toggle for (concurrent) monthly ERA5 requests; progressbar disabled by default

RELEASE_NOTES.rst

@@ -11,9 +11,17 @@ Release Notes
 Upcoming Release
 ================
 
+- Adds option to toggle whether ERA5 downloads are requested in monthly or
+  annual chunks with keyword argument ``cutout.prepare(monthly_requests=True)``.
+  The default is now annual requests. The monthly requests can also be posted
+  concurrently using ``cutout.prepare(monthly_requests=True,
+  concurrent_requests=True)``.
+
 - Improved parallelization of ``atlite.convert.build_line_rating`` by adding
   keyword arguments for ``dask.compute`` (``dask_kwargs={}``) and an option to
-  disable the progressbar (``show_progressbar=True``).
+  disable the progressbar (``show_progress=False``).
+
+- Default to ``show_progress=False`` for performance reasons.
 
 Version 0.2.13
 ==============

atlite/convert.py

@@ -51,7 +51,7 @@ def convert_and_aggregate(
     return_capacity=False,
     capacity_factor=False,
     capacity_factor_timeseries=False,
-    show_progress=True,
+    show_progress=False,
     dask_kwargs={},
     **convert_kwds,
 ):
@@ -91,7 +91,7 @@ def convert_and_aggregate(
     capacity_factor_timeseries : boolean
         If True, the capacity factor time series of the chosen resource for
         each grid cell is computed.
-    show_progress : boolean, default True
+    show_progress : boolean, default False
         Whether to show a progress bar.
     dask_kwargs : dict, default {}
         Dict with keyword arguments passed to `dask.compute`.
@@ -882,7 +882,7 @@ def hydro(
     hydrobasins,
     flowspeed=1,
     weight_with_height=False,
-    show_progress=True,
+    show_progress=False,
     **kwargs,
 ):
     """
@@ -1047,7 +1047,7 @@ def convert_line_rating(
 
 
 def line_rating(
-    cutout, shapes, line_resistance, show_progress=True, dask_kwargs={}, **params
+    cutout, shapes, line_resistance, show_progress=False, dask_kwargs={}, **params
 ):
     """
     Create a dynamic line rating time series based on the IEEE-738 standard.
@@ -1073,7 +1073,7 @@ def line_rating(
     line_resistance : float/series
         Resistance of the lines in Ohm/meter. Alternatively in p.u. system in
         Ohm/1000km (see example below).
-    show_progress : boolean, default True
+    show_progress : boolean, default False
         Whether to show a progress bar.
     dask_kwargs : dict, default {}
         Dict with keyword arguments passed to `dask.compute`.

atlite/data.py

@@ -25,7 +25,14 @@
 from atlite.datasets import modules as datamodules
 
 
-def get_features(cutout, module, features, tmpdir=None):
+def get_features(
+    cutout,
+    module,
+    features,
+    tmpdir=None,
+    monthly_requests=False,
+    concurrent_requests=False,
+):
     """
     Load the feature data for a given module.
 
@@ -39,7 +46,13 @@ def get_features(cutout, module, features, tmpdir=None):
 
     for feature in features:
         feature_data = delayed(get_data)(
-            cutout, feature, tmpdir=tmpdir, lock=lock, **parameters
+            cutout,
+            feature,
+            tmpdir=tmpdir,
+            lock=lock,
+            monthly_requests=monthly_requests,
+            concurrent_requests=concurrent_requests,
+            **parameters,
         )
         datasets.append(feature_data)
 
@@ -115,6 +128,10 @@ def cutout_prepare(
     tmpdir=None,
     overwrite=False,
     compression={"zlib": True, "complevel": 9, "shuffle": True},
+    show_progress=False,
+    dask_kwargs=None,
+    monthly_requests=False,
+    concurrent_requests=False,
 ):
     """
     Prepare all or a selection of features in a cutout.
@@ -147,12 +164,26 @@ def cutout_prepare(
         To efficiently reduce cutout sizes, specify the number of 'least_significant_digits': n here.
         To disable compression, set "complevel" to None.
         Default is {'zlib': True, 'complevel': 9, 'shuffle': True}.
+    show_progress : bool, optional
+        If True, a progress bar is shown. The default is False.
+    dask_kwargs : dict, default {}
+        Dict with keyword arguments passed to `dask.compute`.
+    monthly_requests : bool, optional
+        If True, the data is requested on a monthly basis in ERA5. This is useful for
+        large cutouts, where the data is requested in smaller chunks. The
+        default is False
+    concurrent_requests : bool, optional
+        If True, the monthly data requests are posted concurrently.
+        Only has an effect if `monthly_requests` is True. The default is False.
 
