removed yield function and only made save_daily an option when a path…

… is given

pypam/acoustic_file.py

@@ -454,6 +454,8 @@ def _get_metadata_attrs(self):
                 d = d.__dict__[sub_k]
             if isinstance(d, pathlib.Path):
                 d = str(d)
+            if isinstance(d, bool):
+                d = int(d)
             if d is None:
                 d = 0
             metadata_attrs[k.replace('.', '_')] = d

pypam/acoustic_survey.py

@@ -172,11 +172,13 @@ def _get_metadata_attrs(self):
                 d = d.__dict__[sub_k]
             if isinstance(d, pathlib.Path):
                 d = str(d)
+            if isinstance(d, bool):
+                d = int(d)
             metadata_attrs[k.replace('.', '_')] = d
 
         return metadata_attrs
 
-    def evolution_multiple(self, method_list: list, band_list=None, save_daily=True, output_folder=None, **kwargs):
+    def evolution_multiple(self, method_list: list, band_list=None, save_daily=False, output_folder=None, **kwargs):
         """
         Compute the method in each file and output the evolution
         Returns a xarray DataSet with datetime as index and one row for each bin of each file
@@ -190,28 +192,27 @@ def evolution_multiple(self, method_list: list, band_list=None, save_daily=True,
             represented with a tuple as (low_freq, high_freq). If set to None, the broadband up to the Nyquist
             frequency will be analyzed
         save_daily : boolean
-            Set to True to save/yield daily netcdf files instead of a huge big file (useful for long deployments)
+            Set to True to save daily netcdf files instead of returning a huge big file (useful for long deployments)
         output_folder : str or Path
-            Directory to save the netcdf files
+            Directory to save the netcdf files. Only works with save_daily
         **kwargs :
             Any accepted parameter for the method_name
         """
-        if output_folder is not None:
-            if not isinstance(output_folder, pathlib.Path):
-                output_folder = pathlib.Path(output_folder)
+        if save_daily and output_folder is None:
+            raise ValueError('output_folder must not be none to save daily netcdf files')
+        if isinstance(output_folder, str):
+            output_folder = pathlib.Path(output_folder)
         ds = xarray.Dataset(attrs=self._get_metadata_attrs())
         f = operator.methodcaller('_apply_multiple', method_list=method_list, binsize=self.binsize,
                                   nfft=self.nfft, fft_overlap=self.fft_overlap, bin_overlap=self.bin_overlap,
                                   band_list=band_list, **kwargs)
         start_date, end_date = self.start_end_timestamp()
-        current_date = start_date.date
+        current_date = start_date.date()
         for sound_file in self._files():
-            if save_daily and (sound_file.date.date > current_date):
-                if output_folder is not None:
-                    ds.to_netcdf(output_folder.joinpath('%s.nc' % current_date))
-                else:
-                    yield ds
+            if save_daily and (sound_file.date.date() > current_date):
+                ds.to_netcdf(output_folder.joinpath('%s.nc' % current_date))
                 ds = xarray.Dataset(attrs=self._get_metadata_attrs())
+                current_date = sound_file.date.date()
             ds_output = f(sound_file)
             ds = utils.merge_ds(ds, ds_output, self.file_dependent_attrs)
             self.current_chunk_id += ds.id.max()
@@ -233,32 +234,40 @@ def evolution(self, method_name, band_list=None, **kwargs):
         """
         return self.evolution_multiple(method_list=[method_name], band_list=band_list, **kwargs)
 
-    def evolution_freq_dom(self, method_name, save_daily=True, output_folder=None, **kwargs):
+    def evolution_freq_dom(self, method_name, save_daily=False, output_folder=None, **kwargs):
         """
         Returns the evolution of frequency domain parameters
         Parameters
         ----------
         method_name : str
             Name of the method of the acoustic_file class to compute
+        save_daily : boolean
+            Set to True to save daily netcdf files instead of returning a huge big file (useful for long deployments)
+        output_folder : str or Path
+            Directory to save the netcdf files. Only works with save_daily
         Returns
         -------
         A xarray DataSet with a row per bin with the method name output
         """
+        if save_daily and output_folder is None:
+            raise ValueError('output_folder must not be none to save daily netcdf files')
+        if isinstance(output_folder, str):
+            output_folder = pathlib.Path(output_folder)
         ds = xarray.Dataset(attrs=self._get_metadata_attrs())
         f = operator.methodcaller(method_name, binsize=self.binsize, nfft=self.nfft, fft_overlap=self.fft_overlap,
                                   bin_overlap=self.bin_overlap, **kwargs)
         start_date, end_date = self.start_end_timestamp()
-        current_date = start_date.date
+        current_date = start_date.date()
         for sound_file in self._files():
-            if save_daily and (sound_file.date.date > current_date):
-                if output_folder is not None:
-                    ds.to_netcdf(output_folder.joinpath('%s.nc' % current_date))
-                else:
-                    yield ds
+            if save_daily and (sound_file.date.date() > current_date):
+                ds.to_netcdf(output_folder.joinpath('%s.nc' % current_date))
                 ds = xarray.Dataset(attrs=self._get_metadata_attrs())
+                current_date = sound_file.date.date()
             ds_output = f(sound_file)
             ds = utils.merge_ds(ds, ds_output, self.file_dependent_attrs)
             self.current_chunk_id += ds.id.max()
+        if save_daily:
+            ds.to_netcdf(output_folder.joinpath('%s.nc' % current_date))
         return ds
 
     def timestamps_array(self):

pypam/dataset.py

@@ -161,7 +161,8 @@ def generate_deployment(self, idx):
         ds = xarray.Dataset()
         if self.frequency_features not in [[], None]:
             for f in self.frequency_features:
-                freq_evo = asa.evolution_freq_dom(f, band=None, db=True)
+                freq_evo = asa.evolution_freq_dom(f, band=None, db=True, save_daily=True,
+                                                  output_folder=self.output_folder.joinpath('deployments'))
                 for data_var in freq_evo.data_vars:
                     ds = ds.merge(freq_evo[data_var])
         if self.temporal_features not in [[], None]: