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gobbo_a
2017-05-04 12:42:06 +02:00
parent 71ef4063ef
commit 33e2f84f5e
5 changed files with 295 additions and 10 deletions
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20
devices/CurrentCamera.properties
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@@ -1,16 +1,16 @@
#Wed May 03 16:50:12 CEST 2017
#Thu May 04 11:39:44 CEST 2017
colormap=Flame
colormapAutomatic=true
colormapMax=800.0
colormapMin=0.0
flipHorizontally=false
flipHorizontally=true
flipVertically=false
grayscale=false
imageHeight=1040
imageWidth=1392
imageHeight=2160
imageWidth=2560
invert=false
regionStartX=0
regionStartY=0
regionStartX=1
regionStartY=1
rescaleFactor=1.0
rescaleOffset=0.0
roiHeight=-1
@@ -21,9 +21,9 @@ rotation=0.0
rotationCrop=false
scale=1.0
serverURL=localhost\:10000
spatialCalOffsetX=-50.035945363048164
spatialCalOffsetY=-50.04812319538017
spatialCalScaleX=-1.0
spatialCalScaleY=-1.0
spatialCalOffsetX=-1279.0
spatialCalOffsetY=-1079.0
spatialCalScaleX=-53.020711215318485
spatialCalScaleY=-53.02454840203798
spatialCalUnits=mm
transpose=false

1
script/Diagnostics/WireScan.py
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@@ -164,6 +164,7 @@ finally:
print "Closing stream"
st.close()
# save the entry in the logbook
if do_elog:
if get_option("Generated data file:\n" + get_exec_pars().path +"\n\nSave to ELOG?", "YesNo") == "Yes":

223
script/Diagnostics/sig_process.py Normal file
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@@ -0,0 +1,223 @@
# Signal processing functions
import copy
import numpy as np
import scipy.optimize
# Noise evaluation ####
def noise_evaluation(noise: np.ndarray):
"""
Returns offset and standard deviation of the noise signal.
:param noise: noise signal
:return: Tuple of (noise_offset, noise_sigma))
"""
return np.mean(noise), np.std(noise)
# Processing of BLM signal ####
def blm_remove_spikes(x):
"""
Returns new array with removed 1 and 2 point spikes.
:param x: input array
:return: output array
"""
x = copy.copy(x)
if x.size > 5: # Must have enough sample points
d_x = x[1:] - x[:-1]
maximum = x.max() - x.min()
for i in range(x.size-3):
if d_x[i+1] > 0.5 * maximum and d_x[i+2] < -0.5 * maximum:
# 1 point spike
x[i+2] = (x[i:i+5].sum() - x[i+2])/4
if d_x[i+1] > 0.5 * maximum and d_x[i+2] >= -0.5 * maximum:
# 2 point spikes
x[i+2] = (x[i:i+2].sum() + x[i+4])/3
x[i+3] = (x[i+1] + x[i+4:i+6].sum())/3
# Handle edge points
if d_x[-1] > 0.5 * maximum:
x[-1] = x[-3:-1].sum() / 2
if d_x[-2] > 0.5 * maximum:
x[-2] = (x[-1] + x[-4:-2].sum()) / 3
if d_x[0] < -0.5 * maximum:
x[0] = x[1:3].sum() / 2
if d_x[1] < -0.5*maximum:
x[1] = (x[0] + x[2:4].sum()) / 3
return x
def blm_normalize(x, q):
"""
Returns normalized BLM signal using charge from the BPM
:param x: BLM signal
:param q: BPM charge
:return: output array (same instance as input array)
"""
x = copy.copy(x)
return x / q
# Processing of position data ####
def motor_to_wire_cs(pos, type: str = 'u', center_pos: float = 0.0):
"""
Map wire position from motor coordinates (encoder readout) to wire coordinates. center_pos should be motor position
when wire is in the middle of the pipe.
:param pos: position in motor coordinates
:param type: type of wire 'x', 'y' or 'u' (under 45 degrees to the pipe horizon)
:param center_pos: wire position in motor coordinates when in the middle of the pipe
:return: position in wire coordinates
"""
# u: normal positions, like GARAGE, POSITION, or any wires under 45 degrees to pipe horizon
# x: which crosses pipe vertically (scans in x coordinate)
# y: which crosses pipe horizontally (scans in y coordinate)
w_scale = {'u': 1, 'x': -np.sqrt(2), 'y': np.sqrt(2)}
return (pos-center_pos)/w_scale[type]
def remove_beam_jitter(pos: np.ndarray, bpm1: np.ndarray, bpm2: np.ndarray, d_b1_w=1, d_w_b2=1):
"""
This is a temporary solution which works only for first WSC on SwissFEL.
!!TODO: In future this method must implement correction using online model.
:param pos: array of positions for 1 profile
:param bpm1: array of bpm in front of wsc readings (x or y) for 1 profile
:param bpm2: array of after wsc bpm readings (x or y) for 1 profile
:param d_b1_w: Distance between bpm1 and wsc
:param d_w_b2: Distance between wsc and bpm2
:return: array of corrected positions
"""
beam_position_at_wsc = (bpm1 * d_b1_w + bpm2 * d_w_b2) / (d_b1_w + d_w_b2)
# Reference centroid position is mean value of all measured values
beam_jitter = beam_position_at_wsc - beam_position_at_wsc.mean(0)
# Correct wire position for beam jitter
return pos + beam_jitter
# Profile statistics ####
def profile_gauss_stats(x, y, off=None, amp=None, com=None, sigma=None):
if off is None:
off = y.min() # good enough starting point for offset
if com is None:
com = x[y.argmax()]
if amp is None:
amp = y.max() - off
# For normalised gauss curve sigma=1/(amp*sqrt(2*pi))
if sigma is None:
surface = np.trapz((y-off), x=x)
sigma = surface / (amp * np.sqrt(2 * np.pi))
try:
popt, pcov = scipy.optimize.curve_fit(gauss_fn, x, y, p0=[off, amp, com, sigma], method='lm')
popt[3] = abs(popt[3]) # sigma should be returned as positive
except Exception as e:
print("Gauss fitting not successful.\n" + str(e))
popt = [off, amp, com, abs(sigma)]
return popt
def gauss_fn(x, a, b, c, d):
return a + b * np.exp(-(np.power((x - c), 2) / (2 * np.power(d, 2))))
def profile_rms_stats(x, y, noise_std=0, n_sigma=3.5):
"""
Does the center of mass and RMS calculation on given profile.
:param x: data of x axis (wire position)
:param y: data of y axis (blm current)
:param noise_std: standard deviation of noise
:param n_sigma: multiplication of window size (n_sigma*sigma) where sigma is first estimation of sigma
:return: rms of signal
"""
if x.shape == y.shape:
# Windowing the signal
# 1. All points bellow +sigma are treated as noise and ignored
# 2. If point is higher than +sigma and neighbour points are noise, then it is also treated as noise and ignored
# 3. All sets of multiple non noise points in a row are treated as signal_candidates
signal_flag = False
signal_start = 0
signal_candidates = list()
for i in range(y.size):
if y[i] < noise_std:
# Not part of the signal. Check if this is the end of signal candidate.
if signal_flag and i-signal_start > 1:
signal_candidates.append((signal_start, i-1))
signal_flag = False
elif not signal_flag:
# Start of potential new signal candidate
signal_flag = True
signal_start = i
# Take care of last signal candidate if last point is higher than noise (signal_flag is still True)
if signal_flag and (y.size - signal_start - 1) > 1:
signal_candidates.append((signal_start, y.size-1))
if len(signal_candidates) == 0:
# Only noise, no signal. Cannot calculate rms
print("RMS calculation not successful. No signal detected.\n")
return None, None
# Evaluate candidates
candidates_val = [y[s[0]:s[1]].sum() for s in signal_candidates]
signal_boundaries = signal_candidates[candidates_val.index(max(candidates_val))]
# Calculate RMS values (at this point they are underestimated, but sigma is then used to extend the signal
# region of interest which gives a better result (biggest surface)
com_estimate, rms_estimate = com_rms(x[signal_boundaries[0]:signal_boundaries[1]],
y[signal_boundaries[0]:signal_boundaries[1]])
# Indexes of useful signal (-1 is here because original algorithm calculates this a bit different and
# because it does the floor() of calculated function
# Define direction of scan
if x[0] < x[-1]: # increasing position
low_boundary = np.argmax(x >= com_estimate - n_sigma * rms_estimate)-1
high_boundary = np.argmax(x >= com_estimate + n_sigma * rms_estimate)-1
else: # decreasing position
high_boundary = np.argmax(x <= com_estimate - n_sigma * rms_estimate)-1
low_boundary = np.argmax(x <= com_estimate + n_sigma * rms_estimate)-1
if high_boundary <= 0:
high_boundary = x.size
if low_boundary < 0:
low_boundary = 0
if (high_boundary-low_boundary) < 4:
print("RMS calculation not successful. Resolution to low.\n")
return None, None
# Calculate final RMS and centroid on ROI data
if high_boundary == x.size:
com, rms = com_rms(x[low_boundary:], y[low_boundary:])
else:
com, rms = com_rms(x[low_boundary:high_boundary], y[low_boundary:high_boundary])
return com, rms
else:
return None, None
def com_rms(x, y):
# Centre of mass and rms
com = (x * y).sum() / y.sum()
com2 = (x * x * y).sum() / y.sum()
rms = np.sqrt(np.abs(com2 - com * com))
return com, rms

31
script/Diagnostics/sig_process_wrapper.py Normal file
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@@ -0,0 +1,31 @@
from jeputils import *
MODULE = "Diagnostics/sig_process"
def noise_evaluation(noise):
return call_jep(MODULE, "noise_evaluation", [to_npa(noise),])
def blm_remove_spikes(x):
ret = call_jep(MODULE, "blm_remove_spikes", [to_npa(x),])
return None if ret is None else ret.data
def blm_normalize(x, q):
ret = call_jep(MODULE, "blm_normalize", [to_npa(x), q])
return None if ret is None else ret.data
def motor_to_wire_cs(pos, ctype = 'u', center_pos = 0.0):
return call_jep(MODULE, "motor_to_wire_cs", [pos, ctype, center_pos ])
def remove_beam_jitter(pos, bpm1, bpm2, d_b1_w=1, d_w_b2=1):
ret = call_jep(MODULE, "remove_beam_jitter", [to_npa(pos),to_npa(bpm1), to_npa(bpm2), d_b1_w, d_w_b2 ])
return None if ret is None else ret.data
def profile_gauss_stats(x, y, off=None, amp=None, com=None, sigma=None):
ret = call_jep(MODULE, "profile_gauss_stats", [to_npa(x), to_npa(y), off, amp, com, sigma])
return None if ret is None else ret.data
def profile_rms_stats(x, y, noise_std=0, n_sigma=3.5):
return call_jep(MODULE, "profile_rms_stats", [to_npa(x), to_npa(y), noise_std, n_sigma])

30
script/test/TestSigProcess.py Normal file
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@@ -0,0 +1,30 @@
run("Diagnostics/sig_process_wrapper")
#Loading test data
#p=get_plots("Wire Scan")[0]
#s=p.getSeries(0)
#indexes = sorted(range(len(s.x)),key=lambda x:s.x[x])
#x,y = [s.x[x] for x in indexes], [s.y[x] for x in indexes]
