svn commit: r1518283 - /incubator/climate/trunk/examples/simple_model_to_model_bias.py

[go...@apache.org]

Author: goodale
Date: Wed Aug 28 16:48:24 2013
New Revision: 1518283

URL: http://svn.apache.org/r1518283
Log:
CLIMATE-263: Simple Model-to-Model Test - Progress

* Broke out the individual Steps
* Added Spatial Regridding Example
* Added Bias Metric
* Added Evaluation Run 

Modified:
    incubator/climate/trunk/examples/simple_model_to_model_bias.py

Modified: incubator/climate/trunk/examples/simple_model_to_model_bias.py
URL: http://svn.apache.org/viewvc/incubator/climate/trunk/examples/simple_model_to_model_bias.py?rev=1518283&r1=1518282&r2=1518283&view=diff
==============================================================================
--- incubator/climate/trunk/examples/simple_model_to_model_bias.py (original)
+++ incubator/climate/trunk/examples/simple_model_to_model_bias.py Wed Aug 28 16:48:24 2013
@@ -1,13 +1,71 @@
 import datetime
+
+import numpy as np
+
 import ocw.data_source.local as local
 import ocw.dataset_processor as dsp
+import ocw.evaluation as eval
+import ocw.metrics as metrics
 
 # 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"
-# Load the Files into OCW Objects Using the local from ocw.data_source
-dataset_1 = local.load_file(FILE_1, "tasmax")
-dataset_2 = local.load_file(FILE_2, "tasmax")
-# Do Temporal Rebinning to an Annual Time Step using dataset_processor
-dataset_1 = dsp.temporal_rebin(dataset_1, datetime.timedelta(days=365))
-dataset_2 = dsp.temporal_rebin(dataset_2, datetime.timedelta(days=365))
+
+""" Step 1: Load Local NetCDF Files into OCW Dataset Objects """
+print("Loading %s into an OCW Dataset Object" % (FILE_1,))
+knmi_dataset = local.load_file(FILE_1, "tasmax")
+print("KNMI_Dataset.values shape: (times, lats, lons) - %s \n" % (knmi_dataset.values.shape,))
+
+print("Loading %s into an OCW Dataset Object" % (FILE_2,))
+wrf_dataset = local.load_file(FILE_2, "tasmax")
+print("WRF_Dataset.values shape: (times, lats, lons) - %s \n" % (wrf_dataset.values.shape,))
+
+
+
+""" Step 2: Temporally Rebin the Data into an Annual Timestep """
+print("Temporally Rebinning the Datasets to an Annual Timestep")
+knmi_dataset = dsp.temporal_rebin(knmi_dataset, datetime.timedelta(days=365))
+wrf_dataset = dsp.temporal_rebin(wrf_dataset, datetime.timedelta(days=365))
+print("KNMI_Dataset.values shape: %s" % (knmi_dataset.values.shape,))
+print("WRF_Dataset.values shape: %s \n\n" % (wrf_dataset.values.shape,))
+
+
+
+""" Step 3: Spatially Regrid the Dataset Objects to a 1 degree grid """
+#  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()
+
+# 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, 1)
+new_lats = np.arange(min_lat, max_lat, 1)
+
+# 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("Final shape of the KNMI_Dataset: \n"
+      "%s\n" % (knmi_dataset.values.shape, ))
+print("Spatially Regridding the WRF_Dataset...")
+wrf_dataset = dsp.spatial_regrid(wrf_dataset, new_lats, new_lons)
+print("Final shape of the WRF_Dataset: \n"
+      "%s\n" % (wrf_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
+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
+bias_evaluation = eval.Evaluation(knmi_dataset, [wrf_dataset], [bias])
+bias_evaluation.run()
+
+
+
+