def __init__(self,
address,
pickle_dirname=".",
pickle_basename="hist",
roi=None,
hist_min=None,
hist_max=None,
n_slots=None,
**kwds):
"""
@param address Address string XXX Que?!
@param pickle_dirname Directory portion of output pickle file
XXX mean, mu?
@param pickle_basename Filename prefix of output pickle file
image XXX mean, mu?
@param calib_dir Directory with calibration information
@param dark_path Path to input dark image
@param dark_stddev Path to input dark standard deviation
"""
super(pixel_histograms, self).__init__(address=address, **kwds)
self.pickle_dirname = cspad_tbx.getOptString(pickle_dirname)
self.pickle_basename = cspad_tbx.getOptString(pickle_basename)
self.hist_min = cspad_tbx.getOptFloat(hist_min)
self.hist_max = cspad_tbx.getOptFloat(hist_max)
self.n_slots = cspad_tbx.getOptInteger(n_slots)
self.histograms = {}
self.dimensions = None
self.roi = cspad_tbx.getOptROI(roi)
self.values = flex.long()
self.sigma_scaling = False
if self.hist_min is None: self.hist_min = -50
if self.hist_max is None: self.hist_max = 150
if self.n_slots is None: self.n_slots = 200
def __init__(self,
address,
pickle_dirname=".",
pickle_basename="hist",
roi=None,
hist_min=None,
hist_max=None,
n_slots=None,
**kwds):
"""
@param address Address string XXX Que?!
@param pickle_dirname Directory portion of output pickle file
XXX mean, mu?
@param pickle_basename Filename prefix of output pickle file
image XXX mean, mu?
@param calib_dir Directory with calibration information
@param dark_path Path to input dark image
@param dark_stddev Path to input dark standard deviation
"""
super(pixel_histograms, self).__init__(
address=address,
**kwds
)
self.pickle_dirname = cspad_tbx.getOptString(pickle_dirname)
self.pickle_basename = cspad_tbx.getOptString(pickle_basename)
self.hist_min = cspad_tbx.getOptFloat(hist_min)
self.hist_max = cspad_tbx.getOptFloat(hist_max)
self.n_slots = cspad_tbx.getOptInteger(n_slots)
self.histograms = {}
self.dimensions = None
self.roi = cspad_tbx.getOptROI(roi)
self.values = flex.long()
self.sigma_scaling = False
if self.hist_min is None: self.hist_min = -50
if self.hist_max is None: self.hist_max = 150
if self.n_slots is None: self.n_slots = 200
def run(self, flags, sweep=None, observations=None, **kwargs):
obs_x, obs_y = observations.centroids().px_position_xy().parts()
import numpy as np
H, xedges, yedges = np.histogram2d(
obs_x.as_numpy_array(), obs_y.as_numpy_array(),bins=self.nbins)
from scitbx.array_family import flex
H_flex = flex.double(H.flatten().astype(np.float64))
n_slots = min(int(flex.max(H_flex)), 30)
hist = flex.histogram(H_flex, n_slots=n_slots)
slots = hist.slots()
cumulative_hist = flex.long(len(slots))
for i in range(len(slots)):
cumulative_hist[i] = slots[i]
if i > 0:
cumulative_hist[i] += cumulative_hist[i-1]
cumulative_hist = cumulative_hist.as_double()/flex.max(
cumulative_hist.as_double())
cutoff = None
gradients = flex.double()
for i in range(len(slots)-1):
x1 = cumulative_hist[i]
x2 = cumulative_hist[i+1]
g = (x2 - x1)/hist.slot_width()
gradients.append(g)
if (cutoff is None and i > 0 and
g < self.gradient_cutoff and gradients[i-1] < self.gradient_cutoff):
cutoff = hist.slot_centers()[i-1]-0.5*hist.slot_width()
H_flex = flex.double(np.ascontiguousarray(H))
isel = (H_flex > cutoff).iselection()
sel = np.column_stack(np.where(H > cutoff))
for (ix, iy) in sel:
flags.set_selected(
