In [71]:
# Always issue the %pylab command to load numpy, matplotlib and set plotting to interactive mode
# Here we additionally use 'inline' mode to keep the plots in the notebook
%pylab inline
Populating the interactive namespace from numpy and matplotlib
In [72]:
# Now load the KAM module
import KAM
# If you make any changes to KAM and want see how they work dont forget to reload KAM!
reload(KAM)
Out[72]:
<module 'KAM' from 'KAM.pyc'>
In [73]:
# Default Data Database location:
print(KAM.database_location)
#Added 6/12/17 - use a demonstration Data directory ('Data_Demo') for KAM demonstration
KAM.database_location = './Data_Demo/My_Data_Library.h5'
print(KAM.database_location)
Data/My_Data_Library.h5
./Data_Demo/My_Data_Library.h5
In [74]:
# The main object for manipulating and analyzing sweep data is the sweep ojbect.
# You must instantiate it.
swp = KAM.sweep()
In [75]:
# The sweep object can load three types of data:
# 1 - Data stored in the hf5 database
# 2 - Data from touchstone .s2p or .s3p files
# 3 - Data acquired in the lab in Miguel's ScanData format (a .mat file)
# Additionally ScanaData can be loaded from the web if proper URL and Uname/PW are given.
filename = './Data_Demo/S5_truegapdistance.s2p'
swp.load_touchstone(filename, pick_loop = True)
# Here we have avoided picking the look as the current data by specifing pick_loop = True
# Otherwise, we would have done this next:
#index = 0
#swp.pick_loop(index)
# loading th data created a Sweep_Array attribute in the sweep object,
# which contains all sweeps loaded. (1 sweep in this case)
# Sweep_Array is a numpy 'structured'/'record'
swp.Sweep_Array
Out[75]:
array([ (620000000.0, 708779820.0, 0.0, 0.0, 0.0, 0.0, [0.0], 0.0, [(0.367332688337-0.930071762824j), (0.2207536263176-0.975309708459j), (0.2200285412995-0.975473548872j), (0.2010003974258-0.979577247217j), (0.200996151499-0.979578130806j), (0.2009918968893-0.979579016198j), (0.2009876335669-0.979579903398j), (0.2009833615018-0.979580792414j), (0.2009790806639-0.979581683251j), (0.2009747910228-0.979582575915j), (0.2009704925483-0.979583470413j), (0.2009661852098-0.979584366751j), (0.2009618689765-0.979585264936j), (0.2009575438177-0.979586164974j), (0.2009532097024-0.979587066871j), (0.2009488665995-0.979587970634j), (0.2009445144777-0.979588876269j), (0.2009401533056-0.979589783783j), (0.2009357830518-0.979590693183j), (0.2009314036844-0.979591604474j), (0.2009270151717-0.979592517663j), (0.2009226174816-0.979593432758j), (0.200918210582-0.979594349764j), (0.2009137944407-0.979595268689j), (0.2009093690252-0.979596189539j), (0.2009049343029-0.979597112321j), 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dtype=[('Fstart', '<f8'), ('Fstop', '<f8'), ('Heater_Voltage', '<f8'), ('Pinput_dB', '<f8'), ('Preadout_dB', '<f8'), ('Thermometer_Voltage_Bias', '<f8'), ('Temperature_Readings', '<f8', (1,)), ('Temperature', '<f8'), ('S21', '<c16', (1424,)), ('Frequencies', '<f8', (1424,)), ('Q', '<f8'), ('Qc', '<f8'), ('Fr', '<f8'), ('Is_Valid', '?'), ('Chi_Squared', '<f8'), ('Mask', '?', (1424,)), ('R', '<f8'), ('r', '<f8'), ('a', '<f8'), ('b', '<f8'), ('Theta', '<f8'), ('Phi', '<f8'), ('cQ', '<f8'), ('cQc', '<f8'), ('cFr', '<f8'), ('cIs_Valid', '?'), ('cChi_Squared', '<f8'), ('cPhi', '<f8'), ('cTheta', '<f8'), ('cR', '<f8'), ('sQ', '<f8'), ('sQc', '<f8'), ('sFr', '<f8'), ('sIs_Valid', '?'), ('sChi_Squared', '<f8'), ('sPhi', '<f8'), ('sTheta', '<f8'), ('sR', '<f8')])
In [76]:
# Lets look at the data
swp.plot_loop(aspect='equal');
swp.plot_transmission();
In [77]:
# to fit the loop, standard first thing to do is remove any cable delays.
# For a Sonnet simulation this the delay incurred between the port and
# the resonator structure.
swp.remove_cable_delay(Show_Plot = True, Verbose = True)
cable delay is -1.12799481373e-07 seconds by minimum distance method
Out[77]:
-1.1279948137342702e-07
In [78]:
# Oops this did not improve the loop.
# So lets start over, and undo the remove_cable_delay operation.
swp.pick_loop(0)
In [79]:
# next we fit the loop to a circle.
swp.circle_fit(Show_Plot = True)
#%config InlineBackend.close_figures=False
In [80]:
# next we perform the phase fit get the resonance parameters
swp.phase_fit(Fit_Method = 'Multiple',Verbose = True, Show_Plot = True)
Duplicates cuts:
1 duplicate frequencies removed from loop data, 1423 remaining data points
Radius cut:
0 points < r_loop*0.75 or > r_loop*1.75 found and removed, 1423 remaining data points
Bandwidth cut:
1352 points outside of fr_est +/- 8*FWHM_est removed, 71 remaining data points
Angle jump cut:
19 points with discontinuous jumps in loop angle removed, 52 remaining data points
Initial Guess:
Loop rotation 177.327340925 deg, fr 708592560.0, Q 656104.222222
Nelder-Mead Minimzation Result:
final_simplex: (array([[ -6.55304199e-01, 1.17173216e+09, 8.26727985e+05],
[ -6.55304199e-01, 1.17173216e+09, 8.26727985e+05],
[ -6.55304199e-01, 1.17173216e+09, 8.26727985e+05],
[ -6.55304199e-01, 1.17173216e+09, 8.26727985e+05]]), array([ 278.68988831, 278.68988831, 278.68988831, 278.68988831]))
fun: 278.68988830583845
message: 'Optimization terminated successfully.'
nfev: 326
nit: 131
status: 0
success: True
x: array([ -6.55304199e-01, 1.17173216e+09, 8.26727985e+05])
Powell Minimzation Result:
direc: array([[ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00],
[ -3.06766066e-03, -7.38126165e+00, -2.86575327e+02],
[ -4.42674318e-07, 0.00000000e+00, -1.58145188e-02]])
fun: 0.23627109100973051
message: 'Optimization terminated successfully.'
nfev: 1555
nit: 8
status: 0
success: True
x: array([ -2.81430300e+00, 7.08592399e+08, 4.38761837e+05])
Newton-CG Minimzation Result:
fun: 2.5214028389886503
jac: array([ 0.011852023, 0.00031352662, 1.582763e-05], dtype=float128)
message: 'Optimization terminated successfully.'
nfev: 4
nhev: 3
nit: 3
njev: 6
status: 0
success: True
x: array([-2.7650326, 7.0859243e+08, 656103.87], dtype=float128)
In [81]:
# Resonance parameters can be directly accessed in the loop object
print swp.loop.fr
print swp.loop.Q
print swp.loop.Qi
print swp.loop.Qc
708592399.078
438761.837379
28857406.5458
445535.979915
In [82]:
# There is also a metadata object in the sweep object that holds a lot of info
# about the loaded data. For sonnet data most metadata is None. lets view as a dict..
swp.metadata.__dict__
Out[82]:
{'Atten_Added_At_NA': None,
'Atten_At_4K': None,
'Atten_NA_Input': None,
'Atten_NA_Output': None,
'Atten_RTAmp_Input': None,
'Box': None,
'Cable_Calibration': None,
'Data_Source': './Data_Demo/S5_truegapdistance.s2p',
'Digitizer': None,
'Electrical_Delay': -1.1279948137342702e-07,
'Feedline_Impedance': None,
'Fridge_Base_Temp': None,
'Fridge_Run_Start_Date': None,
'Fsteps': 1424,
'Ground_Plane': None,
'Ground_Plane_Thickness': None,
'IFBW': None,
'LNA': None,
'Meaurement_Duration': None,
'Min_Freq_Resolution': 6.2345379213483111e-05,
'Minimum_Q': None,
'NA_Average_Factor': None,
'Notes': None,
'Num_Heater_Voltages': None,
'Num_Points_Per_Scan': None,
'Num_Powers': None,
'Num_Ranges': None,
'Num_Temperatures': 0,
'Press': None,
'RTAmp': None,
'RTAmp_In_Use': None,
'Resonator_Eeff': None,
'Resonator_Impedance': None,
'Resonator_Thickness': None,
'Resonator_Width': None,
'Run': 'S5_truegapdistance',
'Sensor': None,
'System_Calibration': None,
'Temperature_Calibration': None,
'Test_Location': None,
'Thermometer_Configuration': None,
'Time_Created': '06/27/2014 05:42:39',
'Wait_Time': None}
In [83]:
# We can save the data for future use into a hf5 database
swp.save_hf5(filename = KAM.database_location, overwrite = True)
Speficied h5 database exists and will be updated.
