Merge pull request #140 from pastas/series_testing

Improve series comparison and some user feedback

pastastore/base.py

@@ -15,6 +15,7 @@
 import pastas as ps
 from numpy import isin
 from packaging.version import parse as parse_version
+from pandas.testing import assert_series_equal
 from pastas.io.pas import PastasEncoder
 from tqdm.auto import tqdm
 
@@ -30,7 +31,7 @@ class BaseConnector(ABC):
 
     Class holds base logic for dealing with time series and Pastas Models. Create your
     own Connector to a data source by writing a a class that inherits from this
-    BaseConnector. Your class has to override each abstractmethod and abstractproperty.
+    BaseConnector. Your class has to override each abstractmethod and property.
     """
 
     _default_library_names = [
@@ -47,6 +48,10 @@ class BaseConnector(ABC):
     # True for pastas>=0.23.0 and False for pastas<=0.22.0
     USE_PASTAS_VALIDATE_SERIES = False if PASTAS_LEQ_022 else True
 
+    # set series equality comparison settings (using assert_series_equal)
+    SERIES_EQUALITY_ABSOLUTE_TOLERANCE = 1e-10
+    SERIES_EQUALITY_RELATIVE_TOLERANCE = 0.0
+
     def __repr__(self):
         """Representation string of the object."""
         return (
@@ -670,22 +675,27 @@ def upsert_stress(
         metadata["kind"] = kind
         self._upsert_series("stresses", series, name, metadata=metadata)
 
-    def del_models(self, names: Union[list, str]) -> None:
+    def del_models(self, names: Union[list, str], verbose: bool = True) -> None:
         """Delete model(s) from the database.
 
         Parameters
         ----------
         names : str or list of str
             name(s) of the model to delete
+        verbose : bool, optional
+            print information about deleted models, by default True
         """
-        for n in self._parse_names(names, libname="models"):
+        names = self._parse_names(names, libname="models")
+        for n in names:
             mldict = self.get_models(n, return_dict=True)
             oname = mldict["oseries"]["name"]
             self._del_item("models", n)
             self._del_oseries_model_link(oname, n)
         self._clear_cache("_modelnames_cache")
+        if verbose:
+            print(f"Deleted {len(names)} model(s) from database.")
 
-    def del_model(self, names: Union[list, str]) -> None:
+    def del_model(self, names: Union[list, str], verbose: bool = True) -> None:
         """Delete model(s) from the database.
 
         Alias for del_models().
@@ -694,10 +704,14 @@ def del_model(self, names: Union[list, str]) -> None:
         ----------
         names : str or list of str
             name(s) of the model to delete
+        verbose : bool, optional
+            print information about deleted models, by default True
         """
-        self.del_models(names=names)
+        self.del_models(names=names, verbose=verbose)
 
-    def del_oseries(self, names: Union[list, str], remove_models: bool = False):
+    def del_oseries(
+        self, names: Union[list, str], remove_models: bool = False, verbose: bool = True
+    ):
         """Delete oseries from the database.
 
         Parameters
@@ -706,29 +720,38 @@ def del_oseries(self, names: Union[list, str], remove_models: bool = False):
             name(s) of the oseries to delete
         remove_models : bool, optional
             also delete models for deleted oseries, default is False
+        verbose : bool, optional
+            print information about deleted oseries, by default True
         """
         names = self._parse_names(names, libname="oseries")
         for n in names:
             self._del_item("oseries", n)
         self._clear_cache("oseries")
+        if verbose:
+            print(f"Deleted {len(names)} oseries from database.")
         # remove associated models from database
         if remove_models:
             modelnames = list(
                 chain.from_iterable([self.oseries_models.get(n, []) for n in names])
             )
-            self.del_models(modelnames)
+            self.del_models(modelnames, verbose=verbose)
 
-    def del_stress(self, names: Union[list, str]):
+    def del_stress(self, names: Union[list, str], verbose: bool = True):
         """Delete stress from the database.
 
         Parameters
         ----------
         names : str or list of str
             name(s) of the stress to delete
+        verbose : bool, optional
+            print information about deleted stresses, by default True
         """
-        for n in self._parse_names(names, libname="stresses"):
+        names = self._parse_names(names, libname="stresses")
+        for n in names:
             self._del_item("stresses", n)
         self._clear_cache("stresses")
+        if verbose:
+            print(f"Deleted {len(names)} stress(es) from database.")
 
     def _get_series(
         self,
@@ -1665,11 +1688,18 @@ def _check_oseries_in_store(self, ml: Union[ps.Model, dict]):
                 so = ml.oseries.series_original
             else:
                 so = ml.oseries._series_original
-            if not so.dropna().equals(s_org):
+            try:
+                assert_series_equal(
+                    so.dropna(),
+                    s_org,
+                    atol=self.SERIES_EQUALITY_ABSOLUTE_TOLERANCE,
+                    rtol=self.SERIES_EQUALITY_RELATIVE_TOLERANCE,
+                )
+            except AssertionError as e:
                 raise ValueError(
                     f"Cannot add model because model oseries '{name}'"
-                    " is different from stored oseries!"
-                )
+                    " is different from stored oseries! See stacktrace for differences."
+                ) from e
 
     def _check_stresses_in_store(self, ml: Union[ps.Model, dict]):
         """Check if stresses time series are contained in PastaStore (internal method).
@@ -1699,11 +1729,19 @@ def _check_stresses_in_store(self, ml: Union[ps.Model, dict]):
                             so = s.series_original
                         else:
                             so = s._series_original
-                        if not so.equals(s_org):
+                        try:
+                            assert_series_equal(
+                                so,
+                                s_org,
+                                atol=self.SERIES_EQUALITY_ABSOLUTE_TOLERANCE,
+                                rtol=self.SERIES_EQUALITY_RELATIVE_TOLERANCE,
+                            )
+                        except AssertionError as e:
                             raise ValueError(
                                 f"Cannot add model because model stress "
-                                f"'{s.name}' is different from stored stress!"
-                            )
+                                f"'{s.name}' is different from stored stress! "
+                                "See stacktrace for differences."
+                            ) from e
         elif isinstance(ml, dict):
             for sm in ml["stressmodels"].values():
                 classkey = "stressmodel" if PASTAS_LEQ_022 else "class"