def __init__(self, sliceColName=None, sliceColUnits=None,
bins=None, binMin=None, binMax=None, binsize=None,
verbose=True, badval=0):
"""
'sliceColName' is the name of the data column to use for slicing.
'sliceColUnits' lets the user set the units (for plotting purposes) of the slice column.
'bins' can be a numpy array with the binpoints for sliceCol or a single integer value
(if a single value, this will be used as the number of bins, together with data min/max or binMin/Max),
as in numpy's histogram function.
If 'binsize' is used, this will override the bins value and will be used together with the data min/max
or binMin/Max to set the binpoint values.
Bins work like numpy histogram bins: the last 'bin' value is end value of last bin;
all bins except for last bin are half-open ([a, b>), the last one is ([a, b]).
"""
super(OneDSlicer, self).__init__(verbose=verbose, badval=badval)
self.sliceColName = sliceColName
self.columnsNeeded = [sliceColName]
self.bins = bins
self.binMin = binMin
self.binMax = binMax
self.binsize = binsize
if sliceColUnits is None:
co = ColInfo()
self.sliceColUnits = co.getUnits(self.sliceColName)
else:
self.sliceColUnits = sliceColUnits
self.slicer_init = {'sliceColName':self.sliceColName, 'sliceColUnits':sliceColUnits,
'badval':badval}
self.plotFuncs = [OneDBinnedData,]
def __init__(self,
sliceColName=None,
sliceColUnits=None,
bins=None,
binMin=None,
binMax=None,
binsize=None,
verbose=True,
badval=0,
cumulative=True,
forceNoFfmpeg=False):
"""
The 'movieSlicer' acts similarly to the OneDSlicer (slices on one data column).
However, the data slices from the movieSlicer are intended to be fed to another slicer, which then
(together with a set of Metrics) calculates metric values + plots at each slice created by the movieSlicer.
The job of the movieSlicer is to track those slices and put them together into a movie.
'sliceColName' is the name of the data column to use for slicing.
'sliceColUnits' lets the user set the units (for plotting purposes) of the slice column.
'bins' can be a numpy array with the binpoints for sliceCol or a single integer value
(if a single value, this will be used as the number of bins, together with data min/max or binMin/Max),
as in numpy's histogram function.
If 'binsize' is used, this will override the bins value and will be used together with the data min/max
or binMin/Max to set the binpoint values.
Bins work like numpy histogram bins: the last 'bin' value is end value of last bin;
all bins except for last bin are half-open ([a, b>), the last one is ([a, b]).
The movieSlicer stitches individual frames together into a movie using ffmpeg. Thus, on
instantiation it checks that ffmpeg is available and will raise and exception if not.
This behavior can be overriden using forceNoFfmpeg = True (in order to create a movie later perhaps).
"""
# Check for ffmpeg.
if not forceNoFfmpeg:
try:
fnull = open(os.devnull, 'w')
subprocess.check_call(['which', 'ffmpeg'], stdout=fnull)
except CalledProcessError:
raise Exception(
'Could not find ffmpeg on the system, so will not be able to create movie.'
' Use forceNoFfmpeg=True to override this error and create individual images.'
)
super(MovieSlicer, self).__init__(verbose=verbose, badval=badval)
self.sliceColName = sliceColName
self.columnsNeeded = [sliceColName]
self.bins = bins
self.binMin = binMin
self.binMax = binMax
self.binsize = binsize
self.cumulative = cumulative
if sliceColUnits is None:
co = ColInfo()
self.sliceColUnits = co.getUnits(self.sliceColName)
self.slicer_init = {
'sliceColName': self.sliceColName,
'sliceColUnits': sliceColUnits,
'badval': badval
}
def __init__(self, sliceColName=None, sliceColUnits=None,
bins=None, binMin=None, binMax=None, binsize=None,
verbose=True, badval=0, cumulative=True, forceNoFfmpeg=False):
"""
The 'movieSlicer' acts similarly to the OneDSlicer (slices on one data column).
However, the data slices from the movieSlicer are intended to be fed to another slicer, which then
(together with a set of Metrics) calculates metric values + plots at each slice created by the movieSlicer.
