Basic version with no checking - if cached data exists use it

tupak/result.py

@@ -12,7 +12,24 @@ except ImportError:
             "You do not have the optional module chainconsumer installed")
 
 
+def result_file_name(outdir, label):
+    """ Returns the standard filename used for a result file """
+    return '{}/{}_result.h5'.format(outdir, label)
+
+
+def read_in_result(outdir, label):
+    filename = result_file_name(outdir, label)
+    if os.path.isfile(filename):
+        return Result(deepdish.io.load(filename))
+    else:
+        return None
+
+
 class Result(dict):
+    def __init__(self, dictionary=None):
+        if type(dictionary) is dict:
+            for key in dictionary:
+                setattr(self, key, dictionary[key])
 
     def __getattr__(self, name):
         try:
@@ -33,7 +50,7 @@ class Result(dict):
                         self.logzerr))
 
     def save_to_file(self, outdir, label):
-        file_name = '{}/{}_result.h5'.format(outdir, label)
+        file_name = result_file_name(outdir, label)
         if os.path.isdir(outdir) is False:
             os.makedirs(outdir)
         if os.path.isfile(file_name):

tupak/sampler.py

@@ -7,7 +7,7 @@ import sys
 import numpy as np
 import matplotlib.pyplot as plt
 
-from .result import Result
+from .result import Result, read_in_result
 from .prior import Prior, fill_priors
 from . import utils
 from . import prior
@@ -54,6 +54,7 @@ class Sampler(object):
         self.kwargs = kwargs
 
         self.result = result
+        self.check_cached_result()
 
         self.log_summary_for_sampler()
 
@@ -179,6 +180,9 @@ class Sampler(object):
     def run_sampler(self):
         pass
 
+    def check_cached_result(self):
+        self.cached_result = read_in_result(self.outdir, self.label)
+
     def log_summary_for_sampler(self):
         logging.info("Using sampler {} with kwargs {}".format(
             self.__class__.__name__, self.kwargs))
@@ -405,6 +409,10 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
         sampler = sampler_class(likelihood, priors, sampler, outdir=outdir,
                                 label=label, use_ratio=use_ratio,
                                 **sampler_kwargs)
+        if sampler.cached_result:
+            logging.info("Using cached result")
+            return sampler.cached_result
+
         result = sampler.run_sampler()
         result.noise_logz = likelihood.noise_log_likelihood()
         if use_ratio: