Merge branch 'master' of https://github.com/BrianWilson1/climate

ocw-parallel/README.md

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+# ocw-parallel

ocw-parallel/functions.py

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+# 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 sys, os, datetime, urllib, urlparse
+from datetime import timedelta
+import numpy as np
+
+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
+
+
+def loadDataset(urlAndVariable, dir=None):
+    '''Load a dataset (variable), returning a Dataset object.'''
+    shortName = None
+    if len(urlAndVariable) == 3:
+        url, variable, shortName = urlAndVariable
+    else:
+        url, variable = urlAndVariable
+    f = retrieveFile(url, dir)
+    ds = local.load_file(f, variable)
+    if shortName is None or shortName == '':
+        shortName = f + '?' + variable
+    ds.name = shortName
+    shape = ds.values.shape
+    if len(shape) == 3:
+        coords = '(time, lat, lon)'
+    elif len(shape) == 2:
+        coords = '(lat, lon)'
+    print >>sys.stderr, 'loadDataset: File %s has variable %s with shape %s: %s' % (f, variable, coords, shape) 
+    return ds
+
+def loadDatasets(urlAndVariables, dir=None):
+    return [loadDataset(uv, dir) for uv in urlAndVariables]
+
+
+def temporalRegrid(dataset, timeRes=timedelta(days=365)):
+    '''Temporally rebin a dataset variable to a specified time resolution (timedelta object).'''
+    dataset = dsp.temporal_rebin(dataset, timeRes)
+    name = dataset.name
+    if name is None: name = ''
+    print >>sys.stderr, 'temporalRebin: Dataset %s has new shape %s' % (name, str(dataset.values.shape))
+    return dataset
+
+def temporalRegrids(datasets, timeRes=timedelta(days=365)):
+    '''Temporally rebin dataset variables to a specified time resolution (timedelta object).'''
+    return [temporalRegrid(d, timeRes) for d in datasets]
+
+
+def spatialBounds(dataset): return dataset.spatial_boundaries()
+
+def commonSpatialBounds(datasets):
+    '''Compute overlapping (intersection) spatial bounds of many datasets.'''
+    bounds = [spatialBounds(ds) for ds in datasets]
+    for i, b in enumerate(bounds):
+        name = datasets[i].name
+        if name is None or name == '': name = str(i)
+        print >>sys.stderr, 'commonSpatialBounds: Dataset %s has boundaries: lat (%s, %s), lon (%s, %s).' % \
+                                (name, b[0], b[1], b[2], b[3])
+    minLat = max([b[0] for b in bounds])
+    maxLat = min([b[1] for b in bounds])
+    minLon = max([b[2] for b in bounds])
+    maxLon = min([b[3] for b in bounds])
+    print >>sys.stderr, 'commonSpatialBounds: Common boundaries are: lat (%s, %s), lon (%s, %s).' % \
+                            (minLat, maxLat, minLon, maxLon)
+    return (minLat, maxLat, minLon, maxLon)
+
+def generateLatLonGrid(latGrid, lonGrid):
+    '''Generate a uniform lat/lon grid at set resolutions, where latGrid is a tuple of (latMin, latMax, latRes).'''
+    minLat, maxLat, latRes = map(float, latGrid)
+    minLon, maxLon, lonRes = map(float, lonGrid)
+    lats = np.arange(minLat, maxLat, float(latRes))
+    lons = np.arange(minLon, maxLon, float(lonRes))
+    return (lats, lons)
+
+def commonLatLonGrid(datasets, latRes, lonRes):
+    '''Find common (intersect) lat/lon bounds and construct new grid with specified lat/lon resolutions.'''
+    minLat, maxLat, minLon, maxLon = commonSpatialBounds(datasets)
+    latGrid = (minLat, maxLat, latRes)
+    lonGrid = (minLon, maxLon, lonRes)
+    return generateLatLonGrid(latGrid, lonGrid)
+
+
+def spatialRegrid(dataset, lats, lons):
+    '''Spatially regrid dataset variable to a new grid with specified resolution, where lats & lons
+are the new coordinate vectors.
+    '''
+    return dsp.spatial_regrid(dataset, lats, lons)
+
+def spatialRegrids(datasets, lats, lons):
+    '''Spatially regrid dataset variables to a new grid with specified resolution, where lats & lons
+are the new coordinate vectors.
+    '''
+    return [spatialRegrid(d, lats, lons) for d in datasets]
+
+
+def lookupMetrics(metricNames,
+                  availableMetrics={'Bias': metrics.Bias, 'StdDevRatio': metrics.StdDevRatio,
+                                    'PatternCorrelation': metrics.PatternCorrelation}):
+    '''Return a list of metric objects given a list of string names.'''
+    metrics = []
+    for name in metricNames:
+        try:
+            m = availableMetrics[name]
+            metrics.append(m())
+        except:
+            print >>sys.stderr, 'lookupMetrics: Error, No metric named %s' % name
+    return metrics
+
+def computeMetrics(datasets, metricNames=['Bias'], subregions=None):
+    '''Compute one or more metrics comparing multiple target datasets to a reference dataset.
+This routine assumes that the datasets have already been regridded so that there grid dimensions
+are identical.
+    '''
+    metrics = lookupMetrics(metricNames)
+    if len(metrics) != len(metricNames):
+        print >>sys.stderr, 'computeMetrics: Error, Illegal or misspelled metric name.'
+    eval = evaluation.Evaluation(datasets[0], datasets[1:], metrics)
+    print >>sys.stderr, 'computeMetrics: Evaluating metrics %s . . .' % str(metricNames)
+    eval.run()
+    return eval.results
+
+
+def compareVariablesWithMetrics(datasetUrlsAndVarNames,         # URL's pointing to datasets, first one is reference, rest are targets; 
+                                                                # each tuple can be (datasetUrl, variableName, shortName), shortName optional
+                           metrics,                             # list of metrics to compute (by name)
+                           outputName,                          # root name for outputs
+                           timeRes=timedelta(days=365),         # time resolution to regrid all variables to
+                           latRes=1., lonRes=1.,                # lat/lon resolutions to regrid all variables to
+                           subregions=None,                     # list of subregion boundaries
+                           dir='./'):                           # directory for outputs, defaults to current working dir
+    '''Compare multiple target variables to a reference variable, returning the computed metric(s)
+after temporally rebinning to a common time resolution and a common spatial (lat/lon) resolution.
+    '''
+    datasets = loadDatasets(datasetUrlsAndVarNames, dir)
+
+    datasets = temporalRegrids(datasets, timeRes)
+
+    newLats, newLons = commonLatLonGrid(datasets, latRes, lonRes)
+    datasets = spatialRegrids(datasets, newLats, newLons)
+
+#    datasets = maskMissingValues(datasets, missingValues)
+
+    metrics = computeMetrics(datasets, metrics, subregions)
+    return (newLats, newLons, metrics)
+
+
+def plotBias(metric, lats, lons, outputName, **config):
+    '''Plot the bias of the reference datasets compared to multiple targets.'''
+    plotFile = outputName + '.png'
+    print 'plotBias: Writing %s' % plotFile
+    plotter.draw_contour_map(metric, lats, lons, outputName, **config)
+    return plotFile
+
+
+# Utilities follow.
+
+def isLocalFile(url):
+    '''Check if URL is a local path.'''
+    u = urlparse.urlparse(url)
+    if u.scheme == '' or u.scheme == 'file':
+        if not path.exists(u.path):
+            print >>sys.stderr, 'isLocalFile: File at local path does not exist: %s' % u.path
+        return (True, u.path)
+    else:
+        return (False, u.path)
+
+def retrieveFile(url, dir=None):
+    '''Retrieve a file from a URL, or if it is a local path then verify it exists.'''
+    if dir is None: dir = './'
+    ok, path = isLocalFile(url)
+    fn = os.path.split(path)[1]
+    outPath = os.path.join(dir, fn)
+    if not ok:
+        if os.path.exists(outPath):
+            print >>sys.stderr, 'retrieveFile: Using cached file: %s' % outPath
+        else:
+            try:
+                print >>sys.stderr, 'retrieveFile: Retrieving (URL) %s to %s' % (url, outPath)
+                urllib.urlretrieve(url, outPath)
+            except:
+                print >>sys.stderr, 'retrieveFile: Cannot retrieve file at URL: %s' % url
+                return None
+    return outPath    
+
+
+# Tests and main follow.
+
+def test1(urlsAndVars, outputName, **config):
+    '''Test compareManyWithMetrics routine.'''
+    lats, lons, metrics = compareVariablesWithMetrics(urlsAndVars, ['Bias'], outputName, timedelta(days=365), 1, 1)
+    print metrics
+
+    config = {'gridshape': (4, 5),
+              'ptitle': 'TASMAX Bias of WRF Compared to KNMI (1989 - 2008)',
+              'subtitles': range(1989, 2009, 1)}
+    plotFile = plotBias(metrics[0][0], lats, lons, outputName, **config)
+    return plotFile
+
+
+def main(args):
+    '''Main routine to provide command line capability.'''
+    nTest = int(args[0])
+    url1 = args[1]
+    var1 = args[2]
+    outputName = args[3]
+    url2 = args[4]
+    var2 = args[5]
+    urlsAndVars = [(url1, var1),  (url2, var2)]
+    if nTest == 1:
+        return test1(urlsAndVars, outputName)
+    elif nTest == 2:
+        return test2(urlsAndVars, outputName)
+
+if __name__ == '__main__':
+    print main(sys.argv[1:])
+
+
+# python functions.py 1 "http://zipper.jpl.nasa.gov/dist/AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_tasmax.nc" "tasmax" "wrf_bias_compared_to_knmi" "http://zipper.jpl.nasa.gov/dist/AFRICA_UC-WRF311_CTL_ERAINT_MM_50km-rg_1989-2008_tasmax.nc" "tasmax"
+