Change meaning of default None for weight_function in PersistenceImage

- ``None`` corresponding the identity means that there really is a non-trivial weighting in that case. Semantically, this does not seem correct ("None" should mean no weighting at all)

gtda/diagrams/_metrics.py

@@ -10,7 +10,7 @@
 from sklearn.utils import gen_even_slices
 from sklearn.utils.validation import _num_samples
 
-from ._utils import identity, _subdiagrams, _sample_image
+from ._utils import _subdiagrams, _sample_image
 from ..externals.modules.gtda_bottleneck import bottleneck_distance
 from ..externals.modules.gtda_wasserstein import wasserstein_distance
 from ..utils.intervals import Interval

gtda/diagrams/representations.py

@@ -14,7 +14,7 @@
 
 from ._metrics import betti_curves, landscapes, heats, \
     persistence_images, silhouettes
-from ._utils import identity, _subdiagrams, _bin
+from ._utils import _subdiagrams, _bin
 from ..base import PlotterMixin
 from ..plotting import plot_heatmap
 from ..utils._docs import adapt_fit_transform_docs
@@ -752,9 +752,10 @@ class PersistenceImage(BaseEstimator, TransformerMixin, PlotterMixin):
         dimension, to sample during :meth:`fit`.
 
     weight_function : callable or None, default: ``None``
-        Function mapping the 1D array of persistence values of the points of an
-        input diagram to a 1D array of weights. ``None`` is equivalent to
-        passing the identity function.
+        Function mapping the 1D array of sampled persistence values (see
+        :attr:`samplings_`) to a 1D array of weights. ``None`` is equivalent to
+        passing ``numpy.ones_like``. More weight can be given to regions of
+        high persistence by passing a monotonic function, e.g. the identity.
 
     n_jobs : int or None, optional, default: ``None``
         The number of jobs to use for the computation. ``None`` means 1 unless
@@ -851,7 +852,7 @@ def fit(self, X, y=None):
             self.get_params(), self._hyperparameters, exclude=["n_jobs"])
 
         if self.weight_function is None:
-            self.effective_weight_function_ = identity
+            self.effective_weight_function_ = np.ones_like
         else:
             self.effective_weight_function_ = self.weight_function