pass var names as kwargs

.gitignore

@@ -1,4 +1,7 @@
 *.pyc
 example.py
 .DS_store
-tests/test_results/*.nc
+tests/test_results/*.nc
+*.egg-info
+build
+dist

basininflow/inflow.py

@@ -53,7 +53,11 @@ def create_inflow_file(lsm_data: str,
                        timestep: datetime.timedelta = None,
                        cumulative: bool = False,
                        file_label: str = None,
-                       force_positive_runoff: bool = False, ) -> None:
+                       force_positive_runoff: bool = False,
+                       runoff_var: str = None,
+                       x_var: str = None,
+                       y_var: str = None,
+                       time_var: str = None, ) -> None:
     """
     Generate inflow files for use with RAPID.
 
@@ -81,6 +85,14 @@ def create_inflow_file(lsm_data: str,
         Label to include in the file name for organization purposes.
     force_positive_runoff: bool, optional
            Set all negative values to zero. Default is False
+    runoff_var: str, optional
+        Name of the runoff variable in the LSM data. If None, will search lsm dataset for recognized names and guess
+    x_var: str, optional
+        Name of the x variable in the LSM data. If None, will search lsm dataset for recognized names and guess
+    y_var: str, optional
+        Name of the y variable in the LSM data. If None, will search lsm dataset for recognized names and guess
+    time_var: str, optional
+        Name of the time variable in the LSM data. If None, will search lsm dataset for recognized names and guess
     """
     # Ensure that every input file exists
     if weight_table is not None and not os.path.exists(weight_table):
@@ -108,15 +120,24 @@ def create_inflow_file(lsm_data: str,
     logging.info(f'Opening LSM files {lsm_data[0] if type(lsm_data) == list else lsm_data}')
     with xr.open_mfdataset(lsm_data) as ds:
         # Select the variable names
-        runoff_variable = [x for x in ['ro', 'RO', 'runoff', 'RUNOFF'] if x in ds.variables][0]
-        lon_variable = [x for x in ['lon', 'longitude', 'LONGITUDE', 'LON'] if x in ds.variables][0]
-        lat_variable = [x for x in ['lat', 'latitude', 'LATITUDE', 'LAT'] if x in ds.variables][0]
+        if not runoff_var:
+            logging.warning('Runoff variable provided. Guessing from default names')
+            runoff_var = [x for x in ['ro', 'RO', 'runoff', 'RUNOFF'] if x in ds.variables][0]
+        if not x_var:
+            logging.warning('X variable not given. Guessing from default names.')
+            x_var = [x for x in ['x', 'lon', 'longitude', 'LONGITUDE', 'LON'] if x in ds.variables][0]
+        if not y_var:
+            logging.warning('Y variable not given. Guessing from default names.')
+            y_var = [x for x in ['y', 'lat', 'latitude', 'LATITUDE', 'LAT'] if x in ds.variables][0]
+        if not time_var:
+            logging.warning('Time variable not given. Guessing from default names.')
+            time_var = [x for x in ['time', 'TIME', ] if x in ds.variables][0]
 
         # Check that the input table dimensions match the dataset dimensions
         # This gets us the shape, while ignoring the time dimension
-        variable_dims = ds[runoff_variable].dims
-        dataset_shape = [ds[runoff_variable].shape[variable_dims.index(lat_variable)],
-                         ds[runoff_variable].shape[variable_dims.index(lon_variable)]]
+        variable_dims = ds[runoff_var].dims
+        dataset_shape = [ds[runoff_var].shape[variable_dims.index(y_var)],
+                         ds[runoff_var].shape[variable_dims.index(x_var)]]
 
         if weight_table is None:
             # find a file in the input_dir which contains f"weight*{dataset_shape[0]}x{dataset_shape[1]}.csv"
@@ -151,11 +172,11 @@ def create_inflow_file(lsm_data: str,
         lat_indices = weight_df['lat_index'].values  # - min_lat_idx
         lon_indices = weight_df['lon_index'].values  # - min_lon_idx
 
-        ds = ds[runoff_variable]
+        ds = ds[runoff_var]
 
         # Get approximate sizes of arrays and check if we have enough memory
-        out_array_size = ds['time'].shape[0] * sorted_rivid_array.shape[0]
-        in_array_size = ds['time'].shape[0] * n_wt_rows
+        out_array_size = ds[time_var].shape[0] * sorted_rivid_array.shape[0]
+        in_array_size = ds[time_var].shape[0] * n_wt_rows
         if ds.ndim == 4:
             in_array_size *= 2
         total_size = out_array_size + in_array_size
@@ -175,7 +196,7 @@ def create_inflow_file(lsm_data: str,
 
         # get the time array from the dataset
         logging.info('Reading Time values')
-        datetime_array = ds['time'].to_numpy()
+        datetime_array = ds[time_var].to_numpy()
 
         logging.info('Reading Runoff values')
         if ds.ndim == 3:

environment.yml

@@ -1,7 +1,7 @@
 name: inflows
 channels:
-  - defaults
   - conda-forge
+  - defaults
 dependencies:
   - dask
   - netcdf4