pyEPR.core_distributed_analysis module#

Main distributed analysis module to use pyEPR.

Contains code to connect to Ansys and to analyze HFSS files using the EPR method.

This module handles the microwave part of the analysis and connection to

Further contains code to be able to do autogenerated reports,

Copyright Zlatko Minev, Zaki Leghtas, and the pyEPR team 2015, 2016, 2017, 2018, 2019, 2020

class pyEPR.core_distributed_analysis.DistributedAnalysis(*args, **kwargs)[source]#

Bases: object

DISTRIBUTED ANALYSIS of layout and microwave results.

Main computation class & interface with HFSS.

This class defines a DistributedAnalysis object which calculates and saves Hamiltonian parameters from an HFSS simulation.

Further, it allows one to calculate dissipation, etc.

calc_Q_external(variation, freq_GHz, U_E=None)[source]#

Calculate the coupling Q of mode m with each port p Expected that you have specified the mode before calling this

Parameters:

  • variation (str) – A string identifier of the variation,

  • '0' (such as)

  • '1'

  • ...

calc_avg_current_J_surf_mag(variation: str, junc_rect: str, junc_line)[source]#

Peak current I_max for mode J in junction J.

The average is over the surface of the junction (spatial average).

Parameters:

  • variation (str) – A string identifier of the variation, such as ‘0’, ‘1’, …

  • junc_rect (str) – name of rectangle to integrate over

  • junc_line (str) – name of junction line to integrate over

Returns:

Value of peak current

calc_current(fields, line: str)[source]#

Function to calculate Current based on line. Not in use.

Parameters:

line (str) – integration line between plates - name

calc_current_using_line_voltage(variation: str, junc_line_name: str, junc_L_Henries: float, Cj_Farads: float = None)[source]#

Peak current I_max for prespecified mode calculating line voltage across junction.

Make sure that you have set the correct variation in HFSS before running this

Parameters:

  • variation – variation number

  • junc_line_name – name of the HFSS line spanning the junction

  • junc_L_Henries – junction inductance in henries

  • Cj_Farads – junction cap in Farads

  • TODO – Smooth?

calc_energy_electric(variation: str = None, obj: str = 'AllObjects', volume: str = 'Deprecated', smooth: bool = False, obj_dims: int = 3)[source]#

Calculates two times the peak electric energy, or 4 times the RMS, \(4*\mathcal{E}_{\mathrm{elec}}\) (since we do not divide by 2 and use the peak phasors).

\[\mathcal{E}_{\mathrm{elec}}=\frac{1}{4}\mathrm{Re}\int_{V}\mathrm{d}v\vec{E}_{\text{max}}^{*}\overleftrightarrow{\epsilon}\vec{E}_{\text{max}}\]
Parameters:

  • variation (str) – A string identifier of the variation, such as ‘0’, ‘1’, …

  • obj (string | 'AllObjects') – Name of the object to integrate over

  • smooth (bool | False) – Smooth the electric field or not when performing calculation

  • obj_dims (int | 3) – 1 - line, 2 - surface, 3 - volume. Default volume

Example

Example use to calculate the energy participation ratio (EPR) of a substrate

1ℰ_total  = epr_hfss.calc_energy_electric(obj='AllObjects')
2ℰ_substr = epr_hfss.calc_energy_electric(obj='Box1')
3print(f'Energy in substrate = {100*ℰ_substr/ℰ_total:.1f}%')
calc_energy_magnetic(variation: str = None, obj: str = 'AllObjects', volume: str = 'Deprecated', smooth: bool = False, obj_dims: int = 3)[source]#

See calc_energy_electric.

Parameters:

  • variation (str) – A string identifier of the variation, such as ‘0’, ‘1’, …

  • volume (string | 'AllObjects') – Name of the volume to integrate over

  • smooth (bool | False) – Smooth the electric field or not when performing calculation

  • obj_dims (int | 3) – 1 - line, 2 - surface, 3 - volume. Default volume

calc_line_current(variation, junc_line_name)[source]#
calc_p_electric_volume(name_dielectric3D, relative_to='AllObjects', variation=None, E_total=None)[source]#

Calculate the dielectric energy-participation ratio of a 3D object (one that has volume) relative to the dielectric energy of a list of objects.

