From 20c42486b82d00bc6dfef799d8fda99f72a75f55 Mon Sep 17 00:00:00 2001
From: Laurent Mercadier <laurent.mercadier@xfel.eu>
Date: Mon, 18 Nov 2019 21:43:55 +0100
Subject: [PATCH] Adds axisRange parameter, returns fitting function when
full=True
---
knife_edge.py | 50 +++++++++++++++++++++++++++++++++++---------------
1 file changed, 35 insertions(+), 15 deletions(-)
diff --git a/knife_edge.py b/knife_edge.py
index 224b8a8..e7b906b 100644
--- a/knife_edge.py
+++ b/knife_edge.py
@@ -8,10 +8,12 @@ import matplotlib.pyplot as plt
import numpy as np
from scipy.special import erfc
from scipy.optimize import curve_fit
+import bisect
-def knife_edge(nrun, axisKey='scannerX', signalKey='FastADC4peaks', p0=None, full=False, plot=False):
+def knife_edge(nrun, axisKey='scannerX', signalKey='FastADC4peaks',
+ axisRange=[None,None], p0=None, full=False, plot=False):
''' Calculates the beam radius at 1/e^2 from a knife-edge scan by fitting with
- erfc function: f(a, u) = a*erfc(u) or f(a, u) = a*erfc(-u) where
+ erfc function: f(a,b,u) = a*erfc(u)+b or f(a,b,u) = a*erfc(-u)+b where
u = sqrt(2)*(x-x0)/w0 with w0 the beam radius at 1/e^2 and x0 the beam center.
Inputs:
nrun: xarray Dataset containing the detector signal and the motor
@@ -19,22 +21,24 @@ def knife_edge(nrun, axisKey='scannerX', signalKey='FastADC4peaks', p0=None, ful
axisKey: string, key of the axis against which the knife-edge is
performed.
signalKey: string, key of the detector signal.
- p0: list, initial parameters used for the fit: x0, w0, a. If None, a beam
+ axisRange: list of length 2, minimum and maximum values between which to apply
+ the fit.
+ p0: list, initial parameters used for the fit: x0, w0, a, b. If None, a beam
radius of 100 um is assumed.
full: bool: If False, returns the beam radius and standard error. If True,
returns the popt, pcov list of parameters and covariance matrix from
- curve_fit.
+ curve_fit as well as the fitting function.
plot: bool: If True, plots the data and the result of the fit.
Outputs:
If full is False, ndarray with beam radius at 1/e^2 in mm and standard
error from the fit in mm. If full is True, returns popt and pcov from
curve_fit function.
'''
- def integPowerUp(x, x0, w0, a):
- return a*erfc(-np.sqrt(2)*(x-x0)/w0)
+ def integPowerUp(x, x0, w0, a, b):
+ return a*erfc(-np.sqrt(2)*(x-x0)/w0) + b
- def integPowerDown(x, x0, w0, a):
- return a*erfc(np.sqrt(2)*(x-x0)/w0)
+ def integPowerDown(x, x0, w0, a, b):
+ return a*erfc(np.sqrt(2)*(x-x0)/w0) + b
#get the number of pulses per train from the signal source:
dim = nrun[signalKey].dims[1]
@@ -46,22 +50,38 @@ def knife_edge(nrun, axisKey='scannerX', signalKey='FastADC4peaks', p0=None, ful
sortIdx = np.argsort(positions)
positions = positions[sortIdx]
intensities = nrun[signalKey].values.flatten()[sortIdx]
-
+
+ if axisRange[0] is None or axisRange[0] < positions[0]:
+ idxMin = 0
+ else:
+ if axisRange[0] >= positions[-1]:
+ raise ValueError('The minimum value of axisRange is too large')
+ idxMin = bisect.bisect(positions, axisRange[0])
+ if axisRange[1] is None or axisRange[1] > positions[-1]:
+ idxMax = None
+ else:
+ 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]
+
# estimate a linear slope fitting the data to determine which function to fit
slope = np.cov(positions, intensities)[0][1]/np.var(positions)
if slope < 0:
func = integPowerDown
- funcStr = 'a*erfc(np.sqrt(2)*(x-x0)/w0)'
+ funcStr = 'a*erfc(np.sqrt(2)*(x-x0)/w0) + b'
else:
func = integPowerUp
- funcStr = 'a*erfc(-np.sqrt(2)*(x-x0)/w0)'
+ funcStr = 'a*erfc(-np.sqrt(2)*(x-x0)/w0) + b'
if p0 is None:
- p0 = [np.mean(positions), 0.1, np.max(intensities)/2]
+ p0 = [np.mean(positions), 0.1, np.max(intensities)/2, 0]
popt, pcov = curve_fit(func, positions, intensities, 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*1e3))
- print('a = %e +/- %e '%(popt[2], pcov[2,2]**0.5*1e3))
+ print('x0 = (%.3f +/- %.3f) mm'%(popt[0], pcov[0,0]**0.5))
+ print('a = %e +/- %e '%(popt[2], pcov[2,2]**0.5))
+ print('b = %e +/- %e '%(popt[3], pcov[3,3]**0.5))
if plot:
xfit = np.linspace(positions.min(), positions.max(), 1000)
@@ -78,6 +98,6 @@ def knife_edge(nrun, axisKey='scannerX', signalKey='FastADC4peaks', p0=None, ful
plt.title(nrun.attrs['runFolder'])
plt.tight_layout()
if full:
- return popt, pcov
+ return popt, pcov, func
else:
return np.array([popt[1], pcov[1,1]**0.5])
--
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