Merge pull request #12 from joezuntz/single_gaussian_fisher_fix

Single gaussian fisher fix

cosmosis/gaussian_likelihood.py

@@ -316,8 +316,12 @@ def __init__(self, options):
         else:
             self.log_det_constant = 0.0
 
-        self.likelihood_only = options.get_bool('likelihood_only', 'False')
-
+        self.likelihood_only = options.get_bool('likelihood_only', False)
+
+        if not self.likelihood_only:
+            self.chol = sigma
+
+
     def build_data(self):
         """Sub-classes can over-ride this if they wish, to generate 
         the data point in a more complex way"""

cosmosis/samplers/test/test_sampler.py

@@ -5,6 +5,11 @@
 
 
 class TestSampler(Sampler):
+    # The name of this class makes pytest think that it is a
+    # test to be run, instead of a tool for testing likelhioods.
+    # This should stop that from happening.
+    __test__ = False
+
     needs_output = False
 
     def config(self):

cosmosis/test/test_gaussian.py

@@ -1,6 +1,6 @@
 from cosmosis.runtime import FunctionModule
 from cosmosis.datablock import DataBlock
-from cosmosis.gaussian_likelihood import GaussianLikelihood
+from cosmosis.gaussian_likelihood import GaussianLikelihood, SingleValueGaussianLikelihood
 import numpy as np
 import os
 
@@ -38,6 +38,41 @@ def build_covariance(self):
 
     assert np.isclose(block["likelihoods", "lll_like"], -3*np.log(0.1)/2)
 
+def test_single_gaussian():
+
+    class MySingleLikelihood(SingleValueGaussianLikelihood):
+        section = "sec"
+        name = "name"
+        like_name = "xxx"
+        mean = 3.0
+        sigma = 0.1
+
+
+    mod = MySingleLikelihood.as_module("my2")
+
+    # no extra config info
+    mod.setup({"my2":{"include_norm":True, "likelihood_only": False}})
+
+    block = DataBlock()
+
+    block["sec", "name"] = 4.0
+    status = mod.execute(block)
+
+    # check cholesky correctly calculated
+    assert np.isclose(mod.data.chol, 0.1)
+
+    assert status == 0
+    assert np.isclose(block["data_vector", "xxx_chi2"], 100)
+    assert np.isclose(block["data_vector", "xxx_log_det"], 2*np.log(0.1))
+    assert block["data_vector", "xxx_n"] == 1
+
+    assert np.isclose(block["data_vector", "xxx_theory"], 4.0)
+    assert np.isclose(block["data_vector", "xxx_data"], 3.0)
+    assert np.isclose(block["data_vector", "xxx_inverse_covariance"], 100.0)
+    # sim should be within 10 sigma!
+    assert 3.8 < block["data_vector", "xxx_simulation"] < 4.2
+
+    assert np.isclose(block["likelihoods", "xxx_like"], -50.0 - np.log(0.1))
 
 
 if __name__ == '__main__':

cosmosis/version.py

@@ -1 +1 @@
-__version__ = '2.0.2'
+__version__ = '2.0.3'