The job of the movieSlicer is to track those slices and put them together into a movie.
'sliceColName' is the name of the data column to use for slicing.
'sliceColUnits' lets the user set the units (for plotting purposes) of the slice column.
'bins' can be a numpy array with the binpoints for sliceCol or a single integer value
(if a single value, this will be used as the number of bins, together with data min/max or binMin/Max),
as in numpy's histogram function.
If 'binsize' is used, this will override the bins value and will be used together with the data min/max
or binMin/Max to set the binpoint values.
Bins work like numpy histogram bins: the last 'bin' value is end value of last bin;
all bins except for last bin are half-open ([a, b>), the last one is ([a, b]).
The movieSlicer stitches individual frames together into a movie using ffmpeg. Thus, on
instantiation it checks that ffmpeg is available and will raise and exception if not.
This behavior can be overriden using forceNoFfmpeg = True (in order to create a movie later perhaps).
"""
# Check for ffmpeg.
if not forceNoFfmpeg:
try:
fnull = open(os.devnull, 'w')
subprocess.check_call(['which', 'ffmpeg'], stdout=fnull)
except CalledProcessError:
raise Exception('Could not find ffmpeg on the system, so will not be able to create movie.'
' Use forceNoFfmpeg=True to override this error and create individual images.')
super(MovieSlicer, self).__init__(verbose=verbose, badval=badval)
self.sliceColName = sliceColName
self.columnsNeeded = [sliceColName]
self.bins = bins
self.binMin = binMin
self.binMax = binMax
self.binsize = binsize
self.cumulative = cumulative
if sliceColUnits is None:
co = ColInfo()
self.sliceColUnits = co.getUnits(self.sliceColName)
self.slicer_init = {'sliceColName':self.sliceColName, 'sliceColUnits':sliceColUnits,
'badval':badval}
def _findReqCols(self):
"""Find the columns needed by the metrics, slicers, and stackers.
If there are any additional stackers required, instatiate them and add them to
the self.stackers list.
(default stackers have to be instantiated to determine what additional columns
are needed from database).
"""
# Find the columns required by the metrics and slicers (including if they come from stackers).
colInfo = ColInfo()
dbcolnames = set()
defaultstackers = set()
# Look for the source for the columns for the slicer.
for col in self.slicer.columnsNeeded:
colsource = colInfo.getDataSource(col)
if colsource != colInfo.defaultDataSource:
defaultstackers.add(colsource)
else:
dbcolnames.add(col)
# Look for the source of columns for this metric (only).
# We can't use the colRegistry here because we want the columns for this metric only.
for col in self.metric.colNameArr:
colsource = colInfo.getDataSource(col)
if colsource != colInfo.defaultDataSource:
defaultstackers.add(colsource)
else:
dbcolnames.add(col)
# Remove explicity instantiated stackers from defaultstacker set.
for s in self.stackerList:
if s.__class__ in defaultstackers:
defaultstackers.remove(s.__class__)
# Instantiate and add the remaining default stackers.
for s in defaultstackers:
self.stackerList.append(s())
# Add the columns needed by all stackers to the list to grab from the database.
for s in self.stackerList:
for col in s.colsReq:
dbcolnames.add(col)
# Remove 'metricdata' from dbcols if it ended here by default.
if 'metricdata' in dbcolnames:
dbcolnames.remove('metricdata')
self.dbCols = dbcolnames
def _findReqCols(self):
"""Find the columns needed by the metrics, slicers, and stackers.
If there are any additional stackers required, instatiate them and add them to
the self.stackers list.
(default stackers have to be instantiated to determine what additional columns
are needed from database).
