diff options
Diffstat (limited to 'numpy/lib/twodim_base.py')
| -rw-r--r-- | numpy/lib/twodim_base.py | 39 |
1 files changed, 18 insertions, 21 deletions
diff --git a/numpy/lib/twodim_base.py b/numpy/lib/twodim_base.py index 336f23c64..d168e0fca 100644 --- a/numpy/lib/twodim_base.py +++ b/numpy/lib/twodim_base.py @@ -558,9 +558,11 @@ def histogram2d(x, y, bins=10, range=None, normed=False, weights=None): Parameters ---------- x : array_like, shape (N,) - An array containing the x coordinates of the points to be histogrammed. + An array containing the x coordinates of the points to be + histogrammed. y : array_like, shape (N,) - An array containing the y coordinates of the points to be histogrammed. + An array containing the y coordinates of the points to be + histogrammed. bins : int or [int, int] or array_like or [array, array], optional The bin specification: @@ -578,13 +580,13 @@ def histogram2d(x, y, bins=10, range=None, normed=False, weights=None): ``[[xmin, xmax], [ymin, ymax]]``. All values outside of this range will be considered outliers and not tallied in the histogram. normed : bool, optional - If False, returns the number of samples in each bin. If True, returns - the bin density, i.e. the bin count divided by the bin area. + If False, returns the number of samples in each bin. If True, + returns the bin density ``bin_count / sample_count / bin_area``. 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 False, the - values of the returned histogram are equal to the sum of the weights - belonging to the samples falling into each bin. + 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 + False, the values of the returned histogram are equal to the sum of + the weights belonging to the samples falling into each bin. Returns ------- @@ -604,20 +606,15 @@ def histogram2d(x, y, bins=10, range=None, normed=False, weights=None): Notes ----- - When `normed` is True, then the returned histogram is the sample density, - defined such that: - - .. math:: - \\sum_{i=0}^{nx-1} \\sum_{j=0}^{ny-1} H_{i,j} \\Delta x_i \\Delta y_j = 1 - - where `H` is the histogram array and :math:`\\Delta x_i \\Delta y_i` - the area of bin ``{i,j}``. + When `normed` is True, then the returned histogram is the sample + density, defined such that the sum over bins of the product + ``bin_value * bin_area`` is 1. Please note that the histogram does not follow the Cartesian convention - where `x` values are on the abcissa and `y` values on the ordinate axis. - Rather, `x` is histogrammed along the first dimension of the array - (vertical), and `y` along the second dimension of the array (horizontal). - This ensures compatibility with `histogramdd`. + where `x` values are on the abscissa and `y` values on the ordinate + axis. Rather, `x` is histogrammed along the first dimension of the + array (vertical), and `y` along the second dimension of the array + (horizontal). This ensures compatibility with `histogramdd`. Examples -------- @@ -651,7 +648,7 @@ def histogram2d(x, y, bins=10, range=None, normed=False, weights=None): >>> ax = fig.add_subplot(131) >>> ax.set_title('imshow: equidistant') >>> im = plt.imshow(H, interpolation='nearest', origin='low', - extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]]) + extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]]) pcolormesh can display exact bin edges: |