def test_json(self):
job_json = self.job.json
self.assertTrue(isinstance(job_json['measurements'], list))
self.assertEqual(len(job_json['measurements']), 1)
self.assertTrue(isinstance(job_json['blobs'], list))
self.assertEqual(len(job_json['blobs']), 1)
# Rebuild from JSON
job2 = Job.from_json(job_json)
for m1, m2 in zip(self.job.measurements, job2.measurements):
self.assertEqual(m1.quantity, m2.quantity)
class JobTestCase(unittest.TestCase):
"""Test Job classes."""
def setUp(self):
meas = DemoMeasurement()
self.job = Job(measurements=[meas])
def test_json(self):
job_json = self.job.json
self.assertTrue(isinstance(job_json['measurements'], list))
self.assertEqual(len(job_json['measurements']), 1)
self.assertTrue(isinstance(job_json['blobs'], list))
self.assertEqual(len(job_json['blobs']), 1)
# Rebuild from JSON
job2 = Job.from_json(job_json)
for m1, m2 in zip(self.job.measurements, job2.measurements):
self.assertEqual(m1.quantity, m2.quantity)
def test_roundtrip(self):
# Manually use temporary directories here,
# because I can't figure out how to get py.test tmpdir fixture
# to work in the unittest.TestCase context.
tmp_dir = tempfile.mkdtemp()
out_file_name = os.path.join(tmp_dir, "job_test.json")
# Write JSON
self.job.write_json(out_file_name)
# Rebuild from JSON
with open(out_file_name, 'r') as f:
job2 = Job.from_json(json.load(f))
for m1, m2 in zip(self.job.measurements, job2.measurements):
self.assertEqual(m1.quantity, m2.quantity)
# Cleanup our temp files
os.remove(out_file_name)
os.removedirs(tmp_dir)
def test_json_deserialization(self):
job = Job(measurements=[self.meas])
job_json = job.json
meas_doc = job_json['measurements'][0]
blobs_doc = job_json['blobs']
# Rebuild from JSON
m2 = DeserializedMeasurement.from_json(meas_doc, blobs_json=blobs_doc)
self.assertEqual(self.meas.metric.name, m2.metric.name)
self.assertEqual(self.meas.quantity, m2.quantity)
for k, param in self.meas.parameters.items():
self.assertEqual(param.quantity, m2.parameters[k].quantity)
for k, extra in self.meas.extras.items():
self.assertEqual(extra.quantity, m2.extras[k].quantity)
for k, blob in self.meas._linked_blobs.items():
for kk, datum in blob.datums.items():
self.assertEqual(datum.quantity,
m2._linked_blobs[k].datums[kk].quantity)
def load_json_output(filepath):
"""Read JSON from a file into a job object.
Currently just does a trivial de-serialization with no checking
to make sure that one results with a valid validate.base.job object.
Parameters
----------
filepath : `str`
Source file name for JSON output.
Returns
-------
job : A `validate.base.job` object.
"""
with open(filepath, 'r') as infile:
json_data = json.load(infile)
return Job.from_json(json_data)
def test_roundtrip(self):
# Manually use temporary directories here,
# because I can't figure out how to get py.test tmpdir fixture
# to work in the unittest.TestCase context.
tmp_dir = tempfile.mkdtemp()
out_file_name = os.path.join(tmp_dir, "job_test.json")
# Write JSON
self.job.write_json(out_file_name)
# Rebuild from JSON
with open(out_file_name, 'r') as f:
job2 = Job.from_json(json.load(f))
for m1, m2 in zip(self.job.measurements, job2.measurements):
self.assertEqual(m1.quantity, m2.quantity)
# Cleanup our temp files
os.remove(out_file_name)
os.removedirs(tmp_dir)
def setUp(self):
meas = DemoMeasurement()
self.job = Job(measurements=[meas])
def runOneFilter(repo,
visitDataIds,
metrics,
brightSnr=100,
makeJson=True,
filterName=None,
outputPrefix='',
useJointCal=False,
verbose=False,
**kwargs):
"""Main executable for the case where there is just one filter.
Plot files and JSON files are generated in the local directory
prefixed with the repository name (where '_' replace path separators),
unless overriden by specifying `outputPrefix`.
E.g., Analyzing a repository "CFHT/output"
will result in filenames that start with "CFHT_output_".
Parameters
----------
repo : string or Butler
A Butler or a repository URL that can be used to construct one.
dataIds : list of dict
List of `butler` data IDs of Image catalogs to compare to reference.
The `calexp` pixel image is needed for the photometric calibration
unless useJointCal is True, in which the `photoCalib` and `wcs`
datasets are used instead. Note that these have data IDs that include
the tract number.
metrics : `dict` or `collections.OrderedDict`
Dictionary of `lsst.validate.base.Metric` instances. Typically this is
data from ``validate_drp``\ 's ``metrics.yaml`` and loaded with
`lsst.validate.base.load_metrics`.
brightSnr : float, optional
Minimum SNR for a star to be considered bright
makeJson : bool, optional
Create JSON output file for metrics. Saved to current working directory.
outputPrefix : str, optional
Specify the beginning filename for output files.
filterName : str, optional
Name of the filter (bandpass).
useJointCal : bool, optional
Use jointcal/meas_mosaic outputs to calibrate positions and fluxes.
verbose : bool, optional
Output additional information on the analysis steps.
"""
matchedDataset = MatchedMultiVisitDataset(repo,
visitDataIds,
useJointCal=useJointCal,
verbose=verbose)
photomModel = PhotometricErrorModel(matchedDataset)
astromModel = AstrometricErrorModel(matchedDataset)
linkedBlobs = {'photomModel': photomModel, 'astromModel': astromModel}
job = Job(blobs=[matchedDataset, photomModel, astromModel])
for x in (1, 2, 3):
amxName = 'AM{0:d}'.format(x)
afxName = 'AF{0:d}'.format(x)
adxName = 'AD{0:d}'.format(x)
AMxMeasurement(metrics[amxName],
matchedDataset,
filterName,
job=job,
linkedBlobs=linkedBlobs,
verbose=verbose)
for specName in metrics[afxName].get_spec_names(
filter_name=filterName):
AFxMeasurement(metrics[afxName],
matchedDataset,
job.get_measurement(amxName),
filterName,
specName,
job=job,
linkedBlobs=linkedBlobs,
verbose=verbose)
ADxMeasurement(metrics[adxName],
matchedDataset,
job.get_measurement(amxName),
filterName,
specName,
job=job,
linkedBlobs=linkedBlobs,
verbose=verbose)
PA1Measurement(metrics['PA1'],
matchedDataset,
filterName,
job=job,
linkedBlobs=linkedBlobs,
verbose=verbose)
for specName in metrics['PA2'].get_spec_names(filter_name=filterName):
PA2Measurement(metrics['PA2'],
matchedDataset,
pa1=job.get_measurement('PA1'),
filter_name=filterName,
spec_name=specName,
verbose=verbose,
job=job,
linkedBlobs=linkedBlobs)
for specName in metrics['PF1'].get_spec_names(filter_name=filterName):
PF1Measurement(metrics['PF1'],
matchedDataset,
job.get_measurement('PA1'),
filterName,
specName,
verbose=verbose,
job=job,
linkedBlobs=linkedBlobs)
if makeJson:
job.write_json(outputPrefix + '.json')
return job