def test_clipping_of_log():
# issue 804
M, L, C = Path.MOVETO, Path.LINETO, Path.CLOSEPOLY
points = [(0.2, -99), (0.4, -99), (0.4, 20), (0.2, 20), (0.2, -99)]
codes = [M, L, L, L, C]
path = Path(points, codes)
# something like this happens in plotting logarithmic histograms
trans = mtransforms.BlendedGenericTransform(mtransforms.Affine2D(),
scale.LogTransform(10, 'clip'))
tpath = trans.transform_path_non_affine(path)
result = tpath.iter_segments(trans.get_affine(),
clip=(0, 0, 100, 100),
simplify=False)
tpoints, tcodes = zip(*result)
assert_allclose(tcodes, [M, L, L, L, C])
def sketch_model(mod, axes=None, shade=True):
from matplotlib import transforms
axes = getaxes(axes)
trans = transforms.BlendedGenericTransform(axes.transAxes, axes.transData)
for dis in mod.discontinuities():
color = shades[-1]
axes.axhline(dis.z, color=dark(color), lw=1.5)
if dis.name is not None:
axes.text(0.90,
dis.z,
dis.name,
transform=trans,
va='center',
ha='right',
color=dark(color),
bbox=dict(ec=dark(color),
fc=light(color, 0.3),
pad=8,
lw=1))
for ilay, lay in enumerate(mod.layers()):
if isinstance(lay, cake.GradientLayer):
tab = shades
else:
tab = shades2
color = tab[ilay % len(tab)]
if shade:
axes.axhspan(lay.ztop,
lay.zbot,
fc=color,
ec=dark(color),
label='abc')
if lay.name is not None:
axes.text(0.95, (lay.ztop + lay.zbot) * 0.5,
lay.name,
transform=trans,
va='center',
ha='right',
color=dark(color),
bbox=dict(ec=dark(color),
fc=light(color, 0.3),
pad=8,
lw=1))
input_dims = 1
output_dims = 1
is_separable = False
has_inverse = True
def __init__(self):
mtransforms.Transform.__init__(self)
def transform_non_affine(self, wl):
return (wl * un.k).to(un.micron, equivalencies=un.spectral()).value
def inverted(self):
return Micron2WNTransform()
aux_trans = mtransforms.BlendedGenericTransform(
Micron2WNTransform(), mtransforms.IdentityTransform())
fig = plt.figure(1)
for n in range(1, 4, 1):
ax_mn = SubplotHost(fig, 1, 3, n)
fig.add_subplot(ax_mn)
ax_mn.set_xlabel('Wavelength (micron)')
# x_micron=np.array([15,10,5,3])
# ax_mn.set_xticks(x_micron)
# ax_mn.set_xlim(5,15)
ax_mn.set_ylim(0, 6)
ax_mn.set_xscale('log')
test_spectrum = np.genfromtxt('sample_spectrum.dpt', delimiter=',')
output_dims = 1
is_separable = False
has_inverse = True
def __init__(self):
mtransforms.Transform.__init__(self)
def transform_non_affine(self, wl):
return (wl*un.micron).to(un.k, equivalencies=un.spectral()).value
def inverted(self):
return WN2MicronTransform()
aux_trans = mtransforms.BlendedGenericTransform(WN2MicronTransform(), mtransforms.IdentityTransform())
fig = plt.figure(1)
fig.set_size_inches(7,4)
offset=0
ax_wn = SubplotHost(fig, 1, 1, 1)
fig.add_subplot(ax_wn)
ax_mn = ax_wn.twin(aux_trans)
ax_mn.set_viewlim_mode("transform")
# x_micron=np.array([12, 10, 8, 6])
# ax_mn.set_xticks(x_micron)
ax_mn.xaxis.tick_bottom()
