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-rw-r--r--doc/release/1.15.0-notes.rst6
-rw-r--r--numpy/lib/histograms.py26
-rw-r--r--numpy/lib/tests/test_histograms.py16
-rw-r--r--numpy/lib/tests/test_twodim_base.py6
-rw-r--r--numpy/lib/twodim_base.py14
5 files changed, 48 insertions, 20 deletions
diff --git a/doc/release/1.15.0-notes.rst b/doc/release/1.15.0-notes.rst
index 8961de300..728465f1e 100644
--- a/doc/release/1.15.0-notes.rst
+++ b/doc/release/1.15.0-notes.rst
@@ -277,6 +277,12 @@ The ``range`` argument of `histogramdd` can now contain ``None`` values to
indicate that the range for the corresponding axis should be computed from the
data. Previously, this could not be specified on a per-axis basis.
+The normed arguments of ``histogramdd`` and ``histogram2d`` have been renamed
+-----------------------------------------------------------------------------
+These arguments are now called ``density``, which is consistent with
+``histogram``. The old argument continues to work, but the new name should be
+preferred.
+
``np.r_`` works with 0d arrays, and ``np.ma.mr_`` works with ``np.ma.masked``
----------------------------------------------------------------------------
0d arrays passed to the `r_` and `mr_` concatenation helpers are now treated as
diff --git a/numpy/lib/histograms.py b/numpy/lib/histograms.py
index ad7215504..422b356f7 100644
--- a/numpy/lib/histograms.py
+++ b/numpy/lib/histograms.py
@@ -812,7 +812,8 @@ def histogram(a, bins=10, range=None, normed=None, weights=None,
return n, bin_edges
-def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
+def histogramdd(sample, bins=10, range=None, normed=None, weights=None,
+ density=None):
"""
Compute the multidimensional histogram of some data.
@@ -845,9 +846,14 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
An entry of None in the sequence results in the minimum and maximum
values being used for the corresponding dimension.
The default, None, is equivalent to passing a tuple of D None values.
+ density : bool, optional
+ 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
- If False, returns the number of samples in each bin. If True,
- returns the bin density ``bin_count / sample_count / bin_volume``.
+ 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,
@@ -961,8 +967,18 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
core = D*(slice(1, -1),)
hist = hist[core]
- # Normalize if normed is True
- if normed:
+ # 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()
for i in _range(D):
shape = np.ones(D, int)
diff --git a/numpy/lib/tests/test_histograms.py b/numpy/lib/tests/test_histograms.py
index d22aa5a27..f136b5c81 100644
--- a/numpy/lib/tests/test_histograms.py
+++ b/numpy/lib/tests/test_histograms.py
@@ -547,13 +547,13 @@ class TestHistogramdd(object):
# Check normalization
ed = [[-2, 0, 2], [0, 1, 2, 3], [0, 1, 2, 3]]
- H, edges = histogramdd(x, bins=ed, normed=True)
+ H, edges = histogramdd(x, bins=ed, density=True)
assert_(np.all(H == answer / 12.))
