Incremental progress on model ensemble example * Dropped extra print statements * Adjusted the existing Evaluation object to run against multiple datasets * Change the output plotter settings to support the new datasets
Project: http://git-wip-us.apache.org/repos/asf/climate/repo Commit: http://git-wip-us.apache.org/repos/asf/climate/commit/f4a67429 Tree: http://git-wip-us.apache.org/repos/asf/climate/tree/f4a67429 Diff: http://git-wip-us.apache.org/repos/asf/climate/diff/f4a67429 Branch: refs/heads/master Commit: f4a67429eb3027a2a3217dcceb999814525dd419 Parents: 4f26cca Author: cgoodale <[email protected]> Authored: Mon Jul 7 15:18:52 2014 -0700 Committer: cgoodale <[email protected]> Committed: Mon Jul 7 15:18:52 2014 -0700 ---------------------------------------------------------------------- examples/model_ensemble_to_rcmed.py | 58 ++++++++++++++++---------------- 1 file changed, 29 insertions(+), 29 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/climate/blob/f4a67429/examples/model_ensemble_to_rcmed.py ---------------------------------------------------------------------- diff --git a/examples/model_ensemble_to_rcmed.py b/examples/model_ensemble_to_rcmed.py index 33f1219..544a638 100644 --- a/examples/model_ensemble_to_rcmed.py +++ b/examples/model_ensemble_to_rcmed.py @@ -32,7 +32,7 @@ import ocw.plotter as plotter # File URL leader FILE_LEADER = "http://zipper.jpl.nasa.gov/dist/"; # This way we can easily adjust the time span of the retrievals -YEARS = 3 +YEARS = 1 # Two Local Model Files FILE_1 = "AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_tasmax.nc" FILE_2 = "AFRICA_UC-WRF311_CTL_ERAINT_MM_50km-rg_1989-2008_tasmax.nc" @@ -55,10 +55,9 @@ else: # Load local knmi model data knmi_dataset = local.load_file(FILE_1, "tasmax") knmi_dataset.name = "AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_tasmax" -print(knmi_dataset) + wrf311_dataset = local.load_file(FILE_2, "tasmax") wrf311_dataset.name = "AFRICA_UC-WRF311_CTL_ERAINT_MM_50km-rg_1989-2008_tasmax" -print(wrf311_dataset) @@ -81,7 +80,7 @@ dataset_id = int(cru_31['dataset_id']) parameter_id = int(cru_31['parameter_id']) # The spatial_boundaries() function returns the spatial extent of the dataset -min_lat, max_lat, min_lon, max_lon = knmi_dataset.spatial_boundaries() +min_lat, max_lat, min_lon, max_lon = wrf311_dataset.spatial_boundaries() print("Calculating the Maximum Overlap in Time for the datasets") @@ -99,7 +98,6 @@ end_time = datetime.datetime(start_time.year + YEARS, start_time.month, start_ti print("Final Overlap 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, @@ -110,42 +108,44 @@ cru31_dataset = rcmed.parameter_dataset(dataset_id, start_time, end_time) -import sys; sys.exit() """ Step 3: Resample Datasets so they are the same shape """ -print("CRU31_Dataset.values shape: (times, lats, lons) - %s" % (cru31_dataset.values.shape,)) -print("KNMI_Dataset.values shape: (times, lats, lons) - %s" % (knmi_dataset.values.shape,)) -print("Our two datasets have a mis-match in time. We will subset on time to %s years\n" % YEARS) +print("Temporally Rebinning the Datasets to a Monthly Timestep") +# To run monthly temporal Rebinning use a timedelta of 30 days. +knmi_dataset = dsp.temporal_rebin(knmi_dataset, datetime.timedelta(days=30)) +wrf311_dataset = dsp.temporal_rebin(wrf311_dataset, datetime.timedelta(days=30)) +cru31_dataset = dsp.temporal_rebin(cru31_dataset, datetime.timedelta(days=30)) + +# Running Temporal Rebin early helps negate the issue of datasets being