Table /RunS5_truegapdistance/T201406270542 exists. Overwriting...
table metadata Resonator_Eeff not defined and is set to None
table metadata Test_Location not defined and is set to None
table metadata Atten_RTAmp_Input not defined and is set to None
table metadata Thermometer_Configuration not defined and is set to None
table metadata Cable_Calibration not defined and is set to None
table metadata Fridge_Run_Start_Date not defined and is set to None
table metadata Minimum_Q not defined and is set to None
table metadata Atten_Added_At_NA not defined and is set to None
table metadata Atten_NA_Input not defined and is set to None
table metadata Sensor not defined and is set to None
table metadata Num_Heater_Voltages not defined and is set to None
table metadata Ground_Plane not defined and is set to None
table metadata Atten_At_4K not defined and is set to None
table metadata IFBW not defined and is set to None
table metadata Ground_Plane_Thickness not defined and is set to None
table metadata Wait_Time not defined and is set to None
table metadata Meaurement_Duration not defined and is set to None
table metadata Digitizer not defined and is set to None
table metadata Press not defined and is set to None
table metadata LNA not defined and is set to None
table metadata Notes not defined and is set to None
table metadata Box not defined and is set to None
table metadata NA_Average_Factor not defined and is set to None
table metadata Num_Points_Per_Scan not defined and is set to None
table metadata Fridge_Base_Temp not defined and is set to None
table metadata Num_Powers not defined and is set to None
table metadata RTAmp_In_Use not defined and is set to None
table metadata Feedline_Impedance not defined and is set to None
table metadata Temperature_Calibration not defined and is set to None
table metadata Atten_NA_Output not defined and is set to None
table metadata System_Calibration not defined and is set to None
table metadata Num_Ranges not defined and is set to None
table metadata Resonator_Thickness not defined and is set to None
table metadata Resonator_Width not defined and is set to None
table metadata Resonator_Impedance not defined and is set to None
table metadata RTAmp not defined and is set to None
In [84]:
# Now lets load and look at another data set.
filename = './Data_Demo/Coupling6_rescale2_full_23pH.s2p'
swp.load_touchstone(filename)
In [85]:
# Again We will look at the data. But this time we use the fact the each of these port functions
# return a Reutrns a tuple, (fig, ax, line), where fig is the figure object, ax is the axes object
# and line is the line object for the plotted data. We'll maipulate the image by adding a marker at
# the transmission minimum
(fig0, ax0, line0) = swp.plot_loop(aspect='equal', show = False)
# show = False because inline mode does not like to update plots thare are already rendered.
(fig1, ax1, line1) = swp.plot_transmission(show = False)
# find the min transmission point and plot it as a star
fr = np.extract(np.abs(swp.loop.z).min() == np.abs(swp.loop.z),swp.loop.freq)[0]
zr = np.extract(np.abs(swp.loop.z).min() == np.abs(swp.loop.z),swp.loop.z)[0]
point0 = ax0.plot(zr.real, zr.imag,'y*',markersize = 20)
point1 = ax1.plot(fr, 20*np.log10(abs(zr)),'y*',markersize = 20)
plt.show()
In [86]:
# lets trim this data so that we only have points that are 3*FWHM bandwidth away from the
# transmission minimum. This will reduce the number of points we have to worry about...
swp.trim_loop(N=3, Verbose = True)
# see what was done
swp.plot_transmission(show = True);
Bandwidth cut:
54 points outside of fr_est +/- 3*FWHM_est removed, 66 remaining data points
In [87]:
# Now we circle fit and then fit the resonance parameters to the data.
# *It's important to always circle fit before phase fit.*
swp.circle_fit(Show_Plot = True)
In [88]:
swp.phase_fit(Fit_Method = 'Multiple',Verbose = True, Show_Plot = True)
# Note choosing Nelder-Mead because Powell/Newton-CG do not converge.
Duplicates cuts:
1 duplicate frequencies removed from loop data, 65 remaining data points
Radius cut:
0 points < r_loop*0.75 or > r_loop*1.75 found and removed, 65 remaining data points
Bandwidth cut:
0 points outside of fr_est +/- 8*FWHM_est removed, 65 remaining data points
Angle jump cut:
0 points with discontinuous jumps in loop angle removed, 65 remaining data points
Initial Guess:
Loop rotation 180.110758943 deg, fr 1459241300.0, Q 29184.826
Nelder-Mead Minimzation Result:
final_simplex: (array([[ -1.02904873e+00, 2.54308290e+09, 3.57008280e+04],
[ -1.02904873e+00, 2.54308290e+09, 3.57008280e+04],
[ -1.02904873e+00, 2.54308290e+09, 3.57008280e+04],
[ -1.02904873e+00, 2.54308290e+09, 3.57008280e+04]]), array([ 121.92365007, 121.92365007, 121.92365007, 121.92365007]))
fun: 121.92365006584528
message: 'Optimization terminated successfully.'
