Merge pull request #159 from California-Planet-Search/next-release

Version 1.1.7

.travis.yml

@@ -24,9 +24,10 @@ before_install:
     - sudo ln -s /run/shm /dev/shm
 
 install:
-    - conda install --yes pip numpy scipy matplotlib cython pandas nose nbformat nbconvert jupyter_client ipykernel
+    - conda install --yes pip numpy scipy matplotlib cython pandas nose nbformat nbconvert jupyter_client ipykernel tornado==4.5.3
     - if [ ${py} == "2" ]; then conda install --yes configparser==3.5.0b2; fi
-    - pip install coveralls celerite
+    - pip install coveralls pybind11
+    - pip install celerite
     - pip install .
     - python setup.py build_ext -i
 

MANIFEST.in

@@ -2,3 +2,4 @@ global-include src/*.pyx
 global-include src/*.c
 include requirements.txt
 include example_data/*
+recursive-include radvel/templates *

README.md

@@ -13,7 +13,7 @@ General Toolkit for Modeling Radial Velocities.
 
 ## Attribution
 
-Written by BJ Fulton, Erik Petigura, Sarah Blunt, and Evan Sinukoff. Fulton et al. (submitted to PASP)
+Written by BJ Fulton, Erik Petigura, Sarah Blunt, and Evan Sinukoff. [Fulton et al. (2017)](http://adsabs.harvard.edu/cgi-bin/bib_query?arXiv:1801.01947)
 
 Please cite the [original publication](http://adsabs.harvard.edu/abs/2018arXiv180101947F) and the following DOI if you make use of this software in your research.
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.580821.svg)](https://doi.org/10.5281/zenodo.580821)

radvel/__init__.py

@@ -13,7 +13,7 @@
 __all__=['model', 'likelihood', 'posterior', 'mcmc', 'prior', 'utils',
          'fitting', 'report', 'cli', 'driver', 'gp']
 
-__version__ = '1.1.6'
+__version__ = '1.1.7'
 
 MODULEDIR, filename = os.path.split(__file__)
 DATADIR = os.path.join(sys.prefix, 'radvel_example_data')

radvel/cli.py

@@ -176,7 +176,7 @@ def main():
         args.outputdir = outdir
 
     if not os.path.isdir(args.outputdir):
-        os.mkdir(outdir)
+        os.mkdir(args.outputdir)
 
     args.func(args)
 

radvel/driver.py

@@ -269,7 +269,7 @@ def tables(args):
     post = radvel.posterior.load(status.get('fit', 'postfile'))
     chains = pd.read_csv(status.get('mcmc', 'chainfile'))
     report = radvel.report.RadvelReport(P, post, chains)
-
+    tabletex = radvel.report.TexTable(report)
     for tabtype in args.type:
         print("Generating LaTeX code for {} table".format(tabtype))
 
@@ -281,9 +281,9 @@ def tables(args):
             report = radvel.report.RadvelReport(
                 P, post, chains, compstats=compstats
             )
-            tex = report.tabletex(tabtype='nplanets')
+            tex = tabletex.tab_comparison()
         else:
-            tex = report.tabletex(tabtype=tabtype)
+            tex = tabletex.tab_comparison()
 
         saveto = os.path.join(
             args.outputdir, '{}_{}_.tex'.format(conf_base,tabtype)

radvel/mcmc.py

@@ -263,33 +263,6 @@ def mcmc(post, nwalkers=50, nrun=10000, ensembles=8, checkinterval=50, burnGR=1.
 
     return df
 
-def draw_models_from_chain(mod, chain, t, nsamples=50):
-    """Draw Models from Chain
-    
-    Given an MCMC chain of parameters, draw representative parameters
-    and synthesize models.
-
-    Args:
-        mod (radvel.RVmodel) : RV model
-        chain (DataFrame): pandas DataFrame with different values from MCMC 
-            chain
-        t (array): time range over which to synthesize models
-        nsamples (int): number of draws
-    
-    Returns:
-        array: 2D array with the different models as different rows
-    """
-
-    np.random.seed(0)
-    chain_samples = chain.ix[np.random.choice(chain.index, nsamples)]
-    models = []
-    for i in chain_samples.index:
-        params = np.array( chain.ix[i, mod.vary_parameters] )
-        params = mod.array_to_params(params)
-        models += [mod.model(params, t)]
-    models = np.vstack(models)
-    return models
-
 
 def gelman_rubin(pars0, minTz, maxGR):
     """Gelman-Rubin Statistic