diff --git a/tupak/result.py b/tupak/result.py
index 60f6165079755848eedb8d13b209931b2cfe0a13..649727fd8771d5485fbf8ebac937bf92e1d94fb8 100644
--- a/tupak/result.py
+++ b/tupak/result.py
@@ -12,7 +12,25 @@ 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):
+ """ Read in a saved .h5 data file """
+ 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:
@@ -25,15 +43,19 @@ class Result(dict):
def __repr__(self):
"""Print a summary """
- return ("nsamples: {:d}\n"
- "noise_logz: {:6.3f}\n"
- "logz: {:6.3f} +/- {:6.3f}\n"
- "log_bayes_factor: {:6.3f} +/- {:6.3f}\n"
- .format(len(self.samples), self.noise_logz, self.logz, self.logzerr, self.log_bayes_factor,
- self.logzerr))
+ if hasattr(self, 'samples'):
+ return ("nsamples: {:d}\n"
+ "noise_logz: {:6.3f}\n"
+ "logz: {:6.3f} +/- {:6.3f}\n"
+ "log_bayes_factor: {:6.3f} +/- {:6.3f}\n"
+ .format(len(self.samples), self.noise_logz, self.logz,
+ self.logzerr, self.log_bayes_factor, self.logzerr))
+ else:
+ return ''
def save_to_file(self, outdir, label):
- file_name = '{}/{}_result.h5'.format(outdir, label)
+ """ Writes the Result to a deepdish h5 file """
+ file_name = result_file_name(outdir, label)
if os.path.isdir(outdir) is False:
os.makedirs(outdir)
if os.path.isfile(file_name):
@@ -194,3 +216,19 @@ class Result(dict):
self.posterior['chi_p'] = max(self.posterior.a_1 * np.sin(self.posterior.tilt_1),
(4 * self.posterior.q + 3) / (3 * self.posterior.q + 4) * self.posterior.q
* self.posterior.a_2 * np.sin(self.posterior.tilt_2))
+
+ def check_attribute_match_to_other_object(self, name, other_object):
+ """ Check attribute name exists in other_object and is the same """
+ A = getattr(self, name, False)
+ B = getattr(other_object, name, False)
+ logging.debug('Checking {} value: {}=={}'.format(name, A, B))
+ if (A is not False) and (B is not False):
+ typeA = type(A)
+ typeB = type(B)
+ if typeA == typeB:
+ if typeA in [str, float, int, dict, list]:
+ return A == B
+ elif typeA in [np.ndarray]:
+ return np.all(A == B)
+ return False
+
diff --git a/tupak/sampler.py b/tupak/sampler.py
index 970aa150fb6b03fb67fe78091b7154afe613c108..c9f028c86c275fcacfb529520f0c5d05b35316b5 100644
--- a/tupak/sampler.py
+++ b/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()
@@ -188,9 +189,26 @@ class Sampler(object):
def run_sampler(self):
pass
+ def check_cached_result(self):
+ """ Check if the cached data file exists and can be used """
+ logging.debug("Checking cached data")
+ self.cached_result = read_in_result(self.outdir, self.label)
+ if self.cached_result:
+ check_keys = ['search_parameter_keys', 'fixed_parameter_keys',
+ 'kwargs']
+ use_cache = True
+ for key in check_keys:
+ if self.cached_result.check_attribute_match_to_other_object(
+ key, self) is False:
+ logging.debug("Cached value {} is unmatched".format(key))
+ use_cache = False
+ if use_cache is False:
+ self.cached_result = None
+
def log_summary_for_sampler(self):
- logging.info("Using sampler {} with kwargs {}".format(
- self.__class__.__name__, self.kwargs))
+ if self.cached_result is False:
+ logging.info("Using sampler {} with kwargs {}".format(
+ self.__class__.__name__, self.kwargs))
class Nestle(Sampler):
@@ -367,7 +385,7 @@ class Ptemcee(Sampler):
def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
sampler='nestle', use_ratio=True, injection_parameters=None,
- **sampler_kwargs):
+ **kwargs):
"""
The primary interface to easy parameter estimation
@@ -392,7 +410,7 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
injection_parameters: dict
A dictionary of injection parameters used in creating the data (if
using simulated data). Appended to the result object and saved.
- **sampler_kwargs:
+ **kwargs:
All kwargs are passed directly to the samplers `run` functino
Returns
@@ -413,7 +431,11 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
sampler_class = globals()[sampler.title()]
sampler = sampler_class(likelihood, priors, sampler, outdir=outdir,
label=label, use_ratio=use_ratio,
- **sampler_kwargs)
+ **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:
@@ -423,7 +445,8 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
result.log_bayes_factor = result.logz - result.noise_logz
result.injection_parameters = injection_parameters
result.fixed_parameter_keys = [key for key in priors if isinstance(key, prior.DeltaFunction)]
- # result.prior = prior # Removed as this breaks the saving of the data
+ result.priors = priors
+ result.kwargs = sampler.kwargs
result.samples_to_data_frame()
result.save_to_file(outdir=outdir, label=label)
return result