# Check that H has the correct shape.
H, edges = histogramdd(x, (2, 3, 4),
range=[[-1, 1], [0, 3], [0, 4]],
- normed=True)
+ density=True)
answer = np.array([[[0, 1, 0, 0], [0, 0, 1, 0], [1, 0, 0, 0]],
[[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0]]])
assert_array_almost_equal(H, answer / 6., 4)
@@ -599,10 +599,10 @@ class TestHistogramdd(object):
def test_weights(self):
v = np.random.rand(100, 2)
hist, edges = histogramdd(v)
- n_hist, edges = histogramdd(v, normed=True)
+ n_hist, edges = histogramdd(v, density=True)
w_hist, edges = histogramdd(v, weights=np.ones(100))
assert_array_equal(w_hist, hist)
- w_hist, edges = histogramdd(v, weights=np.ones(100) * 2, normed=True)
+ w_hist, edges = histogramdd(v, weights=np.ones(100) * 2, density=True)
assert_array_equal(w_hist, n_hist)
w_hist, edges = histogramdd(v, weights=np.ones(100, int) * 2)
assert_array_equal(w_hist, 2 * hist)
@@ -708,7 +708,7 @@ class TestHistogramdd(object):
assert_equal(hist[0, 0], 1)
- def test_normed_non_uniform_2d(self):
+ def test_density_non_uniform_2d(self):
# Defines the following grid:
#
# 0 2 8
@@ -732,14 +732,14 @@ class TestHistogramdd(object):
assert_equal(hist, relative_areas)
# resulting histogram should be uniform, since counts and areas are propotional
- hist, edges = histogramdd((y, x), bins=(y_edges, x_edges), normed=True)
+ hist, edges = histogramdd((y, x), bins=(y_edges, x_edges), density=True)
assert_equal(hist, 1 / (8*8))
- def test_normed_non_uniform_1d(self):
+ def test_density_non_uniform_1d(self):
# compare to histogram to show the results are the same
v = np.arange(10)
bins = np.array([0, 1, 3, 6, 10])
hist, edges = histogram(v, bins, density=True)
- hist_dd, edges_dd = histogramdd((v,), (bins,), normed=True)
+ hist_dd, edges_dd = histogramdd((v,), (bins,), density=True)
assert_equal(hist, hist_dd)
assert_equal(edges, edges_dd[0])
diff --git a/numpy/lib/tests/test_twodim_base.py b/numpy/lib/tests/test_twodim_base.py
index d3a072af3..bf93b4adb 100644
--- a/numpy/lib/tests/test_twodim_base.py
+++ b/numpy/lib/tests/test_twodim_base.py
@@ -208,7 +208,7 @@ class TestHistogram2d(object):
x = array([1, 1, 2, 3, 4, 4, 4, 5])
y = array([1, 3, 2, 0, 1, 2, 3, 4])
H, xed, yed = histogram2d(
- x, y, (6, 5), range=[[0, 6], [0, 5]], normed=True)
+ x, y, (6, 5), range=[[0, 6], [0, 5]], density=True)
answer = array(
[[0., 0, 0, 0, 0],
[0, 1, 0, 1, 0],
@@ -220,11 +220,11 @@ class TestHistogram2d(object):
assert_array_equal(xed, np.linspace(0, 6, 7))
assert_array_equal(yed, np.linspace(0, 5, 6))
- def test_norm(self):
+ def test_density(self):
x = array([1, 2, 3, 1, 2, 3, 1, 2, 3])
y = array([1, 1, 1, 2, 2, 2, 3, 3, 3])
H, xed, yed = histogram2d(
- x, y, [[1, 2, 3, 5], [1, 2, 3, 5]], normed=True)
+ x, y, [[1, 2, 3, 5], [1, 2, 3, 5]], density=True)
answer = array([[1, 1, .5],
[1, 1, .5],
[.5, .5, .25]])/9.
diff --git a/numpy/lib/twodim_base.py b/numpy/lib/twodim_base.py
index cca316e9a..98efba191 100644
--- a/numpy/lib/twodim_base.py
+++ b/numpy/lib/twodim_base.py
@@ -530,7 +530,8 @@ def vander(x, N=None, increasing=False):
return v
-def histogram2d(x, y, bins=10, range=None, normed=False, weights=None):
+def histogram2d(x, y, bins=10, range=None, normed=None, weights=None,
+ density=None):
"""
Compute the bi-dimensional histogram of two data samples.
@@ -560,9 +561,14 @@ def histogram2d(x, y, bins=10, range=None, normed=False, weights=None):
(if not specified explicitly in the `bins` parameters):
``[[xmin, xmax], [ymin, ymax]]``. All values outside of this range
will be considered outliers and not tallied in the histogram.
+ density : bool, optional
+ 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_area``.
normed : bool, optional
- If False, returns the number of samples in each bin. If True,
- returns the bin density ``bin_count / sample_count / bin_area``.
+ An alias for the density argument that behaves identically. To avoid
+ confusion with the broken normed argument to `histogram`, `density`
+ should be preferred.
weights : array_like, shape(N,), optional
An array of values ``w_i`` weighing each sample ``(x_i, y_i)``.
Weights are normalized to 1 if `normed` is True. If `normed` is
@@ -652,7 +658,7 @@ def histogram2d(x, y, bins=10, range=None, normed=False, weights=None):
if N != 1 and N != 2:
xedges = yedges = asarray(bins)
bins = [xedges, yedges]
- hist, edges = histogramdd([x, y], bins, range, normed, weights)
+ hist, edges = histogramdd([x, y], bins, range, normed, weights, density)
return hist, edges[0], edges[1]
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