Merge pull request #340 from California-Planet-Search/next-release

Version 1.4.3

docs/autocorrintro.rst

@@ -36,7 +36,7 @@ using the ``minAfactor`` argument when calling the MCMC.
 
 The autocorrelation factor is calculated for each parameter and the minimum of these values
 is returned in real time as the MCMC run progresses.
-Once the minimum autocorrelation factor is below ``minAfactor``, this criterion
+Once the minimum autocorrelation factor is above ``minAfactor``, this criterion
 for convergence is met. Whether or not ``minAfactor`` has been satisfied can be seen in the autocorrelation plot below. Once the
 maximum autocorrelation time has passed the dashed line labeled 'Autocorrelation Factor Criterion,' the chain is likely converged. After five consecutive status checks appear past the dashed line, the MCMC will halt if all other criterion have also been met.
 

docs/tutorials/GaussianProcess-tutorial.ipynb

@@ -270,7 +270,7 @@
     "                                          )\n",
     "    # Add in instrument parameters\n",
     "    like.params['gamma_'+tel_suffix] = radvel.Parameter(value=np.mean(vel[indices]), vary=False, linear=True)\n",
-    "    like.params['jit_'+tel_suffix] = radvel.Parameter(value=jit_guesses[tel_suffix], vary=False, linear=True) \n",
+    "    like.params['jit_'+tel_suffix] = radvel.Parameter(value=jit_guesses[tel_suffix], vary=True)\n",
     "    likes.append(like)\n",
     "\n",
     "\n",
@@ -594,4 +594,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

radvel/__init__.py

@@ -29,7 +29,7 @@ def _custom_warningfmt(msg, *a, **b):
 __all__ = ['model', 'likelihood', 'posterior', 'mcmc', 'prior', 'utils',
          'fitting', 'report', 'cli', 'driver', 'gp']
 
-__version__ = '1.4.2'
+__version__ = '1.4.3'
 __spec__ = __name__
 __package__ = __path__[0]
 

radvel/mcmc.py

@@ -235,7 +235,7 @@ def mcmc(post, nwalkers=50, nrun=10000, ensembles=8, checkinterval=50, minAfacto
             if proceedname is None:
                 raise ValueError('proceed set to true but no proceedname provided')
             else:
-                h5p = h5py.File(savename, 'r')
+                h5p = h5py.File(proceedname, 'r')
                 msg = 'Loading chains and run information from previous MCMC'
                 print(msg)
             statevars.prechains = []
@@ -328,7 +328,7 @@ def mcmc(post, nwalkers=50, nrun=10000, ensembles=8, checkinterval=50, minAfacto
             if not proceed:
                 statevars.initial_positions.append(p0)
             else:
-                statevars.initial_positions.append(statevars.prechains[i][-1, :, :])
+                statevars.initial_positions.append(statevars.prechains[e][-1, :, :])
             statevars.samplers.append(emcee.EnsembleSampler(statevars.nwalkers, statevars.ndim, post.logprob_array,
                                                             threads=1))
 

radvel/model.py

@@ -229,13 +229,13 @@ def dict_to_vector(self):
         self.vector = vector
 
     def vector_names(self):
-        names = [0] * (len(self.params.keys()))
+        names = [0] * (len(self.params.keys()) + 2)
         for key in self.params.keys():
             try:
                 names[self.indices[key]] = key
             except KeyError:
                 pass
-        self.names = names
+        self.names = [i for i in names if type(i) == str]
 
     def vector_to_dict(self):
         for key in self.params.keys():

radvel/plot/orbit_plots.py

@@ -4,6 +4,7 @@
 from matplotlib import pyplot as pl
 from matplotlib.ticker import MaxNLocator
 from astropy.time import Time
+import copy
 
 import radvel
 from radvel import plot
@@ -65,7 +66,7 @@ def __init__(self, post, saveplot=None, epoch=2450000, yscale_auto=False, yscale
                  figwidth=7.5, fit_linewidth=2.0, set_xlim=None, text_size=9, highlight_last=False,
                  show_rms=False, legend_kwargs=dict(loc='best'), status=None):
 
-        self.post = post
+        self.post = copy.deepcopy(post)
         self.saveplot = saveplot
         self.epoch = epoch
         self.yscale_auto = yscale_auto
@@ -103,7 +104,8 @@ def __init__(self, post, saveplot=None, epoch=2450000, yscale_auto=False, yscale
 
         # convert params to synth basis
         synthparams = self.post.params.basis.to_synth(self.post.params)
-        self.post.params.update(synthparams)
+        self.post.params = synthparams
+        self.post.vector.dict_to_vector()
 
         self.model = self.post.likelihood.model
         self.rvtimes = self.post.likelihood.x