Merge pull request #318 from California-Planet-Search/next-release

Version 1.4.1

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__ = '1.4.0'
+__version__ = '1.4.1'
 __spec__ = __name__
 __package__ = __path__[0]
 

radvel/cli.py

@@ -113,6 +113,9 @@ def main():
     psr_mcmc.add_argument('--proceed', dest='proceed', action='store_true',
                           help='If True, MCMC chains will resume from the previous run'
                           )
+    psr_mcmc.add_argument('--headless', dest='headless', action='store_true',
+                          help='If True, convergence stats will not display in real time'
+                          )
     psr_mcmc.set_defaults(func=radvel.driver.mcmc)
 
     # Derive physical parameters

radvel/driver.py

@@ -195,7 +195,7 @@ def mcmc(args):
                          minAfactor=args.minAfactor, maxArchange=args.maxArchange, burnAfactor=args.burnAfactor,
                          burnGR=args.burnGR, maxGR=args.maxGR, minTz=args.minTz, minsteps=args.minsteps,
                          minpercent=args.minpercent, thin=args.thin, serial=args.serial, save=args.save,
-                         savename=backend_loc, proceed=args.proceed, proceedname=backend_loc)
+                         savename=backend_loc, proceed=args.proceed, proceedname=backend_loc, headless=args.headless)
 
     mintz = statevars.mintz
     maxgr = statevars.maxgr

radvel/likelihood.py

@@ -143,18 +143,15 @@ def set_vary_params(self, param_values_array):
         param_values_array = list(param_values_array)
         i = 0
         try:
-            for index in self.vary_params:
-                self.vector.vector[index][0] = param_values_array[i]
-                i += 1
-            assert i == len(param_values_array), \
-                "Length of array must match number of varied parameters"
+            if len(self.vary_params) != len(param_values_array):
+                self.list_vary_params()
         except AttributeError:
             self.list_vary_params()
-            for index in self.vary_params:
-                self.vector.vector[index][0] = param_values_array[i]
-                i += 1
-            assert i == len(param_values_array), \
-                "Length of array must match number of varied parameters"
+        for index in self.vary_params:
+            self.vector.vector[index][0] = param_values_array[i]
+            i += 1
+        assert i == len(param_values_array), \
+            "Length of array must match number of varied parameters"
 
     def get_vary_params(self):
         try:

radvel/mcmc.py

@@ -101,7 +101,7 @@ def _status_message_CLI(statevars):
     statevars.screen.refresh()
 
 
-def convergence_check(minAfactor, maxArchange, maxGR, minTz, minsteps, minpercent):
+def convergence_check(minAfactor, maxArchange, maxGR, minTz, minsteps, minpercent, headless):
     """Check for convergence
 
     Check for convergence for a list of emcee samplers
@@ -116,6 +116,7 @@ def convergence_check(minAfactor, maxArchange, maxGR, minTz, minsteps, minpercen
             will start after the minsteps threshold or the minpercent threshold has been hit.
         minpercent (float): Minimum percentage of total steps before convergence tests are performed. Convergence checks
             will start after the minsteps threshold or the minpercent threshold has been hit.
+        headless (bool): if set to true, the convergence statistics will not be displayed in real time.
     """
 
     statevars.ar = 0
@@ -165,10 +166,11 @@ def convergence_check(minAfactor, maxArchange, maxGR, minTz, minsteps, minpercen
         else:
             statevars.mixcount = 0
 
-    if isnotebook():
-        _status_message_NB(statevars)
-    else:
-        _status_message_CLI(statevars)
+    if not headless:
+        if isnotebook():
+            _status_message_NB(statevars)
+        else:
+            _status_message_CLI(statevars)
 
 
 def _domcmc(input_tuple):
@@ -186,7 +188,7 @@ def _domcmc(input_tuple):
 
 def mcmc(post, nwalkers=50, nrun=10000, ensembles=8, checkinterval=50, minAfactor=40, maxArchange=.03, burnAfactor=25,
          burnGR=1.03, maxGR=1.01, minTz=1000, minsteps=1000, minpercent=5, thin=1, serial=False, save=False,
-         savename=None, proceed=False, proceedname=None):
+         savename=None, proceed=False, proceedname=None, headless=False):
     """Run MCMC
     Run MCMC chains using the emcee EnsambleSampler
     Args:
@@ -215,6 +217,7 @@ def mcmc(post, nwalkers=50, nrun=10000, ensembles=8, checkinterval=50, minAfacto
         savename (string): location of h5py file where MCMC chains will be saved for future use
         proceed (bool): set to true to continue a previously saved run
         proceedname (string): location of h5py file with previously MCMC run chains
+        headless (bool): if set to true, the convergence statistics will not display in real time
     Returns:
         DataFrame: DataFrame containing the MCMC samples
     """
@@ -385,7 +388,7 @@ def mcmc(post, nwalkers=50, nrun=10000, ensembles=8, checkinterval=50, minAfacto
             statevars.interval = t2 - t1
 
             convergence_check(minAfactor=minAfactor, maxArchange=maxArchange, maxGR=maxGR, minTz=minTz,
-                              minsteps=minsteps, minpercent=minpercent)
+                              minsteps=minsteps, minpercent=minpercent, headless=headless)
 
             if save:
                 for i, sampler in enumerate(statevars.samplers):

radvel/tests/test_api.py

@@ -36,6 +36,7 @@ class _args(types.SimpleNamespace):
     savename = 'rawchains.h5'
     proceed = False
     proceedname = None
+    headless=False
 
 
 def _standard_run(setupfn, arguments):