def Main(i,fitted_data):
saveDirectory = '/home/kaspar/Desktop/1nmlow'
fileDirectory = '/home/kaspar/Desktop/1nmlow/'
pulsetime = 340*10**(-15)
mybuttonslist = []
f = ReadAllFiles(fileDirectory)[i]
data = pd.read_csv(fileDirectory+f) # grab the data
z = data.iloc[0:,0]/10**3 # z in meters
ydata = data.iloc[0:,2] #the transmission
guess = [0.0006,0.25]
params, params_covariance = optimize.curve_fit(ClosedScan,z, ydata,guess)
print f
print params
print params_covariance
energy,L,alpah = GetParams(f)
z0 = params[0]
q0 = params[1]
fig, ax = plt.subplots()
plt.ylabel('Transmission')
plt.xlabel('z(m)')
mycolumn = "PtSe$_2$" +str(L*10**9) +"nm closed scan "+str(energy/10**-9)+"nJ"
plt.title(mycolumn) # title
myscat = plt.scatter(z,ydata)
improvedZ = np.linspace(-0.05,0.05,500)
myfit, = plt.plot(improvedZ,ClosedScan(improvedZ,params[0],params[1]),color='red')
center = plt.axvline(x=0, color='black')
plt.subplots_adjust(right = 0.75, bottom=0.25)
a0 = q0
f0 = z0
axcolor = 'lightgoldenrodyellow'
axfreq = plt.axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor)
axamp = plt.axes([0.25, 0.15, 0.65, 0.03], axisbg=axcolor)
xvals = plt.axes([0.92, 0.25, 0.03, 0.65], axisbg=axcolor)
yvals = plt.axes([0.85, 0.25, 0.03, 0.65], axisbg=axcolor)
xslider = VertSlider(xvals, 'x offset', -0.01, 0.01, valinit=0,valfmt='%.3f')
yslider = VertSlider(yvals, 'y offset', -0.3, 0.3, valinit=0,valfmt='%.4f')
myZ0 = Slider(axfreq, 'Z0', 0.00001, 0.05, valinit=f0,valfmt='%.5f')
myQ0 = Slider(axamp, 'q0', -1, 1, valinit=a0,valfmt='%.5f')
def update(val):
z0 = myZ0.val
q0 = myQ0.val
yshift = yslider.val
xshift = xslider.val
shiftedy = ClosedScan(improvedZ,z0,q0)+yshift
myfit.set_ydata(shiftedy)
myfit.set_xdata(improvedZ+xshift)
fig.canvas.draw_idle()
myZ0.on_changed(update)
myQ0.on_changed(update)
xslider.on_changed(update)
yslider.on_changed(update)
resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
def reset(event):
myZ0.reset()
myQ0.reset()
xslider.reset()
yslider.reset()
button.on_clicked(reset)
plt.show(block=False)
answer = raw_input('Hit enter to continue\n')
z0 = myZ0.val
q0 = myQ0.val
yshift = yslider.val
xshift = xslider.val
plt.close()
plt.ylabel('Transmission')
plt.xlabel('z(m)')
mycolumn = "PtSe$_2$" +str(L*10**9) +"nm closed scan "+str(energy/10**-9)+"nJ"
plt.title(mycolumn) # title
myscat = plt.scatter(z,ydata)
improvedZ = np.linspace(-0.05,0.05,500)
myfit, = plt.plot(improvedZ+xshift,ClosedScan(improvedZ,z0,q0)+yshift,color='red')
z0 = z0*10**3
plt.text(0.8,0.8,"$Z_0 (mm)$ ="+str('%.2f' % z0)+"\n"+" $\Delta \Phi_0$ = "+str('%.2f' % q0)+'\n' ,horizontalalignment='center',
verticalalignment='center',
transform = ax.transAxes)
name = re.sub('correct.csv','',f)
plt.savefig( saveDirectory+f+"closed.png")
plt.close()
return button
def Main(myfile, fitted_data, saveDirectory, fileDirectory):
"""The main function of the program"""
f = myfile # ith file name from list of files in directory
