def onselectcirc(center, radius, x, y):
self.center = center
self.radius = radius
if radius < 0.5:
i, j = int(x), int(
y) # Test if the selected area is smaller than a spaxel
ind = self.cube.get_lindex(
(i, j)
) # In that case we display the spectrum corresponding to
self.update_spec(
ind
) # the spaxel where the mouse button has been released.
self.dot.set_visible(
True
) # We also display a dot on this spaxel on the image window
if ((i == 14) and (j == 0)):
self.text['numlens1'].set_text('%3.0d' % (ind + 1))
else:
self.text['numlens1'].set_text('%3.0d' % (ind + 2))
self.print_nbspec(1)
self.dot.set_data((i + 0.5, i + 0.5), (j + 0.5, j + 0.5))
pylab.show()
else:
radius = sqrt((x - self.center[0])**2 +
(y - self.center[1])**2)
self.integ_aperture()
self.print_nbspec()
self.dot.set_visible(False)
pylab.show()
def integ_aperture(self):
print "int_ap(1)"
if self.erase_flag:
self.axes[2].cla()
r = sqrt((self.cube.i - self.center[0] + 0.5)**2 +
(self.cube.j - self.center[1] + 0.5)**2)
self.cube.plot_spec(mask=(r <= self.radius), ax=self.axes[2])
self.erase_flag = True
self.update_sp_stat()
def update_sp_stat(self):
"""Calculate the statistics of the spectrum/spectra selected and print it"""
n = len(self.axes[2].lines) - 1
data = self.axes[2].lines[n].get_ydata()
med = scipy.median(data)
Mean = scipy.mean(data)
sigma = scipy.std(data)
disp = sqrt(scipy.median((data - med)**2))
self.text['mean_sp1'].set_text('%8.2f' % Mean)
self.text['sigma_sp1'].set_text('%8.2f' % sigma)
self.text['median_sp1'].set_text('%8.2f' % med)
self.text['med_disp_sp1'].set_text('%8.2f' % disp)
def print_nbspec(self, n=None):
"""Calculate how many spectra was selected in the reconstructed image and print it"""
print "nbspec"
if n == None:
r = sqrt((self.cube.i - self.center[0] + 0.5)**2 +
(self.cube.j - self.center[1] + 0.5)**2)
nbspec = len(compress(r <= self.radius, r))
else:
nbspec = n
if nbspec <= 1:
self.text['nbspec'].set_text('%i spectrum selected' % nbspec)
else:
self.text['nbspec'].set_text('%i spectra selected' % nbspec)
pylab.show()
def release(self, event):
'on button release event'
if self.pressx is None or self.ignore(event): return
if self.pressy is None or self.ignore(event): return
self.canvas.draw()
self.center = [self.pressx, self.pressy]
self.radius = sqrt((event.xdata-self.center[0])**2 + (event.ydata-self.center[1])**2)
y = event.ydata
x = event.xdata
if self.minspan is not None and radius<self.minspan: return
self.onselect(self.center, self.radius, x, y)
self.pressx = None
self.pressy = None
return False
def onmove(self, event):
'on motion notify event'
if self.pressx is None or self.ignore(event): return
if self.pressy is None or self.ignore(event): return
self.center = [self.pressx,self.pressy]
self.radius = sqrt((event.xdata-self.center[0])**2 + (event.ydata-self.center[1])**2)
if self.radius > 0.5:
self.circ.set_visible(True)
else:
self.circ.set_visible(False)
self.circ.verts = [(v[0]*self.radius+self.center[0],v[1]*self.radius+self.center[1]) for v in self.unit_verts]
pylab.draw()
self.update()
return False
def update_slice(self, ind=None):
print "up_sl(1)"
if ind != None:
self.slice = transpose(self.cube.slice2d(ind, coord='p'))
self.sl_med = scipy.median(ravel(self.slice))
self.sl_mdisp = sqrt(
