def test_mse(): """ Test for mse """ # example 1 x = np.arange(10) y = np.arange(10) + 2 mse_pred = 4. mse_bias_pred = 2. ** 2 mse_obs, _, mse_bias, _ = met.mse(x, y) nptest.assert_equal(mse_obs, mse_pred) nptest.assert_equal(mse_bias, mse_bias_pred) # example 2, with outlier x = np.arange(10) y = np.arange(10) + 2 y[-1] = 51. mse_pred = 180. mse_bias_pred = 36. mse_obs, _, mse_bias, _ = met.mse(x, y) nptest.assert_almost_equal(mse_obs, mse_pred, 6) nptest.assert_almost_equal(mse_bias, mse_bias_pred, 6)
def test_mse(): """ Test for mse """ # example 1 x = np.arange(10) y = np.arange(10) + 2 mse_pred = 4. mse_bias_pred = 2.**2 mse_obs, _, mse_bias, _ = met.mse(x, y) nptest.assert_equal(mse_obs, mse_pred) nptest.assert_equal(mse_bias, mse_bias_pred) # example 2, with outlier x = np.arange(10) y = np.arange(10) + 2 y[-1] = 51. mse_pred = 180. mse_bias_pred = 36. mse_obs, _, mse_bias, _ = met.mse(x, y) nptest.assert_almost_equal(mse_obs, mse_pred, 6) nptest.assert_almost_equal(mse_bias, mse_bias_pred, 6)
def calc_metrics(self, data, gpi_info): dataset = super(BasicMetricsPlusMSE, self).calc_metrics(data, gpi_info) if len(data) < self.min_obs: return dataset x, y = data['ref'].values, data[self.other_name].values mse, mse_corr, mse_bias, mse_var = metrics.mse(x, y) dataset['mse'][0] = mse dataset['mse_corr'][0] = mse_corr dataset['mse_bias'][0] = mse_bias dataset['mse_var'][0] = mse_var return dataset
def calc_metrics(self, data, gpi_info): dataset = super(BasicMetricsPlusMSE, self).calc_metrics(data, gpi_info) if len(data) < 10: return dataset x, y = data["ref"].values, data[self.other_name].values mse, mse_corr, mse_bias, mse_var = metrics.mse(x, y) dataset["mse"][0] = mse dataset["mse_corr"][0] = mse_corr dataset["mse_bias"][0] = mse_bias dataset["mse_var"][0] = mse_var return dataset
def test_rmsd_mse(): """ Test for rmsd and mse """ # example 1 x = np.random.randn(1000) y = np.random.randn(1000) rmsd_pred = met.rmsd(x, y) mse_pred, _, _, _ = met.mse(x, y) nptest.assert_almost_equal(rmsd_pred ** 2, mse_pred, 6)
def test_rmsd_mse(): """ Test for rmsd and mse """ # example 1 x = np.random.randn(1000) y = np.random.randn(1000) rmsd_pred = met.rmsd(x, y) mse_pred, _, _, _ = met.mse(x, y) nptest.assert_almost_equal(rmsd_pred**2, mse_pred, 6)
def test_mse(arange_testdata): """ Test for mse """ with pytest.deprecated_call(): # example 1 x, y = arange_testdata mse_pred = 4. mse_bias_pred = 2.**2 mse_obs, _, mse_bias, _ = met.mse(x, y) nptest.assert_equal(mse_obs, mse_pred) nptest.assert_equal(mse_bias, mse_bias_pred) # example 2, with outlier y[-1] = 51. mse_pred = 180. mse_bias_pred = 36. mse_obs, _, mse_bias, _ = met.mse(x, y) nptest.assert_almost_equal(mse_obs, mse_pred, 6) nptest.assert_almost_equal(mse_bias, mse_bias_pred, 6)
def compare_data(ismn_data, validation_data, scaling='linreg', anomaly=None): """ Compare data from an ISMN station to the defined validation datasets. Parameters ---------- ismn_data: pandas.Dataframe Data from the ISMN used as a reference validation_data: dict Dictionary of pandas.DataFrames, One for each dataset to compare against scaling: string, optional Scaling method to use. anomaly: string If set then the validation is done for anomalies. """ insitu_label = 'soil moisture' if anomaly != None: if anomaly == 'climatology': ascat_clim = anomaly_calc.calc_climatology( ascat_masked[ascat_label]) insitu_clim = anomaly_calc.calc_climatology( ismn_data['soil moisture']) ascat_anom = anomaly_calc.calc_anomaly(ascat_masked[ascat_label], climatology=ascat_clim) ascat_masked[ascat_label] = ascat_anom.values insitu_anom = anomaly_calc.calc_anomaly(ISMN_data['insitu'], climatology=insitu_clim) ISMN_data['insitu'] = insitu_anom.values if anomaly == 'average': ascat_anom = anomaly_calc.calc_anomaly(ascat_masked[ascat_label]) ascat_masked[ascat_label] = ascat_anom.values insitu_anom = anomaly_calc.calc_anomaly(ISMN_data['insitu']) ISMN_data['insitu'] = insitu_anom.values ascat_masked = ascat_masked.dropna() ISMN_data = ISMN_data.dropna() for dname in validation_data: vdata = validation_data[dname] vdata_label = 'cci_sm' matched_data = temp_match.matching(ismn_data, vdata, window=1) if scaling != 'noscale' and scaling != 'porosity': scaled_data = scale.add_scaled(matched_data, label_in=vdata_label, label_scale=insitu_label, method=scaling) scaled_label = vdata_label + '_scaled_' + scaling scaled_data = scaled_data[[insitu_label, scaled_label]] elif scaling == 'noscale': scaled_data = matched_data[[insitu_label, vdata_label]] scaled_label = vdata_label # scaled_data.rename(columns={'insitu': ISMN_ts_name}, inplace=True) labels, values = scaled_data.to_dygraph_format() ascat_insitu = {'labels': labels, 'data': values} x, y = scaled_data[insitu_label].values, scaled_data[scaled_label].values kendall, p_kendall = sc_stats.kendalltau(x.tolist(), y.tolist()) spearman, p_spearman = sc_stats.spearmanr(x, y) pearson, p_pearson = sc_stats.pearsonr(x, y) rmsd = metrics.rmsd(x, y) bias = metrics.bias(y, x) mse, mse_corr, mse_bias, mse_var = metrics.mse(x, y) statistics = { 'kendall': { 'v': '%.2f' % kendall, 'p': '%.4f' % p_kendall }, 'spearman': { 'v': '%.2f' % spearman, 'p': '%.4f' % p_spearman }, 'pearson': { 'v': '%.2f' % pearson, 'p': '%.4f' % p_pearson }, 'bias': '%.4f' % bias, 'rmsd': { 'rmsd': '%.4f' % np.sqrt(mse), 'rmsd_corr': '%.4f' % np.sqrt(mse_corr), 'rmsd_bias': '%.4f' % np.sqrt(mse_bias), 'rmsd_var': '%.4f' % np.sqrt(mse_var) }, 'mse': { 'mse': '%.4f' % mse, 'mse_corr': '%.4f' % mse_corr, 'mse_bias': '%.4f' % mse_bias, 'mse_var': '%.4f' % mse_var } } scaling_options = { 'noscale': 'No scaling', 'porosity': 'Scale using porosity', 'linreg': 'Linear Regression', 'mean_std': 'Mean - standard deviation', 'min_max': 'Minimum,maximum', 'lin_cdf_match': 'Piecewise <br> linear CDF matching', 'cdf_match': 'CDF matching' } settings = { 'scaling': scaling_options[scaling], # 'snow_depth': mask['snow_depth'], # 'surface_temp': mask['st_l1'], # 'air_temp': mask['air_temp'] } era_data = {'labels': [], 'data': []} output_data = { 'validation_data': ascat_insitu, 'masking_data': era_data, 'statistics': statistics, 'settings': settings } return output_data, 1