docs updates and series bug

docs/source/_examples/plot_fitting.py

@@ -0,0 +1,142 @@
+"""
+=====================
+Measuring Time Delays
+=====================
+
+A series of examples demonstrating various fitting options 
+with SNTD.
+"""
+
+###############################################################
+# There are 3 methods built into SNTD to measure time delays 
+# (parallel, series, color). They are accessed by the same 
+# function:`~sntd.fit_data`. Here ``myMISN`` could be generated
+# in the simulation example of the documentation. The true
+# delay for all of these fits is 50 days.
+# 
+# **Parallel:**
+
+import sntd
+
+ex_1,ex_2=sntd.load_example_data()
+myMISN=sntd.table_factory([ex_1,ex_2],telescopename='HST',object_name='example_SN')
+
+fitCurves=sntd.fit_data(myMISN,snType='Ia', models='salt2-extended',bands=['F110W','F160W'],
+                params=['x0','t0','x1','c'],constants={'z':1.4},refImage='image_1',cut_time=[-50,30],
+                bounds={'t0':(-20,20),'x1':(-3,3),'c':(-.5,.5),'mu':(.5,2)},fitOrder=['image_2','image_1'],
+                method='parallel',microlensing=None,modelcov=False,npoints=100)
+print(fitCurves.parallel.time_delays)
+print(fitCurves.parallel.time_delay_errors)
+print(fitCurves.parallel.magnifications)
+print(fitCurves.parallel.magnification_errors)
+fitCurves.plot_object(showFit=True,method='parallel')
+fitCurves.plot_fit(method='parallel',par_image='image_1')
+fitCurves.plot_fit(method='parallel',par_image='image_2')
+
+#####################################################################################################################################
+# Note that the bounds for the 't0' parameter are not absolute, the actual peak time will be estimated (unless t0_guess is defined)
+# and the defined bounds will be added to this value. Similarly for amplitude, where bounds are multiplicative
+#
+# Other methods are called in a similar fashion, with a couple of extra arguments:
+# 
+# **Series:**
+
+
+fitCurves=sntd.fit_data(myMISN,snType='Ia', models='salt2-extended',bands=['F110W','F160W'],
+        params=['x1','c'],constants={'z':1.4},refImage='image_1',cut_time=[-50,30],
+        bounds={'td':(-20,20),'mu':(.5,2),'x1':(-3,3),'c':(-.5,.5)},
+        method='series',npoints=100)
+
+
+print(fitCurves.series.time_delays)
+print(fitCurves.series.time_delay_errors)
+print(fitCurves.series.magnifications)
+print(fitCurves.series.magnification_errors)
+fitCurves.plot_object(showFit=True,method='series')
+fitCurves.plot_fit(method='series')
+
+############################################################### 
+# **Color:**
+
+
+
+fitCurves=sntd.fit_data(myMISN,snType='Ia', models='salt2-extended',bands=['F110W','F160W'],
+                    params=['c'],constants={'z':1.4,'x1':fitCurves.images['image_1'].fits.model.get('x1')},refImage='image_1',
+                    bounds={'td':(-20,20),'mu':(.5,2),'c':(-.5,.5)},cut_time=[-50,30],
+                    method='color',microlensing=None,modelcov=False,npoints=100,maxiter=None)
+
+print(fitCurves.color.time_delays)
+print(fitCurves.color.time_delay_errors)
+fitCurves.plot_object(showFit=True,method='color')
+fitCurves.plot_fit(method='color')
+
+#################################################################################################################################################################
+# You can include your fit from the parallel method as a prior on light curve and time delay parameters in the series/color methods with the "fit_prior" command:
+
+
+
+fitCurves_parallel=sntd.fit_data(myMISN,snType='Ia', models='salt2-extended',bands=['F110W','F160W'],
+                	params=['x0','t0','x1','c'],constants={'z':1.4},refImage='image_1',
+                	bounds={'t0':(-20,20),'x1':(-3,3),'c':(-.5,.5),'mu':(.5,2)},fitOrder=['image_2','image_1'],cut_time=[-50,30],
+               	    method='parallel',microlensing=None,modelcov=False,npoints=100,maxiter=None)
+fitCurves_color=sntd.fit_data(myMISN,snType='Ia', models='salt2-extended',bands=['F110W','F160W'],cut_time=[-50,30],
+                	params=['c'],constants={'z':1.4,'x1':fitCurves.images['image_1'].fits.model.get('x1')},refImage='image_1',
+                	bounds={'td':(-20,20),'mu':(.5,2),'c':(-.5,.5)},fit_prior=fitCurves_parallel,
+                	method='color',microlensing=None,modelcov=False,npoints=100,maxiter=None)
+
+print(fitCurves_parallel.parallel.time_delays)
+print(fitCurves_parallel.parallel.time_delay_errors)
+print(fitCurves_color.color.time_delays)
+print(fitCurves_color.color.time_delay_errors)
+###################################################################################################################################
+# **Fitting Using Extra Propagation Effects**
+# 
+# You might also want to include other propagation effects in your fitting model, and fit relevant parameters. This can be done by
+# simply adding effects to an SNCosmo model, in the same way as if you were fitting a single SN with SNCosmo. First we can add some
+# extreme dust in the source and lens frames (your final simulations may look slightly different as **c** is chosen randomly):
+
+
+
+myMISN2 = sntd.createMultiplyImagedSN(sourcename='salt2-extended', snType='Ia', redshift=1.4,z_lens=.53, bands=['F110W','F160W'],
+              zp=[26.9,26.2], cadence=5., epochs=35.,time_delays=[10., 70.], magnifications=[20,10],
+              objectName='My Type Ia SN',telescopename='HST',av_lens=1.5,
+              av_host=1)
+print('lensebv:',myMISN2.images['image_1'].simMeta['lensebv'],
+     'hostebv:',myMISN2.images['image_1'].simMeta['hostebv'], 
+     'c:',myMISN2.images['image_1'].simMeta['c'])
+
+################################################################################
+# Okay, now we can fit the MISN first without taking these effects into account:
+
+
+
+fitCurves_dust=sntd.fit_data(myMISN2,snType='Ia', models='salt2-extended',bands=['F110W','F160W'],
+                                                     params=['x0','x1','t0','c'],npoints=200,
+                                                     constants={'z':1.4},minsnr=1,cut_time=[-50,30],
+                                                     bounds={'t0':(-15,15),'x1':(-3,3),'c':(-.5,.5)})
+print(fitCurves_dust.parallel.time_delays)
+print(fitCurves_dust.parallel.time_delay_errors)
+print('c:',fitCurves_dust.images['image_1'].fits.model.get('c'))
+fitCurves_dust.plot_object(showFit=True)
+########################################################################################################################
+# We can see that the fitter has done reasonably well, and the time delay is still accurate (True delay is 50 days). 
+# However, one issue is that the measured value for **c** is vastly different than the actual value 
+# as it attempts to compensate for extinction without a propagation effect. Now let's add in the propagation effects:
+
+import sncosmo
+dust = sncosmo.CCM89Dust()
+salt2_model=sncosmo.Model('salt2-extended',effects=[dust,dust],effect_names=['lens','host'],effect_frames=['free','rest'])
+fitCurves_dust=sntd.fit_data(myMISN2,snType='Ia', models=salt2_model,bands=['F110W','F160W'],npoints=200,
+                    params=['x0','x1','t0','c','lensebv','hostebv'],minsnr=1,cut_time=[-50,30],
+                    constants={'z':1.4,'lensr_v':3.1,'lensz':0.53,'hostr_v':3.1},
+                    bounds={'t0':(-15,15),'x1':(-3,3),'c':(-.5,.5),'lensebv':(0,1.),'hostebv':(0,1.)})
+
+print(fitCurves_dust.parallel.time_delays)
+print(fitCurves_dust.parallel.time_delay_errors)
+print('c:',fitCurves_dust.images['image_1'].fits.model.get('c'),
+      'lensebv:',fitCurves_dust.images['image_1'].fits.model.get('lensebv'),
+      'hostebv:',fitCurves_dust.images['image_1'].fits.model.get('hostebv'))
+fitCurves_dust.plot_object(showFit=True)
+########################################################################################################################
+# Now the measured value for **c** is much closer to reality, and the measured times of peak are somewhat
+# more accurate. 

