def test_plot_fert_rates_save_fig(tmpdir):
totpers = base_params.S
min_yr = 20
max_yr = 100
fert_data = (np.array([0.0, 0.0, 0.3, 12.3, 47.1, 80.7, 105.5, 98.0,
49.3, 10.4, 0.8, 0.0, 0.0]) / 2000)
age_midp = np.array([9, 10, 12, 16, 18.5, 22, 27, 32, 37, 42, 47,
55, 56])
fert_func = si.interp1d(age_midp, fert_data, kind='cubic')
fert_rates = np.random.uniform(size=totpers)
parameter_plots.plot_fert_rates(
fert_func, age_midp, totpers, min_yr, max_yr, fert_data,
fert_rates, output_dir=tmpdir)
img = mpimg.imread(os.path.join(tmpdir, 'fert_rates.png'))
assert isinstance(img, np.ndarray)
def test_plot_fert_rates():
totpers = base_params.S
min_yr = 20
max_yr = 100
fert_data = (np.array([0.0, 0.0, 0.3, 12.3, 47.1, 80.7, 105.5, 98.0,
49.3, 10.4, 0.8, 0.0, 0.0]) / 2000)
age_midp = np.array([9, 10, 12, 16, 18.5, 22, 27, 32, 37, 42, 47,
55, 56])
fert_func = si.interp1d(age_midp, fert_data, kind='cubic')
fert_rates = np.random.uniform(size=totpers)
fig = parameter_plots.plot_fert_rates(
fert_func, age_midp, totpers, min_yr, max_yr, fert_data,
fert_rates)
assert fig
def get_fert(totpers, min_yr, max_yr, graph=False):
'''
This function generates a vector of fertility rates by model period
age that corresponds to the fertility rate data by age in years
(Source: National Vital Statistics Reports, Volume 64, Number 1,
January 15, 2015, Table 3, final 2013 data
http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_01.pdf)
Args:
totpers (int): total number of agent life periods (E+S), >= 3
min_yr (int): age in years at which agents are born, >= 0
max_yr (int): age in years at which agents die with certainty,
>= 4
graph (bool): =True if want graphical output
Returns:
fert_rates (Numpy array): fertility rates for each model period
of life
'''
# Get current population data (2013) for weighting
pop_file = os.path.join(
CUR_PATH, 'data', 'demographic', 'pop_data.csv')
pop_data = pd.read_csv(pop_file, thousands=',')
pop_data_samp = pop_data[(pop_data['Age'] >= min_yr - 1) &
(pop_data['Age'] <= max_yr - 1)]
curr_pop = np.array(pop_data_samp['2013'], dtype='f')
curr_pop_pct = curr_pop / curr_pop.sum()
# Get fertility rate by age-bin data
fert_data = (np.array([0.0, 0.0, 0.3, 12.3, 47.1, 80.7, 105.5, 98.0,
49.3, 10.4, 0.8, 0.0, 0.0]) / 2000)
# Mid points of age bins
age_midp = np.array([9, 10, 12, 16, 18.5, 22, 27, 32, 37, 42, 47,
55, 56])
# Generate interpolation functions for fertility rates
fert_func = si.interp1d(age_midp, fert_data, kind='cubic')
# Calculate average fertility rate in each age bin using trapezoid
# method with a large number of points in each bin.
binsize = (max_yr - min_yr + 1) / totpers
num_sub_bins = float(10000)
len_subbins = (np.float64(100 * num_sub_bins)) / totpers
age_sub = (np.linspace(np.float64(binsize) / num_sub_bins,
np.float64(max_yr),
int(num_sub_bins*max_yr)) - 0.5 *
np.float64(binsize) / num_sub_bins)
curr_pop_sub = np.repeat(np.float64(curr_pop_pct) / num_sub_bins,
num_sub_bins)
fert_rates_sub = np.zeros(curr_pop_sub.shape)
pred_ind = (age_sub > age_midp[0]) * (age_sub < age_midp[-1])
age_pred = age_sub[pred_ind]
fert_rates_sub[pred_ind] = np.float64(fert_func(age_pred))
fert_rates = np.zeros(totpers)
end_sub_bin = 0
for i in range(totpers):
beg_sub_bin = int(end_sub_bin)
end_sub_bin = int(np.rint((i + 1) * len_subbins))
fert_rates[i] = ((
curr_pop_sub[beg_sub_bin:end_sub_bin] *
fert_rates_sub[beg_sub_bin:end_sub_bin]).sum() /
curr_pop_sub[beg_sub_bin:end_sub_bin].sum())
if graph:
pp.plot_fert_rates(fert_func, age_midp, totpers, min_yr, max_yr,
fert_data, fert_rates, output_dir=OUTPUT_DIR)
return fert_rates