"""
# Find the columns required by the metrics and slicers (including if they come from stackers).
colInfo = ColInfo()
dbcolnames = set()
defaultstackers = set()
# Look for the source for the columns for the slicer.
for col in self.slicer.columnsNeeded:
colsource = colInfo.getDataSource(col)
if colsource != colInfo.defaultDataSource:
defaultstackers.add(colsource)
else:
dbcolnames.add(col)
# Look for the source of columns for this metric (only).
# We can't use the colRegistry here because we want the columns for this metric only.
for col in self.metric.colNameArr:
colsource = colInfo.getDataSource(col)
if colsource != colInfo.defaultDataSource:
defaultstackers.add(colsource)
else:
dbcolnames.add(col)
# Remove explicity instantiated stackers from defaultstacker set.
for s in self.stackerList:
if s.__class__ in defaultstackers:
defaultstackers.remove(s.__class__)
# Instantiate and add the remaining default stackers.
for s in defaultstackers:
self.stackerList.append(s())
# Add the columns needed by all stackers to the list to grab from the database.
for s in self.stackerList:
for col in s.colsReq:
dbcolnames.add(col)
# Remove 'metricdata' from dbcols if it ended here by default.
if 'metricdata' in dbcolnames:
dbcolnames.remove('metricdata')
self.dbCols = dbcolnames
def __init__(self,
sliceColName=None,
sliceColUnits=None,
bins=None,
binMin=None,
binMax=None,
binsize=None,
verbose=True,
badval=0):
"""
'sliceColName' is the name of the data column to use for slicing.
'sliceColUnits' lets the user set the units (for plotting purposes) of the slice column.
'bins' can be a numpy array with the binpoints for sliceCol or a single integer value
(if a single value, this will be used as the number of bins, together with data min/max or binMin/Max),
as in numpy's histogram function.
If 'binsize' is used, this will override the bins value and will be used together with the data min/max
or binMin/Max to set the binpoint values.
Bins work like numpy histogram bins: the last 'bin' value is end value of last bin;
all bins except for last bin are half-open ([a, b>), the last one is ([a, b]).
"""
super(OneDSlicer, self).__init__(verbose=verbose, badval=badval)
self.sliceColName = sliceColName
self.columnsNeeded = [sliceColName]
self.bins = bins
self.binMin = binMin
self.binMax = binMax
self.binsize = binsize
if sliceColUnits is None:
co = ColInfo()
self.sliceColUnits = co.getUnits(self.sliceColName)
else:
self.sliceColUnits = sliceColUnits
self.slicer_init = {
'sliceColName': self.sliceColName,
'sliceColUnits': sliceColUnits,
'badval': badval
}
self.plotFuncs = [
OneDBinnedData,
]
class MetricBundle(object):
"""The MetricBundle is defined by a combination of a (single) metric, slicer and
constraint - together these define a unique combination of an opsim benchmark.
An example would be: a CountMetric, a HealpixSlicer, and a sqlconstraint 'filter="r"'.
After the metric is evaluated over the slicePoints of the slicer, the resulting
metric values are saved in the MetricBundle.
The MetricBundle also saves the summary metrics to be used to generate summary
statistics over those metric values, as well as the resulting summary statistic values.
Plotting parameters and display parameters (for showMaf) are saved in the MetricBundle,
as well as additional metadata such as the opsim run name, and relevant stackers and maps
to apply when calculating the metric values.
"""
colInfo = ColInfo()
def __init__(self,
metric,
slicer,
constraint=None,
sqlconstraint=None,
stackerList=None,
runName='opsim',
metadata=None,
plotDict=None,
displayDict=None,
summaryMetrics=None,
mapsList=None,
fileRoot=None,
plotFuncs=None):
# Set the metric.
if not isinstance(metric, metrics.BaseMetric):
raise ValueError('metric must be an lsst.sims.maf.metrics object')
self.metric = metric
# Set the slicer.
if not isinstance(slicer, slicers.BaseSlicer):
raise ValueError('slicer must be an lsst.sims.maf.slicers object')
self.slicer = slicer
# Set the constraint.
self.constraint = constraint
if self.constraint is None:
# Provide backwards compatibility for now - phase out sqlconstraint eventually.
if sqlconstraint is not None:
warnings.warn(
'Future warning - "sqlconstraint" will be deprecated in favor of '
'"constraint" in a future release.')