This is as a function relative to another object or all objects.

When all objects are specified, this does not include any energy that might be stored in any lumped elements or lumped capacitors.

Returns:

ℰ_object/ℰ_total, (ℰ_object, _total)

calc_p_junction(variation, U_H, U_E, Ljs, Cjs)[source]#

For a single specific mode. Expected that you have specified the mode before calling this, set_mode().

Expected to precalc U_H and U_E for mode, will return pandas pd.Series object:

  • junc_rect = [‘junc_rect1’, ‘junc_rect2’] name of junc rectangles to integrate H over

  • junc_len = [0.0001] specify in SI units; i.e., meters

  • LJs = [8e-09, 8e-09] SI units

  • calc_sign = [‘junc_line1’, ‘junc_line2’]

WARNING: Cjs is experimental.

This function assumes there are no lumped capacitors in model.

Parameters:

  • variation (str) – A string identifier of the variation,

  • '0' (such as)

  • '1'

  • ...

Note

U_E and U_H are the total peak energy (NOT twice as in U_ and U_H other places).

Warning

Potential errors: If you dont have a line or rect by the right name you will prob get an error of the type: com_error: (-2147352567, ‘Exception occurred.’, (0, None, None, None, 0, -2147024365), None)

calc_p_junction_single(mode, variation, U_E=None, U_H=None)[source]#

This function is used in the case of a single junction only. For multiple junctions, see calc_p_junction().

Assumes no lumped capacitive elements.

property design#

Ansys design class handle

do_EPR_analysis(variations: list = None, modes: list = None, append_analysis: bool = True)[source]#

Run the full EPR field extraction and save results to disk.

Iterates over all requested variations and eigenmodes, computes EPR participation ratios (p_mj), zero-point fluctuations (φ_zpf), junction currents and voltages, and saves the results to an HDF5/pickle file readable by QuantumAnalysis.

Parameters:

  • variations (list of str, optional) – Variation labels to analyse (e.g. ['0', '1']). Defaults to all solved variations.

  • modes (list of int, optional) – Eigenmode indices to include (e.g. [0, 2, 3] to skip mode 1). Defaults to all modes. Use consistent indices when later calling analyze_all_variations().

  • append_analysis (bool, optional) – If True (default), skip variations already present in the results file. Set to False to recompute and overwrite everything.

Returns:

Results are written to self.data_filename. Load them with:

epra = epr.QuantumAnalysis(eprd.data_filename)

Return type:

None

Note

Assumes low dissipation (high-Q). Lumped capacitor support (Cj_variable) is experimental — see the EPR paper for theoretical background.

Example

eprd = epr.DistributedAnalysis(pinfo)
eprd.do_EPR_analysis()
# or for a subset:
eprd.do_EPR_analysis(variations=['0', '2'], modes=[0, 1])
get_Qdielectric(dielectric, mode, variation, U_E=None)[source]#
get_Qseam(seam, mode, variation, U_H=None)[source]#

Calculate the contribution to Q of a seam, by integrating the current in the seam with finite conductance: set in the config file ref: http://arxiv.org/pdf/1509.01119.pdf

get_Qseam_sweep(seam, mode, variation, variable, values, unit, U_H=None, pltresult=True)[source]#

Q due to seam loss.

values = [‘5mm’,’6mm’,’7mm’] ref: http://arxiv.org/pdf/1509.01119.pdf

get_Qsurface(mode, variation, name, U_E=None, material_properties=None)[source]#

Calculate the contribution to Q of a dielectric layer of dirt on a given surface. Set the dirt thickness and loss tangent in the config file ref: http://arxiv.org/pdf/1509.01854.pdf

get_Qsurface_all(mode, variation, U_E=None)[source]#

Calculate the contribution to Q of a dielectric layer of dirt on all surfaces. Set the dirt thickness and loss tangent in the config file ref: http://arxiv.org/pdf/1509.01854.pdf

get_ansys_frequencies_all(vs='variation')[source]#

Return all ansys frequencies and quality factors vs a variation

Returns a multi-index pandas DataFrame

get_ansys_variables()[source]#

Get ansys variables for all variations

Returns:

Return a dataframe of variables as index and columns as the variations

get_ansys_variations()[source]#

Will update ansys information and result the list of variations.

