def mask2d(self):
return utils.flatten_2x1s(self.mask)
def mask2d(self):
return utils.flatten_2x1s( self.mask )
def __init__(self, raw_image, mask=None, filename='my_mask', fmt='pypad'):
"""
Instantiate an interactive masking session.
Parameters
----------
raw_image : np.ndarray
A shape (4, 8, 185, 388) array containing a reference image that
the user will use to guide their masking.
mask : padmask.PadMask
A PadMask object to modify. If `None` (default), generate a new
mask.
filename : str
The name of the file to generate at the end of the session.
fmt : str
The file format of `filename` to write.
"""
self.print_gui_help()
self.filename = filename
self.file_fmt = fmt
if not raw_image.shape == (4, 16, 185, 194):
raise ValueError("`raw_image` must have shape: (4, 16, 185, 194)")
if mask == None:
self.mask = PadMask()
elif isinstance(mask, PadMask):
self.mask = mask
else:
raise TypeError(
'`mask` argument must be a pypad.mask.PadMask object')
# inject a new mask type into our PadMask obj
m = self.mask._blank_mask()
if not 'manual' in self.mask._masks.keys():
self.mask._inject_mask('manual', m)
# deal with negative values
if not 'negatives' in self.mask._masks.keys():
self.mask._inject_mask('negatives', m.copy())
self.mask._masks['negatives'][raw_image <= 0.0] = 0
print "Masked: %d negative pixels" % np.sum(
np.logical_not(self.mask._masks['negatives']))
# we're going to plot the log of the image, so do that up front
self.raw_image_4d = raw_image
self.raw_image = utils.flatten_2x1s(raw_image)
self.log_image = self.raw_image.copy()
self.log_image[self.log_image < 0.0] = 0.0
self.log_image = np.log10(self.log_image + 1.0)
# populate an array containing the indices of all pixels in the image
mg = np.meshgrid(np.arange(self.raw_image.shape[0]),
np.arange(self.raw_image.shape[1]))
self.points = np.vstack((mg[0].flatten(), mg[1].flatten())).T
# create a colormap with masked pixels clearly highlighted
self.palette = plt.cm.PuOr_r # reversed purple-orange -- base cm
self.palette.set_under(color='green')
# draw the main GUI, which is an image that can be interactively masked
plt.figure(figsize=(9, 6))
self.ax = plt.subplot(111)
self.im = self.ax.imshow(
(self.log_image * self.mask.mask2d) - 1e-10,
cmap=self.palette,
origin='lower',
interpolation='nearest',
vmin=1e-10,
aspect=1,
extent=[0, self.log_image.shape[0], 0, self.log_image.shape[1]])
self.lc, = self.ax.plot((0, 0), (0, 0),
'-+m',
linewidth=1,
markersize=8,
markeredgewidth=1)
self.lm, = self.ax.plot((0, 0), (0, 0),
'-+m',
linewidth=1,
markersize=8,
markeredgewidth=1)
self.line_corner = (0, 0)
self.xy = None
self.lines_xy = None
self.single_px = None # for masking single pixels
self.colorbar = plt.colorbar(self.im, pad=0.01)
self.colorbar.set_label(r'$\log_{10}$ Intensity')
cidb = plt.connect('button_press_event', self.on_click)
cidk = plt.connect('key_press_event', self.on_keypress)
cidm = plt.connect('motion_notify_event', self.on_move)
plt.xlim([0, self.log_image.shape[0]])
plt.ylim([0, self.log_image.shape[1]])
self.ax.get_xaxis().set_ticks([])
self.ax.get_yaxis().set_ticks([])
# add toggle buttons that allow the user to turn on and off std masks
# I used to have this in its own nice function, but MPL didn't like
# that for some reason... there is probably a better way, I just dont
# know the innerds of MPL enough --TJL
axcolor = 'lightgoldenrodyellow'
ax1 = plt.axes([0.04, 0.7, 0.12, 0.08])
self.b1 = ToggleButton(ax1,
'nonbonded',
color=axcolor,
hovercolor='0.975')
self.b1.on_turned_on(self.mask.mask_nonbonded)
self.b1.on_turned_off(self.mask.remove_mask, 'nonbonded')
self.b1.on_turned_on(self.update_image)
self.b1.on_turned_off(self.update_image)
ax2 = plt.axes([0.04, 0.6, 0.12, 0.08])
self.b2 = ToggleButton(ax2,
'row 13',
color=axcolor,
hovercolor='0.975')
self.b2.on_turned_on(self.mask.mask_row13)
self.b2.on_turned_off(self.mask.remove_mask, 'row13')
self.b2.on_turned_on(self.update_image)
self.b2.on_turned_off(self.update_image)
ax3 = plt.axes([0.04, 0.5, 0.12, 0.08])
self.b3 = ToggleButton(ax3,
'borders',
color=axcolor,
hovercolor='0.975')
self.b3.on_turned_on(self._set_borderwidth)
self.mask_border_cid = self.b3.on_turned_on(self.mask.mask_borders)
self.b3.on_turned_off(self.mask.remove_mask, 'border')
self.b3.on_turned_on(self.update_image)
self.b3.on_turned_off(self.update_image)
ax4 = plt.axes([0.04, 0.4, 0.12, 0.08])
self.b4 = ToggleButton(ax4,
'threshold',
color=axcolor,
hovercolor='0.975')
self.b4.on_turned_on(self._set_threshold)
self.mask_threshold_cid = self.b4.on_turned_on(
self.mask.mask_threshold, self.raw_image_4d, None, None)
self.b4.on_turned_off(self.mask.remove_mask, 'threshold')
self.b4.on_turned_on(self.update_image)
self.b4.on_turned_off(self.update_image)
plt.show()
return
def __init__(self, raw_image, mask=None, filename='my_mask', fmt='pypad'):
"""
Instantiate an interactive masking session.