self.constraint = sqlconstraint
if self.constraint is None:
self.constraint = ''
# Set the stackerlist if applicable.
if stackerList is not None:
if isinstance(stackerList, stackers.BaseStacker):
self.stackerList = [
stackerList,
]
else:
self.stackerList = []
for s in stackerList:
if s is None:
pass
else:
if not isinstance(s, stackers.BaseStacker):
raise ValueError(
'stackerList must only contain lsst.sims.maf.stackers objs'
)
self.stackerList.append(s)
else:
self.stackerList = []
# Set the 'maps' to apply to the slicer, if applicable.
if mapsList is not None:
if isinstance(mapsList, maps.BaseMap):
self.mapsList = [
mapsList,
]
else:
self.mapsList = []
for m in mapsList:
if not isinstance(m, maps.BaseMap):
raise ValueError(
'mapsList must only contain lsst.sims.maf.maps objects'
)
self.mapsList.append(m)
else:
self.mapsList = []
# If the metric knows it needs a particular map, add it to the list.
mapNames = [mapName.__class__.__name__ for mapName in self.mapsList]
if hasattr(self.metric, 'maps'):
for mapName in self.metric.maps:
if mapName not in mapNames:
tempMap = getattr(maps, mapName)()
self.mapsList.append(tempMap)
mapNames.append(mapName)
# Add the summary stats, if applicable.
self.setSummaryMetrics(summaryMetrics)
# Set the provenance/metadata.
self._buildMetadata(metadata)
# Set the run name and build the output filename base (fileRoot).
self.setRunName(runName)
# Reset fileRoot, if provided.
if fileRoot is not None:
self.fileRoot = fileRoot
# Determine the columns needed from the database.
self._findReqCols()
# Set the plotting classes/functions.
self.setPlotFuncs(plotFuncs)
# Set the plotDict and displayDicts.
self.plotDict = {}
self.setPlotDict(plotDict)
# Update/set displayDict.
self.displayDict = {}
self.setDisplayDict(displayDict)
# This is where we store the metric values and summary stats.
self.metricValues = None
self.summaryValues = None
def _resetMetricBundle(self):
"""Reset all properties of MetricBundle.
"""
self.metric = None
self.slicer = None
self.constraint = None
self.stackerList = []
self.summaryMetrics = []
self.plotFuncs = []
self.mapsList = None
self.runName = 'opsim'
self.metadata = ''
self.dbCols = None
self.fileRoot = None
self.plotDict = {}
self.displayDict = {}
self.metricValues = None
self.summaryValues = None
def _setupMetricValues(self):
"""Set up the numpy masked array to store the metric value data.
"""
dtype = self.metric.metricDtype
# Can't store healpix slicer mask values in an int array.
if dtype == 'int':
dtype = 'float'
if self.metric.shape == 1:
shape = self.slicer.shape
else:
shape = (self.slicer.shape, self.metric.shape)
self.metricValues = ma.MaskedArray(data=np.empty(shape, dtype),
mask=np.zeros(shape, 'bool'),
fill_value=self.slicer.badval)
def _buildMetadata(self, metadata):
"""If no metadata is provided, process the constraint
(by removing extra spaces, quotes, the word 'filter' and equal signs) to make a metadata version.
e.g. 'filter = "r"' becomes 'r'
"""
if metadata is None:
self.metadata = self.constraint.replace('=', '').replace(
'filter', '').replace("'", '')
self.metadata = self.metadata.replace('"', '').replace(' ', ' ')
self.metadata.strip(' ')
else:
self.metadata = metadata
def _buildFileRoot(self):
"""
Build an auto-generated output filename root (i.e. minus the plot type or .npz ending).
"""
# Build basic version.
self.fileRoot = '_'.join([
self.runName, self.metric.name, self.metadata,
self.slicer.slicerName[:4].upper()
])
# Sanitize output name if needed.
self.fileRoot = utils.nameSanitize(self.fileRoot)
def _findReqCols(self):
"""Find the columns needed by the metrics, slicers, and stackers.
If there are any additional stackers required, instatiate them and add them to
the self.stackers list.
(default stackers have to be instantiated to determine what additional columns
are needed from database).