Returns:

("Cj='2fF' Lj='12nH'",
"Cj='2fF' Lj='12.5nH'",
"Cj='2fF' Lj='13nH'",
"Cj='2fF' Lj='13.5nH'",
"Cj='2fF' Lj='14nH'")

Return type:

For example

get_convergence(variation='0')[source]#
Parameters:

  • variation (str) – A string identifier of the variation,

  • '0' (such as)

  • '1'

  • ...

Returns:

A pandas DataFrame object

1    Solved Elements     Max Delta Freq. % Pass Number
21           128955              NaN
32               167607          11.745000
43               192746          3.208600
54               199244          1.524000

get_convergence_vs_pass(variation='0')[source]#

Makes a plot in HFSS that return a pandas dataframe

Parameters:

  • variation (str) – A string identifier of the variation,

  • '0' (such as)

  • '1'

  • ...

Returns:

Returns a convergence vs pass number of the eignemode freqs.

1    re(Mode(1)) [g]     re(Mode(2)) [g] re(Mode(3)) [g]
2Pass []
31       4.643101        4.944204        5.586289
42       5.114490        5.505828        6.242423
53       5.278594        5.604426        6.296777

get_freqs_bare(variation: str)[source]#

Warning

Outdated. Do not use. To be deprecated

Parameters:

variation (str) – A string identifier of the variation, such as ‘0’, ‘1’, …

Returns:

[type] – [description]

get_freqs_bare_pd(variation: str, frame=True)[source]#

Return the freq and Qs of the solved modes for a variation. I.e., the Ansys solved frequencies.

Parameters:

  • variation (str) – A string identifier of the variation, such as ‘0’, ‘1’, …

  • dataframe (frame {bool} -- if True returns)

  • series. (else tuple of)

Returns:

If frame = True, then a multi-index Dataframe that looks something like this

                Freq. (GHz)  Quality Factor
variation mode
0         0        5.436892             1020
        1        7.030932             50200
1         0        5.490328             2010
        1        7.032116             104500

If frame = False, then a tuple of two Series, such as (Fs, Qs) – Tuple of pandas.Series objects; the row index is the mode number

get_junc_len_dir(variation: str, junc_line)[source]#

Return the length and direction of a junction defined by a line

Parameters:

  • variation (str) – simulation variation

  • junc_line (str) – polyline object

Returns:

junction length uj (list of 3 floats): x,y,z components of the unit vector tangent to the junction line

Return type:

jl (float)

get_junctions_L_and_C(variation: str)[source]#

Returns a pandas Series with the index being the junction name as specified in the project_info.

The values in the series are numeric and in SI base units, i.e., not nH but Henries, and not fF but Farads.

Parameters:

  • variation (str) – label such as ‘0’ or ‘all’, in which case return

  • variations (pandas table for all)

get_mesh_statistics(variation='0')[source]#
Parameters:

  • variation (str) – A string identifier of the variation,

  • '0' (such as)

  • '1'

  • ...

Returns: A pandas dataframe, such as

1    Name        Num Tets    Min edge    length          Max edge length     RMS edge length Min tet vol     Max tet vol     Mean tet vol    Std Devn (vol)
20   Region      909451          0.000243    0.860488        0.037048            6.006260e-13        0.037352        0.000029        6.268190e-04
31   substrate       1490356     0.000270    0.893770        0.023639            1.160090e-12        0.031253        0.000007        2.309920e-04
get_nominal_variation_index()[source]#
Returns:

A string identifies, such as ‘0’ or ‘1’, that labels the nominal variation index number.

