals': [i for i in pop_log_vec],'automargin': True,'side':'right'}
}], # 'tickformat': yax_form_log,
label="Logarithmic",
method="relayout"
)
]),
x= 0.5,
xanchor="right",
pad={"r": 5, "t": 30, "b": 10, "l": 5},
active=0,
y=-0.13,
showactive=True,
direction='up',
yanchor="top"
)],
legend = dict(
font=dict(size=font_size*(20/24)),
x = 0.5,
y = 1.03,
xanchor= 'center',
yanchor= 'bottom'
),
legend_orientation = 'h',
legend_title = '<b> Key </b>',
yaxis2 = dict(
title = 'Population',
overlaying='y1',
range = yax['range'],
side='right',
ticktext = [population_format(0.01*vec[i]) for i in range(len(pop_vec_lin))],
tickvals = [i for i in pop_vec_lin],
automargin=True
)
)
return {'data': lines_to_plot, 'layout': layout}
def generate_csv(data_to_save,
population_frame,
filename,
input_type=None,
time_vec=None):
category_map = {}
for key in categories.keys():
category_map[str(categories[key]['index'])] = key
print(category_map)
if input_type == 'percentile':
csv_sol = np.transpose(data_to_save)
solution_csv = pd.DataFrame(csv_sol)
col_names = []
for i in range(csv_sol.shape[1]):
col_names.append(categories[category_map[str(i)]]['longname'])
solution_csv.columns = col_names
solution_csv['Time'] = time_vec
# this is our dataframe to be saved
elif input_type == 'raw':
final_frame = pd.DataFrame()
for key, value in tqdm(data_to_save.items()):
csv_sol = np.transpose(value['y']) # age structured
solution_csv = pd.DataFrame(csv_sol)
# setup column names
col_names = []
number_categories_with_age = csv_sol.shape[1]
for i in range(number_categories_with_age):
ii = i % params.number_compartments
jj = floor(i / params.number_compartments)
col_names.append(
categories[category_map[str(ii)]]['longname'] + ': ' +
str(np.asarray(population_frame.Age)[jj]))
solution_csv.columns = col_names
solution_csv['Time'] = value['t']
for j in range(len(
categories.keys())): # params.number_compartments
solution_csv[categories[category_map[str(j)]]
['longname']] = value['y_plot'][
j] # summary/non age-structured
(R0, latentRate, removalRate, hospRate, deathRateICU,
deathRateNoIcu) = key
solution_csv['R0'] = [R0] * solution_csv.shape[0]
solution_csv['latentRate'] = [latentRate] * solution_csv.shape[0]
solution_csv['removalRate'] = [removalRate] * solution_csv.shape[0]
solution_csv['hospRate'] = [hospRate] * solution_csv.shape[0]
solution_csv['deathRateICU'] = [deathRateICU
] * solution_csv.shape[0]
solution_csv['deathRateNoIcu'] = [deathRateNoIcu
] * solution_csv.shape[0]
final_frame = pd.concat([final_frame, solution_csv],
ignore_index=True)
solution_csv = final_frame
#this is our dataframe to be saved
elif input_type == 'solution':
csv_sol = np.transpose(data_to_save[0]['y']) # age structured
solution_csv = pd.DataFrame(csv_sol)
# setup column names
col_names = []
number_categories_with_age = csv_sol.shape[1]
for i in range(number_categories_with_age):
ii = i % params.number_compartments
jj = floor(i / params.number_compartments)
col_names.append(categories[category_map[str(ii)]]['longname'] +
': ' + str(np.asarray(population_frame.Age)[jj]))
solution_csv.columns = col_names
solution_csv['Time'] = data_to_save[0]['t']
for j in range(len(categories.keys())): # params.number_compartments
solution_csv[categories[category_map[str(j)]]
['longname']] = data_to_save[0]['y_plot'][
j] # summary/non age-structured
# this is our dataframe to be saved
# save it
solution_csv.to_csv(
os.path.join(os.path.dirname(cwd), 'CSV_output/' + filename + '.csv'))
return None
def run_model(
self,
T_stop,
population,
population_frame,
infection_matrix,
beta,
control_dict, # control
latentRate=params.latent_rate,
removalRate=params.removal_rate,
hospRate=params.hosp_rate,
deathRateICU=params.death_rate_with_ICU,
deathRateNoIcu=params.death_rate # more params
):
E0 = 0 # exposed
I0 = 1 / population # sympt
A0 = 1 / population # asympt
R0 = 0 # recovered
H0 = 0 # hospitalised/needing hospital care
C0 = 0 # critical (cared)
D0 = 0 # dead
O0 = 0 # offsite
Q0 = 0 # quarantined
U0 = 0 # critical (uncared)
S0 = 1 - I0 - R0 - C0 - H0 - D0 - O0 - Q0 - U0
age_categories = int(population_frame.shape[0])
y0 = np.zeros(params.number_compartments * age_categories)
population_vector = np.asarray(population_frame.Population_structure)
# initial conditions
for i in range(age_categories):
y0[params.S_ind + i *
params.number_compartments] = (population_vector[i] / 100) * S0
y0[params.E_ind + i *
params.number_compartments] = (population_vector[i] / 100) * E0
y0[params.I_ind + i *
params.number_compartments] = (population_vector[i] / 100) * I0
y0[params.A_ind + i *
params.number_compartments] = (population_vector[i] / 100) * A0
y0[params.R_ind + i *
params.number_compartments] = (population_vector[i] / 100) * R0
y0[params.H_ind + i *
params.number_compartments] = (population_vector[i] / 100) * H0
y0[params.C_ind + i *
params.number_compartments] = (population_vector[i] / 100) * C0
y0[params.D_ind + i *
params.number_compartments] = (population_vector[i] / 100) * D0
y0[params.O_ind + i *
params.number_compartments] = (population_vector[i] / 100) * O0
y0[params.Q_ind + i *
params.number_compartments] = (population_vector[i] / 100) * Q0
y0[params.U_ind + i *
params.number_compartments] = (population_vector[i] / 100) * U0
symptomatic_prob = np.asarray(population_frame.p_symptomatic)
hospital_prob = np.asarray(population_frame.p_hospitalised)
critical_prob = np.asarray(population_frame.p_critical)
sol = ode(self.ode_system).set_f_params(
infection_matrix,
age_categories,
symptomatic_prob,
hospital_prob,
critical_prob,
beta, # params
latentRate,
removalRate,
hospRate,
deathRateICU,
deathRateNoIcu, # more params
control_dict['better_hygiene'],
control_dict['remove_symptomatic'],
control_dict['remove_high_risk'],
control_dict['ICU_capacity'] # control params
)
tim = np.linspace(0, T_stop, T_stop + 1) # 1 time value per day
sol.set_initial_value(y0, tim[0])
y_out = np.zeros((len(y0), len(tim)))
i2 = 0
y_out[:, 0] = sol.y
for t in tim[1:]:
if sol.successful():
sol.integrate(t)
i2 = i2 + 1
y_out[:, i2] = sol.y
else:
raise RuntimeError('ode solver unsuccessful')
y_plot = np.zeros((len(categories.keys()), len(tim)))
for name in calculated_categories:
y_plot[categories[name]['index'], :] = y_out[
categories[name]['index'], :]
for i in range(1, population_frame.shape[0]): # age_categories
y_plot[categories[name]['index'], :] = y_plot[
categories[name]['index'], :] + y_out[
categories[name]['index'] +
i * params.number_compartments, :]
for name in change_in_categories: # daily change in
name_changed_var = name[
-1] # name of the variable we want daily change of
y_plot[categories[name]['index'], :] = np.concatenate(
[[0],
np.diff(y_plot[categories[name_changed_var]['index'], :])])
# finally,
E = y_plot[categories['CE']['index'], :]
I = y_plot[categories['CI']['index'], :]
A = y_plot[categories['CA']['index'], :]
y_plot[categories['Ninf']['index'], :] = [
E[i] + I[i] + A[i] for i in range(len(E))
] # change in total number of people with active infection
return {'y': y_out, 't': tim, 'y_plot': y_plot}
def simulate_R0_unmitigated(
R_0,
column,
t_stop=200
): # gives solution for middle R0, as well as solutions for a range of R0s between an upper and lower bound
from plots import plot_by_age
import sys, os
cwd = os.getcwd()
sys.path.append(os.path.abspath(os.path.join('..', 'base')))
from functions import simulator
from configs.baseline import camp, population_frame, population, control_dict
from initialise_parameters import params, categories
# infection_matrix = np.asarray(pd.read_csv(os.path.join(os.path.dirname(cwd),'Parameters/Contact_matrix.csv'))) #np.ones((population_frame.shape[0],population_frame.shape[0]))
infection_matrix = np.asarray(
pd.read_csv(
os.path.join(os.path.dirname(cwd),
'Parameters/Contact_matrix_' + camp + '.csv'))
) #np.ones((population_frame.shape[0],population_frame.shape[0]))
infection_matrix = infection_matrix[:, 1:]
next_generation_matrix = np.matmul(
0.01 * np.diag(population_frame.Population_structure),
infection_matrix)
largest_eigenvalue = max(
np.linalg.eig(next_generation_matrix)[0]) # max eigenvalue
beta = R_0 * params.removal_rate
beta = np.real(
(1 / largest_eigenvalue) * beta) # in case eigenvalue imaginary
sols_raw = {}
result = simulator().run_model(T_stop=t_stop,
infection_matrix=infection_matrix,
population=population,
population_frame=population_frame,
beta=beta,
control_dict=control_dict)
sols_raw[beta * largest_eigenvalue / params.removal_rate] = result
final_frame = pd.DataFrame()
category_map = {
'0': 'S',
'1': 'E',
'2': 'I',
'3': 'A',
'4': 'R',
'5': 'H',
'6': 'C',
'7': 'D',
'8': 'O',
'9': 'CS', # change in S
'10': 'CE', # change in E
'11': 'CI', # change in I
'12': 'CA', # change in A
'13': 'CR', # change in R
'14': 'CH', # change in H
'15': 'CC', # change in C
'16': 'CD', # change in D
'17': 'CO', # change in O
'18': 'Ninf',
}
for key, value in sols_raw.items():
csv_sol = np.transpose(value['y']) # age structured
solution_csv = pd.DataFrame(csv_sol)
# setup column names
col_names = []
number_categories_with_age = csv_sol.shape[1]
for i in range(number_categories_with_age):
ii = i % params.number_compartments
jj = floor(i / params.number_compartments)
col_names.append(categories[category_map[str(ii)]]['longname'] +
': ' + str(np.asarray(population_frame.Age)[jj]))
solution_csv.columns = col_names
solution_csv['Time'] = value['t']
for j in range(len(categories.keys())): # params.number_compartments
solution_csv[categories[category_map[str(j)]]['longname']] = value[
'y_plot'][j] # summary/non age-structured
solution_csv['R0'] = [key] * solution_csv.shape[0]
final_frame = pd.concat([final_frame, solution_csv], ignore_index=True)
solution_csv = final_frame
#this is our dataframe to be saved
plot_by_age(column, solution_csv)