docs/source/_examples/micro.py → docs/source/_examples/plot_micro.py

@@ -20,7 +20,7 @@
 
 
 
-myMISN2 = sntd.createMultiplyImagedSN(sourcename='salt2-extended', snType='Ia', redshift=1.2,z_lens=.5, bands=['F110W','F160W'],
+myMISN = sntd.createMultiplyImagedSN(sourcename='salt2-extended', snType='Ia', redshift=1.2,z_lens=.5, bands=['F110W','F160W'],
                    zp=[26.8,26.2], cadence=5., epochs=35.,time_delays=[10., 70.], magnifications=[7,3.5],
        objectName='My Type Ia SN',telescopename='HST', microlensing_type='AchromaticMicrolensing',microlensing_params=myML)
-myMISN2.plot_object(showMicro=True)
+myMISN.plot_object(showMicro=True)

docs/source/_examples/plot_sim.py

@@ -0,0 +1,26 @@
+"""
+=====================
+Simulating Supernovae
+=====================
+
+Simulating a multiply-imaged supernova.
+"""
+
+###############################################################
+# Create a simulated multiply-imaged supernova that we can then fit,
+# with no microlensing included in the simulation. Note that your final
+# printed information will be different, as this is a randomly generated
+# supernova.
+# 
+# **No Microlensing**
+
+import sntd
+
+myMISN = sntd.createMultiplyImagedSN(sourcename='salt2-extended', snType='Ia', redshift=1.4,z_lens=.53, bands=['F110W','F160W'],
+             zp=[26.8,26.2], cadence=5., epochs=35.,time_delays=[20., 70.], magnifications=[10,5],
+ objectName='My Type Ia SN',telescopename='HST')
+print(myMISN)
+myMISN.plot_object()
+
+
+