Coverage for python/lsst/pipe/tasks/characterizeImage.py : 27%

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1#
2# LSST Data Management System
3# Copyright 2008-2015 AURA/LSST.
4#
5# This product includes software developed by the
6# LSST Project (http://www.lsst.org/).
7#
8# This program is free software: you can redistribute it and/or modify
9# it under the terms of the GNU General Public License as published by
10# the Free Software Foundation, either version 3 of the License, or
11# (at your option) any later version.
12#
13# This program is distributed in the hope that it will be useful,
14# but WITHOUT ANY WARRANTY; without even the implied warranty of
15# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
16# GNU General Public License for more details.
17#
18# You should have received a copy of the LSST License Statement and
19# the GNU General Public License along with this program. If not,
20# see <https://www.lsstcorp.org/LegalNotices/>.
21#
22import numpy as np
24from lsstDebug import getDebugFrame
25import lsst.afw.table as afwTable
26import lsst.pex.config as pexConfig
27import lsst.pipe.base as pipeBase
28import lsst.daf.base as dafBase
29import lsst.pipe.base.connectionTypes as cT
30from lsst.afw.math import BackgroundList
31from lsst.afw.table import SourceTable, SourceCatalog, IdFactory
32from lsst.meas.algorithms import SubtractBackgroundTask, SourceDetectionTask, MeasureApCorrTask
33from lsst.meas.algorithms.installGaussianPsf import InstallGaussianPsfTask
34from lsst.meas.astrom import RefMatchTask, displayAstrometry
35from lsst.meas.algorithms import LoadIndexedReferenceObjectsTask
36from lsst.obs.base import ExposureIdInfo
37from lsst.meas.base import SingleFrameMeasurementTask, ApplyApCorrTask, CatalogCalculationTask
38from lsst.meas.deblender import SourceDeblendTask
39from .measurePsf import MeasurePsfTask
40from .repair import RepairTask
42__all__ = ["CharacterizeImageConfig", "CharacterizeImageTask"]
45class CharacterizeImageConnections(pipeBase.PipelineTaskConnections,
46 dimensions=("instrument", "visit", "detector")):
47 exposure = cT.Input(
48 doc="Input exposure data",
49 name="postISRCCD",
50 storageClass="ExposureF",
51 dimensions=["instrument", "visit", "detector"],
52 )
53 characterized = cT.Output(
54 doc="Output characterized data.",
55 name="icExp",
56 storageClass="ExposureF",
57 dimensions=["instrument", "visit", "detector"],
58 )
59 sourceCat = cT.Output(
60 doc="Output source catalog.",
61 name="icSrc",
62 storageClass="SourceCatalog",
63 dimensions=["instrument", "visit", "detector"],
64 )
65 backgroundModel = cT.Output(
66 doc="Output background model.",
67 name="icExpBackground",
68 storageClass="Background",
69 dimensions=["instrument", "visit", "detector"],
70 )
71 outputSchema = cT.InitOutput(
72 doc="Schema of the catalog produced by CharacterizeImage",
73 name="icSrc_schema",
74 storageClass="SourceCatalog",
75 )
78class CharacterizeImageConfig(pipeBase.PipelineTaskConfig,
79 pipelineConnections=CharacterizeImageConnections):
81 """!Config for CharacterizeImageTask"""
82 doMeasurePsf = pexConfig.Field(
83 dtype=bool,
84 default=True,
85 doc="Measure PSF? If False then for all subsequent operations use either existing PSF "
86 "model when present, or install simple PSF model when not (see installSimplePsf "
87 "config options)"
88 )
89 doWrite = pexConfig.Field(
90 dtype=bool,
91 default=True,
92 doc="Persist results?",
93 )
94 doWriteExposure = pexConfig.Field(
95 dtype=bool,
96 default=True,
97 doc="Write icExp and icExpBackground in addition to icSrc? Ignored if doWrite False.",
98 )
99 psfIterations = pexConfig.RangeField(
100 dtype=int,
101 default=2,
102 min=1,
103 doc="Number of iterations of detect sources, measure sources, "
104 "estimate PSF. If useSimplePsf is True then 2 should be plenty; "
105 "otherwise more may be wanted.",
106 )
107 background = pexConfig.ConfigurableField(
108 target=SubtractBackgroundTask,
109 doc="Configuration for initial background estimation",
110 )
111 detection = pexConfig.ConfigurableField(
112 target=SourceDetectionTask,
113 doc="Detect sources"
114 )
115 doDeblend = pexConfig.Field(
116 dtype=bool,
117 default=True,
118 doc="Run deblender input exposure"
119 )
120 deblend = pexConfig.ConfigurableField(
121 target=SourceDeblendTask,
122 doc="Split blended source into their components"
123 )
124 measurement = pexConfig.ConfigurableField(
125 target=SingleFrameMeasurementTask,