"""
# Find all the columns needed by metric and slicer.
knownCols = self.slicer.columnsNeeded + list(self.metric.colNameArr)
# For the stackers already set up, find their required columns.
for s in self.stackerList:
knownCols += s.colsReq
knownCols = set(knownCols)
# Track sources of all of these columns.
self.dbCols = set()
newstackers = set()
for col in knownCols:
if self.colInfo.getDataSource(
col) == self.colInfo.defaultDataSource:
self.dbCols.add(col)
else:
# New default stackers could come from metric/slicer or stackers.
newstackers.add(self.colInfo.getDataSource(col))
# Remove already-specified stackers from default list.
for s in self.stackerList:
if s.__class__ in newstackers:
newstackers.remove(s.__class__)
# Loop and check if stackers are introducing new columns or stackers.
while len(newstackers) > 0:
# Check for the sources of the columns in any of the new stackers.
newCols = []
for s in newstackers:
newstacker = s()
newCols += newstacker.colsReq
self.stackerList.append(newstacker)
newCols = set(newCols)
newstackers = set()
for col in newCols:
if self.colInfo.getDataSource(
col) == self.colInfo.defaultDataSource:
self.dbCols.add(col)
else:
newstackers.add(self.colInfo.getDataSource(col))
for s in self.stackerList:
if s.__class__ in newstackers:
newstackers.remove(s.__class__)
# A Bit of cleanup.
# Remove 'metricdata' from dbcols if it ended here by default.
if 'metricdata' in self.dbCols:
self.dbCols.remove('metricdata')
if 'None' in self.dbCols:
self.dbCols.remove('None')
def setSummaryMetrics(self, summaryMetrics):
"""Set (or reset) the summary metrics for the metricbundle.
Parameters
----------
summaryMetrics : List[BaseMetric]
Instantiated summary metrics to use to calculate summary statistics for this metric.
"""
if summaryMetrics is not None:
if isinstance(summaryMetrics, metrics.BaseMetric):
self.summaryMetrics = [summaryMetrics]
else:
self.summaryMetrics = []
for s in summaryMetrics:
if not isinstance(s, metrics.BaseMetric):
raise ValueError(
'SummaryStats must only contain lsst.sims.maf.metrics objects'
)
self.summaryMetrics.append(s)
else:
# Add identity metric to unislicer metric values (to get them into resultsDB).
if self.slicer.slicerName == 'UniSlicer':
self.summaryMetrics = [metrics.IdentityMetric()]
else:
self.summaryMetrics = []
def setPlotFuncs(self, plotFuncs):
"""Set or reset the plotting functions.
The default is to use all the plotFuncs associated with the slicer, which
is what happens in self.plot if setPlotFuncs is not used to override self.plotFuncs.
Parameters
----------
plotFuncs : List[BasePlotter]
The plotter or plotters to use to generate visuals for this metric.
"""
if plotFuncs is not None:
if plotFuncs is isinstance(plotFuncs, plots.BasePlotter):
self.plotFuncs = [plotFuncs]
else:
self.plotFuncs = []
for pFunc in plotFuncs:
if not isinstance(pFunc, plots.BasePlotter):
raise ValueError(
'plotFuncs should contain instantiated ' +
'lsst.sims.maf.plotter objects.')
self.plotFuncs.append(pFunc)
else:
self.plotFuncs = []
for pFunc in self.slicer.plotFuncs:
if isinstance(pFunc, plots.BasePlotter):
self.plotFuncs.append(pFunc)
else:
self.plotFuncs.append(pFunc())
def setPlotDict(self, plotDict):
"""Set or update any property of plotDict.
Parameters
----------
plotDict : dict
A dictionary of plotting parameters.
The usable keywords vary with each lsst.sims.maf.plots Plotter.
"""
# Don't auto-generate anything here - the plotHandler does it.
if plotDict is not None:
self.plotDict.update(plotDict)
# Check for bad zp or normVal values.
if 'zp' in self.plotDict:
if self.plotDict['zp'] is not None:
if not np.isfinite(self.plotDict['zp']):
warnings.warn(
'Warning! Plot zp for %s was infinite: removing zp from plotDict'
% (self.fileRoot))
del self.plotDict['zp']
if 'normVal' in self.plotDict:
if self.plotDict['normVal'] == 0:
warnings.warn(
'Warning! Plot normalization value for %s was 0: removing normVal from plotDict'
% (self.fileRoot))
del self.plotDict['normVal']
def setDisplayDict(self, displayDict=None, resultsDb=None):
"""Set or update any property of displayDict.
