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PODAAC.py @ 959

Revision 959, 183.0 KB (checked in by jjr8, 14 months ago)
* Incremented build number. * Fixed documentation bug.

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1	# Datasets/NASA/PODAAC.py - THREDDSCatalogs and OPeNDAPURLs for NASA
2	# PO.DAAC datasets.
3	#
4	# Copyright (C) 2010 Jason J. Roberts
5	#
6	# This program is free software; you can redistribute it and/or
7	# modify it under the terms of the GNU General Public License
8	# as published by the Free Software Foundation; either version 2
9	# of the License, or (at your option) any later version.
10	#
11	# This program is distributed in the hope that it will be useful,
12	# but WITHOUT ANY WARRANTY; without even the implied warranty of
13	# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
14	# GNU General Public License (available in the file LICENSE.TXT)
15	# for more details.
16	#
17	# You should have received a copy of the GNU General Public License
18	# along with this program; if not, write to the Free Software
19	# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
20
21	import datetime
22	import os
23
24	from GeoEco.Datasets import Dataset, QueryableAttribute, Grid
25	from GeoEco.Datasets.Collections import DirectoryTree
26	from GeoEco.Datasets.GDAL import GDALDataset
27	from GeoEco.Datasets.OPeNDAP import THREDDSCatalog, OPeNDAPURL, OPeNDAPGrid
28	from GeoEco.Datasets.Virtual import TimeSeriesGridStack, GridSliceCollection, MaskedGrid, ClippedGrid, RotatedGlobalGrid, ClimatologicalGridCollection, DerivedGrid
29	from GeoEco.DynamicDocString import DynamicDocString
30	from GeoEco.Internationalization import _
31	from GeoEco.Types import *
32
33
34	class MODISL3SSTTimeSeries(Grid):
35	__doc__ = DynamicDocString()
36
37	def __init__(self, satellite, temporalResolution, spatialResolution, geophysicalParameter, variableName=u'l3m_data', qualityLevel=0, timeout=60, maxRetryTime=300, cacheDirectory=None):
38	self.__doc__.Obj.ValidateMethodInvocation()
39
40	# Construct a THREDDSCatalog for the variable requested by the
41	# caller and wrap it in a TimeSeriesGridStack to create a 3D
42	# grid with dimensions tyx.
43
44	queryableAttributeValues = {u'Satellite': satellite,
45	u'SatelliteCode': satellite[0].upper(),
46	u'TemporalResolution': temporalResolution,
47	u'TemporalResolutionCode': {u'daily': u'DAY', u'8day': u'8D', u'monthly': u'MO', u'annual': u'YR'}[temporalResolution],
48	u'SpatialResolution': spatialResolution,
49	u'SpatialResolutionCode': spatialResolution[0],
50	u'Algorithm': {u'sst': u'sst', u'nsst': u'sst', u'sst4': u'sst4'}[geophysicalParameter],
51	u'Wavelength': {u'sst': u'11um', u'nsst': u'11um', u'sst4': u'4um'}[geophysicalParameter],
52	u'GeophysicalParameter': geophysicalParameter}
53
54	if temporalResolution == u'daily':
55	pathParsingExpressions=[r'(?P<Year>\d\d\d\d)',
56	r'(?P<DayOfYear>\d\d\d)',
57	r'%s(?P<Year>\d\d\d\d)(?P<DayOfYear>\d\d\d)(?P<EndDate>\d{0})\.L3m_%s_%s_%s\.bz2' % (queryableAttributeValues[u'SatelliteCode'], queryableAttributeValues[u'TemporalResolutionCode'], geophysicalParameter, queryableAttributeValues[u'SpatialResolutionCode'])]
58	else:
59	pathParsingExpressions=[r'(?P<Year>\d\d\d\d)',
60	r'%s(?P<Year>\d\d\d\d)(?P<DayOfYear>\d\d\d)(?P<EndDate>\d\d\d\d\d\d\d)\.L3m_%s_%s_%s\.bz2' % (queryableAttributeValues[u'SatelliteCode'], queryableAttributeValues[u'TemporalResolutionCode'], geophysicalParameter, queryableAttributeValues[u'SpatialResolutionCode'])]
61
62	if variableName == u'l3m_data':
63	if geophysicalParameter != u'sst4':
64	displayName = _(u'MODIS %(satellite)s L3 mapped %(temporalResolution)s %(spatialResolution)s thermal IR %(geophysicalParameter)s from NASA PO.DAAC') % {u'satellite': satellite[0].upper() + satellite[1:], u'temporalResolution': temporalResolution, u'spatialResolution': spatialResolution, u'geophysicalParameter': geophysicalParameter.upper()}
65	else:
66	displayName = _(u'MODIS %(satellite)s L3 mapped %(temporalResolution)s %(spatialResolution)s mid-IR %(geophysicalParameter)s from NASA PO.DAAC') % {u'satellite': satellite[0].upper() + satellite[1:], u'temporalResolution': temporalResolution, u'spatialResolution': spatialResolution, u'geophysicalParameter': geophysicalParameter.upper()}
67	elif geophysicalParameter != u'sst4':
68	displayName = _(u'MODIS %(satellite)s L3 mapped %(temporalResolution)s %(spatialResolution)s thermal IR %(geophysicalParameter)s quality level from NASA PO.DAAC') % {u'satellite': satellite[0].upper() + satellite[1:], u'temporalResolution': temporalResolution, u'spatialResolution': spatialResolution, u'geophysicalParameter': geophysicalParameter.upper()}
69	else:
70	displayName = _(u'MODIS %(satellite)s L3 mapped %(temporalResolution)s %(spatialResolution)s mid-IR %(geophysicalParameter)s quality level from NASA PO.DAAC') % {u'satellite': satellite[0].upper() + satellite[1:], u'temporalResolution': temporalResolution, u'spatialResolution': spatialResolution, u'geophysicalParameter': geophysicalParameter.upper()}
71
72	grid = TimeSeriesGridStack(THREDDSCatalog(url='http://opendap.jpl.nasa.gov/opendap/allData/modis/L3/%s/%s/%s/%s/catalog.xml' % (satellite, queryableAttributeValues[u'Wavelength'], spatialResolution, temporalResolution),
73	pathParsingExpressions=pathParsingExpressions,
74	displayName=displayName,
75	timeout=timeout,
76	maxRetryTime=maxRetryTime,
77	queryableAttributes=self._GetQueryableAttributesForTimeSlices(),
78	queryableAttributeValues=queryableAttributeValues,
79	lazyPropertyValues=self._GetLazyPropertyValuesForTimeSlices(variableName, spatialResolution, temporalResolution, geophysicalParameter),
80	cacheDirectory=cacheDirectory),
81	reportProgress=False)
82
83	# If the caller requested a specific quality level, create a
84	# THREDDSCatalog for the 'l3m_qual' variable and use it to
85	# mask the 3D grid.
86
87	if qualityLevel is not None and qualityLevel in [0, 1]:
88	grid = MaskedGrid(grid,
89	masks=[TimeSeriesGridStack(THREDDSCatalog(url='http://opendap.jpl.nasa.gov/opendap/allData/modis/L3/%s/%s/%s/%s/catalog.xml' % (satellite, queryableAttributeValues[u'Wavelength'], spatialResolution, temporalResolution),
90	pathParsingExpressions=pathParsingExpressions,
91	displayName=_(u'quality level'),
92	timeout=timeout,
93	maxRetryTime=maxRetryTime,
94	queryableAttributes=self._GetQueryableAttributesForTimeSlices(),
95	queryableAttributeValues=queryableAttributeValues,
96	lazyPropertyValues=self._GetLazyPropertyValuesForTimeSlices('l3m_qual', spatialResolution, temporalResolution, geophysicalParameter),
97	cacheDirectory=cacheDirectory),
98	reportProgress=False)],
99	operators=[u'>'],
100	values=[qualityLevel])
101
102	# Assign our _WrappedGrid instance variable.
103
104	self._WrappedGrid = grid
105
106	def __getattribute__(self, name):
107	if name.startswith('__') or name in ['_WrappedGrid', '_GetTimeCoordsFromQueryableAttributeValues', '_GetQueryableAttributesForTimeSlices', '_GetLazyPropertyValuesForTimeSlices', 'CreateArcGISRasters', 'CreateClimatologicalArcGISRasters', 'InterpolateAtArcGISPoints', 'CreateCayulaCornillonFrontsAsArcGISRasters']:
108	return object.__getattribute__(self, name)
109	return object.__getattribute__(object.__getattribute__(self, '_WrappedGrid'), name)
110
111	def __setattr__(self, name, value):
112	if name.startswith('__') or name in ['_WrappedGrid', '_GetTimeCoordsFromQueryableAttributeValues', '_GetQueryableAttributesForTimeSlices', '_GetLazyPropertyValuesForTimeSlices', 'CreateArcGISRasters', 'CreateClimatologicalArcGISRasters', 'InterpolateAtArcGISPoints', 'CreateCayulaCornillonFrontsAsArcGISRasters']:
113	object.__setattr__(self, name, value)
114	else:
115	setattr(object.__getattribute__(self, '_WrappedGrid'), name, value)
116
117	@classmethod
118	def _GetQueryableAttributesForTimeSlices(cls):
119
120	# First define a function used to calculate the value of the
121	# EndDate queryable attribute from the value of the DateTime
122	# queryable attribute.
123
124	def _GetEndDate(selfOrDict, startDate):
125
126	# Get the temporal resolution.
127
128	temporalResolution = None
129	if isinstance(selfOrDict, dict):
130	if u'TemporalResolution' in selfOrDict:
131	temporalResolution = selfOrDict[u'TemporalResolution']
132	else:
133	temporalResolution = selfOrDict.GetQueryableAttributeValue(u'TemporalResolution')
134
135	# Calculate the end date string from the temporal resolution.
136
137	if temporalResolution is not None:
138	temporalResolution = temporalResolution.lower()
139
140	if temporalResolution == u'daily':
141	return ''
142
143	if temporalResolution == u'8day':
144	return unicode((startDate + datetime.timedelta(days=7)).strftime('%Y%j'))
145
146	if temporalResolution == u'monthly':
147	if startDate.month == 12:
148	return unicode((datetime.datetime(startDate.year + 1, 1, 1) - datetime.timedelta(days=1)).strftime('%Y%j'))
149	return unicode((datetime.datetime(startDate.year, startDate.month + 1, 1) - datetime.timedelta(days=1)).strftime('%Y%j'))
150
151	if temporalResolution == u'annual':
152	return unicode((datetime.datetime(startDate.year + 1, 1, 1) - datetime.timedelta(days=1)).strftime('%Y%j'))
153
154	return None
155
156	# Now return a tuple of the queryable attributes. The EndDate
157	# queryable attribute makes use of the function.
158
159	return (QueryableAttribute(u'Satellite', _(u'Satellite'), UnicodeStringTypeMetadata(allowedValues=[u'aqua', u'terra'], makeLowercase=True)),
160	QueryableAttribute(u'SatelliteCode', _(u'Satellite abbreviation code'), UnicodeStringTypeMetadata(allowedValues=[u'a', u't'], makeLowercase=True)),
161	QueryableAttribute(u'TemporalResolution', _(u'Temporal resolution'), UnicodeStringTypeMetadata(allowedValues=[u'daily', u'8day', u'monthly', u'annual'], makeLowercase=True)),
162	QueryableAttribute(u'TemporalResolutionCode', _(u'Temporal resolution abbreviation code'), UnicodeStringTypeMetadata(allowedValues=[u'DAY', u'8D', u'MO', u'YR'], makeLowercase=True)),
163	QueryableAttribute(u'SpatialResolution', _(u'Spatial resolution'), UnicodeStringTypeMetadata(allowedValues=[u'4km', u'9km'], makeLowercase=True)),
164	QueryableAttribute(u'SpatialResolutionCode', _(u'Spatial resolution abbreviation code'), UnicodeStringTypeMetadata(allowedValues=[u'4', u'9'], makeLowercase=True)),
165	QueryableAttribute(u'Algorithm', _(u'Algorithm'), UnicodeStringTypeMetadata(allowedValues=[u'sst', u'sst4'], makeLowercase=True)),
166	QueryableAttribute(u'Wavelength', _(u'Primary wavelength'), UnicodeStringTypeMetadata(allowedValues=[u'11um', u'4um'], makeLowercase=True)),
167	QueryableAttribute(u'GeophysicalParameter', _(u'Geophysical parameter'), UnicodeStringTypeMetadata(allowedValues=[u'sst', u'nsst', u'sst4'], makeLowercase=True)),
168	QueryableAttribute(u'DateTime', _(u'Start date'), DateTimeTypeMetadata()),
169	QueryableAttribute(u'EndDate', _(u'End date string'), UnicodeStringTypeMetadata(mustMatchRegEx=ur'\d\d\d\d\d\d\d\d'), derivedFromAttr=u'DateTime', derivedValueFunc=_GetEndDate),
170	QueryableAttribute(u'VariableName', _(u'Variable'), UnicodeStringTypeMetadata(allowedValues=[u'l3m_data', u'l3m_qual'], makeLowercase=True)))
171
172	@classmethod
173	def _GetLazyPropertyValuesForTimeSlices(cls, variableName, spatialResolution, temporalResolution, geophysicalParameter):
174	import numpy
175	return {'VariableTypes': ['Grid'],
176	'VariableNames': [variableName],
177	'SpatialReference': Dataset.ConvertSpatialReference('proj4', '+proj=latlong +ellps=WGS84 +datum=WGS84 +no_defs', 'obj'),
178	'Dimensions': 'yx',
179	'Shape': {'4km': (4320, 8640), '9km': (2160, 4320)}[spatialResolution],
180	'CoordDependencies': (None, None),
181	'CoordIncrements': {'4km': (180./4320,360./8640), '9km': (180./2160,360./4320)}[spatialResolution],
182	'TIncrement': {'daily': 1, '8day': 8, 'monthly': 1, u'annual': 1}[temporalResolution],
183	'TIncrementUnit': {'daily': 'day', '8day': 'day', 'monthly': 'month', u'annual': 'year'}[temporalResolution],
184	'TSemiRegularity': {'daily': None, '8day': 'annual', 'monthly': None, u'annual': None}[temporalResolution],
185	'TCountPerSemiRegularPeriod': {'daily': None, '8day': 46, 'monthly': None, u'annual': None}[temporalResolution],
186	'TCornerCoordType': 'min',
187	'TOffsetFromParsedTime': {'sst': None, 'nsst': -0.5, 'sst4': -0.5}[geophysicalParameter], # Nighttime datasets start 1/2 days prior to the date and time parsed from the file (i.e. they start at 12:00:00 on the previous date, rather than 00:00:00 on the parsed date)
188	'CornerCoords': {'4km': (-90 + 180./4320/2, -180 + 360./8640/2), '9km': (-90 + 180./2160/2, -180 + 360./4320/2)}[spatialResolution],
189	'PhysicalDimensions': 'yx',
190	'PhysicalDimensionsFlipped': (True, False),
191	'UnscaledDataType': {'l3m_data': 'uint16', 'l3m_qual': 'uint8'}[variableName],
192	'UnscaledNoDataValue': {'l3m_data': 65535, 'l3m_qual': 255}[variableName],
193	'ScaledDataType': {'l3m_data': 'float32', 'l3m_qual': None}[variableName],
194	'ScaledNoDataValue': {'l3m_data': numpy.cast['float32'](0.000717185*65535 - 2), 'l3m_qual': None}[variableName],
195	'ScalingFunction': {'l3m_data': lambda data: numpy.cast['float32'](0.000717185*data - 2), 'l3m_qual': None}[variableName],
196	'UnscalingFunction': {'l3m_data': lambda data: numpy.cast['uint16'](numpy.round((2 + data)/0.000717185)), 'l3m_qual': None}[variableName]}
197
198	@classmethod
199	def _GetTimeCoordsFromQueryableAttributeValues(cls, queryableAttributeValues):
200	try:
201	if u'DateTime' in queryableAttributeValues and isinstance(queryableAttributeValues[u'DateTime'], datetime.datetime):
202	startDate = queryableAttributeValues[u'DateTime']
203	temporalResolution = None
204
205	if u'GeophysicalParameter' in queryableAttributeValues and isinstance(queryableAttributeValues[u'GeophysicalParameter'], basestring) and queryableAttributeValues[u'GeophysicalParameter'].lower() in [u'nsst', u'sst4']:
206	tOffset = datetime.timedelta(-0.5)
207	else:
208	tOffset = datetime.timedelta(0)
209
210	if u'TemporalResolution' in queryableAttributeValues and isinstance(queryableAttributeValues[u'TemporalResolution'], basestring) and queryableAttributeValues[u'TemporalResolution'] in [u'daily', '8day', 'monthly', 'annual']:
211	temporalResolution = queryableAttributeValues[u'TemporalResolution']
212	elif u'TemporalResolutionCode' in queryableAttributeValues and isinstance(queryableAttributeValues[u'TemporalResolutionCode'], basestring) and queryableAttributeValues[u'TemporalResolutionCode'] in [u'DAY', '8D', 'MO', 'YR']:
213	temporalResolution = queryableAttributeValues[u'TemporalResolutionCode']
214	else:
215	return [startDate + tOffset, None, None]
216
217	if temporalResolution in [u'daily', u'DAY']:
218	return [startDate + tOffset, startDate + datetime.timedelta(0.5) + tOffset, startDate + datetime.timedelta(1) + tOffset - datetime.timedelta(seconds=1)]
219
220	if temporalResolution in [u'8day', u'8D']:
221	if startDate.month == 12 and startDate.day >= 24:
222	endDate = datetime.datetime(startDate.year + 1, 1, 1)
223	return [startDate + tOffset, startDate + (endDate - startDate) / 2 + tOffset, endDate + tOffset - datetime.timedelta(seconds=1)]
224	else:
225	return [startDate + tOffset, startDate + datetime.timedelta(4) + tOffset, startDate + datetime.timedelta(8) + tOffset - datetime.timedelta(seconds=1)]
226
227	if temporalResolution in [u'monthly', u'MO']:
228	if startDate.month == 12:
229	endDate = datetime.datetime(startDate.year + 1, 1, 1)
230	else:
231	endDate = datetime.datetime(startDate.year, startDate.month + 1, 1)
232	return [startDate + tOffset, startDate + (endDate - startDate) / 2 + tOffset, endDate + tOffset - datetime.timedelta(seconds=1)]
233
234	if temporalResolution in [u'annual', u'YR']:
235	return [startDate + tOffset, datetime.datetime(startDate.year, 7, 1) + tOffset, datetime.datetime(startDate.year + 1, 1, 1) + tOffset - datetime.timedelta(seconds=1)]
236
237	return [None, None, None]
238	except:
239	return [None, None, None]
240
241	@classmethod
242	def CreateArcGISRasters(cls, satellite, temporalResolution, spatialResolution, geophysicalParameter, variableName,
243	outputWorkspace, mode=u'add', rasterNameExpressions=[u'%(Satellite)s', u'%(Wavelength)s', u'%(SpatialResolution)s', u'%(TemporalResolution)s', u'%%Y', u'%(SatelliteCode).1s%%Y%%j%(EndDate)s.L3m_%(TemporalResolutionCode).3s_%(GeophysicalParameter)s_%(SpatialResolutionCode).1s_%(VariableName)s.img'], rasterCatalog=None,
244	qualityLevel=0,
245	rotationOffset=None, spatialExtent=None, startDate=None, endDate=None,
246	timeout=60, maxRetryTime=300, cacheDirectory=None,
247	useUnscaledData=False, calculateStatistics=True, buildRAT=False, buildPyramids=False):
248	cls.__doc__.Obj.ValidateMethodInvocation()
249
250	# Construct a MODISL3SSTTimeSeries instance and rotate and
251	# clip it as requested.
252
253	grid = MODISL3SSTTimeSeries(satellite, temporalResolution, spatialResolution, geophysicalParameter, variableName, qualityLevel, timeout, maxRetryTime, cacheDirectory)
254
255	try:
256	if rotationOffset is not None:
257	grid = RotatedGlobalGrid(grid, rotationOffset, u'Map units')
258
259	xMin, yMin, xMax, yMax = None, None, None, None
260	if spatialExtent is not None:
261	from GeoEco.Types import EnvelopeTypeMetadata
262	xMin, yMin, xMax, yMax = EnvelopeTypeMetadata.ParseFromArcGISString(spatialExtent)
263
264	if spatialExtent is not None or startDate is not None or endDate is not None:
265	if startDate is not None:
266	startDate = datetime.datetime(startDate.year, startDate.month, startDate.day, 0, 0, 0)
267	if endDate is not None:
268	endDate = datetime.datetime(endDate.year, endDate.month, endDate.day, 23, 59, 59)
269
270	grid = ClippedGrid(grid, u'Map coordinates', xMin=xMin, xMax=xMax, yMin=yMin, yMax=yMax, tMin=startDate, tMax=endDate)
271
272	# Construct an ArcGISWorkspace instance and import time slices
273	# from the MODISL3SSTTimeSeries instance.
274
275	from GeoEco.Datasets.ArcGIS import ArcGISWorkspace, ArcGISRaster
276
277	workspace = ArcGISWorkspace(outputWorkspace, ArcGISRaster, pathCreationExpressions=rasterNameExpressions, cacheTree=True, queryableAttributes=cls._GetQueryableAttributesForTimeSlices())
278	workspace.ImportDatasets(GridSliceCollection(grid).QueryDatasets(), mode, useUnscaledData=useUnscaledData, calculateStatistics=calculateStatistics, buildRAT=buildRAT, buildPyramids=buildPyramids)
279
280	if rasterCatalog is not None:
281	workspace.ToRasterCatalog(rasterCatalog, grid.GetSpatialReference(u'ArcGIS'), tQACoordType=u'min', tCoordFunction=cls._GetTimeCoordsFromQueryableAttributeValues, overwriteExisting=True)
282
283	finally:
284	grid.Close()
285
286	# Return successfully.
287
288	return outputWorkspace
289
290	@classmethod
291	def CreateClimatologicalArcGISRasters(cls, satellite, temporalResolution, spatialResolution, geophysicalParameter, variableName,
292	statistic, binType,
293	outputWorkspace, mode=u'add', rasterNameExpressions=[u'%(Satellite)s', u'%(Wavelength)s', u'%(SpatialResolution)s', u'%(TemporalResolution)s', u'%(ClimatologyBinType)s_Climatology', u'%(Satellite)s_%(GeophysicalParameter)s_%(VariableName)s_%(ClimatologyBinName)s_%(Statistic)s.img'],
294	qualityLevel=0,
295	binDuration=1, startDayOfYear=1,
296	rotationOffset=None, spatialExtent=None, startDate=None, endDate=None,
297	timeout=60, maxRetryTime=300, cacheDirectory=None,
298	calculateStatistics=True, buildPyramids=False):
299	cls.__doc__.Obj.ValidateMethodInvocation()
300
301	# Construct a MODISL3SSTTimeSeries instance and rotate and
302	# clip it as requested.
303
304	grid = MODISL3SSTTimeSeries(satellite, temporalResolution, spatialResolution, geophysicalParameter, variableName, qualityLevel, timeout, maxRetryTime, cacheDirectory)
305
306	try:
307	if rotationOffset is not None:
308	grid = RotatedGlobalGrid(grid, rotationOffset, u'Map units')
309
310	xMin, yMin, xMax, yMax = None, None, None, None
311	if spatialExtent is not None:
312	from GeoEco.Types import EnvelopeTypeMetadata
313	xMin, yMin, xMax, yMax = EnvelopeTypeMetadata.ParseFromArcGISString(spatialExtent)
314
315	if spatialExtent is not None or startDate is not None or endDate is not None:
316	if startDate is not None:
317	startDate = datetime.datetime(startDate.year, startDate.month, startDate.day, 0, 0, 0)
318	if endDate is not None:
319	endDate = datetime.datetime(endDate.year, endDate.month, endDate.day, 23, 59, 59)
320
321	grid = ClippedGrid(grid, u'Map coordinates', xMin=xMin, xMax=xMax, yMin=yMin, yMax=yMax, tMin=startDate, tMax=endDate)
322
323	# Construct a ClimatologicalGridCollection from the
324	# MODISL3SSTTimeSeries instance. Construct an
325	# ArcGISWorkspace instance and import the
326	# ClimatologicalGridCollection into it.
327
328	from GeoEco.Datasets.ArcGIS import ArcGISWorkspace, ArcGISRaster
329
330	collection = ClimatologicalGridCollection(grid, statistic, binType, binDuration, startDayOfYear, reportProgress=True)
331	workspace = ArcGISWorkspace(outputWorkspace, ArcGISRaster, pathCreationExpressions=rasterNameExpressions, cacheTree=True, queryableAttributes=tuple(collection.GetAllQueryableAttributes()))
332	workspace.ImportDatasets(collection.QueryDatasets(), mode, calculateStatistics=calculateStatistics, buildPyramids=buildPyramids)
333
334	finally:
335	grid.Close()
336
337	# Return successfully.
338
339	return outputWorkspace
340
341	@classmethod
342	def InterpolateAtArcGISPoints(cls, satellite, temporalResolution, spatialResolution, geophysicalParameter, variableName,
343	points, valueField, tField, method=u'Nearest', where=None, noDataValue=None,
344	qualityLevel=0,
345	timeout=60, maxRetryTime=300, cacheDirectory=None,
346	orderByFields=None, numBlocksToCacheInMemory=256, xBlockSize=16, yBlockSize=16, tBlockSize=3):
347	cls.__doc__.Obj.ValidateMethodInvocation()
348
349	grid = MODISL3SSTTimeSeries(satellite, temporalResolution, spatialResolution, geophysicalParameter, variableName, qualityLevel, timeout, maxRetryTime, cacheDirectory)
350
351	try:
352	if orderByFields is not None:
353	orderBy = u', '.join(map(lambda f: f + u' ASC', orderByFields))
354	else:
355	from GeoEco.ArcGIS import GeoprocessorManager
356	if GeoprocessorManager.GetArcGISMajorVersion() > 9 or GeoprocessorManager.GetArcGISMinorVersion() >= 2:
357	orderBy = tField + u' ASC'
358	else:
359	orderBy = None
360
361	from GeoEco.Datasets.ArcGIS import ArcGISTable
362	from GeoEco.SpatialAnalysis.Interpolation import Interpolator
363	Interpolator.InterpolateGridsValuesForTableOfPoints([grid], ArcGISTable(points), [valueField], tField=tField, where=where, orderBy=orderBy, method=method, noDataValue=noDataValue, gridsWrap=True, numBlocksToCacheInMemory=numBlocksToCacheInMemory, xBlockSize=xBlockSize, yBlockSize=yBlockSize, tBlockSize=tBlockSize)
364
365	finally:
366	grid.Close()
367
368	return points
369
370	@classmethod
371	def CreateCayulaCornillonFrontsAsArcGISRasters(cls, satellite, temporalResolution, spatialResolution, geophysicalParameter, minPopMeanDifference,
372	outputWorkspace, mode=u'add', rasterNameExpressions=[u'%(Satellite)s', u'%(Wavelength)s_fronts', u'%(SpatialResolution)s', u'%(TemporalResolution)s', u'%%Y', '%(Satellite)s_%(TemporalResolution)s_%%Y%%j_%(GeophysicalParameter)s_%(ImageType)s.img'],
373	medianFilterWindowSize=3, histogramWindowSize=20, histogramWindowStride=1, minPropNonMaskedCells=0.65, minPopProp=0.25, minTheta=0.76, minSinglePopCohesion=0.88, minGlobalPopCohesion=0.90, threads=1,
374	fillHoles=20, thin=True, minSize=10,
375	qualityLevel=1,
376	rotationOffset=None, spatialExtent=None, startDate=None, endDate=None,
377	timeout=60, maxRetryTime=300, cacheDirectory=None,
378	calculateStatistics=True, buildRAT=False, buildPyramids=False,
379	outputCandidateCounts=False, outputFrontCounts=False, outputWindowStatusCodes=False, outputWindowStatusValues=False):
380	cls.__doc__.Obj.ValidateMethodInvocation()
381
382	# Construct a MODISL3SSTTimeSeries instance and rotate
383	# and clip it as requested.
384
385	grid = MODISL3SSTTimeSeries(satellite, temporalResolution, spatialResolution, geophysicalParameter, u'l3m_data', qualityLevel, timeout, maxRetryTime, cacheDirectory)
386
387	try:
388	if rotationOffset is not None:
389	grid = RotatedGlobalGrid(grid, rotationOffset, u'Map units')
390
391	xMin, yMin, xMax, yMax = None, None, None, None
392	if spatialExtent is not None:
393	from GeoEco.Types import EnvelopeTypeMetadata
394	xMin, yMin, xMax, yMax = EnvelopeTypeMetadata.ParseFromArcGISString(spatialExtent)
395
396	if spatialExtent is not None or startDate is not None or endDate is not None:
397	if startDate is not None:
398	startDate = datetime.datetime(startDate.year, startDate.month, startDate.day, 0, 0, 0)
399	if endDate is not None:
400	endDate = datetime.datetime(endDate.year, endDate.month, endDate.day, 23, 59, 59)
401
402	grid = ClippedGrid(grid, u'Map coordinates', xMin=xMin, xMax=xMax, yMin=yMin, yMax=yMax, tMin=startDate, tMax=endDate)
403
404	# Construct a CayulaCornillonFrontsInGrid collection using
405	# the requested parameters.
406
407	from GeoEco.OceanographicAnalysis.Fronts import CayulaCornillonFrontsInGrid
408
409	collection = CayulaCornillonFrontsInGrid(grid,
410	wrapEdges=grid.Shape[-1] == MODISL3SSTTimeSeries._GetLazyPropertyValuesForTimeSlices('l3m_data', spatialResolution, temporalResolution, geophysicalParameter)['Shape'][-1],
411	medianFilterWindowSize=medianFilterWindowSize,
412	histogramWindowSize=histogramWindowSize,
413	histogramWindowStride=histogramWindowStride,
414	minPropNonMaskedCells=minPropNonMaskedCells,
415	minPopProp=minPopProp,
416	minPopMeanDifference=minPopMeanDifference,
417	minTheta=minTheta,
418	minSinglePopCohesion=minSinglePopCohesion,
419	minGlobalPopCohesion=minGlobalPopCohesion,
420	threads=threads,
421	fillHoles=fillHoles,
422	thin=thin,
423	minSize=minSize)
424	try:
425	try:
426	# Construct an ArcGISWorkspace instance and import
427	# time slices from the CayulaCornillonFrontsInGrid
428	# instance.
429
430	expression = "ImageType = 'floc'"
431	if outputCandidateCounts:
432	expression += " OR ImageType = 'ccnt'"
433	if outputFrontCounts:
434	expression += " OR ImageType = 'fcnt'"
435	if outputWindowStatusCodes:
436	expression += " OR ImageType = 'wsco'"
437	if outputWindowStatusValues:
438	expression += " OR ImageType = 'wsvl'"
439
440	from GeoEco.Datasets.ArcGIS import ArcGISWorkspace, ArcGISRaster
441
442	workspace = ArcGISWorkspace(outputWorkspace, ArcGISRaster, pathCreationExpressions=rasterNameExpressions, cacheTree=True, queryableAttributes=tuple(collection.GetAllQueryableAttributes()))
443	workspace.ImportDatasets(collection.QueryDatasets(expression), mode, calculateStatistics=calculateStatistics, buildRAT=buildRAT, buildPyramids=buildPyramids)
444
445	# If we caught an exception, log it now. Then delete
446	# the collection object, to ensure its memory is
447	# freed. Normally we don't bother doing this but the
448	# memory it holds can be substantial.
449
450	except:
451	from GeoEco.Logging import Logger
452	Logger.LogExceptionAsError()
453	finally:
454	del collection
455	finally:
456	grid.Close()
457
458	# Return successfully.
459
460	return outputWorkspace
461
462
463	class _GHRSSTLevel4THREDDSCatalog(THREDDSCatalog):
464	__doc__ = DynamicDocString()
465
466	def __init__(self, displayName=None, timeout=60, maxRetryTime=120, cacheDirectory=None):
467	super(_GHRSSTLevel4THREDDSCatalog, self).__init__('http://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/L4/catalog.xml',
468	[r'(?P<AreaCode>[^\-]+)',
469	r'(?P<RDACCode>[^\-]+)',
470	r'(?P<ProductCode2>[^\-]+)',
471	r'(?P<Year>\d\d\d\d)',
472	r'(?P<DayOfYear>\d\d\d)',
473	r'(?P<Year>\d\d\d\d)(?P<Month>\d\d)(?P<Day>\d\d)-(?P<RDACCode>[^\-]+)-L4(?P<ProductType>[^\-]+)-(?P<AreaCode>[^\-]+)-v(?P<GDSVersion>\d\d)-fv(?P<FileVersion>[^\-]+)-(?P<ProductCode>[^\-]+)(?:\.nc\|\.nc\.bz2\|\.nc\.gz)'],
474	displayName=displayName,
475	timeout=timeout,
476	maxRetryTime=maxRetryTime,
477	queryableAttributes=(QueryableAttribute(u'RDACCode', _(u'RDAC code'), UnicodeStringTypeMetadata()),
478	QueryableAttribute(u'ProductType', _(u'Product type'), UnicodeStringTypeMetadata()),
479	QueryableAttribute(u'AreaCode', _(u'Area code'), UnicodeStringTypeMetadata()),
480	QueryableAttribute(u'GDSVersion', _(u'GDS version'), IntegerTypeMetadata()),
481	QueryableAttribute(u'FileVersion', _(u'File version'), UnicodeStringTypeMetadata()),
482	QueryableAttribute(u'ProductCode', _(u'Product code'), UnicodeStringTypeMetadata()),
483	QueryableAttribute(u'ProductCode2', _(u'Product code 2'), UnicodeStringTypeMetadata()),
484	QueryableAttribute(u'DateTime', _(u'Start date'), DateTimeTypeMetadata())),
485	cacheDirectory=cacheDirectory)
486
487	def _ConstructOPeNDAPURL(self, url, timeout, maxRetryTime, queryableAttributeValues, cacheDirectory):
488	return _GHRSSTLevel4OPeNDAPURL(url,
489	timeout=timeout,
490	maxRetryTime=maxRetryTime,
491	parentCollection=self,
492	queryableAttributeValues=queryableAttributeValues,
493	lazyPropertyValues={'VariableTypes': ['Grid', 'Grid'],
494	'VariableNames': ['analysed_sst', 'analysis_error']},
495	cacheDirectory=cacheDirectory)
496
497
498	class _GHRSSTLevel4OPeNDAPURL(OPeNDAPURL):
499	__doc__ = DynamicDocString()
500
501	def _ConstructOPeNDAPGrid(self, variableName, variableType):
502	return _GHRSSTLevel4OPeNDAPGrid(self, variableName, variableType)
503
504
505	class _GHRSSTLevel4OPeNDAPGrid(OPeNDAPGrid):
506	__doc__ = DynamicDocString()
507
508	def __init__(self, opendapURLObj, variableName, variableType):
509	self.__doc__.Obj.ValidateMethodInvocation()
510
511	# Perform additional validation.
512
513	if variableName not in ['analysed_sst', 'analysis_error']:
514	raise ValueError(_(u'This tool does not support the "%(variableName)s" variable. The supported variables are "analysed_sst" and "analysis_error".') % {u'variableName': variableName})
515
516	# Initialize the base class.
517
518	super(_GHRSSTLevel4OPeNDAPGrid, self).__init__(opendapURLObj,
519	variableName,
520	variableType,
521	lazyPropertyValues={'SpatialReference': Dataset.ConvertSpatialReference('proj4', '+proj=latlong +ellps=WGS84 +datum=WGS84 +no_defs', 'obj'),
522	'Dimensions': u'tyx',
523	'TIncrement': 1,
524	'TIncrementUnit': 'day',
525	'TSemiRegularity': None,
526	'TCountPerSemiRegularPeriod': None,
527	'TCornerCoordType': 'min',
528	'CoordDependencies': (None, None, None),
529	'PhysicalDimensions': u'tyx'})
530
531	def _GetLazyPropertyPhysicalValue(self, name):
532
533	# If the caller is asking for the CoordIncrements or
534	# CornerCoords, calculate them from the lon and lat OPeNDAP
535	# variables, which give the center coordinates of the cells.
536
537	self.ParentCollection._Open()
538
539	if name in ['CoordIncrements', 'CornerCoords', 'PhysicalDimensionsFlipped']:
540	lon = self.ParentCollection._PydapDataset['lon']
541	lat = self.ParentCollection._PydapDataset['lat']
542
543	yIncrement = abs((lat[-1] - lat[0]) / (lat.shape[0] - 1))
544
545	if os.path.basename(self.ParentCollection.URL).find('-GLOB-') >= 0: # If it is a global dataset, assume 360 degrees longitude and calculate the x increment at full precision.
546	xIncrement = 360. / self.Shape[-1]
547	elif lon[0] > lon[-1]:
548	xIncrement = abs((lon[-1] + 360. - lon[0]) / (lon.shape[0] - 1)) # For datasets such as ABOM-L4HRfnd-AUS-RAMSSA_09km that cross the 180th meridian, use a 0-to-360 coordinate system
549	else:
550	xIncrement = abs((lon[-1] - lon[0]) / (lon.shape[0] - 1))
551
552	if abs(yIncrement - xIncrement) < 0.000001: # If x and y increments are almost exactly the same, force them to be exactly the same so that the cells are perfectly square
553	yIncrement = xIncrement
554
555	self.SetLazyPropertyValue('CoordIncrements', (1., yIncrement, xIncrement))
556
557	if lat[-1] > lat[0]:
558	self.SetLazyPropertyValue('CornerCoords', (datetime.datetime(1980, 1, 1), lat[0], lon[0])) # Use fake time coordinate; the true time coordinates are parsed from the file name.
559	self.SetLazyPropertyValue('PhysicalDimensionsFlipped', (False, False, False))
560	else:
561	self.SetLazyPropertyValue('CornerCoords', (datetime.datetime(1980, 1, 1), lat[-1], lon[0])) # Use fake time coordinate; the true time coordinates are parsed from the file name.
562	self.SetLazyPropertyValue('PhysicalDimensionsFlipped', (False, True, False))
563
564	return self.GetLazyPropertyValue(name)
565
566	# If the caller is asking for the scaled data type, scaling
567	# function, or unscaling function, determine these from the
568	# data type and add_offset and scale_factor attributes of this
569	# OPeNDAP variable.
570
571	if name in ['ScaledDataType', 'ScalingFunction', 'UnscalingFunction']:
572	v = self._GetOPeNDAPVariable()
573	if self.GetLazyPropertyValue('UnscaledDataType').startswith('float') or not hasattr(v, 'scale_factor') or not hasattr(v, 'add_offset'):
574	return None
575
576	import numpy
577
578	self.SetLazyPropertyValue('ScaledDataType', u'float32')
579	self.SetLazyPropertyValue('ScalingFunction', lambda data: numpy.cast['float32'](v.scale_factor*data + v.add_offset))
580	self.SetLazyPropertyValue('UnscalingFunction', lambda data: numpy.cast[str(self.GetLazyPropertyValue('UnscaledDataType'))](numpy.round((data - v.add_offset)/v.scale_factor)))
581
582	return self.GetLazyPropertyValue(name)
583
584	# If the caller is asking for the UnscaledNoDataValue, get it
585	# from the _FillValue attribute of this OPeNDAP variable.
586
587	if name == 'UnscaledNoDataValue':
588	v = self._GetOPeNDAPVariable()
589	if not hasattr(v, '_FillValue'):
590	return None
591	if not self.GetLazyPropertyValue('UnscaledDataType').startswith('float'):
592	return int(v._FillValue)
593	return v._FillValue
594
595	# If the caller is asking for the ScaledNoDataValue, calculate
596	# it.
597
598	if name == 'ScaledNoDataValue':
599	scalingFunction = self.GetLazyPropertyValue('ScalingFunction')
600	unscaledNoDataValue = self.GetLazyPropertyValue('UnscaledNoDataValue')
601	if scalingFunction is None or unscaledNoDataValue is None:
602	return None
603	return scalingFunction(unscaledNoDataValue)
604
605	# Otherwise let the base class get this property.
606
607	return super(_GHRSSTLevel4OPeNDAPGrid, self)._GetLazyPropertyPhysicalValue(name)
608
609
610	class GHRSSTLevel4(Grid):
611	__doc__ = DynamicDocString()
612
613	def __init__(self, variableName, rdacCode, productType, areaCode, gdsVersion, fileVersion, productCode, productCode2=None, convertToCelsius=True, timeout=60, maxRetryTime=300, cacheDirectory=None):
614	self.__doc__.Obj.ValidateMethodInvocation()
615
616	# Construct a _GHRSSTLevel4THREDDSCatalog and wrap it in a
617	# TimeSeriesGridStack to create a 3D grid with dimensions tyx.
618
619	if productCode2 is None:
620	productCode2 = productCode
621
622	if rdacCode == 'REMSS' and areaCode == 'GLOB' and productCode == 'mw_ir_rt_OI':
623	datasetSpecificExpression = 'AND (Year > 2008 OR Year = 2008 AND DayOfYear >= 199)' # Hack to speed up querying for mw_ir_rt_OI, which is stored in same directory as mw_ir_OI but starts at a much later date.
624	else:
625	datasetSpecificExpression = ''
626
627	grid = TimeSeriesGridStack(_GHRSSTLevel4THREDDSCatalog(_('GHRSST L4 product %s-L4%s-%s-%s hosted by NASA PO.DAAC') % (rdacCode, productType, areaCode, productCode), timeout, maxRetryTime, cacheDirectory),
628	expression = "VariableName = '%s' AND RDACCode = '%s' AND ProductType = '%s' AND AreaCode = '%s' AND GDSVersion = %i AND FileVersion = '%s' AND ProductCode = '%s' AND ProductCode2 = '%s' %s" % (variableName, rdacCode, productType, areaCode, gdsVersion, fileVersion, productCode, productCode2, datasetSpecificExpression),
629	reportProgress=False)
630
631	# If the caller requested that we convert Kelvin temperatures
632	# to Celsius, wrap the grid.
633
634	if convertToCelsius:
635	qav = {}
636	for qa in grid.GetAllQueryableAttributes():
637	qav[qa.Name] = grid.GetQueryableAttributeValue(qa.Name)
638
639	grid = DerivedGrid([grid],
640	'self._Grids[0].Data.__getitem__(tuple(sliceList)) - 273.15',
641	grid.DisplayName,
642	grid.DataType,
643	grid.NoDataValue - 273.15,
644	queryableAttributes=tuple(grid.GetAllQueryableAttributes()),
645	queryableAttributeValues=qav)
646
647	# Assign our _WrappedGrid instance variable.
648
649	self._WrappedGrid = grid
650
651	def __getattribute__(self, name):
652	if name.startswith('__') or name in ['_WrappedGrid', '_PODAAC_Products', '_RotateAndClip', 'CreateArcGISRasters', 'CreateClimatologicalArcGISRasters', 'InterpolateAtArcGISPoints', 'CreateCayulaCornillonFrontsAsArcGISRasters']:
653	return object.__getattribute__(self, name)
654	return object.__getattribute__(object.__getattribute__(self, '_WrappedGrid'), name)
655
656	def __setattr__(self, name, value):
657	if name.startswith('__') or name in ['_WrappedGrid', '_PODAAC_Products', '_RotateAndClip', 'CreateArcGISRasters', 'CreateClimatologicalArcGISRasters', 'InterpolateAtArcGISPoints', 'CreateCayulaCornillonFrontsAsArcGISRasters']:
658	object.__setattr__(self, name, value)
659	else:
660	setattr(object.__getattribute__(self, '_WrappedGrid'), name, value)
661
662	# In the lists below the items are [rdacCode, productType, areaCode, gdsVersion, fileVersion, productCode, productCode2]
663
664	_PODAAC_Products = {u'ABOM-L4HRfnd-AUS-RAMSSA_09km': ['ABOM', 'HRfnd', 'AUS', 1, '01_0', 'RAMSSA_09km', 'RAMSSA_09km'],
665	u'ABOM-L4LRfnd-GLOB-GAMSSA_28km': ['ABOM', 'LRfnd', 'GLOB', 1, '01_0', 'GAMSSA_28km', 'GAMSSA_28km'],
666	u'DMI-L4UHfnd-NSEABALTIC-DMI_OI': ['DMI', 'UHfnd', 'NSEABALTIC', 1, '01', 'DMI_OI', 'DMI_OI'],
667	u'EUR-L4UHRfnd-MED-ODYSSEA': ['EUR', 'UHRfnd', 'MED', 1, '01', 'ODYSSEA', 'ODYSSEA'],
668	u'EUR-L4UHRfnd-NWE-ODYSSEA': ['EUR', 'UHRfnd', 'NWE', 1, '01', 'ODYSSEA', 'ODYSSEA'],
669	u'JPL-L4UHfnd-GLOB-MUR': ['JPL', 'UHfnd', 'GLOB', 1, '03', 'MUR', 'MUR'],
670	u'JPL_OUROCEAN-L4UHfnd-GLOB-G1SST': ['JPL_OUROCEAN', 'UHfnd', 'GLOB', 1, '01_0', 'G1SST', 'G1SST'],
671	u'NAVO-L4HR1m-GLOB-K10_SST': ['NAVO', 'HR1m', 'GLOB', 1, '01_0', 'K10_SST', 'K10_SST'],
672	u'NCDC-L4LRblend-GLOB-AVHRR_AMSR_OI': ['NCDC', 'LRblend', 'GLOB', 1, '02_0', 'AVHRR_AMSR_OI', 'AVHRR_AMSR_OI'],
673	u'NCDC-L4LRblend-GLOB-AVHRR_OI': ['NCDC', 'LRblend', 'GLOB', 1, '02_0', 'AVHRR_OI', 'AVHRR_OI'],
674	#u'REMSS-L4HRfnd-GLOB-mw_ir_OI': ['REMSS', 'HRfnd', 'GLOB', 1, '01', 'mw_ir_OI', 'mw_ir_OI'], # PO.DAAC's copy of this dataset is not up to date. I emailed them and they are working on it.
675	#u'REMSS-L4HRfnd-GLOB-mw_ir_rt_OI': ['REMSS', 'HRfnd', 'GLOB', 1, '01', 'mw_ir_rt_OI', 'mw_ir_OI'], # PO.DAAC's copy of this dataset is not up to date. I emailed them and they are working on it.
676	u'UKMO-L4HRfnd-GLOB-OSTIA': ['UKMO', 'HRfnd', 'GLOB', 1, '02', 'OSTIA', 'OSTIA']}
677
678	@classmethod
679	def _RotateAndClip(cls, grid, rotationOffset, spatialExtent, startDate, endDate):
680	if rotationOffset is not None:
681	grid = RotatedGlobalGrid(grid, rotationOffset, u'Map units')
682
683	xMin, yMin, xMax, yMax = None, None, None, None
684	if spatialExtent is not None:
685	from GeoEco.Types import EnvelopeTypeMetadata
686	xMin, yMin, xMax, yMax = EnvelopeTypeMetadata.ParseFromArcGISString(spatialExtent)
687
688	if spatialExtent is not None or startDate is not None or endDate is not None:
689	if startDate is not None:
690	startDate = datetime.datetime(startDate.year, startDate.month, startDate.day, 0, 0, 0)
691	if endDate is not None:
692	endDate = datetime.datetime(endDate.year, endDate.month, endDate.day, 23, 59, 59)
693
694	grid = ClippedGrid(grid, u'Map coordinates', xMin=xMin, xMax=xMax, yMin=yMin, yMax=yMax, tMin=startDate, tMax=endDate)
695
696	return grid
697
698	@classmethod
699	def CreateArcGISRasters(cls, product, variableName,
700	outputWorkspace, mode=u'add', rasterNameExpressions=[u'%(AreaCode)s', u'%(RDACCode)s', u'%(ProductCode)s', u'%(VariableName)s', u'%%Y', u'%%Y%%m%%d-%(RDACCode)s-L4%(ProductType)s-%(AreaCode)s-v%(GDSVersion)02i-fv%(FileVersion)s-%(ProductCode)s-%(VariableName)s.img'], rasterCatalog=None,
701	rotationOffset=None, spatialExtent=None, startDate=None, endDate=None,
702	timeout=60, maxRetryTime=300, cacheDirectory=None,
703	convertToCelsius=True, useUnscaledData=False, calculateStatistics=True, buildRAT=False, buildPyramids=False):
704	cls.__doc__.Obj.ValidateMethodInvocation()
705	[rdacCode, productType, areaCode, gdsVersion, fileVersion, productCode, productCode2] = cls._PODAAC_Products[product]
706	grid = cls(variableName, rdacCode, productType, areaCode, gdsVersion, fileVersion, productCode, productCode2, convertToCelsius and variableName == u'analysed_sst' and not useUnscaledData, timeout, maxRetryTime, cacheDirectory)
707	try:
708	from GeoEco.Datasets.ArcGIS import ArcGISWorkspace, ArcGISRaster
709	grid = cls._RotateAndClip(grid, rotationOffset, spatialExtent, startDate, endDate)
710	workspace = ArcGISWorkspace(outputWorkspace, ArcGISRaster, pathCreationExpressions=rasterNameExpressions, cacheTree=True, queryableAttributes=tuple(grid.GetAllQueryableAttributes() + [QueryableAttribute(u'DateTime', _(u'Date'), DateTimeTypeMetadata())]))
711	workspace.ImportDatasets(GridSliceCollection(grid, tQACoordType=u'min').QueryDatasets(), mode, useUnscaledData=useUnscaledData, calculateStatistics=calculateStatistics, buildRAT=buildRAT, buildPyramids=buildPyramids)
712	if rasterCatalog is not None:
713	workspace.ToRasterCatalog(rasterCatalog, grid.GetSpatialReference(u'ArcGIS'), tQACoordType=u'min', tCoordFunction=lambda qav: [qav[u'DateTime'], qav[u'DateTime'] + datetime.timedelta(0.5), qav[u'DateTime'] + datetime.timedelta(1) - datetime.timedelta(seconds=1)], overwriteExisting=True)
714	finally:
715	grid.Close()
716	return outputWorkspace
717
718	@classmethod
719	def CreateClimatologicalArcGISRasters(cls, product, variableName,
720	statistic, binType,
721	outputWorkspace, mode=u'add', rasterNameExpressions=[u'%(AreaCode)s', u'%(RDACCode)s', u'%(ProductCode)s', u'%(VariableName)s', u'%(ClimatologyBinType)s_Climatology', u'%(RDACCode)s-L4%(ProductType)s-%(AreaCode)s-v%(GDSVersion)02i-fv%(FileVersion)s-%(ProductCode)s-%(VariableName)s-%(ClimatologyBinName)s-%(Statistic)s.img'],
722	binDuration=1, startDayOfYear=1,
723	rotationOffset=None, spatialExtent=None, startDate=None, endDate=None,
724	timeout=60, maxRetryTime=300, cacheDirectory=None,
725	convertToCelsius=True, calculateStatistics=True, buildPyramids=False):
726	cls.__doc__.Obj.ValidateMethodInvocation()
727	[rdacCode, productType, areaCode, gdsVersion, fileVersion, productCode, productCode2] = cls._PODAAC_Products[product]
728	grid = cls(variableName, rdacCode, productType, areaCode, gdsVersion, fileVersion, productCode, productCode2, convertToCelsius and variableName == u'analysed_sst', timeout, maxRetryTime, cacheDirectory)
729	try:
730	from GeoEco.Datasets.ArcGIS import ArcGISWorkspace, ArcGISRaster
731	grid = cls._RotateAndClip(grid, rotationOffset, spatialExtent, startDate, endDate)
732	collection = ClimatologicalGridCollection(grid, statistic, binType, binDuration, startDayOfYear, reportProgress=True)
733	workspace = ArcGISWorkspace(outputWorkspace, ArcGISRaster, pathCreationExpressions=rasterNameExpressions, cacheTree=True, queryableAttributes=tuple(collection.GetAllQueryableAttributes()))
734	workspace.ImportDatasets(collection.QueryDatasets(), mode, calculateStatistics=calculateStatistics, buildPyramids=buildPyramids)
735	finally:
736	grid.Close()
737	return outputWorkspace
738
739	@classmethod
740	def InterpolateAtArcGISPoints(cls, product, variableName,
741	points, valueField, tField, method=u'Nearest', where=None, noDataValue=None, convertToCelsius=True,
742	timeout=60, maxRetryTime=300, cacheDirectory=None,
743	orderByFields=None, numBlocksToCacheInMemory=256, xBlockSize=64, yBlockSize=64, tBlockSize=1):
744	cls.__doc__.Obj.ValidateMethodInvocation()
745	[rdacCode, productType, areaCode, gdsVersion, fileVersion, productCode, productCode2] = cls._PODAAC_Products[product]
746	grid = cls(variableName, rdacCode, productType, areaCode, gdsVersion, fileVersion, productCode, productCode2, convertToCelsius and variableName == u'analysed_sst', timeout, maxRetryTime, cacheDirectory)
747	try:
748	if orderByFields is not None:
749	orderBy = u', '.join(map(lambda f: f + u' ASC', orderByFields))
750	else:
751	from GeoEco.ArcGIS import GeoprocessorManager
752	if GeoprocessorManager.GetArcGISMajorVersion() > 9 or GeoprocessorManager.GetArcGISMinorVersion() >= 2:
753	orderBy = tField + u' ASC'
754	else:
755	orderBy = None
756	from GeoEco.Datasets.ArcGIS import ArcGISTable
757	from GeoEco.SpatialAnalysis.Interpolation import Interpolator
758	Interpolator.InterpolateGridsValuesForTableOfPoints([grid], ArcGISTable(points), [valueField], tField=tField, where=where, orderBy=orderBy, method=method, noDataValue=noDataValue, gridsWrap=True, numBlocksToCacheInMemory=numBlocksToCacheInMemory, xBlockSize=xBlockSize, yBlockSize=yBlockSize, tBlockSize=tBlockSize)
759	finally:
760	grid.Close()
761	return points
762
763	@classmethod
764	def CreateCayulaCornillonFrontsAsArcGISRasters(cls, product, minPopMeanDifference,
765	outputWorkspace, mode=u'add', rasterNameExpressions=[u'%(AreaCode)s', u'%(RDACCode)s', u'%(ProductCode)s', u'%(VariableName)s', u'%%Y', '%%Y%%m%%d-%(RDACCode)s-L4%(ProductType)s-%(AreaCode)s-v%(GDSVersion)02i-fv%(FileVersion)s-%(ProductCode)s-%(ImageType)s.img'],
766	medianFilterWindowSize=3, histogramWindowSize=32, histogramWindowStride=1, minPropNonMaskedCells=0.65, minPopProp=0.25, minTheta=0.76, minSinglePopCohesion=0.90, minGlobalPopCohesion=0.92, threads=1,
767	fillHoles=20, thin=True, minSize=10,
768	rotationOffset=None, spatialExtent=None, startDate=None, endDate=None,
769	timeout=60, maxRetryTime=300, cacheDirectory=None,
770	calculateStatistics=True, buildRAT=False, buildPyramids=False,
771	outputCandidateCounts=False, outputFrontCounts=False, outputWindowStatusCodes=False, outputWindowStatusValues=False):
772	cls.__doc__.Obj.ValidateMethodInvocation()
773
774	# Construct a GHRSSTLevel4 instance and rotate and clip it as
775	# requested.
776
777	[rdacCode, productType, areaCode, gdsVersion, fileVersion, productCode, productCode2] = cls._PODAAC_Products[product]
778	grid = cls(u'analysed_sst', rdacCode, productType, areaCode, gdsVersion, fileVersion, productCode, productCode2, False, timeout, maxRetryTime, cacheDirectory)
779	try:
780	grid = cls._RotateAndClip(grid, rotationOffset, spatialExtent, startDate, endDate)
781
782	# Construct a CayulaCornillonFrontsInGrid collection using
783	# the requested parameters.
784
785	from GeoEco.OceanographicAnalysis.Fronts import CayulaCornillonFrontsInGrid
786
787	collection = CayulaCornillonFrontsInGrid(grid,
788	wrapEdges='-GLOB-' in product and spatialExtent is None,
789	medianFilterWindowSize=medianFilterWindowSize,
790	histogramWindowSize=histogramWindowSize,
791	histogramWindowStride=histogramWindowStride,
792	minPropNonMaskedCells=minPropNonMaskedCells,
793	minPopProp=minPopProp,
794	minPopMeanDifference=minPopMeanDifference,
795	minTheta=minTheta,
796	minSinglePopCohesion=minSinglePopCohesion,
797	minGlobalPopCohesion=minGlobalPopCohesion,
798	threads=threads,
799	fillHoles=fillHoles,
800	thin=thin,
801	minSize=minSize)
802	try:
803	try:
804	# Construct an ArcGISWorkspace instance and import
805	# time slices from the CayulaCornillonFrontsInGrid
806	# instance.
807
808	expression = "ImageType = 'floc'"
809	if outputCandidateCounts:
810	expression += " OR ImageType = 'ccnt'"
811	if outputFrontCounts:
812	expression += " OR ImageType = 'fcnt'"
813	if outputWindowStatusCodes:
814	expression += " OR ImageType = 'wsco'"
815	if outputWindowStatusValues:
816	expression += " OR ImageType = 'wsvl'"
817
818	from GeoEco.Datasets.ArcGIS import ArcGISWorkspace, ArcGISRaster
819
820	workspace = ArcGISWorkspace(outputWorkspace, ArcGISRaster, pathCreationExpressions=rasterNameExpressions, cacheTree=True, queryableAttributes=tuple(collection.GetAllQueryableAttributes()))
821	workspace.ImportDatasets(collection.QueryDatasets(expression), mode, calculateStatistics=calculateStatistics, buildRAT=buildRAT, buildPyramids=buildPyramids)
822
823	# If we caught an exception, log it now. Then delete
824	# the collection object, to ensure its memory is
825	# freed. Normally we don't bother doing this but the
826	# memory it holds can be substantial.
827
828	except:
829	from GeoEco.Logging import Logger
830	Logger.LogExceptionAsError()
831	finally:
832	del collection
833	finally:
834	grid.Close()
835
836	# Return successfully.
837
838	return outputWorkspace
839
840
841	class QuikSCATL3TimeSeries(TimeSeriesGridStack):
842	__doc__ = DynamicDocString()
843
844	@classmethod
845	def GetQueryableAttributesForTimeSlices(cls):
846	return (QueryableAttribute(u'DateTime', _(u'Date'), DateTimeTypeMetadata()),
847	QueryableAttribute(u'ProcessingDateTime', _(u'Processing date and time'), UnicodeStringTypeMetadata(mustMatchRegEx=ur'\d\d\d\d\d\d\d\d\d\d\d\d')),
848	QueryableAttribute(u'VariableName', _(u'Variable'), UnicodeStringTypeMetadata(allowedValues=[u'asc_avg_wind_speed', u'des_avg_wind_speed', u'asc_avg_wind_vel_u', u'des_avg_wind_vel_u', u'asc_avg_wind_vel_v', u'des_avg_wind_vel_v', u'asc_avg_wind_speed_sq', u'des_avg_wind_speed_sq', u'asc_wvc_count', u'des_wvc_count', u'asc_time_frac', u'des_time_frac', u'asc_rain_prob', u'des_rain_prob', u'asc_rain_flag', u'des_rain_flag'], makeLowercase=True)))
849
850	def __init__(self, variableName, timeout=60, maxRetryTime=300, cacheDirectory=None):
851	# TODO: validation
852
853	# Initialize our display name.
854
855	self._DisplayName = _(u'QuikSCAT L3 %(variableName)s from NASA PODAAC via OPeNDAP') % {u'variableName': variableName}
856
857	# Construct a THREDDSCatalog for the caller's parameters.
858
859	queryableAttributes = QuikSCATL3TimeSeries.GetQueryableAttributesForTimeSlices()
860
861	import numpy
862	lazyPropertyValues = {'VariableTypes': ['Grid'],
863	'VariableNames': [variableName],
864	'SpatialReference': Dataset.ConvertSpatialReference('proj4', '+proj=latlong +ellps=WGS84 +datum=WGS84 +no_defs', 'obj'),
865	'Dimensions': 'yx',
866	'Shape': (720, 1440),
867	'CoordDependencies': (None, None),
868	'CoordIncrements': (0.25, 0.25),
869	'TIncrement': 1,
870	'TIncrementUnit': 'day',
871	'TSemiRegularity': None,
872	'TCountPerSemiRegularPeriod': None,
873	'TCornerCoordType': 'min',
874	'CornerCoords': (-89.875, 0.125),
875	'PhysicalDimensions': 'yx',
876	'PhysicalDimensionsFlipped': (False, False),
877	'UnscaledDataType': {u'asc_avg_wind_speed': 'uint16',
878	u'des_avg_wind_speed': 'uint16',
879	u'asc_avg_wind_vel_u': 'int16',
880	u'des_avg_wind_vel_u': 'int16',
881	u'asc_avg_wind_vel_v': 'int16',
882	u'des_avg_wind_vel_v': 'int16',
883	u'asc_avg_wind_speed_sq': 'uint32',
884	u'des_avg_wind_speed_sq': 'uint32',
885	u'asc_wvc_count': 'int32', # OPeNDAP version of the data uses int32, even though the HDF files use int8. This may be because OPeNDAP does not support int8, only uint8.
886	u'des_wvc_count': 'int32',
887	u'asc_time_frac': 'uint16',
888	u'des_time_frac': 'uint16',
889	u'asc_rain_prob': 'uint16',
890	u'des_rain_prob': 'uint16',
891	u'asc_rain_flag': 'int32', # Again, OPeNDAP version of the data uses int32, even though the HDF files use int8.
892	u'des_rain_flag': 'int32'}[variableName],
893	'UnscaledNoDataValue': None,
894	'ScaledDataType': {u'asc_avg_wind_speed': 'float32',
895	u'des_avg_wind_speed': 'float32',
896	u'asc_avg_wind_vel_u': 'float32',
897	u'des_avg_wind_vel_u': 'float32',
898	u'asc_avg_wind_vel_v': 'float32',
899	u'des_avg_wind_vel_v': 'float32',
900	u'asc_avg_wind_speed_sq': 'float32',
901	u'des_avg_wind_speed_sq': 'float32',
902	u'asc_wvc_count': None,
903	u'des_wvc_count': None,
904	u'asc_time_frac': 'float32',
905	u'des_time_frac': 'float32',
906	u'asc_rain_prob': 'float32',
907	u'des_rain_prob': 'float32',
908	u'asc_rain_flag': None,
909	u'des_rain_flag': None}[variableName],
910	'ScaledNoDataValue': None,
911	'ScalingFunction': {u'asc_avg_wind_speed': lambda data: numpy.cast['float32'](data) * numpy.cast['float32'](0.01),
912	u'des_avg_wind_speed': lambda data: numpy.cast['float32'](data) * numpy.cast['float32'](0.01),
913	u'asc_avg_wind_vel_u': lambda data: numpy.cast['float32'](data) * numpy.cast['float32'](0.01),
914	u'des_avg_wind_vel_u': lambda data: numpy.cast['float32'](data) * numpy.cast['float32'](0.01),
915	u'asc_avg_wind_vel_v': lambda data: numpy.cast['float32'](data) * numpy.cast['float32'](0.01),
916	u'des_avg_wind_vel_v': lambda data: numpy.cast['float32'](data) * numpy.cast['float32'](0.01),
917	u'asc_avg_wind_speed_sq': lambda data: numpy.cast['float32'](data) * numpy.cast['float32'](0.01),
918	u'des_avg_wind_speed_sq': lambda data: numpy.cast['float32'](data) * numpy.cast['float32'](0.01),
919	u'asc_wvc_count': None,
920	u'des_wvc_count': None,
921	u'asc_time_frac': lambda data: numpy.cast['float32'](data) * numpy.cast['float32'](0.00002),
922	u'des_time_frac': lambda data: numpy.cast['float32'](data) * numpy.cast['float32'](0.00002),
923	u'asc_rain_prob': lambda data: numpy.cast['float32'](data) * numpy.cast['float32'](0.001),
924	u'des_rain_prob': lambda data: numpy.cast['float32'](data) * numpy.cast['float32'](0.001),
925	u'asc_rain_flag': None,
926	u'des_rain_flag': None}[variableName],
927	'UnscalingFunction': {u'asc_avg_wind_speed': lambda data: numpy.cast['uint16'](numpy.round(data / numpy.cast['float32'](0.01))),
928	u'des_avg_wind_speed': lambda data: numpy.cast['uint16'](numpy.round(data / numpy.cast['float32'](0.01))),
929	u'asc_avg_wind_vel_u': lambda data: numpy.cast['int16'](numpy.round(data / numpy.cast['float32'](0.01))),
930	u'des_avg_wind_vel_u': lambda data: numpy.cast['int16'](numpy.round(data / numpy.cast['float32'](0.01))),
931	u'asc_avg_wind_vel_v': lambda data: numpy.cast['int16'](numpy.round(data / numpy.cast['float32'](0.01))),
932	u'des_avg_wind_vel_v': lambda data: numpy.cast['int16'](numpy.round(data / numpy.cast['float32'](0.01))),
933	u'asc_avg_wind_speed_sq': lambda data: numpy.cast['int32'](numpy.round(data / numpy.cast['float32'](0.01))),
934	u'des_avg_wind_speed_sq': lambda data: numpy.cast['int32'](numpy.round(data / numpy.cast['float32'](0.01))),
935	u'asc_wvc_count': None,
936	u'des_wvc_count': None,
937	u'asc_time_frac': lambda data: numpy.cast['int16'](numpy.round(data / numpy.cast['float32'](0.00002))),
938	u'des_time_frac': lambda data: numpy.cast['int16'](numpy.round(data / numpy.cast['float32'](0.00002))),
939	u'asc_rain_prob': lambda data: numpy.cast['int16'](numpy.round(data / numpy.cast['float32'](0.001))),
940	u'des_rain_prob': lambda data: numpy.cast['int16'](numpy.round(data / numpy.cast['float32'](0.001))),
941	u'asc_rain_flag': None,
942	u'des_rain_flag': None}[variableName]}
943
944	pathParsingExpressions=[r'(?P<Year>\d\d\d\d)',
945	r'QS_XWGRD3_(?P<Year>\d\d\d\d)(?P<DayOfYear>\d\d\d)\.(?P<ProcessingDateTime>\d\d\d\d\d\d\d\d\d\d\d)\.gz']
946
947	threddsCatalog = THREDDSCatalog(url='http://dods.jpl.nasa.gov/opendap/ocean_wind/quikscat/L3/data/catalog.xml',
948	pathParsingExpressions=pathParsingExpressions,
949	timeout=timeout,
950	maxRetryTime=maxRetryTime,
951	queryableAttributes=queryableAttributes,
952	lazyPropertyValues=lazyPropertyValues,
953	cacheDirectory=cacheDirectory)
954
955	# Initialize the base class.
956
957	super(QuikSCATL3TimeSeries, self).__init__(threddsCatalog, None)
958
959	def _GetDisplayName(self):
960	return self._DisplayName
961
962
963	class QuikSCATL3TimeSlicesInDirectory(DirectoryTree):
964	__doc__ = DynamicDocString()
965
966	def __init__(self,
967	path,
968	pathParsingExpressions=[r'(?P<Year>\d\d\d\d)',
969	r'QS_XWGRD3_(?P<Year>\d\d\d\d)(?P<DayOfYear>\d\d\d)\.(?P<ProcessingDateTime>\d\d\d\d\d\d\d\d\d\d\d)_(?P<VariableName>asc_avg_wind_speed\|des_avg_wind_speed\|asc_avg_wind_vel_u\|des_avg_wind_vel_u\|asc_avg_wind_vel_v\|des_avg_wind_vel_v\|asc_avg_wind_speed_sq\|des_avg_wind_speed_sq\|asc_wvc_count\|des_wvc_count\|asc_time_frac\|des_time_frac\|asc_rain_prob\|des_rain_prob\|asc_rain_flag\|des_rain_flag)\.img'],
970	datasetType=GDALDataset,
971	pathCreationExpressions=['%%Y',
972	'QS_XWGRD3_%%Y%%j.%(ProcessingDateTime)s_%(VariableName)s.img'],
973	cacheTree=True):
974
975	super(QuikSCATL3TimeSlicesInDirectory, self).__init__(path=path,
976	pathParsingExpressions=pathParsingExpressions,
977	datasetType=datasetType,
978	pathCreationExpressions=pathCreationExpressions,
979	cacheTree=cacheTree,
980	queryableAttributes=QuikSCATL3TimeSeries.GetQueryableAttributesForTimeSlices())
981
982
983	###############################################################################
984	# Metadata: module
985	###############################################################################
986
987	from GeoEco.ArcGIS import ArcGISDependency
988	from GeoEco.Dependencies import PythonAggregatedModuleDependency
989	from GeoEco.Datasets.ArcGIS import _UseUnscaledDataDescription, _CalculateStatisticsDescription, _BuildRATDescription, _BuildPyramidsDescription
990	from GeoEco.Datasets.Virtual import TimeSeriesGridStack, GridSliceCollection, MaskedGrid, ClippedGrid, RotatedGlobalGrid
991	from GeoEco.Metadata import *
992	from GeoEco.OceanographicAnalysis.Fronts import CayulaCornillonEdgeDetection, _SIEDDescription, _SIEDReferences, _WindowStatusCodes, _WindowStatusValues
993	from GeoEco.Types import *
994
995	AddModuleMetadata(shortDescription=_(u'THREDDSCatalogs and OPeNDAPURLs for NASA PO.DAAC datasets.')) # TODO: Better description
996
997	###############################################################################
998	# Metadata: MODISL3SSTTimeSeries class
999	###############################################################################
1000
1001	_MODISL3SSTTimeSeries_LongDescription = _(
1002	u"""The NASA Jet Propulsion Laboratory (JPL) `Physical Oceanography
1003	Distributed Active Archive Center <http://podaac.jpl.nasa.gov/>`_
1004	(PO.DAAC) publishes four collections of sea surface temperature (SST)
1005	images gathered by the Moderate Resolution Imaging Spectroradiometer
1006	(MODIS) carried by the Terra and Aqua satellites:
1007
1008	* `Product 162 <http://podaac.jpl.nasa.gov/PRODUCTS/p162.html>`_ -
1009	MODIS Terra Global Level 3 Mapped Thermal IR SST
1010
1011	* `Product 163 <http://podaac.jpl.nasa.gov/PRODUCTS/p163.html>`_ -
1012	MODIS Terra Global Level 3 Mapped Mid-IR SST
1013
1014	* `Product 184 <http://podaac.jpl.nasa.gov/PRODUCTS/p184.html>`_ -
1015	MODIS Aqua Global Level 3 Mapped Thermal IR SST
1016
1017	* `Product 185 <http://podaac.jpl.nasa.gov/PRODUCTS/p185.html>`_ -
1018	MODIS Aqua Global Level 3 Mapped Mid-IR SST
1019	""")
1020
1021	_MODISReferences = _(
1022	u"""References
1023
1024	PO.DAAC's
1025	`MODIS SST Data Set Guide <ftp://podaac.jpl.nasa.gov/pub/sea_surface_temperature/modis/doc/modis_sst.gd.html>`_
1026	provides more information about the MODIS SST datasets.
1027
1028	Please see
1029	`these instructions <http://podaac.jpl.nasa.gov/WEB_INFO/citations.html>`_
1030	on how to acknowledge your use of PO.DAAC products.""")
1031
1032	AddClassMetadata(MODISL3SSTTimeSeries,
1033	shortDescription=_(u'Time series of MODIS SST images published by NASA JPL PO.DAAC.'),
1034	longDescription=_MODISL3SSTTimeSeries_LongDescription + _(
1035	u"""
1036	PO.DAAC presents each collection as a time series of 2D HDF files that
1037	may be accessed through a variety of protocols, including `OPeNDAP <http://opendap.org/>`_.
1038	Given a satellite name, temporal resolution, spatial resolution, and
1039	desired geophysical parameter, this class efficiently accesses the
1040	specified time series of images using OPeNDAP, representing them as a
1041	single 3D grid with dimensions x, y, and time.
1042
1043	""") + _MODISReferences) # TODO: Add more documentation showing use of class from Python code.
1044
1045	# Constructor
1046
1047	AddMethodMetadata(MODISL3SSTTimeSeries.__init__,
1048	shortDescription=_(u'Constructs a new MODISL3SSTTimeSeries instance.'),
1049	isExposedToPythonCallers=True,
1050	dependencies=[PythonAggregatedModuleDependency('numpy')])
1051
1052	AddArgumentMetadata(MODISL3SSTTimeSeries.__init__, u'self',
1053	typeMetadata=ClassInstanceTypeMetadata(cls=MODISL3SSTTimeSeries),
1054	description=_(u'MODISL3SSTTimeSeries instance.'))
1055
1056	AddArgumentMetadata(MODISL3SSTTimeSeries.__init__, u'satellite',
1057	typeMetadata=UnicodeStringTypeMetadata(allowedValues=[u'Aqua', u'Terra'], makeLowercase=True),
1058	description=_(
1059	u"""MODIS satellite to use, one of:
1060
1061	* Aqua - the Aqua datasets start in July 2002.
1062
1063	* Terra - the Terra datasets start in February 2000.
1064	"""),
1065	arcGISDisplayName=_(u'Satellite'))
1066
1067	AddArgumentMetadata(MODISL3SSTTimeSeries.__init__, u'temporalResolution',
1068	typeMetadata=UnicodeStringTypeMetadata(allowedValues=[u'Daily', u'8day', u'Monthly', u'Annual'], makeLowercase=True),
1069	description=_(
1070	u"""Temporal resolution to use, one of:
1071
1072	* Daily - daily images. There are 365 during normal years and 366
1073	during leap years.
1074
1075	* 8day - 8-day images. There are 46 per year. The first image of the
1076	year starts on January 1. The duration of the last image of the year
1077	is five days during normal years and six days during leap years.
1078
1079	* Monthly - monthly images.
1080
1081	* Annual - annual images.
1082
1083	Although NASA may publish MODIS SST images at other temporal
1084	resolutions, they are not supported at this time. If you need one of those
1085	products, please contact the author of this tool to see if support may
1086	be added.
1087
1088	The satellites experience occasional transient failures that prevent
1089	data from being collected. NASA opted not to produce any images for
1090	these periods. These missing images are represented as time slices
1091	filled with the NoData value."""),
1092	arcGISDisplayName=_(u'Temporal resolution'))
1093
1094	AddArgumentMetadata(MODISL3SSTTimeSeries.__init__, u'spatialResolution',
1095	typeMetadata=UnicodeStringTypeMetadata(allowedValues=[u'4km', u'9km'], makeLowercase=True),
1096	description=_(
1097	u"""Spatial resolution to use, one of:
1098
1099	* 4km - the grid has a cell size of 1/24 geographic degree, or about
1100	4.64 km at the equator, with 8640 columns and 4320 rows.
1101
1102	* 9km - the grid has a cell size of 1/12 geographic degree, or about
1103	9.28 km at the equator, with 4320 columns and 2160 rows.
1104	"""),
1105	arcGISDisplayName=_(u'Spatial resolution'))
1106
1107	AddArgumentMetadata(MODISL3SSTTimeSeries.__init__, u'geophysicalParameter',
1108	typeMetadata=UnicodeStringTypeMetadata(allowedValues=[u'SST', u'NSST', u'SST4'], makeLowercase=True),
1109	description=_(
1110	u"""Geophysical parameter to use, one of:
1111
1112	* SST - daytime SST derived from the 11 and 12 micrometer thermal IR
1113	infrared (IR) bands (MODIS channels 31 and 32)
1114
1115	* NSST - nighttime SST derived from the 11 and 12 micrometer thermal
1116	IR infrared (IR) bands (MODIS channels 31 and 32)
1117
1118	* SST4 -nighttime SST derived from the 3.8-4.1 micrometer mid-IR
1119	infrared (IR) bands (MODIS channels 20, 22, and 23)
1120
1121	The geophysical parameter determines the time of day that the data
1122	apply to. During the daytime, the satellites pass over the equator at
1123	slightly different times, 10:30 for Terra and 13:30 for Aqua, but for
1124	simplicity it is assumed that they both pass over at 12:00. Therefore,
1125	a daily image of the SST parameter (daytime temperature) is assumed to
1126	apply from 00:00 to 23:59 on the date of the image. The nighttime
1127	passes occur 12 hours earlier. Thus a daily image of the NSST or SST4
1128	parameter (nighttime temperature) is assumed to apply from 12:00 on
1129	the previous day to 11:59 of the date of the image. This scheme
1130	reflects what is described by the Start Time and End Time global
1131	attributes of the MODIS L3 HDF files."""),
1132	arcGISDisplayName=_(u'Geophysical parameter'))
1133
1134	AddArgumentMetadata(MODISL3SSTTimeSeries.__init__, u'variableName',
1135	typeMetadata=UnicodeStringTypeMetadata(allowedValues=[u'l3m_data', u'l3m_qual'], makeLowercase=True),
1136	description=_(
1137	u"""Dataset variable to use, one of:
1138
1139	* l3m_data - the value of the geophysical parameter (SST). The data
1140	type will be 32-bit floating point, or 16-bit unsigned integers if
1141	the original unscaled values are requested.
1142
1143	* l3m_qual - the pixel quality level. The data type will be unsigned
1144	8-bit integer. According to the PO.DAAC MODIS SST documentation, the
1145	possible values are 0 (good), 1 (questionable), and 2 (clouds). The
1146	documentation also mentions the value 3 (bad, other than clouds) but
1147	it appears that NASA already converts these to No Data in both the
1148	l3m_qual and l3m_data images.
1149	"""),
1150	arcGISDisplayName=_(u'Dataset variable'))
1151
1152	AddArgumentMetadata(MODISL3SSTTimeSeries.__init__, u'qualityLevel',
1153	typeMetadata=IntegerTypeMetadata(canBeNone=True, allowedValues=[0, 1, 2]),
1154	description=_(
1155	u"""Pixel quality level for masking the grid, one of:
1156
1157	* 0 - pixels with quality level 0 will have data; pixels with quality
1158	levels 1 or 2 will be set to No Data.
1159
1160	* 1 - pixels with quality levels 0 and 1 will have data; pixels with
1161	quality level 2 will be set to No Data.
1162
1163	* 2 - all pixels will have data.
1164
1165	The PO.DAAC MODIS SST documentation states that the possible quality
1166	levels are 0 (good), 1 (questionable), and 2 (clouds), and recommends
1167	that only quality level 0 pixels be used, but notes that quality level
1168	1 pixels can be used "under some circumstances". The documentation
1169	also mentions the quality level 3 (bad, other than clouds). These
1170	appear to include land, satellite malfunctions, and dense clouds, and
1171	are converted to No Data by NASA automatically."""),
1172	arcGISDisplayName=_(u'Pixel quality level'),
1173	arcGISCategory=_(u'Masking options'))
1174
1175	CopyArgumentMetadata(THREDDSCatalog.__init__, u'timeout', MODISL3SSTTimeSeries.__init__, u'timeout')
1176	CopyArgumentMetadata(THREDDSCatalog.__init__, u'maxRetryTime', MODISL3SSTTimeSeries.__init__, u'maxRetryTime')
1177	CopyArgumentMetadata(THREDDSCatalog.__init__, u'cacheDirectory', MODISL3SSTTimeSeries.__init__, u'cacheDirectory')
1178
1179	AddResultMetadata(MODISL3SSTTimeSeries.__init__, u'grid',
1180	typeMetadata=ClassInstanceTypeMetadata(cls=MODISL3SSTTimeSeries),
1181	description=_(u'MODISL3SSTTimeSeries instance.'))
1182
1183	# Public method: MODISL3SSTTimeSeries.CreateArcGISRasters
1184
1185	AddMethodMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters,
1186	shortDescription=_(u'Creates rasters for MODIS Level 3 SST images published by NASA JPL PO.DAAC.'),
1187	longDescription=_MODISL3SSTTimeSeries_LongDescription + _(
1188	u"""
1189	Given a satellite name, temporal resolution, spatial resolution, and
1190	desired geophysical parameter, this tool efficiently downloads the
1191	specified images using the `OPeNDAP <http://opendap.org/>`_ protocol
1192	and creates corresponding rasters.
1193
1194	""" + _MODISReferences),
1195	isExposedToPythonCallers=True,
1196	isExposedByCOM=True,
1197	isExposedAsArcGISTool=True,
1198	arcGISDisplayName=_(u'Create Rasters for PO.DAAC MODIS L3 SST'),
1199	arcGISToolCategory=_(u'Data Products\\NASA JPL PO.DAAC\\MODIS Global Level 3 Mapped SST'),
1200	dependencies=[ArcGISDependency(9, 1), PythonAggregatedModuleDependency('numpy')])
1201
1202	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'cls',
1203	typeMetadata=ClassOrClassInstanceTypeMetadata(cls=MODISL3SSTTimeSeries),
1204	description=_(u'MODISL3SSTTimeSeries class or instance.'))
1205
1206	CopyArgumentMetadata(MODISL3SSTTimeSeries.__init__, u'satellite', MODISL3SSTTimeSeries.CreateArcGISRasters, u'satellite')
1207	CopyArgumentMetadata(MODISL3SSTTimeSeries.__init__, u'temporalResolution', MODISL3SSTTimeSeries.CreateArcGISRasters, u'temporalResolution')
1208	CopyArgumentMetadata(MODISL3SSTTimeSeries.__init__, u'spatialResolution', MODISL3SSTTimeSeries.CreateArcGISRasters, u'spatialResolution')
1209	CopyArgumentMetadata(MODISL3SSTTimeSeries.__init__, u'geophysicalParameter', MODISL3SSTTimeSeries.CreateArcGISRasters, u'geophysicalParameter')
1210	CopyArgumentMetadata(MODISL3SSTTimeSeries.__init__, u'variableName', MODISL3SSTTimeSeries.CreateArcGISRasters, u'variableName')
1211
1212	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'outputWorkspace',
1213	typeMetadata=ArcGISWorkspaceTypeMetadata(createParentDirectories=True),
1214	description=_(
1215	u"""Directory or geodatabase to receive the rasters.
1216
1217	Unless you have a specific reason to store the rasters in a
1218	geodatabase, we recommend you store them in a directory because it
1219	will be much faster and allows the rasters to be organized in a tree.
1220	If you do store the rasters in a geodatabase, you must change the
1221	Raster Name Expressions parameter; see below for more
1222	information."""),
1223	arcGISDisplayName=_(u'Output workspace'))
1224
1225	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'mode',
1226	typeMetadata=UnicodeStringTypeMetadata(allowedValues=[u'Add', u'Replace'], makeLowercase=True),
1227	description=_(
1228	u"""Overwrite mode, one of:
1229
1230	* Add - create rasters that do not exist and skip those that already
1231	exist. This is the default.
1232
1233	* Replace - create rasters that do not exist and overwrite those that
1234	already exist.
1235
1236	'Add' should be appropriate for nearly all situations. One situation
1237	in which 'Replace' is appropriate is when the data publisher
1238	reprocesses the entire dataset with improved algorithms and releases
1239	updated images. In that case, you may wish to recreate all of your
1240	rasters using the updated data.
1241
1242	The ArcGIS Overwrite Outputs geoprocessing setting has no effect on
1243	this tool. If 'Replace' is selected the rasters will be overwritten,
1244	regardless of the ArcGIS Overwrite Outputs setting."""),
1245	arcGISDisplayName=_(u'Overwrite mode'))
1246
1247	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'rasterNameExpressions',
1248	typeMetadata=ListTypeMetadata(elementType=UnicodeStringTypeMetadata(), minLength=1),
1249	description=_(
1250	u"""List of expressions specifying how the output rasters should be
1251	named.
1252
1253	The default expression assumes you are storing rasters in a file
1254	system directory and creates them in a tree structure with names that
1255	imitate those used by NASA. When storing rasters in a directory, the
1256	final expression specifies the file name of the raster and any
1257	preceding expressions specify subdirectories. The extension of the
1258	final expression determines the output raster format: .asc for ArcInfo
1259	ASCII Grid, .bmp for BMP, .gif for GIF, .img for an ERDAS IMAGINE
1260	file, .jpg for JPEG, .jp2 for JPEG 2000, .png for PNG, .tif for
1261	GeoTIFF, or no extension for ArcInfo Binary Grid. The default
1262	expression uses .img.
1263
1264	When storing rasters in a geodatabase, you should provide only one
1265	expression. That expression specifies the raster's name.
1266
1267	Each expression may contain any sequence of characters permitted by
1268	the output workspace. Each expression may optionally contain one or
1269	more of the following case-sensitive codes. The tool replaces the
1270	codes with appropriate values when creating each raster:
1271
1272	* %(Satellite)s - name of the satellite, either "aqua" or "terra".
1273
1274	* %(SatelliteCode)s - abbreviation for the satellite, either "A" or
1275	"T", used in NASA's file naming scheme.
1276
1277	* %(TemporalResolution)s - temporal resolution, either "daily",
1278	"8day", "monthly", or "annual".
1279
1280	* %(TemporalResolutionCode)s - abbreviation for the temporal
1281	resolution, either "DAY", "8D", "MO", or "YR", used in NASA's file
1282	naming scheme.
1283
1284	* %(SpatialResolution)s - spatial resolution, either "4km" or "9km".
1285
1286	* %(SpatialResolutionCode)s - abbreviation for the spatial
1287	resolution, either "4" or "9", used in NASA's file naming scheme.
1288
1289	* %(GeophysicalParameter)s - geophysical parameter represented in the
1290	output raster, either "sst", "nsst", or "sst4".
1291
1292	* %(Algorithm)s - algorithm used to estimate the geophysical
1293	parameter from the raw sensor values, either "sst" for the sst or
1294	nsst geophysical parameter, or "sst4" for the sst4 geophysical
1295	parameter. The algorithm name is used in NASA's file naming scheme.
1296
1297	* %(Wavelength)s - an alternative abbreviation for the algorithm used
1298	to estimate the geophysical parameter, either "11um" for the sst or
1299	nsst geophysical parameter, or "4um" for the sst4 geophysical
1300	parameter. The abbreviation refers to the wavelengths of the MODIS
1301	bands used in the algorithm, where 11um refers to the "thermal IR"
1302	bands and 4um refers to the "mid-IR" bands. The abbreviation is used
1303	in NASA's file naming scheme.
1304
1305	* %(VariableName)s - dataset variable represented in the output
1306	raster, either "l3m_data" for the value of the geophysical parameter
1307	or "l3m_qual" for the pixel quality level.
1308
1309	* %%Y - four-digit year of the raster.
1310
1311	* %%m - two-digit month of the first day of the raster.
1312
1313	* %%d - two-digit day of the month of the first day of the raster.
1314
1315	* %%j - three-digit day of the year of the first day of the raster.
1316
1317	* %(EndDate)s - date of the last day of the raster in the format
1318	"YYYYjjj" where YYYY is the four-digit year and jjj is the
1319	three-digit day of the year. This date is used in NASA's file naming
1320	scheme for temporal resolutions other than "daily".
1321	"""),
1322	arcGISDisplayName=_(u'Raster name expressions'))
1323
1324	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'rasterCatalog',
1325	typeMetadata=ArcGISRasterCatalogTypeMetadata(canBeNone=True, mustBeDifferentThanArguments=[u'outputWorkspace'], createParentDirectories=True),
1326	description=_(
1327	u"""Raster catalog to create.
1328
1329	This parameter requires ArcGIS 9.3 or later.
1330
1331	If this parameter is specified, after the tool finishes creating
1332	rasters, it will create an unmanaged raster catalog and import all of
1333	the rasters in the output workspace into it. The catalog will have
1334	fields for all of the codes specified in the Raster Name Expressions
1335	as well as fields for the start date, center date, and end date of
1336	each raster. You can then use the catalog to create animations using
1337	the ArcGIS 10 Time Slider.
1338
1339	WARNING: The raster catalog will be deleted and re-created each time
1340	the tool is executed. Beware of this if you plan to add your own
1341	fields to the catalog after it has been created."""),
1342	direction=u'Output',
1343	arcGISDisplayName=_(u'Output raster catalog'),
1344	dependencies=[ArcGISDependency(9, 3)])
1345
1346	CopyArgumentMetadata(MODISL3SSTTimeSeries.__init__, u'qualityLevel', MODISL3SSTTimeSeries.CreateArcGISRasters, u'qualityLevel')
1347
1348	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'rotationOffset',
1349	typeMetadata=FloatTypeMetadata(canBeNone=True),
1350	description=_(
1351	u"""Degrees to rotate the output rasters about the polar axis. If not
1352	provided, the output rasters will be centered on the Prime Meridian
1353	and have x coordinates ranging from -180 to 180.
1354
1355	Use this parameter to shift the center longitude to a different
1356	location. Positive values shift it to the east, negative values to the
1357	west. For example, to center the output rasters on the Pacific ocean
1358	and use x coordinates ranging from 0 to 360 rather than -180 to 180,
1359	provide 180 for this parameter.
1360
1361	The rasters can only be rotated in whole grid cells. The value you
1362	provide will be rounded off to the closest cell."""),
1363	arcGISDisplayName=_(u'Rotate raster by'),
1364	arcGISCategory=_(u'Spatiotemporal extent'))
1365
1366	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'spatialExtent',
1367	typeMetadata=EnvelopeTypeMetadata(canBeNone=True),
1368	description=_(
1369	u"""Spatial extent of the output rasters, in degrees. If not provided,
1370	the spatial extent will not be clipped.
1371
1372	This parameter is applied after the rotation parameter and uses
1373	coordinates that result after rotation. For example, if the rotation
1374	parameter was 180, the resulting rasters will have x coordinates
1375	ranging from 0 to 360. The spatial extent should be expressed in those
1376	coordinates.
1377
1378	The rasters can only be clipped in whole grid cells. The values you
1379	provide will be rounded off to the closest cell."""),
1380	arcGISDisplayName=_(u'Spatial extent'),
1381	arcGISCategory=_(u'Spatiotemporal extent'))
1382
1383	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'startDate',
1384	typeMetadata=DateTimeTypeMetadata(canBeNone=True),
1385	description=_(
1386	u"""Start date for the rasters to create. Rasters will be created for
1387	images that occur on or after the start date and on or before the end
1388	date. If the start date is not provided, the date of the oldest image
1389	will be used.
1390
1391	The time component of the start date is ignored."""),
1392	arcGISDisplayName=_(u'Start date'),
1393	arcGISCategory=_(u'Spatiotemporal extent'))
1394
1395	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'endDate',
1396	typeMetadata=DateTimeTypeMetadata(canBeNone=True),
1397	description=_(
1398	u"""End date for the rasters to create. Rasters will be created for
1399	images that occur on or after the start date and on or before the end
1400	date. If the end date is not provided, the date of the most recent
1401	image will be used.
