def population_diagnostic(simulation):
figures_directory = create_or_get_figures_directory(simulation)
directory = os.path.dirname(simulation.data_source.output_path)
uniform_weight = simulation.uniform_weight
population = None
for csv_file in ['births', 'deaths', 'migrants']:
simulation_data_frame = pandas.read_csv(os.path.join(directory, csv_file + '.csv'))
simulation_data_frame.period = (simulation_data_frame['period'] / 100).round().astype(int)
simulation_data_frame.set_index('period', inplace = True)
insee_data_frame = pandas.DataFrame({
'{}_insee'.format(csv_file): get_insee_projection(csv_file, 'total', function = 'sum')
})
insee_data_frame.index.name = 'period'
data_frame = pandas.concat([simulation_data_frame * uniform_weight, insee_data_frame], axis = 1)
if population is not None:
population = pandas.concat([population, data_frame], axis = 1)
else:
population = data_frame
population.dropna(inplace = True)
population.index.name = 'year'
ax = population.plot(
title = 'Components of population growth',
style = ['k-', 'k--', 'g-', 'g--', 'b-', 'b--'],
xticks = [period for period in list(population.index.astype(int))],
)
fig = ax.get_figure()
plt.gca().xaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x)))
fig.savefig(os.path.join(figures_directory, 'population_growth_compoennts.png'))
del ax, fig
return population
def plot_dependance_csv(simulation):
figures_directory = create_or_get_figures_directory(simulation)
panel_simulation = extract_dependance_csv(simulation)
panel_simulation = panel_simulation.squeeze() / 1000
panel_simulation.index.name = None
panel_simulation.rename(
columns = dict(
male = "hommes",
female = "femmes",
),
inplace = True
)
plt.figure()
ax = panel_simulation.plot(
xticks = [period for period in list(panel_simulation.index.astype(int))],
cmap = 'PuBu'
)
ax.set_ylabel(u"effectifs en milliers")
ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.2), ncol=3)
plt.gca().xaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x)))
fig = ax.get_figure()
fig.savefig(os.path.join(figures_directory, 'dependance.png'))
del ax, fig
def plot_ratio_demographique(simulation):
figures_directory = create_or_get_figures_directory(simulation)
panel_simulation = extract_population_by_age_csv(simulation)
panel_simulation.drop('total', axis = 'major', inplace = True)
def separate(age):
age = int(age)
if age <= 19:
return 'kids'
elif age <= 59:
return 'active'
elif age > 59:
return 'retired'
ratio_60 = panel_simulation['total'].groupby(separate, axis = 0).sum()
ratio_60 = ratio_60.loc['retired', :]/ratio_60.loc['active', :]
def plot_population2(simulation):
figures_directory = create_or_get_figures_directory(simulation)
panel_simulation = extract_population_csv(simulation)
for gender in ['total', 'male', 'female']:
data_frame_insee = get_data_frame_insee(gender)
data_frame_simulation = panel_simulation[gender].drop('total')
data_frame_simulation.index = data_frame_simulation.index.astype(int)
data_frame_simulation.sort_index(inplace = True)
absolute_diff = (
data_frame_simulation - data_frame_insee[data_frame_simulation.columns]
)
relative_diff = (
data_frame_simulation - data_frame_insee[data_frame_simulation.columns]
) / data_frame_insee[data_frame_simulation.columns]
plt.figure()
ax = relative_diff.T.plot(title = gender)
fig = ax.get_figure()
fig.savefig(os.path.join(figures_directory, 'population_rel_diff_{}.png'.format(gender)))
del ax, fig
for gender in ['total', 'male', 'female']:
data_frame_insee_total = get_data_frame_insee(gender).sum()
data_frame_simulation = panel_simulation[gender].drop('total')
data_frame_simulation.index = data_frame_simulation.index.astype(int)
data_frame_simulation.sort_index(inplace = True)
data_frame_simulation_total = data_frame_simulation.sum()
data_frame_insee_total = data_frame_insee_total.loc[data_frame_simulation_total.index].copy()
plt.figure()
ax = data_frame_insee_total.plot(label = 'insee', style = 'b-')
data_frame_simulation_total.plot(label = 'til', style = 'r-', ax = ax)
ax.legend()
fig = ax.get_figure()
fig.savefig(os.path.join(figures_directory, 'population_{}.png'.format(gender)))
del ax, fig
