def calc_metrics(self, data, gpi_info):
"""
calculates the desired statistics
Parameters
----------
data : pd.DataFrame
with >2 columns, the first column is the reference dataset
named 'ref' other columns are the datasets to compare against
named 'other_i'
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 = super(IntercomparisonMetrics,
self).calc_metrics(data, gpi_info)
subset = np.ones(len(data), dtype=bool)
n_obs = subset.sum()
if n_obs < self.min_obs:
return dataset
dataset['n_obs'][0] = n_obs
# calculate Pearson correlation
pearson_R, pearson_p = df_metrics.pearsonr(data)
pearson_R, pearson_p = pearson_R._asdict(), pearson_p._asdict()
# calculate Spearman correlation
spea_rho, spea_p = df_metrics.spearmanr(data)
spea_rho, spea_p = spea_rho._asdict(), spea_p._asdict()
# calculate bias
bias_nT = df_metrics.bias(data)
bias_dict = bias_nT._asdict()
# calculate RMSD
rmsd = df_metrics.rmsd(data)
rmsd_dict = rmsd._asdict()
# calculate MSE
mse, mse_corr, mse_bias, mse_var = df_metrics.mse(data)
mse_dict, mse_corr_dict, mse_bias_dict, mse_var_dict = \
mse._asdict(), mse_corr._asdict(), mse_bias._asdict(), mse_var._asdict()
# calculate RSS
rss = df_metrics.RSS(data)
rss_dict = rss._asdict()
# calulcate tau
if self.calc_tau:
tau, p_tau = df_metrics.kendalltau(data)
tau_dict, p_tau_dict = tau._asdict(), p_tau._asdict()
else:
tau = p_tau = p_tau_dict = tau_dict = None
# No extra scaling is performed here.
# always scale for ubRMSD with mean std
# calculate ubRMSD
data_scaled = scale(data, method='mean_std')
ubRMSD_nT = df_metrics.ubrmsd(data_scaled)
ubRMSD_dict = ubRMSD_nT._asdict()
for tds_name in self.tds_names:
R, p_R = pearson_R[tds_name], pearson_p[tds_name]
rho, p_rho = spea_rho[tds_name], spea_p[tds_name]
bias = bias_dict[tds_name]
mse = mse_dict[tds_name]
mse_corr = mse_corr_dict[tds_name]
mse_bias = mse_bias_dict[tds_name]
mse_var = mse_var_dict[tds_name]
rmsd = rmsd_dict[tds_name]
ubRMSD = ubRMSD_dict[tds_name]
rss = rss_dict[tds_name]
if tau_dict and p_tau_dict:
tau = tau_dict[tds_name]
p_tau = p_tau_dict[tds_name]
split_tds_name = tds_name.split(self.ds_names_split)
tds_name_key = self.ds_names_split.join([
self.ds_names_lut[split_tds_name[0]],
self.ds_names_lut[split_tds_name[1]]
])
dataset[self.metric_ds_split.join(['R', tds_name_key])][0] = R
dataset[self.metric_ds_split.join(['p_R', tds_name_key])][0] = p_R
dataset[self.metric_ds_split.join(['rho', tds_name_key])][0] = rho
dataset[self.metric_ds_split.join(['p_rho',
tds_name_key])][0] = p_rho
dataset[self.metric_ds_split.join(['BIAS',
tds_name_key])][0] = bias
dataset[self.metric_ds_split.join(['mse', tds_name_key])][0] = mse
dataset[self.metric_ds_split.join(['mse_corr',
tds_name_key])][0] = mse_corr
dataset[self.metric_ds_split.join(['mse_bias',
tds_name_key])][0] = mse_bias
dataset[self.metric_ds_split.join(['mse_var',
tds_name_key])][0] = mse_var
dataset[self.metric_ds_split.join(['RMSD',
tds_name_key])][0] = rmsd
dataset[self.metric_ds_split.join(['urmsd',
tds_name_key])][0] = ubRMSD
dataset[self.metric_ds_split.join(['RSS', tds_name_key])][0] = rss
if self.calc_tau:
dataset[self.metric_ds_split.join(['tau',
tds_name_key])][0] = tau
dataset[self.metric_ds_split.join(['p_tau',
tds_name_key])][0] = p_tau
return dataset
def calc_metrics(self, data, gpi_info):
"""
Calculate Triple Collocation metrics
Parameters
----------
data : pd.DataFrame
with >2 columns, the first column is the reference dataset named 'ref'
other columns are the data sets to compare against named 'other_i'
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['gpi'][0] = gpi_info[0]
dataset['lon'][0] = gpi_info[1]
dataset['lat'][0] = gpi_info[2]
if self.metadata_template != None:
for key, value in self.metadata_template.items():
dataset[key][0] = gpi_info[3][key]
# number of observations
subset = np.ones(len(data), dtype=bool)
n_obs = subset.sum()
if n_obs < self.min_obs:
return dataset
dataset['n_obs'][0] = n_obs
# calculate Pearson correlation
pearson_R, pearson_p = df_metrics.pearsonr(data)
pearson_R, pearson_p = pearson_R._asdict(), pearson_p._asdict()
# calculate Spearman correlation
spea_rho, spea_p = df_metrics.spearmanr(data)
spea_rho, spea_p = spea_rho._asdict(), spea_p._asdict()
# calculate bias
bias_nT = df_metrics.bias(data)
bias_dict = bias_nT._asdict()
# calculate RMSD
rmsd = df_metrics.rmsd(data)
rmsd_dict = rmsd._asdict()
# calculate MSE
mse, mse_corr, mse_bias, mse_var = df_metrics.mse(data)
mse_dict = mse._asdict()
mse_corr_dict = mse_corr._asdict()
mse_bias_dict = mse_bias._asdict()
mse_var_dict = mse_var._asdict()
# calculate RSS
rss = df_metrics.RSS(data)
rss_dict = rss._asdict()
