In [1]:
%pylab inline
Populating the interactive namespace from numpy and matplotlib
In [2]:
cd /Users/miguel_daal/Documents/Projects/Python_Res_Fit/KAM
/Users/miguel_daal/Documents/Projects/Python_Res_Fit/KAM
In [3]:
import KAM
In [4]:
reload(KAM)
Out[4]:
<module 'KAM' from 'KAM.pyc'>
In [5]:
#sweep.metadata.__dict__
#----------
#s = sweep.Sweep_Array.view(np.recarray)
#s.dtype.names
#---------
#Power_Sweeps[7].metadata.__dict__
#Power_Sweeps[8].Sweep_Array.view(np.recarray).Pinput_dB
#-----------
#for sweep in Power_Sweeps:
# print('{0}, {1} | {2} {3} '.format(sweep.metadata.Run, sweep.metadata.Sensor, sweep.metadata.Atten_Added_At_NA, sweep.Sweep_Array.view(np.recarray).Pinput_dB[0]))
#----------
#for sweep in Power_Sweeps:
# if sweep.metadata.Atten_Added_At_NA != 0.0:
# sweep.Sweep_Array['Preadout_dB'] = sweep.Sweep_Array['Preadout_dB']-sweep.metadata.Atten_Added_At_NA
# print('{0}, {1} | {2} {3} '.format(sweep.metadata.Run, sweep.metadata.Sensor, sweep.metadata.Atten_Added_At_NA, sweep.Sweep_Array.view(np.recarray).Pinput_dB[0]))
#---------------
In [6]:
run -i Load_Datasets.py
In [7]:
fig = plt.figure( figsize=(8, 8), dpi=100)
ax = fig.add_subplot(111)
lines = {}
#Power_Sweeps[8].Sweep_Array['Preadout_dB'] =Power_Sweeps[8].Sweep_Array['Preadout_dB']+20
#Power_Sweeps[7].Sweep_Array['Preadout_dB'] =Power_Sweeps[7].Sweep_Array['Preadout_dB']+20
for sweep in Power_Sweeps:#[Power_Sweeps[5],Power_Sweeps[8],Power_Sweeps[1], Power_Sweeps[7]]:#Power_Sweeps:
Pr = sweep.Sweep_Array['Preadout_dB']
Qc = sweep.Sweep_Array['Qc']
Q = sweep.Sweep_Array['Q']
Fr = sweep.Sweep_Array['Fr']
Qi = 1.0/((1.0/Q)-(1.0/Qc))
line = ax.plot(Pr,Qi, label = sweep.metadata.Run + ' '+sweep.metadata.Sensor +' ' +sweep.metadata.Ground_Plane, linewidth=3,linestyle= '-', marker = 'x' )
ax.legend(loc = 'best', fontsize=10,scatterpoints =1, numpoints = 1, labelspacing = .1)
#ax.set_ylim((-50000.0, 400000.0))
ax.set_ylim((-50000.0, 500000.0)) #for Qc
ax.set_xlim((-140.0, -20.0))
plt.show()
In [30]:
#show elliptical loops
#show df/f
#show temp vs time
#show
sweep = Run51aP
print(sweep.Sweep_Array['Pinput_dB'])
def plot_routine(index):
sweep.pick_loop(index)
sweep.decompress_gain(Compression_Calibration_Index = -1, Show_Plot = False, Verbose = False)
sweep.remove_cable_delay(Show_Plot = False, Verbose = False)
sweep.circle_fit(Show_Plot = True)
sweep.phase_fit(Fit_Method = 'Multiple',Verbose = True, Show_Plot = True)
print(sweep.loop.Phase_Fit_Method)
plot_routine(4)
[-55. -54.75 -54.5 -54.25 -54. -53.75 -53.5 -53.25 -53. -52.75
-52.5 -52.25 -52. -51.75 -51.5 -51.25 -51. -50.75 -50.5 -50.25 -50.
-49.75 -49.5 -49.25 -49. -48.75 -48.5 -48.25 -48. -47.75 -47.5
-47.25 -47. -46.75 -46.5 -46.25 -46. -45.75 -45.5 -45.25 -45. -44.75
-44.5 -44.25 -44. -43.75 -43.5 -43.25 -43. -42.75 -42.5 -42.25 -42.
-41.75 -41.5 -41.25 -41. -40.75 -40.5 -40.25 -40. -39.75 -39.5
-39.25 -39. -38.75 -38.5 -38.25 -38. -37.75 -37.5 -37.25 -37. -36.75
-36.5 -36.25 -36. -35.75 -35.5 -35.25 -35. -34.75 -34.5 -34.25 -34.