126 doc="Measure sources"
127 )
128 doApCorr = pexConfig.Field(
129 dtype=bool,
130 default=True,
131 doc="Run subtasks to measure and apply aperture corrections"
132 )
133 measureApCorr = pexConfig.ConfigurableField(
134 target=MeasureApCorrTask,
135 doc="Subtask to measure aperture corrections"
136 )
137 applyApCorr = pexConfig.ConfigurableField(
138 target=ApplyApCorrTask,
139 doc="Subtask to apply aperture corrections"
140 )
141 # If doApCorr is False, and the exposure does not have apcorrections already applied, the
142 # active plugins in catalogCalculation almost certainly should not contain the characterization plugin
143 catalogCalculation = pexConfig.ConfigurableField(
144 target=CatalogCalculationTask,
145 doc="Subtask to run catalogCalculation plugins on catalog"
146 )
147 useSimplePsf = pexConfig.Field(
148 dtype=bool,
149 default=True,
150 doc="Replace the existing PSF model with a simplified version that has the same sigma "
151 "at the start of each PSF determination iteration? Doing so makes PSF determination "
152 "converge more robustly and quickly.",
153 )
154 installSimplePsf = pexConfig.ConfigurableField(
155 target=InstallGaussianPsfTask,
156 doc="Install a simple PSF model",
157 )
158 refObjLoader = pexConfig.ConfigurableField(
159 target=LoadIndexedReferenceObjectsTask,
160 doc="reference object loader",
161 )
162 ref_match = pexConfig.ConfigurableField(
163 target=RefMatchTask,
164 doc="Task to load and match reference objects. Only used if measurePsf can use matches. "
165 "Warning: matching will only work well if the initial WCS is accurate enough "
166 "to give good matches (roughly: good to 3 arcsec across the CCD).",
167 )
168 measurePsf = pexConfig.ConfigurableField(
169 target=MeasurePsfTask,
170 doc="Measure PSF",
171 )
172 repair = pexConfig.ConfigurableField(
173 target=RepairTask,
174 doc="Remove cosmic rays",
175 )
176 checkUnitsParseStrict = pexConfig.Field(
177 doc="Strictness of Astropy unit compatibility check, can be 'raise', 'warn' or 'silent'",
178 dtype=str,
179 default="raise",
180 )
182 def setDefaults(self):
183 super().setDefaults()
184 # just detect bright stars; includeThresholdMultipler=10 seems large,
185 # but these are the values we have been using
186 self.detection.thresholdValue = 5.0
187 self.detection.includeThresholdMultiplier = 10.0
188 self.detection.doTempLocalBackground = False
189 # do not deblend, as it makes a mess
190 self.doDeblend = False
191 # measure and apply aperture correction; note: measuring and applying aperture
192 # correction are disabled until the final measurement, after PSF is measured
193 self.doApCorr = True
194 # minimal set of measurements needed to determine PSF
195 self.measurement.plugins.names = [
196 "base_PixelFlags",
197 "base_SdssCentroid",
198 "base_SdssShape",
199 "base_GaussianFlux",
200 "base_PsfFlux",
201 "base_CircularApertureFlux",
202 ]
204 def validate(self):
205 if self.doApCorr and not self.measurePsf:
206 raise RuntimeError("Must measure PSF to measure aperture correction, "
207 "because flags determined by PSF measurement are used to identify "
208 "sources used to measure aperture correction")
210## \addtogroup LSST_task_documentation
211## \{
212## \page CharacterizeImageTask
213## \ref CharacterizeImageTask_ "CharacterizeImageTask"
214## \copybrief CharacterizeImageTask
215## \}
218class CharacterizeImageTask(pipeBase.PipelineTask, pipeBase.CmdLineTask):
219 r"""!Measure bright sources and use this to estimate background and PSF of an exposure
221 @anchor CharacterizeImageTask_
223 @section pipe_tasks_characterizeImage_Contents Contents
225 - @ref pipe_tasks_characterizeImage_Purpose
226 - @ref pipe_tasks_characterizeImage_Initialize
227 - @ref pipe_tasks_characterizeImage_IO
228 - @ref pipe_tasks_characterizeImage_Config
229 - @ref pipe_tasks_characterizeImage_Debug
232 @section pipe_tasks_characterizeImage_Purpose Description
234 Given an exposure with defects repaired (masked and interpolated over, e.g. as output by IsrTask):
235 - detect and measure bright sources
236 - repair cosmic rays
237 - measure and subtract background
238 - measure PSF
240 @section pipe_tasks_characterizeImage_Initialize Task initialisation
242 @copydoc \_\_init\_\_
244 @section pipe_tasks_characterizeImage_IO Invoking the Task
246 If you want this task to unpersist inputs or persist outputs, then call
247 the `runDataRef` method (a thin wrapper around the `run` method).
