def standardAngleMetrics(colname, replace_colname=None): """A set of standard simple metrics for some quantity which is a wrap-around angle. Parameters ---------- colname : str The column name to apply the metrics to. replace_colname: str or None, opt Value to replace colname with in the metricName. i.e. if replace_colname='' then metric name is Mean, instead of Mean Airmass, or if replace_colname='seeingGeom', then metric name is Mean seeingGeom instead of Mean seeingFwhmGeom. Default is None, which does not alter the metric name. Returns ------- List of configured metrics. """ standardAngleMetrics = [ metrics.MeanAngleMetric(colname), metrics.RmsAngleMetric(colname), metrics.FullRangeAngleMetric(colname), metrics.MinMetric(colname), metrics.MaxMetric(colname) ] if replace_colname is not None: for m in standardAngleMetrics: if len(replace_colname) > 0: m.name = m.name.replace('%s' % colname, '%s' % replace_colname) else: m.name = m.name.rstrip(' %s' % colname) return standardAngleMetrics
def meanRADec(colmap=None, runName='opsim', extraSql=None, extraMetadata=None): """Plot the range of RA/Dec as a function of night. Parameters ---------- colmap : dict, opt A dictionary with a mapping of column names. Default will use OpsimV4 column names. runName : str, opt The name of the simulated survey. Default is "opsim". extraSql : str, opt Additional constraint to add to any sql constraints (e.g. 'night<365') Default None, for no additional constraints. extraMetadata : str, opt Additional metadata to add before any below (i.e. "WFD"). Default is None. """ if colmap is None: colmap = ColMapDict('opsimV4') bundleList = [] plotBundles = [] group = 'RA Dec coverage' subgroup = 'All visits' if extraMetadata is not None: subgroup = extraMetadata displayDict = {'group': group, 'subgroup': subgroup, 'order': 0} ra_metrics = [metrics.MeanAngleMetric(colmap['ra']), metrics.FullRangeAngleMetric(colmap['ra'])] dec_metrics = [metrics.MeanMetric(colmap['dec']), metrics.MinMetric(colmap['dec']), metrics.MaxMetric(colmap['dec'])] for m in ra_metrics: slicer = slicers.OneDSlicer(sliceColName=colmap['night'], binsize=1) if not colmap['raDecDeg']: plotDict = {'yMin': np.radians(-5), 'yMax': np.radians(365)} else: plotDict = {'yMin': -5, 'yMax': 365} bundle = mb.MetricBundle(m, slicer, extraSql, metadata=extraMetadata, displayDict=displayDict, plotDict=plotDict) bundleList.append(bundle) for m in dec_metrics: slicer = slicers.OneDSlicer(sliceColName=colmap['night'], binsize=1) bundle = mb.MetricBundle(m, slicer, extraSql, metadata=extraMetadata, displayDict=displayDict) bundleList.append(bundle) # Set the runName for all bundles and return the bundleDict. for b in bundleList: b.setRunName(runName) return mb.makeBundlesDictFromList(bundleList), plotBundles
def testFullRangeAngleMetric(self): """Test full range angle metric.""" dv1 = np.arange(0, 32, 2.5) dv2 = (dv1 - 20.0) % 360. dv1 = np.array(list(zip(dv1)), dtype=[('testdata', 'float')]) dv2 = np.array(list(zip(dv2)), dtype=[('testdata', 'float')]) testmetric = metrics.FullRangeAngleMetric('testdata') result1 = testmetric.run(dv1) result2 = testmetric.run(dv2) self.assertAlmostEqual(result1, result2) dv = np.arange(0, 358, 5) dv = np.array(list(zip(dv)), dtype=[('testdata', 'float')]) result = testmetric.run(dv) self.assertAlmostEqual(result, 355) dv = np.random.rand(10000)*360.0 dv = np.array(list(zip(dv)), dtype=[('testdata', 'float')]) result = testmetric.run(dv) result = result self.assertGreater(result, 355)