     Returns
     -------
     cutout : atlite.Cutout
         Cutout with prepared data. The variables are stored in `cutout.data`.
     """
+    if dask_kwargs is None:
+        dask_kwargs = {}
+
     if cutout.prepared and not overwrite:
         logger.info("Cutout already prepared.")
         return cutout
@@ -174,7 +205,14 @@ def cutout_prepare(
             continue
         logger.info(f"Calculating and writing with module {module}:")
         missing_features = missing_vars.index.unique("feature")
-        ds = get_features(cutout, module, missing_features, tmpdir=tmpdir)
+        ds = get_features(
+            cutout,
+            module,
+            missing_features,
+            tmpdir=tmpdir,
+            monthly_requests=monthly_requests,
+            concurrent_requests=concurrent_requests,
+        )
         prepared |= set(missing_features)
 
         cutout.data.attrs.update(dict(prepared_features=list(prepared)))
@@ -198,8 +236,11 @@ def cutout_prepare(
         # Delayed writing for large cutout
         # cf. https://stackoverflow.com/questions/69810367/python-how-to-write-large-netcdf-with-xarray
         write_job = ds.to_netcdf(tmp, compute=False)
-        with ProgressBar():
-            write_job.compute()
+        if show_progress:
+            with ProgressBar(minimum=2):
+                write_job.compute(**dask_kwargs)
+        else:
+            write_job.compute(**dask_kwargs)
         if cutout.path.exists():
             cutout.data.close()
             cutout.path.unlink()

atlite/datasets/era5.py

@@ -20,6 +20,7 @@
 import numpy as np
 import pandas as pd
 import xarray as xr
+from dask import compute, delayed
 from numpy import atleast_1d
 
 from atlite.gis import maybe_swap_spatial_dims
@@ -274,7 +275,7 @@ def _area(coords):
     return [y1, x0, y0, x1]
 
 
-def retrieval_times(coords, static=False):
+def retrieval_times(coords, static=False, monthly_requests=False):
     """
     Get list of retrieval cdsapi arguments for time dimension in coordinates.
 
@@ -287,6 +288,11 @@ def retrieval_times(coords, static=False):
     Parameters
     ----------
     coords : atlite.Cutout.coords
+    static : bool, optional
+    monthly_requests : bool, optional
+        If True, the data is requested on a monthly basis. This is useful for
+        large cutouts, where the data is requested in smaller chunks. The
+        default is False
 
     Returns
     -------
@@ -305,12 +311,21 @@ def retrieval_times(coords, static=False):
     times = []
     for year in time.year.unique():
         t = time[time.year == year]
-        for month in t.month.unique():
+        if monthly_requests:
+            for month in t.month.unique():
+                query = {
+                    "year": str(year),
+                    "month": str(month),
+                    "day": list(t[t.month == month].day.unique()),
+                    "time": ["%02d:00" % h for h in t[t.month == month].hour.unique()],
+                }
+                times.append(query)
+        else:
             query = {
                 "year": str(year),
-                "month": str(month),
-                "day": list(t[t.month == month].day.unique()),
-                "time": ["%02d:00" % h for h in t[t.month == month].hour.unique()],
+                "month": list(t.month.unique()),
+                "day": list(t.day.unique()),
+                "time": ["%02d:00" % h for h in t.hour.unique()],
             }
             times.append(query)
     return times
@@ -377,7 +392,15 @@ def retrieve_data(product, chunks=None, tmpdir=None, lock=None, **updates):
     return ds
 
 
-def get_data(cutout, feature, tmpdir, lock=None, **creation_parameters):
+def get_data(
+    cutout,
+    feature,
+    tmpdir,
+    lock=None,
+    monthly_requests=False,
+    concurrent_requests=False,
+    **creation_parameters,
+):
     """
     Retrieve data from ECMWFs ERA5 dataset (via CDS).
 