249 If you already have the inputs unpersisted and do not want to persist the output
250 then it is more direct to call the `run` method:
252 @section pipe_tasks_characterizeImage_Config Configuration parameters
254 See @ref CharacterizeImageConfig
256 @section pipe_tasks_characterizeImage_Debug Debug variables
258 The @link lsst.pipe.base.cmdLineTask.CmdLineTask command line task@endlink interface supports a flag
259 `--debug` to import `debug.py` from your `$PYTHONPATH`; see @ref baseDebug for more about `debug.py`.
261 CharacterizeImageTask has a debug dictionary with the following keys:
262 <dl>
263 <dt>frame
264 <dd>int: if specified, the frame of first debug image displayed (defaults to 1)
265 <dt>repair_iter
266 <dd>bool; if True display image after each repair in the measure PSF loop
267 <dt>background_iter
268 <dd>bool; if True display image after each background subtraction in the measure PSF loop
269 <dt>measure_iter
270 <dd>bool; if True display image and sources at the end of each iteration of the measure PSF loop
271 See @ref lsst.meas.astrom.displayAstrometry for the meaning of the various symbols.
272 <dt>psf
273 <dd>bool; if True display image and sources after PSF is measured;
274 this will be identical to the final image displayed by measure_iter if measure_iter is true
275 <dt>repair
276 <dd>bool; if True display image and sources after final repair
277 <dt>measure
278 <dd>bool; if True display image and sources after final measurement
279 </dl>
281 For example, put something like:
282 @code{.py}
283 import lsstDebug
284 def DebugInfo(name):
285 di = lsstDebug.getInfo(name) # N.b. lsstDebug.Info(name) would call us recursively
286 if name == "lsst.pipe.tasks.characterizeImage":
287 di.display = dict(
288 repair = True,
289 )
291 return di
293 lsstDebug.Info = DebugInfo
294 @endcode
295 into your `debug.py` file and run `calibrateTask.py` with the `--debug` flag.
297 Some subtasks may have their own debug variables; see individual Task documentation.
298 """
300 # Example description used to live here, removed 2-20-2017 by MSSG
302 ConfigClass = CharacterizeImageConfig
303 _DefaultName = "characterizeImage"
304 RunnerClass = pipeBase.ButlerInitializedTaskRunner
306 def runQuantum(self, butlerQC, inputRefs, outputRefs):
307 inputs = butlerQC.get(inputRefs)
308 if 'exposureIdInfo' not in inputs.keys():
309 exposureIdInfo = ExposureIdInfo()
310 exposureIdInfo.expId, exposureIdInfo.expBits = butlerQC.quantum.dataId.pack("visit_detector",
311 returnMaxBits=True)
312 inputs['exposureIdInfo'] = exposureIdInfo
313 outputs = self.run(**inputs)
314 butlerQC.put(outputs, outputRefs)
316 def __init__(self, butler=None, refObjLoader=None, schema=None, **kwargs):
317 """!Construct a CharacterizeImageTask
319 @param[in] butler A butler object is passed to the refObjLoader constructor in case
320 it is needed to load catalogs. May be None if a catalog-based star selector is
321 not used, if the reference object loader constructor does not require a butler,
322 or if a reference object loader is passed directly via the refObjLoader argument.
323 @param[in] refObjLoader An instance of LoadReferenceObjectsTasks that supplies an
324 external reference catalog to a catalog-based star selector. May be None if a
325 catalog star selector is not used or the loader can be constructed from the
326 butler argument.
327 @param[in,out] schema initial schema (an lsst.afw.table.SourceTable), or None
328 @param[in,out] kwargs other keyword arguments for lsst.pipe.base.CmdLineTask
329 """
330 super().__init__(**kwargs)
332 if schema is None:
333 schema = SourceTable.makeMinimalSchema()
334 self.schema = schema
335 self.makeSubtask("background")
336 self.makeSubtask("installSimplePsf")
337 self.makeSubtask("repair")
338 self.makeSubtask("measurePsf", schema=self.schema)
339 if self.config.doMeasurePsf and self.measurePsf.usesMatches:
340 if not refObjLoader:
341 self.makeSubtask('refObjLoader', butler=butler)
342 refObjLoader = self.refObjLoader
343 self.makeSubtask("ref_match", refObjLoader=refObjLoader)
344 self.algMetadata = dafBase.PropertyList()
345 self.makeSubtask('detection', schema=self.schema)
346 if self.config.doDeblend:
347 self.makeSubtask("deblend", schema=self.schema)
348 self.makeSubtask('measurement', schema=self.schema, algMetadata=self.algMetadata)
349 if self.config.doApCorr:
350 self.makeSubtask('measureApCorr', schema=self.schema)
351 self.makeSubtask('applyApCorr', schema=self.schema)
352 self.makeSubtask('catalogCalculation', schema=self.schema)
353 self._initialFrame = getDebugFrame(self._display, "frame") or 1
354 self._frame = self._initialFrame
355 self.schema.checkUnits(parse_strict=self.config.checkUnitsParseStrict)
356 self.outputSchema = afwTable.SourceCatalog(self.schema)
358 def getInitOutputDatasets(self):
359 outputCatSchema = afwTable.SourceCatalog(self.schema)
360 outputCatSchema.getTable().setMetadata(self.algMetadata)
361 return {'outputSchema': outputCatSchema}
363 @pipeBase.timeMethod
364 def runDataRef(self, dataRef, exposure=None, background=None, doUnpersist=True):
365 """!Characterize a science image and, if wanted, persist the results
367 This simply unpacks the exposure and passes it to the characterize method to do the work.
369 @param[in] dataRef: butler data reference for science exposure
370 @param[in,out] exposure exposure to characterize (an lsst.afw.image.ExposureF or similar).
371 If None then unpersist from "postISRCCD".
372 The following changes are made, depending on the config:
373 - set psf to the measured PSF
374 - set apCorrMap to the measured aperture correction
375 - subtract background
376 - interpolate over cosmic rays
377 - update detection and cosmic ray mask planes
378 @param[in,out] background initial model of background already subtracted from exposure
379 (an lsst.afw.math.BackgroundList). May be None if no background has been subtracted,
380 which is typical for image characterization.
381 A refined background model is output.
382 @param[in] doUnpersist if True the exposure is read from the repository
383 and the exposure and background arguments must be None;
384 if False the exposure must be provided.
385 True is intended for running as a command-line task, False for running as a subtask
387 @return same data as the characterize method
388 """
389 self._frame = self._initialFrame # reset debug display frame
390 self.log.info("Processing %s" % (dataRef.dataId))
392 if doUnpersist:
393 if exposure is not None or background is not None:
394 raise RuntimeError("doUnpersist true; exposure and background must be None")
395 exposure = dataRef.get("postISRCCD", immediate=True)
396 elif exposure is None:
397 raise RuntimeError("doUnpersist false; exposure must be provided")
399 exposureIdInfo = dataRef.get("expIdInfo")
401 charRes = self.run(
402 exposure=exposure,
403 exposureIdInfo=exposureIdInfo,
404 background=background,
405 )
407 if self.config.doWrite:
408 dataRef.put(charRes.sourceCat, "icSrc")
409 if self.config.doWriteExposure:
410 dataRef.put(charRes.exposure, "icExp")
411 dataRef.put(charRes.background, "icExpBackground")
413 return charRes
415 @pipeBase.timeMethod
416 def run(self, exposure, exposureIdInfo=None, background=None):
417 """!Characterize a science image
419 Peforms the following operations:
420 - Iterate the following config.psfIterations times, or once if config.doMeasurePsf false:
421 - detect and measure sources and estimate PSF (see detectMeasureAndEstimatePsf for details)
422 - interpolate over cosmic rays
423 - perform final measurement
425 @param[in,out] exposure exposure to characterize (an lsst.afw.image.ExposureF or similar).
