def simulate(self,
name="Main",
y0_dict=None,
show_figure=True,
filename=None):
"""
Simulate ODE models with setted parameter values and show it as a figure.
Args:
name (str): phase series name. If 'Main', main PhaseSeries will be used
y0_dict (dict):
- key (str): variable name
- value (float): initial value
- dictionary of initial values or None
- if model will be changed in the later phase, must be specified
show_figure (bool):
- if True, show the result as a figure.
filename (str): filename of the figure, or None (show figure)
Returns:
(pandas.DataFrame)
Index:
reset index
Columns:
- Date (str): date, like 31Dec2020
- Country (str): country/region name
- Province (str): province/prefecture/state name
- variables of the models (int): Confirmed (int) etc.
"""
name = self.MAIN if name == "Main" else name
df = self.series_dict[name].summary()
# Future phases must be added in advance
if self.FUTURE not in df[self.TENSE].unique():
raise KeyError(
f"Future phases of {name} scenario must be registered by Scenario.add_phase() in advance."
)
# Simulation
dim_df, start_objects = self._simulate(name=name, y0_dict=y0_dict)
dim_df = dim_df.set_index(self.DATE).resample("D").mean()
dim_df = dim_df.astype(np.int64)
fig_df = dim_df.copy()
dim_df[self.DATE] = dim_df.index.strftime(self.DATE_FORMAT)
dim_df = dim_df.reset_index(drop=True)
dim_df = dim_df.loc[:, [self.DATE, *dim_df.columns.tolist()[:-1]]]
# Return dataframe if figure is not needed
if not show_figure:
return dim_df
# Show figure
fig_cols_set = set(fig_df.columns) & set(self.FIG_COLUMNS)
fig_cols = [col for col in self.FIG_COLUMNS if col in fig_cols_set]
line_plot(fig_df[fig_cols],
title=f"{self.area}: Predicted number of cases",
filename=filename,
y_integer=True,
v=start_objects[1:])
return dim_df
def records(self, show_figure=True, filename=None):
"""
Return the records as a dataframe.
@show_figure <bool>:
- if True, show the records as a line-plot.
@filename <str>: filename of the figure, or None (show figure)
"""
df = self.jhu_data.subset(self.country, province=self.province)
if not show_figure:
return df
line_plot(df.set_index(self.DATE).drop(self.C, axis=1),
f"{self.area}: Cases over time",
y_integer=True,
filename=filename)
return df
def param_history(self,
targets=None,
name="Main",
divide_by_first=True,
show_figure=True,
filename=None,
box_plot=True,
**kwargs):
"""
Return subset of summary.
@targets <list[str]/str>: parameters to show (Rt etc.)
@name <str>: phase series name
- if 'Main', main PhaseSeries will be used
@divide_by_first <bool>: if True, divide the values by 1st phase's values
@box_plot <bool>: if True, box plot. if False, line plot.
@show_figure <bool>:
- if True, show the result as a figure.
@filename <str>: filename of the figure, or None (show figure)
@kwargs: keword arguments of pd.DataFrame.plot or line_plot()
@return <pd.DataFrame>
"""
if filename is not None:
plt.switch_backend("Agg")
name = self.MAIN if name == "Main" else name
if name not in self.series_dict.keys():
raise KeyError(f"@name {name} scenario has not been registered.")
df = self.series_dict[name].summary()
model_param_nest = [m.PARAMETERS for m in self.model_dict.values()]
model_day_nest = [m.DAY_PARAMETERS for m in self.model_dict.values()]
model_parameters = self.flatten(model_param_nest)
model_day_params = self.flatten(model_day_nest)
selectable_cols = [
self.N, *model_parameters, self.RT, *model_day_params
]
targets = [targets] if isinstance(targets, str) else targets
targets = selectable_cols if targets is None else targets
if not set(targets).issubset(set(selectable_cols)):
raise KeyError(
f"@targets must be a subset of {', '.join(selectable_cols)}.")
df = df.loc[:, targets]
if divide_by_first:
df = df / df.iloc[0, :]
title = f"{self.area}: Ratio to 1st phase parameters ({name} scenario)"
else:
title = f"{self.area}: History of parameter values ({name} scenario)"
if box_plot:
df.plot.bar(title=title)
plt.xticks(rotation=0)
if divide_by_first or self.RT in targets:
plt.axhline(y=1.0, color="black", linestyle=":")
plt.legend(bbox_to_anchor=(1.02, 0),
loc="lower left",
borderaxespad=0)
plt.tight_layout()
if filename is None:
plt.show()
return df
plt.savefig(filename,
bbox_inches="tight",
transparent=False,
dpi=300)
plt.clf()
return df
_df = df.reset_index(drop=True)
_df.index = _df.index + 1
h = 1.0 if divide_by_first else None
line_plot(_df,
title=title,
xlabel="Phase",
ylabel=str(),
math_scale=False,
h=h,
show_figure=show_figure,
filename=filename)
def simulate(self,
name="Main",
y0_dict=None,
show_figure=True,
filename=None):
"""
Simulate ODE models with setted parameter values.
@name <str>: phase series name
- if 'Main', main PhaseSeries will be used
@y0_dict <doct[str]=float>:
- dictionary of initial values or None
- if model will be changed in the later phase, must be specified
@show_figure <bool>:
- if True, show the result as a figure.
