CLIMATE-826 ValueError in model_ensemble_to_rcmed.py
diff --git a/examples/model_ensemble_to_rcmed.py b/examples/model_ensemble_to_rcmed.py
index 185aa2e..787367b 100644
--- a/examples/model_ensemble_to_rcmed.py
+++ b/examples/model_ensemble_to_rcmed.py
@@ -130,14 +130,10 @@
cru_start = datetime.datetime.strptime(cru_31['start_date'], "%Y-%m-%d")
cru_end = datetime.datetime.strptime(cru_31['end_date'], "%Y-%m-%d")
-knmi_start, knmi_end = knmi_dataset.temporal_boundaries()
# Set the Time Range to be the year 1989
start_time = datetime.datetime(1989, 1, 1)
end_time = datetime.datetime(1989, 12, 1)
-print("Time Range is: %s to %s" % (start_time.strftime("%Y-%m-%d"),
- end_time.strftime("%Y-%m-%d")))
-
print("Fetching data from RCMED...")
cru31_dataset = rcmed.parameter_dataset(dataset_id,
parameter_id,
@@ -150,15 +146,29 @@
""" Step 3: Resample Datasets so they are the same shape """
+# Running Temporal Rebin early helps negate the issue of datasets being on different
+# days of the month (1st vs. 15th)
print("Temporally Rebinning the Datasets to an Annual Timestep")
# To run annual temporal Rebinning,
knmi_dataset = dsp.temporal_rebin(knmi_dataset, temporal_resolution='annual')
+dataset_start, dataset_end = knmi_dataset.temporal_boundaries()
+start_time = max([start_time, dataset_start])
+end_time = min([end_time, dataset_end])
+
wrf311_dataset = dsp.temporal_rebin(
wrf311_dataset, temporal_resolution='annual')
-cru31_dataset = dsp.temporal_rebin(cru31_dataset, temporal_resolution='annual')
+dataset_start, dataset_end = wrf311_dataset.temporal_boundaries()
+start_time = max([start_time, dataset_start])
+end_time = min([end_time, dataset_end])
-# Running Temporal Rebin early helps negate the issue of datasets being on different
-# days of the month (1st vs. 15th)
+cru31_dataset = dsp.temporal_rebin(cru31_dataset, temporal_resolution='annual')
+dataset_start, dataset_end = cru31_dataset.temporal_boundaries()
+start_time = max([start_time, dataset_start])
+end_time = min([end_time, dataset_end])
+
+print("Time Range is: %s to %s" % (start_time.strftime("%Y-%m-%d"),
+ end_time.strftime("%Y-%m-%d")))
+
# Create a Bounds object to use for subsetting
new_bounds = Bounds(lat_min=min_lat, lat_max=max_lat, lon_min=min_lon,
lon_max=max_lon, start=start_time, end=end_time)
@@ -214,7 +224,7 @@
lats = new_lats
lons = new_lons
fname = OUTPUT_PLOT
-gridshape = (3, 1) # Using a 3 x 1 since we have a 1 year of data for 3 models
+gridshape = (3, start_time.year - end_time.year + 1) # Using a 3 x N since we have a N year(s) of data for 3 models
plotnames = ["KNMI", "WRF311", "ENSEMBLE"]
for i in np.arange(3):
plot_title = "TASMAX Bias of CRU 3.1 vs. %s (%s - %s)" % (
