Merge EE high endpoint branch

demo.py

@@ -1,26 +1,22 @@
 import ee
-import pyproj
-from gridwxcomp.prep_metadata import prep_metadata, get_subgrid_bounds
+from gridwxcomp.prep_metadata import prep_metadata
 from gridwxcomp.ee_download import download_grid_data
 from gridwxcomp.plot import daily_comparison, monthly_comparison, station_bar_plot
 from gridwxcomp.calc_bias_ratios import calc_bias_ratios
 from gridwxcomp.spatial import make_grid, interpolate
 from gridwxcomp.util import reproject_crs_for_bounds
 
 
-# initialize earth engine
-ee.Initialize()
 # name of the dataset comparison is being made with
 gridded_dataset_name = 'conus404'
-# Path to station metadata file with lat/long coords
-station_meta_path = './gridwxcomp/example_data/Station_Data.txt'
 # local path for config file
-config_path = f'./gridwxcomp/example_data/gridwxcomp_config_{gridded_dataset_name}.ini'
+config_path = f'gridwxcomp/example_data/gridwxcomp_config_{gridded_dataset_name}.ini'
+# Path to station metadata file with lat/long coords
+station_meta_path = 'gridwxcomp/example_data/Station_Data.txt'
 
-# name of bucket data will be exported to once the earth engine calls complete
-export_bucket = 'openet'
-# path in bucket to export to
-export_path = f'bias_correction_gridwxcomp_testing/gridwxcomp_{gridded_dataset_name}/'
+# initialize earth engine
+ee.Authenticate()
+ee.Initialize(opt_url='https://earthengine-highvolume.googleapis.com')
 
 # local path for prep_metadata output
 gridwxcomp_input = f'{gridded_dataset_name}_gridwxcomp_metadata.csv'
@@ -79,33 +75,27 @@
         'crs_id': 'ESRI:102004'}
 }
 
-# User has option to automatically pull bounds, aee TODO in function over assumptions
+# User has option to automatically pull bounds, see TODO in function over assumptions
 # projection_dict['wgs84']['bounds'] = get_subgrid_bounds(gridwxcomp_input, config_path, buffer=25)
 
 projection_dict['lcc']['bounds'] = reproject_crs_for_bounds(
     projection_dict['wgs84']['bounds'], projection_dict['lcc']['resolution'],
     projection_dict['wgs84']['crs_id'], projection_dict['lcc']['crs_id'], 0)
 
-
 '''
 download_grid_data
-   The purpose of this module is to make calls to the earth engine API to export gridded timeseries at the station
-        points contained within the station metadata file generated by prep_metadata. Exported data will be saved to
-        a cloud storage bucket that the user specifies.
-
-   The user will need to manually download the data via gsutil before proceeding further. The module amends the
-        station metadata file to have paths for the gridded dataset as they would exist locally once the user has
-        downloaded them.
+    The purpose of this module is to make calls to the earth engine API to 
+        export timeseries for the gridded dataset at the station locations 
+        contained within the station metadata file generated by prep_metadata.
+
+    The data is saved locally as it is downloaded, and the metadata file
+        generated by prep_metadata is amended with paths to the downloaded files.
 
-   Example: the example run is with the dataset conus404, the gridded path will be filled in such that if you
-        download the 'conus404' folder from the bucket to the local directory of trial_runs.py it will work without
-        further modification.
 '''
 download_grid_data(
     gridwxcomp_input,
     config_path,
-    export_bucket=export_bucket,
-    export_path=export_path,
+    local_folder=None,
     force_download=False)
 
 '''
@@ -139,12 +129,9 @@
     config_path,
     dataset_name=gridded_dataset_name)
 
-for var in [
-    'tmax',
-    'tmin',
-    'eto']:  # Iterate over vars in list. Valid entries found in calc_bias_ratios.py VAR_LIST
-    # path to bias ratios output file
-    ratio_filepath = f'{output_dir}/{var}_summary_comp_1980_2020.csv'
+for var in ['prcp', 'etr', 'eto']:  # Iterate over vars in list. Valid entries found in calc_bias_ratios.py VAR_LIST
+    ratio_filepath = f'{output_dir}/{var}_summary_comp_all_yrs.csv'  # path to bias ratios output file
+
     interpolation_out_path = (f'{var}_invdistnn_p{params["power"]}_'  # directory for interpolation outputs
                               f's{params["smoothing"]}_maxpoints{params["max_points"]}_radius{params["radius"]}')
 
@@ -173,15 +160,15 @@
         comparison_var=var,
         grid_id=None,
         day_limit=10,
-        years='1980-2020',
+        years='all',
         comp=True)
 
     '''
     station_bar_plot
         Generates boxplots of the bias ratios to visualize overall performance.
         Requires the outputs of calc_bias_ratios as an input.
     '''
-    station_bar_plot(ratio_filepath, bar_plot_layer='growseason_mean')
+    station_bar_plot(ratio_filepath, bar_plot_layer='grow_mean')
 
     '''
     make grid

gridwxcomp/calc_bias_ratios.py

@@ -23,7 +23,7 @@
 from .util import parse_yr_filter, read_config, read_data, convert_units
 
 
-VAR_LIST = ['tmax', 'tmin', 'tdew', 'rs', 'wind', 'ea', 'rhmax', 'rhmin', 'rhavg', 'eto', 'etr']
+VAR_LIST = ['tmax', 'tmin', 'tdew', 'rs', 'wind', 'ea', 'rhmax', 'rhmin', 'rhavg', 'eto', 'etr', 'prcp']
 GROW_THRESH = 65
 SUM_THRESH = 35
 ANN_THRESH = 125