def metadataBasicsAngle(value, colmap=None, runName='opsim', valueName=None, groupName=None, extraSql=None, extraMetadata=None, nside=64, ditherStacker=None, ditherkwargs=None): """Calculate basic metrics on visit metadata 'value', where value is a wrap-around angle. Calculates extended standard metrics (with unislicer) on the quantity (all visits and per filter), makes histogram of the value (all visits and per filter), Parameters ---------- value : str The column name for the quantity to evaluate. (column name in the database or created by a stacker). colmap : dict or None, opt A dictionary with a mapping of column names. Default will use OpsimV4 column names. runName : str, opt The name of the simulated survey. Default is "opsim". valueName : str, opt The name of the value to be reported in the resultsDb and added to the metric. This is intended to help standardize metric comparison between sim versions. value = name as it is in the database (seeingFwhmGeom, etc). valueName = name to be recorded ('seeingGeom', etc.). Default is None, which will match 'value'. groupName : str, opt The group name for this quantity in the displayDict. Default is the same as 'valueName', capitalized. extraSql : str, opt Additional constraint to add to any sql constraints (e.g. 'propId=1' or 'fieldID=522'). Default None, for no additional constraints. extraMetadata : str, opt Additional metadata to add before any below (i.e. "WFD"). Default is None. nside : int, opt Nside value for healpix slicer. Default 64. If "None" is passed, the healpixslicer-based metrics will be skipped. ditherStacker: str or lsst.sims.maf.stackers.BaseDitherStacker Optional dither stacker to use to define ra/dec columns. ditherkwargs: dict, opt Optional dictionary of kwargs for the dither stacker. Returns ------- metricBundleDict """ if colmap is None: colmap = ColMapDict('opsimV4') bundleList = [] if valueName is None: valueName = value if groupName is None: groupName = valueName.capitalize() subgroup = extraMetadata else: groupName = groupName.capitalize() subgroup = valueName.capitalize() if subgroup is None: subgroup = 'All visits' displayDict = {'group': groupName, 'subgroup': subgroup} raCol, decCol, degrees, ditherStacker, ditherMeta = radecCols( ditherStacker, colmap, ditherkwargs) extraMetadata = combineMetadata(extraMetadata, ditherMeta) # Set up basic all and per filter sql constraints. filterlist, colors, orders, sqls, metadata = filterList( all=True, extraSql=extraSql, extraMetadata=extraMetadata) stackerList = [ditherStacker] # Summarize values over all and per filter. slicer = slicers.UniSlicer() for f in filterlist: for m in standardAngleMetrics(value, replace_colname=valueName): displayDict['caption'] = '%s for %s.' % (m.name, metadata[f]) displayDict['order'] = orders[f] bundle = mb.MetricBundle(m, slicer, sqls[f], stackerList=stackerList, metadata=metadata[f], displayDict=displayDict) bundleList.append(bundle) # Histogram values over all and per filter. for f in filterlist: displayDict['caption'] = 'Histogram of %s' % (value) if valueName != value: displayDict['caption'] += ' (%s)' % (valueName) displayDict['caption'] += ' for %s.' % (metadata[f]) displayDict['order'] = orders[f] m = metrics.CountMetric(value, metricName='%s Histogram' % (valueName)) slicer = slicers.OneDSlicer(sliceColName=value) bundle = mb.MetricBundle(m, slicer, sqls[f], stackerList=stackerList, metadata=metadata[f], displayDict=displayDict) bundleList.append(bundle) # Make maps of min/median/max for all and per filter, per RA/Dec, with standard summary stats. mList = [] mList.append( metrics.MeanAngleMetric(value, metricName='AngleMean %s' % (valueName))) mList.append( metrics.FullRangeAngleMetric(value, metricName='AngleRange %s' % (valueName))) mList.append( metrics.RmsAngleMetric(value, metricName='AngleRms %s' % (valueName))) slicer = slicers.HealpixSlicer(nside=nside, latCol=decCol, lonCol=raCol, latLonDeg=degrees) subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()] for f in filterlist: for m in mList: displayDict['caption'] = 'Map of %s' % m.name if valueName != value: displayDict['caption'] += ' (%s)' % value displayDict['caption'] += ' for %s.' % metadata[f] displayDict['order'] = orders[f] bundle = mb.MetricBundle(m, slicer, sqls[f], stackerList=stackerList, metadata=metadata[f], plotFuncs=subsetPlots, displayDict=displayDict, summaryMetrics=standardSummary()) bundleList.append(bundle) # Set the runName for all bundles and return the bundleDict. for b in bundleList: b.setRunName(runName) return mb.makeBundlesDictFromList(bundleList)