REL: v0.4.0

docs/source/installation.rst

@@ -5,3 +5,12 @@ Installation
 At the command line::
 
     $ pip install lightcurve-fitting
+
+To install the stable version directly from GitHub::
+
+    $ pip install git+https://github.com/griffin-h/lightcurve_fitting.git
+
+If you want the bleeding-edge "development" version (not recommended)::
+
+    $ pip install git+https://github.com/griffin-h/lightcurve_fitting.git@dev
+

docs/source/release-history.rst

@@ -2,6 +2,21 @@
 Release History
 ===============
 
+v0.4.0 (2022-02-08)
+-------------------
+* Fix bug in min/max validity times when using intrinsic scatter parameter
+* Change prior on blackbody temperature from log-uniform to uniform
+* Don't italicize some filter names
+* Return axes objects in light curve corner plot
+* Give option to plot magnitudes in light curve corner plot
+* Fix plotting of wavelength when units are supplied
+* Add option to calculate phase in rest-frame hours
+* Issue warning when filters do not receive extinction correction
+* Switch from to generic filter curves from the SVO Filter Profile Service where possible
+* Add progress bars for MCMC fitting
+* Add option to save chain plots. Burn-in and sampling plots are combined into the same figure.
+* Add option to save chain in bolometric light curve fitting
+
 v0.3.0 (2021-09-22)
 -------------------
 * Switch the default table format from ``'ascii.fixed_width'`` to just ``'ascii'``

docs/source/usage.rst

@@ -169,7 +169,7 @@ If you used the Rabinak & Waxman option, the model fails even earlier, but you w
 .. code-block:: python
 
     p_mean = sampler.flatchain.mean(axis=0)
-    t_max = ShockCooling2.t_max(*p_mean)
+    t_max = ShockCooling2.t_max(p_mean)
     print(t_max)
     if lc_early['MJD'].max() > t_max:
         print('Warning: your model is not valid for all your observations')

lightcurve_fitting.ipynb

@@ -134,7 +134,7 @@
    "outputs": [],
    "source": [
     "p_mean = sampler.flatchain.mean(axis=0)\n",
-    "t_max = model.t_max(*p_mean)\n",
+    "t_max = model.t_max(p_mean)\n",
     "print(t_max)\n",
     "if lc_early['MJD'].max() > t_max:\n",
     "    print('Warning: your model is not valid for all your observations')"

lightcurve_fitting/bolometric.py

@@ -53,7 +53,7 @@ def pseudo(temp, radius, z, filter0=filtdict['I'], filter1=filtdict['U'], cutoff
 
 
 def blackbody_mcmc(epoch1, z, p0=None, show=False, outpath='.', nwalkers=10, burnin_steps=200, steps=100,
-                   T_range=(1., 100.), R_range=(0.01, 1000.), cutoff_freq=np.inf, prev_temp=None):
+                   T_range=(1., 100.), R_range=(0.01, 1000.), cutoff_freq=np.inf, prev_temp=None, save_chains=False):
     """
     Fit a blackbody spectrum to a spectral energy distribution using a Markov-chain Monte Carlo routine
 
@@ -83,6 +83,8 @@ def blackbody_mcmc(epoch1, z, p0=None, show=False, outpath='.', nwalkers=10, bur
         Cutoff frequency (in terahertz) for a modified blackbody spectrum (see https://doi.org/10.3847/1538-4357/aa9334)
     prev_temp : scipy.stats.gaussian_kde, optional
         Temperature distribution from fitting the previous epoch's SED. If given, this is used as the prior.
+    save_chains : bool, optional
+        If True, save the MCMC chain histories to the directory ``outpath``. Default: only save the corner plot
 
