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import numpy as np
from io import BytesIO
from matplotlib import pyplot as plt
from scipy import interpolate
from matplotlib import image
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.transforms import Bbox
from matplotlib.patches import Ellipse
def devectorize_axes(ax=None, dpi=None, transparent=True):
"""Convert axes contents to a png.
This is useful when plotting many points, as the size of the saved file
can become very large otherwise.
Parameters
----------
ax : Axes instance (optional)
Axes to de-vectorize. If None, this uses the current active axes
(plt.gca())
dpi: int (optional)
resolution of the png image. If not specified, the default from
'savefig.dpi' in rcParams will be used
transparent : bool (optional)
if True (default) then the PNG will be made transparent
Returns
-------
ax : Axes instance
the in-place modified Axes instance
Examples
--------
The code can be used in the following way::
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from astroML.plotting.tools import devectorize_axes
>>> fig, ax = plt.subplots()
>>> x, y = np.random.random((2, 10000))
>>> ax.scatter(x, y) # doctest: +IGNORE_OUTPUT
>>> devectorize_axes(ax) # doctest: +IGNORE_OUTPUT
The resulting figure will be much smaller than the vectorized version.
"""
if ax is None:
ax = plt.gca()
fig = ax.figure
axlim = ax.axis()
# setup: make all visible spines (axes & ticks) & text invisible
# we need to set these back later, so we save their current state
_sp = {}
_txt_vis = [t.get_visible() for t in ax.texts]
for k in ax.spines:
_sp[k] = ax.spines[k].get_visible()
ax.spines[k].set_visible(False)
for t in ax.texts:
t.set_visible(False)
_xax = ax.xaxis.get_visible()
_yax = ax.yaxis.get_visible()
_patch = ax.patch.get_visible()
ax.patch.set_visible(False)
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
# convert canvas to PNG
extents = ax.bbox.extents / fig.dpi
output = BytesIO()
plt.savefig(output, format='png', dpi=dpi,
transparent=transparent,
bbox_inches=Bbox([extents[:2], extents[2:]]))
output.seek(0)
im = image.imread(output)
# clear everything on axis (but not text)
# https://discourse.matplotlib.org/t/recommended-way-of-deleting-lines/22526
for art in list(ax.lines) + list(ax.patches) + list(ax.tables) + list(ax.artists) + list(ax.images):
art.remove()
# Show the image
ax.imshow(im, extent=axlim, aspect='auto', interpolation='nearest')
# restore all the spines & text
for k in ax.spines:
ax.spines[k].set_visible(_sp[k])
for t, v in zip(ax.texts, _txt_vis):
t.set_visible(v)
ax.patch.set_visible(_patch)
ax.xaxis.set_visible(_xax)
ax.yaxis.set_visible(_yax)
if plt.isinteractive():
plt.draw()
return ax
def discretize_cmap(cmap, N):
"""Return a discrete colormap from the continuous colormap cmap.
Parameters
----------
cmap : colormap instance, eg. cm.jet.
N : Number of colors.
Returns
-------
cmap_d : discretized colormap
Example
-------
>>> from matplotlib import cm
>>> djet = discretize_cmap(cm.jet, 5)
"""
cdict = cmap._segmentdata.copy()
# N colors
colors_i = np.linspace(0, 1., N)
# N+1 indices
indices = np.linspace(0, 1., N + 1)
for key in ('red', 'green', 'blue'):
# Find the N colors
D = np.array(cdict[key])
interp = interpolate.interp1d(D[:, 0], D[:, 1])
colors = interp(colors_i)
# Place these colors at the correct indices.
A = np.zeros((N + 1, 3), float)
A[:, 0] = indices
A[1:, 1] = colors
A[:-1, 2] = colors
# Create a tuple for the dictionary.
L = []
for color in A:
L.append(tuple(color))
cdict[key] = tuple(L)
# Return colormap object.
return LinearSegmentedColormap('colormap', cdict, 1024)
def draw_ellipse(mu, C, scales=[1, 2, 3], ax=None, **kwargs):
if ax is None:
ax = plt.gca()
# find principal components and rotation angle of ellipse
sigma_x2 = C[0, 0]
sigma_y2 = C[1, 1]
sigma_xy = C[0, 1]
alpha = 0.5 * np.arctan2(2 * sigma_xy,
(sigma_x2 - sigma_y2))
tmp1 = 0.5 * (sigma_x2 + sigma_y2)
tmp2 = np.sqrt(0.25 * (sigma_x2 - sigma_y2) ** 2 + sigma_xy ** 2)
sigma1 = np.sqrt(tmp1 + tmp2)
sigma2 = np.sqrt(tmp1 - tmp2)
for scale in scales:
ax.add_patch(Ellipse((mu[0], mu[1]),
2 * scale * sigma1, 2 * scale * sigma2,
alpha * 180. / np.pi,
**kwargs))
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