

In [1]:

    
%matplotlib inline 
import optoanalysis
from numpy import pi

Example of how to load a data file and analyse / visualise various properties



In [2]:

    
optoanalysis.properties["default_fig_size"] = (7, 5) # sets default fig size for optoanalysis plots



In [3]:

    
data = optoanalysis.load_data("testData.raw")









    



Loading data from testData.raw

Plotting the time trace



In [4]:

    
data.plot_time_data(timeStart=0, timeEnd=200, units='us')









    












    Out[4]:





(<matplotlib.figure.Figure at 0x7f8d4169ec88>,
 <matplotlib.axes._subplots.AxesSubplot at 0x7f8d413f5ac8>)

Plotting the power spectral density (PSD)



In [5]:

    
fig, ax = data.plot_PSD([0, 300]); # Plot PSD of loaded data

Fitting to a peak in the PSD



In [6]:

    
W_trap_z, A, Gamma, fig, ax = data.get_fit_auto(75e3); # fit to Z peak of loaded data and extracts the parameters A (The A parameter), W_trap_z (the trap frequency in Z) and Gamma (the damping on Z)









    



found best






    












    




A: 548621211346.4878 +- 1.518599585030561% 
Trap Frequency: 466605.4199560843 +- 0.014086349536246044% 
Big Gamma: 3992.323366562553 +- 3.2497134412173843%

Extracting further parameters from the data



In [7]:

    
# next 3 lines are just me getting the pressure value from a file
with open("testDataPressure.dat", 'r') as file: 
    for line in file:
        pressure = float(line.split("mbar")[0])

R, M, ConvFactorZ = data.extract_parameters(pressure, 0.15) # extracts the radius of the particle, mass and conversion factor for the z degree of freedom (used to convert volts to meters of particle motion)



In [8]:

    
print(R, M, ConvFactorZ)









    



(1.45+/-0.22)e-07 (2.3+/-0.7)e-17 (1.10+/-0.34)e+06



In [9]:

    
F_trap_z = W_trap_z/(2*pi)



In [10]:

    
print(F_trap_z)

Plotting the phase space representation of a degree of motion



In [11]:

    
fig, axscatter, axhistx, axhisty, cbar = data.plot_phase_space(F_trap_z.n, ConvFactorZ, FractionOfSampleFreq=3, figsize=[7, 7]); # this filters for the z motion, calculates the phase space of this motion and plots it using the qplots library









    



filtering data



In [12]:

    
W_trap_x, A, Gamma, fig, ax = data.get_fit_auto(160e3); # fits to the x peak
R, M, ConvFactorX = data.extract_parameters(pressure, 0.15) # extracts parameters from x peak

W_trap_y, A, Gamma, fig, ax = data.get_fit_auto(180e3); # fits to the y peak
R, M, ConvFactorY = data.extract_parameters(pressure, 0.15)# extracts parameters from y peak









    



found best






    












    




A: 41671522193.62844 +- 1.5048839857496519% 
Trap Frequency: 1059286.9112359448 +- 0.008296517428258338% 
Big Gamma: 4530.375868165643 +- 3.2509953331880586% 
found best






    












    




A: 29346150102.71962 +- 1.5008668421878193% 
Trap Frequency: 1161182.0446442377 +- 0.007815161493549834% 
Big Gamma: 4404.643455512914 +- 3.3146231843259844%



In [13]:

    
print(ConvFactorZ, ConvFactorX, ConvFactorY) # conversion factor for z is larger than conversion factor for x and y (which have similar values) as our detection more sesitive to z than x and y









    



(1.10+/-0.34)e+06 (2.3+/-0.7)e+05 (2.1+/-0.6)e+05

Extracting the motion in volts by filtering



In [14]:

    
z, x, y, t, fig, ax = data.extract_ZXY_motion([75e3, 165e3, 180e3], 10e3, [10e3, 10e3, 10e3], subSampleFraction=3) # extracts the motion of z, x and y









