def test_fit_gaussian_2d(self):
import matplotlib.pylab as plt
# Create the gaussian data
Xin, Yin = plt.mgrid[0:201, 0:201]
data = self._gaussian(3, 100, 100, 20, 40)(Xin, Yin) + \
np.random.random(Xin.shape)
plt.clf()
ax2 = plt.axes([0, 0.52, 1.0, 0.4])
ax2.matshow(data, cmap=plt.cm.gist_earth_r)
params = ap.fitGaussianImage(data)
fit = self._gaussian(*params)
ax2.contour(fit(*np.indices(data.shape)), cmap=plt.cm.cool)
(height, x, y, width_x, width_y) = params
plt.text(0.95, 0.05, """
x : %.1f
y : %.1f
width_x : %.1f
width_y : %.1f""" %(x, y, width_x, width_y),
fontsize=16, horizontalalignment='right',
verticalalignment='bottom', transform=ax2.transAxes)
ax1 = plt.axes([0, 0.08, 1.0, 0.4])
def test_fit_gaussian_image_l0(self):
import matplotlib.pylab as plt
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
d1 = ap.catools.caget('LTB-BI:BD1{VF1}Img1:ArrayData')
self.assertEqual(len(d1), 1220*1620)
d2 = np.reshape(d1, (1220, 1620))
params = ap.fitGaussianImage(d2)
fit = self._gaussian(*params)
plt.clf()
extent=(500, 1620, 400, 1220)
#plt.contour(fit(*np.indices(d2.shape)), cmap=plt.cm.copper)
plt.imshow(d2, interpolation="nearest", cmap=plt.cm.bwr)
ax = plt.gca()
ax.set_xlim(750, 850)
ax.set_ylim(550, 650)
(height, y, x, width_y, width_x) = params
#axins = zoomed_inset_axes(ax, 6, loc=1)
#axins.contour(fit(*np.indices(d2.shape)), cmap=plt.cm.cool)
#axins.set_xlim(759, 859)
#axins.set_ylim(559, 660)
#mark_inset(ax, axins, loc1=2, loc2=4, fc='none', ec='0.5')
plt.text(0.95, 0.05, """
x : %.1f
y : %.1f
width_x : %.1f
width_y : %.1f""" %(x, y, width_x, width_y),
fontsize=16, horizontalalignment='right',
verticalalignment='bottom', transform=ax.transAxes)
plt.savefig(figname("test2.png"))
