Ejemplo n.º 1
0
    def calc_metrics(self, data, gpi_info):
        """
        calculates the desired statistics

        Parameters
        ----------
        data : pandas.DataFrame
            with 2 columns, the first column is the reference dataset
            named 'ref'
            the second column the dataset to compare against named 'other'
        gpi_info : tuple
            of (gpi, lon, lat)

        Notes
        -----
        Kendall tau is calculation is optional at the moment
        because the scipy implementation is very slow which is problematic for
        global comparisons
        """
        dataset = copy.deepcopy(self.result_template)

        dataset['n_obs'][0] = len(data)
        dataset['gpi'][0] = gpi_info[0]
        dataset['lon'][0] = gpi_info[1]
        dataset['lat'][0] = gpi_info[2]

        if len(data) < 10:
            return dataset

        x, y = data['ref'].values, data[self.other_name].values
        R, p_R = metrics.pearsonr(x, y)
        rho, p_rho = metrics.spearmanr(x, y)
        RMSD = metrics.rmsd(x, y)
        BIAS = metrics.bias(x, y)

        dataset['R'][0], dataset['p_R'][0] = R, p_R
        dataset['rho'][0], dataset['p_rho'][0] = rho, p_rho
        dataset['RMSD'][0] = RMSD
        dataset['BIAS'][0] = BIAS

        if self.calc_tau:
            tau, p_tau = metrics.kendalltau(x, y)
            dataset['tau'][0], dataset['p_tau'][0] = tau, p_tau

        return dataset
Ejemplo n.º 2
0
    def calc_metrics(self, data, gpi_info):
        """
        calculates the desired statistics

        Parameters
        ----------
        data : pandas.DataFrame
            with 2 columns, the first column is the reference dataset
            named 'ref'
            the second column the dataset to compare against named 'other'
        gpi_info : tuple
            of (gpi, lon, lat)

        Notes
        -----
        Kendall tau is calculation is optional at the moment
        because the scipy implementation is very slow which is problematic for
        global comparisons
        """
        dataset = copy.deepcopy(self.result_template)

        dataset["n_obs"][0] = len(data)
        dataset["gpi"][0] = gpi_info[0]
        dataset["lon"][0] = gpi_info[1]
        dataset["lat"][0] = gpi_info[2]

        if len(data) < 10:
            return dataset

        x, y = data["ref"].values, data[self.other_name].values
        R, p_R = metrics.pearsonr(x, y)
        rho, p_rho = metrics.spearmanr(x, y)
        RMSD = metrics.rmsd(x, y)
        BIAS = metrics.bias(x, y)

        dataset["R"][0], dataset["p_R"][0] = R, p_R
        dataset["rho"][0], dataset["p_rho"][0] = rho, p_rho
        dataset["RMSD"][0] = RMSD
        dataset["BIAS"][0] = BIAS

        if self.calc_tau:
            tau, p_tau = metrics.kendalltau(x, y)
            dataset["tau"][0], dataset["p_tau"][0] = tau, p_tau

        return dataset
Ejemplo n.º 3
0
        
        plt.scatter(matched_data[scaled_ascat_label].values,matched_data[label_insitu].values)
        plt.xlabel(scaled_ascat_label)
        plt.ylabel(label_insitu)
        plt.show()
        
        #calculate correlation coefficients, RMSD, bias, Nash Sutcliffe
        x, y = matched_data[scaled_ascat_label].values, matched_data[label_insitu].values
        
        print "ISMN time series:",ISMN_time_series
        print "compared to"
        print ascat_time_series
        print "Results:"
        print "Pearson's (R,p_value)", metrics.pearsonr(x, y)
        print "Spearman's (rho,p_value)", metrics.spearmanr(x, y)
        print "Kendalls's (tau,p_value)", metrics.kendalltau(x, y)
        print "RMSD", metrics.rmsd(x, y)
        print "Bias", metrics.bias(x, y)
        print "Nash Sutcliffe", metrics.nash_sutcliffe(x, y)
        
        
    i += 1
    
    #only show the first 2 stations, otherwise this program would run a long time
    #and produce a lot of plots
    if i >= 2:
        break    
    


Ejemplo n.º 4
0
        scaled_data.plot(secondary_y=[label_ascat])
        plt.show()

        plt.scatter(matched_data[scaled_ascat_label].values,
                    matched_data[label_insitu].values)
        plt.xlabel(scaled_ascat_label)
        plt.ylabel(label_insitu)
        plt.show()

        #calculate correlation coefficients, RMSD, bias, Nash Sutcliffe
        x, y = matched_data[scaled_ascat_label].values, matched_data[
            label_insitu].values

        print "ISMN time series:", ISMN_time_series
        print "compared to"
        print ascat_time_series
        print "Results:"
        print "Pearson's (R,p_value)", metrics.pearsonr(x, y)
        print "Spearman's (rho,p_value)", metrics.spearmanr(x, y)
        print "Kendalls's (tau,p_value)", metrics.kendalltau(x, y)
        print "RMSD", metrics.rmsd(x, y)
        print "Bias", metrics.bias(x, y)
        print "Nash Sutcliffe", metrics.nash_sutcliffe(x, y)

    i += 1

    #only show the first 2 stations, otherwise this program would run a long time
    #and produce a lot of plots
    if i >= 2:
        break