I just fixed a small bug when bins were not filled and C was specified.
Attached is the revised version.
Index: lib/matplotlib/axes.py
===================================================================
--- lib/matplotlib/axes.py (revision 5892)
+++ lib/matplotlib/axes.py (working copy)
@@ -4941,24 +4941,35 @@
scatter.__doc__ = cbook.dedent(scatter.__doc__) % martist.kwdocd
- def hexbin(self, x, y, gridsize = 100, bins = None,
+ def hexbin(self, x, y, C = None, gridsize = 100, bins = None,
xscale = 'linear', yscale = 'linear',
cmap=None, norm=None, vmin=None, vmax=None,
alpha=1.0, linewidths=None, edgecolors='none',
+ reduce_C_function = np.mean,
**kwargs):
"""
call signature::
- hexbin(x, y, gridsize = 100, bins = None,
+ hexbin(x, y, C = None, gridsize = 100, bins = None,
xscale = 'linear', yscale = 'linear',
cmap=None, norm=None, vmin=None, vmax=None,
alpha=1.0, linewidths=None, edgecolors='none'
+ reduce_C_function = np.mean,
**kwargs)
Make a hexagonal binning plot of *x* versus *y*, where *x*,
- *y* are 1-D sequences of the same length, *N*.
+ *y* are 1-D sequences of the same length, *N*. If *C* is None
+ (the default), this is a histogram of the number of occurences
+ of the observations at (x[i],y[i]).
- *x* and/or *y* may be masked arrays, in which case only
+ If *C* is specified, it specifies values at the coordinate
+ (x[i],y[i]). These values are accumulated for each hexagonal
+ bin and then reduced according to *reduce_C_function*, which
+ defaults to numpy's mean function (np.mean). (If *C* is
+ specified, it must also be a 1-D sequence of the same length
+ as *x* and *y*.)
+
+ *x*, *y* and/or *C* may be masked arrays, in which case only
unmasked points will be plotted.
Optional keyword arguments:
@@ -5049,7 +5060,7 @@
self._process_unit_info(xdata=x, ydata=y, kwargs=kwargs)
- x, y = cbook.delete_masked_points(x, y)
+ x, y, C = cbook.delete_masked_points(x, y, C)
# Set the size of the hexagon grid
if iterable(gridsize):
@@ -5087,22 +5098,59 @@
nx2 = nx
ny2 = ny
n = nx1*ny1+nx2*ny2
- counts = np.zeros(n)
- lattice1 = counts[:nx1*ny1]
- lattice2 = counts[nx1*ny1:]
- lattice1.shape = (nx1,ny1)
- lattice2.shape = (nx2,ny2)
d1 = (x-ix1)**2 + 3.0 * (y-iy1)**2
d2 = (x-ix2-0.5)**2 + 3.0 * (y-iy2-0.5)**2
bdist = (d1<d2)
- for i in range(len(x)):
- if bdist[i]:
- lattice1[ix1[i], iy1[i]]+=1
- else:
- lattice2[ix2[i], iy2[i]]+=1
+ if C is None:
+ accum = np.zeros(n)
+ # Create appropriate views into "accum" array.
+ lattice1 = accum[:nx1*ny1]
+ lattice2 = accum[nx1*ny1:]
+ lattice1.shape = (nx1,ny1)
+ lattice2.shape = (nx2,ny2)
+ for i in range(len(x)):
+ if bdist[i]:
+ lattice1[ix1[i], iy1[i]]+=1
+ else:
+ lattice2[ix2[i], iy2[i]]+=1
+ else:
+ # create accumulation arrays
+ lattice1 = np.empty((nx1,ny1),dtype=object)
+ for i in range(nx1):
+ for j in range(ny1):
+ lattice1[i,j] = []
+ lattice2 = np.empty((nx2,ny2),dtype=object)
+ for i in range(nx2):
+ for j in range(ny2):
+ lattice2[i,j] = []
+
+ for i in range(len(x)):
+ if bdist[i]:
+ lattice1[ix1[i], iy1[i]].append( C[i] )
+ else:
+ lattice2[ix2[i], iy2[i]].append( C[i] )
+
+ for i in range(nx1):
+ for j in range(ny1):
+ vals = lattice1[i,j]
+ if len(vals):
+ lattice1[i,j] = reduce( reduce_C_function, vals )
+ else:
+ lattice1[i,j] = np.nan
+ for i in range(nx2):
+ for j in range(ny2):
+ vals = lattice2[i,j]
+ if len(vals):
+ lattice2[i,j] = reduce( reduce_C_function, vals )
+ else:
+ lattice2[i,j] = np.nan
+
+ accum = np.hstack(( lattice1.astype(float).ravel(), lattice2.astype(float).ravel() ))
+ good_idxs = ~np.isnan(accum)
+
px = xmin + sx * np.array([ 0.5, 0.5, 0.0, -0.5, -0.5, 0.0])
py = ymin + sy * np.array([-0.5, 0.5, 1.0, 0.5, -0.5, -1.0]) / 3.0
@@ -5112,6 +5160,11 @@
polygons[:,nx1*ny1:,0] = np.repeat(np.arange(nx2) + 0.5, ny2)
polygons[:,nx1*ny1:,1] = np.tile(np.arange(ny2), nx2) + 0.5
+ if C is not None:
+ # remove accumulation bins with no data
+ polygons = polygons[:,good_idxs,:]
+ accum = accum[good_idxs]
+
polygons = np.transpose(polygons, axes=[1,0,2])
polygons[:,:,0] *= sx
polygons[:,:,1] *= sy
@@ -5150,20 +5203,20 @@
transOffset = self.transData,
)
- # Transform the counts if needed
+ # Transform accum if needed
if bins=='log':
- counts = np.log10(counts+1)
+ accum = np.log10(accum+1)
elif bins!=None:
if not iterable(bins):
- minimum, maximum = min(counts), max(counts)
+ minimum, maximum = min(accum), max(accum)
bins-=1 # one less edge than bins
bins = minimum + (maximum-minimum)*np.arange(bins)/bins
bins = np.sort(bins)
- counts = bins.searchsorted(counts)
+ accum = bins.searchsorted(accum)
if norm is not None: assert(isinstance(norm, mcolors.Normalize))
if cmap is not None: assert(isinstance(cmap, mcolors.Colormap))
- collection.set_array(counts)
+ collection.set_array(accum)
collection.set_cmap(cmap)
collection.set_norm(norm)
collection.set_alpha(alpha)
Index: examples/pylab_examples/hexbin_demo2.py
===================================================================
--- examples/pylab_examples/hexbin_demo2.py (revision 0)
+++ examples/pylab_examples/hexbin_demo2.py (revision 0)
@@ -0,0 +1,54 @@
+"""
+hexbin is an axes method or pyplot function that is essentially
+a pcolor of a 2-D histogram with hexagonal cells. It can be
+much more informative than a scatter plot; in the first subplot
+below, try substituting 'scatter' for 'hexbin'.
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.mlab as mlab
+
+delta = 0.025
+x = y = np.arange(-3.0, 3.0, delta)
+X, Y = np.meshgrid(x, y)
+Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
+Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
+Z = Z2-Z1 # difference of Gaussians
+
+x = X.ravel()
+y = Y.ravel()
+z = Z.ravel()
+
+if 1:
+ # make some points 20 times more common than others, but same mean
+ xcond = (-1 < x) & (x < 1)
+ ycond = (-2 < y) & (y < 0)
+ cond = xcond & ycond
+ xnew = x[cond]
+ ynew = y[cond]
+ znew = z[cond]
+ for i in range(20):
+ x = np.hstack((x,xnew))
+ y = np.hstack((y,ynew))
+ z = np.hstack((z,znew))
+
+xmin = x.min()
+xmax = x.max()
+ymin = y.min()
+ymax = y.max()
+
+plt.subplot(211)
+plt.hexbin(x,y, C=z )
+plt.axis([xmin, xmax, ymin, ymax])
+cb = plt.colorbar()
+cb.set_label('mean value')
+
+plt.subplot(212)
+plt.hexbin(x,y)
+plt.axis([xmin, xmax, ymin, ymax])
+cb = plt.colorbar()
+cb.set_label('N observations')
+
+plt.show()
+
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