1402
1403	The time component of the end date is ignored."""),
1404	arcGISDisplayName=_(u'End date'),
1405	arcGISCategory=_(u'Spatiotemporal extent'))
1406
1407	CopyArgumentMetadata(THREDDSCatalog.__init__, u'timeout', MODISL3SSTTimeSeries.CreateArcGISRasters, u'timeout')
1408	CopyArgumentMetadata(THREDDSCatalog.__init__, u'maxRetryTime', MODISL3SSTTimeSeries.CreateArcGISRasters, u'maxRetryTime')
1409	CopyArgumentMetadata(THREDDSCatalog.__init__, u'cacheDirectory', MODISL3SSTTimeSeries.CreateArcGISRasters, u'cacheDirectory')
1410
1411	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'useUnscaledData',
1412	typeMetadata=BooleanTypeMetadata(),
1413	description=_UseUnscaledDataDescription,
1414	arcGISDisplayName=_(u'Use unscaled data'),
1415	arcGISCategory=_(u'Additional raster processing options'))
1416
1417	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'calculateStatistics',
1418	typeMetadata=BooleanTypeMetadata(),
1419	description=_CalculateStatisticsDescription,
1420	arcGISDisplayName=_(u'Calculate statistics'),
1421	arcGISCategory=_(u'Additional raster processing options'))
1422
1423	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'buildRAT',
1424	typeMetadata=BooleanTypeMetadata(),
1425	description=_BuildRATDescription,
1426	arcGISDisplayName=_(u'Build raster attribute tables'),
1427	arcGISCategory=_(u'Additional raster processing options'))
1428
1429	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'buildPyramids',
1430	typeMetadata=BooleanTypeMetadata(),
1431	description=_BuildPyramidsDescription,
1432	arcGISDisplayName=_(u'Build pyramids'),
1433	arcGISCategory=_(u'Additional raster processing options'))
1434
1435	AddResultMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'updatedOutputWorkspace',
1436	typeMetadata=ArcGISWorkspaceTypeMetadata(),
1437	description=_(u'Updated output workspace.'),
1438	arcGISDisplayName=_(u'Updated output workspace'),
1439	arcGISParameterDependencies=[u'outputWorkspace'])
1440
1441	# Public method: MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters
1442
1443	AddMethodMetadata(MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters,
1444	shortDescription=_(u'Creates climatological rasters from MODIS Level 3 SST images published by NASA JPL PO.DAAC.'),
1445	longDescription=_MODISL3SSTTimeSeries_LongDescription + _(
1446	u"""
1447	This tool produces rasters showing the climatological average value
1448	(or other statistic) of a time series of MODIS SST images. Given a
1449	specification of the desired MODIS SST time series, a statistic, and a
1450	climatological bin definition, this tool efficiently downloads the
1451	images using the `OPeNDAP <http://opendap.org/>`_ protocol, classifies
1452	them into bins, and produces a single raster for each bin. Each cell
1453	of the raster is produced by calculating the statistic on the values
1454	of that cell extracted from all of the rasters in the bin.
1455
1456	""" + _MODISReferences),
1457	isExposedToPythonCallers=True,
1458	isExposedByCOM=True,
1459	isExposedAsArcGISTool=True,
1460	arcGISDisplayName=_(u'Create Climatological Rasters for PO.DAAC MODIS L3 SST'),
1461	arcGISToolCategory=_(u'Data Products\\NASA JPL PO.DAAC\\MODIS Global Level 3 Mapped SST'),
1462	dependencies=[ArcGISDependency(9, 1), PythonAggregatedModuleDependency('numpy')])
1463
1464	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'cls', MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'cls')
1465	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'satellite', MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'satellite')
1466	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'temporalResolution', MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'temporalResolution')
1467	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'spatialResolution', MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'spatialResolution')
1468	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'geophysicalParameter', MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'geophysicalParameter')
1469	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'variableName', MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'variableName')
1470
1471	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'statistic',
1472	typeMetadata=UnicodeStringTypeMetadata(allowedValues=[u'Count', u'Maximum', u'Mean', u'Minimum', u'Range', u'Standard Deviation', u'Sum'], makeLowercase=True),
1473	description=_(
1474	u"""Statistic to calculate for each cell, one of:
1475
1476	* Count - number of images in which the cell had data.
1477
1478	* Maximum - maximum value for the cell.
1479
1480	* Mean - mean value for the cell, calculated as the sum divided by the
1481	count.
1482
1483	* Minimum - minimum value for the cell.
1484
1485	* Range - range for the cell, calculated as the maximum minus the
1486	minimum.
1487
1488	* Standard Deviation - sample standard deviation for the cell
1489	(i.e. the standard deviation estimated using Bessel's correction).
1490	In order to calculate this, there must be at least two images with
1491	data for the cell.
1492
1493	* Sum - the sum for the cell.
1494	"""),
1495	arcGISDisplayName=_(u'Statistic'))
1496
1497	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'binType',
1498	typeMetadata=UnicodeStringTypeMetadata(allowedValues=[u'Daily', u'Monthly', u'Cumulative'], makeLowercase=True),
1499	description=_(
1500	u"""Climatology bins to use, one of:
1501
1502	* Daily - daily bins. Images will be classified into bins according to
1503	their days of the year. The number of days in each bin is determined
1504	by the Climatology Bin Duration parameter (which defaults to 1). The
1505	number of bins is calculated by dividing 365 by the bin duration. If
1506	there is no remainder, then that number of bins will be created;
1507	images for the 366th day of leap years will be counted in the bin
1508	that includes day 365. For example, if the bin duration is 5, 73
1509	bins will be created. The first will be for days 1-5, the second
1510	will be for days 5-10, and so on; the 73rd bin will be for days
1511	361-365 during normal years and 361-366 during leap years. If
1512	dividing 365 by the bin duration does yield a remainder, then one
1513	additional bin will be created to hold the remaining days. For
1514	example, if the bin duration is 8, 46 bins will be created. The
1515	first will be for days 1-8, the second for days 9-16, and so on; the
1516	46th will be for days 361-365 during normal years and 361-366 during
1517	leap years.
1518
1519	* Monthly - monthly bins. Images will be classified into bins according to
1520	their months of the year. The number of months in each bin is
1521	determined by the Climatology Bin Duration parameter (which defaults
1522	to 1). The number of bins is calculated by dividing 12 by the bin
1523	duration. If there is no remainder, then that number of bins will be
1524	created. For example, if the bin duration is 3, there will be four
1525	bins: January-March, April-June, July-September, and
1526	October-December. If there is a remainder, then one additional bin
1527	will be created. For example, if the bin duration is 5, 3 bins will
1528	be created: January-May, June-October, November-December.
1529
1530	* Cumulative - one bin. A single climatology raster will be calculated
1531	from the entire dataset. The Bin Duration parameter is ignored.
1532
1533	For Daily and Monthly, to adjust when the bins start (e.g. to center a
1534	4-bin seasonal climatology on solstices and equinoxes), use the Start
1535	Climatology At This Day Of The Year parameter."""),
1536	arcGISDisplayName=_(u'Climatology bin type'))
1537
1538	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'outputWorkspace', MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'outputWorkspace')
1539
1540	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'mode',
1541	typeMetadata=UnicodeStringTypeMetadata(allowedValues=[u'Add', u'Replace'], makeLowercase=True),
1542	description=_(
1543	u"""Overwrite mode, one of:
1544
1545	* Add - create rasters that do not exist and skip those that already
1546	exist. This is the default.
1547
1548	* Replace - create rasters that do not exist and overwrite those that
1549	already exist. Choose this option when you want to regenerate the
1550	climatologies using the latest images.
1551
1552	The ArcGIS Overwrite Outputs geoprocessing setting has no effect on
1553	this tool. If 'Replace' is selected the rasters will be overwritten,
1554	regardless of the ArcGIS Overwrite Outputs setting."""),
1555	arcGISDisplayName=_(u'Overwrite mode'))
1556
1557	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'rasterNameExpressions',
1558	typeMetadata=ListTypeMetadata(elementType=UnicodeStringTypeMetadata(), minLength=1),
1559	description=_(
1560	u"""List of expressions specifying how the output rasters should be
1561	named.
1562
1563	The default expression assumes you are storing rasters in a file
1564	system directory and creates them in a tree structure. When storing
1565	rasters in a directory, the final expression specifies the file name
1566	of the raster and any preceding expressions specify subdirectories.
1567	The extension of the final expression determines the output raster
1568	format: .asc for ArcInfo ASCII Grid, .bmp for BMP, .gif for GIF, .img
1569	for an ERDAS IMAGINE file, .jpg for JPEG, .jp2 for JPEG 2000, .png for
1570	PNG, .tif for GeoTIFF, or no extension for ArcInfo Binary Grid. The
1571	default expression uses .img.
1572
1573	When storing rasters in a geodatabase, you should provide only one
1574	expression. That expression specifies the raster's name.
1575
1576	Each expression may contain any sequence of characters permitted by
1577	the output workspace. Each expression may optionally contain one or
1578	more of the following case-sensitive codes. The tool replaces the
1579	codes with appropriate values when creating each raster:
1580
1581	* %(Satellite)s - name of the satellite, either "aqua" or "terra".
1582
1583	* %(SatelliteCode)s - abbreviation for the satellite, either "A" or
1584	"T", used in NASA's file naming scheme.
1585
1586	* %(TemporalResolution)s - temporal resolution, either "daily",
1587	"8day", "monthly", or "annual".
1588
1589	* %(TemporalResolutionCode)s - abbreviation for the temporal
1590	resolution, either "DAY", "8D", "MO", or "YR", used in NASA's file
1591	naming scheme.
1592
1593	* %(SpatialResolution)s - spatial resolution, either "4km" or "9km".
1594
1595	* %(SpatialResolutionCode)s - abbreviation for the spatial
1596	resolution, either "4" or "9", used in NASA's file naming scheme.
1597
1598	* %(GeophysicalParameter)s - geophysical parameter represented in the
1599	output raster, either "sst", "nsst", or "sst4".
1600
1601	* %(Algorithm)s - algorithm used to estimate the geophysical
1602	parameter from the raw sensor values, either "sst" for the sst or
1603	nsst geophysical parameter, or "sst4" for the sst4 geophysical
1604	parameter. The algorithm name is used in NASA's file naming scheme.
1605
1606	* %(Wavelength)s - an alternative abbreviation for the algorithm used
1607	to estimate the geophysical parameter, either "11um" for the sst or
1608	nsst geophysical parameter, or "4um" for the sst4 geophysical
1609	parameter. The abbreviation refers to the wavelengths of the MODIS
1610	bands used in the algorithm, where 11um refers to the "thermal IR"
1611	bands and 4um refers to the "mid-IR" bands. The abbreviation is used
1612	in NASA's file naming scheme.
1613
1614	* %(VariableName)s - dataset variable represented in the output
1615	raster, either "l3m_data" for the value of the geophysical parameter
1616	or "l3m_qual" for the pixel quality level.
1617
1618	* %(ClimatologyBinType)s - type of the climatology bin, either "Daily"
1619	if 1-day bins, "Xday" if multi-day bins (X is replaced by the
1620	duration), "Monthly" if 1-month bins, "Xmonth" if multi-month bins,
1621	or "Cumulative".
1622
1623	* %(ClimatologyBinName)s - name of the climatology bin corresponding
1624	represented by the output raster, either "dayXXX" for 1-day bins
1625	(XXX is replaced by the day of the year), "daysXXXtoYYY" for
1626	multi-day bins (XXX is replaced by the first day of the bin, YYY is
1627	replaced by the last day), "monthXX" for 1-month bins (XX is
1628	replaced by the month), "monthXXtoYY" (XX is replaced by the first
1629	month of the bin, YY by the last month), or "cumulative".
1630
1631	* %(Statistic)s - statistic that was calculated, in lowercase and with
1632	spaces replaced by underscores; one of: "count", "maximum", "mean",
1633	"minimum", "range", "standard_deviation", "Sum".
1634
1635	If the Bin Type is "Daily", the following additional codes are
1636	available:
1637
1638	* %(FirstDay)i - first day of the year of the climatology bin
1639	represented by the output raster.
1640
1641	* %(LastDay)i - last day of the year of the climatology bin
1642	represented by the output raster. For 1-day climatologies, this will
1643	be the same as %(FirstDay)i.
1644
1645	If the Bin Type is "Monthly", the following additional codes are
1646	available:
1647
1648	* %(FirstMonth)i - first month of the climatology bin represented by
1649	the output raster.
1650
1651	* %(DayOfFirstMonth)i - first day of the first month of the
1652	climatology bin represented by the output raster.
1653
1654	* %(LastMonth)i - last month of the climatology bin represented by
1655	the output raster.
1656
1657	* %(DayOfLastMonth)i - last day of the last month of the climatology
1658	bin represented by the output raster.
1659
1660	Note that the additional codes are integers and may be formatted using
1661	"printf"-style formatting codes. For example, to format the FirstDay
1662	as a three-digit number with leading zeros::
1663
1664	%(FirstDay)03i
1665	"""),
1666	arcGISDisplayName=_(u'Raster name expressions'))
1667
1668	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'qualityLevel', MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'qualityLevel')
1669
1670	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'binDuration',
1671	typeMetadata=IntegerTypeMetadata(minValue=1),
1672	description=_(
1673	u"""Duration of each bin, in days or months, when the Bin Type is
1674	Daily or Monthly, respectively. The default is 1. See the Bin Type
1675	parameter for more information."""),
1676	arcGISDisplayName=_(u'Climatology bin duration'),
1677	arcGISCategory=_(u'Climatology options'))
1678
1679	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'startDayOfYear',
1680	typeMetadata=IntegerTypeMetadata(minValue=1, maxValue=365),
1681	description=_(
1682	u"""Use this parameter to create bin defintions that deviate from the
1683	traditional calendar. The interpretation of this parameter depends on
1684	the Bin Type:
1685
1686	* Daily - this parameter defines the day of the year of the first
1687	climatology bin. For example, if this parameter is 100 and the Bin
1688	Duration is 10, the first bin will be numbered 100-109. The bin
1689	spanning the end of the year will be numbered 360-004. The last bin
1690	will be numbered 095-099. To define a four-bin climatology with bins
1691	that are centered approximately on the equinoxes and solstices
1692	(i.e., a seasonal climatology), set the Bin Duration to 91 and the
1693	start day to 36 (February 5). This will produce bins with dates
1694	036-126, 127-217, 218-308, and 309-035.
1695
1696	* Monthly - this parameter defines the day of the year of the first
1697	climatology bin, and the day of the month of that bin will be used
1698	as the first day of the month of all of the bins. For example, if
1699	this parameter is 46, which is February 15, and the Bin Duration is
1700	1, then the bins will be February 15 - March 14, March 15 - April
1701	14, April 15 - May 14, and so on. Calculations involving this
1702	parameter always assume a 365 day year (a non-leap year). To define
1703	a four-bin climatology using the months traditionally associated
1704	with spring, summer, fall, and winter in many northern hemisphere
1705	cultures, set the Bin Duration to 3 and the start day to 60 (March
1706	1). This will produce bins with months 03-05, 06-08, 09-11, and
1707	12-02.
1708
1709	* Cumulative - this parameter is ignored.
1710	"""),
1711	arcGISDisplayName=_(u'Start climatology at this day of the year'),
1712	arcGISCategory=_(u'Climatology options'))
1713
1714	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'rotationOffset', MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'rotationOffset')
1715	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'spatialExtent', MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'spatialExtent')
1716	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'startDate', MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'startDate')
1717	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'endDate', MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'endDate')
1718	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'timeout', MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'timeout')
1719	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'maxRetryTime', MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'maxRetryTime')
1720	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'cacheDirectory', MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'cacheDirectory')
1721	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'calculateStatistics', MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'calculateStatistics')
1722	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'buildPyramids', MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'buildPyramids')
1723
1724	CopyResultMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'updatedOutputWorkspace', MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'updatedOutputWorkspace')
1725
1726	# Public method: MODISL3SSTTimeSeries.InterpolateAtArcGISPoints
1727
1728	AddMethodMetadata(MODISL3SSTTimeSeries.InterpolateAtArcGISPoints,
1729	shortDescription=_(u'Interpolates PO.DAAC MODIS Level 3 SST values at points.'),
1730	longDescription=_MODISL3SSTTimeSeries_LongDescription + _(
1731	u"""
1732	Given a satellite name, temporal resolution, spatial resolution, and
1733	desired geophysical parameter, this tool interpolates the value of
1734	that parameter at the given points. This tool performs the same basic
1735	operation as the ArcGIS Spatial Analyst's Extract Values to Points
1736	tool, but it reads the MODIS data directly from NASA's servers using
1737	the `OPeNDAP <http://opendap.org/>`_ protocol, rather than reading
1738	rasters stored on your machine.
1739
1740	""" + _MODISReferences),
1741	isExposedToPythonCallers=True,
1742	isExposedByCOM=True,
1743	isExposedAsArcGISTool=True,
1744	arcGISDisplayName=_(u'Interpolate PO.DAAC MODIS L3 SST at Points'),
1745	arcGISToolCategory=_(u'Data Products\\NASA JPL PO.DAAC\\MODIS Global Level 3 Mapped SST'),
1746	dependencies=[ArcGISDependency(9, 1, requiresCOMInstantiation=True), PythonAggregatedModuleDependency('numpy')])
1747
1748	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'cls', MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'cls')
1749	CopyArgumentMetadata(MODISL3SSTTimeSeries.__init__, u'satellite', MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'satellite')
1750	CopyArgumentMetadata(MODISL3SSTTimeSeries.__init__, u'temporalResolution', MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'temporalResolution')
1751	CopyArgumentMetadata(MODISL3SSTTimeSeries.__init__, u'spatialResolution', MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'spatialResolution')
1752	CopyArgumentMetadata(MODISL3SSTTimeSeries.__init__, u'geophysicalParameter', MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'geophysicalParameter')
1753	CopyArgumentMetadata(MODISL3SSTTimeSeries.__init__, u'variableName', MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'variableName')
1754
1755	AddArgumentMetadata(MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'points',
1756	typeMetadata=ArcGISFeatureLayerTypeMetadata(mustExist=True, allowedShapeTypes=[u'Point']),
1757	description=_(
1758	u"""Points at which values should be interpolated.
1759
1760	The SST data use the WGS 1984 geographic coordinate system. It is
1761	recommended but not required that the points use the same coordinate
1762	system. If they do not, this tool will attempt to project the points
1763	to the WGS 1984 coordinate system prior to doing the interpolation.
1764	This may fail if a datum transformation is required, in which case you
1765	will have to manually project the points to the WGS 1984 coordinate
1766	system before using this tool."""),
1767	arcGISDisplayName=_(u'Point features'))
1768
1769	AddArgumentMetadata(MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'valueField',
1770	typeMetadata=ArcGISFieldTypeMetadata(mustExist=True, allowedFieldTypes=[u'short', u'long', u'float', u'double']),
1771	description=_(
1772	u"""Field of the points to receive the interpolated values.
1773
1774	The field must have a floating-point or integer data type. If the
1775	field cannot represent the interpolated value at full precision, the
1776	closest approximation will be stored and a warning will be issued.
1777	This will happen, for example, when you interpolate SST values into an
1778	integer field."""),
1779	arcGISDisplayName=_(u'Field to receive the interpolated values'),
1780	arcGISParameterDependencies=[u'points'])
1781
1782	AddArgumentMetadata(MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'tField',
1783	typeMetadata=ArcGISFieldTypeMetadata(mustExist=True, allowedFieldTypes=[u'date']),
1784	description=_(
1785	u"""Field of the points that specifies the date and time of the point.
1786
1787	The field must have a date or datetime data type. If the field can
1788	only represent dates with no time component, the time will assumed to
1789	be 00:00:00."""),
1790	arcGISDisplayName=_(u'Date field'),
1791	arcGISParameterDependencies=[u'points'])
1792
1793	AddArgumentMetadata(MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'method',
1794	typeMetadata=UnicodeStringTypeMetadata(allowedValues=[u'Nearest', u'Linear'], makeLowercase=True),
1795	description=_(
1796	u"""Interpolation method to use, one of:
1797
1798	* Nearest - nearest neighbor interpolation. The interpolated value
1799	will simply be the value of the cell that contains the point. This
1800	is the default.
1801
1802	* Linear - linear interpolation (also known as trilinear
1803	interpolation). This method is suitable for the l3m_data variable
1804	(SST), a continuous variable, but is not appropriate for the
1805	l3m_qual variable, a categorical variable (use nearest neighbor
1806	instead). This method averages the values of the eight nearest cells
1807	in the x, y, and time dimensions, weighting the contribution of each
1808	cell by the area of it that would be covered by a hypothetical cell
1809	centered on the point being interpolated. If the cell containing the
1810	point contains NoData, the result is NoData. If any of the other
1811	seven cells contain NoData, they are omitted from the average, and
1812	the result is based on the weighted average of the cells that do
1813	contain data. This is the same algorithm implemented by the ArcGIS
1814	Spatial Analyst's Extract Values to Points tool.
1815	"""),
1816	arcGISDisplayName=_(u'Interpolation method'))
1817
1818	AddArgumentMetadata(MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'where',
1819	typeMetadata=SQLWhereClauseTypeMetadata(canBeNone=True),
1820	description=_(
1821	u"""SQL WHERE clause expression that specifies the subset of points to
1822	use. If this parameter is not provided, all of the points will be
1823	used.
1824
1825	The exact syntax of this expression depends on the type of feature
1826	class you're using. ESRI recommends you reference fields using the
1827	following syntax:
1828
1829	* For shapefiles, ArcInfo coverages, or feature classes stored in file
1830	geodatabases, ArcSDE geodatabases, or ArcIMS, enclose field names in
1831	double quotes: "MY_FIELD"
1832
1833	* For feature classes stored in personal geodatabases, enclose field
1834	names in square brackets: [MY_FIELD].
1835	"""),
1836	arcGISDisplayName=_(u'Where clause'),
1837	arcGISCategory=_(u'Interpolation options'),
1838	arcGISParameterDependencies=[u'points'])
1839
1840	AddArgumentMetadata(MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'noDataValue',
1841	typeMetadata=FloatTypeMetadata(canBeNone=True),
1842	description=_(
1843	u"""Value to use when the interpolated value is NoData.
1844
1845	If a value is not provided for this parameter, a database NULL value
1846	will be stored in the field when the interpolated value is NoData. If
1847	the field cannot store NULL values, as is the case with shapefiles,
1848	the value -9999 will be used."""),
1849	arcGISDisplayName=_(u'Value to use when the interpolated value is NoData'),
1850	arcGISCategory=_(u'Interpolation options'))
1851
1852	CopyArgumentMetadata(MODISL3SSTTimeSeries.__init__, u'qualityLevel', MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'qualityLevel')
1853	CopyArgumentMetadata(MODISL3SSTTimeSeries.__init__, u'timeout', MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'timeout')
1854	CopyArgumentMetadata(MODISL3SSTTimeSeries.__init__, u'maxRetryTime', MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'maxRetryTime')
1855	CopyArgumentMetadata(MODISL3SSTTimeSeries.__init__, u'cacheDirectory', MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'cacheDirectory')
1856
1857	AddArgumentMetadata(MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'orderByFields',
1858	typeMetadata=ListTypeMetadata(elementType=ArcGISFieldTypeMetadata(mustExist=True), minLength=1, canBeNone=True),
1859	description=_(
1860	u"""Fields for defining the order in which the points are processed.