data = pd.read_csv(fileDirectory + f) # grab the data
z = data.iloc[0:, 0] / 10**3 # z in meters
ydata = data.iloc[0:, 1] #the transmission
global energy, L, alpha, leff, pulsetime # define some globals
pulsetime = 340 * 10**(-15) # the pulse time
energy, L, alpha = GetParams(f)
leff = L_eff(L, alpha)
#########################
# fitting of data
guess = [0.009, 0.5] # initial guess
params, params_covariance = optimize.curve_fit(OpenScanv3, z, ydata,
guess) # the fitting
z0 = params[0] # 1st param
b = params[1] # the NL absorbtion coefficent -> 2nd param
if z0 < 0:
z0 = -z0 # make usre it hasnt made z0 negative
w0 = BeamWaist(z0) # the beam width
i0 = Intensity(w0, energy, pulsetime) # the intensity at focus
# the one sigma error for the paramters
perr = np.sqrt(np.diag(params_covariance))
w0Err = BeamWaistErr(w0, z0, perr[0])
i0Err = IntensityErr(i0, w0Err)
bErr = perr[1]
#print the initial values
print params
print perr
print w0, w0Err
print i0, i0Err
print b, bErr
#create a plot of the transmission and fitted curve
fig, ax = plt.subplots()
plt.ylabel('Transmission')
plt.xlabel('z(m)')
# example SampleX
samplename = f.split('_')[0]
mytitle = str(samplename) + str(L * 10**9) + "nm open scan " + str(
energy / 10**-9) + "nJ" # title
plt.title(mytitle) # title
myscat = plt.scatter(z, ydata) # data scatter
plt.ylim(ax.get_ylim())
improvedZ = np.linspace(-0.05, 0.05, 500) # the x data for the fit
myfit, = plt.plot(improvedZ,
OpenScanv3(improvedZ, params[0], params[1]),
color='red') # plot the fitted curve
plt.subplots_adjust(right=0.75, bottom=0.25) # alignment
# intial slider values
a0 = b
f0 = z0
# make sliders for interative plot
axcolor = 'lightgoldenrodyellow'
axz0 = plt.axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor) # z0 slider
axb0 = plt.axes([0.25, 0.15, 0.65, 0.03], axisbg=axcolor) # beta slider
xvals = plt.axes([0.92, 0.25, 0.03, 0.65],
axisbg=axcolor) # x and y adjsutment sliders
yvals = plt.axes([0.85, 0.25, 0.03, 0.65], axisbg=axcolor)
# slider implementation
xslider = VertSlider(xvals,
'x offset',
-0.01,
0.01,
valinit=0,
valfmt='%.3f')
yslider = VertSlider(yvals,
'y offset',
-0.3,
0.3,
valinit=0,
valfmt='%.4f')
myZ0 = Slider(axz0, 'Z0', 0.00001, 0.05, valinit=f0, valfmt='%.5f')
myB0 = Slider(axb0,
'beta',
-0.000005,
0.000005,
valinit=a0,
valfmt='%.10f')
# update function for matplotlibs sliders
def update(val):
z0 = myZ0.val
q0 = myB0.val
yshift = yslider.val
xshift = xslider.val
shiftedy = OpenScanv3(improvedZ, z0, q0) + yshift
myfit.set_ydata(shiftedy)
myfit.set_xdata(improvedZ + xshift)
fig.canvas.draw_idle()
myZ0.on_changed(update)
myB0.on_changed(update)
xslider.on_changed(update)
yslider.on_changed(update)
# reset button for sliders
resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
def reset(event):
myZ0.reset()
myB0.reset()
xslider.reset()
yslider.reset()
button.on_clicked(reset)
#show the plot
plt.show(block=False)
# wait for user to finish fitting exit and enter input...
answer = raw_input('Hit enter to continue\n')
# retrive slider values
z0 = myZ0.val
b = myB0.val
yshift = yslider.val
xshift = xslider.val
# close plot to prevent memory overload
plt.close()
# ensure z0 is not less then 0 again
if z0 < 0:
z0 = -z0
w0 = BeamWaist(z0) # the beam width
i0 = Intensity(w0, energy, pulsetime) # the intensity at focus
# the one sigma error
perr = np.sqrt(np.diag(params_covariance))
w0Err = BeamWaistErr(w0, z0, perr[0])
i0Err = IntensityErr(i0, w0Err)
bErr = perr[1]
# print the final versions
print "Z0 = ", z0, "beta = ", b
print "err0r = ", perr
print "w0 = ", w0, w0Err
print "i0 = ", i0, i0Err
print "b = ", b, bErr
#make the final plot
fig, ax = plt.subplots()
plt.ylabel('Transmission')
plt.xlabel('z(m)')
plt.title(mytitle) # title
plt.scatter(z, ydata)
improvedZ = np.linspace(-0.05, 0.05, 500)
myfit, = plt.plot(improvedZ + xshift,
OpenScanv3(improvedZ, z0, b) + yshift,
color='red')
# format some of the data into the standard form
z0 = z0 * 10**3
w0 = w0 * 10**6
i0 = i0 / (10**(9) * 100 * 100)
energy = energy / 10**-9
L = L * 10**9
# add some text showing the values
plt.text(0.8,
0.8,
"$Z_0 (mm)$ =" + str('%.2f' % z0) + "\n" + '$w_0(\mu m)$ = ' +
str('%.2f' % w0),
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes)
plt.text(0.2,
0.8,
"$I_0 (GW cm^{-2}) = $" + str('%.2f' % i0) + "\n" +
r"$\beta = $" + str(b),
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes)
# make a sname for the entry to be put into a csv
name = str(re.sub('.csv', '', f)) # remove .csv from the title
plt.savefig(saveDirectory + name + '.png',
bboxinches='tight') # save image
plt.close()
print name, L, energy, params[0], perr[0], params[1], perr[
1], z0, b, xshift, yshift, w0, w0Err, i0, i0Err, alpha
params[0] = params[0] * 10**3 # ensure z0 is in meters for the csv
perr[0] = perr[0] * 10**3
# add an entry to the csv
fitted_data.loc[i] = [
name, L, energy, params[0], perr[0], params[1], perr[1], z0, b, xshift,
yshift, w0, w0Err, i0, i0Err, alpha
]
return button # return button this ensures the plot works (if removed the reset button will not work)
def Main(i, fitted_data):
saveDirectory = '/home/kaspar/Desktop/1nmlow'
fileDirectory = '/home/kaspar/Desktop/1nmlow/'
pulsetime = 340 * 10**(-15)
mybuttonslist = []
f = ReadAllFiles(fileDirectory)[i]
data = pd.read_csv(fileDirectory + f) # grab the data
z = data.iloc[0:, 0] / 10**3 # z in meters
ydata = data.iloc[0:, 2] #the transmission
guess = [0.0006, 0.25]
params, params_covariance = optimize.curve_fit(ClosedScan, z, ydata, guess)
print f
print params
print params_covariance
energy, L, alpah = GetParams(f)
z0 = params[0]
q0 = params[1]
fig, ax = plt.subplots()
plt.ylabel('Transmission')
plt.xlabel('z(m)')
mycolumn = "PtSe$_2$" + str(L * 10**9) + "nm closed scan " + str(
energy / 10**-9) + "nJ"
plt.title(mycolumn) # title
myscat = plt.scatter(z, ydata)
improvedZ = np.linspace(-0.05, 0.05, 500)
myfit, = plt.plot(improvedZ,
ClosedScan(improvedZ, params[0], params[1]),
color='red')
center = plt.axvline(x=0, color='black')
plt.subplots_adjust(right=0.75, bottom=0.25)
a0 = q0
f0 = z0
axcolor = 'lightgoldenrodyellow'
axfreq = plt.axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor)
axamp = plt.axes([0.25, 0.15, 0.65, 0.03], axisbg=axcolor)
xvals = plt.axes([0.92, 0.25, 0.03, 0.65], axisbg=axcolor)