-33.75 -33.5 -33.25 -33. -32.75 -32.5 -32.25 -32. -31.75 -31.5
-31.25 -31. -30.75 -30.5 -30.25 -30. -29.75 -29.5 -29.25 -29. -28.75
-28.5 -28.25 -28. -27.75 -27.5 -27.25 -27. -26.75 -26.5 -26.25 -26.
-25.75 -25.5 -25.25 -25. -24.75 -24.5 -24.25 -24. -23.75 -23.5
-23.25 -23. -22.75 -22.5 -22.25 -22. -21.75 -21.5 -21.25 -21. -20.75
-20.5 -20.25 -20. -19.75 -19.5 -19.25 -19. -18.75 -18.5 -18.25 -18.
-17.75 -17.5 -17.25 -17. ]
Duplicates cuts:
1 duplicate frequencies removed from loop data, 799 remaining data points
Radius cut:
0 points < r_loop*0.75 found and removed, 799 remaining data points
Bandwidth cut:
0 points outside of fr_est +/- 10*FWHM_est removed, 799 remaining data points
Angle jump cut:
2 points with loop angle step > 345 deg removed, 797 remaining data points
Initial Guess:
Loop rotation 6.24410974455, fr 713147361.702, Q 106406.114286
Nelder-Mead Minimzation Result:
status: 1
nfev: 1001
success: False
fun: 5.7857111398026593
x: array([ 3.13686329e+00, 7.13147003e+08, 1.05492209e+05])
message: 'Maximum number of function evaluations has been exceeded.'
nit: 408
Powell Minimzation Result:
status: 0
success: True
direc: array([[ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00],
[ 5.67354476e-03, -1.85403951e+01, -1.23498918e+01],
[ -4.08857833e-16, 6.47803101e-12, 1.74327055e-10]])
nfev: 655
fun: 5.1330065706035182
x: array([ 3.13610560e+00, 7.13147005e+08, 1.11981909e+05])
message: 'Optimization terminated successfully.'
nit: 5
Newton-CG Minimzation Result:
status: 2
success: False
njev: 16
nfev: 26
fun: 236.01839863341144
x: array([ 2.5756553, 7.1314736e+08, 106406.11], dtype=float128)
message: 'Desired error not necessarily achieved due to precision loss.'
nhev: 3
jac: array([ 9065.0648, -0.62840698, -0.00080927737], dtype=float128)
Powell
In [9]:
plot_routine(2)
Duplicates cuts:
3 duplicate frequencies removed from loop data, 2397 remaining data points
Radius cut:
0 points < r_loop*0.75 found and removed, 2397 remaining data points
Bandwidth cut:
0 points outside of fr_est +/- 10*FWHM_est removed, 2397 remaining data points
Angle jump cut:
0 points with loop angle step > 345 deg removed, 2397 remaining data points
Initial Guess:
Loop rotation 0.061767160208, fr 652812326.717, Q 55845.5084633
Nelder-Mead Minimzation Result:
status: 0
nfev: 350
success: True
fun: 11791.263111653503
x: array([ 9.81358012e-01, 2.64700900e+09, 2.48643015e+04])
message: 'Optimization terminated successfully.'
nit: 146
Powell Minimzation Result:
status: 0
success: True
direc: array([[ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00],
[ 0.00000000e+00, 1.00000000e+00, 0.00000000e+00],
[ -1.00895635e-01, 5.16140202e+02, 2.55565865e+03]])
nfev: 948
fun: 42.07779683018699
x: array([ 3.08764409e+00, 6.52814331e+08, 7.15935584e+04])
message: 'Optimization terminated successfully.'
nit: 8
Newton-CG Minimzation Result:
status: 2
success: False
njev: 10
nfev: 22
fun: 25321.390499577457
x: array([ 6.17671602e-02, 6.52812327e+08, 5.58455085e+04])
message: 'Desired error not necessarily achieved due to precision loss.'
nhev: 1
jac: array([ 16118.897, -1.5506575, -0.038836894], dtype=float128)
Powell
In [24]:
sweep.Sweep_Array.shape
Out[24]:
(153,)
In [11]:
#Gain decompression seems to work but does not loop circular
Run46aPh.pick_loop(-2)
Run46aPh.decompress_gain(Compression_Calibration_Index = -1, Show_Plot = True, Verbose = False)
Run46aPh.remove_cable_delay(Show_Plot = False, Verbose = False)
Run46aPh.circle_fit(Show_Plot = True)
Run46aPh.phase_fit(Fit_Method = 'Multiple',Verbose = True, Show_Plot = True)
/Users/miguel_daal/anaconda/lib/python2.7/site-packages/matplotlib/figure.py:1595: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.