Parameters
----------
displayDict : Optional[dict]
Dictionary of display parameters for showMaf.
Expected keywords: 'group', 'subgroup', 'order', 'caption'.
'group', 'subgroup', and 'order' control where the metric results are shown on the showMaf page.
'caption' provides a caption to use with the metric results.
These values are saved in the results database.
resultsDb : Optional[ResultsDb]
A MAF results database, used to save the display parameters.
"""
# Set up a temporary dictionary with the default values.
tmpDisplayDict = {
'group': None,
'subgroup': None,
'order': 0,
'caption': None
}
# Update from self.displayDict (to use existing values, if present).
tmpDisplayDict.update(self.displayDict)
# And then update from any values being passed now.
if displayDict is not None:
tmpDisplayDict.update(displayDict)
# Reset self.displayDict to this updated dictionary.
self.displayDict = tmpDisplayDict
# If we still need to auto-generate a caption, do it.
if self.displayDict['caption'] is None:
if self.metric.comment is None:
caption = self.metric.name + ' calculated on a %s basis' % (
self.slicer.slicerName)
if self.constraint != '' and self.constraint is not None:
caption += ' using a subset of data selected via %s.' % (
self.constraint)
else:
caption += '.'
else:
caption = self.metric.comment
if 'zp' in self.plotDict:
caption += ' Values plotted with a zeropoint of %.2f.' % (
self.plotDict['zp'])
if 'normVal' in self.plotDict:
caption += ' Values plotted with a normalization value of %.2f.' % (
self.plotDict['normVal'])
self.displayDict['caption'] = caption
if resultsDb:
# Update the display values in the resultsDb.
metricId = resultsDb.updateMetric(self.metric.name,
self.slicer.slicerName,
self.runName, self.constraint,
self.metadata, None)
resultsDb.updateDisplay(metricId, self.displayDict)
def setRunName(self, runName, updateFileRoot=True):
"""Set (or reset) the runName. FileRoot will be updated accordingly if desired.
Parameters
----------
runName: str
Run Name, which will become part of the fileRoot.
fileRoot: bool, opt
Flag to update the fileRoot with the runName. Default True.
"""
self.runName = runName
if updateFileRoot:
self._buildFileRoot()
def writeDb(self, resultsDb=None, outfileSuffix=None):
"""Write the metricValues to the database
"""
if outfileSuffix is not None:
outfile = self.fileRoot + '_' + outfileSuffix + '.npz'
else:
outfile = self.fileRoot + '.npz'
if resultsDb is not None:
metricId = resultsDb.updateMetric(self.metric.name,
self.slicer.slicerName,
self.runName, self.constraint,
self.metadata, outfile)
resultsDb.updateDisplay(metricId, self.displayDict)
def write(self,
comment='',
outDir='.',
outfileSuffix=None,
resultsDb=None):
"""Write metricValues (and associated metadata) to disk.
Parameters
----------
comment : Optional[str]
Any additional comments to add to the output file
outDir : Optional[str]
The output directory
outfileSuffix : Optional[str]
Additional suffix to add to the output files (typically a numerical suffix for movies)
resultsD : Optional[ResultsDb]
Results database to store information on the file output
"""
if outfileSuffix is not None:
outfile = self.fileRoot + '_' + outfileSuffix + '.npz'
else:
outfile = self.fileRoot + '.npz'
self.slicer.writeData(os.path.join(outDir, outfile),
self.metricValues,
metricName=self.metric.name,
simDataName=self.runName,
constraint=self.constraint,
metadata=self.metadata + comment,
displayDict=self.displayDict,
plotDict=self.plotDict)
if resultsDb is not None:
self.writeDb(resultsDb=resultsDb)
def outputJSON(self):
"""Set up and call the baseSlicer outputJSON method, to output to IO string.