@filename <str>: filename of the figure, or None (show figure)
@return <pd.DataFrame>
- index <int>: reseted index
- Date <str>: date, like 31Dec2020
- Country <str>: country/region name
- Province <str>: province/prefecture/state name
- variables of the models <int>: Confirmed <int> etc.
"""
# TODO: Refactoring, split this method
name = self.MAIN if name == "Main" else name
df = self.series_dict[name].summary()
# Future must be added in advance
if self.FUTURE not in df[self.TENSE].unique():
raise KeyError(
f"Future phases of {name} scenario must be registered by Scenario.add_phase() in advance."
)
simulator = ODESimulator(
self.country,
province="-" if self.province is None else self.province)
start_dates = list()
for phase in df.index:
model_name = df.loc[phase, self.ODE]
model = self.model_dict[model_name]
start_obj = self.date_obj(df.loc[phase, self.START])
start_dates.append(start_obj)
end_obj = self.date_obj(df.loc[phase, self.END])
phase_seconds = (end_obj - start_obj).total_seconds() + 1
step_n = round(phase_seconds / (60 * self.tau))
population = df.loc[phase, self.N]
param_dict = df[model.PARAMETERS].to_dict(orient="index")[phase]
if phase == self.num2str(1):
# Calculate intial values
nondim_data = NondimData(self.clean_df,
country=self.country,
province=self.province)
nondim_df = nondim_data.make(model, population)
init_index = [
date_obj
for (date_obj,
_) in self.series_dict[name].phase_dict.items()
if date_obj == start_obj
][0]
y0_dict_phase = {
v: nondim_df.loc[init_index, v]
for v in model.VARIABLES
}
else:
try:
y0_dict_phase = y0_dict.copy()
except AttributeError:
y0_dict_phase = None
simulator.add(model,
step_n,
population,
param_dict=param_dict,
y0_dict=y0_dict_phase)
simulator.run()
dim_df = simulator.dim(self.tau, df.loc[self.num2str(1), self.START])
dim_df = dim_df.set_index(self.DATE).resample("D").mean()
# TODO: smoothing the values
dim_df = dim_df.astype(np.int64)
fig_df = dim_df.copy()
dim_df[self.DATE] = dim_df.index.strftime(self.DATE_FORMAT)
dim_df = dim_df.reset_index(drop=True)
dim_df = dim_df.loc[:, [self.DATE, *dim_df.columns.tolist()[:-1]]]
if not show_figure:
return dim_df
# Show figure
fig_cols_set = set(fig_df.columns) & set(self.FIG_COLUMNS)
fig_cols = [col for col in self.FIG_COLUMNS if col in fig_cols_set]
# TODO: add vertical lines to line-plot with tau and step_n
line_plot(fig_df[fig_cols],
title=f"{self.area}: Predicted number of cases",
filename=filename,
y_integer=True,
v=start_dates[1:])
return dim_df
def param_history(self,
targets=None,
name="Main",
divide_by_first=True,
show_figure=True,
filename=None,
show_box_plot=True,
**kwargs):
"""
Return subset of summary.
Args:
targets (list[str]/str): parameters to show (Rt etc.)
name (str): phase series name
divide_by_first (bool): if True, divide the values by 1st phase's values
show_box_plot (bool): if True, box plot. if False, line plot.
show_figure (bool): If True, show the result as a figure.
filename (str): filename of the figure, or None (show figure)
kwargs: keword arguments of pd.DataFrame.plot or line_plot()
Returns:
(pandas.DataFrame)
Notes:
If 'Main' was used as @name, main PhaseSeries will be used.
"""
if "box_plot" in kwargs.keys():
raise KeyError("Please use 'show_box_plot', not 'box_plot'")
name = self.MAIN if name == "Main" else name
if name not in self.series_dict.keys():
raise KeyError(f"@name {name} scenario has not been registered.")
df = self.series_dict[name].summary()
model_param_nest = [m.PARAMETERS for m in self.model_dict.values()]
model_day_nest = [m.DAY_PARAMETERS for m in self.model_dict.values()]
model_parameters = self.flatten(model_param_nest)
model_day_params = self.flatten(model_day_nest)
selectable_cols = [
self.N, *model_parameters, self.RT, *model_day_params
]
targets = [targets] if isinstance(targets, str) else targets
targets = selectable_cols if targets is None else targets
if not set(targets).issubset(set(selectable_cols)):
raise KeyError(
f"@targets must be a subset of {', '.join(selectable_cols)}.")
df = df.loc[:, targets]
if divide_by_first:
df = df / df.iloc[0, :]
title = f"{self.area}: Ratio to 1st phase parameters ({name} scenario)"
else:
title = f"{self.area}: History of parameter values ({name} scenario)"
if show_box_plot:
h_values = [1.0] if divide_by_first or self.RT in targets else None
box_plot(df, title, h=h_values, filename=filename)
return df
_df = df.reset_index(drop=True)
_df.index = _df.index + 1
h = 1.0 if divide_by_first else None
line_plot(_df,
title=title,
xlabel="Phase",
ylabel=str(),
math_scale=False,
h=h,
show_figure=show_figure,
filename=filename)
return df