     Returns
     -------
@@ -104,7 +106,7 @@ def log_prior(p):  # p = [T, R]
         elif prev_temp is not None:
             return prev_temp.logpdf(p[0]) - np.log(p[1])
         else:
-            return -np.sum(np.log(p))
+            return -np.log(p[1])
 
     def log_likelihood(p, filtobj, y, dy):
         y_fit = blackbody_to_filters(filtobj, p[0], p[1], z, cutoff_freq)
@@ -155,6 +157,9 @@ def log_posterior(p, filtobj, y, dy):
     filename = os.path.join(outpath, f'{mjdavg:.1f}.png')
     print(filename)
     f4.savefig(filename)
+    if save_chains:
+        chain_filename = os.path.join(outpath, f'{mjdavg:.1f}.npy')
+        np.save(chain_filename, sampler.flatchain)
     if show:
         plt.show()
     else:
@@ -458,7 +463,7 @@ def plot_color_curves(t, colors=None, fmt='o', limit_length=0.1, xcol='MJD'):
 
 def calculate_bolometric(lc, z, outpath='.', res=1., nwalkers=10, burnin_steps=200, steps=100,
                          T_range=(1., 100.), R_range=(0.01, 1000.), save_table_as=None, min_nfilt=3,
-                         cutoff_freq=np.inf, show=False, colors=None, do_mcmc=True):
+                         cutoff_freq=np.inf, show=False, colors=None, do_mcmc=True, save_chains=False):
     """
     Calculate the full bolometric light curve from a table of broadband photometry
 
@@ -469,7 +474,7 @@ def calculate_bolometric(lc, z, outpath='.', res=1., nwalkers=10, burnin_steps=2
     z : float
         Redshift between the blackbody (rest frame) and the filters (observed frame)
     outpath : str, optional
-        Directory to which to save the corner plots. Default: current directory
+        Directory to which to save the corner plots and MCMC chains. Default: current directory
     res : float, optional
         Approximate resolution for grouping, in days. Default: 1 day.
     nwalkers : int, optional
@@ -495,6 +500,8 @@ def calculate_bolometric(lc, z, outpath='.', res=1., nwalkers=10, burnin_steps=2
     do_mcmc : bool, optional
         If True (default), also fit the spectral energy distribution with an MCMC routine. This is slower but gives
         more realistic uncertainties.
+    save_chains : bool, optional
+        If True, save the MCMC chain histories to the directory ``outpath``. Default: only save the corner plot
 
     Returns
     -------
@@ -567,7 +574,7 @@ def calculate_bolometric(lc, z, outpath='.', res=1., nwalkers=10, burnin_steps=2
                 raise ValueError
             sampler = blackbody_mcmc(epoch1, z, p0, outpath=outpath, nwalkers=nwalkers, burnin_steps=burnin_steps,
                                      steps=steps, T_range=T_range, R_range=R_range, cutoff_freq=cutoff_freq, show=show,
-                                     prev_temp=prev_temp)
+                                     prev_temp=prev_temp, save_chains=save_chains)
             L_mcmc_opt = pseudo(sampler.flatchain[:, 0], sampler.flatchain[:, 1], z, cutoff_freq=cutoff_freq)
             (T_mcmc, R_mcmc), (dT0_mcmc, dR0_mcmc), (dT1_mcmc, dR1_mcmc) = median_and_unc(sampler.flatchain)
             L_mcmc, dL_mcmc0, dL_mcmc1 = median_and_unc(L_mcmc_opt)

lightcurve_fitting/filters.py

@@ -38,6 +38,8 @@ class Filter:
         The line color used when plotting photometry in this filter. Default: same as ``color``.
     textcolor : str, tuple, optional
         The color used when printing the name of this filter. Default: same as ``linecolor``.
+    italics : bool, optional
+        Italicize the filter name when used with LaTeX. Default: True.
 
     Attributes
     ----------
@@ -53,6 +55,8 @@ class Filter:
         The line color used when plotting photometry in this filter
     textcolor : str, tuple
         The color used when printing the name of this filter
+    italics : bool
+        Italicize the filter name when used with LaTeX
     mec : str, tuple
         The marker edge color used when plotting photometry in this filter
     plotstyle : dict
@@ -86,7 +90,7 @@ class Filter:
     """The names of recognized filters listed in (approximate) decreasing order of effective frequency"""
 