1861
1862	The points may be processed faster if they are ordered
1863	spatiotemporally, such that points that are close in space and time
1864	are processed sequentially. Ordering the points this way increases the
1865	probability that the value of a given point can be interpolated from
1866	data that is cached in memory, rather than from data that must be read
1867	from the disk or network, which is much slower. Choose fields that
1868	faciliate this. For example, if your points represent the locations of
1869	animals tracked by satellite telemetry, order the processing first by
1870	the animal ID and then by the transmission date or number.
1871
1872	If you omit this parameter, the Date Field will be used automatically.
1873
1874	This parameter requires ArcGIS 9.2 or later."""),
1875	arcGISDisplayName=_(u'Order by fields'),
1876	arcGISCategory=_(u'Performance tuning options'),
1877	arcGISParameterDependencies=[u'points'],
1878	dependencies=[ArcGISDependency(9, 2)])
1879
1880	AddArgumentMetadata(MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'numBlocksToCacheInMemory',
1881	typeMetadata=IntegerTypeMetadata(minValue=0, canBeNone=True),
1882	description=_(
1883	u"""Maximum number of blocks of SST data to cache in memory.
1884
1885	To minimize the number of times that the disk or network must be
1886	accessed, this tool employs a simple caching strategy, in addition to
1887	disk caching described by the Cache Directory parameter. When it
1888	processes the first point, it reads a square block of cells centered
1889	on that point and caches it in memory. When it processes the second
1890	and subsequent points, it first checks whether the cells needed for
1891	that point are contained by the block cached in memory. If so, it
1892	processes that point using the in-memory block, rather than reading
1893	from disk or the network again. If not, it reads another square block
1894	centered on that point and adds it to the cache.
1895
1896	The tool processes the remaining points, adding additional blocks to
1897	the cache, as needed. To prevent the cache from exhausing all memory,
1898	it is only permitted to grow to the size specified by this parameter.
1899	When the cache is full but a new block is needed, the oldest block is
1900	discarded to make room for the newest block.
1901
1902	The maximum size of the cache in bytes may be calculated by
1903	multiplying this parameter by the block size parameters and the data
1904	type of the geophysical parameter (either 4 bytes for the l3m_data or
1905	2 bytes for l3m_qual). For example, if this parameter is 128 and the
1906	blocks are x=32 by y=32 by t=2 and you're interpolating the l3m_data
1907	parameter (SST) which is 4 bytes, the maximum size of the cache is
1908	1048576 bytes (1 MB).
1909
1910	If this parameter is 0, no blocks will be cached in memory."""),
1911	arcGISDisplayName=_(u'Number of blocks of data to cache in memory'),
1912	arcGISCategory=_(u'Performance tuning options'))
1913
1914	AddArgumentMetadata(MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'xBlockSize',
1915	typeMetadata=IntegerTypeMetadata(minValue=0, canBeNone=True),
1916	description=_(
1917	u"""Size of the blocks of SST data to cache in memory, in the x
1918	direction (longitude). The size is given as the number of cells.
1919
1920	If this parameter is 0, no blocks will be cached in memory."""),
1921	arcGISDisplayName=_(u'In-memory cache block size, in X direction'),
1922	arcGISCategory=_(u'Performance tuning options'))
1923
1924	AddArgumentMetadata(MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'yBlockSize',
1925	typeMetadata=IntegerTypeMetadata(minValue=0, canBeNone=True),
1926	description=_(
1927	u"""Size of the blocks of SST data to cache in memory, in the y
1928	direction (latitude). The size is given as the number of cells.
1929
1930	If this parameter is 0, no blocks will be cached in memory."""),
1931	arcGISDisplayName=_(u'In-memory cache block size, in Y direction'),
1932	arcGISCategory=_(u'Performance tuning options'))
1933
1934	AddArgumentMetadata(MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'tBlockSize',
1935	typeMetadata=IntegerTypeMetadata(minValue=0, canBeNone=True),
1936	description=_(
1937	u"""Size of the blocks of SST data to cache in memory, in the t
1938	direction (time). The size is given as the number of cells.
1939
1940	If this parameter is 0, no blocks will be cached in memory."""),
1941	arcGISDisplayName=_(u'In-memory cache block size, in T direction'),
1942	arcGISCategory=_(u'Performance tuning options'))
1943
1944	AddResultMetadata(MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'updatedPoints',
1945	typeMetadata=ArcGISFeatureLayerTypeMetadata(),
1946	description=_(u'Updated points.'),
1947	arcGISDisplayName=_(u'Updated points'),
1948	arcGISParameterDependencies=[u'points'])
1949
1950	# Public method: MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters
1951
1952	AddMethodMetadata(MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters,
1953	shortDescription=_(u'Creates rasters indicating the positions of fronts in MODIS Level 3 SST images published by NASA JPL PO.DAAC, using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.'),
1954	longDescription=_MODISL3SSTTimeSeries_LongDescription + _(
1955	u"""
1956
1957	Given a satellite name, temporal resolution, spatial resolution, and
1958	desired SST product, this tool efficiently downloads the corresponding
1959	time series of MODIS Level 3 SST images, executes the Cayula and Cornillon
1960	SIED algorithm to identify fronts, and creates rasters showing the
1961	locations of the fronts.
1962
1963	This tool is complicated and has a lot of parameters. The complex
1964	dynamics of the ocean, the presence of clouds, the difficulty of
1965	detecting and masking them, and the inability to resolve fine-scale
1966	thermal features using the spatial resolution of MODIS Level 3
1967	products make it hard to obtain perfect results. For the best chance
1968	of success, please read all of the documentation carefully.
1969
1970	We configured the default parameters of this tool to balance a number
1971	of tradeoffs. These defaults are different than those used in Cayula
1972	and Cornillon's original study. For example, the original study used a
1973	32x32 moving window while ours is somewhat smaller because we found
1974	that the algorithm appeared to detect more fronts in MODIS images when
1975	a smaller window was used. This is probably due to the fact that
1976	Cayula and Cornillon's original data had a spatial resolution of about
1977	1 km, while MODIS Level 3 has a resolution of about 4.6 km. The
1978	theoretical basis of the algorithm requires that only one front appear
1979	in the window; the 32x32 window appeared to be too large to work well
1980	with MODIS's coarser resolution, often enclosing multiple fronts.
1981
1982	We believe our default values work better for the MODIS Level 3 images
1983	than Cayula and Cornillon's original values, but we have not attempted
1984	to formally validate the results. Please keep that in mind when
1985	applying this tool in your own studies.
1986
1987	Note that the Front Cleanup Options, which are enabled by default,
1988	require that MATLAB 2007b or the MATLAB Component Runtime (MCR) 7.7 be
1989	installed. You can download a free copy of the MCR 7.7 from
1990	http://code.nicholas.duke.edu/projects/mget/wiki/MCR. If you do not
1991	wish to install MATLAB or the MCR, you must disable the Front Cleanup
1992	Options.
1993
1994	The SIED Algorithm
1995
1996	""" + _SIEDDescription +
1997	"""
1998	""" + _MODISReferences +
1999	"""
2000
2001	""" + _SIEDReferences),
2002	isExposedToPythonCallers=True,
2003	isExposedByCOM=True,
2004	isExposedAsArcGISTool=True,
2005	arcGISDisplayName=_(u'Find Cayula-Cornillon Fronts in PO.DAAC MODIS L3 SST'),
2006	arcGISToolCategory=_(u'Data Products\\NASA JPL PO.DAAC\\MODIS Global Level 3 Mapped SST'),
2007	dependencies=[ArcGISDependency(9, 1), PythonAggregatedModuleDependency('numpy')])
2008
2009	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'cls', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'cls')
2010	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'satellite', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'satellite')
2011
2012	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'temporalResolution', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'temporalResolution')
2013	MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters.__doc__.Obj.GetArgumentByName(u'temporalResolution').Description += _(
2014	u"""
2015
2016	The choice of an appropriate temporal resolution is critical to the
2017	successful operation of the Cayula and Cornillon algorithm. The algorithm
2018	is designed to operate on instantaneous images of SST, not averages.
2019	Therefore, for best results, choose daily temporal resolution.
2020
2021	You may be tempted to try a lower temporal resolution, such as 8-day
2022	or monthly, because there are fewer clouds. The problem is that the
2023	SST gradients are not as sharp because SST has been averaged over
2024	several images. If the fronts have moved around over the duration of
2025	those images, as is often the case, the SST gradient will be smoothed
2026	out spatially and the algorithm will function poorly.
2027
2028	Rather than choosing a lower temporal resolution, consider using daily
2029	and then compositing the resulting frontal images. For example, rather
2030	than using 8-day images, find the fronts in the 8 daily images and
2031	then add them together. This is similar to how Cayula and Cornillon's
2032	(1996) multiple-image edge detection (MIED) works. Also consider using
2033	both daytime and nighttime images in your composites.""")
2034
2035	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'spatialResolution', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'spatialResolution')
2036	MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters.__doc__.Obj.GetArgumentByName(u'spatialResolution').Description += _(
2037	u"""
2038
2039	The default Cayula and Cornillon algorithm parameters are intended to be
2040	used with the 4km MODIS products. If you use the 9km products, you
2041	might achieve better results by adjusting the parameter values,
2042	particularly the Histogram Window Size parameter.""")
2043
2044	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'geophysicalParameter', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'geophysicalParameter')
2045
2046	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'minPopMeanDifference',
2047	typeMetadata=FloatTypeMetadata(mustBeGreaterThan=0.),
2048	description=_(
2049	u"""Minimum difference, in degrees C, between the mean temperatures of
2050	two adjacent populations of pixels for a front to be detected between
2051	those two populations.
2052
2053	The Cayula and Cornillon algorithm passes a moving window over the
2054	image, checking each window for a bimodal distribution in the
2055	temperatures of the pixels within it. When the algorithm detects a
2056	bimodal distribution, it computes the mean temperatures of the two
2057	populations and compares the difference between the means to this
2058	threshold. If the difference is less than this threshold, the
2059	algorithm concludes there is no front present and moves on to the next
2060	window.
2061
2062	You can use this parameter to eliminate weak fronts by selecting a
2063	value that corresponds to a desired minimum mean temperature
2064	difference. Larger values will detect fewer fronts; smaller values
2065	will detect more fronts. However, bear in mind that
2066	`this document <ftp://podaac-ftp.jpl.nasa.gov/allData/modis/docs/MODIS_SST_Guide_Doc.pdf>`_
2067	MODIS SST data can only be considered accurate to +/- 0.4 degrees C.
2068	We do not advise thresholds below that value."""),
2069	arcGISDisplayName=_(u'Front detection threshold'))
2070
2071	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'outputWorkspace',
2072	typeMetadata=ArcGISWorkspaceTypeMetadata(createParentDirectories=True),
2073	description=_(
2074	u"""Directory or geodatabase to receive the rasters.
2075
2076	Unless you have a specific reason to store the rasters in a
2077	geodatabase, we recommend you store them in a directory because it
2078	will be much faster and allows the rasters to be organized in a tree.
2079	If you do store the rasters in a geodatabase, you must change the
2080	Raster Name Expressions parameter; the documentation for that
2081	parameter for more information.
2082
2083	The front location images will be written as 8-bit signed integer
2084	rasters. Each pixel can have one of three values:
2085
2086	* NoData - the pixel was never a candidate for containing a front,
2087	either because it was masked or because because it did not appear in
2088	any histogram windows that had sufficiently large numbers of
2089	non-masked pixels to proceed with the histogramming step of the
2090	Cayula and Cornillon algorithm.
2091
2092	* 0 - The pixel was a candidate for containing a front -- it was not
2093	masked and it appeared in at least one histogram window with a
2094	sufficient number of non-masked pixels to proceed with the
2095	histogramming step -- but it was never marked as a front pixel in
2096	any of the histogram windows it appeared in.
2097
2098	* 1 - The pixel was a candidate for containing a front and it was marked
2099	as a front pixel in at least one of the histogram windows it
2100	appeared in.
2101
2102	You may also instruct this tool to write several kinds of diagnostic
2103	outputs. These can help you understand why a front was or was not
2104	identified at a specific location. Please see the documentation for
2105	the diagnostic outputs for more information.
2106
2107	The front location images described above are constructed by
2108	performing a classification of two of the diagnostic outputs, the
2109	"candidate counts" and "front counts" images, and then applying the
2110	Fill Holes, Thin, and Minimum Front Size options."""),
2111	arcGISDisplayName=_(u'Output workspace'))
2112
2113	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'mode', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'mode')
2114
2115	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'rasterNameExpressions',
2116	typeMetadata=ListTypeMetadata(elementType=UnicodeStringTypeMetadata(), minLength=1),
2117	description=_(
2118	u"""List of expressions specifying how the output rasters should be
2119	named.
2120
2121	The default expression assumes you are storing rasters in a file
2122	system directory and creates them in a tree structure. When storing
2123	rasters in a directory, the final expression specifies the file name
2124	of the raster and any preceding expressions specify subdirectories.
2125	The extension of the final expression determines the output raster
2126	format: .asc for ArcInfo ASCII Grid, .bmp for BMP, .gif for GIF, .img
2127	for an ERDAS IMAGINE file, .jpg for JPEG, .jp2 for JPEG 2000, .png for
2128	PNG, .tif for GeoTIFF, or no extension for ArcInfo Binary Grid. The
2129	default expression uses .img.
2130
2131	When storing rasters in a geodatabase, you should provide only one
2132	expression. That expression specifies the raster's name.
2133
2134	Each expression may contain any sequence of characters permitted by
2135	the output workspace. Each expression may optionally contain one or
2136	more of the following case-sensitive codes. The tool replaces the
2137	codes with appropriate values when creating each raster:
2138
2139	* %(Satellite)s - name of the satellite, either "aqua" or "terra".
2140
2141	* %(SatelliteCode)s - abbreviation for the satellite, either "A" or
2142	"T", used in NASA's file naming scheme.
2143
2144	* %(TemporalResolution)s - temporal resolution, either "daily",
2145	"8day", "monthly", or "annual".
2146
2147	* %(TemporalResolutionCode)s - abbreviation for the temporal
2148	resolution, either "DAY", "8D", "MO", or "YR", used in NASA's file
2149	naming scheme.
2150
2151	* %(SpatialResolution)s - spatial resolution, either "4km" or "9km".
2152
2153	* %(SpatialResolutionCode)s - abbreviation for the spatial
2154	resolution, either "4" or "9", used in NASA's file naming scheme.
2155
2156	* %(GeophysicalParameter)s - geophysical parameter used to produce the
2157	output raster, either "sst", "nsst", or "sst4".
2158
2159	* %(Algorithm)s - algorithm used to estimate the geophysical
2160	parameter from the raw sensor values, either "sst" for the sst or
2161	nsst geophysical parameter, or "sst4" for the sst4 geophysical
2162	parameter. The algorithm name is used in NASA's file naming scheme.
2163
2164	* %(Wavelength)s - an alternative abbreviation for the algorithm used
2165	to estimate the geophysical parameter, either "11um" for the sst or
2166	nsst geophysical parameter, or "4um" for the sst4 geophysical
2167	parameter. The abbreviation refers to the wavelengths of the MODIS
2168	bands used in the algorithm, where 11um refers to the "thermal IR"
2169	bands and 4um refers to the "mid-IR" bands. The abbreviation is used
2170	in NASA's file naming scheme.
2171
2172	* %(ImageType)s - type of image represented in the output raster,
2173	either "floc" (front locations), "ccnt" (candidate counts), "fcnt"
2174	(front counts), "wsco" (window status codes), or "wsvl" (window
2175	status values).
2176
2177	* %%Y - four-digit year of the raster.
2178
2179	* %%m - two-digit month of the first day of the raster.
2180
2181	* %%d - two-digit day of the month of the first day of the raster.
2182
2183	* %%j - three-digit day of the year of the first day of the raster.
2184
2185	* %(EndDate)s - date of the last day of the raster in the format
2186	"YYYYjjj" where YYYY is the four-digit year and jjj is the
2187	three-digit day of the year. This date is used in NASA's file naming
2188	scheme for temporal resolutions other than "daily".
2189	"""),
2190	arcGISDisplayName=_(u'Raster name expressions'))
2191
2192	CopyArgumentMetadata(CayulaCornillonEdgeDetection.DetectEdgesInSingleImage, u'medianFilterWindowSize', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'medianFilterWindowSize')
2193	CopyArgumentMetadata(CayulaCornillonEdgeDetection.DetectEdgesInSingleImage, u'histogramWindowSize', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'histogramWindowSize')
2194	CopyArgumentMetadata(CayulaCornillonEdgeDetection.DetectEdgesInSingleImage, u'histogramWindowStride', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'histogramWindowStride')
2195	CopyArgumentMetadata(CayulaCornillonEdgeDetection.DetectEdgesInSingleImage, u'minPropNonMaskedCells', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'minPropNonMaskedCells')
2196	CopyArgumentMetadata(CayulaCornillonEdgeDetection.DetectEdgesInSingleImage, u'minPopProp', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'minPopProp')
2197	CopyArgumentMetadata(CayulaCornillonEdgeDetection.DetectEdgesInSingleImage, u'minTheta', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'minTheta')
2198	CopyArgumentMetadata(CayulaCornillonEdgeDetection.DetectEdgesInSingleImage, u'minSinglePopCohesion', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'minSinglePopCohesion')
2199	CopyArgumentMetadata(CayulaCornillonEdgeDetection.DetectEdgesInSingleImage, u'minGlobalPopCohesion', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'minGlobalPopCohesion')
2200	CopyArgumentMetadata(CayulaCornillonEdgeDetection.DetectEdgesInSingleImage, u'threads', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'threads')
2201	CopyArgumentMetadata(CayulaCornillonEdgeDetection.DetectEdgesInSingleImage, u'fillHoles', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'fillHoles')
2202	CopyArgumentMetadata(CayulaCornillonEdgeDetection.DetectEdgesInSingleImage, u'thin', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'thin')
2203	CopyArgumentMetadata(CayulaCornillonEdgeDetection.DetectEdgesInSingleImage, u'minSize', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'minSize')
2204
2205	CopyArgumentMetadata(MODISL3SSTTimeSeries.__init__, u'qualityLevel', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'qualityLevel')
2206
2207	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'rotationOffset', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'rotationOffset')
2208	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'spatialExtent', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'spatialExtent')
2209	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'startDate', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'startDate')
2210	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'endDate', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'endDate')
2211	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'timeout', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'timeout')
2212	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'maxRetryTime', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'maxRetryTime')
2213	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'cacheDirectory', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'cacheDirectory')
2214	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'calculateStatistics', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'calculateStatistics')
2215	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'buildRAT', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'buildRAT')
2216	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'buildPyramids', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'buildPyramids')
2217
2218	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'outputCandidateCounts',
2219	typeMetadata=BooleanTypeMetadata(),
2220	description=_(
2221	u"""If True, candidate counts diagnostic images will be created.
2222
2223	Candidate counts images contain 16-bit unsigned integers and indicate
2224	how many times each pixel of the corresponding SST image was a
2225	candidate for containing a front, i.e. the number of times it appeared
2226	in a histogram window that had a sufficiently large number of
2227	non-masked pixels to proceed with the histogramming step of the
2228	Cayula and Cornillon algorithm. If the histogram window stride is less
2229	than the window size, successive histogram windows will overlap, and
2230	many pixels will have candidate counts greater than 1. Masked pixels
2231	can never be candidates for containing a front so they will always
2232	have the value 0."""),
2233	arcGISDisplayName=_(u'Create candidate counts rasters'),
2234	arcGISCategory=_(u'Optional diagnostic outputs'))
2235
2236	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'outputFrontCounts',
2237	typeMetadata=BooleanTypeMetadata(),
2238	description=_(
2239	u"""If True, front counts diagnostic images will be created.
2240
2241	Front counts images contain 16-bit unsigned integers and indicate how
2242	many times each pixel of the corresponding SST image was found to
2243	contain a front. The value will range from 0 (it never contained a
2244	front) to the candidate count value for the pixel (it always contained
2245	a front in every histogram window that contained it).
2246
2247	The Front Cleanup Options are not applied to the front counts
2248	image."""),
2249	arcGISDisplayName=_(u'Create front counts rasters'),
2250	arcGISCategory=_(u'Optional diagnostic outputs'))
2251
2252	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'outputWindowStatusCodes',
2253	typeMetadata=BooleanTypeMetadata(),
2254	description=_(
2255	u"""If True, diagnostic images of window status codes will be created.
2256
2257	Window status code images contain 8-bit unsigned integers that
2258	indicate the result of the algorithm for the histogram window centered
2259	on the pixel. You can use this image to diagnose why the algorithm did
2260	not find fronts in a particular region of your image.
2261
2262	The algorithm result will be one of these code numbers:
2263
2264	""" + _WindowStatusCodes),
2265	arcGISDisplayName=_(u'Create window status code rasters'),
2266	arcGISCategory=_(u'Optional diagnostic outputs'))
2267
2268	AddArgumentMetadata(MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'outputWindowStatusValues',
2269	typeMetadata=BooleanTypeMetadata(),
2270	description=_(
2271	u"""If True, diagnostic images of window status values will be
2272	created.
2273
2274	Window status values images contain 32-bit floating point values
2275	to be used in conjunction with the window status codes when diagnosing
2276	the results of the algorithm. The value of each pixel depends on the
2277	window status code for that pixel:
2278
2279	""" + _WindowStatusValues),
2280	arcGISDisplayName=_(u'Create window status value rasters'),
2281	arcGISCategory=_(u'Optional diagnostic outputs'))
2282
2283	CopyResultMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'updatedOutputWorkspace', MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'updatedOutputWorkspace')
2284
2285	###############################################################################
2286	# Metadata: _GHRSSTLevel4THREDDSCatalog class
2287	###############################################################################
2288
2289	AddClassMetadata(_GHRSSTLevel4THREDDSCatalog,
2290	shortDescription=_(u'THREDDSCatalog for the NASA JPL PO.DAAC GHRSST L4 datasets.'),
2291	longDescription=_(
2292	u"""This class is not intended to be used directly by external
2293	callers. Use GHRSSTLevel4 instead."""))
2294
2295	###############################################################################
2296	# Metadata: _GHRSSTLevel4OPeNDAPURL class
2297	###############################################################################
2298
2299	AddClassMetadata(_GHRSSTLevel4OPeNDAPURL,
2300	shortDescription=_(u'OPeNDAPURL for a NASA JPL PO.DAAC GHRSST L4 file.'),
2301	longDescription=_(
2302	u"""This class is not intended to be used directly by external
2303	callers. Use GHRSSTLevel4 instead."""))