426 The following changes are made:
427 - update or set psf
428 - set apCorrMap
429 - update detection and cosmic ray mask planes
430 - subtract background and interpolate over cosmic rays
431 @param[in] exposureIdInfo ID info for exposure (an lsst.obs.base.ExposureIdInfo).
432 If not provided, returned SourceCatalog IDs will not be globally unique.
433 @param[in,out] background initial model of background already subtracted from exposure
434 (an lsst.afw.math.BackgroundList). May be None if no background has been subtracted,
435 which is typical for image characterization.
437 @return pipe_base Struct containing these fields, all from the final iteration
438 of detectMeasureAndEstimatePsf:
439 - exposure: characterized exposure; image is repaired by interpolating over cosmic rays,
440 mask is updated accordingly, and the PSF model is set
441 - sourceCat: detected sources (an lsst.afw.table.SourceCatalog)
442 - background: model of background subtracted from exposure (an lsst.afw.math.BackgroundList)
443 - psfCellSet: spatial cells of PSF candidates (an lsst.afw.math.SpatialCellSet)
444 """
445 self._frame = self._initialFrame # reset debug display frame
447 if not self.config.doMeasurePsf and not exposure.hasPsf():
448 self.log.warn("Source catalog detected and measured with placeholder or default PSF")
449 self.installSimplePsf.run(exposure=exposure)
451 if exposureIdInfo is None:
452 exposureIdInfo = ExposureIdInfo()
454 # subtract an initial estimate of background level
455 background = self.background.run(exposure).background
457 psfIterations = self.config.psfIterations if self.config.doMeasurePsf else 1
458 for i in range(psfIterations):
459 dmeRes = self.detectMeasureAndEstimatePsf(
460 exposure=exposure,
461 exposureIdInfo=exposureIdInfo,
462 background=background,
463 )
465 psf = dmeRes.exposure.getPsf()
466 psfSigma = psf.computeShape().getDeterminantRadius()
467 psfDimensions = psf.computeImage().getDimensions()
468 medBackground = np.median(dmeRes.background.getImage().getArray())
469 self.log.info("iter %s; PSF sigma=%0.2f, dimensions=%s; median background=%0.2f" %
470 (i + 1, psfSigma, psfDimensions, medBackground))
472 self.display("psf", exposure=dmeRes.exposure, sourceCat=dmeRes.sourceCat)
474 # perform final repair with final PSF
475 self.repair.run(exposure=dmeRes.exposure)
476 self.display("repair", exposure=dmeRes.exposure, sourceCat=dmeRes.sourceCat)
478 # perform final measurement with final PSF, including measuring and applying aperture correction,
479 # if wanted
480 self.measurement.run(measCat=dmeRes.sourceCat, exposure=dmeRes.exposure,
481 exposureId=exposureIdInfo.expId)
482 if self.config.doApCorr:
483 apCorrMap = self.measureApCorr.run(exposure=dmeRes.exposure, catalog=dmeRes.sourceCat).apCorrMap
484 dmeRes.exposure.getInfo().setApCorrMap(apCorrMap)
485 self.applyApCorr.run(catalog=dmeRes.sourceCat, apCorrMap=exposure.getInfo().getApCorrMap())
486 self.catalogCalculation.run(dmeRes.sourceCat)
488 self.display("measure", exposure=dmeRes.exposure, sourceCat=dmeRes.sourceCat)
490 return pipeBase.Struct(
491 exposure=dmeRes.exposure,
492 sourceCat=dmeRes.sourceCat,
493 background=dmeRes.background,
494 psfCellSet=dmeRes.psfCellSet,
496 characterized=dmeRes.exposure,
497 backgroundModel=dmeRes.background
498 )
500 @pipeBase.timeMethod
501 def detectMeasureAndEstimatePsf(self, exposure, exposureIdInfo, background):
502 """!Perform one iteration of detect, measure and estimate PSF
504 Performs the following operations:
505 - if config.doMeasurePsf or not exposure.hasPsf():
506 - install a simple PSF model (replacing the existing one, if need be)
507 - interpolate over cosmic rays with keepCRs=True
508 - estimate background and subtract it from the exposure
509 - detect, deblend and measure sources, and subtract a refined background model;
510 - if config.doMeasurePsf:
511 - measure PSF
513 @param[in,out] exposure exposure to characterize (an lsst.afw.image.ExposureF or similar)
514 The following changes are made:
515 - update or set psf
516 - update detection and cosmic ray mask planes
517 - subtract background
518 @param[in] exposureIdInfo ID info for exposure (an lsst.obs_base.ExposureIdInfo)
519 @param[in,out] background initial model of background already subtracted from exposure
520 (an lsst.afw.math.BackgroundList).
