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# This file is part of verify. 

# 

# Developed for the LSST Data Management System. 

# This product includes software developed by the LSST Project 

# (https://www.lsst.org). 

# See the COPYRIGHT file at the top-level directory of this distribution 

# for details of code ownership. 

# 

# This program is free software: you can redistribute it and/or modify 

# it under the terms of the GNU General Public License as published by 

# the Free Software Foundation, either version 3 of the License, or 

# (at your option) any later version. 

# 

# This program is distributed in the hope that it will be useful, 

# but WITHOUT ANY WARRANTY; without even the implied warranty of 

# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

# GNU General Public License for more details. 

# 

# You should have received a copy of the GNU General Public License 

# along with this program. If not, see <https://www.gnu.org/licenses/>. 

 

__all__ = ["MetricTask"] 

 

import abc 

 

import lsst.pex.config 

import lsst.pipe.base as pipeBase 

 

 

class MetricTask(pipeBase.Task, metaclass=abc.ABCMeta): 

"""A base class for tasks that compute one metric from input datasets. 

 

Parameters 

---------- 

*args 

**kwargs 

Constructor parameters are the same as for 

`lsst.pipe.base.PipelineTask`. 

 

Notes 

----- 

In general, both the ``MetricTask``'s metric and its input data are 

configurable. Metrics may be associated with a data ID at any level of 

granularity, including repository-wide. 

 

Like `lsst.pipe.base.PipelineTask`, this class should be customized by 

overriding one of `run` or `adaptArgsAndRun` and by providing an 

`~lsst.pipe.base.InputDatasetField` for each parameter of `run`. For 

requirements on these methods that are specific to ``MetricTask``, see 

`adaptArgsAndRun`. 

 

.. note:: 

The API is designed to make it easy to convert all ``MetricTasks`` to 

`~lsst.pipe.base.PipelineTask` later, but this class is *not* a 

`~lsst.pipe.base.PipelineTask` and does not work with activators, 

quanta, or `lsst.daf.butler`. 

""" 

 

# TODO: create a specialized MetricTaskConfig once metrics have 

# Butler datasets 

ConfigClass = lsst.pex.config.Config 

 

def __init__(self, **kwargs): 

super().__init__(**kwargs) 

 

def adaptArgsAndRun(self, inputData, inputDataIds, outputDataId): 

"""Compute a metric from in-memory data. 

 

Parameters 

---------- 

inputData : `dict` from `str` to any 

Dictionary whose keys are the names of input parameters and values 

are Python-domain data objects (or lists of objects) retrieved 

from data butler. Accepting lists of objects is strongly 

recommended; this allows metrics to vary their granularity up to 

the granularity of the input data without the need for extensive 

code changes. Input objects may be `None` to represent 

missing data. 

inputDataIds : `dict` from `str` to `list` of dataId 

Dictionary whose keys are the names of input parameters and values 

are data IDs (or lists of data IDs) that the task consumes for 

corresponding dataset type. Data IDs are guaranteed to match data 

objects in ``inputData``. 

outputDataId : `dict` from `str` to dataId 

Dictionary containing a single key, ``"measurement"``, which maps 

to a single data ID for the measurement. The data ID must have the 

same granularity as the metric. 

 

Returns 

------- 

struct : `lsst.pipe.base.Struct` 

A `~lsst.pipe.base.Struct` containing at least the 

following component: 

 

- ``measurement``: the value of the metric identified by 

`getOutputMetricName`, computed from ``inputData`` 

(`lsst.verify.Measurement` or `None`). The measurement is 

guaranteed to contain not only the value of the metric, but also 

any mandatory supplementary information. 

 

Raises 

------ 

lsst.verify.tasks.MetricComputationError 

Raised if an algorithmic or system error prevents calculation 

of the metric. Examples include corrupted input data or 

unavoidable exceptions raised by analysis code. The 

`~lsst.verify.tasks.MetricComputationError` should be chained to a 

more specific exception describing the root cause. 

 

Not having enough data for a metric to be applicable is not an 

error, and should not trigger this exception. 

 

Notes 

----- 

This implementation calls `run` on the contents of ``inputData``, 

followed by calling `addStandardMetadata` on the result before 

returning it. Any subclass that overrides this method must also call 

`addStandardMetadata` on its measurement before returning it. 

 

`adaptArgsAndRun` and `run` should assume they take multiple input 

datasets, regardless of the expected metric granularity. Doing so lets 

metrics be defined with a different granularity from the Science 

Pipelines processing, and allows for the aggregation (or lack thereof) 

of the metric to be controlled by the task configuration with no code 

changes. This rule may be broken if it is impossible for more than one 

copy of a dataset to exist. 

 

All input data must be treated as optional. This maximizes the 

``MetricTask``'s usefulness for incomplete pipeline runs or runs with 

optional processing steps. If a metric cannot be calculated because 

the necessary inputs are missing, the ``MetricTask`` must return `None` 

in place of the measurement. 

 

Examples 

-------- 

Consider a metric that characterizes PSF variations across the entire 

field of view, given processed images. Then, if `run` has the 

signature ``run(images)``: 

 

.. code-block:: py 

 

inputData = {'images': [image1, image2, ...]} 

inputDataIds = {'images': [{'visit': 42, 'ccd': 1}, 

{'visit': 42, 'ccd': 2}, 

...]} 

outputDataId = {'measurement': {'visit': 42}} 

result = task.adaptArgsAndRun( 

inputData, inputDataIds, outputDataId) 

""" 

result = self.run(**inputData) 

if result.measurement is not None: 

self.addStandardMetadata(result.measurement, 

outputDataId["measurement"]) 

return result 

 

@classmethod 

def getInputDatasetTypes(cls, config): 

"""Return input dataset types for this task. 

 

Parameters 

---------- 

config : ``cls.ConfigClass`` 

Configuration for this task. 

 

Returns 

------- 

datasets : `dict` from `str` to `str` 

Dictionary where the key is the name of the input dataset (must 

match a parameter to `run`) and the value is the name of its 

Butler dataset type. 

 

Notes 

----- 

The default implementation searches ``config`` for 

`~lsst.pipe.base.InputDatasetConfig` fields, much like 

`lsst.pipe.base.PipelineTask.getInputDatasetTypes` does. 

""" 

datasets = {} 

for key, value in config.items(): 

if isinstance(value, lsst.pipe.base.InputDatasetConfig): 

datasets[key] = value.name 

return datasets 

 

@classmethod 

@abc.abstractmethod 

def getOutputMetricName(cls, config): 

"""Identify the metric calculated by this ``MetricTask``. 

 

Parameters 

---------- 

config : ``cls.ConfigClass`` 

Configuration for this ``MetricTask``. 

 

Returns 

------- 

metric : `lsst.verify.Name` 

The name of the metric computed by objects of this class when 

configured with ``config``. 

""" 

 

def addStandardMetadata(self, measurement, outputDataId): 

"""Add data ID-specific metadata required for all metrics. 

 

This method currently does not add any metadata, but may do so 

in the future. 

 

Parameters 

---------- 

measurement : `lsst.verify.Measurement` 

The `~lsst.verify.Measurement` that the metadata are added to. 

outputDataId : ``dataId`` 

The data ID to which the measurement applies, at the appropriate 

level of granularity. 

 

Notes 

----- 

This method must be called by any subclass that overrides 

`adaptArgsAndRun`, but should be ignored otherwise. It should not be 

overridden by subclasses. 

 

This method is not responsible for shared metadata like the execution 

environment (which should be added by this ``MetricTask``'s caller), 

nor for metadata specific to a particular metric (which should be 

added when the metric is calculated). 

 

.. warning:: 

This method's signature will change whenever additional data needs 

to be provided. This is a deliberate restriction to ensure that all 

subclasses pass in the new data as well. 

""" 

pass