fix: try inferring both long and short period tides for FES (#343)

tsutterley · Sep 27, 2024 · 2aa4938 · 2aa4938
1 parent da9de5e
commit 2aa4938
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Showing 5 changed files with 31 additions and 35 deletions.
diff --git a/pyTMD/compute.py b/pyTMD/compute.py
@@ -403,7 +403,7 @@ def tide_elevations(
             if INFER_MINOR:
                 MINOR = pyTMD.predict.infer_minor(ts.tide[i], hc, c,
                     deltat=deltat[i], corrections=nodal_corrections,
-                    minor=minor_constituents, frequency=model.frequency)
+                    minor=minor_constituents)
             else:
                 MINOR = np.ma.zeros_like(TIDE)
             # add major and minor components and reform grid
@@ -418,7 +418,7 @@ def tide_elevations(
         if INFER_MINOR:
             minor = pyTMD.predict.infer_minor(ts.tide, hc, c,
                 deltat=deltat, corrections=nodal_corrections,
-                minor=minor_constituents, frequency=model.frequency)
+                minor=minor_constituents)
             tide.data[:] += minor.data[:]
     elif (TYPE.lower() == 'time series'):
         nstation = len(x)
@@ -432,7 +432,7 @@ def tide_elevations(
             if INFER_MINOR:
                 MINOR = pyTMD.predict.infer_minor(ts.tide, HC, c,
                     deltat=deltat, corrections=nodal_corrections,
-                    minor=minor_constituents, frequency=model.frequency)
+                    minor=minor_constituents)
             else:
                 MINOR = np.ma.zeros_like(TIDE)
             # add major and minor components
@@ -636,7 +636,7 @@ def tide_currents(
                 if INFER_MINOR:
                     MINOR = pyTMD.predict.infer_minor(ts.tide[i], hc, c,
                         deltat=deltat[i], corrections=nodal_corrections,
-                        minor=minor_constituents, frequency=model.frequency)
+                        minor=minor_constituents)
                 else:
                     MINOR = np.ma.zeros_like(TIDE)
                 # add major and minor components and reform grid
@@ -651,7 +651,7 @@ def tide_currents(
             if INFER_MINOR:
                 minor = pyTMD.predict.infer_minor(ts.tide, hc, c,
                     deltat=deltat, corrections=nodal_corrections,
-                    minor=minor_constituents, frequency=model.frequency)
+                    minor=minor_constituents)
                 tide[t].data[:] += minor.data[:]
         elif (TYPE.lower() == 'time series'):
             nstation = len(x)
@@ -665,7 +665,7 @@ def tide_currents(
                 if INFER_MINOR:
                     MINOR = pyTMD.predict.infer_minor(ts.tide, HC, c,
                         deltat=deltat, corrections=nodal_corrections,
-                        minor=minor_constituents, frequency=model.frequency)
+                        minor=minor_constituents)
                 else:
                     MINOR = np.ma.zeros_like(TIDE)
                 # add major and minor components

diff --git a/pyTMD/io/model.py b/pyTMD/io/model.py
@@ -11,7 +11,6 @@
         add file_format and nodal correction attributes
         export database as a dataclass for easier access
         added variable name and descriptions for long period tides
-        added attribute for model bulk frequencies for long period tides
     Updated 08/2024: added attribute for minor constituents to infer
         allow searching over iterable glob strings in definition files
         added option to try automatic detection of definition file format
@@ -302,16 +301,6 @@ def corrections(self) -> str:
         else:
             return part1
 
-    @property
-    def frequency(self) -> str:
-        """
-        Returns the frequency type for the model
-        """
-        if self.variable in ('tide_lpe',):
-            return 'long'
-        else:
-            return 'short'
-
     @property
     def file_format(self) -> str:
         """

diff --git a/pyTMD/predict.py b/pyTMD/predict.py
@@ -57,6 +57,7 @@
 """
 from __future__ import annotations
 
+import logging
 import numpy as np
 import pyTMD.arguments
 import pyTMD.astro
@@ -310,11 +311,8 @@ def infer_minor(
         time correction for converting to Ephemeris Time (days)
     corrections: str, default 'OTIS'
         use nodal corrections from OTIS/ATLAS or GOT/FES models
-    frequency: str, default 'short'
-        frequency of tidal constituents to infer
-
-            - 'short': diurnal and semidiurnal constituents
-            - 'long': long-period constituents
+    raise_exception: bool, default False
+        Raise a ``ValueError`` if major constituents are not found
     minor: list or None, default None
         tidal constituent IDs
 
@@ -343,14 +341,13 @@ def infer_minor(
     # set default keyword arguments
     kwargs.setdefault('deltat', 0.0)
     kwargs.setdefault('corrections', 'OTIS')
-    kwargs.setdefault('frequency', 'short')
+    kwargs.setdefault('raise_exception', False)
     # list of minor constituents
     kwargs.setdefault('minor', None)
     # infer the minor tidal constituents
-    if kwargs['frequency'] in ('short',):
-        dh = _infer_short_period(t, zmajor, constituents, **kwargs)
-    elif kwargs['frequency'] in ('long',):
-        dh = _infer_long_period(t, zmajor, constituents, **kwargs)
+    dh = 0.0
+    dh += _infer_short_period(t, zmajor, constituents, **kwargs)
+    dh += _infer_long_period(t, zmajor, constituents, **kwargs)
     # return the inferred values
     return dh
 