    



filtering Z
filtering X
filtering Y



In [15]:

    
znm = z/ConvFactorZ.n*1e9

Plotting Wigner



In [16]:

    
sample_freq = data.SampleFreq/3



In [17]:

    
wigner, centres = optoanalysis.get_wigner(znm, F_trap_z.n, sample_freq, histbins=1000)



In [18]:

    
optoanalysis.plot_wigner3d(wigner, centres, "nm")









    Out[18]:





(<matplotlib.figure.Figure at 0x7f8d3eb4ac50>,
 <matplotlib.axes._subplots.Axes3DSubplot at 0x7f8d3eb43828>)



In [19]:

    
optoanalysis.plot_wigner2d(wigner, centres)









    Out[19]:





(<matplotlib.figure.Figure at 0x7f8d3e8af0b8>,
 <matplotlib.axes._axes.Axes at 0x7f8d3ebaa828>,
 <matplotlib.axes._axes.Axes at 0x7f8d27d3b9b0>,
 <matplotlib.axes._axes.Axes at 0x7f8d3f0b8208>,
 <matplotlib.colorbar.Colorbar at 0x7f8d0c38c668>)

Animating the particle's motion



In [20]:

    
optoanalysis.animate(z, x, y, [ConvFactorZ.n, ConvFactorX.n, ConvFactorY.n], t, 150, timeSteps=25) # animates the motion of z, x and y









    



This will take ~ 0.08458333333333333 minutes

Plotting multiple data files and calculating temperature with a reference data set



In [21]:

    
PRef = 2.703
Pcooled = 0.0232

We can load the reference (uncooled at high pressure) and cooled data



In [22]:

    
ref = optoanalysis.load_data("CH1_RUN00000001_REPEAT0000.raw")
cooldata = optoanalysis.load_data("CH1_RUN00000036_REPEAT0000.raw")









    



Loading data from CH1_RUN00000001_REPEAT0000.raw
Loading data from CH1_RUN00000036_REPEAT0000.raw






    



/home/ash/anaconda2/envs/python3/lib/python3.5/site-packages/scipy/signal/spectral.py:772: UserWarning: nperseg = 1000000, is greater than input length = 250002, using nperseg = 250002
  'using nperseg = {1:d}'.format(nperseg, x.shape[-1]))

we can plot the 2 PSDs together



In [23]:

    
optoanalysis.multi_plot_PSD([ref, cooldata], [0, 200])









    












    Out[23]:





(<matplotlib.figure.Figure at 0x7f8d07e760f0>,
 <matplotlib.axes._subplots.AxesSubplot at 0x7f8d07bc1668>)



In [24]:

    
ref.get_PSD(0.5e5)









    Out[24]:





(array([  0.00000000e+00,   4.99999994e+01,   9.99999988e+01, ...,
          1.24989999e+06,   1.24994999e+06,   1.24999999e+06]),
 array([  1.28149734e-08,   6.24827096e-08,   5.09595420e-08, ...,
          1.10449946e-09,   1.16330389e-09,   3.97229587e-10]))

We can fit to the Z, X and Y peaks



In [25]:

    
W_trap_z, A, Gamma, fig, ax = ref.get_fit_auto(60e3, MaxWidth=5000); # fits to the z peak
R, M, ConvFactorZ = ref.extract_parameters(PRef, 0.15) # extracts parameters from z peak

W_trap_x, A, Gamma, fig, ax = ref.get_fit_auto(150e3); # fits to the x peak
R, M, ConvFactorX = ref.extract_parameters(PRef, 0.15) # extracts parameters from x peak

W_trap_y, A, Gamma, fig, ax = ref.get_fit_auto(166e3, MaxWidth=10e3); # fits to the y peak
R, M, ConvFactorY = ref.extract_parameters(PRef, 0.15)# extracts parameters from y peak









    



found best






    












    




A: 1176625371481.0798 +- 8.485884211418862% 
Trap Frequency: 388312.81845422875 +- 0.05534503465645279% 
Big Gamma: 7075.83189874807 +- 7.357867729789741% 
found best