x=[-200.30429237268825, -200.2650700434188, -200.22115208318002, -199.9457671375377, -199.86345548879072, -199.85213073174933, -199.35687977133284, -199.13811861090275, -197.97304970346386, -197.2952215624348, -195.09076092936948, -192.92276048970703, -191.96871876227698, -189.49577852322938, -187.9652790409825, -183.63756456925222, -180.04899765472996, -178.43839623242422, -174.07311671294445, -172.0410133577918, -165.90824309893102, -160.99771795989466, -159.30176653939253, -154.27688897558514, -152.0854103810786, -145.75652847587313, -140.80843828908465, -139.23982133191495, -134.27073891256106, -132.12649284133064, -125.95947209775511, -121.00309550337462, -119.26736932643232, -114.2706655484383, -112.07393889578914, -105.72295990367157, -100.8088439880125, -99.2034906238494, -94.30042325164636, -92.15010048151461, -85.92203653534293, -81.03913275494665, -79.27412793784428, -74.33487658582118, -72.06274362408762, -65.76562628131825, -60.91255356825276, -59.20334389560392, -54.33286972659312, -52.19387171350535, -45.94978737932291, -41.03014719193582, -39.301602568238906, -34.35572209014114, -32.04464301272608, -25.8221033382824, -20.922074315528747, -19.21590299233186, -14.31090212502093, -12.217203140101386, -5.9283722049240435, -0.9863587170369246, 0.7408048387279834, 5.71126832601389, 7.972628957879352, 14.204559894256546, 19.11839959633025, 20.8218087836657, 25.678748486941828, 27.822718344586864, 34.062659474970715, 38.9745656819391, 40.77409719734158, 45.72080631619803, 47.974156754056835, 54.23453768983539, 59.12020360609568, 60.77306570712026, 65.70734521458867, 67.8344660434617, 74.03187028154134, 78.96532114824849, 80.76070945985495, 85.74802197591286, 87.9140889204674, 94.18082276873524, 99.25790470037091, 100.68454787413205, 105.7213026221542, 107.79483801526698, 113.99555681638138, 119.0707052529143, 120.72715813056156, 125.77551384921307, 127.91257836719551, 134.2011330887875, 139.23043006997628, 140.71673537840158, 145.76288138835983, 147.80216629676042, 154.06420451405637, 159.0846626604798, 160.76183155710717, 165.73699067536242, 167.9265357747636, 173.96705069576544, 178.2522282751915, 179.9042617354548, 183.54586165856657, 185.23269803071796, 189.41678143751972, 191.87149157986588, 192.8741468985015, 195.0241934550453, 195.966634211846, 197.9821647518146, 198.99006812859284, 199.33202054855676, 199.91897441965887, 200.11536227958896, 200.22280936469997, 200.25181179127208]
y=[11.0, 6.0, 8.0, 5.0, 11.0, 7.0, 18.0, 11.0, 12.0, 10.0, 8.0, 6.0, 16.0, 4.0, 12.0, 9.0, 15.0, 14.0, 8.0, 20.0, 15.0, 8.0, 9.0, 11.0, 13.0, 12.0, 13.0, 15.0, 13.0, 20.0, 10.0, 7.0, 17.0, 11.0, 20.0, 13.0, 13.0, 23.0, 14.0, 10.0, 17.0, 15.0, 20.0, 16.0, 14.0, 13.0, 18.0, 22.0, 9.0, 20.0, 12.0, 14.0, 17.0, 19.0, 14.0, 14.0, 23.0, 19.0, 15.0, 20.0, 20.0, 21.0, 20.0, 23.0, 22.0, 15.0, 10.0, 17.0, 21.0, 15.0, 23.0, 23.0, 25.0, 18.0, 16.0, 21.0, 22.0, 16.0, 16.0, 14.0, 19.0, 20.0, 18.0, 20.0, 23.0, 13.0, 16.0, 20.0, 25.0, 15.0, 15.0, 17.0, 22.0, 26.0, 19.0, 30.0, 25.0, 17.0, 17.0, 23.0, 16.0, 27.0, 21.0, 21.0, 26.0, 27.0, 21.0, 17.0, 20.0, 20.0, 21.0, 19.0, 25.0, 19.0, 13.0, 23.0, 20.0, 20.0, 18.0, 20.0, 19.0, 25.0]
y[30] = 100
#Calling wrapper methods
rem= blm_remove_spikes(y)
print "Noise: ", noise_evaluation(rem)
print "Wire X: ", motor_to_wire_cs(20000, 'x', 2000)
nor = blm_normalize(rem, 2.0)
s2 = [i + random.random()*10 for i in nor]
jit = remove_beam_jitter(x, nor, s2, d_b1_w=1, d_w_b2=1)
[com, rms] = profile_rms_stats(x, nor,noise_std=0, n_sigma=3.5)
print "Rms: ", [com, rms]
[off, amp, com, sigma] = profile_gauss_stats(x, nor, off=None, amp=None, com=None, sigma=None)
print "Gauss: ", [off, amp, com, sigma]
from mathutils import Gaussian
f = Gaussian(amp, com, sigma)
gauss = [f.value(i)+off for i in x]
#Plotting results
plot([y,rem, nor, gauss, s2, jit], ["signal", "rem", "nor", "gauss", "s2", "jit"], xdata = x, title="Fit")