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From jo...@apache.org
Subject [1/2] git commit: CLIMATE-425 Example for Temporal STD Metric
Date Fri, 16 May 2014 20:19:34 GMT
Repository: climate
Updated Branches:
  refs/heads/master 0edac0b1b -> 4929e2cfb


CLIMATE-425 Example for Temporal STD Metric


Project: http://git-wip-us.apache.org/repos/asf/climate/repo
Commit: http://git-wip-us.apache.org/repos/asf/climate/commit/89f5d0c4
Tree: http://git-wip-us.apache.org/repos/asf/climate/tree/89f5d0c4
Diff: http://git-wip-us.apache.org/repos/asf/climate/diff/89f5d0c4

Branch: refs/heads/master
Commit: 89f5d0c4f485f9fbc3b6a296b8608fc6dda9b90f
Parents: b6db8f8
Author: Maziyar Boustani <maziyar_b4@yahoo.com>
Authored: Thu May 15 15:50:50 2014 -0700
Committer: Maziyar Boustani <maziyar_b4@yahoo.com>
Committed: Thu May 15 15:50:50 2014 -0700

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 examples/simple_model_tstd.py | 86 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 86 insertions(+)
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http://git-wip-us.apache.org/repos/asf/climate/blob/89f5d0c4/examples/simple_model_tstd.py
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diff --git a/examples/simple_model_tstd.py b/examples/simple_model_tstd.py
new file mode 100644
index 0000000..46ff7ae
--- /dev/null
+++ b/examples/simple_model_tstd.py
@@ -0,0 +1,86 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+
+import urllib
+
+import ocw.data_source.local as local
+import ocw.evaluation as evaluation
+import ocw.metrics as metrics
+import ocw.plotter as plotter
+
+# File URL leader
+FILE_LEADER = "http://zipper.jpl.nasa.gov/dist/"
+# One Local Model Files
+FILE_1 = "AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_tasmax.nc"
+
+# Filename for the output image/plot (without file extension)
+OUTPUT_PLOT = "knmi_temporal_std"
+
+# Download necessary NetCDF file
+urllib.urlretrieve(FILE_LEADER + FILE_1, FILE_1)
+
+""" Step 1: Load Local NetCDF File into OCW Dataset Objects """
+print "Loading %s into an OCW Dataset Object" % (FILE_1,)
+# 'tasmax' is variable name of values
+knmi_dataset = local.load_file(FILE_1, "tasmax")
+
+print "KNMI_Dataset.values shape: (times, lats, lons) - %s \n" % (knmi_dataset.values.shape,)
+
+# Acessing latittudes and longitudes of netCDF file
+lats = knmi_dataset.lats
+lons = knmi_dataset.lons
+
+""" Step 2:  Build a Metric to use for Evaluation - Temporal STD for this example """
+# You can build your own metrics, but OCW also ships with some common metrics
+print "Setting up a Temporal STD metric to use for evaluation"
+std = metrics.TemporalStdDev()
+
+""" Step 3: 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"
+# Temporal STD Metric gets one target dataset then reference dataset should be None
+std_evaluation = evaluation.Evaluation(None, [knmi_dataset], [std])
+print "Executing the Evaluation using the object's run() method"
+std_evaluation.run()
+
+""" Step 4: 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 = std_evaluation.unary_results[0][0]
+print "The results are of type: %s" % type(results)
+
+# From the temporal std output I want to make a Contour Map of the region
+print "Generating a contour map using ocw.plotter.draw_contour_map()"
+
+fname = OUTPUT_PLOT
+gridshape = (4, 5) # 20 Years worth of plots. 20 rows in 1 column
+plot_title = "TASMAX Temporal Standard Deviation (1989 - 2008)"
+sub_titles = range(1989, 2009, 1)
+
+plotter.draw_contour_map(results, lats, lons, fname,
+                         gridshape=gridshape, ptitle=plot_title,
+                         subtitles=sub_titles)


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