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replaced bladeNr+bladeZ by dZ

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stahn
2024-04-30 16:49:05 +02:00
parent ca81d4134b
commit f7b127fcca
1 changed files with 32 additions and 32 deletions
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64
life_histogrammer.py
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@@ -1,4 +1,4 @@
__version__ = '2024-03-15'
__version__ = '2024-03-30'
import os
import sys
@@ -35,26 +35,26 @@ def pixel2quantity():
x = (31 - bZ) * det.dX
bladeAngle = np.rad2deg( 2. * np.arcsin(0.5*det.bladeZ / detectorDistance) )
delta = (det.nBlades/2. - bladeNr) * bladeAngle - np.rad2deg( np.arctan(bZ*det.dZ / ( detectorDistance + bZ * det.dX) ) )
quantity = np.vstack((bladeNr.T, bZ.T, bY.T, delta.T, x.T)).T
dZ = bladeNr * 32 + bZ
quantity = np.vstack((dZ.T, bY.T, delta.T, x.T)).T
return quantity
#==============================================================================
def analyse_ev(event_e, tof_e, yMin, yMax, thetaMin, thetaMax):
data_e = np.zeros((len(event_e), 10), dtype=float)
data_e = np.zeros((len(event_e), 9), dtype=float)
# data_e column description:
# 0: wall time / s
# 1: pixelID
# 2: blade number
# 3: z on blade
# 4: y on blade
# 5: delta / deg = angle on detector
# 6: path within detector / mm
# 7: lambda / angstrom
# 8: theta / deg
# 9: q_z / angstrom^-1
# 2: z on detector
# 3: y on detector
# 4: delta / deg = angle on detector
# 5: path within detector / mm
# 6: lambda / angstrom
# 7: theta / deg
# 8: q_z / angstrom^-1
data_e[:,0] = tof_e[:]
data_e[:,1] = event_e[:]
@@ -69,39 +69,39 @@ def analyse_ev(event_e, tof_e, yMin, yMax, thetaMin, thetaMax):
logging.warning(f'## strange times: {np.shape(filter_e)[0]-np.shape(data_e)[0]}')
pixelLookUp = pixel2quantity()
data_e[:,2:7] = pixelLookUp[np.int_(data_e[:,1])-1,:]
data_e[:,2:6] = pixelLookUp[np.int_(data_e[:,1])-1,:]
#================================
# filter y range
filter_e = (yMin <= data_e[:,4]) & (data_e[:,4] <= yMax)
filter_e = (yMin <= data_e[:,3]) & (data_e[:,3] <= yMax)
data_e = data_e[filter_e,:]
# correct tof for beam size effect at chopper
data_e[:,0] -= ( data_e[:,5] / 180. ) * tau
data_e[:,0] -= ( data_e[:,4] / 180. ) * tau
# effective flight path length
#data_e[:,6] = chopperDetectorDistance + data_e[:,6]
#data_e[:,5] = chopperDetectorDistance + data_e[:,5]
# calculate lambda
hdm = 6.626176e-34/1.674928e-27 # h / m
data_e[:,7] = 1.e13 * data_e[:,0] * hdm / ( chopperDetectorDistance + data_e[:,6] )
data_e[:,6] = 1.e13 * data_e[:,0] * hdm / ( chopperDetectorDistance + data_e[:,5] )
# theta
data_e[:,8] = nu - mu + data_e[:,5]
data_e[:,7] = nu - mu + data_e[:,4]
# gravity compensation
data_e[:,8] += np.rad2deg( np.arctan( 3.07e-10 * ( detectorDistance + data_e[:,6]) * data_e[:,7] * data_e[:,7] ) )
data_e[:,7] += np.rad2deg( np.arctan( 3.07e-10 * ( detectorDistance + data_e[:,5]) * data_e[:,6] * data_e[:,6] ) )
# filter theta range
filter_l = (thetaMin <= data_e[:,8]) & (data_e[:,8] <= thetaMax)
filter_l = (thetaMin <= data_e[:,7]) & (data_e[:,7] <= thetaMax)
data_e = data_e[filter_l,:]
# q_z
data_e[:,9] = 4*np.pi * np.sin( np.deg2rad( data_e[:,8] ) ) / data_e[:,7]
data_e[:,8] = 4*np.pi * np.sin( np.deg2rad( data_e[:,7] ) ) / data_e[:,6]
# filter q_z range
#filter_e = (qMin < data_e[:,I7]) & (data_e[:,7] < qMax)
#filter_e = (qMin < data_e[:,6]) & (data_e[:,6] < qMax)
#data_e = data_e[filter_e,:]
return data_e
@@ -278,9 +278,9 @@ class PlotSelection:
cmap = mpl.cm.gnuplot(np.arange(256))
cmap[:1, :] = np.array([256/256, 255/256, 236/256, 1])
cmap = mpl.colors.ListedColormap(cmap, name='myColorMap', N=cmap.shape[0])
I_yt, bins_y, bins_t = np.histogram2d(data_e[:,4], data_e[:,8], bins = (self.y_grid(), self.theta_grid()))
I_lt, bins_l, bins_t = np.histogram2d(data_e[:,7], data_e[:,8], bins = (self.lamda_grid(), self.theta_grid()))
