svn commit: r1521238 - /incubator/climate/trunk/examples/knmi_to_cru31_full_bias.py

[go...@apache.org]

Author: goodale
Date: Mon Sep  9 18:42:20 2013
New Revision: 1521238

URL: http://svn.apache.org/r1521238
Log:
CLIMATE-264: Complete Model to RCMED Dataset comparison.

Added:
    incubator/climate/trunk/examples/knmi_to_cru31_full_bias.py

Added: incubator/climate/trunk/examples/knmi_to_cru31_full_bias.py
URL: http://svn.apache.org/viewvc/incubator/climate/trunk/examples/knmi_to_cru31_full_bias.py?rev=1521238&view=auto
==============================================================================
--- incubator/climate/trunk/examples/knmi_to_cru31_full_bias.py (added)
+++ incubator/climate/trunk/examples/knmi_to_cru31_full_bias.py Mon Sep  9 18:42:20 2013
@@ -0,0 +1,147 @@
+import datetime
+
+import numpy as np
+
+import ocw.data_source.local as local
+import ocw.data_source.rcmed as rcmed
+from ocw.dataset import Bounds as Bounds
+import ocw.dataset_processor as dsp
+import ocw.evaluation as evaluation
+import ocw.metrics as metrics
+import ocw.plotter as plotter
+
+# This way we can easily adjust the time span of the retrievals
+YEARS = 3
+# Two Local Model Files 
+MODEL = "AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_tasmax.nc"
+# Filename for the output image/plot (without file extension)
+OUTPUT_PLOT = "cru_31_tmax_knmi_africa_bias_full"
+
+""" Step 1: Load Local NetCDF File into OCW Dataset Objects """
+print("Loading %s into an OCW Dataset Object" % (MODEL,))
+knmi_dataset = local.load_file(MODEL, "tasmax")
+print("KNMI_Dataset.values shape: (times, lats, lons) - %s \n" % (knmi_dataset.values.shape,))
+
+""" Step 2: Fetch an OCW Dataset Object from the data_source.rcmed module """
+print("Working with the rcmed interface to get CRU3.1 Daily-Max Temp")
+metadata = rcmed.get_parameters_metadata()
+cru_31 = [m for m in metadata if m['longname'] == "CRU3.1 Daily-Maximum Temperature"][0]
+
+""" The RCMED API uses the following function to query, subset and return the 
+raw data from the database:
+
+rcmed.parameter_dataset(dataset_id, parameter_id, min_lat, max_lat, min_lon, 
+                        max_lon, start_time, end_time)
+
+The first two required params are in the cru_31 variable we defined earlier
+"""
+# Must cast to int since the rcmed api requires ints
+dataset_id = int(cru_31['dataset_id'])
+parameter_id = int(cru_31['parameter_id'])
+
+print("We are going to use the Model to constrain the Spatial Domain")
+#  The spatial_boundaries() function returns the spatial extent of the dataset
+print("The KNMI_Dataset spatial bounds (min_lat, max_lat, min_lon, max_lon) are: \n"
+      "%s\n" % (knmi_dataset.spatial_boundaries(), ))
+print("The KNMI_Dataset spatial resolution (lat_resolution, lon_resolution) is: \n"
+      "%s\n\n" % (knmi_dataset.spatial_resolution(), ))
+min_lat, max_lat, min_lon, max_lon = knmi_dataset.spatial_boundaries()
+
+print("Calculating the Maximum Overlap in Time for the datasets")
+
+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.time_range()
+# Grab the Max Start Time
+start_time = max([cru_start, knmi_start])
+# Grab the Min End Time
+end_time = min([cru_end, knmi_end])
+print("Overlap computed to be: %s to %s" % (start_time.strftime("%Y-%m-%d"),
+                                          end_time.strftime("%Y-%m-%d")))
+print("We are going to grab the first %s year(s) of data" % YEARS)
+end_time = datetime.datetime(start_time.year + YEARS, start_time.month, start_time.day)
+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,
+                                        min_lat,
+                                        max_lat,
+                                        min_lon,
+                                        max_lon,
+                                        start_time,
+                                        end_time)
+
+""" 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)
+
+# Create a Bounds object to use for subsetting
+new_bounds = Bounds(min_lat, max_lat, min_lon, max_lon, start_time, end_time)
+knmi_dataset = dsp.subset(new_bounds, knmi_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
+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...")
+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, ))
+ 
+""" 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()
+
+""" Step 5: Create an Evaluation Object using Datasets and our Metric """
+# The Evaluation Class Signature is:
+# Evaluation(reference, targets, metrics, subregions=None)
+# 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])
+print("Executing the Evaluation using the object's run() method")
+bias_evaluation.run()
+ 
+""" Step 6: Make a Plot from the Evaluation.results """
+# The Evaluation.results are a set of nested lists to support many different
+# possible Evaluation scenarios.
+#
+# The Evaluation results docs say:
+# The shape of results is (num_metrics, num_target_datasets) if no subregion
+# Accessing the actual results when we have used 1 metric and 1 dataset is
+# done this way:
+print("Accessing the Results of the Evaluation run")
+results = bias_evaluation.results[0][0]
+ 
+# From the bias output I want to make a Contour Map of the region
+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"]
+ 
+plotter.draw_contour_map(results, lats, lons, fname,
+                         gridshape=gridshape, ptitle=plot_title, 
+                         subtitles=sub_titles)
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