Parameters
----------
raw_image : np.ndarray
A shape (4, 8, 185, 388) array containing a reference image that
the user will use to guide their masking.
mask : padmask.PadMask
A PadMask object to modify. If `None` (default), generate a new
mask.
filename : str
The name of the file to generate at the end of the session.
fmt : str
The file format of `filename` to write.
"""
self.print_gui_help()
self.filename = filename
self.file_fmt = fmt
if not raw_image.shape == (4, 16, 185, 194):
raise ValueError("`raw_image` must have shape: (4, 16, 185, 194)")
if mask == None:
self.mask = PadMask()
elif isinstance(mask, PadMask):
self.mask = mask
else:
raise TypeError('`mask` argument must be a pypad.mask.PadMask object')
# inject a new mask type into our PadMask obj
m = self.mask._blank_mask()
if not 'manual' in self.mask._masks.keys():
self.mask._inject_mask('manual', m)
# deal with negative values
if not 'negatives' in self.mask._masks.keys():
self.mask._inject_mask('negatives', m.copy())
self.mask._masks['negatives'][raw_image <= 0.0] = 0
print "Masked: %d negative pixels" % np.sum(np.logical_not(self.mask._masks['negatives']))
# we're going to plot the log of the image, so do that up front
self.raw_image_4d = raw_image
self.raw_image = utils.flatten_2x1s(raw_image)
self.log_image = self.raw_image.copy()
self.log_image[self.log_image < 0.0] = 0.0
self.log_image = np.log10(self.log_image + 1.0)
# populate an array containing the indices of all pixels in the image
mg = np.meshgrid( np.arange(self.raw_image.shape[0]),
np.arange(self.raw_image.shape[1]) )
self.points = np.vstack((mg[0].flatten(), mg[1].flatten())).T
# create a colormap with masked pixels clearly highlighted
self.palette = plt.cm.PuOr_r # reversed purple-orange -- base cm
self.palette.set_under(color='green')
# draw the main GUI, which is an image that can be interactively masked
plt.figure(figsize=(9,6))
self.ax = plt.subplot(111)
self.im = self.ax.imshow( (self.log_image * self.mask.mask2d) - 1e-10, cmap=self.palette,
origin='lower', interpolation='nearest', vmin=1e-10, aspect=1,
extent=[0, self.log_image.shape[0], 0, self.log_image.shape[1]] )
self.lc, = self.ax.plot((0,0),(0,0),'-+m', linewidth=1, markersize=8, markeredgewidth=1)
self.lm, = self.ax.plot((0,0),(0,0),'-+m', linewidth=1, markersize=8, markeredgewidth=1)
self.line_corner = (0,0)
self.xy = None
self.lines_xy = None
self.single_px = None # for masking single pixels
self.colorbar = plt.colorbar(self.im, pad=0.01)
self.colorbar.set_label(r'$\log_{10}$ Intensity')
cidb = plt.connect('button_press_event', self.on_click)
cidk = plt.connect('key_press_event', self.on_keypress)
cidm = plt.connect('motion_notify_event', self.on_move)
plt.xlim([0, self.log_image.shape[0]])
plt.ylim([0, self.log_image.shape[1]])
self.ax.get_xaxis().set_ticks([])
self.ax.get_yaxis().set_ticks([])
# add toggle buttons that allow the user to turn on and off std masks
# I used to have this in its own nice function, but MPL didn't like
# that for some reason... there is probably a better way, I just dont
# know the innerds of MPL enough --TJL
axcolor = 'lightgoldenrodyellow'
ax1 = plt.axes([0.04, 0.7, 0.12, 0.08])
self.b1 = ToggleButton(ax1, 'nonbonded', color=axcolor, hovercolor='0.975')
self.b1.on_turned_on(self.mask.mask_nonbonded)
self.b1.on_turned_off(self.mask.remove_mask, 'nonbonded')
self.b1.on_turned_on(self.update_image)
self.b1.on_turned_off(self.update_image)
ax2 = plt.axes([0.04, 0.6, 0.12, 0.08])
self.b2 = ToggleButton(ax2, 'row 13', color=axcolor, hovercolor='0.975')
self.b2.on_turned_on(self.mask.mask_row13)
self.b2.on_turned_off(self.mask.remove_mask, 'row13')
self.b2.on_turned_on(self.update_image)
self.b2.on_turned_off(self.update_image)
ax3 = plt.axes([0.04, 0.5, 0.12, 0.08])
self.b3 = ToggleButton(ax3, 'borders', color=axcolor, hovercolor='0.975')
self.b3.on_turned_on(self._set_borderwidth)
self.mask_border_cid = self.b3.on_turned_on(self.mask.mask_borders)
self.b3.on_turned_off(self.mask.remove_mask, 'border')
self.b3.on_turned_on(self.update_image)
self.b3.on_turned_off(self.update_image)
ax4 = plt.axes([0.04, 0.4, 0.12, 0.08])
self.b4 = ToggleButton(ax4, 'threshold', color=axcolor, hovercolor='0.975')
self.b4.on_turned_on(self._set_threshold)
self.mask_threshold_cid = self.b4.on_turned_on(self.mask.mask_threshold, self.raw_image_4d, None, None)
self.b4.on_turned_off(self.mask.remove_mask, 'threshold')
self.b4.on_turned_on(self.update_image)
self.b4.on_turned_off(self.update_image)
plt.show()
return