yvals = plt.axes([0.85, 0.25, 0.03, 0.65], axisbg=axcolor)
xslider = VertSlider(xvals,
'x offset',
-0.01,
0.01,
valinit=0,
valfmt='%.3f')
yslider = VertSlider(yvals,
'y offset',
-0.3,
0.3,
valinit=0,
valfmt='%.4f')
myZ0 = Slider(axfreq, 'Z0', 0.00001, 0.05, valinit=f0, valfmt='%.5f')
myQ0 = Slider(axamp, 'q0', -1, 1, valinit=a0, valfmt='%.5f')
def update(val):
z0 = myZ0.val
q0 = myQ0.val
yshift = yslider.val
xshift = xslider.val
shiftedy = ClosedScan(improvedZ, z0, q0) + yshift
myfit.set_ydata(shiftedy)
myfit.set_xdata(improvedZ + xshift)
fig.canvas.draw_idle()
myZ0.on_changed(update)
myQ0.on_changed(update)
xslider.on_changed(update)
yslider.on_changed(update)
resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
def reset(event):
myZ0.reset()
myQ0.reset()
xslider.reset()
yslider.reset()
button.on_clicked(reset)
plt.show(block=False)
answer = raw_input('Hit enter to continue\n')
z0 = myZ0.val
q0 = myQ0.val
yshift = yslider.val
xshift = xslider.val
plt.close()
plt.ylabel('Transmission')
plt.xlabel('z(m)')
mycolumn = "PtSe$_2$" + str(L * 10**9) + "nm closed scan " + str(
energy / 10**-9) + "nJ"
plt.title(mycolumn) # title
myscat = plt.scatter(z, ydata)
improvedZ = np.linspace(-0.05, 0.05, 500)
myfit, = plt.plot(improvedZ + xshift,
ClosedScan(improvedZ, z0, q0) + yshift,
color='red')
z0 = z0 * 10**3
plt.text(0.8,
0.8,
"$Z_0 (mm)$ =" + str('%.2f' % z0) + "\n" + " $\Delta \Phi_0$ = " +
str('%.2f' % q0) + '\n',
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes)
name = re.sub('correct.csv', '', f)
plt.savefig(saveDirectory + f + "closed.png")
plt.close()
return button
def Main(myfile,fitted_data,saveDirectory,fileDirectory):
"""The main function of the program"""
f = myfile# ith file name from list of files in directory
data = pd.read_csv(fileDirectory+f) # grab the data
z = data.iloc[0:,0]/10**3 # z in meters
ydata = data.iloc[0:,1] #the transmission
global energy,L,alpha,leff,pulsetime # define some globals
pulsetime = 340*10**(-15) # the pulse time
energy,L,alpha = GetParams(f)
leff = L_eff(L,alpha)
#########################
# fitting of data
guess= [0.009,0.5] # initial guess
params, params_covariance = optimize.curve_fit(OpenScanv3,z, ydata, guess) # the fitting
z0 = params[0] # 1st param
b = params[1] # the NL absorbtion coefficent -> 2nd param
if z0 < 0:
z0 = -z0 # make usre it hasnt made z0 negative
w0 = BeamWaist(z0) # the beam width
i0 = Intensity(w0,energy,pulsetime) # the intensity at focus
# the one sigma error for the paramters
perr = np.sqrt(np.diag(params_covariance))
w0Err = BeamWaistErr(w0,z0,perr[0])
i0Err = IntensityErr(i0,w0Err)
bErr = perr[1]
#print the initial values
print params
print perr
print w0,w0Err
print i0,i0Err
print b,bErr
#create a plot of the transmission and fitted curve
fig, ax = plt.subplots()
plt.ylabel('Transmission')
plt.xlabel('z(m)')
# example SampleX
samplename = f.split('_')[0]
mytitle = str(samplename)+str(L*10**9) +"nm open scan "+str(energy/10**-9)+"nJ" # title
plt.title(mytitle) # title
myscat = plt.scatter(z,ydata) # data scatter
plt.ylim( ax.get_ylim() )
improvedZ = np.linspace(-0.05,0.05,500) # the x data for the fit
myfit, = plt.plot(improvedZ,OpenScanv3(improvedZ,params[0],params[1]),color='red') # plot the fitted curve
plt.subplots_adjust(right = 0.75, bottom=0.25) # alignment
# intial slider values
a0 = b
f0 = z0
