lsst.pipe.tasks provides many of the Task classes that drive the LSST Science Pipelines. The command-line tasks listed here are useful data processing entry points for most users. You can also assemble your own pipelines by combining individual tasks through their Python APIs.

lsst.pipe.tasks does not provide all the tasks and command-line tasks in the LSST Science Pipelines. For a complete list of all available tasks, see Task index and for an introduction to processing data see Getting started with the LSST Science Pipelines.

## Contributing¶

lsst.pipe.tasks is developed at https://github.com/lsst/pipe_tasks. You can find Jira issues for this module under the pipe_tasks component.

Assemble a coadded image from a set of warps (coadded temporary exposures).
!Calibrate an exposure: measure sources and perform astrometric and photometric calibration
!Measure bright sources and use this to estimate background and PSF of an exposure
Assemble a compareWarp coadded image from a set of warps by masking artifacts detected by comparing PSF-matched warps.
Task to consolidate HealSparse property maps.
Concatenate sourceTable list into a per-visit sourceTable_visit
Assemble DCR coadded images from a set of warps.
Detect sources on a DCR coadd.
Task to compute Healsparse property maps.
Subtract an image from a template and measure the result
!Warp and optionally PSF-Match calexps onto an a common projection.
Merge dcrCoadd detections from multiple subfilters.
Merge measurements from multiple subfilters.
Task that computes the number of science sources created through deblending.
Task that computes the number of science sources that have been deblended.
Assemble a coadded image from a set of coadded temporary exposures, being careful to clip & flag areas with potential artifacts.
Transform/standardize a source catalog
!Image difference Task used in the Winter 2013 data challege. Enables testing the effects of registration shifts and scatter.
Write source table to parquet

Deblend the sources in a merged catalog
!Example command-line task that computes simple statistics on an image
Retrieve data from a repository, e.g. for plotting or analysis purposes
!Make a DiscreteSkyMap in a repository, using the bounding box of a set of calexps.
!Make a sky map in a repository
!Assemble raw data, fit the PSF, detect and measure, and fit WCS and zero-point

An abstract base class for subtasks that inject fake sources into images to test completeness and other aspects of the processing.
Select up to a maximum number of the best-seeing images using their Wcs.
Task that will parse the filename and/or its contents to get the required information to populate the calibration registry.
Task that will generate the calibration registry for the Mapper
Task to compute exposure summary statistics.
!Example task to compute sigma-clipped mean and standard deviation of an image
!Example task to compute mean and standard deviation of an image
Task for making a HealSparse input map.
Task that generates registry for calibration images
Task that will ingest images into the data repository
Load multi-band reference objects from a reference catalog.
Find streaks or other straight lines in image data.
Base class for data processing tasks.
Task that will parse the filename and/or its contents to get the required information for putting the file in the correct location and populating the registry.
Task that will ingest images into the data repository
Task that will generate the registry for the Mapper
Select images using their Wcs and cuts on the PSF properties
Task that will generate the registry for the Mapper
Task to register (align) multiple images.
Scale the variance in a MaskedImage
Compute scale factor to scale exposures to a desired photometric zero point
Add isPrimaryKey to a given schema.
A task to warp and PSF-match an exposure
Select images using their Wcs

### Configurations¶

Colorterm
!Colorterm correction for one pair of filters
ColortermDict
A mapping of physical filter label to Colorterm
ColortermLibrary
!A mapping of photometric reference catalog name or glob to ColortermDict
CullPeaksConfig
! @anchor CullPeaksConfig
DatabaseSelectImagesConfig
Base configuration for subclasses of BaseSelectImagesTask that use a database
InitialPsfConfig
!Describes the initial PSF used for detection and measurement before we do PSF determination.

## Python API reference¶

#### Classes¶

 AssembleCoaddTask(*args, **kwargs) Assemble a coadded image from a set of warps (coadded temporary exposures). AssembleCoaddConnections(*[, config]) AssembleCoaddConfig Configuration parameters for the AssembleCoaddTask. SafeClipAssembleCoaddTask(*args, **kwargs) Assemble a coadded image from a set of coadded temporary exposures, being careful to clip & flag areas with potential artifacts. SafeClipAssembleCoaddConfig Configuration parameters for the SafeClipAssembleCoaddTask. CompareWarpAssembleCoaddTask(*args, **kwargs) Assemble a compareWarp coadded image from a set of warps by masking artifacts detected by comparing PSF-matched warps. CompareWarpAssembleCoaddConfig

#### Class Inheritance Diagram¶

 DcrAssembleCoaddConnections(*[, config]) DcrAssembleCoaddTask(*args, **kwargs) Assemble DCR coadded images from a set of warps. DcrAssembleCoaddConfig
 NumberDeblendedSourcesMetricTask(**kwargs) Task that computes the number of science sources that have been deblended. NumberDeblendedSourcesMetricConfig NumberDeblendChildSourcesMetricTask(**kwargs) Task that computes the number of science sources created through deblending. NumberDeblendChildSourcesMetricConfig