def main3():
import psana
ds = psana.DataSource("exp=cxid9114:run=62")
events = ds.events()
det = psana.Detector('CxiDs2.0:Cspad.0')
dark = det.pedestals(62)
gain_map = det.gain_mask(62) == 1
plt.imshow(gain_map[0])
plt.show()
mask = mask_utils.mask_small_regions(gain_map)
mask2 = np.load("details_mask.npy")
mask *= mask2
start = 0
for i in range(100):
ev = events.next()
if ev is None:
continue
if i < start:
continue
data = det.calib(ev, cmpars=(5, 0, 0, 0, 0))
#data = det.calib(ev, cmpars=(1,25,25,100,1))
if data is None:
continue
plt.imshow(gain_map[0])
plt.show()
xlow, ylow, xhigh, yhigh, new_data = get_gain_dists(
data, gain_map, mask)
low_g0, low_g1, fit_low = fit_utils.fit_low_gain_dist(xlow,
ylow,
plot=1)
high_g0, high_g1, fit_high = fit_utils.fit_high_gain_dist(xhigh,
yhigh,
plot=1)
plt.figure()
plt.imshow(data[0], vmin=-10, vmax=50, cmap='gnuplot')
plt.show()
def correct_panels(data, gain_map, mask, plot=False):
xlow, ylow, xhigh, yhigh, new_data = get_gain_dists(data, gain_map, mask)
low_g0, low_g1, fit_low = fit_utils.fit_low_gain_dist(xlow,
ylow,
plot=plot)
high_g0, high_g1, fit_high = fit_utils.fit_high_gain_dist(xhigh,
yhigh,
plot=plot)
low_1phot = xlow[low_g1.argmax()]
#high_1phot = xhigh[high_g1.argmax()]
high_1phot = xhigh[np.argmax(utils.smooth(yhigh, window_size=30)[220:300])
+ 220]
print "Low gain 1 photon peak: %.4f ADU" % low_1phot
print "High gain 1 photon peak: %.4f ADU" % high_1phot
gain = high_1phot / low_1phot
print "Estimated gain: %.4f" % gain
low_0phot_wid = fit_low.params['wid0']
high_0phot_wid = fit_high.params['wid0']
bg_gain = high_0phot_wid / low_0phot_wid
print "Estimated dark-current gain: %.4f" % bg_gain
cutoff = low_1phot - 1 * fit_low.params['wid1'].value / np.sqrt(2.)
print "Estimated low-gain dark-current cutoff ADU: %.4f" % cutoff
#cutoff = 1.85
#gain = 6.85
#bg_gain = 1.95
lowgain_photons = np.logical_and(new_data > cutoff, gain_map)
new_data[gain_map] = new_data[gain_map] * bg_gain
new_data[lowgain_photons] = new_data[lowgain_photons] * gain / bg_gain
return new_data
if fix_gain:
data32 = np.array([
np.hstack([pan_data[i * 2], pan_data[i * 2 + 1]])
for i in range(32)
])
# undo the nominal low-to-high gain correction
data32[loader.gain] /= loader.nominal_gain_val # this is default 6.85
outg = gain_utils.get_gain_dists(data32,
loader.gain,
loader.cspad_mask,
plot=False,
norm=True)
fit = fit_utils.fit_low_gain_dist(outg[0], outg[1], plot=plot)
fitH = fit_utils.fit_high_gain_dist(outg[2], outg[3], plot=plot)
bgg = fitH[2].params['wid0'].value / fit[2].params['wid0'].value
low2highG = fitH[2].params['mu1'].value / fit[2].params['mu1'].value
lowGres = np.sum(fit[2].residual**2)
highGres = np.sum(fitH[2].residual**2)
all_lowGres.append([lowGres] * Nref)
all_highGres.append([highGres] * Nref)
all_g.append([low2highG] * Nref)
all_bgg.append([bgg] * Nref)
data32[loader.gain] = data32[loader.gain] * low2highG
phot3_g = fitH[2].params['mu1'].value
get_ipython().magic(u'cd results') get_ipython().magic(u'cd run62') get_ipython().magic(u'ls ') from dxtbx.model.experiment_list import ExperimentListFactory data = [x.as_numpy_array() for x in loader.get_raw_data(54598)] data32 = array([hstack([data[i*2], data[i*2+1]]) for i in range(32)]) data2 = data32.copy() data2[loader.gain] /= loader.nominal_gain_val imshow(data2[0], vmin=-20, vmax=30) figure(1) figure() imshow(data2[0], vmin=-20, vmax=30) figure();imshow(data32[0], vmin=-20, vmax=30) out2 = gain_utils.get_gain_dists(data2, loader.gain, loader.cspad_mask, plot=False, norm=True) fit2 = fit_utils.fit_low_gain_dist(out2[0], out2[1], plot=1) fit2H = fit_utils.fit_high_gain_dist(out2[2], out2[3], plot=1) fit2H fit2H[0] fit2H[1] fit2H[2] fit2H[2].x fit2H[2].param fit2H[2].params fit2H[2].params['mu1'] fit2H[2].params['mu1'].value fit2H[2].params['mu1'].value fit2[2].params['mu1'].value 27.402076353130738 / 4.0863837771690239 data3 = data2.copy() data3[loader.gain] *= 6.70570309774341 figure();imshow(data3[0], vmin=-20, vmax=30)