Merge ed1fa31 into 31452c2

AegeanTools/fitting.py

@@ -847,85 +847,6 @@ def bias_correct(params, data, acf=None):
     return
 
 
-def condon_errors(source, theta_n, psf=None):
-    """
-    Calculate the parameter errors for a fitted source
-    using the description of Condon'97
-    All parameters are assigned errors, assuming that all params were fit.
-    If some params were held fixed then these errors are overestimated.
-
-    Parameters
-    ----------
-    source : :class:`AegeanTools.models.SimpleSource`
-        The source which was fit.
-
-    theta_n : float or None
-        A measure of the beam sampling. (See Condon'97).
-
-    psf : :class:`AegeanTools.wcs_helpers.Beam`
-        The psf at the location of the source.
-
-    Returns
-    -------
-    None
-
-    """
-
-    # indices for the calculation or rho
-    alphas = {'amp': (3. / 2, 3. / 2),
-              'major': (5. / 2, 1. / 2),
-              'xo': (5. / 2, 1. / 2),
-              'minor': (1. / 2, 5. / 2),
-              'yo': (1. / 2, 5. / 2),
-              'pa': (1. / 2, 5. / 2)}
-
-    major = source.a / 3600.  # degrees
-    minor = source.b / 3600.  # degrees
-    phi = np.radians(source.pa)  # radians
-    if psf is not None:
-        a, b, _ = psf.get_psf_sky2sky(source.ra, source.dec)
-        theta_n = np.sqrt(a * b)
-
-    if theta_n is None:
-        source.err_a = source.err_b = source.err_peak_flux = source.err_pa = source.err_int_flux = 0.0
-        return
-
-    smoothing = major * minor / (theta_n ** 2)
-    factor1 = (1 + (theta_n / major)**2)
-    factor2 = (1 + (theta_n / minor)**2)
-    snr = source.peak_flux / source.local_rms
-    # calculation of rho2 depends on the parameter being used so we lambda this into a function
-    def rho2(x): return smoothing / 4 * \
-        factor1 ** alphas[x][0] * factor2 ** alphas[x][1] * snr ** 2
-
-    source.err_peak_flux = source.peak_flux * np.sqrt(2 / rho2('amp'))
-    source.err_a = major * np.sqrt(2 / rho2('major')) * 3600.  # arcsec
-    source.err_b = minor * np.sqrt(2 / rho2('minor')) * 3600.  # arcsec
-
-    err_xo2 = 2. / rho2('xo') * major ** 2 / (8 * np.log(2))  # Condon'97 eq 21
-    err_yo2 = 2. / rho2('yo') * minor ** 2 / (8 * np.log(2))
-    source.err_ra = np.sqrt(err_xo2 * np.sin(phi) **
-                            2 + err_yo2 * np.cos(phi)**2)
-    source.err_dec = np.sqrt(err_xo2 * np.cos(phi) **
-                             2 + err_yo2 * np.sin(phi)**2)
-
-    if (major == 0) or (minor == 0):
-        source.err_pa = ERR_MASK
-    # if major/minor are very similar then we should not be able to figure out what pa is.
-    elif abs(2 * (major-minor) / (major+minor)) < 0.01:
-        source.err_pa = ERR_MASK
-    else:
-        source.err_pa = np.degrees(
-            np.sqrt(4 / rho2('pa')) * (major * minor / (major ** 2 - minor ** 2)))
-
-    # integrated flux error
-    err2 = (source.err_peak_flux / source.peak_flux) ** 2
-    err2 += (theta_n ** 2 / (major * minor)) * \
-        ((source.err_a / source.a) ** 2 + (source.err_b / source.b) ** 2)
-    source.err_int_flux = source.int_flux * np.sqrt(err2)
-    return
-
-
 def errors(source, model, wcshelper):
     """
     Convert pixel based errors into sky coord errors

AegeanTools/source_finder.py

@@ -594,7 +594,6 @@ def characterise_islands(
       'best' - Uncertainties measured based on covariance matrix of the
                fit and of the data
                See Hancock et al. 2018 for a description of this process.
-      'condon' - Uncertainties are *calculated* based on Condon'98 (?year)
       'raw' - uncertainties directly from the covariance matrix only
       'none' or None - No uncertainties, all will be set to -1.
 

scripts/aegean

@@ -132,8 +132,6 @@ if __name__ == "__main__":
                         help="Use this regions file to restrict source finding in this image.\nUse MIMAS region (.mim) files.")
     group4.add_argument('--nocov', dest='docov', action="store_false", default=True,
                         help="Don't use the covariance of the data in the fitting proccess. [Default = False]")
-    group4.add_argument('--condon', dest='condon', action="store_true", default=False,
-                        help="replace errors with those suggested by Condon'97. [Default = False]")
 
     # priorized fitting
     group5 = parser.add_argument_group(
@@ -381,18 +379,6 @@ if __name__ == "__main__":
                                  progress=options.progress, regroup_eps=options.regroup_eps)
 
     sources = sf.sources
-    # if --condon is set then we replace all the errors with those described by Condon'97
-    if options.condon:
-        # theta_N is the FWHM of the smoothing kernel (the noise correlation)
-        # which in this case is the same as the synthesized beam FWHM
-        if options.beam:
-            theta_n = np.sqrt(options.beam.a * options.beam.b)
-            psf = None
-        else:
-            psf = sf.global_data.psfhelper
-            theta_n = None
-        for s in sources:
-            fitting.condon_errors(s, theta_n=theta_n, psf=psf)
 
     log.info("found {0} sources total".format(len(sources)))
     if len(sources) > 0 and options.tables:

tests/test_fitting.py

@@ -178,40 +178,6 @@ def test_bias_correct():
     fitting.bias_correct(model, data)
 
 
-def test_condon_errs():
-    """Test that we can create Condon errors"""
-    source = models.ComponentSource()
-    source.ra = 0
-    source.dec = 1
-    source.a = 10
-    source.b = 10
-    source.pa = 0
-    source.local_rms = 0.1
-    source.peak_flux = 1
-    source.int_flux = 1
-
-    fitting.condon_errors(source, None)
-    if not (source.err_a == 0):
-        raise AssertionError(
-            "err_a should be zero but is {0}".format(source.err_a))
-
-    fitting.condon_errors(source, theta_n=8.)
-    if not (source.err_a > 0):
-        raise AssertionError("err_a should be non-zero")
-
-    # test that we can get a PA error
-    source.a = 20
-    fitting.condon_errors(source, None)
-    if source.err_pa < 0:
-        raise AssertionError("err_pa<0")
-
-    psf = wcs_helpers.WCSHelper.from_file('tests/test_files/1904-66_SIN.fits')
-    try:
-        fitting.condon_errors(source, theta_n=8., psf=psf)
-    except AttributeError as e:
-        raise AssertionError("condon_errors failed with psf")
-
-
 if __name__ == "__main__":
     # introspect and run all the functions starting with 'test'
     for f in dir():