refac: removed verbose default options from catalog readers

tests: updated vcr

Author: pabloitu

csep/__init__.py

@@ -3,23 +3,17 @@
 import time
 
 from csep._version import __version__
-
-from csep.core import forecasts
+from csep.core import catalog_evaluations
 from csep.core import catalogs
+from csep.core import forecasts
 from csep.core import poisson_evaluations
-from csep.core import catalog_evaluations
 from csep.core import regions
+from csep.core.exceptions import CSEPCatalogException
+from csep.core.forecasts import GriddedForecast, CatalogForecast
 from csep.core.repositories import (
     load_json,
     write_json
 )
-
-from csep.core.exceptions import CSEPCatalogException
-
-from csep.utils import datasets
-from csep.utils import readers
-
-from csep.core.forecasts import GriddedForecast, CatalogForecast
 from csep.models import (
     EvaluationResult,
     CatalogNumberTestResult,
@@ -28,7 +22,8 @@
     CatalogPseudolikelihoodTestResult,
     CalibrationTestResult
 )
-
+from csep.utils import datasets
+from csep.utils import readers
 from csep.utils.time_utils import (
     utc_now_datetime,
     strptime_to_utc_datetime,
@@ -366,7 +361,7 @@ def query_gcmt(start_time, end_time, min_magnitude=5.0,
                max_depth=None,
                catalog_id=None,
                min_latitude=None, max_latitude=None,
-               min_longitude=None, max_longitude=None):
+               min_longitude=None, max_longitude=None, verbose=True):
 
     eventlist = readers._query_gcmt(start_time=start_time,
                                      end_time=end_time,
@@ -381,6 +376,17 @@ def query_gcmt(start_time, end_time, min_magnitude=5.0,
                                    name='gCMT',
                                    catalog_id=catalog_id,
                                    date_accessed=utc_now_datetime())
+
+    if verbose:
+        print("Downloaded catalog from GCMT with following parameters")
+        print("Start Date: {}\nEnd Date: {}".format(str(catalog.start_time),
+                                                    str(catalog.end_time)))
+        print("Min Latitude: {} and Max Latitude: {}".format(catalog.min_latitude,
+                                                             catalog.max_latitude))
+        print("Min Longitude: {} and Max Longitude: {}".format(catalog.min_longitude,
+                                                               catalog.max_longitude))
+        print("Min Magnitude: {}".format(catalog.min_magnitude))
+        print(f"Found {catalog.event_count} events in the gns catalog.")
     return catalog
 
 

csep/core/binomial_evaluations.py

@@ -126,7 +126,7 @@ def _simulate_catalog(sim_cells, sampling_weights, sim_fore, random_numbers=None
 
 
 def _binary_likelihood_test(forecast_data, observed_data, num_simulations=1000, random_numbers=None, 
-                              seed=None, use_observed_counts=True, verbose=True, normalize_likelihood=False):
+                              seed=None, use_observed_counts=True, verbose=False, normalize_likelihood=False):
     """  Computes binary conditional-likelihood test from CSEP using an efficient simulation based approach.
     
     Args:

csep/core/brier_evaluations.py

@@ -71,7 +71,7 @@ def _simulate_catalog(sim_cells, sampling_weights, random_numbers=None):
 
 
 def _brier_score_test(forecast_data, observed_data, num_simulations=1000,
-                      random_numbers=None, seed=None, verbose=True):
+                      random_numbers=None, seed=None, verbose=False):
     """  Computes the Brier consistency test conditional on the total observed
     number of events
 

csep/core/catalog_evaluations.py

@@ -23,7 +23,7 @@
     from csep.core.forecasts import CatalogForecast
 
 
-def number_test(forecast, observed_catalog, verbose=True):
+def number_test(forecast, observed_catalog, verbose=False):
     """ Performs the number test on a catalog-based forecast.
 
     The number test builds an empirical distribution of the event counts for each data. By default, this
@@ -62,7 +62,7 @@ def number_test(forecast, observed_catalog, verbose=True):
     return result
 
 
-def spatial_test(forecast, observed_catalog, verbose=True):
+def spatial_test(forecast, observed_catalog, verbose=False):
     """ Performs spatial test for catalog-based forecasts.
 
 
@@ -148,7 +148,7 @@ def spatial_test(forecast, observed_catalog, verbose=True):
     return result
 
 
-def magnitude_test(forecast, observed_catalog, verbose=True):
+def magnitude_test(forecast, observed_catalog, verbose=False):
     """ Performs magnitude test for catalog-based forecasts """
     test_distribution = []
 
@@ -224,7 +224,7 @@ def magnitude_test(forecast, observed_catalog, verbose=True):
     return result
 
 
-def pseudolikelihood_test(forecast, observed_catalog, verbose=True):
+def pseudolikelihood_test(forecast, observed_catalog, verbose=False):
     """ Performs the spatial pseudolikelihood test for catalog forecasts.
 
     Performs the spatial pseudolikelihood test as described by Savran et al., 2020. The tests uses a pseudolikelihood

csep/core/forecasts.py

@@ -669,7 +669,7 @@ def magnitude_counts(self):
             self.get_expected_rates()
         return self.expected_rates.magnitude_counts()
 
-    def get_event_counts(self, verbose=True):
+    def get_event_counts(self, verbose=False):
         """ Returns a numpy array containing the number of event counts for each catalog.
 
             Note: This function can take a while to compute if called without already iterating through a forecast that
@@ -730,7 +730,7 @@ def get_expected_rates(self, verbose=False):
                                                   magnitudes=self.magnitudes, name=self.name)
             return self.expected_rates
 
-    def plot(self, plot_args = None, verbose=True, **kwargs):
+    def plot(self, plot_args = None, verbose=False, **kwargs):
         plot_args = plot_args or {}
         if self.expected_rates is None:
             self.get_expected_rates(verbose=verbose)

csep/core/poisson_evaluations.py

@@ -601,7 +601,7 @@ def _simulate_catalog(num_events, sampling_weights, sim_fore,
 
 def _poisson_likelihood_test(forecast_data, observed_data,
                              num_simulations=1000, random_numbers=None,
-                             seed=None, use_observed_counts=True, verbose=True,
+                             seed=None, use_observed_counts=True, verbose=False,
                              normalize_likelihood=False):
     """
 	Computes the likelihood-test from CSEP using an efficient simulation based approach.

tests/artifacts/Comcat/vcr_detail.yaml

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