dlogX -> logdX (#306)

* dlogX -> logdX

* bumped version

* Actually added the error this time

* Added logdX tests

README.rst

@@ -2,7 +2,7 @@
 anesthetic: nested sampling post-processing
 ===========================================
 :Authors: Will Handley and Lukas Hergt
-:Version: 2.0.0-beta.40
+:Version: 2.0.0-beta.41
 :Homepage: https://github.com/handley-lab/anesthetic
 :Documentation: http://anesthetic.readthedocs.io/
 

anesthetic/_version.py

@@ -1 +1 @@
-__version__ = '2.0.0b40'
+__version__ = '2.0.0b41'

anesthetic/samples.py

@@ -872,7 +872,14 @@ def logX(self, nsamples=None):
         logX.name = 'logX'
         return logX
 
+    # TODO: remove this in version >= 2.1
     def dlogX(self, nsamples=None):
+        # noqa: disable=D102
+        raise NotImplementedError(
+            "This is anesthetic 1.0 syntax. You should instead use logdX."
+            )
+
+    def logdX(self, nsamples=None):
         """Compute volume of shell of loglikelihood.
 
         Parameters
@@ -892,10 +899,10 @@ def dlogX(self, nsamples=None):
         logX = self.logX(nsamples)
         logXp = logX.shift(1, fill_value=0)
         logXm = logX.shift(-1, fill_value=-np.inf)
-        dlogX = np.log(1 - np.exp(logXm-logXp)) + logXp - np.log(2)
-        dlogX.name = 'dlogX'
+        logdX = np.log(1 - np.exp(logXm-logXp)) + logXp - np.log(2)
+        logdX.name = 'logdX'
 
-        return dlogX
+        return logdX
 
     def _betalogL(self, beta=None):
         """Log(L**beta) convenience function.
@@ -958,20 +965,20 @@ def logw(self, nsamples=None, beta=None):
         if np.ndim(nsamples) > 0:
             return nsamples
 
-        dlogX = self.dlogX(nsamples)
+        logdX = self.logdX(nsamples)
         betalogL = self._betalogL(beta)
 
-        if dlogX.ndim == 1 and betalogL.ndim == 1:
-            logw = dlogX + betalogL
-        elif dlogX.ndim > 1 and betalogL.ndim == 1:
-            logw = dlogX.add(betalogL, axis=0)
-        elif dlogX.ndim == 1 and betalogL.ndim > 1:
-            logw = betalogL.add(dlogX, axis=0)
+        if logdX.ndim == 1 and betalogL.ndim == 1:
+            logw = logdX + betalogL
+        elif logdX.ndim > 1 and betalogL.ndim == 1:
+            logw = logdX.add(betalogL, axis=0)
+        elif logdX.ndim == 1 and betalogL.ndim > 1:
+            logw = betalogL.add(logdX, axis=0)
         else:
-            cols = MultiIndex.from_product([betalogL.columns, dlogX.columns])
-            dlogX = dlogX.reindex(columns=cols, level='samples')
+            cols = MultiIndex.from_product([betalogL.columns, logdX.columns])
+            logdX = logdX.reindex(columns=cols, level='samples')
             betalogL = betalogL.reindex(columns=cols, level='beta')
-            logw = betalogL+dlogX
+            logw = betalogL+logdX
         return logw
 
     def logZ(self, nsamples=None, beta=None):

tests/test_samples.py

@@ -506,6 +506,30 @@ def test_logX():
     assert (abs(logX.mean(axis=1) - pc.logX()) < logX.std(axis=1) * 3).all()
 
 
+def test_logdX():
+    np.random.seed(3)
+    pc = read_chains('./tests/example_data/pc')
+
+    logdX = pc.logdX()
+    assert isinstance(logdX, WeightedSeries)
+    assert_array_equal(logdX.index, pc.index)
+
+    nsamples = 10
+
+    logdX = pc.logdX(nsamples=nsamples)
+    assert isinstance(logdX, WeightedDataFrame)
+    assert_array_equal(logdX.index, pc.index)
+    assert_array_equal(logdX.columns, np.arange(nsamples))
+    assert logdX.columns.name == 'samples'
+
+    assert not (logdX > 0).to_numpy().any()
+
+    n = 1000
+    logdX = pc.logdX(n)
+
+    assert (abs(logdX.mean(axis=1) - pc.logdX()) < logdX.std(axis=1) * 3).all()
+
+
 def test_logbetaL():
     np.random.seed(3)
     pc = read_chains('./tests/example_data/pc')
@@ -1347,6 +1371,9 @@ def test_old_gui():
     with pytest.raises(NotImplementedError):
         make_1d_axes(['x0', 'y0'], tex={'x0': '$x_0$', 'y0': '$y_0$'})
 
+    with pytest.raises(NotImplementedError):
+        samples.dlogX(1000)
+
 
 def test_groupby_stats():
     mcmc = read_chains('./tests/example_data/cb')