Added data-truth correlation. Metrics can now be selected as an optio…

…n in the triple_collocation_validate function.

tests/test_triple_collocation.py

@@ -43,7 +43,7 @@ def test_triple_collocation_simulated_data():
     assert set(keys_lvl_2) == set(dict_data.keys())
 
     assert ref in keys_lvl_2
-    assert len(tc_result['data_sources'][ref].keys()) == 8
+    assert len(tc_result['data_sources'][ref].keys()) == 6
 
 
 # def test_triple_collocation_real_data():

wavy/triple_collocation.py

@@ -218,13 +218,19 @@ def SNR_dB(A, B, C, var_err_A):
     return 10*np.log10(sensitivity_estimates(A, B, C)/var_err_A)
 
 
-def triple_collocation_validate(result_dict, ref=None):
+def triple_collocation_validate(result_dict, 
+                                metric_list=['var_est','rmse','si',
+                                             'rho','mean','std'], 
+                                ref=None):
     '''
     Runs the triple collocation given a dictionary
     containing three measurements, returns results
     in a dictionary.
 
     results_dict: {'name of measurement':list of values}
+    metric_list: List of the metrics to return, among 'var_est',
+    'rmse', 'si', 'rho', 'sens', 'snr', 'snr_db', 'fmse', 'mean',
+    'std'
     ref: Name of one of the measurements, must correspond
     to one key of results_dict
 
@@ -247,44 +253,71 @@ def triple_collocation_validate(result_dict, ref=None):
     cov_bc = np.cov(B,C)
     cov_ac = np.cov(A,C)
 
+    # Sensitivity
     sens = [(cov_ab[0][1]*cov_ac[0][1])/cov_bc[0][1],
             (cov_ab[0][1]*cov_bc[0][1])/cov_ac[0][1],
             (cov_bc[0][1]*cov_ac[0][1])/cov_ab[0][1]]
 
+    # Estimate of the variance of random error
     var_est = [cov_ab[0][0] - sens[0],
-               cov_ab[1][1] - sens[1],
-               cov_bc[1][1] - sens[2]]
+	       cov_ab[1][1] - sens[1],
+	       cov_bc[1][1] - sens[2]]
 
+    # Root Mean Square Error
     rmse = [np.sqrt(var_est[0]),
             np.sqrt(var_est[1]),
             np.sqrt(var_est[2])]
 
-    si = [rmse[0]/mean_ref*100,
-          rmse[1]/mean_ref*100,
-          rmse[2]/mean_ref*100]
+    # Scatter Index
+    if 'si' in metric_list:
+        si = [rmse[0]/mean_ref*100,
+	      rmse[1]/mean_ref*100,
+	      rmse[2]/mean_ref*100]
 
+    # Signal to Noise Ratio
     snr = [sens[0]/var_est[0],
            sens[1]/var_est[1],
            sens[2]/var_est[2]]
 
-    fmse = [1/(1+snr[0]),
-            1/(1+snr[1]),
-            1/(1+snr[2])]
-
-    snr_db = [10*np.log10(snr[0]),
-              10*np.log10(snr[1]),
-              10*np.log10(snr[2])]
+    # Fractional Mean Squared Error
+    if 'fmse' in metric_list:
+        fmse = [1/(1+snr[0]),
+                1/(1+snr[1]),
+                1/(1+snr[2])]
+
+    # Signal to Noise Ratio (dB)
+    if 'snr_db' in metric_list:
+        snr_db = [10*np.log10(snr[0]),
+                  10*np.log10(snr[1]),
+                  10*np.log10(snr[2])]
+
+    # Data truth correlation
+    if 'rho' in metric_list:
+        rho = [sens[0]/cov_ab[0][0],
+               sens[1]/cov_ab[1][1],
+               sens[2]/cov_bc[1][1]]
 
     for i,k in enumerate(measure_names):
-
-        tc_validate['data_sources'][k]['var_est'] = var_est[i]
-        tc_validate['data_sources'][k]['RMSE'] = rmse[i]
-        tc_validate['data_sources'][k]['SI'] = si[i]
-        tc_validate['data_sources'][k]['sensitivity'] = sens[i]
-        tc_validate['data_sources'][k]['fMSE'] = fmse[i]
-        tc_validate['data_sources'][k]['SNR_dB'] = snr_db[i]
-        tc_validate['data_sources'][k]['mean'] = np.mean(measures[i])
-        tc_validate['data_sources'][k]['std'] = np.std(measures[i])
+        if 'var_est' in metric_list:
+            tc_validate['data_sources'][k]['var_est'] = var_est[i]
+        if 'rmse' in metric_list:
+            tc_validate['data_sources'][k]['RMSE'] = rmse[i]
+        if 'si' in metric_list:
+            tc_validate['data_sources'][k]['SI'] = si[i]
+        if 'sens' in metric_list:
+            tc_validate['data_sources'][k]['sensitivity'] = sens[i]
+        if 'rho' in metric_list:
+            tc_validate['data_sources'][k]['rho'] = rho[i]
+        if 'snr' in metric_list:
+            tc_validate['data_sources'][k]['SNR'] = snr[i]
+        if 'fmse' in metric_list:
+            tc_validate['data_sources'][k]['fMSE'] = fmse[i]
+        if 'snr_db' in metric_list:
+            tc_validate['data_sources'][k]['SNR_dB'] = snr_db[i]
+        if 'mean' in metric_list:
+            tc_validate['data_sources'][k]['mean'] = np.mean(measures[i])
+        if 'std' in metric_list:
+            tc_validate['data_sources'][k]['std'] = np.std(measures[i])
 
     return tc_validate