warnings.warn("This figure includes Axes that are not "
Duplicates cuts:
1 duplicate frequencies removed from loop data, 799 remaining data points
Radius cut:
57 points < r_loop*0.75 found and removed, 742 remaining data points
Bandwidth cut:
0 points outside of fr_est +/- 10*FWHM_est removed, 742 remaining data points
Angle jump cut:
5 points with loop angle step > 345 deg removed, 737 remaining data points
Initial Guess:
Loop rotation 5.96219149542, fr 689174461.408, Q 11876.8127611
Nelder-Mead Minimzation Result:
status: 1
nfev: 1002
success: False
fun: 1439.9030826635117
x: array([ 5.84310844e+00, 6.89101838e+08, 7.87738580e+03])
message: 'Maximum number of function evaluations has been exceeded.'
nit: 452
Powell Minimzation Result:
status: 0
success: True
direc: array([[ 1.00000000e+00, 0.00000000e+00, 0.00000000e+00],
[ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00],
[ 1.26150126e-01, -3.17760845e+03, -8.09092039e+01]])
nfev: 832
fun: 14.998043482944322
x: array([ 3.42258439e+00, 6.89166296e+08, 1.63254910e+04])
message: 'Optimization terminated successfully.'
nit: 8
Newton-CG Minimzation Result:
status: 2
success: False
njev: 15
nfev: 25
fun: 268.9058924360007
x: array([ 2.7042398, 6.8917446e+08, 11876.813], dtype=float128)
message: 'Desired error not necessarily achieved due to precision loss.'
nhev: 3
jac: array([ 8681.6017, -0.2652777, -0.12906769], dtype=float128)
In [12]:
fig = plt.figure( figsize=(8, 8), dpi=100)
ax = fig.add_subplot(111)
lines = {}
for sweep in Power_Sweeps:
Pr = sweep.Sweep_Array['Preadout_dB']
Fr = sweep.Sweep_Array['Fr']
Fr0 = Fr[min(Pr)==Pr]
dF = (Fr-Fr0)/Fr
line = ax.plot(Pr,dF, label = sweep.metadata.Run + ' '+sweep.metadata.Sensor, linewidth=3,linestyle= '-')
ax.legend(loc = 'best', fontsize=10,scatterpoints =1, numpoints = 1, labelspacing = .1)
plt.show()
In [12]:
In [13]:
temp = KAM.thermometry()
Run48a_Temps = [(0.65, 0.653), (0.6, 0.63), (0.55,0.575), (0.5, 0.535), (0.45, 0.498), (0.4,0.455), (0.35,0.395), (0.3,0.325), (0.25, 0.258), (0.2,.206), (0.15, 0.154), (0.1,0.106), (0.0, 0.051)]
path = '/Users/miguel_daal/Desktop/Some_Data/'
filename = path + 'Run48a/TP_Sweep/Run48a'
temp.load_MonitoringVI_file(filename, temp_list = Run48a_Temps)
/Users/miguel_daal/anaconda/lib/python2.7/site-packages/scipy/signal/signaltools.py:427: RuntimeWarning: invalid value encountered in less
out = where(lVar < noise, lMean, res)
In [14]:
fig = plt.figure( figsize=(10, 10), dpi=100)
ax = fig.add_subplot(111)
for sweep in TP_Sweeps:
T = sweep.Sweep_Array['Temperature']
Pr = sweep.Sweep_Array['Preadout_dB']
Qc = sweep.Sweep_Array['Qc']
Q = sweep.Sweep_Array['Q']
Qi = 1.0/((1.0/Q)-(1.0/Qc))
Fr = sweep.Sweep_Array['Fr']
#Fr0 = Fr[min(Pr)==Pr]
#dF = (Fr-Fr0)/Fr
line = ax.plot(T,Qi, label = sweep.metadata.Run + ' '+sweep.metadata.Sensor, linewidth=3,linestyle= '-')
ax.legend(loc = 'best', fontsize=10,scatterpoints =1, numpoints = 1, labelspacing = .1)
ax.set_title('Qi versus Temp and Power')
#ax.set_ylim((-1, .1))
#ax.set_xlim((-140.0, -20.0))
plt.show()
In [15]:
fig = plt.figure( figsize=(10, 10), dpi=100)
ax = fig.add_subplot(111)
for sweep in [TP_Sweeps[1]]:
T = sweep.Sweep_Array['Temperature']
Pr = sweep.Sweep_Array['Preadout_dB']
Qc = sweep.Sweep_Array['Qc']
Q = sweep.Sweep_Array['Q']
Qi = 1.0/((1.0/Q)-(1.0/Qc))
Fr = sweep.Sweep_Array['Fr']
Fr0 = Fr[min(Pr)==Pr][0]
