DEP: Remove `normed=` keyword argument from histogroms

The normed keyword argument has been deprecated for a long time.
This removes it, replacing its position with the new density
argument.

1 files changed, 9 insertions, 68 deletions

diff --git a/numpy/lib/histograms.py b/numpy/lib/histograms.py
index 98182f1c4..f7feb2531 100644
--- a/numpy/lib/histograms.py
+++ b/numpy/lib/histograms.py
@@ -671,13 +671,12 @@ def histogram_bin_edges(a, bins=10, range=None, weights=None):
 
 
 def _histogram_dispatcher(
-        a, bins=None, range=None, normed=None, weights=None, density=None):
+        a, bins=None, range=None, density=None, weights=None):
     return (a, bins, weights)
 
 
 @array_function_dispatch(_histogram_dispatcher)
-def histogram(a, bins=10, range=None, normed=None, weights=None,
-              density=None):
+def histogram(a, bins=10, range=None, density=None, weights=None):
     r"""
     Compute the histogram of a dataset.
 
@@ -704,16 +703,6 @@ def histogram(a, bins=10, range=None, normed=None, weights=None,
         computation as well. While bin width is computed to be optimal
         based on the actual data within `range`, the bin count will fill
         the entire range including portions containing no data.
-    normed : bool, optional
-
-        .. deprecated:: 1.6.0
-
-        This is equivalent to the `density` argument, but produces incorrect
-        results for unequal bin widths. It should not be used.
-
-        .. versionchanged:: 1.15.0
-            DeprecationWarnings are actually emitted.
-
     weights : array_like, optional
         An array of weights, of the same shape as `a`.  Each value in
         `a` only contributes its associated weight towards the bin count
@@ -728,8 +717,6 @@ def histogram(a, bins=10, range=None, normed=None, weights=None,
         histogram values will not be equal to 1 unless bins of unity
         width are chosen; it is not a probability *mass* function.
 
-        Overrides the ``normed`` keyword if given.
-
     Returns
     -------
     hist : array
@@ -891,46 +878,15 @@ def histogram(a, bins=10, range=None, normed=None, weights=None,
 
         n = np.diff(cum_n)
 
-    # density overrides the normed keyword
-    if density is not None:
-        if normed is not None:
-            # 2018-06-13, numpy 1.15.0 (this was not noisily deprecated in 1.6)
-            warnings.warn(
-                    "The normed argument is ignored when density is provided. "
-                    "In future passing both will result in an error.",
-                    DeprecationWarning, stacklevel=3)
-        normed = None
-
     if density:
         db = np.array(np.diff(bin_edges), float)
         return n/db/n.sum(), bin_edges
-    elif normed:
-        # 2018-06-13, numpy 1.15.0 (this was not noisily deprecated in 1.6)
-        warnings.warn(
-                "Passing `normed=True` on non-uniform bins has always been "
-                "broken, and computes neither the probability density "
-                "function nor the probability mass function. "
-                "The result is only correct if the bins are uniform, when "
-                "density=True will produce the same result anyway. "
-                "The argument will be removed in a future version of "
-                "numpy.",
-                np.VisibleDeprecationWarning, stacklevel=3)
-
-        # this normalization is incorrect, but
-        db = np.array(np.diff(bin_edges), float)
-        return n/(n*db).sum(), bin_edges
-    else:
-        if normed is not None:
-            # 2018-06-13, numpy 1.15.0 (this was not noisily deprecated in 1.6)
-            warnings.warn(
-                    "Passing normed=False is deprecated, and has no effect. "
-                    "Consider passing the density argument instead.",
-                    DeprecationWarning, stacklevel=3)
-        return n, bin_edges
+
+    return n, bin_edges
 
 
-def _histogramdd_dispatcher(sample, bins=None, range=None, normed=None,
-                            weights=None, density=None):
+def _histogramdd_dispatcher(sample, bins=None, range=None, density=None,
+                            weights=None):
     if hasattr(sample, 'shape'):  # same condition as used in histogramdd
         yield sample
     else:
@@ -941,8 +897,7 @@ def _histogramdd_dispatcher(sample, bins=None, range=None, normed=None,
 
 
 @array_function_dispatch(_histogramdd_dispatcher)
-def histogramdd(sample, bins=10, range=None, normed=None, weights=None,
-                density=None):
+def histogramdd(sample, bins=10, range=None, density=None, weights=None):
     """
     Compute the multidimensional histogram of some data.
 
@@ -979,20 +934,16 @@ def histogramdd(sample, bins=10, range=None, normed=None, weights=None,
         If False, the default, returns the number of samples in each bin.
         If True, returns the probability *density* function at the bin,
         ``bin_count / sample_count / bin_volume``.
-    normed : bool, optional
-        An alias for the density argument that behaves identically. To avoid
-        confusion with the broken normed argument to `histogram`, `density`
-        should be preferred.
     weights : (N,) array_like, optional
         An array of values `w_i` weighing each sample `(x_i, y_i, z_i, ...)`.
-        Weights are normalized to 1 if normed is True. If normed is False,
+        Weights are normalized to 1 if density is True. If density is False,
         the values of the returned histogram are equal to the sum of the
         weights belonging to the samples falling into each bin.
 
     Returns
     -------
     H : ndarray
-        The multidimensional histogram of sample x. See normed and weights
+        The multidimensional histogram of sample x. See density and weights
         for the different possible semantics.
     edges : list
         A list of D arrays describing the bin edges for each dimension.
@@ -1104,16 +1055,6 @@ def histogramdd(sample, bins=10, range=None, normed=None, weights=None,
     core = D*(slice(1, -1),)
     hist = hist[core]
 
-    # handle the aliasing normed argument
-    if normed is None:
-        if density is None:
-            density = False
-    elif density is None:
-        # an explicit normed argument was passed, alias it to the new name
-        density = normed
-    else:
-        raise TypeError("Cannot specify both 'normed' and 'density'")
-
     if density:
         # calculate the probability density function
         s = hist.sum()