# calculate ubRMSD
# todo: we could use the TC derived scaling parameters here?
data_scaled = scale(data, method='mean_std')
ubRMSD_nT = df_metrics.ubrmsd(data_scaled)
ubRMSD_dict = ubRMSD_nT._asdict()
# calulcate tau
if self.calc_tau:
tau, p_tau = df_metrics.kendalltau(data)
tau_dict, p_tau_dict = tau._asdict(), p_tau._asdict()
else:
tau = p_tau = p_tau_dict = tau_dict = None
# calculate TC metrics
ref_ind = np.where(np.array(data.columns) == self.ref_name)[0][0]
snrs, err_stds, betas = df_metrics.tcol_snr(data, ref_ind=ref_ind)
snr_dict = self._tc_res_dict(snrs)
err_std_dict = self._tc_res_dict(err_stds)
beta_dict = self._tc_res_dict(betas)
# store TC results
for thds_name in self.thds_names:
snr = snr_dict[thds_name]
err_std = err_std_dict[thds_name]
beta = beta_dict[thds_name]
split_thds_name = thds_name.split(self.ds_names_split)
thds_name_key = self.ds_names_split.join([
self.ds_names_lut[split_thds_name[0]],
self.ds_names_lut[split_thds_name[1]],
self.ds_names_lut[split_thds_name[2]]
])
for metr, res in dict(snr=snr, err_std=err_std, beta=beta).items():
for ds, ds_res in res.items():
m_ds = "{}_{}".format(metr, self.ds_names_lut[ds])
n = '{}{}{}'.format(m_ds, self.metric_ds_split,
thds_name_key)
if n in dataset.keys():
dataset[n][0] = ds_res
# Store basic metrics results
for tds_name in self.tds_names:
R, p_R = pearson_R[tds_name], pearson_p[tds_name]
rho, p_rho = spea_rho[tds_name], spea_p[tds_name]
bias = bias_dict[tds_name]
mse = mse_dict[tds_name]
mse_corr = mse_corr_dict[tds_name]
mse_bias = mse_bias_dict[tds_name]
mse_var = mse_var_dict[tds_name]
rmsd = rmsd_dict[tds_name]
ubRMSD = ubRMSD_dict[tds_name]
rss = rss_dict[tds_name]
if tau_dict and p_tau_dict:
tau = tau_dict[tds_name]
p_tau = p_tau_dict[tds_name]
split_tds_name = tds_name.split(self.ds_names_split)
tds_name_key = self.ds_names_split.join([
self.ds_names_lut[split_tds_name[0]],
self.ds_names_lut[split_tds_name[1]]
])
dataset[self.metric_ds_split.join(['R', tds_name_key])][0] = R
dataset[self.metric_ds_split.join(['p_R', tds_name_key])][0] = p_R
dataset[self.metric_ds_split.join(['rho', tds_name_key])][0] = rho
dataset[self.metric_ds_split.join(['p_rho',
tds_name_key])][0] = p_rho
dataset[self.metric_ds_split.join(['BIAS',
tds_name_key])][0] = bias
dataset[self.metric_ds_split.join(['mse', tds_name_key])][0] = mse
dataset[self.metric_ds_split.join(['mse_corr',
tds_name_key])][0] = mse_corr
dataset[self.metric_ds_split.join(['mse_bias',
tds_name_key])][0] = mse_bias
dataset[self.metric_ds_split.join(['mse_var',
tds_name_key])][0] = mse_var
dataset[self.metric_ds_split.join(['RMSD',
tds_name_key])][0] = rmsd
dataset[self.metric_ds_split.join(['urmsd',
tds_name_key])][0] = ubRMSD
dataset[self.metric_ds_split.join(['RSS', tds_name_key])][0] = rss
if self.calc_tau:
dataset[self.metric_ds_split.join(['tau',
tds_name_key])][0] = tau
dataset[self.metric_ds_split.join(['p_tau',
tds_name_key])][0] = p_tau
return dataset