This may not be in the solved list!s

get_previously_analyzed()[source]#

Return previously analyzed data.

Does not yet handle data that was previously saved in a filename.

get_variable_vs_variations(variable: str, convert: bool = True)[source]#

Get ansys variables

Return HFSS variable from self.get_ansys_variables() as a pandas series vs variations.

Parameters:

convert (bool) – Convert to a numeric quantity if possible using the ureg

get_variables(variation=None)[source]#

Get ansys variables.

Parameters:

variation (str) – A string identifier of the variation, such as ‘0’, ‘1’, …

get_variation_string(variation=None)[source]#

Solved variation string identifier.

Parameters:

variation (str) – A string identifier of the variation, such as ‘0’, ‘1’, …

Returns:

Return the list variation string of parameters in ansys used to identify the variation.

"$test='0.25mm' Cj='2fF' Lj='12.5nH'"

get_variations()[source]#

An array of strings corresponding to solved variations corresponding to the selected Setup.

Returns:

Returns a list of strings that give the variation labels for HFSS.

OrderedDict([
    ('0', "Cj='2fF' Lj='12nH'"),
    ('1', "Cj='2fF' Lj='12.5nH'"),
    ('2', "Cj='2fF' Lj='13nH'"),
    ('3', "Cj='2fF' Lj='13.5nH'"),
    ('4', "Cj='2fF' Lj='14nH'")])

has_fields(variation: str = None)[source]#

Determine if fields exist for a particular solution. Just calls self.solutions.has_fields(variation_string)

Parameters:

variation (str) – String of variation label, such as ‘0’ or ‘1’. If None, gets the nominal variation

hfss_report_f_convergence(variation='0', save_csv=True)[source]#

Create a report inside HFSS to plot the converge of freq and style it.

Saves report to csv file.

Returns a convergence vs pass number of the eignemode freqs. Returns a pandas dataframe:

    re(Mode(1)) [g] re(Mode(2)) [g] re(Mode(3)) [g]
Pass []
1   4.643101        4.944204        5.586289
2   5.114490        5.505828        6.242423
3   5.278594        5.604426        6.296777
hfss_report_full_convergence(fig=None, _display=True)[source]#

Plot a full report of teh convergences of an eigenmode analysis for a a given variation. Makes a plot inside hfss too.

Keyword Arguments:

  • (default (_display {bool} -- Force display or not.) – {None})

  • (default – {True})

Returns:

[type] – [description]

load(filepath=None)[source]#

Utility function to load results file

Keyword Arguments:

(default (filepath {[type]} -- [description]) – {None})

property n_variations#

Number of solved variations, corresponding to the selected Setup.

property options#

Project info options

property project#

Ansys project class handle

quick_plot_frequencies(swp_variable='variations', ax=None)[source]#

Quick plot of frequencies from HFSS

static results_variations_on_inside(results: dict)[source]#

Switches the order on result of variations. Reverse dict.

save(project_info: dict = None)[source]#

Save results to self.data_filename

Keyword Arguments:

(default (project_info {dict} -- [description]) – {None})

set_mode(mode_num, phase=0)[source]#

Set source excitations should be used for fields post processing. Counting modes from 0 onward

set_variation(variation: str)[source]#

Set the ansys design to a solved variation. This will change all local variables!

Warning: not tested with global variables.

property setup#

Ansys setup class handle. Could be None.

setup_data()[source]#

Set up folder paths for saving data to.

Sets the save filename with the current time.

Saves to Path(config.root_dir) / self.project.name / self.design.name

update_ansys_info() → None[source]#

Refresh cached information from the live Ansys session.

Call this after changing the number of eigenmodes, adding or removing a parametric sweep variation, or modifying any design variable — any operation that changes the solved-variation list or eigenmode count without restarting Python.

Updates#

self.n_modes, self._list_variations, self.variations, self._nominal_variation, and self._hfss_variables.

variations#

List of variation indices, which are strings of ints, such as [‘0’, ‘1’]

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