nfev: 338
nit: 137
status: 0
success: True
x: array([ -1.02904873e+00, 2.54308290e+09, 3.57008280e+04])
Powell Minimzation Result:
direc: array([[ -4.72566949e-05, -8.85895372e-01, 5.05443248e-01],
[ 1.95830148e-01, 7.07233315e+03, 6.48561820e+01],
[ 1.05808021e-08, 0.00000000e+00, 4.99523928e-04]])
fun: 3.8495583864987808e-05
message: 'Optimization terminated successfully.'
nfev: 1465
nit: 12
status: 0
success: True
x: array([ -3.62303056e+00, 1.45925593e+09, 1.89781918e+04])
Newton-CG Minimzation Result:
fun: 0.7252197186074516
jac: array([-3.0774967e-07, -0.00012885975, 0.00023711664], dtype=float128)
message: 'Maximum number of iterations has been exceeded.'
nfev: 51
nhev: 50
nit: 50
njev: 100
status: 1
success: False
x: array([-3.7146771, 1.4592515e+09, 22555.474], dtype=float128)
In [89]:
# Not lets save this data in our hf5 database
swp.save_hf5(filename = KAM.database_location, overwrite = True)
Speficied h5 database exists and will be updated.
Table /RunCoupling6_rescale2_full_23pH/T201406261405 exists. Overwriting...
table metadata Resonator_Eeff not defined and is set to None
table metadata Test_Location not defined and is set to None
table metadata Atten_RTAmp_Input not defined and is set to None
table metadata Thermometer_Configuration not defined and is set to None
table metadata Cable_Calibration not defined and is set to None
table metadata Fridge_Run_Start_Date not defined and is set to None
table metadata Minimum_Q not defined and is set to None
table metadata Atten_Added_At_NA not defined and is set to None
table metadata Atten_NA_Input not defined and is set to None
table metadata Sensor not defined and is set to None
table metadata Num_Heater_Voltages not defined and is set to None
table metadata Ground_Plane not defined and is set to None
table metadata Atten_At_4K not defined and is set to None
table metadata IFBW not defined and is set to None
table metadata Ground_Plane_Thickness not defined and is set to None
table metadata Wait_Time not defined and is set to None
table metadata Meaurement_Duration not defined and is set to None
table metadata Digitizer not defined and is set to None
table metadata Press not defined and is set to None
table metadata LNA not defined and is set to None
table metadata Notes not defined and is set to None
table metadata Box not defined and is set to None
table metadata NA_Average_Factor not defined and is set to None
table metadata Num_Points_Per_Scan not defined and is set to None
table metadata Fridge_Base_Temp not defined and is set to None
table metadata Num_Powers not defined and is set to None
table metadata RTAmp_In_Use not defined and is set to None
table metadata Feedline_Impedance not defined and is set to None
table metadata Temperature_Calibration not defined and is set to None
table metadata Electrical_Delay not defined and is set to None
table metadata Atten_NA_Output not defined and is set to None
table metadata System_Calibration not defined and is set to None
table metadata Num_Ranges not defined and is set to None
table metadata Resonator_Thickness not defined and is set to None
table metadata Resonator_Width not defined and is set to None
table metadata Resonator_Impedance not defined and is set to None
table metadata RTAmp not defined and is set to None
In [90]:
# lets see whats in our database and use it to reload first data set
swp.construct_hf5_toc(filename = KAM.database_location)
swp.load_hf5('/RunS5_truegapdistance/T201406270542', filename = KAM.database_location)
[ ('Coupling6_', '06/26/2014 14:05:29', 0, 0, 0, 0, '', '', '/RunCoupling6_rescale2_full_23pH/T201406261405')
('S5_truegap', '06/27/2014 05:42:39', 0, 0, 0, 0, '', '', '/RunS5_truegapdistance/T201406270542')]
In [91]:
# confirm that we have relaoded it by checking the metadata and plotting the data and
print swp.metadata.Run
swp.pick_loop(0) #forces currentdata to be the data we just loaded
swp.plot_loop();
S5_truegapdistance
In [ ]:
Content source: mdaal/KAM
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