CLIMATE-453 - Add Taylor Diagram creation example
Project: http://git-wip-us.apache.org/repos/asf/climate/repo Commit: http://git-wip-us.apache.org/repos/asf/climate/commit/91211e2e Tree: http://git-wip-us.apache.org/repos/asf/climate/tree/91211e2e Diff: http://git-wip-us.apache.org/repos/asf/climate/diff/91211e2e Branch: refs/heads/master Commit: 91211e2e4f9a11e161efd7b030e4bf1f647ec8ae Parents: e9437d0 Author: Michael Joyce <[email protected]> Authored: Wed Jun 4 10:39:05 2014 -0700 Committer: Michael Joyce <[email protected]> Committed: Wed Jun 4 10:39:05 2014 -0700 ---------------------------------------------------------------------- examples/taylor_diagram_example.py | 98 +++++++++++++++++++++++++++++++++ 1 file changed, 98 insertions(+) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/climate/blob/91211e2e/examples/taylor_diagram_example.py ---------------------------------------------------------------------- diff --git a/examples/taylor_diagram_example.py b/examples/taylor_diagram_example.py new file mode 100644 index 0000000..202954a --- /dev/null +++ b/examples/taylor_diagram_example.py @@ -0,0 +1,98 @@ +import datetime +import sys +from os import path +import urllib + +import numpy + +from ocw.dataset import Bounds +import ocw.data_source.local as local +import ocw.dataset_processor as dsp +import ocw.evaluation as evaluation +import ocw.metrics as metrics +import ocw.plotter as plotter + +FILE_LEADER = "http://zipper.jpl.nasa.gov/dist/"; +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" + +# Download some example NetCDF files for the evaluation +################################################################################ +if not path.exists(FILE_1): + urllib.urlretrieve(FILE_LEADER + FILE_1, FILE_1) + +if not path.exists(FILE_2): + urllib.urlretrieve(FILE_LEADER + FILE_2, FILE_2) + +# Load the example datasets into OCW Dataset objects. We want to load +# the 'tasmax' variable values. We'll also name the datasets for use +# when plotting. +################################################################################ +knmi_dataset = local.load_file(FILE_1, "tasmax") +wrf_dataset = local.load_file(FILE_2, "tasmax") + +knmi_dataset.name = "knmi" +wrf_dataset.name = "wrf" + +# Date values from loaded datasets might not always fall on reasonable days. +# With monthly data, we could have data falling on the 1st, 15th, or some other +# day of the month. Let's fix that real quick. +################################################################################ +knmi_dataset = dsp.normalize_dataset_datetimes(knmi_dataset, 'monthly') +wrf_dataset = dsp.normalize_dataset_datetimes(wrf_dataset, 'monthly') + +# We're only going to run this evaluation over a years worth of data. We'll +# make a Bounds object and use it to subset our datasets. +################################################################################ +subset = Bounds(-45, 42, -24, 60, datetime.datetime(1989, 1, 1), datetime.datetime(1989, 12, 1)) +knmi_dataset = dsp.subset(subset, knmi_dataset) +wrf_dataset = dsp.subset(subset, wrf_dataset) + +# Temporally re-bin the data into a monthly timestep. +################################################################################ +knmi_dataset = dsp.temporal_rebin(knmi_dataset, datetime.timedelta(days=30)) +wrf_dataset = dsp.temporal_rebin(wrf_dataset, datetime.timedelta(days=30)) + +# Spatially regrid the datasets onto a 1 degree grid. +################################################################################ +# Get the bounds of the reference dataset and use it to create a new +# set of lat/lon values on a 1 degree step +# Using the bounds we will create a new set of lats and lons on 1 degree step +min_lat, max_lat, min_lon, max_lon = knmi_dataset.spatial_boundaries() +new_lons = numpy.arange(min_lon, max_lon, 1) +new_lats = numpy.arange(min_lat, max_lat, 1) + +# Spatially regrid datasets using the new_lats, new_lons numpy arrays +knmi_dataset = dsp.spatial_regrid(knmi_dataset, new_lats, new_lons) +wrf_dataset = dsp.spatial_regrid(wrf_dataset, new_lats, new_lons) + +# Load the metrics that we want to use for the evaluation. +################################################################################ +sstdr = metrics.SpatialStdDevRatio() +pc = metrics.PatternCorrelation() + +# Create our new evaluation object. The knmi dataset is the evaluations +# reference dataset. We then provide a list of 1 or more target datasets +# to use for the evaluation. In this case, we only want to use the wrf dataset. +# Then we pass a list of all the metrics that we want to use in the evaluation. +################################################################################ +test_evaluation = evaluation.Evaluation(knmi_dataset, [wrf_dataset], [sstdr, pc]) +test_evaluation.run() + +# Pull our the evaluation results and prepare them for drawing a Taylor diagram. +################################################################################ +spatial_stddev_ratio = test_evaluation.results[0][0] +# Pattern correlation results are a tuple, so we need to index and grab +# the component we care about. +spatial_correlation = test_evaluation.results[0][1][0] + +taylor_data = numpy.array([[spatial_stddev_ratio], [spatial_correlation]]).transpose() + +# Draw our taylor diagram! +################################################################################ +plotter.draw_taylor_diagram(taylor_data, + [wrf_dataset.name], + knmi_dataset.name, + fname='taylor_plot', + fmt='png', + frameon=False)