@@ -392,6 +415,13 @@ def get_data(cutout, feature, tmpdir, lock=None, **creation_parameters):
         `atlite.datasets.era5.features`
     tmpdir : str/Path
         Directory where the temporary netcdf files are stored.
+    monthly_requests : bool, optional
+        If True, the data is requested on a monthly basis in ERA5. This is useful for
+        large cutouts, where the data is requested in smaller chunks. The
+        default is False
+    concurrent_requests : bool, optional
+        If True, the monthly data requests are posted concurrently.
+        Only has an effect if `monthly_requests` is True.
     **creation_parameters :
         Additional keyword arguments. The only effective argument is 'sanitize'
         (default True) which sets sanitization of the data on or off.
@@ -428,6 +458,11 @@ def retrieve_once(time):
     if feature in static_features:
         return retrieve_once(retrieval_times(coords, static=True)).squeeze()
 
-    datasets = map(retrieve_once, retrieval_times(coords))
+    time_chunks = retrieval_times(coords, monthly_requests=monthly_requests)
+    if concurrent_requests:
+        delayed_datasets = [delayed(retrieve_once)(chunk) for chunk in time_chunks]
+        datasets = compute(*delayed_datasets)
+    else:
+        datasets = map(retrieve_once, time_chunks)
 
     return xr.concat(datasets, dim="time").sel(time=coords["time"])

atlite/datasets/gebco.py

@@ -45,7 +45,14 @@ def get_data_gebco_height(xs, ys, gebco_path):
     )
 
 
-def get_data(cutout, feature, tmpdir, **creation_parameters):
+def get_data(
+    cutout,
+    feature,
+    tmpdir,
+    monthly_requests=False,
+    concurrent_requests=False,
+    **creation_parameters
+):
     """
     Get the gebco height data.
 
@@ -56,6 +63,10 @@ def get_data(cutout, feature, tmpdir, **creation_parameters):
         Takes no effect, only here for consistency with other dataset modules.
     tmpdir : str
         Takes no effect, only here for consistency with other dataset modules.
+    monthly_requests : bool
+        Takes no effect, only here for consistency with other dataset modules.
+    concurrent_requests : bool
+        Takes no effect, only here for consistency with other dataset modules.
     **creation_parameters :
         Must include `gebco_path`.
 

atlite/datasets/sarah.py

@@ -160,7 +160,9 @@ def hourly_mean(ds):
     return ds
 
 
-def get_data(cutout, feature, tmpdir, lock=None, **creation_parameters):
+def get_data(
+    cutout, feature, tmpdir, lock=None, monthly_requests=False, **creation_parameters
+):
     """
     Load stored SARAH data and reformat to matching the given cutout.
 
@@ -173,6 +175,10 @@ def get_data(cutout, feature, tmpdir, lock=None, **creation_parameters):
     feature : str
         Name of the feature data to retrieve. Must be in
         `atlite.datasets.sarah.features`
+    monthly_requests : bool
+        Takes no effect, only here for consistency with other dataset modules.
+    concurrent_requests : bool
+        Takes no effect, only here for consistency with other dataset modules.
     **creation_parameters :
         Mandatory arguments are:
             * 'sarah_dir', str. Directory of the stored SARAH data.

atlite/gis.py

@@ -674,7 +674,7 @@ def _process_func(i):
 
 
 def compute_availabilitymatrix(
-    cutout, shapes, excluder, nprocesses=None, disable_progressbar=False
+    cutout, shapes, excluder, nprocesses=None, disable_progressbar=True
 ):
     """
     Compute the eligible share within cutout cells in the overlap with shapes.

atlite/hydro.py

@@ -41,7 +41,7 @@ def find_upstream_basins(meta, hid):
     return hids
 
 
-def determine_basins(plants, hydrobasins, show_progress=True):
+def determine_basins(plants, hydrobasins, show_progress=False):
     if isinstance(hydrobasins, str):
         hydrobasins = gpd.read_file(hydrobasins)
 
@@ -81,7 +81,7 @@ def determine_basins(plants, hydrobasins, show_progress=True):
 
 
 def shift_and_aggregate_runoff_for_plants(
-    basins, runoff, flowspeed=1, show_progress=True
+    basins, runoff, flowspeed=1, show_progress=False
 ):
     inflow = xr.DataArray(
         np.zeros((len(basins.plants), runoff.indexes["time"].size)),

test/test_preparation_and_conversion.py

@@ -678,6 +678,13 @@ def test_pv_era5_and_era5t(tmp_path_factory):
             cutout, time=str(first_day_prev_month), skip_optimal_sum_test=True
         )
 
+    @staticmethod
+    @pytest.mark.parametrize("concurrent_requests", [True, False])
+    def test_era5_monthly_requests(tmp_path_factory, concurrent_requests):
+        tmp_path = tmp_path_factory.mktemp("era5_mon")
+        cutout = Cutout(path=tmp_path / "era5", module="era5", bounds=BOUNDS, time=TIME)
+        cutout.prepare(monthly_requests=True, concurrent_requests=concurrent_requests)
+
     @staticmethod
     def test_wind_era5(cutout_era5):
         return wind_test(cutout_era5)