Creating measurement classes

The MeasurementBase abstract base class defines a standard interface for writing classes that make measurements of metrics. MeasurementBase ensures that measurements, along with metadata, can be serialized and submitted to the SQUASH metric monitoring service.

This page covers the usage of MeasurementBase for creating measurement classes.

A minimal measurement class

At a minimum, measurement classes must subclass MeasurementBase. This is a basic template for a measurement class:

import os
import astropy.units as u
from lsst.utils import getPackageDir
from lsst.validate.base import MeasurementBase

class PA1Measurement(MeasurementBase):

    def __init__(self, yaml_path):

        self.metric = Metric.from_yaml('PA1', yaml_path=yaml_path)

        # measurement code
        # ...

        # This is the scalar measurement value; always as an astropy.unit.Quantity
        self.quantity = 2. * u.mmag

yaml_path = os.path.join(getPackageDir('validate_drp'),
pa1_measurement = PA1Measurement(yaml_path)

In a measurement class, the MeasurementBase.metric attribute must be set with a Metric instance (this is required by the MeasurementBase abstract base class). In this example, the Metric.from_yaml class method constructs a Metric instance for PA1 from the metrics.yaml file built into validate_drp.

Storing a measurement quantity

The purpose of a measurement class is to make a measurement; those calculations should occur in a measurement instance’s __init__ method. Any data required for a measurement should be provided as arguments to the measurement class’s __init__ method.

The measurement result is stored in a MeasurementBase.quantity attribute. MeasurementBase.quantity must be a scalar astropy.units.Quantity. The units of MeasurementBase.quantity must be compatible with the units of the Metric. If a measurement class is unable to make a measurement, MeasurementBase.quantity should be None.

Registering measurement parameters

Often a measurement code is customized with parameters. As a means of lightweight provenance, the measurement API provides a MeasurementBase.register_parameter method to declare these parameters so that they’re persisted to the database:

class PA1Measurement(MeasurementBase):

    def __init__(self, yaml_path, num_random_shuffles=50):

        self.metric = Metric.from_yaml(self.label, yaml_path)

                                description='Number of random shuffles')

        # ... measurement code

In this example, the PA1Measurement class registers a parameter named num_random_shuffles.

A parameter’s ‘quantity’ can be a astropy.units.Quantity, str, bool or a unitless int. In this example, num_random_shuffles doesn’t have physical units, so it is a unitless int.

Accessing parameter values as object attributes

Once registered, the values of parameters are available as instance attributes. Continuing the PA1Measurement example:

pa1 = PA1Measurment(num_random_shuffles=50)
pa1.num_random_shuffles  # == 50

Through attribute access, a parameter’s value can be both read and updated. Remember that a parameter can only be set with a Quantity, str, bool, or int type.

Accessing parameters as Datum objects

Although the values of parameters can be accessed through object attributes, they are stored internally as Datum objects. You can access these Datums as items of the parameters attribute:

pa1.parameters['num_random_shuffles'].quantity  # 50
pa1.parameters['num_random_shuffles'].unit  # None, as a unitless int
pa1.parameters['num_random_shuffles'].label  # 'num_random_shuffles'
pa1.parameters['num_random_shuffles'].description  # 'Number of random shuffles'

Alternative ways of registering parameters

The register_parameter() method is flexible in terms of its arguments. For example, it’s possible to first register a parameter and set its value later:

                        description='Number of random shuffles')
# ...
self.num_random_shuffles = 50

Here, a label is not set; in this case the label defaults to the name of the parameter itself.

It’s also possible to provide a Datum to MeasurementBase.register_parameter:

                       datum=Datum(50, '', label='shuffles',
                                   description='Number of random shuffles'))

This can be useful when copying a parameter already available as a Datum.

Storing extra measurement outputs

Although metric measurements are strictly scalar values, your measurement might yield additional data that you want make available through the SQUASH dashboard. These additional data are called extras.

Registering extras is similar to registering parameters, except that the MeasurementBase.register_extra method is used. As an example, the PA1 measurement code (lsst.validate.drp.calcsrd.PA1Measurement) stores the inter-quartile range, RMS, and magnitude difference of stars observed across multiple random visits, along with the mean magnitude observed star:

class PA1Measurement(MeasurementBase):

       def __init__(self, yaml_path, num_random_shuffles=50):

           self.metric = Metric.from_yaml(self.label, yaml_path=yaml_path)

           # register extras
                               description='Photometric repeatability RMS of '
                                           'stellar pairs for each random sampling')
                               description='Photometric repeatability IQR of '
                                           'stellar pairs for each random sample')
                               description='Difference magnitudes of stellar source pairs'
                                           'for each random sample')
                               description='Mean magnitude of pairs of stellar '
                                           'sources matched across visits, for '
                                           'each random sample.')

           # ... make measurements

           # Set values of extras
           self.rms = np.array([pa1.rms for pa1 in pa1Samples]) * u.mmag
           self.iqr = np.array([pa1.iqr for pa1 in pa1Samples]) * u.mmag
           self.mag_diff = np.array([pa1.mag_diffs for pa1 in pa1Samples]) * u.mmag
           self.mag_mean = np.array([pa1.mag_mean for pa1 in pa1Samples]) * u.mag

           # The scalar metric measurement
           self.quantity = np.mean(self.iqr)

MeasurementBase.register_extra works just like the MeasurementBase.register_parameter() method. Specifically, the value of the extra can be set at registration time, or afterwards by setting an instance attribute (shown above). An extra can also be registered with a pre-made Datum object.

Accessing and updating the values and Datum objects of measurement extras

Extras are stored internally as Datum objects. You can access these Datums as key values of the MeasurementBase.extras attribute. Following the PA1 measurement example:

pa1 = PA1Measurement()
pa1.extras['rms'].quantity  # == pa1.rms
pa1.extras['rms'].unit  # u.Unit('mmag')
pa1.extras['rms'].label  # 'rms'
pa1.extras['rms'].decription  # 'Photometric repeatability RMS ...'