.. redirect-from:: /users/prev_whats_new/whats_new_0.99
Table of Contents
Jae-Joon Lee has written two new guides :ref:`legend_guide` and :ref:`plotting-guide-annotation`. Michael Sarahan has written :ref:`image_tutorial`. John Hunter has written two new tutorials on working with paths and transformations: :ref:`paths` and :ref:`transforms_tutorial`.
Reinier Heeres has ported John Porter's mplot3d over to the new matplotlib transformations framework, and it is now available as a toolkit mpl_toolkits.mplot3d (which now comes standard with all mpl installs). See :ref:`mplot3d-examples-index` and :ref:`mplot3d`.
.. plot::
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
plt.style.use('classic')
X = np.arange(-5, 5, 0.25)
Y = np.arange(-5, 5, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)
fig = plt.figure()
ax = Axes3D(fig, auto_add_to_figure=False)
fig.add_axes(ax)
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.viridis)
plt.show()
Jae-Joon Lee has added a new toolkit to ease displaying multiple images in matplotlib, as well as some support for curvilinear grids to support the world coordinate system. The toolkit is included standard with all new mpl installs. See :ref:`axes_grid1-examples-index`, :ref:`axisartist-examples-index`, :ref:`axes_grid1_users-guide-index` and :ref:`axisartist_users-guide-index`
.. plot::
from mpl_toolkits.axes_grid1.axes_rgb import RGBAxes
def get_demo_image():
# prepare image
delta = 0.5
extent = (-3, 4, -4, 3)
x = np.arange(-3.0, 4.001, delta)
y = np.arange(-4.0, 3.001, delta)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = (Z1 - Z2) * 2
return Z, extent
def get_rgb():
Z, extent = get_demo_image()
Z[Z < 0] = 0.
Z = Z / Z.max()
R = Z[:13, :13]
G = Z[2:, 2:]
B = Z[:13, 2:]
return R, G, B
fig = plt.figure()
plt.style.use('classic')
ax = RGBAxes(fig, [0.1, 0.1, 0.8, 0.8])
r, g, b = get_rgb()
ax.imshow_rgb(r, g, b, origin="lower")
ax.RGB.set_xlim(0., 9.5)
ax.RGB.set_ylim(0.9, 10.6)
plt.show()
Andrew Straw has added the ability to place "axis spines" -- the lines that denote the data limits -- in various arbitrary locations. No longer are your axis lines constrained to be a simple rectangle around the figure -- you can turn on or off left, bottom, right and top, as well as "detach" the spine to offset it away from the data. See :doc:`/gallery/spines/spine_placement_demo` and :class:`matplotlib.spines.Spine`.
.. plot::
def adjust_spines(ax, spines):
for loc, spine in ax.spines.items():
if loc in spines:
spine.set_position(('outward', 10)) # outward by 10 points
else:
spine.set_color('none') # don't draw spine
# turn off ticks where there is no spine
if 'left' in spines:
ax.yaxis.set_ticks_position('left')
else:
# no yaxis ticks
ax.yaxis.set_ticks([])
if 'bottom' in spines:
ax.xaxis.set_ticks_position('bottom')
else:
# no xaxis ticks
ax.xaxis.set_ticks([])
fig = plt.figure()
plt.style.use('classic')
x = np.linspace(0, 2*np.pi, 100)
y = 2*np.sin(x)
ax = fig.add_subplot(2, 2, 1)
ax.plot(x, y)
adjust_spines(ax, ['left'])
ax = fig.add_subplot(2, 2, 2)
ax.plot(x, y)
adjust_spines(ax, [])
ax = fig.add_subplot(2, 2, 3)
ax.plot(x, y)
adjust_spines(ax, ['left', 'bottom'])
ax = fig.add_subplot(2, 2, 4)
ax.plot(x, y)
adjust_spines(ax, ['bottom'])
plt.show()