on different +# days of the month (1st vs. 15th) # Create a Bounds object to use for subsetting new_bounds = Bounds(min_lat, max_lat, min_lon, max_lon, start_time, end_time) + +# DEBUG +print new_bounds +print knmi_dataset +print wrf311_dataset + knmi_dataset = dsp.subset(new_bounds, knmi_dataset) +wrf311_dataset = dsp.subset(new_bounds, wrf311_dataset) + -print("CRU31_Dataset.values shape: (times, lats, lons) - %s" % (cru31_dataset.values.shape,)) -print("KNMI_Dataset.values shape: (times, lats, lons) - %s \n" % (knmi_dataset.values.shape,)) -print("Temporally Rebinning the Datasets to a Single Timestep") -# To run FULL temporal Rebinning use a timedelta > 366 days. I used 999 in this example -knmi_dataset = dsp.temporal_rebin(knmi_dataset, datetime.timedelta(days=999)) -cru31_dataset = dsp.temporal_rebin(cru31_dataset, datetime.timedelta(days=999)) -print("KNMI_Dataset.values shape: %s" % (knmi_dataset.values.shape,)) -print("CRU31_Dataset.values shape: %s \n\n" % (cru31_dataset.values.shape,)) - """ Spatially Regrid the Dataset Objects to a 1/2 degree grid """ -# Using the bounds we will create a new set of lats and lons on 1 degree step +# Using the bounds we will create a new set of lats and lons on 1/2 degree step new_lons = np.arange(min_lon, max_lon, 0.5) new_lats = np.arange(min_lat, max_lat, 0.5) # Spatially regrid datasets using the new_lats, new_lons numpy arrays -print("Spatially Regridding the KNMI_Dataset...") knmi_dataset = dsp.spatial_regrid(knmi_dataset, new_lats, new_lons) -print("Spatially Regridding the CRU31_Dataset...") +wrf311_dataset = dsp.spatial_regrid(wrf311_dataset, new_lats, new_lons) cru31_dataset = dsp.spatial_regrid(cru31_dataset, new_lats, new_lons) -print("Final shape of the KNMI_Dataset:%s" % (knmi_dataset.values.shape, )) -print("Final shape of the CRU31_Dataset:%s" % (cru31_dataset.values.shape, )) - + +# Generate an ensemble dataset from knmi and wrf models +ensemble_dataset = dsp.ensemble([knmi_dataset, wrf311_dataset]) + """ Step 4: Build a Metric to use for Evaluation - Bias for this example """ -# You can build your own metrics, but OCW also ships with some common metrics print("Setting up a Bias metric to use for evaluation") bias = metrics.Bias() @@ -155,7 +155,7 @@ bias = metrics.Bias() # Evaluation can take in multiple targets and metrics, so we need to convert # our examples into Python lists. Evaluation will iterate over the lists print("Making the Evaluation definition") -bias_evaluation = evaluation.Evaluation(knmi_dataset, [cru31_dataset], [bias]) +bias_evaluation = evaluation.Evaluation(cru31_dataset, [knmi_dataset, wrf311_dataset, ensemble_dataset], [bias]) print("Executing the Evaluation using the object's run() method") bias_evaluation.run() @@ -176,9 +176,9 @@ print("Generating a contour map using ocw.plotter.draw_contour_map()") lats = new_lats lons = new_lons fname = OUTPUT_PLOT -gridshape = (1, 1) # Using a 1 x 1 since we have a single Bias for the full time range -plot_title = "TASMAX Bias of KNMI Compared to CRU 3.1 (%s - %s)" % (start_time.strftime("%Y/%d/%m"), end_time.strftime("%Y/%d/%m")) -sub_titles = ["Full Temporal Range"] +gridshape = (3, 12) # Using a 3 x 12 since we have a 1 year of monthly data for 3 models +plot_title = "TASMAX Bias of CRU 3.1 vs. KNMI, WRF311 and ENSEMBLE (%s - %s)" % (start_time.strftime("%Y/%d/%m"), end_time.strftime("%Y/%d/%m")) +sub_titles = ["Monthly Time Step"] plotter.draw_contour_map(results, lats, lons, fname, gridshape=gridshape, ptitle=plot_title,