2304
2305	###############################################################################
2306	# Metadata: _GHRSSTLevel4OPeNDAPGrid class
2307	###############################################################################
2308
2309	AddClassMetadata(_GHRSSTLevel4OPeNDAPGrid,
2310	shortDescription=_(u'OPeNDAPGrid for a variable of a NASA JPL PO.DAAC GHRSST L4 file.'),
2311	longDescription=_(
2312	u"""This class is not intended to be used directly by external
2313	callers. Use GHRSSTLevel4 instead."""))
2314
2315	# Constructor
2316
2317	AddMethodMetadata(_GHRSSTLevel4OPeNDAPGrid.__init__,
2318	shortDescription=_(u'Constructs a new _GHRSSTLevel4OPeNDAPGrid instance.'))
2319
2320	AddArgumentMetadata(_GHRSSTLevel4OPeNDAPGrid.__init__, u'self',
2321	typeMetadata=ClassInstanceTypeMetadata(cls=_GHRSSTLevel4OPeNDAPGrid),
2322	description=_(u'_GHRSSTLevel4OPeNDAPGrid instance.'))
2323
2324	AddArgumentMetadata(_GHRSSTLevel4OPeNDAPGrid.__init__, u'opendapURLObj',
2325	typeMetadata=ClassInstanceTypeMetadata(cls=_GHRSSTLevel4OPeNDAPURL),
2326	description=_(u'_GHRSSTLevel4OPeNDAPURL instance that contains us.'))
2327
2328	AddArgumentMetadata(_GHRSSTLevel4OPeNDAPGrid.__init__, u'variableName',
2329	typeMetadata=UnicodeStringTypeMetadata(allowedValues=[u'analysed_sst', u'analysis_error']),
2330	description=_(u'OPeNDAP variable name.'))
2331
2332	AddArgumentMetadata(_GHRSSTLevel4OPeNDAPGrid.__init__, u'variableType',
2333	typeMetadata=UnicodeStringTypeMetadata(allowedValues=[u'Grid']),
2334	description=_(u'OPeNDAP variable type.'))
2335
2336	AddResultMetadata(_GHRSSTLevel4OPeNDAPGrid.__init__, u'grid',
2337	typeMetadata=ClassInstanceTypeMetadata(cls=_GHRSSTLevel4OPeNDAPGrid),
2338	description=_(u'_GHRSSTLevel4OPeNDAPGrid instance.'))
2339
2340	###############################################################################
2341	# Metadata: GHRSSTLevel4 class
2342	###############################################################################
2343
2344	_GHRSSTLevel4_LongDescription = _(
2345	u"""The `Group for High-Resolution Sea Surface Temperature (GHRSST) <http://www.ghrsst.org/>`_
2346	provides a new generation of global high-resolution (<10km) SST
2347	products to the operational oceanographic, meteorological, climate and
2348	general scientific community. The overall aim of the GHRSST is to
2349	provide the best quality sea surface temperature data for applications
2350	in short, medium and decadal/climate time scales in the most cost
2351	effective and efficient manner through international collaboration and
2352	scientific innovation.
2353
2354	This %(name)s accesses L4 gap-free gridded SST products published in
2355	near real time by the GHRSST GDAC Global Data Assembly Center (GDAC)
2356	at the `NASA JPL Physical Oceanography Distributed Active Archive Center (PO.DAAC) <http://podaac.jpl.nasa.gov/SeaSurfaceTemperature/GHRSST>`_.
2357	These products provide regional and global daily cloud-free estimates
2358	of SST at spatial resolutions ranging from 0.25 degrees to down 1 km.
2359	To fill in cloudy areas, data from multiple satellite and in-situ
2360	sensors are combined and regions without any data are filled in
2361	various interpolation and modeling techniques.
2362
2363	All products accessed by this %(name)s are published at a daily
2364	timestep. Some products are updated on a continual basis and available
2365	in near real time; others are updated infrequently and are intended
2366	mainly for historical analysis. The temporal extent, spatial
2367	resolution and extent, and sensors and interpolation technique used
2368	vary by product. All products use the WGS 1984 geographic coordinate
2369	system. GHRSST temperatures are published in kelvin. By default, this
2370	%(name)s converts them to degrees Celsius but provides an option to
2371	obtain the original kelvin values.
2372
2373	All products are obtained from PO.DAAC using the OPeNDAP protocol.
2374	This %(name)s supports most but not all of the GHRSST L4 products
2375	hosted by PO.DAAC. If you see a product on PO.DAAC that is not
2376	available with this tool, please contact the MGET development team for
2377	assistance.""")
2378
2379	AddClassMetadata(GHRSSTLevel4,
2380	shortDescription=_(u'A Grid representing a GHRSST L4 product hosted by NASA JPL PO.DAAC.'),
2381	longDescription=_GHRSSTLevel4_LongDescription % {u'name': 'class'})
2382
2383	# Constructor
2384
2385	AddMethodMetadata(GHRSSTLevel4.__init__,
2386	shortDescription=_(u'Constructs a new GHRSSTLevel4 instance.'))
2387
2388	AddArgumentMetadata(GHRSSTLevel4.__init__, u'self',
2389	typeMetadata=ClassInstanceTypeMetadata(cls=GHRSSTLevel4),
2390	description=_(u'GHRSSTLevel4 instance.'))
2391
2392	AddArgumentMetadata(GHRSSTLevel4.__init__, u'variableName',
2393	typeMetadata=UnicodeStringTypeMetadata(allowedValues=[u'analysed_sst', u'analysis_error']),
2394	description=_(
2395	u"""GHRSST variable to access, one of:
2396
2397	* analysed_sst - GHRSST product-specific estimate of SST.
2398
2399	* analysis_error - GHRSST product-specific estimate of of the error in
2400	the SST estimate.
2401
2402	Please see the product documentation for details about what these
2403	variables mean and how they were calculated. These variable names are
2404	case-sensitive."""),
2405	arcGISDisplayName=_(u'GHRSST variable'))
2406
2407	AddArgumentMetadata(GHRSSTLevel4.__init__, u'rdacCode',
2408	typeMetadata=UnicodeStringTypeMetadata(),
2409	description=_(
2410	u"""The code of the GHRSST Regional Data Access Center (RDAC) that
2411	produced the product. These codes are case-sensitive, typically upper
2412	case, and assigned by GHRSST."""))
2413
2414	AddArgumentMetadata(GHRSSTLevel4.__init__, u'productType',
2415	typeMetadata=UnicodeStringTypeMetadata(),
2416	description=_(
2417	u"""GHRSST product type code. These codes are case-sensitive, assigned
2418	by GHRSST, and indicate the overall resolution and type of SST
2419	estimated by the product. Typical codes include "LRfnd", "HRfnd",
2420	"UHfnd", "HR1m", and "LRblend"."""))
2421
2422	AddArgumentMetadata(GHRSSTLevel4.__init__, u'areaCode',
2423	typeMetadata=UnicodeStringTypeMetadata(),
2424	description=_(
2425	u"""The region code of the the product. These codes are
2426	case-sensitive, typically upper case, and assigned by GHRSST. A list
2427	of codes may be seen on the PO.DAAC OPeNDAP server at
2428	http://podaac-opendap.jpl.nasa.gov/opendap/hyrax/allData/ghrsst/data/L4/"""))
2429
2430	AddArgumentMetadata(GHRSSTLevel4.__init__, u'gdsVersion',
2431	typeMetadata=IntegerTypeMetadata(minValue=1),
2432	description=_(
2433	u"""Version of the GHRSST Data Specification (GDS) used by the
2434	product. At the time of this writing, all products accessed by this
2435	function were at GDS version 1."""))
2436
2437	AddArgumentMetadata(GHRSSTLevel4.__init__, u'fileVersion',
2438	typeMetadata=UnicodeStringTypeMetadata(),
2439	description=_(
2440	u"""Version of the file. This is a case-sensitive string that is
2441	product specific and assigned by the RDAC that produced the
2442	product."""))
2443
2444	AddArgumentMetadata(GHRSSTLevel4.__init__, u'productCode',
2445	typeMetadata=UnicodeStringTypeMetadata(),
2446	description=_(
2447	u"""The RDAC-assigned product code that appears at the end of the
2448	GHRSST file name."""))
2449
2450	AddArgumentMetadata(GHRSSTLevel4.__init__, u'productCode2',
2451	typeMetadata=UnicodeStringTypeMetadata(canBeNone=True),
2452	description=_(
2453	u"""The RDAC-assigned product code that appears in the PO.DAAC OPeNDAP
2454	catalog structure below the RDAC level. For example, in the URL
2455	http://podaac-opendap.jpl.nasa.gov/opendap/hyrax/allData/ghrsst/data/L4/GLOB/REMSS/mw_ir_OI/2012/001/20120103-REMSS-L4HRfnd-GLOB-v01-fv03-mw_ir_rt_OI.nc.gz,
2456	the productCode is "mw_ir_rt_OI" while the productCode2 is "mw_ir_OI".
2457
2458	Typically the productCode2 is the same as the same as the productCode,
2459	in which case you should omit the productCode2 (leave it None). Only a
2460	few products have a different productCode2."""))
2461
2462	AddArgumentMetadata(GHRSSTLevel4.__init__, u'convertToCelsius',
2463	typeMetadata=BooleanTypeMetadata(),
2464	description=_(
2465	u"""If True (the default), temperature values will be converted from
2466	kelvin to degrees Celsius. If False, temperature values will be in the
2467	original kelvin values."""),
2468	arcGISDisplayName=_(u'Convert temperatures to Celsius'),
2469	arcGISCategory=_(u'Additional raster processing options'))
2470
2471	CopyArgumentMetadata(THREDDSCatalog.__init__, u'timeout', GHRSSTLevel4.__init__, u'timeout')
2472	CopyArgumentMetadata(THREDDSCatalog.__init__, u'maxRetryTime', GHRSSTLevel4.__init__, u'maxRetryTime')
2473	CopyArgumentMetadata(THREDDSCatalog.__init__, u'cacheDirectory', GHRSSTLevel4.__init__, u'cacheDirectory')
2474
2475	AddResultMetadata(GHRSSTLevel4.__init__, u'grid',
2476	typeMetadata=ClassInstanceTypeMetadata(cls=GHRSSTLevel4),
2477	description=_(u'GHRSSTLevel4 instance.'))
2478
2479	# Public method: GHRSSTLevel4.CreateArcGISRasters
2480
2481	AddMethodMetadata(GHRSSTLevel4.CreateArcGISRasters,
2482	shortDescription=_(u'Creates rasters for a GHRSST L4 product hosted by NASA JPL PO.DAAC.'),
2483	longDescription=_GHRSSTLevel4_LongDescription % {u'name': 'tool'},
2484	isExposedToPythonCallers=True,
2485	isExposedByCOM=True,
2486	isExposedAsArcGISTool=True,
2487	arcGISDisplayName=_(u'Create Rasters for GHRSST L4 SST'),
2488	arcGISToolCategory=_(u'Data Products\\NASA JPL PO.DAAC\\GHRSST L4 SST'),
2489	dependencies=[ArcGISDependency(9, 1), PythonAggregatedModuleDependency('numpy')])
2490
2491	AddArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'cls',
2492	typeMetadata=ClassOrClassInstanceTypeMetadata(cls=GHRSSTLevel4),
2493	description=_(u'GHRSSTLevel4 class or instance.'))
2494
2495	AddArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'product',
2496	typeMetadata=UnicodeStringTypeMetadata(allowedValues=sorted(GHRSSTLevel4._PODAAC_Products.keys())),
2497	description=_(
2498	u"""GHRSST L4 product to access. Currently, the following products are
2499	supported:
2500
2501	* `ABOM-L4HRfnd-AUS-RAMSSA_09km <http://podaac.jpl.nasa.gov/dataset/ABOM-L4HRfnd-AUS-RAMSSA_09km>`_.
2502	* `ABOM-L4LRfnd-GLOB-GAMSSA_28km <http://podaac.jpl.nasa.gov/dataset/ABOM-L4LRfnd-GLOB-GAMSSA_28km>`_.
2503	* `DMI-L4UHfnd-NSEABALTIC-DMI_OI <http://podaac.jpl.nasa.gov/dataset/DMI-L4UHfnd-NSEABALTIC-DMI_OI>`_.
2504	* `EUR-L4UHRfnd-MED-ODYSSEA <http://podaac.jpl.nasa.gov/dataset/EUR-L4UHRfnd-MED-ODYSSEA>`_.
2505	* `EUR-L4UHRfnd-NWE-ODYSSEA <http://podaac.jpl.nasa.gov/dataset/EUR-L4UHRfnd-NWE-ODYSSEA>`_.
2506	* `JPL-L4UHfnd-GLOB-MUR <http://podaac.jpl.nasa.gov/dataset/JPL-L4UHfnd-GLOB-MUR>`_.
2507	* `JPL_OUROCEAN-L4UHfnd-GLOB-G1SST <http://podaac.jpl.nasa.gov/dataset/JPL_OUROCEAN-L4UHfnd-GLOB-G1SST>`_.
2508	* `NAVO-L4HR1m-GLOB-K10_SST <http://podaac.jpl.nasa.gov/dataset/NAVO-L4HR1m-GLOB-K10_SST>`_.
2509	* `NCDC-L4LRblend-GLOB-AVHRR_AMSR_OI <http://podaac.jpl.nasa.gov/dataset/NCDC-L4LRblend-GLOB-AVHRR_AMSR_OI>`_.
2510	* `NCDC-L4LRblend-GLOB-AVHRR_OI <http://podaac.jpl.nasa.gov/dataset/NCDC-L4LRblend-GLOB-AVHRR_OI>`_.
2511	* `UKMO-L4HRfnd-GLOB-OSTIA <http://podaac.jpl.nasa.gov/dataset/UKMO-L4HRfnd-GLOB-OSTIA>`_.
2512
2513	All products use the WGS 1984 geographic coordinate system and are
2514	published at a daily timestep. Some products are updated on a
2515	continual basis and available in near real time; others are updated
2516	infrequently and are intended mainly for historical analysis. The
2517	temporal extent, spatial resolution and extent, and sensors and
2518	interpolation technique used vary by product. Please see the products'
2519	documentation for details.
2520
2521	This tool supports most but not all of the GHRSST L4 products hosted
2522	by PO.DAAC. If you see a product on PO.DAAC that is not available with
2523	this tool, please contact the MGET development team for
2524	assistance."""),
2525	arcGISDisplayName=_(u'GHRSST L4 product'))
2526
2527	CopyArgumentMetadata(GHRSSTLevel4.__init__, u'variableName', GHRSSTLevel4.CreateArcGISRasters, u'variableName')
2528
2529	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'outputWorkspace', GHRSSTLevel4.CreateArcGISRasters, u'outputWorkspace')
2530	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'mode', GHRSSTLevel4.CreateArcGISRasters, u'mode')
2531
2532	AddArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'rasterNameExpressions',
2533	typeMetadata=ListTypeMetadata(elementType=UnicodeStringTypeMetadata(), minLength=1),
2534	description=_(
2535	u"""List of expressions specifying how the output rasters should be
2536	named.
2537
2538	The default expression assumes you are storing rasters in a file
2539	system directory and creates them in a tree structure with names that
2540	imitate those used by NASA. When storing rasters in a directory, the
2541	final expression specifies the file name of the raster and any
2542	preceding expressions specify subdirectories. The extension of the
2543	final expression determines the output raster format: .asc for ArcInfo
2544	ASCII Grid, .bmp for BMP, .gif for GIF, .img for an ERDAS IMAGINE
2545	file, .jpg for JPEG, .jp2 for JPEG 2000, .png for PNG, .tif for
2546	GeoTIFF, or no extension for ArcInfo Binary Grid. The default
2547	expression uses .img.
2548
2549	When storing rasters in a geodatabase, you should provide only one
2550	expression. That expression specifies the raster's name.
2551
2552	Each expression may contain any sequence of characters permitted by
2553	the output workspace. Each expression may optionally contain one or
2554	more of the following case-sensitive codes. The tool replaces the
2555	codes with appropriate values when creating each raster:
2556
2557	* %(AreaCode)s - GHRSST abbreviation indicating the spatial extent of
2558	the product, such as "GLOB" for global extent or "AUS" for
2559	Australia.
2560
2561	* %(RDACCode)s - GHRSST abbreviation for the Regonal Data Assembly
2562	Center (RDAC) that produced the product.
2563
2564	* %(ProductCode)s - name of the product, assigned by the RDAC.
2565
2566	* %(VariableName)s - GHRSST variable represented in the output raster,
2567	either "analysed_sst" or "analysis_error".
2568
2569	* %(ProductType)s - GHRSST product type code that indicates the
2570	overall resolution and type of SST estimated by the product. For
2571	example, "UHfnd" indicates ultra-high resolution (< 5 km spatial
2572	resolution) foundational SST. Please see the GHRSST documentation
2573	for the formal meanings of these codes.
2574
2575	* %(GDSVersion)02i - version of the GHRSST Data Specification (GDS)
2576	used by the product. At the time of this writing, all products
2577	accessed by this function were at GDS version 1, which will be
2578	rendered as 01 by the expression %(GDSVersion)02i.
2579
2580	* %(FileVersion)s - version of the data file itself. This is
2581	product-specific and assigned by the RDAC that produced the product.
2582
2583	* %%Y - four-digit year of the raster.
2584
2585	* %%m - two-digit month of the first day of the raster.
2586
2587	* %%d - two-digit day of the month of the first day of the raster.
2588
2589	* %%j - three-digit day of the year of the first day of the raster.
2590	"""),
2591	arcGISDisplayName=_(u'Raster name expressions'))
2592
2593	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'rasterCatalog', GHRSSTLevel4.CreateArcGISRasters, u'rasterCatalog')
2594	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'rotationOffset', GHRSSTLevel4.CreateArcGISRasters, u'rotationOffset')
2595	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'spatialExtent', GHRSSTLevel4.CreateArcGISRasters, u'spatialExtent')
2596	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'startDate', GHRSSTLevel4.CreateArcGISRasters, u'startDate')
2597	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'endDate', GHRSSTLevel4.CreateArcGISRasters, u'endDate')
2598	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'timeout', GHRSSTLevel4.CreateArcGISRasters, u'timeout')
2599	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'maxRetryTime', GHRSSTLevel4.CreateArcGISRasters, u'maxRetryTime')
2600	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'cacheDirectory', GHRSSTLevel4.CreateArcGISRasters, u'cacheDirectory')
2601
2602	CopyArgumentMetadata(GHRSSTLevel4.__init__, u'convertToCelsius', GHRSSTLevel4.CreateArcGISRasters, u'convertToCelsius')
2603
2604	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'useUnscaledData', GHRSSTLevel4.CreateArcGISRasters, u'useUnscaledData')
2605	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'calculateStatistics', GHRSSTLevel4.CreateArcGISRasters, u'calculateStatistics')
2606	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'buildRAT', GHRSSTLevel4.CreateArcGISRasters, u'buildRAT')
2607	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateArcGISRasters, u'buildPyramids', GHRSSTLevel4.CreateArcGISRasters, u'buildPyramids')
2608
2609	AddResultMetadata(GHRSSTLevel4.CreateArcGISRasters, u'updatedOutputWorkspace',
2610	typeMetadata=ArcGISWorkspaceTypeMetadata(),
2611	description=_(u'Updated output workspace.'),
2612	arcGISDisplayName=_(u'Updated output workspace'),
2613	arcGISParameterDependencies=[u'outputWorkspace'])
2614
2615	# Public method: GHRSSTLevel4.CreateClimatologicalArcGISRasters
2616
2617	AddMethodMetadata(GHRSSTLevel4.CreateClimatologicalArcGISRasters,
2618	shortDescription=_(u'Creates climatological rasters for a GHRSST L4 product hosted by NASA JPL PO.DAAC.'),
2619	longDescription=_(
2620	u""" This tool produces rasters showing the climatological average
2621	value (or other statistic) of a time series of GHRSST L4 SST images.
2622	Given a desired GHRSST L4 product, a statistic, and a climatological
2623	bin definition, this tool efficiently downloads the images, classifies
2624	them into bins, and produces a single raster for each bin. Each cell
2625	of the raster is produced by calculating the statistic on the values
2626	of that cell extracted from all of the rasters in the bin.
2627
2628	""" + _GHRSSTLevel4_LongDescription) % {u'name': 'tool'},
2629	isExposedToPythonCallers=True,
2630	isExposedByCOM=True,
2631	isExposedAsArcGISTool=True,
2632	arcGISDisplayName=_(u'Create Climatological Rasters for GHRSST L4 SST'),
2633	arcGISToolCategory=_(u'Data Products\\NASA JPL PO.DAAC\\GHRSST L4 SST'),
2634	dependencies=[ArcGISDependency(9, 1), PythonAggregatedModuleDependency('numpy')])
2635
2636	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'cls', GHRSSTLevel4.CreateClimatologicalArcGISRasters, u'cls')
2637	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'product', GHRSSTLevel4.CreateClimatologicalArcGISRasters, u'product')
2638	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'variableName', GHRSSTLevel4.CreateClimatologicalArcGISRasters, u'variableName')
2639
2640	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'statistic', GHRSSTLevel4.CreateClimatologicalArcGISRasters, u'statistic')
2641	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'binType', GHRSSTLevel4.CreateClimatologicalArcGISRasters, u'binType')
2642	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'outputWorkspace', GHRSSTLevel4.CreateClimatologicalArcGISRasters, u'outputWorkspace')
2643	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'mode', GHRSSTLevel4.CreateClimatologicalArcGISRasters, u'mode')
2644
2645	AddArgumentMetadata(GHRSSTLevel4.CreateClimatologicalArcGISRasters, u'rasterNameExpressions',
2646	typeMetadata=ListTypeMetadata(elementType=UnicodeStringTypeMetadata(), minLength=1),
2647	description=_(
2648	u"""List of expressions specifying how the output rasters should be
2649	named.
2650
2651	The default expression assumes you are storing rasters in a file
2652	system directory and creates them in a tree structure. When storing
2653	rasters in a directory, the final expression specifies the file name
2654	of the raster and any preceding expressions specify subdirectories.
2655	The extension of the final expression determines the output raster
2656	format: .asc for ArcInfo ASCII Grid, .bmp for BMP, .gif for GIF, .img
2657	for an ERDAS IMAGINE file, .jpg for JPEG, .jp2 for JPEG 2000, .png for
2658	PNG, .tif for GeoTIFF, or no extension for ArcInfo Binary Grid. The
2659	default expression uses .img.
2660
2661	When storing rasters in a geodatabase, you should provide only one
2662	expression. That expression specifies the raster's name.
2663
2664	Each expression may contain any sequence of characters permitted by
2665	the output workspace. Each expression may optionally contain one or
2666	more of the following case-sensitive codes. The tool replaces the
2667	codes with appropriate values when creating each raster:
2668
2669	* %(AreaCode)s - GHRSST abbreviation indicating the spatial extent of
2670	the product, such as "GLOB" for global extent or "AUS" for
2671	Australia.
2672
2673	* %(RDACCode)s - GHRSST abbreviation for the Regonal Data Assembly
2674	Center (RDAC) that produced the product.
2675
2676	* %(ProductCode)s - name of the product, assigned by the RDAC.