((obs_x > xedges[ix]) & (obs_x < xedges[ix+1]) &
(obs_y > yedges[iy]) & (obs_y < yedges[iy+1])), False)
if 0:
from matplotlib import pyplot
fig, ax1 = pyplot.subplots()
extent = [yedges[0], yedges[-1], xedges[0], xedges[-1]]
plot1 = ax1.imshow(H, extent=extent, interpolation="nearest")
pyplot.xlim((0, pyplot.xlim()[1]))
pyplot.ylim((0, pyplot.ylim()[1]))
pyplot.gca().invert_yaxis()
cbar1 = pyplot.colorbar(plot1)
pyplot.axes().set_aspect('equal')
pyplot.show()
fig, ax1 = pyplot.subplots()
ax2 = ax1.twinx()
ax1.scatter(hist.slot_centers()-0.5*hist.slot_width(), cumulative_hist)
ax1.set_ylim(0, 1)
ax2.plot(hist.slot_centers()[:-1]-0.5*hist.slot_width(), gradients)
ymin, ymax = pyplot.ylim()
pyplot.vlines(cutoff, ymin, ymax, color='r')
pyplot.show()
H2 = H.copy()
if cutoff is not None:
H2[np.where(H2 >= cutoff)] = 0
fig, ax1 = pyplot.subplots()
plot1 = ax1.pcolormesh(xedges, yedges, H2)
pyplot.xlim((0, pyplot.xlim()[1]))
pyplot.ylim((0, pyplot.ylim()[1]))
pyplot.gca().invert_yaxis()
cbar1 = pyplot.colorbar(plot1)
pyplot.axes().set_aspect('equal')
pyplot.show()
return flags
def run(self, flags, sweep=None, observations=None, **kwargs):
obs_x, obs_y = observations.centroids().px_position_xy().parts()
import numpy as np
H, xedges, yedges = np.histogram2d(obs_x.as_numpy_array(),
obs_y.as_numpy_array(),
bins=self.nbins)
from scitbx.array_family import flex
H_flex = flex.double(H.flatten().astype(np.float64))
n_slots = min(int(flex.max(H_flex)), 30)
hist = flex.histogram(H_flex, n_slots=n_slots)
slots = hist.slots()
cumulative_hist = flex.long(len(slots))
for i in range(len(slots)):
cumulative_hist[i] = slots[i]
if i > 0:
cumulative_hist[i] += cumulative_hist[i - 1]
cumulative_hist = cumulative_hist.as_double() / flex.max(
cumulative_hist.as_double())
cutoff = None
gradients = flex.double()
for i in range(len(slots) - 1):
x1 = cumulative_hist[i]
x2 = cumulative_hist[i + 1]
g = (x2 - x1) / hist.slot_width()
gradients.append(g)
if (cutoff is None and i > 0 and g < self.gradient_cutoff
and gradients[i - 1] < self.gradient_cutoff):
cutoff = hist.slot_centers()[i - 1] - 0.5 * hist.slot_width()
H_flex = flex.double(np.ascontiguousarray(H))
isel = (H_flex > cutoff).iselection()
sel = np.column_stack(np.where(H > cutoff))
for (ix, iy) in sel:
flags.set_selected(
((obs_x > xedges[ix]) & (obs_x < xedges[ix + 1]) &
(obs_y > yedges[iy]) & (obs_y < yedges[iy + 1])), False)
if 0:
from matplotlib import pyplot
fig, ax1 = pyplot.subplots()
extent = [yedges[0], yedges[-1], xedges[0], xedges[-1]]
plot1 = ax1.imshow(H, extent=extent, interpolation="nearest")
pyplot.xlim((0, pyplot.xlim()[1]))
pyplot.ylim((0, pyplot.ylim()[1]))
pyplot.gca().invert_yaxis()
cbar1 = pyplot.colorbar(plot1)
pyplot.axes().set_aspect('equal')
pyplot.show()
fig, ax1 = pyplot.subplots()
ax2 = ax1.twinx()
ax1.scatter(hist.slot_centers() - 0.5 * hist.slot_width(),
cumulative_hist)
ax1.set_ylim(0, 1)
ax2.plot(hist.slot_centers()[:-1] - 0.5 * hist.slot_width(),
gradients)
ymin, ymax = pyplot.ylim()
pyplot.vlines(cutoff, ymin, ymax, color='r')
pyplot.show()
H2 = H.copy()
if cutoff is not None:
H2[np.where(H2 >= cutoff)] = 0
fig, ax1 = pyplot.subplots()
plot1 = ax1.pcolormesh(xedges, yedges, H2)
pyplot.xlim((0, pyplot.xlim()[1]))
pyplot.ylim((0, pyplot.ylim()[1]))
pyplot.gca().invert_yaxis()
cbar1 = pyplot.colorbar(plot1)
pyplot.axes().set_aspect('equal')
pyplot.show()
return flags
