dlogX -> logdX

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:/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')