# make sliders for interative plot
axcolor = 'lightgoldenrodyellow'
axz0 = plt.axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor) # z0 slider
axb0 = plt.axes([0.25, 0.15, 0.65, 0.03], axisbg=axcolor) # beta slider
xvals = plt.axes([0.92, 0.25, 0.03, 0.65], axisbg=axcolor) # x and y adjsutment sliders
yvals = plt.axes([0.85, 0.25, 0.03, 0.65], axisbg=axcolor)
# slider implementation
xslider = VertSlider(xvals, 'x offset', -0.01, 0.01, valinit=0,valfmt='%.3f')
yslider = VertSlider(yvals, 'y offset', -0.3, 0.3, valinit=0,valfmt='%.4f')
myZ0 = Slider(axz0, 'Z0', 0.00001, 0.05, valinit=f0, valfmt='%.5f')
myB0 = Slider(axb0, 'beta', -0.000005, 0.000005, valinit=a0,valfmt='%.10f')
# update function for matplotlibs sliders
def update(val):
z0 = myZ0.val
q0 = myB0.val
yshift = yslider.val
xshift = xslider.val
shiftedy = OpenScanv3(improvedZ,z0,q0)+yshift
myfit.set_ydata(shiftedy)
myfit.set_xdata(improvedZ+xshift)
fig.canvas.draw_idle()
myZ0.on_changed(update)
myB0.on_changed(update)
xslider.on_changed(update)
yslider.on_changed(update)
# reset button for sliders
resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
def reset(event):
myZ0.reset()
myB0.reset()
xslider.reset()
yslider.reset()
button.on_clicked(reset)
#show the plot
plt.show(block=False)
# wait for user to finish fitting exit and enter input...
answer = raw_input('Hit enter to continue\n')
# retrive slider values
z0 = myZ0.val
b = myB0.val
yshift = yslider.val
xshift = xslider.val
# close plot to prevent memory overload
plt.close()
# ensure z0 is not less then 0 again
if z0 < 0:
z0 = -z0
w0 = BeamWaist(z0) # the beam width
i0 = Intensity(w0,energy,pulsetime) # the intensity at focus
# the one sigma error
perr = np.sqrt(np.diag(params_covariance))
w0Err = BeamWaistErr(w0,z0,perr[0])
i0Err = IntensityErr(i0,w0Err)
bErr = perr[1]
# print the final versions
print "Z0 = ",z0,"beta = ",b
print "err0r = ",perr
print "w0 = ",w0,w0Err
print "i0 = ",i0,i0Err
print "b = ", b,bErr
#make the final plot
fig, ax = plt.subplots()
plt.ylabel('Transmission')
plt.xlabel('z(m)')
plt.title(mytitle) # title
plt.scatter(z,ydata)
improvedZ = np.linspace(-0.05,0.05,500)
myfit, = plt.plot(improvedZ+xshift,OpenScanv3(improvedZ,z0,b)+yshift,color='red')
# format some of the data into the standard form
z0 = z0*10**3
w0 = w0*10**6
i0 = i0/(10**(9)*100*100)
energy = energy/10**-9
L = L*10**9
# add some text showing the values
plt.text(0.8,0.8,"$Z_0 (mm)$ ="+str('%.2f' % z0)+"\n"+'$w_0(\mu m)$ = '+str('%.2f' % w0),horizontalalignment='center',
verticalalignment='center',
transform = ax.transAxes)
plt.text(0.2,0.8,"$I_0 (GW cm^{-2}) = $"+str('%.2f' % i0)+"\n"+r"$\beta = $"+ str(b),horizontalalignment='center',
verticalalignment='center',
transform = ax.transAxes)
# make a sname for the entry to be put into a csv
name = str(re.sub('.csv','',f)) # remove .csv from the title
plt.savefig(saveDirectory+name+'.png',bboxinches = 'tight') # save image
plt.close()
print name,L,energy,params[0],perr[0],params[1],perr[1],z0,b,xshift,yshift,w0,w0Err,i0,i0Err,alpha
params[0] = params[0]*10**3 # ensure z0 is in meters for the csv
perr[0] = perr[0]*10**3
# add an entry to the csv
fitted_data.loc[i] = [name,L,energy,params[0],perr[0],params[1],perr[1],z0,b,xshift,yshift,w0,w0Err,i0,i0Err,alpha]
return button # return button this ensures the plot works (if removed the reset button will not work)