I_q, bins_q = np.histogram(data_e[:,9], bins = self.q_grid())
I_yt, bins_y, bins_t = np.histogram2d(data_e[:,3], data_e[:,7], bins = (self.y_grid(), self.theta_grid()))
I_lt, bins_l, bins_t = np.histogram2d(data_e[:,6], data_e[:,7], bins = (self.lamda_grid(), self.theta_grid()))
I_q, bins_q = np.histogram(data_e[:,8], bins = self.q_grid())
q_lim = 4*np.pi*np.array([ max( np.sin(self.theta_grid()[0]*np.pi/180.)/self.lamda_grid()[-1] , 1e-4 ),
min( np.sin(self.theta_grid()[-1]*np.pi/180.)/self.lamda_grid()[0] , 0.03 )])
if arg == 'lin':
@@ -340,7 +340,7 @@ class PlotSelection:
cmap[:1, :] = np.array([256/256, 255/256, 236/256, 1])
cmap = mpl.colors.ListedColormap(cmap, name='myColorMap', N=cmap.shape[0])
z_grid = np.arange(det.nBlades*32)
I_yz, bins_y, bins_z = np.histogram2d(data_e[:,4], (det.nBlades-data_e[:,2])*32-data_e[:,3], bins = (self.y_grid(), z_grid))
I_yz, bins_y, bins_z = np.histogram2d(data_e[:,3], data_e[:,2], bins = (self.y_grid(), z_grid))
if arg == 'log':
vmin = 0
vmax = max(1, np.log(np.max(I_yt)+.1)/np.log(10)*1.05)
@@ -358,7 +358,7 @@ class PlotSelection:
cmap = mpl.cm.gnuplot(np.arange(256))
cmap[:1, :] = np.array([256/256, 255/256, 236/256, 1])
cmap = mpl.colors.ListedColormap(cmap, name='myColorMap', N=cmap.shape[0])
I_lt, bins_l, bins_t = np.histogram2d(data_e[:,7], data_e[:,8], bins = (self.lamda_grid(), self.theta_grid()))
I_lt, bins_l, bins_t = np.histogram2d(data_e[:,6], data_e[:,7], bins = (self.lamda_grid(), self.theta_grid()))
if arg == 'log':
vmax = max(1, np.log(np.max(I_lt)+.1)/np.log(10)*1.05 )
plt.pcolormesh(bins_l, bins_t, (np.log(I_lt+I_lt[I_lt>0].min()/2)/np.log(10.)).T, cmap=cmap, vmin=0, vmax=vmax)
@@ -391,7 +391,7 @@ class PlotSelection:
time_grid = np.arange(0, tau, 0.0005)
z_grid = np.arange(det.nBlades*32+1)
I_tz, bins_t, bins_z = np.histogram2d(data_e[:,0], 32*det.nBlades-data_e[:,2]*32-data_e[:,3], bins = (time_grid, z_grid))
I_tz, bins_t, bins_z = np.histogram2d(data_e[:,0], data_e[:,2], bins = (time_grid, z_grid))
if arg == 'log':
vmax = max(2., np.log(np.max(I_tz)+.1)/np.log(10)*1.05 )
plt.pcolormesh(bins_t, bins_z, (np.log(I_tz+5.e-1)/np.log(10.)).T, cmap=cmap, vmin=0, vmax=vmax)
@@ -409,7 +409,7 @@ class PlotSelection:
plt.savefig(output, format='png', dpi=150)
def Iq(self, fileNumber, arg, data_e):
I_q, bins_q = np.histogram(data_e[:,9], bins = self.q_grid())
I_q, bins_q = np.histogram(data_e[:,8], bins = self.q_grid())
err_q = np.sqrt(I_q+1)
q_lim = 4*np.pi*np.array([ max( np.sin(self.theta_grid()[0]*np.pi/180.)/self.lamda_grid()[-1] , 1e-4 ),
min( np.sin(self.theta_grid()[-1]*np.pi/180.)/self.lamda_grid()[0] , 0.03 )])
@@ -429,7 +429,7 @@ class PlotSelection:
plt.savefig(output, format='png', dpi=150)
def Il(self, fileNumber, arg, data_e):
I_l, bins_l = np.histogram(data_e[:,7], bins = self.lamda_grid())
I_l, bins_l = np.histogram(data_e[:,6], bins = self.lamda_grid())
if arg == 'lin':
plt.plot(bins_l[:-1], I_l)
plt.ylabel('$I ~/~ \\mathrm{cnts}$')
@@ -442,7 +442,7 @@ class PlotSelection:
plt.savefig(output, format='png', dpi=150)
def It(self, fileNumber, arg, data_e):
I_t, bins_t = np.histogram(data_e[:,8], bins = self.theta_grid())
I_t, bins_t = np.histogram(data_e[:,7], bins = self.theta_grid())
plt.plot( I_t, bins_t[:-1])
plt.xlabel('$\\mathrm{cnts}$')
plt.ylabel('$\\theta ~/~ \\mathrm{deg}$')
@@ -501,7 +501,7 @@ def process(dataPath, ident, clas):
# find and open input file
global ev
data_eSum = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
data_eSum = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0]])
sumTime = 0
number = resolveNumber(dataPath, ident)
@@ -705,7 +705,7 @@ def main():
dataPath = get_dataPath(clas)
logging.basicConfig(level=logging.INFO, format='# %(message)s')
verbous = True
output = 'e2h.png'
output = 'life_plot.png'
process(dataPath, clas.fileIdent, clas)
#==============================================================================