examples/podaac_integration_example.py

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"""
    podaac_integration_example.py

    Use OCW to download a PODACC dataset, evaluate and plot (contour map).

    In this example:

    1. Download a remote PO.DAAC (https://podaac.jpl.nasa.gov/) dataset
       and read it into an OCW dataset object.
    2. Create a temporal STD metric using one of the OCW standard metrics.
    3. Evaluate the dataset against the metric and plot a contour map.

    OCW modules demonstrated:

    1. datasource/podaac_datasource
    2. metrics
    3. evaluation
    4. plotter

"""
from __future__ import print_function

import ocw.data_source.podaac_datasource as podaac
import ocw.evaluation as evaluation
import ocw.metrics as metrics
import ocw.plotter as plotter

datasetId = 'PODAAC-CCF30-01XXX'
variable = 'uwnd'
name = 'PO.DAAC_test_dataset'
OUTPUT_PLOT = "ccmp_temporal_std"
# Step 1: Download remote PO.DAAC Dataset and read it into an OCW Dataset Object.
print("Available Level4 PO.DAAC Granules: %s" % podaac.list_available_extract_granule_dataset_ids())
print("Extracting variable '%s' from  Level4 granule '%s' and converting it into a OCW dataset."
      % (variable, datasetId))
ccmp_dataset = podaac.extract_l4_granule(
    variable=variable, dataset_id=datasetId, name=name)
print("CCMP_Dataset.values shape: (times, lats, lons) - %s \n" %
      (ccmp_dataset.values.shape,))

# Acessing latittudes and longitudes of netCDF file
lats = ccmp_dataset.lats
lons = ccmp_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, [ccmp_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))
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 = "Cross-Calibrated Multi-Platform Temporal Standard Deviation"
sub_titles = range(2002, 2010, 1)

plotter.draw_contour_map(results, lats, lons, fname,
                         gridshape=gridshape, ptitle=plot_title,
                         subtitles=sub_titles)