Analyze output
==============
Once the prediction is stored into a file, we can analyze it. Once again it's recommand to put the analyze in a separate function in order to call them on demand.

We can start by loading data, and prediction::

    def analyze():
        # get the data
        (train_inputs, train_targets, train_timestamps), (test_inputs, test_targets, test_timestamps) = split_data()
        prediction = utils.read_output()

 
Then, you only have to pass the data to the function of the ``hibou.stats`` module. Three functions are available:

1. a month-by-month diurnal cycle to see which month's estimations are best::

        # month-by-month diurnal cycle
        stats.plot_daily_mean(
            test_targets,
            prediction,
            np.vectorize(pd.Timestamp)(ts_test[:, 0]),
            fluxes_names=TARGETS_VAR
        )

2. a test of sensibility on each inputs variable, the results are print in a table::

        # show a table of the sensibility of the models to each inputs variable.
        stats.sensibility_test(model_set, test_inputs, INPUTS_VAR, TARGETS_VAR)

3. a correlation plot between observed and estimated fluxed, the RMSE, PCC and p-value are calculated for each given bin of fluxes::

        # correlation and error per flux bin
        stats.stats.plot_correlation_per_bin(
            test_targets,
            prediction,
            fluxes_names=TARGETS_VAR,
            bins=[
                [-100, 0, 100, 200, 300, 400],  # H bins
                [0, 100, 200, 300, 400, 500],   # LE bins   
            ]
        )

All the plotted figures are saved into ``my_output_dir/output``.