Returns
-------
io
IO object containing JSON data representing the metric bundle data.
"""
io = self.slicer.outputJSON(self.metricValues,
metricName=self.metric.name,
simDataName=self.runName,
metadata=self.metadata,
plotDict=self.plotDict)
return io
def read(self, filename):
"""Read metricValues and associated metadata from disk.
Overwrites any data currently in metricbundle.
Parameters
----------
filename : str
The file from which to read the metric bundle data.
"""
if not os.path.isfile(filename):
raise IOError('%s not found' % filename)
self._resetMetricBundle()
# Set up a base slicer to read data (we don't know type yet).
baseslicer = slicers.BaseSlicer()
# Use baseslicer to read file.
metricValues, slicer, header = baseslicer.readData(filename)
self.slicer = slicer
self.metricValues = metricValues
self.metricValues.fill_value = slicer.badval
# It's difficult to reinstantiate the metric object, as we don't
# know what it is necessarily -- the metricName can be changed.
self.metric = metrics.BaseMetric()
# But, for plot label building, we do need to try to recreate the
# metric name and units.
self.metric.units = ''
if header is not None:
self.metric.name = header['metricName']
if 'plotDict' in header:
if 'units' in header['plotDict']:
self.metric.units = header['plotDict']['units']
self.runName = header['simDataName']
try:
self.constraint = header['constraint']
except KeyError:
self.constraint = header['sqlconstraint']
self.metadata = header['metadata']
if 'plotDict' in header:
self.setPlotDict(header['plotDict'])
if 'displayDict' in header:
self.setDisplayDict(header['displayDict'])
if self.metadata is None:
self._buildMetadata()
path, head = os.path.split(filename)
self.fileRoot = head.replace('.npz', '')
self.setPlotFuncs(None)
def computeSummaryStats(self, resultsDb=None):
"""Compute summary statistics on metricValues, using summaryMetrics (metricbundle list).
Parameters
----------
resultsDb : Optional[ResultsDb]
ResultsDb object to use to store the summary statistic values on disk.
"""
if self.summaryValues is None:
self.summaryValues = {}
if self.summaryMetrics is not None:
# Build array of metric values, to use for (most) summary statistics.
rarr_std = np.array(list(zip(self.metricValues.compressed())),
dtype=[('metricdata', self.metricValues.dtype)
])
for m in self.summaryMetrics:
# The summary metric colname should already be set to 'metricdata', but in case it's not:
m.colname = 'metricdata'
summaryName = m.name.replace(' metricdata',
'').replace(' None', '')
if hasattr(m, 'maskVal'):
# summary metric requests to use the mask value, as specified by itself,
# rather than skipping masked vals.
rarr = np.array(
list(zip(self.metricValues.filled(m.maskVal))),
dtype=[('metricdata', self.metricValues.dtype)])
else:
rarr = rarr_std
if np.size(rarr) == 0:
summaryVal = self.slicer.badval
else:
summaryVal = m.run(rarr)
self.summaryValues[summaryName] = summaryVal
# Add summary metric info to results database, if applicable.
if resultsDb:
metricId = resultsDb.updateMetric(self.metric.name,
self.slicer.slicerName,
self.runName,
self.constraint,
self.metadata, None)
resultsDb.updateSummaryStat(metricId,
summaryName=summaryName,
summaryValue=summaryVal)
def reduceMetric(self,
reduceFunc,
reducePlotDict=None,
reduceDisplayDict=None):
"""Run 'reduceFunc' (any function that operates on self.metricValues).
Typically reduceFunc will be the metric reduce functions, as they are tailored to expect the
metricValues format.
reduceDisplayDict and reducePlotDicts are displayDicts and plotDicts to be
applied to the new metricBundle.
Parameters
----------
reduceFunc : Func
Any function that will operate on self.metricValues (typically metric.reduce* function).
reducePlotDict : Optional[dict]
Plot dictionary for the results of the reduce function.
reduceDisplayDict : Optional[dict]
Display dictionary for the results of the reduce function.
Returns
-------
MetricBundle
New metric bundle, inheriting metadata from this metric bundle, but containing the new
metric values calculated with the 'reduceFunc'.
"""
# Generate a name for the metric values processed by the reduceFunc.
rName = reduceFunc.__name__.replace('reduce', '')
reduceName = self.metric.name + '_' + rName
# Set up metricBundle to store new metric values, and add plotDict/displayDict.
newmetric = deepcopy(self.metric)
newmetric.name = reduceName
newmetric.metricDtype = 'float'
if reducePlotDict is not None:
if 'units' in reducePlotDict:
newmetric.units = reducePlotDict['units']
newmetricBundle = MetricBundle(metric=newmetric,
slicer=self.slicer,
stackerList=self.stackerList,
constraint=self.constraint,
metadata=self.metadata,
runName=self.runName,
plotDict=None,
plotFuncs=self.plotFuncs,
displayDict=None,
summaryMetrics=self.summaryMetrics,
mapsList=self.mapsList,
fileRoot='')
# Build a new output file root name.
newmetricBundle._buildFileRoot()
# Add existing plotDict (except for title/xlabels etc) into new plotDict.
for k, v in self.plotDict.items():
if k not in newmetricBundle.plotDict:
newmetricBundle.plotDict[k] = v
# Update newmetricBundle's plot dictionary with any set explicitly by reducePlotDict.
newmetricBundle.setPlotDict(reducePlotDict)
# Copy the parent metric's display dict into the reduce display dict.
newmetricBundle.setDisplayDict(self.displayDict)
# Set the reduce function display 'order' (this is set in the BaseMetric
# by default, but can be overriden in a metric).
order = newmetric.reduceOrder[rName]
newmetricBundle.displayDict['order'] = order
# And then update the newmetricBundle's display dictionary with any set
# explicitly by reduceDisplayDict.
newmetricBundle.setDisplayDict(reduceDisplayDict)
# Set up new metricBundle's metricValues masked arrays, copying metricValue's mask.
newmetricBundle.metricValues = ma.MaskedArray(
data=np.empty(len(self.slicer), 'float'),
mask=self.metricValues.mask,
fill_value=self.slicer.badval)
# Fill the reduced metric data using the reduce function.
for i, (mVal, mMask) in enumerate(
zip(self.metricValues.data, self.metricValues.mask)):
if not mMask:
newmetricBundle.metricValues.data[i] = reduceFunc(mVal)
return newmetricBundle
def plot(self,
plotHandler=None,
plotFunc=None,
outfileSuffix=None,
savefig=False):
"""
Create all plots available from the slicer. plotHandler holds the output directory info, etc.
Parameters
----------
plotHandler : Optional[PlotHandler]
The plotHandler saves the output location and resultsDb connection for a set of plots.
plotFunc : Optional[BasePlotter]
Any plotter function. If not specified, the plotters in self.plotFuncs will be used.
outfileSuffix : Optional[str]
Optional string to append to the end of the plot output files.
Useful when creating sequences of images for movies.
savefig : Optional[bool]
Flag indicating whether or not to save the figure to disk. Default is False.
Returns
-------
dict
Dictionary of plotType:figure number key/value pairs, indicating what plots were created
and what matplotlib figure numbers were used.
"""
# Generate a plotHandler if none was set.
if plotHandler is None:
plotHandler = plots.PlotHandler(savefig=savefig)
# Make plots.
if plotFunc is not None:
if isinstance(plotFunc, plots.BasePlotter):
plotFunc = plotFunc
else:
plotFunc = plotFunc()
plotHandler.setMetricBundles([self])
plotHandler.setPlotDicts(plotDicts=[self.plotDict], reset=True)
madePlots = {}
if plotFunc is not None:
fignum = plotHandler.plot(plotFunc, outfileSuffix=outfileSuffix)
madePlots[plotFunc.plotType] = fignum
else:
for plotFunc in self.plotFuncs:
fignum = plotHandler.plot(plotFunc,
outfileSuffix=outfileSuffix)
madePlots[plotFunc.plotType] = fignum
return madePlots