scipy.median((ravel(self.slice) - self.sl_med)**2))
self.sl_mean = scipy.mean(
numpy.compress(1 - isnan(self.slice), self.slice))
self.sl_disp = scipy.std(
numpy.compress(1 - isnan(self.slice), self.slice))
if self.hcut0 < self.lcut0: self.lcut0 = self.hcut0
vmin = self.sl_med + self.lcut0 * self.sl_mdisp
vmax = self.sl_med + self.hcut0 * self.sl_mdisp
self.axes[0].images[0].set_array(self.slice)
self.axes[0].images[0].set_clim(vmin, vmax)
self.update_sl_stat(ind)
pylab.show()
def __init__(self, fig, cube):
self.cube = cube
self.cube_med = scipy.median(ravel(self.cube.data))
self.cube_mdisp = sqrt(
scipy.median((ravel(self.cube.data) - self.cube_med)**2))
self.fig = fig
self.axes = fig.get_axes()
ax0 = self.axes[0]
ax1 = self.axes[1]
ax2 = self.axes[2]
ax3 = self.axes[3]
print('etape 1')
# Prepare text strings
self.text = {}
self.text['stat'] = ax3.text(0.4, 0.92, 'statistics')
self.text['stat'].set_fontweight('Bold')
self.text['sl'] = ax3.text(0.04, 0.86, 'current slice')
self.text['sp'] = ax3.text(0.65, 0.85, 'current spectrum')
self.text['mean'] = ax3.text(0.35, 0.75, '......Mean......', color="r")
self.text['sigma'] = ax3.text(0.35,
0.65,
'.......RMS......',
color="b")
self.text['median'] = ax3.text(0.35,
0.55,
'.....Median.....',
color="r")
self.text['med_disp'] = ax3.text(0.35,
0.45,
'...Med. disp....',
color="b")
self.text['wav_range'] = ax3.text(0.45,
0.35,
'...Wav. range',
color="r")
self.text['current_int'] = ax3.text(0.35,
0.25,
'...Current int',
color="b")
self.text['nbspec'] = ax3.text(0.04, 0.10, '', fontname='Times')
self.text['numslice'] = ax3.text(0.5, 0.15, 'Slice : ')
self.text['numlens'] = ax3.text(0.5, 0.05, 'Lens : ')
self.text['nbspec'] = ax3.text(0.02, 0.1, '', fontname='Times')
self.text['mean1'] = ax3.text(0.05, 0.75, '', color="r")
self.text['sigma1'] = ax3.text(0.05, 0.65, '', color="b")
self.text['median1'] = ax3.text(0.05, 0.55, '', color="r")
self.text['med_disp1'] = ax3.text(0.05, 0.45, '', color="b")
self.text['wav_range1'] = ax3.text(0.05, 0.35, '', color="r")
self.text['current_int1'] = ax3.text(0.05, 0.25, '', color="b")
self.text['mean_sp1'] = ax3.text(0.7, 0.75, '', color="r")
self.text['sigma_sp1'] = ax3.text(0.7, 0.65, '', color="b")
self.text['median_sp1'] = ax3.text(0.7, 0.55, '', color="r")
self.text['med_disp_sp1'] = ax3.text(0.7, 0.45, '', color="b")
self.text['numslice1'] = ax3.text(0.65, 0.15, '')
self.text['numlens1'] = ax3.text(0.65, 0.06, '')
self.lcut0 = -3
self.hcut0 = 10
self.lcut1 = -3
self.hcut1 = 10
print('etape 2')
# Display reconstructed image in the whole spectral range
self.slice = transpose(cube.slice2d([0, cube.nslice - 1], coord='p'))
self.sl_med = scipy.median(ravel(self.slice))
self.sl_mdisp = sqrt(scipy.median(
(ravel(self.slice) - self.sl_med)**2))
self.sl_mean = scipy.mean(
numpy.compress(1 - isnan(self.slice), self.slice))
self.sl_disp = scipy.std(
numpy.compress(1 - isnan(self.slice), self.slice))
vmin = self.sl_med + self.lcut0 * self.sl_disp
vmax = self.sl_med + self.hcut0 * self.sl_disp
ax0.imshow(self.slice,
interpolation='nearest',
aspect='preserve',
vmin=vmin,
vmax=vmax,
cmap=pylab.cm.hot,
origin='lower')
print('etape 3')
# Display statistics of the reconstructed image
ind = [0, self.cube.nslice - 1]
self.update_sl_stat(ind)
# Import widgets library to select in the stacked spectra image
try:
from pySNIFS_special import Cursor, HorizontalSpanSelector, CircleSpanSelector
except ImportError:
print "Download pySNIFS_special on CVS to use cube_explorer correctly"
print "Trying to import matplotlib.widgets"
try:
from matplotlib.widgets import Cursor, HorizontalSpanSelector
except ImportError:
print "Download the last version of matplotlib to have widgets library"
else:
cursor = Cursor(
ax1, useblit=False, color='green', linewidth=1
) # Use to display reconstructed image and spectrum at the same time
else:
cursor = Cursor(ax1, useblit=False, color='blue', linewidth=1)
print('etape 4')
# Display the stacked spectra image
vmin = self.cube_med + self.lcut1 * self.cube_mdisp
vmax = self.cube_med + self.hcut1 * self.cube_mdisp
ax1.imshow(transpose(cube.data),
interpolation='nearest',
vmin=vmin,
vmax=vmax,
cmap=pylab.cm.hot,
origin='lower')
print('etape 5')
# Store the x/y limits of both images
ax0_xlim = ax0.get_xlim()
ax0_ylim = ax0.get_ylim()
ax1_xlim = ax1.get_xlim()
ax1_ylim = ax1.get_ylim()
print('etape 6')
# Initialize the dot on the slice window showing the selected spectrum
self.dot, = ax0.plot((0, 0), (0, 0), 'wo')
print('etape 7')
# Dirty trick necessary, otherwise, the axes limits are changed I don't know why...
ax0.set_xlim(ax0_xlim)
ax0.set_ylim(ax0_ylim)
self.dot.set_visible(False)
print('etape 8')
# Initialize the begin selection flags. Any 1/2 button press will set the corresponding window flag
# to false and any 1/2 button release will reset it to True
self.erase_flag = True
pylab.draw()
# Use to select several slices on the stacked image
def onselecthori(xmin, xmax, y):
"""draw the reconstructed image and spectra, and update statistics"""
xm = int(xmin)
xM = int(xmax)
self.update_spec(ind=int(y))
i = self.cube.i[int(y)]
j = self.cube.j[int(y)]
self.dot.set_visible(True)
self.dot.set_data((i + 0.5, i + 0.5), (j + 0.5, j + 0.5))
self.print_nbspec(1)
self.text['numslice1'].set_text('[%4.0d:%4.0d]' % (xm, xM))
if ((i == 14) and (j == 0)):
self.text['numlens1'].set_text('%3.0d' % (int(y) + 1))
else:
self.text['numlens1'].set_text('%3.0d' % (int(y) + 2))
ind = [xm, xM]
self.update_slice(ind)
# Use to select several spectra on the reconstructed image
def onselectcirc(center, radius, x, y):
self.center = center
self.radius = radius
if radius < 0.5:
i, j = int(x), int(
y) # Test if the selected area is smaller than a spaxel
ind = self.cube.get_lindex(
(i, j)
) # In that case we display the spectrum corresponding to
self.update_spec(
ind
) # the spaxel where the mouse button has been released.
self.dot.set_visible(
True
) # We also display a dot on this spaxel on the image window
if ((i == 14) and (j == 0)):
self.text['numlens1'].set_text('%3.0d' % (ind + 1))
else:
self.text['numlens1'].set_text('%3.0d' % (ind + 2))
self.print_nbspec(1)
self.dot.set_data((i + 0.5, i + 0.5), (j + 0.5, j + 0.5))
pylab.show()
else:
radius = sqrt((x - self.center[0])**2 +
(y - self.center[1])**2)
self.integ_aperture()
self.print_nbspec()
self.dot.set_visible(False)
pylab.show()
span1 = HorizontalSpanSelector(ax1,
onselecthori,
useblit=False,
rectprops=dict(alpha=0.5,
facecolor='blue'))
span2 = CircleSpanSelector(ax0,
onselectcirc,
useblit=False,
circprops=dict(alpha=0.5, facecolor='blue'))
pylab.show