2677
2678	* %(VariableName)s - GHRSST variable represented in the output raster,
2679	either "analysed_sst" or "analysis_error".
2680
2681	* %(ProductType)s - GHRSST product type code that indicates the
2682	overall resolution and type of SST estimated by the product. For
2683	example, "UHfnd" indicates ultra-high resolution (< 5 km spatial
2684	resolution) foundational SST. Please see the GHRSST documentation
2685	for the formal meanings of these codes.
2686
2687	* %(GDSVersion)02i - version of the GHRSST Data Specification (GDS)
2688	used by the product. At the time of this writing, all products
2689	accessed by this function were at GDS version 1, which will be
2690	rendered as 01 by the expression %(GDSVersion)02i.
2691
2692	* %(FileVersion)s - version of the data file itself. This is
2693	product-specific and assigned by the RDAC that produced the product.
2694
2695	* %(ClimatologyBinType)s - type of the climatology bin, either "Daily"
2696	if 1-day bins, "Xday" if multi-day bins (X is replaced by the
2697	duration), "Monthly" if 1-month bins, "Xmonth" if multi-month bins,
2698	or "Cumulative".
2699
2700	* %(ClimatologyBinName)s - name of the climatology bin corresponding
2701	represented by the output raster, either "dayXXX" for 1-day bins
2702	(XXX is replaced by the day of the year), "daysXXXtoYYY" for
2703	multi-day bins (XXX is replaced by the first day of the bin, YYY is
2704	replaced by the last day), "monthXX" for 1-month bins (XX is
2705	replaced by the month), "monthXXtoYY" (XX is replaced by the first
2706	month of the bin, YY by the last month), or "cumulative".
2707
2708	* %(Statistic)s - statistic that was calculated, in lowercase and with
2709	spaces replaced by underscores; one of: "count", "maximum", "mean",
2710	"minimum", "range", "standard_deviation", "Sum".
2711
2712	If the Bin Type is "Daily", the following additional codes are
2713	available:
2714
2715	* %(FirstDay)i - first day of the year of the climatology bin
2716	represented by the output raster.
2717
2718	* %(LastDay)i - last day of the year of the climatology bin
2719	represented by the output raster. For 1-day climatologies, this will
2720	be the same as %(FirstDay)i.
2721
2722	If the Bin Type is "Monthly", the following additional codes are
2723	available:
2724
2725	* %(FirstMonth)i - first month of the climatology bin represented by
2726	the output raster.
2727
2728	* %(DayOfFirstMonth)i - first day of the first month of the
2729	climatology bin represented by the output raster.
2730
2731	* %(LastMonth)i - last month of the climatology bin represented by
2732	the output raster.
2733
2734	* %(DayOfLastMonth)i - last day of the last month of the climatology
2735	bin represented by the output raster.
2736
2737	Note that the additional codes are integers and may be formatted using
2738	"printf"-style formatting codes. For example, to format the FirstDay
2739	as a three-digit number with leading zeros::
2740
2741	%(FirstDay)03i
2742	"""),
2743	arcGISDisplayName=_(u'Raster name expressions'))
2744
2745	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'binDuration', GHRSSTLevel4.CreateClimatologicalArcGISRasters, u'binDuration')
2746	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters, u'startDayOfYear', GHRSSTLevel4.CreateClimatologicalArcGISRasters, u'startDayOfYear')
2747
2748	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'rotationOffset', GHRSSTLevel4.CreateClimatologicalArcGISRasters, u'rotationOffset')
2749	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'spatialExtent', GHRSSTLevel4.CreateClimatologicalArcGISRasters, u'spatialExtent')
2750	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'startDate', GHRSSTLevel4.CreateClimatologicalArcGISRasters, u'startDate')
2751	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'endDate', GHRSSTLevel4.CreateClimatologicalArcGISRasters, u'endDate')
2752	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'timeout', GHRSSTLevel4.CreateClimatologicalArcGISRasters, u'timeout')
2753	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'maxRetryTime', GHRSSTLevel4.CreateClimatologicalArcGISRasters, u'maxRetryTime')
2754	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'cacheDirectory', GHRSSTLevel4.CreateClimatologicalArcGISRasters, u'cacheDirectory')
2755	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'convertToCelsius', GHRSSTLevel4.CreateClimatologicalArcGISRasters, u'convertToCelsius')
2756	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'calculateStatistics', GHRSSTLevel4.CreateClimatologicalArcGISRasters, u'calculateStatistics')
2757	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'buildPyramids', GHRSSTLevel4.CreateClimatologicalArcGISRasters, u'buildPyramids')
2758
2759	CopyResultMetadata(GHRSSTLevel4.CreateArcGISRasters, u'updatedOutputWorkspace', GHRSSTLevel4.CreateClimatologicalArcGISRasters, u'updatedOutputWorkspace')
2760
2761	# Public method: GHRSSTLevel4.InterpolateAtArcGISPoints
2762
2763	AddMethodMetadata(GHRSSTLevel4.InterpolateAtArcGISPoints,
2764	shortDescription=_(u'Interpolates values of a GHRSST L4 product hosted by NASA JPL PO.DAAC at points.'),
2765	longDescription=_(
2766	u""" Given a desired GHRSST L4 product, this tool interpolates the
2767	value of that product at the given points. This tool performs the same
2768	basic operation as the ArcGIS Spatial Analyst's Extract Values to
2769	Points tool, but it reads the MODIS data directly from NASA's servers
2770	rather than reading rasters stored on your machine.
2771
2772	""" + _GHRSSTLevel4_LongDescription) % {u'name': 'tool'},
2773	isExposedToPythonCallers=True,
2774	isExposedByCOM=True,
2775	isExposedAsArcGISTool=True,
2776	arcGISDisplayName=_(u'Interpolate GHRSST L4 SST at Points'),
2777	arcGISToolCategory=_(u'Data Products\\NASA JPL PO.DAAC\\GHRSST L4 SST'),
2778	dependencies=[ArcGISDependency(9, 1, requiresCOMInstantiation=True), PythonAggregatedModuleDependency('numpy')])
2779
2780	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'cls', GHRSSTLevel4.InterpolateAtArcGISPoints, u'cls')
2781	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'product', GHRSSTLevel4.InterpolateAtArcGISPoints, u'product')
2782	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'variableName', GHRSSTLevel4.InterpolateAtArcGISPoints, u'variableName')
2783
2784	CopyArgumentMetadata(MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'points', GHRSSTLevel4.InterpolateAtArcGISPoints, u'points')
2785	CopyArgumentMetadata(MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'valueField', GHRSSTLevel4.InterpolateAtArcGISPoints, u'valueField')
2786	CopyArgumentMetadata(MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'tField', GHRSSTLevel4.InterpolateAtArcGISPoints, u'tField')
2787
2788	AddArgumentMetadata(GHRSSTLevel4.InterpolateAtArcGISPoints, u'method',
2789	typeMetadata=UnicodeStringTypeMetadata(allowedValues=[u'Nearest', u'Linear'], makeLowercase=True),
2790	description=_(
2791	u"""Interpolation method to use, one of:
2792
2793	* Nearest - nearest neighbor interpolation. The interpolated value
2794	will simply be the value of the cell that contains the point. This
2795	is the default.
2796
2797	* Linear - linear interpolation (also known as trilinear
2798	interpolation). This method averages the values of the eight nearest
2799	cells in the x, y, and time dimensions, weighting the contribution
2800	of each cell by the area of it that would be covered by a
2801	hypothetical cell centered on the point being interpolated. If the
2802	cell containing the point contains NoData, the result is NoData. If
2803	any of the other seven cells contain NoData, they are omitted from
2804	the average, and the result is based on the weighted average of the
2805	cells that do contain data. This is the same algorithm implemented
2806	by the ArcGIS Spatial Analyst's Extract Values to Points tool.
2807	"""),
2808	arcGISDisplayName=_(u'Interpolation method'))
2809
2810	CopyArgumentMetadata(MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'where', GHRSSTLevel4.InterpolateAtArcGISPoints, u'where')
2811	CopyArgumentMetadata(MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'noDataValue', GHRSSTLevel4.InterpolateAtArcGISPoints, u'noDataValue')
2812
2813	AddArgumentMetadata(GHRSSTLevel4.InterpolateAtArcGISPoints, u'convertToCelsius',
2814	typeMetadata=BooleanTypeMetadata(),
2815	description=_(
2816	u"""If True (the default), temperature values will be converted from
2817	kelvin to degrees Celsius. If False, temperature values will be in the
2818	original kelvin values."""),
2819	arcGISDisplayName=_(u'Convert temperatures to Celsius'),
2820	arcGISCategory=_(u'Interpolation options'))
2821
2822	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'timeout', GHRSSTLevel4.InterpolateAtArcGISPoints, u'timeout')
2823	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'maxRetryTime', GHRSSTLevel4.InterpolateAtArcGISPoints, u'maxRetryTime')
2824	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'cacheDirectory', GHRSSTLevel4.InterpolateAtArcGISPoints, u'cacheDirectory')
2825
2826	CopyArgumentMetadata(MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'orderByFields', GHRSSTLevel4.InterpolateAtArcGISPoints, u'orderByFields')
2827
2828	AddArgumentMetadata(GHRSSTLevel4.InterpolateAtArcGISPoints, u'numBlocksToCacheInMemory',
2829	typeMetadata=IntegerTypeMetadata(minValue=0, canBeNone=True),
2830	description=_(
2831	u"""Maximum number of blocks of SST data to cache in memory.
2832
2833	To minimize the number of times that the disk or network must be
2834	accessed, this tool employs a simple caching strategy, in addition to
2835	disk caching described by the Cache Directory parameter. When it
2836	processes the first point, it reads a square block of cells centered
2837	on that point and caches it in memory. When it processes the second
2838	and subsequent points, it first checks whether the cells needed for
2839	that point are contained by the block cached in memory. If so, it
2840	processes that point using the in-memory block, rather than reading
2841	from disk or the network again. If not, it reads another square block
2842	centered on that point and adds it to the cache.
2843
2844	The tool processes the remaining points, adding additional blocks to
2845	the cache, as needed. To prevent the cache from exhausing all memory,
2846	it is only permitted to grow to the size specified by this parameter.
2847	When the cache is full but a new block is needed, the oldest block is
2848	discarded to make room for the newest block.
2849
2850	The maximum size of the cache in bytes may be calculated by
2851	multiplying this parameter by the block size parameters and then by 2.
2852	For example, if this parameter is 256 and the blocks are x=64 by y=64
2853	by t=1, the maximum size of the cache is 2097152 bytes (2 MB).
2854
2855	If this parameter is 0, no blocks will be cached in memory."""),
2856	arcGISDisplayName=_(u'Number of blocks of data to cache in memory'),
2857	arcGISCategory=_(u'Performance tuning options'))
2858
2859	CopyArgumentMetadata(MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'xBlockSize', GHRSSTLevel4.InterpolateAtArcGISPoints, u'xBlockSize')
2860	CopyArgumentMetadata(MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'yBlockSize', GHRSSTLevel4.InterpolateAtArcGISPoints, u'yBlockSize')
2861	CopyArgumentMetadata(MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'tBlockSize', GHRSSTLevel4.InterpolateAtArcGISPoints, u'tBlockSize')
2862
2863	CopyResultMetadata(MODISL3SSTTimeSeries.InterpolateAtArcGISPoints, u'updatedPoints', GHRSSTLevel4.InterpolateAtArcGISPoints, u'updatedPoints')
2864
2865	# Public method: GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters
2866
2867	AddMethodMetadata(GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters,
2868	shortDescription=_(u'Creates rasters indicating the positions of fronts in GHRSST L4 SST images hosted by NASA JPL PO.DAAC, using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.'),
2869	longDescription=_GHRSSTLevel4_LongDescription + _(
2870	u"""
2871
2872	Given a desired GHRSST L4 product, this tool efficiently downloads the
2873	a time series of SST images, executes the Cayula and Cornillon SIED
2874	algorithm to identify fronts, and creates rasters showing the
2875	locations of the fronts.
2876
2877	This tool is complicated and has a lot of parameters. The complex
2878	dynamics of the ocean and the presence of clouds make it hard to
2879	obtain perfect results, even with the sophisticated algorithms GHRSST
2880	products employ to guess values for cloudy pixels. For the best chance
2881	of success, please read all of the documentation carefully.
2882
2883	In our experience, the SIED front detection algorithm is somewhat
2884	sensitive to the spatial resolution of the SST data it is applied to
2885	and will perform better if several parameters are adjusted. We
2886	configured the default parameter values of this tool to those used in
2887	Cayula and Cornillon's original study. That study was applied to AVHRR
2888	images collected by NOAA polar orbiters. The images had an effective
2889	resolution between 1 and 1.5 km. Therefore the default parameter
2890	values for this tool are appropriate for GHRSST products with similar
2891	resolution (e.g. EUR-L4UHRfnd-MED-ODYSSEA, EUR-L4UHRfnd-NWE-ODYSSEA,
2892	JPL-L4UHfnd-GLOB-MUR, JPL_OUROCEAN-L4UHfnd-GLOB-G1SST).
2893
2894	To apply this tool to higher resolution GHRSST L4 products, we suggest
2895	reducing the Histogram Window Size to a smaller value such as 20. If
2896	you do that, you must also reduce the Minimum Single-Population
2897	Cohesion and Minimum Global-Population Cohesion parameters, as
2898	described in the documentation for those parameters. FOr a Histogram
2899	Window Size fo 20, those two parameters should be set to 0.88 and 0.9,
2900	respectively.
2901
2902	Note that the Front Cleanup Options, which are enabled by default,
2903	require that MATLAB 2007b or the MATLAB Component Runtime (MCR) 7.7 be
2904	installed. You can download a free copy of the MCR 7.7 from
2905	http://code.nicholas.duke.edu/projects/mget/wiki/MCR. If you do not
2906	wish to install MATLAB or the MCR, you must disable the Front Cleanup
2907	Options.
2908
2909	The SIED Algorithm
2910
2911	""" + _SIEDDescription +
2912	"""
2913
2914	""" + _SIEDReferences) % {u'name': 'tool'},
2915	isExposedToPythonCallers=True,
2916	isExposedByCOM=True,
2917	isExposedAsArcGISTool=True,
2918	arcGISDisplayName=_(u'Find Cayula-Cornillon Fronts in GHRSST L4 SST'),
2919	arcGISToolCategory=_(u'Data Products\\NASA JPL PO.DAAC\\GHRSST L4 SST'),
2920	dependencies=[ArcGISDependency(9, 1), PythonAggregatedModuleDependency('numpy')])
2921
2922	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'cls', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'cls')
2923	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'product', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'product')
2924
2925	AddArgumentMetadata(GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'minPopMeanDifference',
2926	typeMetadata=FloatTypeMetadata(mustBeGreaterThan=0.),
2927	description=_(
2928	u"""Minimum difference, in degrees C, between the mean temperatures of
2929	two adjacent populations of pixels for a front to be detected between
2930	those two populations.
2931
2932	The Cayula and Cornillon algorithm passes a moving window over the
2933	image, checking each window for a bimodal distribution in the
2934	temperatures of the pixels within it. When the algorithm detects a
2935	bimodal distribution, it computes the mean temperatures of the two
2936	populations and compares the difference between the means to this
2937	threshold. If the difference is less than this threshold, the
2938	algorithm concludes there is no front present and moves on to the next
2939	window.
2940
2941	You can use this parameter to eliminate weak fronts by selecting a
2942	value that corresponds to a desired minimum mean temperature
2943	difference. Larger values will detect fewer fronts; smaller values
2944	will detect more fronts. However, bear in mind that temperature
2945	estimates derived from most satellite remote sensors are only
2946	considered accurate to within 0.3 to 0.5 degrees C. We do not advise
2947	thresholds below 0.3 to 0.5 degrees C."""),
2948	arcGISDisplayName=_(u'Front detection threshold'))
2949
2950	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'outputWorkspace', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'outputWorkspace')
2951	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'mode', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'mode')
2952
2953	AddArgumentMetadata(GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'rasterNameExpressions',
2954	typeMetadata=ListTypeMetadata(elementType=UnicodeStringTypeMetadata(), minLength=1),
2955	description=_(
2956	u"""List of expressions specifying how the output rasters should be
2957	named.
2958
2959	The default expression assumes you are storing rasters in a file
2960	system directory and creates them in a tree structure. When storing
2961	rasters in a directory, the final expression specifies the file name
2962	of the raster and any preceding expressions specify subdirectories.
2963	The extension of the final expression determines the output raster
2964	format: .asc for ArcInfo ASCII Grid, .bmp for BMP, .gif for GIF, .img
2965	for an ERDAS IMAGINE file, .jpg for JPEG, .jp2 for JPEG 2000, .png for
2966	PNG, .tif for GeoTIFF, or no extension for ArcInfo Binary Grid. The
2967	default expression uses .img.
2968
2969	When storing rasters in a geodatabase, you should provide only one
2970	expression. That expression specifies the raster's name.
2971
2972	Each expression may contain any sequence of characters permitted by
2973	the output workspace. Each expression may optionally contain one or
2974	more of the following case-sensitive codes. The tool replaces the
2975	codes with appropriate values when creating each raster:
2976
2977	* %(AreaCode)s - GHRSST abbreviation indicating the spatial extent of
2978	the product, such as "GLOB" for global extent or "AUS" for
2979	Australia.
2980
2981	* %(RDACCode)s - GHRSST abbreviation for the Regonal Data Assembly
2982	Center (RDAC) that produced the product.
2983
2984	* %(ProductCode)s - name of the product, assigned by the RDAC.
2985
2986	* %(VariableName)s - GHRSST variable represented in the output raster,
2987	either "analysed_sst" or "analysis_error".
2988
2989	* %(ProductType)s - GHRSST product type code that indicates the
2990	overall resolution and type of SST estimated by the product. For
2991	example, "UHfnd" indicates ultra-high resolution (< 5 km spatial
2992	resolution) foundational SST. Please see the GHRSST documentation
2993	for the formal meanings of these codes.
2994
2995	* %(GDSVersion)02i - version of the GHRSST Data Specification (GDS)
2996	used by the product. At the time of this writing, all products
2997	accessed by this function were at GDS version 1, which will be
2998	rendered as 01 by the expression %(GDSVersion)02i.
2999
3000	* %(FileVersion)s - version of the data file itself. This is
3001	product-specific and assigned by the RDAC that produced the product.
3002
3003	* %(ImageType)s - type of image represented in the output raster,
3004	either "floc" (front locations), "ccnt" (candidate counts), "fcnt"
3005	(front counts), "wsco" (window status codes), or "wsvl" (window
3006	status values).
3007
3008	* %%Y - four-digit year of the raster.
3009
3010	* %%m - two-digit month of the first day of the raster.
3011
3012	* %%d - two-digit day of the month of the first day of the raster.
3013
3014	* %%j - three-digit day of the year of the first day of the raster.
3015	"""),
3016	arcGISDisplayName=_(u'Raster name expressions'))
3017
3018	CopyArgumentMetadata(CayulaCornillonEdgeDetection.DetectEdgesInSingleImage, u'medianFilterWindowSize', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'medianFilterWindowSize')
3019	CopyArgumentMetadata(CayulaCornillonEdgeDetection.DetectEdgesInSingleImage, u'histogramWindowSize', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'histogramWindowSize')
3020	CopyArgumentMetadata(CayulaCornillonEdgeDetection.DetectEdgesInSingleImage, u'histogramWindowStride', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'histogramWindowStride')
3021	CopyArgumentMetadata(CayulaCornillonEdgeDetection.DetectEdgesInSingleImage, u'minPropNonMaskedCells', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'minPropNonMaskedCells')
3022	CopyArgumentMetadata(CayulaCornillonEdgeDetection.DetectEdgesInSingleImage, u'minPopProp', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'minPopProp')
3023	CopyArgumentMetadata(CayulaCornillonEdgeDetection.DetectEdgesInSingleImage, u'minTheta', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'minTheta')
3024	CopyArgumentMetadata(CayulaCornillonEdgeDetection.DetectEdgesInSingleImage, u'minSinglePopCohesion', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'minSinglePopCohesion')
3025	CopyArgumentMetadata(CayulaCornillonEdgeDetection.DetectEdgesInSingleImage, u'minGlobalPopCohesion', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'minGlobalPopCohesion')
3026	CopyArgumentMetadata(CayulaCornillonEdgeDetection.DetectEdgesInSingleImage, u'threads', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'threads')
3027	CopyArgumentMetadata(CayulaCornillonEdgeDetection.DetectEdgesInSingleImage, u'fillHoles', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'fillHoles')
3028	CopyArgumentMetadata(CayulaCornillonEdgeDetection.DetectEdgesInSingleImage, u'thin', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'thin')
3029	CopyArgumentMetadata(CayulaCornillonEdgeDetection.DetectEdgesInSingleImage, u'minSize', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'minSize')
3030
3031	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'rotationOffset', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'rotationOffset')
3032	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'spatialExtent', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'spatialExtent')
3033	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'startDate', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'startDate')
3034	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'endDate', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'endDate')
3035	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'timeout', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'timeout')
3036	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'maxRetryTime', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'maxRetryTime')
3037	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'cacheDirectory', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'cacheDirectory')
3038	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'calculateStatistics', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'calculateStatistics')
3039	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'buildRAT', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'buildRAT')
3040	CopyArgumentMetadata(GHRSSTLevel4.CreateArcGISRasters, u'buildPyramids', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'buildPyramids')
3041
3042	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'outputCandidateCounts', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'outputCandidateCounts')
3043	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'outputFrontCounts', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'outputFrontCounts')
3044	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'outputWindowStatusCodes', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'outputWindowStatusCodes')
3045	CopyArgumentMetadata(MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters, u'outputWindowStatusValues', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'outputWindowStatusValues')
3046
3047	CopyResultMetadata(GHRSSTLevel4.CreateArcGISRasters, u'updatedOutputWorkspace', GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters, u'updatedOutputWorkspace')
3048
3049	###############################################################################
3050	# Metadata: QuikSCATL3TimeSeries class
3051	###############################################################################
3052
3053	AddClassMetadata(QuikSCATL3TimeSeries,
3054	shortDescription=_(u'TODO: Add description.'))
3055
3056	# TODO: Add metadata
3057
3058	###############################################################################
3059	# Metadata: QuikSCATL3TimeSlicesInDirectory class
3060	###############################################################################
3061
3062	AddClassMetadata(QuikSCATL3TimeSlicesInDirectory,
3063	shortDescription=_(u'TODO: Add description.'))
3064
3065	# TODO: Add metadata
3066
3067	###############################################################################
3068	# Names exported by this module
3069	###############################################################################
3070
3071	__all__ = ['MODISL3SSTTimeSeries',
3072	'GHRSSTLevel4'
3073	'QuikSCATL3TimeSeries',
3074	'QuikSCATL3TimeSlicesInDirectory']

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