Make it a dict

recipes/daymet/meta.yaml

@@ -1,31 +1,25 @@
-title: 'Global Precipitation Climatology Project'
+title: Daymet
 description: >
-  Global Precipitation Climatology Project (GPCP) Daily Version 1.3 gridded, merged ty
-  satellite/gauge precipitation Climate data Record (CDR) from 1996 to present.
+  Daily Surface Weather and Climatological Summaries (Daymet) provides
+  long-term, continuous, gridded estimates of daily weather and climatology
+  variables by interpolating and extrapolating ground-based observations through
+  statistical modeling techniques. The Daymet data products provide driver data
+  for biogeochemical terrestrial modeling and have myriad applications in many
+  Earth science, natural resource, biodiversity, and agricultural research
+  areas. Daymet weather variables include daily minimum and maximum temperature,
+  precipitation, vapor pressure, shortwave radiation, snow water equivalent, and
+  day length produced on a 1 km x 1 km gridded surface over continental North
+  America and Hawaii from 1980 and over Puerto Rico from 1950 through the end of
+  the most recent full calendar year.
 pangeo_forge_version: '0.9.0'
 pangeo_notebook_version: '2022.06.02'
 recipes:
-  - id: gpcp-from-gcs
-    object: 'recipe:recipe'
-provenance:
-  providers:
-    - name: 'NOAA NCEI'
-      description: 'National Oceanographic & Atmospheric Administration National Centers for Environmental Information'
-      roles:
-        - host
-        - licensor
-      url: https://www.ncei.noaa.gov/products/global-precipitation-climatology-project
-    - name: 'University of Maryland'
-      description: >
-        University of Maryland College Park Earth System Science Interdisciplinary Center
-        (ESSIC) and Cooperative Institute for Climate and Satellites (CICS).
-      roles:
-        - producer
-      url: http://gpcp.umd.edu/
+  dict_object: recipe:recipes
+provenance: {}
   license: 'No constraints on data access or use.'
 maintainers:
-  - name: 'Charles Stern'
-    orcid: '0000-0002-4078-0852'
-    github: cisaacstern
+- name: 'Charles Stern'
+  orcid: '0000-0002-4078-0852'
+  github: cisaacstern
 bakery:
   id: 'pangeo-ldeo-nsf-earthcube'

recipes/daymet/recipe.py

@@ -1,82 +1,73 @@
 import datetime
-import enum
-import os
 
 from pangeo_forge_recipes import patterns
 from pangeo_forge_recipes.recipes import XarrayZarrRecipe
 
 
-# class Region(str, enum.Enum):
-#     NA = 'na'
-#     PR = 'pr'
-#     HI = 'hi'
-
-
-# class Frequency(str, enum.Enum):
-#     DAY = 'daily'
-#     MONTH = 'mon'
-#     YEAR = 'ann'
-
-
-AGG_VARIABLES = {'prcp', 'swe', 'tmax', 'tmin', 'vp'}
-DAILY_VARIABLES = AGG_VARIABLES | {'dayl', 'srad'}
-
+def make_recipe(region, frequency):
+    """
+    Make a daymet recipe for given region with given frequency.
 
-def make_format_function(region: str, frequency: str):
+    region is "na", "pr" or "hi"
+    frequency is "daily", "mon" (monthly) or "ann" (yearly)
+    """
+    # Aggregate variables available
     AGG_VARIABLES = {'prcp', 'swe', 'tmax', 'tmin', 'vp'}
+    # We have a few more variables available daily, in addition to the aggregate ones
     DAILY_VARIABLES = AGG_VARIABLES | {'dayl', 'srad'}
-    if frequency in {"mon", "ann"}:
+
+    if frequency in {'mon', 'ann'}:
+        # Aggregated data - monthly or annual
+        variables = list(AGG_VARIABLES)
+
+        if frequency == 'ann':
+            nitems_per_file = 1
+            kwargs = dict()
+        else:
+            nitems_per_file = 12
+            kwargs = dict(subset_inputs={'time': 12})
 
         def format_function(variable, time):
             # https://thredds.daac.ornl.gov/thredds/fileServer/ornldaac/1855/daymet_v4_prcp_monttl_hi_1980.nc
             assert variable in AGG_VARIABLES
 
-            folder = '1852' if frequency == "ann" else '1855'
+            folder = '1852' if frequency == 'ann' else '1855'
             if variable == 'prcp':
                 agg = 'ttl'
             else:
                 agg = 'avg'
             return f'https://thredds.daac.ornl.gov/thredds/fileServer/ornldaac/{folder}/daymet_v4_{variable}_{frequency}{agg}_{region}_{time:%Y}.nc'
 
     else:
+        variables = list(DAILY_VARIABLES)
+        nitems_per_file = 365
+        kwargs = dict(subset_inputs={'time': 365})
 
         def format_function(variable, time):
             assert variable in DAILY_VARIABLES
             # https://thredds.daac.ornl.gov/thredds/fileServer/ornldaac/1840/daymet_v4_daily_hi_dayl_1980.nc
             return f'https://thredds.daac.ornl.gov/thredds/fileServer/ornldaac/1840/daymet_v4_{frequency}_{region}_{variable}_{time:%Y}.nc'
 
-    return format_function
-
-
-def make_recipe(region, frequency):
-    AGG_VARIABLES = {'prcp', 'swe', 'tmax', 'tmin', 'vp'}
-    DAILY_VARIABLES = AGG_VARIABLES | {'dayl', 'srad'}
-    if frequency == "daily":
-        variables = list(DAILY_VARIABLES)
-        nitems_per_file = 365
-        kwargs = dict(subset_inputs={'time': 365})
-    else:
-        variables = list(AGG_VARIABLES)
-
-        if frequency == "ann":
-            nitems_per_file = 1
-            kwargs = dict()
-        else:
-            nitems_per_file = 12
-            kwargs = dict(subset_inputs={'time': 12})
-
     variable_merge_dim = patterns.MergeDim('variable', keys=variables)
 
     dates = [datetime.datetime(y, 1, 1) for y in range(1980, 2021)]
     concat_dim = patterns.ConcatDim('time', keys=dates, nitems_per_file=nitems_per_file)
 
     pattern = patterns.FilePattern(
-        make_format_function(region, frequency), variable_merge_dim, concat_dim
+        format_function(region, frequency), variable_merge_dim, concat_dim
     )
 
     recipe = XarrayZarrRecipe(pattern, copy_input_to_local_file=True, **kwargs)
 
     return recipe
 
 
-recipe = make_recipe("na", "mon")
+regions = ('na', 'hi', 'pr')
+frequencies = ('mon', 'ann')
+
+recipes = {}
+
+for region in regions:
+    for freq in frequencies:
+        id = f'{region}_{freq}'
+        recipes[id] = make_recipe(region, freq)