Quickstart

After installation use the following example as a way to quickly guide you through the usage of the maxent_disaggregation package. For more detailed examples please see Examples

from maxent_disaggregation import maxent_disagg
import numpy as np

# best guess or mean of the total quantity Y_0 (if available)
mean_aggregate = 10
# best guess of the standard deviation of the total quantity Y_0 (if available)
sd_aggregate = 1
# min/max value of the total quantity Y_o (if applicable/available) (optional)
min_aggregate = 0
max_aggregate = np.inf
# best guess values and uncertainties from proxy data for the shares (x_i) if available (of not available put in np.nan)
shares_disaggregates = [0.4, 0.25, 0.2, 0.15]
sds_shares = [0.1, np.nan, 0.04, 0.001]



# Now draw 10000 samples
samples, _ = maxent_disagg(n=10000, 
                        mean_0=mean_aggregate,
                        sd_0=sd_aggregate,
                        min_0=min_aggregate,
                        max_0=max_aggregate, 
                        shares=shares_disaggregates, 
                        sds=sds_shares, 
                        )

# Now plot the sampled distributions
from maxent_disaggregation import plot_samples_hist
# the input values are provided for the legend
plot_samples_hist(samples, 
                  mean_0=mean_aggregate,
                  sd_0=sd_aggregate, 
                  shares=shares_disaggregates, 
                  sds=sds_shares)

Figure 1: Histograms of the samples for both the disaggregate and aggregate values. The dashed vertical lines indicate the means of the sampled distributions. The input values are given in the legend.

We can also easily plot the covariances between the different disaggrate quantities:

# Plot the covariances between the disaggregates
from maxent_disaggregation import plot_covariances
plot_covariances(samples)

Figure 2: Covariances of the samples for the disaggregate quantities. The dashed vertical lines indicate the means and \(\pm 1\sigma\) of the sampled distributions. The input values are given in the legend.