    



/home/ash/anaconda2/envs/python3/lib/python3.5/site-packages/optoanalysis-4.0.1-py3.5.egg/optoanalysis/optoanalysis.py:495: UserWarning: range is too small, returning NaN
  _warnings.warn("range is too small, returning NaN", UserWarning)






    












    




A: 1571242482189.7734 +- 4.1922644494661725% 
Trap Frequency: 941415.2071361727 +- 0.020924166217541108% 
Big Gamma: 5590.661863199339 +- 6.029150823921562% 
found best






    












    




A: 1775528043681.7756 +- 7.9874541675196555% 
Trap Frequency: 1043451.258463454 +- 0.04017237574947875% 
Big Gamma: 11162.33998715433 +- 8.53404814945303%



In [26]:

    
fz = W_trap_z/(2*pi)
fx = W_trap_x/(2*pi)
fy = W_trap_y/(2*pi)

We can extract the motion in Z, X and Y in nm and animate it



In [27]:

    
z, x, y, t, fig, ax = ref.extract_ZXY_motion([fz, fx, fy], 6e3, [10e3, 10e3, 10e3], subSampleFraction=1) # extracts the motion of z, x and y
optoanalysis.animate(z, x, y, [ConvFactorZ.n, ConvFactorX.n, ConvFactorY.n], t, 150, timeSteps=25, filename="referenceParticle") # animates the motion of z, x and y









    



filtering Z
filtering X
filtering Y






    



/home/ash/anaconda2/envs/python3/lib/python3.5/site-packages/scipy/signal/spectral.py:772: UserWarning: nperseg = 1000000, is greater than input length = 250001, using nperseg = 250001
  'using nperseg = {1:d}'.format(nperseg, x.shape[-1]))






    












    



This will take ~ 0.08458333333333333 minutes



In [ ]:

    
z, x, y, t, fig, ax = cooldata.extract_ZXY_motion([fz, fx, fy], 6e3, [10e3, 10e3, 10e3], subSampleFraction=1) # extracts the motion of z, x and y









    



filtering Z
filtering X
filtering Y



In [ ]:

    
optoanalysis.animate(z, x, y, [ConvFactorZ.n, ConvFactorX.n, ConvFactorY.n], t, 150, timeSteps=25, filename="coolParticle") # animates the motion of z, x and y









    



This will take ~ 0.08458333333333333 minutes

We can calculate the temperature of the cooled dataset in Z by fitting to Z and then using `calc_temp`



In [ ]:

    
W_trap_z, A, Gamma, fig, ax = ref.get_fit_auto(60e3, MaxWidth=5000); # fits to the z peak
W_trap_z, A, Gamma, fig, ax = cooldata.get_fit_auto(60e3, MaxWidth=5000); # fits to the z peak

Now we have fit to z for both the reference and data of interest we can get the temperature



In [ ]:

    
optoanalysis.calc_temp(ref, cooldata)

We can also extract the contribution to the damping from the environment and from cooling



In [ ]:

    
optoanalysis.calc_gamma_components(ref, cooldata)



In [ ]:

Example of how to load a data file and analyse / visualise various properties

Plotting the time trace

Plotting the power spectral density (PSD)

Fitting to a peak in the PSD

Extracting further parameters from the data

Plotting the phase space representation of a degree of motion

Extracting the motion in volts by filtering

Plotting Wigner

Animating the particle's motion

Plotting multiple data files and calculating temperature with a reference data set

We can load the reference (uncooled at high pressure) and cooled data

we can plot the 2 PSDs together

We can fit to the Z, X and Y peaks

We can extract the motion in Z, X and Y in nm and animate it

We can calculate the temperature of the cooled dataset in Z by fitting to Z and then using calc_temp

Now we have fit to z for both the reference and data of interest we can get the temperature

We can also extract the contribution to the damping from the environment and from cooling

We can calculate the temperature of the cooled dataset in Z by fitting to Z and then using `calc_temp`