Customizing Data Processing

One of the many goals of the TOM Toolkit is to enable the simplification of the flow of your data from observations. To that end, there’s some built-in functionality that can be overridden to allow your TOM to work for your use case.

To begin, here’s a brief look at part of the structure of the tom_dataproducts app in the TOM Toolkit:


Let’s start with a quick overview of The file contains the Django models for the dataproducts app–in our case, DataProduct and ReducedDatum. The DataProduct contains information about uploaded or saved DataProducts, such as the file name, file path, and what kind of file it is. The ReducedDatum contains individual science data points that are taken from the DataProduct files. Examples of ReducedDatum points would be individual photometry points or individual spectra.

Each DataProduct also has a data_product_type. The data_product_type is simply a description of what the file is, more or less, and is customizable. The list of supported data_product_types is maintained in

# Define the valid data product types for your TOM. Be careful when removing items, as previously valid types will no
# longer be valid, and may cause issues unless the offending records are modified.
    'photometry': ('photometry', 'Photometry'),
    'fits_file': ('fits_file', 'FITS File'),
    'spectroscopy': ('spectroscopy', 'Spectroscopy'),
    'image_file': ('image_file', 'Image File')

In order to add new data product types, simply add a new key/value pair, with the value being a 2-tuple. The first tuple item is the database value, and the second is the display value.

All data products are automatically “processed” on upload, as well. Of course, that can mean different things to different TOMs! The TOM has two built-in data processors, both of which simply ingest the data into the database, and those are also specified in

    'photometry': 'tom_dataproducts.processors.photometry_processor.PhotometryProcessor',
    'spectroscopy': 'tom_dataproducts.processors.spectroscopy_processor.SpectroscopyProcessor',

When a user either uploads a DataProduct to their TOM, the TOM runs process_data() from the corresponding DataProcessor subclass specified in DATA_PROCESSORS seen above. To illustrate, this is the base DataProcessor class:

import mimetypes


class DataProcessor():

    FITS_MIMETYPES = ['image/fits', 'application/fits']
    PLAINTEXT_MIMETYPES = ['text/plain', 'text/csv']

    mimetypes.add_type('image/fits', '.fits')
    mimetypes.add_type('image/fits', '.fz')
    mimetypes.add_type('application/fits', '.fits')
    mimetypes.add_type('application/fits', '.fz')

    def process_data(self, data_product):

Now let’s look at the built-in data processors. First, let’s check out the PhotometryProcessor, which inherits from DataProcessor:

class PhotometryProcessor(DataProcessor):

    def process_data(self, data_product):
        mimetype = mimetypes.guess_type([0]
        if mimetype in self.PLAINTEXT_MIMETYPES:
            photometry = self._process_photometry_from_plaintext(data_product)
            return [(datum.pop('timestamp'), json.dumps(datum)) for datum in photometry]
            raise InvalidFileFormatException('Unsupported file type')

This class has an implementation of process_data() from the superclass DataProcessor. The implementation calls an internal method _process_photometry_from_plaintext(), which return a list of dicts. Each dict contains the values for the timestamp, magnitude, filter, and error for that photometry point. The list is then transformed into a list of 2-tuples, with the first value being the photometry timestamp, and the second being the JSON-ified remaining values.

Next, let’s look at the SpectroscopyProcessor:

class SpectroscopyProcessor(DataProcessor):

    DEFAULT_WAVELENGTH_UNITS = units.angstrom
    DEFAULT_FLUX_CONSTANT = units.erg / ** 2 / units.second / units.angstrom

    def process_data(self, data_product):

        mimetype = mimetypes.guess_type([0]
        if mimetype in self.FITS_MIMETYPES:
            spectrum, obs_date = self._process_spectrum_from_fits(data_product)
        elif mimetype in self.PLAINTEXT_MIMETYPES:
            spectrum, obs_date = self._process_spectrum_from_plaintext(data_product)
            raise InvalidFileFormatException('Unsupported file type')

        serialized_spectrum = SpectrumSerializer().serialize(spectrum)

        return [(obs_date, serialized_spectrum)]

Just like the PhotometryProcessor, this class inherits from DataProcessor and implements process_data(). This is a requirement for a custom DataProcessor! This process_data() method handles two file types, unlike the previous example, each of which calls an internal method that returns a Spectrum1D object. Again, like the PhotometryProcessor, a list of 2-tuples is created, with the first value being the timestamp, and the second being the JSON spectrum.

You may be wondering why these two methods return lists of 2-tuples, especially when the SpectroscopyProcessor only returns a list of length one. The rationale is to ensure that you, the TOM user, shouldn’t have to worry about the database insertion, so the internal logic handles that aspect, and it can do so whether you return one data point or many data points.

For a custom DataProcessor, there are just a few required steps. The first is to create a class that implements DataProcessor, like so:

from tom_dataproducts.data_processor import DataProcessor

class MyDataProcessor(DataProcessor):

    def process_data(self, data_product):
        # custom data processing here

        return [(timestamp1, json1), (timestamp2, json2), ..., (timestampN, dictN)]

Let’s say that this file lives at mytom/ Now the processor needs to be added to DATA_PROCESSORS, and it can either process a new data product type, or replace an existing one. Let’s replace spectroscopy:

    'photometry': 'tom_dataproducts.processors.photometry_processor.PhotometryProcessor',
    'spectroscopy': 'mytom.my_data_processor.MyDataProcessor',

And that’s it! Now your TOM will run the data processing specific to your case instead of the default one.