522 @return pipe_base Struct containing these fields, all from the final iteration
523 of detect sources, measure sources and estimate PSF:
524 - exposure characterized exposure; image is repaired by interpolating over cosmic rays,
525 mask is updated accordingly, and the PSF model is set
526 - sourceCat detected sources (an lsst.afw.table.SourceCatalog)
527 - background model of background subtracted from exposure (an lsst.afw.math.BackgroundList)
528 - psfCellSet spatial cells of PSF candidates (an lsst.afw.math.SpatialCellSet)
529 """
530 # install a simple PSF model, if needed or wanted
531 if not exposure.hasPsf() or (self.config.doMeasurePsf and self.config.useSimplePsf):
532 self.log.warn("Source catalog detected and measured with placeholder or default PSF")
533 self.installSimplePsf.run(exposure=exposure)
535 # run repair, but do not interpolate over cosmic rays (do that elsewhere, with the final PSF model)
536 self.repair.run(exposure=exposure, keepCRs=True)
537 self.display("repair_iter", exposure=exposure)
539 if background is None:
540 background = BackgroundList()
542 sourceIdFactory = IdFactory.makeSource(exposureIdInfo.expId, exposureIdInfo.unusedBits)
543 table = SourceTable.make(self.schema, sourceIdFactory)
544 table.setMetadata(self.algMetadata)
546 detRes = self.detection.run(table=table, exposure=exposure, doSmooth=True)
547 sourceCat = detRes.sources
548 if detRes.fpSets.background:
549 for bg in detRes.fpSets.background:
550 background.append(bg)
552 if self.config.doDeblend:
553 self.deblend.run(exposure=exposure, sources=sourceCat)
555 self.measurement.run(measCat=sourceCat, exposure=exposure, exposureId=exposureIdInfo.expId)
557 measPsfRes = pipeBase.Struct(cellSet=None)
558 if self.config.doMeasurePsf:
559 if self.measurePsf.usesMatches:
560 matches = self.ref_match.loadAndMatch(exposure=exposure, sourceCat=sourceCat).matches
561 else:
562 matches = None
563 measPsfRes = self.measurePsf.run(exposure=exposure, sources=sourceCat, matches=matches,
564 expId=exposureIdInfo.expId)
565 self.display("measure_iter", exposure=exposure, sourceCat=sourceCat)
567 return pipeBase.Struct(
568 exposure=exposure,
569 sourceCat=sourceCat,
570 background=background,
571 psfCellSet=measPsfRes.cellSet,
572 )
574 def getSchemaCatalogs(self):
575 """Return a dict of empty catalogs for each catalog dataset produced by this task.
576 """
577 sourceCat = SourceCatalog(self.schema)
578 sourceCat.getTable().setMetadata(self.algMetadata)
579 return {"icSrc": sourceCat}
581 def display(self, itemName, exposure, sourceCat=None):
582 """Display exposure and sources on next frame, if display of itemName has been requested
584 @param[in] itemName name of item in debugInfo
585 @param[in] exposure exposure to display
586 @param[in] sourceCat source catalog to display
587 """
588 val = getDebugFrame(self._display, itemName)
589 if not val:
590 return
592 displayAstrometry(exposure=exposure, sourceCat=sourceCat, frame=self._frame, pause=False)
593 self._frame += 1