Comparing outputs to observations

Once observations are implemented in the model, the next step is to compare them with model outputs. The objective of the present step is to “run” pyCIF in forward mode and produce a file comparing prescribed observations with simulations. It is assumed that all inputs and outputs from a pre-existing simulations are available for your model.

A forward mode requires the following model methods to be provided, as they are called by the obsoper plugin:

  1. native2inputs:

    Prepare all inputs for the sub-simulation of interest

  2. outputs2native:

    Read model outputs into pyCIF variables

  3. run:

    Run the model


As mentioned earlier, we assume that all inputs and outputs are already computed. So, native2inputs does not need to do anything. native2inputs is based on the following structure:

def native2inputs(self, datastore, input_type, datei, datef, mode,
                  runsubdir, workdir):
    """Converts data at the model data resolution to model compatible input

        self: the model Plugin
        input_type (str): type of input; should be consistent
            with self.required_inputs as specified in
        datastore: data to convert
        datei, datef: date interval of the sub-simulation
        mode (str): running mode: one of 'fwd', 'adj' and 'tl'
        runsubdir (str): sub-directory for the current simulation
        workdir (str): the directory of the whole pyCIF simulation


    # do nothing


We do not try to really run the model at this step. Therefore, run will only copy pre-computed outputs to the current working directory.

def run(self, runsubdir, mode, workdir, do_simu=True, **kwargs):
    """Run the model

        self: the model Plugin
        runsubdir (str): working directory for the current run
        mode (str): forward or backward
        workdir (str): pyCIF working directory
        do_simu (bool): re-run or not existing simulation


    # Copy or link your outputs to runsubdir


At this step, raw model outputs are processed and put into pyCIF variables. For a forward run, the objective is to recompose observation equivalents. pyCIF expects simulations to be included into a pandas DataFrame.

def outputs2native(self, runsubdir, mode='fwd', dump=True):
    """Reads outputs to pyCIF objects.

    If the mode is 'fwd' or 'tl', only onservation-like outputs are extracted.
    For the 'adj' mode, all outputs relative to model sensitivity are extracted.

    Dumps to a NetCDF file with output concentrations if needed"""

    if not hasattr(self, 'dataobs'):
        self.dataobs = init_empty()

    if not hasattr(self, 'datasensit'):
        self.datasensit = {}

    if mode in ['fwd', 'tl']:
        # If no simulated concentration is available just pass
        file = "{}/mod.txt".format(runsubdir)
        if os.stat(file).st_size == 0:
            info("CHIMERE ran without any observation to be compared with for sub-simu "
            "only CHIMERE's outputs are available")
            self.dataobs.loc[:, 'sim'] = np.nan

        # Read simulated concentrations
        sim = pd.read_csv(file, sep='\s+', header=None)[6]

        # Loop over observations in active species
        mask = self.dataobs['parameter'].str.upper() \

        # Putting values to the local data store
        # Assumes arithmetic averages upto now
        inds = [0] + list(np.cumsum(self.dataobs.loc[mask, 'dtstep'][:-1]))

        column = 'sim' if mode == 'fwd' else 'sim_tl'
        self.dataobs.loc[mask, column] = \
            [sim.iloc[k:k + dt].sum()
             for k, dt in zip(inds, self.dataobs.loc[mask, 'dtstep'])]

        return self.dataobs