     def __init__(self, names, color='k', offset=0, system=None, fnu=3.631e-23, filename='', angstrom=False,
-                 linecolor=None, textcolor=None):
+                 linecolor=None, textcolor=None, italics=True):
         if type(names) == list:
             self.name = names[0]
             self.names = names
@@ -110,6 +114,7 @@ def __init__(self, names, color='k', offset=0, system=None, fnu=3.631e-23, filen
             self.textcolor = textcolor
         else:
             self.textcolor = self.linecolor
+        self.italics = italics
         self.system = system
         if self.system == 'Johnson':
             self.mec = 'k'
@@ -191,6 +196,9 @@ def read_curve(self, show=False, force=False):
                 ax2.set_xlabel('Frequency (THz)')
                 ax2.set_ylabel('Transmission')
 
+            self.wl_eff = wl_eff
+            self.dwl = dwl
+            self.wl_range = (wl_eff.value - wl0, wl1 - wl_eff.value)
             self.freq_eff = freq_eff
             self.dfreq = -dfreq
             self.freq_range = (freq_eff.value - freq0, freq1 - freq_eff.value)
@@ -252,39 +260,42 @@ def _resample_filter_curve(filename, outfile):
     Filter(['UVW2', 'uvw2', 'W2', '2', 'uw2'], '#FF007F', 8, 'Swift', 7.379e-24, 'Swift_UVOT.UVW2.dat', angstrom=True),
     Filter(['UVM2', 'uvm2', 'M2', 'M', 'um2'], 'm', 8, 'Swift', 7.656e-24, 'Swift_UVOT.UVM2.dat', angstrom=True),
     Filter(['UVW1', 'uvw1', 'W1', '1', 'uw1'], '#7F00FF', 4, 'Swift', 9.036e-24, 'Swift_UVOT.UVW1.dat', angstrom=True),
-    Filter(['u', "u'", 'up'], '#4700CC', 3, 'Gunn', filename='sdss-up-183.asci'),  # brightened from '#080017'
+    Filter(['u', "u'", 'up'], '#4700CC', 3, 'Gunn', filename='SLOAN_SDSS.u.dat', angstrom=True),  # brightened from '#080017'
     Filter(['U_S', 's', 'us'], '#230047', 3, 'Swift', 1.419e-23, filename='Swift_UVOT.U.dat', angstrom=True),
-    Filter('U', '#3C0072', 3, 'Johnson', 1.790e-23, filename='jnsn-uv-183.asci'),
-    Filter('B', '#0057FF', 2, 'Johnson', 4.063e-23, filename='jnsn-bu-183.asci'),
+    Filter('U', '#3C0072', 3, 'Johnson', 1.790e-23, filename='Generic_Johnson.U.dat', angstrom=True),
+    Filter('B', '#0057FF', 2, 'Johnson', 4.063e-23, filename='Generic_Johnson.B.dat', angstrom=True),
     Filter(['B_S', 'b', 'bs'], '#4B00FF', 2, 'Swift', 4.093e-23, filename='Swift_UVOT.B.dat', angstrom=True),
-    Filter(['g', "g'", 'gp', 'F475W'], '#00CCFF', 1, 'Gunn', filename='sdss-gp-183.asci'),
+    Filter(['g', "g'", 'gp', 'F475W'], '#00CCFF', 1, 'Gunn', filename='SLOAN_SDSS.g.dat', angstrom=True),
     Filter('g-DECam', '#00CCFF', 1, 'DECam', filename='CTIO_DECam.g.dat', angstrom=True),
     Filter(['c', 'cyan'], 'c', 1, 'ATLAS', filename='ATLAS_cyan.txt'),
-    Filter('V', '#79FF00', 1, 'Johnson', 3.636e-23, filename='jnsn-vx-183.asci', textcolor='#46CC00'),
+    Filter('V', '#79FF00', 1, 'Johnson', 3.636e-23, filename='Generic_Johnson.V.dat', angstrom=True, textcolor='#46CC00'),
     Filter(['V_S', 'v', 'vs'], '#00FF30', 1, 'Swift', 3.664e-23, filename='Swift_UVOT.V.dat', angstrom=True),
-    Filter(['unfilt.', '0', 'Clear', 'C', 'clear'], 'w', 0, 'Itagaki', filename='KAF-1001E.asci', linecolor='k'),
-    Filter('white', 'w', 0, 'MOSFiT', filename='white.txt', linecolor='k'),
-    Filter(['pseudobolometric', 'griz'], 'w', 0, 'MOSFiT', filename='pseudobolometric.txt', linecolor='k'),
+    Filter('Itagaki', 'w', 0, 'Itagaki', filename='KAF-1001E.asci', linecolor='k', italics=False),
