bmarechal / PM100_waist

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Commit fc102800db58ac77d0e40fc9e8520b36b8542739

Authored by bmarechal
1 parent 8b2c182ee1
Exists in master

better method to get x pos from filename

Showing 1 changed file with 3 additions and 1 deletions Inline Diff

1 #!/usr/bin/python
2
from scipy.optimize import curve_fit 1 3 from scipy.optimize import curve_fit
import csv, numpy, glob 2 4 import csv, numpy, glob
from scipy.special import erf 3 5 from scipy.special import erf
import matplotlib.pyplot as plt 4 6 import matplotlib.pyplot as plt
5 7
'''power function to optimize''' 6 8 '''power function to optimize'''
def P(x, Po, Pmax, xo, w): 7 9 def P(x, Po, Pmax, xo, w):
return Po+0.5*Pmax*(1.-erf(2.**0.5*(x-xo)/w)) 8 10 return Po+0.5*Pmax*(1.-erf(2.**0.5*(x-xo)/w))
9 11
10 12
'''load and fit beam section''' 11 13 '''load and fit beam section'''
files = glob.glob('*.dat') 12 14 files = glob.glob('*.dat')
files.sort() 13 15 files.sort()
data_waist = [] 14 16 data_waist = []
plt.close() 15 17 plt.close()
fig, p = plt.subplots(2, 1) 16 18 fig, p = plt.subplots(2, 1)
for f in files: 17 19 for f in files:
with open(f, 'r') as dest_f: 18 20 with open(f, 'r') as dest_f:
raw = csv.reader(dest_f, delimiter = '\t', quotechar = '"') 19 21 raw = csv.reader(dest_f, delimiter = '\t', quotechar = '"')
data = [value for value in raw] 20 22 data = [value for value in raw]
data = numpy.asarray(data, dtype = float) 21 23 data = numpy.asarray(data, dtype = float)
xmes = data[:,0] 22 24 xmes = data[:,0]
Pmes = data[:,1] 23 25 Pmes = data[:,1]
24 26
'''optimization with non-linear least squares method''' 25 27 '''optimization with non-linear least squares method'''
Ppopt, Pcov = curve_fit(P, xmes, Pmes, method = 'trf') 26 28 Ppopt, Pcov = curve_fit(P, xmes, Pmes, method = 'trf')
data_waist.append([int(f[-7:-4]), Ppopt[3]]) 27 29 data_waist.append([int(f.split('-')[-1].split('.')[0]), Ppopt[3]])
28 30
'''plot''' 29 31 '''plot'''
p[0].plot(xmes, Pmes, 'o') 30 32 p[0].plot(xmes, Pmes, 'o')
p[0].plot(numpy.linspace(xmes[0], xmes[-1], 100), P(numpy.linspace(xmes[0], xmes[-1], 100), *Ppopt)) 31 33 p[0].plot(numpy.linspace(xmes[0], xmes[-1], 100), P(numpy.linspace(xmes[0], xmes[-1], 100), *Ppopt))
32 34
p[0].grid() 33 35 p[0].grid()
34 36
'''return waist(z) table''' 35 37 '''return waist(z) table'''
data_waist = numpy.asarray(data_waist, dtype = float) 36 38 data_waist = numpy.asarray(data_waist, dtype = float)
print(data_waist) 37 39 print(data_waist)
38 40
'''waist function to optimize''' 39 41 '''waist function to optimize'''
def W(z, w0, z0): 40 42 def W(z, w0, z0):
return w0*(1.+((z-z0)*1542e-6/(numpy.pi*w0**2))**2)**0.5 41 43 return w0*(1.+((z-z0)*1542e-6/(numpy.pi*w0**2))**2)**0.5
42 44