fixes issue #140 to add support for spatial forecasts (#142)

* fixed imports for spatial_likelihood_function
* added support for forecasts with single magnitude bin
fixes #140
* added additional tests

csep/core/poisson_evaluations.py

@@ -3,6 +3,7 @@
 
 import numpy
 import scipy.stats
+import scipy.spatial
 
 from csep.models import EvaluationResult
 from csep.utils.stats import poisson_joint_log_likelihood_ndarray
@@ -226,7 +227,7 @@ def poisson_spatial_likelihood(forecast, catalog):
     scale = catalog.event_count / forecast.event_count
 
     first_term = -forecast.spatial_counts() * scale
-    second_term = catalog.spatial_counts() * np.log(forecast.spatial_counts() * scale)
+    second_term = catalog.spatial_counts() * numpy.log(forecast.spatial_counts() * scale)
     third_term = -scipy.special.loggamma(catalog.spatial_counts() + 1)
 
     poll = first_term + second_term + third_term

csep/utils/calc.py

@@ -72,7 +72,12 @@ def bin1d_vec(p, bins, tol=None, right_continuous=False):
     bins = numpy.array(bins)
     p = numpy.array(p)
     a0 = numpy.min(bins)
-    h = bins[1] - bins[0]
+    # if user supplies only a single bin, do 2 things: 1) fix right continuous to true, and use of h is arbitrary
+    if bins.size == 1:
+        right_continuous = True
+        h = 1
+    else:
+        h = bins[1] - bins[0]
 
     a0_tol = numpy.abs(a0) * numpy.finfo(numpy.float).eps
     h_tol = numpy.abs(h) * numpy.finfo(numpy.float).eps

tests/test_calc.py

@@ -98,6 +98,14 @@ def test_bin1d_vec_int(self):
         expected = [0, 0, 0, 1, 2]
         self.assertListEqual(test.tolist(), expected)
 
+    def test_bin1d_single_bin1(self):
+        data = [-1, 0, 2, 3, 1, 1.5, 1.0, 0.999999999999999]
+        bin_edges = [1]
+        # purposely leaving right_continous flag=False bc it should be forced in the bin1d_vec function
+        test = bin1d_vec(data, bin_edges)
+        expected = [-1, -1, 0, 0, 0, 0, 0, -1]
+        self.assertListEqual(test.tolist(), expected)
+
     def test_upper_limit_right_continuous(self):
         data = [40, 40, 40]
         bin_edges = [0, 10, 20, 30]