def test_save_animation_smoketest(tmpdir, writer, extension):
try:
# for ImageMagick the rcparams must be patched to account for
# 'convert' being a built in MS tool, not the imagemagick
# tool.
writer._init_from_registry()
except AttributeError:
pass
if not animation.writers.is_available(writer):
skip("writer '%s' not available on this system" % writer)
fig, ax = plt.subplots()
line, = ax.plot([], [])
ax.set_xlim(0, 10)
ax.set_ylim(-1, 1)
def init():
line.set_data([], [])
return line,
def animate(i):
x = np.linspace(0, 10, 100)
y = np.sin(x + i)
line.set_data(x, y)
return line,
# Use temporary directory for the file-based writers, which produce a file
# per frame with known names.
with tmpdir.as_cwd():
anim = animation.FuncAnimation(fig, animate, init_func=init, frames=5)
try:
anim.save('movie.' + extension, fps=30, writer=writer, bitrate=500)
except UnicodeDecodeError:
xfail("There can be errors in the numpy import stack, "
"see issues #1891 and #2679")
def test_save_animation_smoketest(writer, extension):
try:
# for ImageMagick the rcparams must be patched to account for
# 'convert' being a built in MS tool, not the imagemagick
# tool.
writer._init_from_registry()
except AttributeError:
pass
if not animation.writers.is_available(writer):
skip("writer '%s' not available on this system" % writer)
fig, ax = plt.subplots()
line, = ax.plot([], [])
ax.set_xlim(0, 10)
ax.set_ylim(-1, 1)
def init():
line.set_data([], [])
return line,
def animate(i):
x = np.linspace(0, 10, 100)
y = np.sin(x + i)
line.set_data(x, y)
return line,
# Use NamedTemporaryFile: will be automatically deleted
F = tempfile.NamedTemporaryFile(suffix='.' + extension)
F.close()
anim = animation.FuncAnimation(fig, animate, init_func=init, frames=5)
try:
anim.save(F.name, fps=30, writer=writer, bitrate=500)
except UnicodeDecodeError:
xfail("There can be errors in the numpy import stack, "
"see issues #1891 and #2679")
finally:
try:
os.remove(F.name)
except Exception:
pass
def test_agg_filter():
def smooth1d(x, window_len):
s = np.r_[2 * x[0] - x[window_len:1:-1], x,
2 * x[-1] - x[-1:-window_len:-1]]
w = np.hanning(window_len)
y = np.convolve(w / w.sum(), s, mode='same')
return y[window_len - 1:-window_len + 1]
def smooth2d(A, sigma=3):
window_len = max(int(sigma), 3) * 2 + 1
A1 = np.array([smooth1d(x, window_len) for x in np.asarray(A)])
A2 = np.transpose(A1)
A3 = np.array([smooth1d(x, window_len) for x in A2])
A4 = np.transpose(A3)
return A4
class BaseFilter(object):
def prepare_image(self, src_image, dpi, pad):
ny, nx, depth = src_image.shape
padded_src = np.zeros([pad * 2 + ny, pad * 2 + nx, depth],
dtype="d")
padded_src[pad:-pad, pad:-pad, :] = src_image[:, :, :]
return padded_src # , tgt_image
def get_pad(self, dpi):
return 0
def __call__(self, im, dpi):
pad = self.get_pad(dpi)
padded_src = self.prepare_image(im, dpi, pad)
tgt_image = self.process_image(padded_src, dpi)
return tgt_image, -pad, -pad
class OffsetFilter(BaseFilter):
def __init__(self, offsets=None):
if offsets is None:
self.offsets = (0, 0)
else:
self.offsets = offsets
def get_pad(self, dpi):
return int(max(*self.offsets) / 72. * dpi)
def process_image(self, padded_src, dpi):
ox, oy = self.offsets
a1 = np.roll(padded_src, int(ox / 72. * dpi), axis=1)
a2 = np.roll(a1, -int(oy / 72. * dpi), axis=0)
return a2
class GaussianFilter(BaseFilter):
"simple gauss filter"
def __init__(self, sigma, alpha=0.5, color=None):
self.sigma = sigma
self.alpha = alpha
if color is None:
self.color = (0, 0, 0)
else:
self.color = color
def get_pad(self, dpi):
return int(self.sigma * 3 / 72. * dpi)
def process_image(self, padded_src, dpi):
tgt_image = np.zeros_like(padded_src)
aa = smooth2d(padded_src[:, :, -1] * self.alpha,
self.sigma / 72. * dpi)
tgt_image[:, :, -1] = aa
tgt_image[:, :, :-1] = self.color
return tgt_image
class DropShadowFilter(BaseFilter):
def __init__(self, sigma, alpha=0.3, color=None, offsets=None):
self.gauss_filter = GaussianFilter(sigma, alpha, color)
self.offset_filter = OffsetFilter(offsets)
def get_pad(self, dpi):
return max(self.gauss_filter.get_pad(dpi),
self.offset_filter.get_pad(dpi))
def process_image(self, padded_src, dpi):
t1 = self.gauss_filter.process_image(padded_src, dpi)
t2 = self.offset_filter.process_image(t1, dpi)
return t2
if V(np.__version__) < V('1.7.0'):
skip('Disabled on Numpy < 1.7.0')
fig = plt.figure()
ax = fig.add_subplot(111)
# draw lines
l1, = ax.plot([0.1, 0.5, 0.9], [0.1, 0.9, 0.5],
"bo-",
mec="b",
mfc="w",
lw=5,
mew=3,
ms=10,
label="Line 1")
l2, = ax.plot([0.1, 0.5, 0.9], [0.5, 0.2, 0.7],
"ro-",
mec="r",
mfc="w",
lw=5,
mew=3,
ms=10,
label="Line 1")
gauss = DropShadowFilter(4)
for l in [l1, l2]:
# draw shadows with same lines with slight offset.
xx = l.get_xdata()
yy = l.get_ydata()
shadow, = ax.plot(xx, yy)
shadow.update_from(l)
# offset transform
ot = mtransforms.offset_copy(l.get_transform(),
ax.figure,
x=4.0,
y=-6.0,
units='points')
shadow.set_transform(ot)
# adjust zorder of the shadow lines so that it is drawn below the
# original lines
shadow.set_zorder(l.get_zorder() - 0.5)
shadow.set_agg_filter(gauss)
shadow.set_rasterized(True) # to support mixed-mode renderers
ax.set_xlim(0., 1.)
ax.set_ylim(0., 1.)
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
def test_agg_filter():
def smooth1d(x, window_len):
s = np.r_[2*x[0] - x[window_len:1:-1],
x,
2*x[-1] - x[-1:-window_len:-1]]
w = np.hanning(window_len)
y = np.convolve(w/w.sum(), s, mode='same')
return y[window_len-1:-window_len+1]
def smooth2d(A, sigma=3):
window_len = max(int(sigma), 3)*2 + 1
A1 = np.array([smooth1d(x, window_len) for x in np.asarray(A)])
A2 = np.transpose(A1)
A3 = np.array([smooth1d(x, window_len) for x in A2])
A4 = np.transpose(A3)
return A4
class BaseFilter(object):
def prepare_image(self, src_image, dpi, pad):
ny, nx, depth = src_image.shape
padded_src = np.zeros([pad*2 + ny, pad*2 + nx, depth], dtype="d")
padded_src[pad:-pad, pad:-pad, :] = src_image[:, :, :]
return padded_src # , tgt_image
def get_pad(self, dpi):
return 0
def __call__(self, im, dpi):
pad = self.get_pad(dpi)
padded_src = self.prepare_image(im, dpi, pad)
tgt_image = self.process_image(padded_src, dpi)
return tgt_image, -pad, -pad
class OffsetFilter(BaseFilter):
def __init__(self, offsets=None):
if offsets is None:
self.offsets = (0, 0)
else:
self.offsets = offsets
def get_pad(self, dpi):
return int(max(*self.offsets)/72.*dpi)
def process_image(self, padded_src, dpi):
ox, oy = self.offsets
a1 = np.roll(padded_src, int(ox/72.*dpi), axis=1)
a2 = np.roll(a1, -int(oy/72.*dpi), axis=0)
return a2
class GaussianFilter(BaseFilter):
"simple gauss filter"
def __init__(self, sigma, alpha=0.5, color=None):
self.sigma = sigma
self.alpha = alpha
if color is None:
self.color = (0, 0, 0)
else:
self.color = color
def get_pad(self, dpi):
return int(self.sigma*3/72.*dpi)
def process_image(self, padded_src, dpi):
tgt_image = np.zeros_like(padded_src)
aa = smooth2d(padded_src[:, :, -1]*self.alpha,
self.sigma/72.*dpi)
tgt_image[:, :, -1] = aa
tgt_image[:, :, :-1] = self.color
return tgt_image
class DropShadowFilter(BaseFilter):
def __init__(self, sigma, alpha=0.3, color=None, offsets=None):
self.gauss_filter = GaussianFilter(sigma, alpha, color)
self.offset_filter = OffsetFilter(offsets)
def get_pad(self, dpi):
return max(self.gauss_filter.get_pad(dpi),
self.offset_filter.get_pad(dpi))
def process_image(self, padded_src, dpi):
t1 = self.gauss_filter.process_image(padded_src, dpi)
t2 = self.offset_filter.process_image(t1, dpi)
return t2
if V(np.__version__) < V('1.7.0'):
skip('Disabled on Numpy < 1.7.0')
fig = plt.figure()
ax = fig.add_subplot(111)
# draw lines
l1, = ax.plot([0.1, 0.5, 0.9], [0.1, 0.9, 0.5], "bo-",
mec="b", mfc="w", lw=5, mew=3, ms=10, label="Line 1")
l2, = ax.plot([0.1, 0.5, 0.9], [0.5, 0.2, 0.7], "ro-",
mec="r", mfc="w", lw=5, mew=3, ms=10, label="Line 1")
gauss = DropShadowFilter(4)
for l in [l1, l2]:
# draw shadows with same lines with slight offset.
xx = l.get_xdata()
yy = l.get_ydata()
shadow, = ax.plot(xx, yy)
shadow.update_from(l)
# offset transform
ot = mtransforms.offset_copy(l.get_transform(), ax.figure,
x=4.0, y=-6.0, units='points')
shadow.set_transform(ot)
# adjust zorder of the shadow lines so that it is drawn below the
# original lines
shadow.set_zorder(l.get_zorder() - 0.5)
shadow.set_agg_filter(gauss)
shadow.set_rasterized(True) # to support mixed-mode renderers
ax.set_xlim(0., 1.)
ax.set_ylim(0., 1.)
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
