From 77c198d1ee46dab58ff4e4cbedd71fb54cf33e20 Mon Sep 17 00:00:00 2001
From: Laurent Mercadier <laurent.mercadier@xfel.eu>
Date: Fri, 13 Mar 2020 09:53:08 +0100
Subject: [PATCH] Added mnemonics for GPC motors, improved knife edge function
---
Load.py | 12 ++++++++++++
knife_edge.py | 12 ++++++------
2 files changed, 18 insertions(+), 6 deletions(-)
diff --git a/Load.py b/Load.py
index e6f6066..7db62c2 100644
--- a/Load.py
+++ b/Load.py
@@ -237,6 +237,18 @@ mnemonics = {
"GPC_EOS_DelayLine": {'source':'SCS_CDIDET_GRD/MOTOR/IMAGER',
'key':'actualPosition.value',
'dim':None},
+ "GPC_X": {'source':'SCS_GPC_MOV/MOTOR/X',
+ 'key':'actualPosition.value',
+ 'dim':None},
+ "GPC_Y": {'source':'SCS_GPC_MOV/MOTOR/Y',
+ 'key':'actualPosition.value',
+ 'dim':None},
+ "GPC_THETA": {'source':'SCS_GPC_MOV/MOTOR/THETA',
+ 'key':'actualPosition.value',
+ 'dim':None},
+ "GPC_THETAMAG": {'source':'SCS_GPC_MOV/MOTOR/THETAMAG',
+ 'key':'actualPosition.value',
+ 'dim':None},
# FFT
"scannerX": {'source':'SCS_CDIFFT_SAM/LMOTOR/SCANNERX',
diff --git a/knife_edge.py b/knife_edge.py
index e7b906b..099f2f7 100644
--- a/knife_edge.py
+++ b/knife_edge.py
@@ -63,11 +63,11 @@ def knife_edge(nrun, axisKey='scannerX', signalKey='FastADC4peaks',
if axisRange[1] <= positions[0]:
raise ValueError('The maximum value of axisRange is too small')
idxMax = bisect.bisect(positions, axisRange[1]) + 1
- positions = positions[idxMin:idxMax]
- intensities = intensities[idxMin:idxMax]
+ pos_sel = positions[idxMin:idxMax]
+ int_sel = intensities[idxMin:idxMax]
# estimate a linear slope fitting the data to determine which function to fit
- slope = np.cov(positions, intensities)[0][1]/np.var(positions)
+ slope = np.cov(pos_sel, int_sel)[0][1]/np.var(pos_sel)
if slope < 0:
func = integPowerDown
funcStr = 'a*erfc(np.sqrt(2)*(x-x0)/w0) + b'
@@ -75,8 +75,8 @@ def knife_edge(nrun, axisKey='scannerX', signalKey='FastADC4peaks',
func = integPowerUp
funcStr = 'a*erfc(-np.sqrt(2)*(x-x0)/w0) + b'
if p0 is None:
- p0 = [np.mean(positions), 0.1, np.max(intensities)/2, 0]
- popt, pcov = curve_fit(func, positions, intensities, p0=p0)
+ p0 = [np.mean(pos_sel), 0.1, np.max(int_sel)/2, 0]
+ popt, pcov = curve_fit(func, pos_sel, int_sel, p0=p0)
print('fitting function:', funcStr)
print('w0 = (%.1f +/- %.1f) um'%(popt[1]*1e3, pcov[1,1]**0.5*1e3))
print('x0 = (%.3f +/- %.3f) mm'%(popt[0], pcov[0,0]**0.5))
@@ -87,7 +87,7 @@ def knife_edge(nrun, axisKey='scannerX', signalKey='FastADC4peaks',
xfit = np.linspace(positions.min(), positions.max(), 1000)
yfit = func(xfit, *popt)
plt.figure(figsize=(7,4))
- plt.scatter(positions, intensities, color='C1', label='exp', s=2, alpha=0.01)
+ plt.scatter(positions, intensities, color='C1', label='exp', s=2, alpha=0.1)
plt.plot(xfit, yfit, color='C4',
label=r'fit $\rightarrow$ $w_0=$(%.1f $\pm$ %.1f) $\mu$m'%(popt[1]*1e3, pcov[1,1]**0.5*1e3))
leg = plt.legend()
--
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