Error Correction Comparision Between a Reciprocal and Non-Reciprocal Device

The purpose of this notebook is to compare the results of applying functions in pyMez.Code.Analysis.SParameter with the application of corrections directly through StatistiCAL

Overview:

  1. Start StatistiCAL
  2. Select Help->Load Sample Problem with DUT
  3. Press Calibrate
  4. Select Results->Save Corrected DUT->Save as DUT.txt
  5. Select Results->Save 12 Term Error Correction->Save as ErrorCoefficents.txt
  6. Select Results->Save Solution Vector -> Solution_Plus.txt (Saves isolation and switch terms too, Has both recip and non recip)
  7. Select Results->Save 4-port error box-> ErrorBox.s4p
  8. Open and Plot corrected DUT using pyMez (this is an average I think DY202, DY2022, DY202 measured with Rnd noise,etc)
  9. Open DY202 with pyMez, Open SolutionVector, Open ErrorCoefficents
  10. Correct DY202 and store as s2p
  11. Plot DUT, and Corrected DY202 using both methods
In [1]:
import re,os
from pyMez import *

Importing Code.Utils.Names
Importing Code.DataHandlers.NISTModels
The module smithplot was not found,please put it on the python path
Importing Code.DataHandlers.GeneralModels
Importing Code.DataHandlers.TouchstoneModels
The module smithplot was not found,please put it on the python path
Importing Code.DataHandlers.XMLModels
Importing Code.DataHandlers.RadiCALModels
Importing Code.DataHandlers.ZipModels
Importing Code.DataHandlers.Translations
Importing Code.DataHandlers.StatistiCALModels
Importing Code.DataHandlers.MUFModels
Importing Code.Analysis.SParameter
Importing Code.Analysis.Uncertainty
Importing Code.InstrumentControl.Instruments
Importing Code.InstrumentControl.Experiments
Importing Code.FrontEnds.AdvancedInterfaceFrame
C:\ProgramData\Anaconda2\lib\site-packages\matplotlib\__init__.py:1401: UserWarning:  This call to matplotlib.use() has no effect
because the backend has already been chosen;
matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
or matplotlib.backends is imported for the first time.

  warnings.warn(_use_error_msg)