+    Filter('white', 'w', 0, 'MOSFiT', filename='white.txt', linecolor='k', italics=False),
+    Filter(['unfilt.', '0', 'Clear', 'C', 'clear', 'pseudobolometric', 'griz'], 'w', 0, 'MOSFiT',
+           filename='pseudobolometric.txt', linecolor='k', italics=False),
     Filter('G', 'w', 0, 'Gaia', filename='GAIA_GAIA0.G.dat', angstrom=True, linecolor='k'),
-    Filter('Kepler', 'r', 0, 'Kepler', filename='Kepler_Kepler.K.dat', angstrom=True),
-    Filter('TESS', 'r', 0, 'TESS', filename='TESS_TESS.Red.dat', angstrom=True),
-    Filter('w', 'w', 0, 'Gunn', filename='pstr-wx-183.asci', linecolor='k'),
+    Filter('Kepler', 'r', 0, 'Kepler', filename='Kepler_Kepler.K.dat', angstrom=True, italics=False),
+    Filter('TESS', 'r', 0, 'TESS', filename='TESS_TESS.Red.dat', angstrom=True, italics=False),
+    Filter('DLT40', 'w', 0, 'DLT40', filename='QE_E2V_MBBBUV_Broadband.csv', linecolor='k', italics=False),
+    Filter('w', 'w', 0, 'Gunn', filename='PAN-STARRS_PS1.w.dat', angstrom=True, linecolor='k'),
     Filter(['o', 'orange'], 'orange', 0, 'ATLAS', filename='ATLAS_orange.txt'),
-    Filter(['r', "r'", 'rp', 'F625W'], '#FF7D00', 0, 'Gunn', filename='sdss-rp-183.asci'),
+    Filter(['r', "r'", 'rp', 'F625W'], '#FF7D00', 0, 'Gunn', filename='SLOAN_SDSS.r.dat', angstrom=True),
     Filter('r-DECam', '#FF7D00', 0, 'DECam', filename='CTIO_DECam.r.dat', angstrom=True),
-    Filter(['R', 'Rc', 'R_s'], '#FF7000', 0, 'Johnson', 3.064e-23, filename='cous-rs-183.asci'),  # '#CC5900'
-    Filter(['i', "i'", 'ip', 'F775W'], '#90002C', -1, 'Gunn', filename='sdss-ip-183.asci'),
+    Filter(['R', 'Rc', 'R_s'], '#FF7000', 0, 'Johnson', 3.064e-23, filename='Generic_Cousins.R.dat', angstrom=True),  # '#CC5900'
+    Filter(['i', "i'", 'ip', 'F775W'], '#90002C', -1, 'Gunn', filename='SLOAN_SDSS.i.dat', angstrom=True),
     Filter('i-DECam', '#90002C', -1, 'DECam', filename='CTIO_DECam.i.dat', angstrom=True),
-    Filter(['I', 'Ic'], '#66000B', -1, 'Johnson', 2.416e-23, filename='cous-ic-183.asci'),  # brightened from '#1C0003'
-    Filter(['z', "z'", 'Z', 'zs'], '#000000', -2, 'Gunn', filename='pstr-zs-183.asci'),
+    Filter(['I', 'Ic'], '#66000B', -1, 'Johnson', 2.416e-23, filename='Generic_Cousins.I.dat', angstrom=True),  # brightened from '#1C0003'
+    Filter('zs', '#000000', -2, 'Gunn', filename='PAN-STARRS_PS1.z.dat', angstrom=True),
+    Filter(['z', "z'", 'zp'], '#000000', -2, 'Gunn', filename='SLOAN_SDSS.z.dat', angstrom=True),
     Filter('z-DECam', '#000000', -2, 'DECam', filename='CTIO_DECam.z.dat', angstrom=True),
-    Filter('y', 'y', -3, 'Gunn', filename='pstr-yx-183.asci'),
+    Filter('y', 'y', -3, 'Gunn', filename='PAN-STARRS_PS1.y.dat', angstrom=True),
     Filter('y-DECam', 'y', -3, 'DECam', filename='CTIO_DECam.Y.dat', angstrom=True),
     Filter('J', '#444444', -2, 'UKIRT', 1.589e-23, filename='Gemini_Flamingos2.J.dat', angstrom=True),
     Filter('H', '#888888', -3, 'UKIRT', 1.021e-23, filename='Gemini_Flamingos2.H.dat', angstrom=True),
     Filter(['K', 'Ks'], '#CCCCCC', -4, 'UKIRT', 0.640e-23, filename='Gemini_Flamingos2.Ks.dat', angstrom=True),
     Filter('L', 'r', -4, 'UKIRT', 0.285e-23),
-    Filter(['unknown', '?'], 'w', 0, 'unknown', linecolor='k')]
+    Filter(['unknown', '?'], 'w', 0, 'unknown', linecolor='k', italics=False)]
 Filter.order = [f.name for f in all_filters]
 filtdict = {}
 for filt in all_filters:

lightcurve_fitting/filters/Generic_Cousins.I.dat

@@ -0,0 +1,43 @@
+7000 0.000
+7050 0.005
+7100 0.020
+7150 0.050
+7200 0.100
+7250 0.170
+7300 0.330
+7350 0.700
+7400 0.820
+7450 0.900
+7500 0.950
+7550 0.980
+7600 0.990
+7650 0.994
+7700 0.980
+7750 0.950
+7800 0.913
+7850 0.870
+7900 0.830
+7950 0.790
+8000 0.750
+8050 0.710
+8100 0.673
+8150 0.650
+8200 0.630
+8250 0.610
+8300 0.580
+8350 0.550
+8400 0.510
+8450 0.470
+8500 0.405
+8550 0.330
+8600 0.250
+8650 0.180
+8700 0.140
+8750 0.110
+8800 0.080
+8850 0.060
+8900 0.035
+8950 0.020
+9000 0.010
+9050 0.005
+9100 0.000

lightcurve_fitting/filters/Generic_Cousins.R.dat

@@ -0,0 +1,53 @@
+5400 0.000
+5450 0.002
+5500 0.010
+5550 0.030
+5600 0.070
+5650 0.180
+5700 0.400
+5750 0.770
+5800 0.890
+5850 0.960
+5900 0.990
+5950 0.999
+6000 1.000
+6050 0.997
+6100 0.990
+6150 0.976
+6200 0.960
+6250 0.946
+6300 0.930
+6350 0.912
+6400 0.895
+6450 0.880
+6500 0.860
+6550 0.845
+6600 0.825
+6650 0.806
+6700 0.788
+6750 0.765
+6800 0.742
+6850 0.720
+6900 0.700
+6950 0.676
+7000 0.650
+7050 0.626
+7100 0.600
+7150 0.568
+7200 0.530
+7250 0.480
+7300 0.395
+7350 0.300
+7400 0.215
+7450 0.155
+7500 0.120
+7550 0.100
+7600 0.085
+7650 0.075
+7700 0.060
+7750 0.050
+7800 0.040
+7850 0.029
+7900 0.020
+7950 0.010
+8000 0.000

lightcurve_fitting/filters/Generic_Johnson.U.dat

@@ -0,0 +1,13 @@
+3000.0 0.0000000000
+3100.0 0.1000000000
+3200.0 0.6100000000
+3300.0 0.8400000000
+3400.0 0.9300000000
+3500.0 0.9700000000
+3600.0 1.0000000000
+3700.0 0.9700000000
+3800.0 0.7300000000
+3900.0 0.3600000000
+4000.0 0.0500000000
+4100.0 0.0100000000
+4200.0 0.0000000000