Merge pull request #192 from NREL/pp/reV-0.8.0_compat

reV 0.8.0 compatibility

reVX/version.py

@@ -3,4 +3,4 @@
 reVX version number
 """
 
-__version__ = "0.3.52"
+__version__ = "0.3.53"

reVX/wind_dirs/mean_wind_dirs.py

@@ -21,15 +21,13 @@ class MeanWindDirections(Aggregation):
     Then convert to equivalent sc_point_gid
     """
 
-    def __init__(self, res_h5_fpath, excl_fpath, wdir_dsets,
+    def __init__(self, excl_fpath, wdir_dsets,
                  tm_dset='techmap_wtk', excl_dict=None,
                  area_filter_kernel='queen', min_area=None,
                  resolution=128, excl_area=None):
         """
         Parameters
         ----------
-        res_h5_fpath : str
-            Filepath to .h5 file containing wind direction data
         excl_fpath : str
             Filepath to exclusions h5 with techmap dataset.
         wdir_dsets : str | list
@@ -65,7 +63,7 @@ def __init__(self, res_h5_fpath, excl_fpath, wdir_dsets,
                 logger.error(msg)
                 raise ValueError(msg)
 
-        super().__init__(excl_fpath, res_h5_fpath, tm_dset, *wdir_dsets,
+        super().__init__(excl_fpath, tm_dset, *wdir_dsets,
                          excl_dict=excl_dict,
                          area_filter_kernel=area_filter_kernel,
                          min_area=min_area,
@@ -180,12 +178,14 @@ def run_serial(cls, excl_fpath, h5_fpath, tm_dset, *wind_dir_dset,
 
         return agg_out
 
-    def aggregate(self, max_workers=None, sites_per_worker=1000):
+    def aggregate(self, res_h5_fpath, max_workers=None, sites_per_worker=1000):
         """
         Average wind directions to sc_points
 
         Parameters
         ----------
+        res_h5_fpath : str
+            Filepath to .h5 file containing wind direction data
         max_workers : int | None
             Number of cores to run summary on. None is all
             available cpus.
@@ -198,7 +198,7 @@ def aggregate(self, max_workers=None, sites_per_worker=1000):
         agg : dict
             Aggregated values for each aggregation dataset
         """
-        agg = super().aggregate(max_workers=max_workers,
+        agg = super().aggregate(res_h5_fpath, max_workers=max_workers,
                                 sites_per_worker=sites_per_worker)
 
         return agg
@@ -254,15 +254,15 @@ def run(cls, res_h5_fpath, excl_fpath, wdir_dsets,
         agg : dict
             Aggregated values for each aggregation dataset
         """
-        wdir = cls(res_h5_fpath, excl_fpath, wdir_dsets, tm_dset=tm_dset,
+        wdir = cls(excl_fpath, wdir_dsets, tm_dset=tm_dset,
                    excl_dict=excl_dict, area_filter_kernel=area_filter_kernel,
                    min_area=min_area, resolution=resolution,
                    excl_area=excl_area)
 
-        agg = wdir.aggregate(max_workers=max_workers,
+        agg = wdir.aggregate(res_h5_fpath, max_workers=max_workers,
                              sites_per_worker=sites_per_worker)
 
         if out_fpath is not None:
-            wdir.save_agg_to_h5(out_fpath, agg)
+            wdir.save_agg_to_h5(res_h5_fpath, out_fpath, agg)
 
         return agg

reVX/wind_dirs/prominent_wind_dirs.py

@@ -47,8 +47,9 @@ def __init__(self, power_rose_h5_fpath, excl_fpath,
             from the profile transform attribute in excl_fpath.
         """
         log_versions(logger)
-        super().__init__(excl_fpath, power_rose_h5_fpath, tm_dset, agg_dset,
-                         resolution=resolution, excl_area=excl_area)
+        self.power_rose_h5_fpath = power_rose_h5_fpath
+        super().__init__(excl_fpath, tm_dset, agg_dset, resolution=resolution,
+                         excl_area=excl_area)
 
     @classmethod
     def _map_direction_pos(cls, power_rose_h5_fpath):
@@ -168,15 +169,16 @@ def prominent_directions(self, max_workers=None,
             Update meta data table with neighboring supply curve point gids
             and power-rose value at each cardinal direction
         """
-        agg_out = self.aggregate(max_workers=max_workers,
+        agg_out = self.aggregate(self.power_rose_h5_fpath,
+                                 max_workers=max_workers,
                                  sites_per_worker=sites_per_worker)
 
         meta = agg_out.pop('meta')
         neighbor_gids = self._get_neighbors(self._excl_fpath,
                                             meta['sc_point_gid'].values,
                                             resolution=self._resolution)
 
-        dir_pos = self._map_direction_pos(self._h5_fpath)
+        dir_pos = self._map_direction_pos(self.power_rose_h5_fpath)
         sc_pr = agg_out.pop('powerrose_100m')[dir_pos].T
 
         columns = ['{}_pr'.format(d)

requirements.txt

@@ -4,7 +4,7 @@ dask[array]>=2.8
 fiona>=1.9.0
 geopandas>=0.8
 NREL-gaps>=0.3.3
-NREL-reV>=0.7.3
+NREL-reV>=0.8.0
 NREL-rex>=0.2.80
 psycopg2-binary>=2.8
 pyproj>=3.0.1