@@ -400,6 +397,7 @@ def _infer_short_period(
     # set default keyword arguments
     kwargs.setdefault('deltat', 0.0)
     kwargs.setdefault('corrections', 'OTIS')
+    kwargs.setdefault('raise_exception', False)
     # list of minor constituents
     kwargs.setdefault('minor', None)
     # number of constituents
@@ -421,8 +419,12 @@ def _infer_short_period(
             z[:,i] = zmajor[:,j1]
             nz += 1
 
-    if (nz < 6):
+    # raise exception or log error
+    if (nz < 6) and kwargs['raise_exception']:
         raise Exception('Not enough constituents for inference')
+    elif (nz < 6):
+        logging.debug('Not enough constituents for inference')
+        return 0.0
 
     # complete list of minor constituents
     minor_constituents = ['2q1', 'sigma1', 'rho1', 'm1b', 'm1',
@@ -528,6 +530,7 @@ def _infer_long_period(
         `doi: 10.1002/2013JB010830 <https://doi.org/10.1002/2013JB010830>`_
     """
     # set default keyword arguments
+    kwargs.setdefault('raise_exception', False)
     kwargs.setdefault('deltat', 0.0)
     kwargs.setdefault('corrections', 'OTIS')
     # list of minor constituents
@@ -558,8 +561,12 @@ def _infer_long_period(
             z[:,i] = zmajor[:,j1]/amajor[i]
             nz += 1
 
-    if (nz < 3):
+    # raise exception or log error
+    if (nz < 3) and kwargs['raise_exception']:
         raise Exception('Not enough constituents for inference')
+    elif (nz < 3):
+        logging.debug('Not enough constituents for inference')
+        return 0.0
 
     # complete list of minor constituents
     minor_constituents = ['sa', 'ssa', 'sta', 'msm', 'msf',

diff --git a/scripts/compute_tidal_currents.py b/scripts/compute_tidal_currents.py
@@ -346,7 +346,7 @@ def compute_tidal_currents(tide_dir, input_file, output_file,
                 if INFER_MINOR:
                     MINOR = pyTMD.predict.infer_minor(ts.tide[i], hc, c,
                         deltat=deltat[i], corrections=nodal_corrections,
-                        minor=minor_constituents, frequency=model.frequency)
+                        minor=minor_constituents)
                 else:
                     MINOR = np.ma.zeros_like(TIDE)
                 # add major and minor components and reform grid
@@ -362,7 +362,7 @@ def compute_tidal_currents(tide_dir, input_file, output_file,
             if INFER_MINOR:
                 minor = pyTMD.predict.infer_minor(ts.tide, hc, c,
                     deltat=deltat, corrections=nodal_corrections,
-                    minor=minor_constituents, frequency=model.frequency)
+                    minor=minor_constituents)
                 tide[t].data[:] += minor.data[:]
         elif (TYPE == 'time series'):
             tide[t] = np.ma.zeros((nstation,nt), fill_value=FILL_VALUE)
@@ -376,7 +376,7 @@ def compute_tidal_currents(tide_dir, input_file, output_file,
                 if INFER_MINOR:
                     MINOR = pyTMD.predict.infer_minor(ts.tide, HC, c,
                         deltat=deltat, corrections=nodal_corrections,
-                        minor=minor_constituents, frequency=model.frequency)
+                        minor=minor_constituents)
                 else:
                     MINOR = np.ma.zeros_like(TIDE)
                 # add major and minor components

diff --git a/scripts/compute_tidal_elevations.py b/scripts/compute_tidal_elevations.py
@@ -354,7 +354,7 @@ def compute_tidal_elevations(tide_dir, input_file, output_file,
             if INFER_MINOR:
                 MINOR = pyTMD.predict.infer_minor(ts.tide[i], hc, c,
                     deltat=deltat[i], corrections=nodal_corrections,
-                    minor=minor_constituents, frequency=model.frequency)
+                    minor=minor_constituents)
             else:
                 MINOR = np.ma.zeros_like(TIDE)
             # add major and minor components and reform grid
@@ -369,7 +369,7 @@ def compute_tidal_elevations(tide_dir, input_file, output_file,
         if INFER_MINOR:
             minor = pyTMD.predict.infer_minor(ts.tide, hc, c,
                 deltat=deltat, corrections=nodal_corrections,
-                minor=minor_constituents, frequency=model.frequency)
+                minor=minor_constituents)
             tide.data[:] += minor.data[:]
     elif (TYPE == 'time series'):
         tide = np.ma.zeros((nstation,nt), fill_value=FILL_VALUE)
@@ -383,7 +383,7 @@ def compute_tidal_elevations(tide_dir, input_file, output_file,
             if INFER_MINOR:
                 MINOR = pyTMD.predict.infer_minor(ts.tide, HC, c,
                     deltat=deltat, corrections=nodal_corrections,
-                    minor=minor_constituents, frequency=model.frequency)
+                    minor=minor_constituents)
             else:
                 MINOR = np.ma.zeros_like(TIDE)
             # add major and minor components