dF = (Fr-Fr0)/Fr
line = ax.plot(T,dF, label = sweep.metadata.Run + ' '+sweep.metadata.Sensor, linewidth=3,linestyle= '-')
ax.legend(loc = 'best', fontsize=10,scatterpoints =1, numpoints = 1, labelspacing = .1)
ax.set_title('df/f versus temp')
#ax.set_ylim((-1, .1))
#ax.set_xlim((-140.0, -20.0))
plt.show()
In [16]:
for sweep in Power_Sweeps:
print('{0},{1}, {2},{3}, {4},{5}').format(sweep.metadata.Run, sweep.metadata.Sensor, sweep.Sweep_Array['Preadout_dB'],sweep.Sweep_Array['Fr'], sweep.Sweep_Array['Q'], sweep.Sweep_Array['Qc'])
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46a,S8 FHN1 (256um width), [-99.97471667 -94.97471727 -89.97471739 -84.97471733 -79.97471715
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46a,S8 FHN1 (256um width), [-59.97470432 -57.97470039 -55.97469595 -53.97469079 -51.97468529
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45b,S7 FHN1 (128um width), [-99.83070523 -89.83070513 -84.83070495 -79.8307044 -74.83070306
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231339.73985599 249260.52441291 266893.83783431 39179.434085 ]
45a,S6 FHN1 (64um width), [-99.87842355 -89.87842328 -84.87842284 -81.87842193 -79.8784205
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1.63673462e+06 7.30032451e+06 2.00895841e+03]
44b,S5 FHN1 (32um width), [-110.04632715 -105.04632766 -100.04632643 -95.04632457 -90.04632049
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448755.0230688 450793.11328603 457625.12303942 464650.74909301
358555.77387251 472613.24845726 482227.80170837 486729.8188296
495931.53015322 504155.40016677 510159.01619587 523700.0771214
536658.75717174 552998.72348363 565338.6204929 ]
44a,S7 FHN1 (128um width), [-109.869504 -99.86949882 -94.86949848 -89.86949822 -84.86949773
-79.8694964 -74.86949688 -69.8694903 -67.86948356 -65.8694787
-63.86947148 -61.86946399 -59.86945084 -57.86943549 -55.86941686
-53.86940471 -51.86939308 -49.86937245 -47.86934487 -45.86931643],[ 6.62525945e+08 6.62524644e+08 6.62524556e+08 6.62524491e+08
6.62524368e+08 6.62524035e+08 6.62524155e+08 6.62522503e+08
6.62520811e+08 6.62519588e+08 6.62517775e+08 6.62515895e+08
6.62512591e+08 6.62508736e+08 6.62504055e+08 6.62501005e+08
6.62498083e+08 6.62492900e+08 6.62485973e+08 6.62478829e+08], [ 62498.02007326 63754.74379532 64166.8275965 64222.55990652
64049.66284932 63513.67291651 59411.53760051 55314.05224884
59809.98614099 58711.0249355 57508.26800718 44558.09764436
40439.08021394 36734.52324624 33417.97600567 55148.48870701
45616.8398799 39392.90461993 35857.97903009 35850.73822986],[ 184210.31802216 184894.43944515 185230.13354492 184984.08159126
184989.2981194 185109.23274751 176332.3058025 169962.70573171
187796.05111081 189510.63626875 192168.47968076 155156.50703612
148245.74769949 143322.58131922 141499.36032179 266250.18702472
279450.15367397 306557.69415782 346063.33894617 426294.14792894]
In [17]:
print(Run45aP.Sweep_Array['Pinput_dB'])
def plot_routine(index):
Run45aP.pick_loop(index)
Run45aP.decompress_gain(Compression_Calibration_Index = -1, Show_Plot = False, Verbose = False)
Run45aP.remove_cable_delay(Show_Plot = False, Verbose = False)
Run45aP.circle_fit(Show_Plot = True)
Run45aP.phase_fit(Fit_Method = 'Multiple',Verbose = True, Show_Plot = True)
plot_routine(10)
[-55. -45. -40. -37. -35. -33. -31. -29. -27. -25. -23. -21. -19. -17. -15.
-13. -11. -9. -7.]
Duplicates cuts:
2 duplicate frequencies removed from loop data, 1598 remaining data points
Radius cut:
1 points < r_loop*0.75 found and removed, 1597 remaining data points
Bandwidth cut:
0 points outside of fr_est +/- 10*FWHM_est removed, 1597 remaining data points
Angle jump cut:
0 points with loop angle step > 345 deg removed, 1597 remaining data points
Initial Guess:
Loop rotation 6.02259312127, fr 664760396.123, Q 142016.993717
Nelder-Mead Minimzation Result:
status: 0
nfev: 861
success: True
fun: 316.21744314072214
x: array([ 3.45097709e+00, 6.64760865e+08, 3.10700451e+05])
message: 'Optimization terminated successfully.'
nit: 426
Powell Minimzation Result:
status: 0
success: True
direc: array([[ -2.14168170e-03, 1.55454273e+01, -6.52463135e+02],
[ -9.11012388e-02, 2.13530067e+01, 2.64691393e+02],
[ 1.60274973e-06, -1.37452533e-02, -1.62403873e+00]])
nfev: 960
fun: 146.47971580585966
x: array([ 3.37217159e+00, 6.64761216e+08, 1.46396739e+05])
message: 'Optimization terminated successfully.'
nit: 7
Newton-CG Minimzation Result:
status: 2
success: False
njev: 35
nfev: 45
fun: 425.8142715291381
x: array([ 3.862034, 6.647604e+08, 142016.99], dtype=float128)
message: 'Desired error not necessarily achieved due to precision loss.'
nhev: 2
jac: array([ 23484.809, -3.5463679, 0.0025671793], dtype=float128)
In [18]:
plot_routine(5)
Duplicates cuts:
2 duplicate frequencies removed from loop data, 1598 remaining data points
Radius cut:
0 points < r_loop*0.75 found and removed, 1598 remaining data points
Bandwidth cut:
0 points outside of fr_est +/- 10*FWHM_est removed, 1598 remaining data points
Angle jump cut:
0 points with loop angle step > 345 deg removed, 1598 remaining data points
Initial Guess:
Loop rotation 0.0241332978088, fr 664766578.851, Q 167027.82909
Nelder-Mead Minimzation Result:
status: 0
nfev: 369
success: True
fun: 9873.2352690310727
x: array([ 7.33753400e-01, 4.86637210e+09, 1.61134617e+05])
message: 'Optimization terminated successfully.'
nit: 159
Powell Minimzation Result:
status: 0
success: True
direc: array([[ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00],
[ 0.00000000e+00, 1.00000000e+00, 0.00000000e+00],
[ -1.32434803e-02, 2.90441828e+01, 2.92821634e+02]])
nfev: 880
fun: 25.442735127885744
x: array([ 3.14371439e+00, 6.64766888e+08, 1.56219732e+05])
message: 'Optimization terminated successfully.'
nit: 7
Newton-CG Minimzation Result:
status: 2
success: False
njev: 10
nfev: 22
fun: 16006.59150972341
x: array([ 2.41332978e-02, 6.64766579e+08, 1.67027829e+05])
message: 'Desired error not necessarily achieved due to precision loss.'
nhev: 1
jac: array([ 10258.054, -1.5563695, -0.002503048], dtype=float128)
In [19]:
swp = KAM.sweep()
path = '/Users/miguel_daal/Desktop/Some_Data/'
filename = path + 'Calibrations/Cables_140520/Channel_1_Coax_2_50mK_KIDs_Run_50a_ScanData_37mK_2014520183338_Fixed20140625.mat'
swp.load_scandata(filename)
index = 1
swp.pick_loop(index)
swp.fit_cable_loss(freq_range = [500e6, 1e9], Verbose = False, Show_Plot = False)
Field named Thermometer_Configuration is not found.
Field named Thermometer_Voltage_Bias is not found.
Loading Run: Channel 1 Coax 2 50mK (KIDs Run 50a)
There are 1 heater voltage(s), 3 input power(s), and 1 frequecy span(s)
In [20]:
sweep.metadata.Run
25 if sweep.metadata.Run == '44b' else 0
Out[20]:
0
In [21]:
sweep.metadata.Run
Out[21]:
'44a'
In [22]:
f = sweep.loop.freq
f.size
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-22-707cded96496> in <module>()
1 f = sweep.loop.freq
----> 2 f.size
AttributeError: 'NoneType' object has no attribute 'size'
In [ ]:
f_adjacent_distance = np.abs(f[:-1]-f[1:])
In [ ]:
np.where(f_adjacent_distance == 0)
In [ ]:
[f[0],f[1], f[2],f[3]]
In [ ]:
f.dtype
In [ ]:
sweep.loop.z[0]
In [ ]:
long(f[1])
In [ ]:
long(f[2])
In [ ]:
Run45aP.Sweep_Array['Preadout_dB'][18]
In [ ]:
Content source: mdaal/KAM
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