Eliminate prints and add Filter classes

src/lisfloodutilities/gridding/generate_grids.py

@@ -91,7 +91,6 @@ def run(config_filename: str, infolder: str, output_file: str, processing_dates_
             outfilepath = filename.with_suffix('.tiff')
             output_writer_tiff.open(outfilepath)
         grid_data = grid_utils.generate_grid(filename)
-        print('orig: ', grid_data[1519][2250])
         output_writer_netcdf.write(grid_data, file_timestamp)
         if output_tiff:
             output_writer_tiff.write(grid_data, file_timestamp)

src/lisfloodutilities/gridding/lib/filters.py

@@ -0,0 +1,40 @@
+from pathlib import Path
+import pandas as pd
+
+class KiwisFilter():
+
+    def __init__(self, filter_args: dict):
+        self.args = filter_args
+
+    def filter(self, kiwis_files: array) -> array:
+        filtered_data_frames = []
+        for file_path in kiwis_files:
+            df_kiwis = pd.read_csv(file_path, sep="\t")
+            df_kiwis = self.__apply_filter(df_kiwis)
+            filtered_data_frames.append(df_kiwis)
+        return filtered_data_frames
+
+    def __apply_filter(self, df: pd.DataFrame) -> pd.DataFrame:
+        # Get the code to filter kiwis leaving only the rows to be used for point file creation
+        return df
+
+# KIWIS_FILTER_PLUGIN_CLASSES = {'DowgradedObservationsKiwisFilter': {'1295': 1.0}, 'ObservationsKiwisFilter': {'1303': 100.0}}
+
+class DowgradedObservationsKiwisFilter(KiwisFilter):
+
+    def __init__(self, filter_args: dict):
+        super().__init__(filter_args)
+
+    def __apply_filter(self, df: pd.DataFrame) -> pd.DataFrame:
+        df = super().__apply_filter(df)
+        return df
+
+class ObservationsKiwisFilter(KiwisFilter):
+
+    def __init__(self, filter_args: dict):
+        super().__init__(filter_args)
+
+    def __apply_filter(self, df: pd.DataFrame) -> pd.DataFrame:
+        df = super().__apply_filter(df)
+        return df
+

src/lisfloodutilities/gridding/lib/writers.py

@@ -124,14 +124,12 @@ def write_timestep(self, grid: np.ndarray, timestep: int = -1):
                 raise Exception("netCDF Dataset was not initialized. If file already exists, use --force flag to append.")
             timestep_idx = int(timestep / self.time_frequency)
             self.nf.variables[self.netcdf_var_time][timestep_idx] = timestep
-            print('ncdf f(grid): ', grid[1519][2250])
             values = self.setNaN(copy.deepcopy(grid))
             values[values < self.conf.value_min_packed] = np.nan
             values[values > self.conf.value_max_packed] = np.nan
             values[values != self.conf.VALUE_NAN] *= self.conf.scale_factor
             values[values != self.conf.VALUE_NAN] += self.conf.add_offset
             values[np.isnan(values)] = self.conf.VALUE_NAN * self.conf.scale_factor + self.conf.add_offset
-            print('ncdf i(grid): ', values[1519][2250])
             self.nf.variables[self.var_code][timestep_idx, :, :] = values
 
     def opened(self) -> bool:
@@ -290,8 +288,6 @@ def write_timestep(self, grid: np.ndarray, timestep: int = -1):
             ds.GetRasterBand(1).SetScale(self.conf.scale_factor)
             ds.GetRasterBand(1).SetOffset(self.conf.add_offset)
             ds = self.setup_dataset_metadata(ds)
-            print('tiff f(grid): ', grid[1519][2250])
-            print('tiff i(grid): ', grid.astype(np.int16)[1519][2250])
             ds.GetRasterBand(1).WriteArray(grid.astype(np.int16))
             ds.FlushCache()
             ds = None