Importing Code.FrontEnds.ShellPanel
reading user preferences
Importing Code.FrontEnds.IEPanel
Importing Code.FrontEnds.MatplotlibWxPanel
In [2]:
JUPYTER_DIRECTORY=os.getcwd()
TEST_DIRECTORY=os.path.join(JUPYTER_DIRECTORY,"Error_Correction_Comparison_Files")
In [3]:
# File that happens after Results->Save Corrected DUT sparameters
corrected_DUT=S2PV1(os.path.join(TEST_DIRECTORY,"DUT.txt"))
# File that results if you open CalibrateDUT.EXE and use the 12 term correction and then save the result usigng DY202.txt
StatistiCAL_corrected_DUT=S2PV1(os.path.join(TEST_DIRECTORY,"TwelveTermCorrectedDUTStatistiCAL.txt"))
# The files it averaged to get corrected_DUT before averaging
uncorrected_DUTs=map(lambda x:S2PV1(os.path.join(TEST_DIRECTORY,x)),["DY202.txt","DY2022.txt",
                                        "DY202_measured with rnd.txt",
                                        "DY202_measured with rnd1.txt"])
In [15]:
%matplotlib notebook
corrected_DUT.show()
plt.suptitle("DUT File Saved By StatistiCAL ",y=1)
plt.tight_layout()
In [14]:
StatistiCAL_corrected_DUT.show()
plt.suptitle("DUT File Saved By Appling Twelve term using Twelve Term Error Correction ",y=1)
plt.tight_layout()
In [6]:
# open the correction files
twelve_term=TwelveTermErrorModel(os.path.join(TEST_DIRECTORY,"CalCoefficients.txt"))
solution_vector=StatistiCALSolutionModel(os.path.join(TEST_DIRECTORY,"Solution_Plus.txt"))
solution_vector_nonreciprocal=StatistiCALSolutionModel(os.path.join(TEST_DIRECTORY,"Solution_Plus.txt"),reciprocal=False)
switch_terms=S2PV1(os.path.join(TEST_DIRECTORY,"SwitchTerms.txt"))
four_port=SNP(os.path.join(TEST_DIRECTORY,"ErrorBox.s4p"))
In [7]:
# using the correct_switch_terms method of the S2PV1 class
switch_corrected_DUTS=map(lambda x: x.correct_switch_terms(switch_terms.sparameter_complex),uncorrected_DUTs)
In [9]:
# Calculating all the corrections
pyMez_corrected_complex=map(lambda x:correct_sparameters_twelve_term(x.sparameter_complex,
                                                                      twelve_term.complex_data),uncorrected_DUTs)
pyMez_corrected_complex_nonreciprocal=map(lambda x:correct_sparameters_twelve_term(x.sparameter_complex,
                                                                      twelve_term.complex_data,reciprocal=False),
                                uncorrected_DUTs)
pyMez_eight_term_complex=map(lambda x:correct_sparameters_eight_term(x.sparameter_complex,
                                                                      solution_vector.eight_term_correction),uncorrected_DUTs)
pyMez_eight_term_complex_nonreciprocal=map(lambda x:correct_sparameters_eight_term(x.sparameter_complex,
                                                                      solution_vector.eight_term_correction,
                                                                                       reciprocal=False),uncorrected_DUTs)
pyMez_sixteen_term_complex=map(lambda x:correct_sparameters_sixteen_term(x.sparameter_complex,
                                                                   four_port.sparameter_complex),uncorrected_DUTs)
switch_corrected_DUTS=[]
for dut in uncorrected_DUTs:
    dut.correct_switch_terms(switch_terms.sparameter_complex)
    switch_corrected_DUTS.append(dut)
pyMez_eight_term_complex_sw=map(lambda x:correct_sparameters_eight_term(x.corrected_data,
                                                                      solution_vector.eight_term_correction),switch_corrected_DUTS)
In [10]:
pyMez_corrected_DUTS=map(lambda x:S2PV1(None,sparameter_complex=x),pyMez_corrected_complex)
pyMez_corrected_nonreciprocal_DUTS=map(lambda x:S2PV1(None,sparameter_complex=x),
                                           pyMez_corrected_complex_nonreciprocal)
pyMez_eight_term_corrected_DUTS=map(lambda x:S2PV1(None,sparameter_complex=x),pyMez_eight_term_complex)
pyMez_eight_term__nonreciprocal_corrected_DUTS=map(lambda x:S2PV1(None,sparameter_complex=x),
                                                       pyMez_eight_term_complex_nonreciprocal)
pyMez_eight_term_corrected_DUTS_sw=map(lambda x:S2PV1(None,sparameter_complex=x),pyMez_eight_term_complex_sw)
pyMez_sixteen_term_corrected_DUTS=map(lambda x:S2PV1(None,sparameter_complex=x),pyMez_sixteen_term_complex)
In [18]:
# comparison of using both software packages to apply corrections in MA format
compare_s2p_plots([StatistiCAL_corrected_DUT,pyMez_eight_term_corrected_DUTS[0],
                   pyMez_eight_term__nonreciprocal_corrected_DUTS[0]],labels=["SC 12 term","8 term recip","8 term non"]);
In [17]:
# Same plot as above but in RI format
compare_s2p_plots([StatistiCAL_corrected_DUT,pyMez_eight_term_corrected_DUTS[0],
                   pyMez_eight_term__nonreciprocal_corrected_DUTS[0]],format="RI",
                  labels=["SC 12 term","8 term recip","8 term non"]);
In [16]:
# Comparison of using the 8 term and 12 term model for reciprocal and non-reciprocal corrections, 
# with statistiCAL applied correction (saved independently)
compare_s2p_plots([StatistiCAL_corrected_DUT,pyMez_eight_term_corrected_DUTS[0],
                   pyMez_eight_term__nonreciprocal_corrected_DUTS[0],
                   pyMez_corrected_DUTS[0],pyMez_eight_term__nonreciprocal_corrected_DUTS[0]],format="MA",
                  labels=["SC 12 term","8 term recip","8 term non","12 term recip","12 term non"]);