In [6]:

    

%matplotlib inline


    

In [77]:

    

import optoanalysis as oa
import numpy as np
import matplotlib.pyplot as plt
import scipy.constants


    

In [3]:

    

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


    

    




Loading data from testData.raw

In [8]:

    

data.get_fit_auto?


    

In [9]:

    

w0, A, G, _, _ = data.get_fit_auto(70e3)


    

    




/home/ash/anaconda2/envs/python3/lib/python3.5/site-packages/optoanalysis-3.3.7-py3.5-linux-x86_64.egg/optoanalysis/optoanalysis.py:489: UserWarning: range is too small, returning NaN
  _warnings.warn("range is too small, returning NaN", UserWarning)






    




found best






    













    





A: 548633139702.1037 +- 1.518617224721159% 
Trap Frequency: 466605.38925598894 +- 0.014086729888777386% 
Big Gamma: 3992.6414844211226 +- 3.2495973515732843% 

In [26]:

    

f = (w0.n/(2*np.pi))
f2 = 2*(w0.n/(2*np.pi))

data.plot_PSD([f2/1e3-10, f2/1e3+10])


    

    













    
Out[26]:





(<matplotlib.figure.Figure at 0x7fb859e074a8>,
 <matplotlib.axes._subplots.AxesSubplot at 0x7fb859cda358>)

In [27]:

    

w20, A2, G2, _, _ = data.get_fit_auto(f2)


    

    




found best






    













    





A: 5460591816.888507 +- 4.819416460051663% 
Trap Frequency: 932714.1179959807 +- 0.02874926630420712% 
Big Gamma: 25977.35485049896 +- 3.4989473662091903% 

In [43]:

    

z = oa.butterworth_filter(data.voltage[::2], data.SampleFreq/2, f-10e3, f+10e3)
z2 = oa.butterworth_filter(data.voltage[::2], data.SampleFreq/2, f2-10e3, f2+10e3)


    

In [44]:

    

print(len(data.time), len(z), len(z2))


    

    




5000002 2500001 2500001

In [46]:

    

plt.plot(data.time.get_array()[0:5000], z[0:5000])
plt.plot(data.time.get_array()[0:5000], z2[0:5000])


    

    
Out[46]:





[<matplotlib.lines.Line2D at 0x7fb859b659b0>]






    

In [55]:

    

s = 20*np.sin(np.arange(0, 100, 0.01))


    

In [56]:

    

np.sqrt(2*np.mean(s**2))


    

    
Out[56]:





20.043360000950941

In [57]:

    

def MeanAmp(signal):
    return np.sqrt(2*np.mean(signal**2))


    

In [63]:

    

V1 = MeanAmp(z)


    

In [62]:

    

V2 = MeanAmp(z2)


    

In [102]:

    

r = V2/V1


    

In [103]:

    

beta = 4*r


    

In [104]:

    

k0 = (2*np.pi)/(1550e-9)


    

In [181]:

    

NA = 0.999
WaistSize = 1550e-9/(np.pi*NA)
Zr = np.pi*WaistSize**2/1550e-9


    

In [182]:

    

z0 = beta/(k0 - 1/Zr)


    

In [183]:

    

ConvFactor = V1/z0


    

In [184]:

    

ConvFactor


    

    
Out[184]:





307063.15224001213

In [185]:

    

mFromA = 2*scipy.constants.Boltzmann*300/(np.pi*A) * ConvFactor**2 * G


    

In [186]:

    

mFromEquipartition = scipy.constants.Boltzmann*300/(w0**2 * z0**2)


    

In [187]:

    

mFromA


    

    
Out[187]:





1.8093317114343098e-18+/-6.489949826955931e-20

In [188]:

    

mFromEquipartition


    

    
Out[188]:





4.821247647238404e-18+/-1.3583122666710175e-21

In [189]:

    

def CalcR(Mass):
    density = 1800
    Radius = (3*Mass/(4*np.pi*density))**(1/3)
    return Radius


    

In [190]:

    

CalcR(mFromA)*1e9


    

    
Out[190]:





62.14206680587009+/-0.7429978946825894

In [191]:

    

CalcR(mFromEquipartition)*1e9


    

    
Out[191]:





86.15214769978056+/-0.008090680226565776

In [192]:

    

0.05**0.5


    

    
Out[192]:





0.22360679774997896

In [206]:

    

0.05**2*50


    

    
Out[206]:





0.12500000000000003

In [ ]: