Electromagnetic Analysis Module

Electromagnetic Analysis Module for MMPP

Advanced electromagnetic analysis tools for FMR modes including: - Poynting vector analysis - Radiation patterns - Energy flow visualization - Q-factor analysis - Mode coupling analysis

class mmpp.fft.electromagnetic_analysis.ElectromagneticAnalysisConfig(mu_0=1.25663706127e-06, epsilon_0=8.8541878188e-12, c=299792458.0, gamma=221100.0, far_field_distance=0.001, radiation_theta_points=181, radiation_phi_points=361, figsize=(15, 10), dpi=150, poynting_colormap='cmc.hawaii', radiation_colormap='cmc.tokyo', energy_integration_method='simpson', q_factor_method='energy_decay')[source]

Bases: object

Configuration for electromagnetic analysis.

Parameters:

mu_0 (float)
epsilon_0 (float)
c (float)
gamma (float)
far_field_distance (float)
radiation_theta_points (int)
radiation_phi_points (int)
figsize (tuple[float, float])
dpi (int)
poynting_colormap (str)
radiation_colormap (str)
energy_integration_method (str)
q_factor_method (str)

mu_0: float = 1.25663706127e-06

epsilon_0: float = 8.8541878188e-12

c: float = 299792458.0

gamma: float = 221100.0

far_field_distance: float = 0.001

radiation_theta_points: int = 181

radiation_phi_points: int = 361

figsize: tuple[float, float] = (15, 10)

dpi: int = 150

poynting_colormap: str = 'cmc.hawaii'

radiation_colormap: str = 'cmc.tokyo'

energy_integration_method: str = 'simpson'

q_factor_method: str = 'energy_decay'

__init__(mu_0=1.25663706127e-06, epsilon_0=8.8541878188e-12, c=299792458.0, gamma=221100.0, far_field_distance=0.001, radiation_theta_points=181, radiation_phi_points=361, figsize=(15, 10), dpi=150, poynting_colormap='cmc.hawaii', radiation_colormap='cmc.tokyo', energy_integration_method='simpson', q_factor_method='energy_decay')

Parameters:

mu_0 (float)
epsilon_0 (float)
c (float)
gamma (float)
far_field_distance (float)
radiation_theta_points (int)
radiation_phi_points (int)
figsize (tuple[float, float])
dpi (int)
poynting_colormap (str)
radiation_colormap (str)
energy_integration_method (str)
q_factor_method (str)

class mmpp.fft.electromagnetic_analysis.PoyntingVectorAnalysis(config=None)[source]

Bases: object

Analysis of electromagnetic energy flow using Poynting vector.

Parameters:: config (Optional[ElectromagneticAnalysisConfig])

__init__(config=None)[source]

Initialize Poynting vector analysis.

Parameters:: config (Optional[ElectromagneticAnalysisConfig])

compute_poynting_vector(E_field, H_field)[source]

Compute Poynting vector S = (1/μ₀) * E × H

Return type:: ndarray

Parameters:

E_fieldnp.ndarray: Electric field array with shape (ny, nx, 3)
H_fieldnp.ndarray: Magnetic field array with shape (ny, nx, 3)

Returns:

: poynting_vector : np.ndarray

Poynting vector with shape (ny, nx, 3)

param E_field:
type E_field:: ndarray
param H_field:
type H_field:: ndarray

compute_energy_density(E_field, H_field, epsilon_r=1.0, mu_r=1.0)[source]

Compute electromagnetic energy density.

Return type:: dict[str, ndarray]

Parameters:

E_fieldnp.ndarray: Electric field array
H_fieldnp.ndarray: Magnetic field array
epsilon_rfloat: Relative permittivity
mu_rfloat: Relative permeability

Returns:

: energy_densities : dict

Dictionary with electric, magnetic, and total energy densities

param E_field:
type E_field:: ndarray
param H_field:
type H_field:: ndarray
param epsilon_r:
type epsilon_r:: float
param mu_r:
type mu_r:: float

visualize_energy_flow(poynting_vector, energy_density, extent=None, title='Electromagnetic Energy Flow')[source]

Visualize energy flow using Poynting vector and energy density.

Return type:: Figure

Parameters:

poynting_vectornp.ndarray: Poynting vector array (ny, nx, 3)
energy_densitynp.ndarray: Energy density array (ny, nx)
extenttuple, optional: Spatial extent [x_min, x_max, y_min, y_max]
titlestr: Plot title

Returns:

: fig : plt.Figure

Matplotlib figure

param poynting_vector:
type poynting_vector:: ndarray
param energy_density:
type energy_density:: ndarray
param extent:
type extent:: Optional[tuple[float, float, float, float]]
param title:
type title:: str

class mmpp.fft.electromagnetic_analysis.RadiationPatternAnalysis(config=None)[source]

Bases: object

Analysis of far-field radiation patterns.

Parameters:: config (Optional[ElectromagneticAnalysisConfig])

__init__(config=None)[source]

Initialize radiation pattern analysis.

Parameters:: config (Optional[ElectromagneticAnalysisConfig])

compute_far_field(current_density, spatial_extent, frequency)[source]

Compute far-field radiation pattern from current density.

Return type:: dict[str, ndarray]

Parameters:

current_densitynp.ndarray: Current density array (ny, nx, 3)
spatial_extenttuple: Spatial extent [x_min, x_max, y_min, y_max] in meters
frequencyfloat: Frequency in Hz

Returns:

: far_field : dict

Dictionary with theta, phi arrays and E_theta, E_phi components

param current_density:
type current_density:: ndarray
param spatial_extent:
type spatial_extent:: tuple[float, float, float, float]
param frequency:
type frequency:: float

visualize_radiation_pattern(far_field_data, title='Radiation Pattern')[source]

Visualize 3D radiation pattern.

Return type:: Figure

Parameters:

far_field_datadict: Far-field data from compute_far_field
titlestr: Plot title

Returns:

: fig : plt.Figure

Matplotlib figure

param far_field_data:
type far_field_data:: dict[str, ndarray]
param title:
type title:: str

class mmpp.fft.electromagnetic_analysis.QFactorAnalysis(config=None)[source]

Bases: object

Analysis of quality factor and mode losses.

Parameters:: config (Optional[ElectromagneticAnalysisConfig])

__init__(config=None)[source]

Initialize Q-factor analysis.

Parameters:: config (Optional[ElectromagneticAnalysisConfig])

compute_q_factor_spectral(frequencies, spectrum, peak_freq)[source]

Compute Q-factor from spectral width method.

Return type:: float

Parameters:

frequenciesnp.ndarray: Frequency array
spectrumnp.ndarray: Power spectrum
peak_freqfloat: Peak frequency

Returns:

: q_factor : float

Quality factor

param frequencies:
type frequencies:: ndarray
param spectrum:
type spectrum:: ndarray
param peak_freq:
type peak_freq:: float

compute_mode_lifetime(q_factor, frequency)[source]

Compute mode lifetime from Q-factor.

Return type:: float

Parameters:

q_factorfloat: Quality factor
frequencyfloat: Mode frequency in Hz

Returns:

: lifetime : float

Mode lifetime in seconds

param q_factor:
type q_factor:: float
param frequency:
type frequency:: float

mmpp.fft.electromagnetic_analysis.analyze_electromagnetic_properties(mode_data, config=None)[source]

Comprehensive electromagnetic analysis of FMR mode data.

Return type:: dict[str, Any]

Parameters:

mode_dataFMRModeData: Mode data to analyze
configElectromagneticAnalysisConfig, optional: Analysis configuration

Returns:

: analysis_results : dict

Dictionary containing all analysis results

param config:
type config:: Optional[ElectromagneticAnalysisConfig]

mmpp.fft.electromagnetic_analysis.create_comprehensive_em_report(mode_data, analysis_results, config=None)[source]

Create comprehensive electromagnetic analysis report.

Return type:: Figure

Parameters:

mode_dataFMRModeData: Original mode data
analysis_resultsdict: Results from analyze_electromagnetic_properties
configElectromagneticAnalysisConfig, optional: Configuration

Returns:

: fig : plt.Figure

Comprehensive report figure

param analysis_results:
type analysis_results:: dict[str, Any]
param config:
type config:: Optional[ElectromagneticAnalysisConfig]

Configuration

ElectromagneticAnalysisConfig

class mmpp.fft.electromagnetic_analysis.ElectromagneticAnalysisConfig(mu_0=1.25663706127e-06, epsilon_0=8.8541878188e-12, c=299792458.0, gamma=221100.0, far_field_distance=0.001, radiation_theta_points=181, radiation_phi_points=361, figsize=(15, 10), dpi=150, poynting_colormap='cmc.hawaii', radiation_colormap='cmc.tokyo', energy_integration_method='simpson', q_factor_method='energy_decay')[source]

Bases: object

Configuration for electromagnetic analysis.

Parameters:

mu_0 (float)
epsilon_0 (float)
c (float)
gamma (float)
far_field_distance (float)
radiation_theta_points (int)
radiation_phi_points (int)
figsize (tuple[float, float])
dpi (int)
poynting_colormap (str)
radiation_colormap (str)
energy_integration_method (str)
q_factor_method (str)

mu_0: float = 1.25663706127e-06

epsilon_0: float = 8.8541878188e-12

c: float = 299792458.0

gamma: float = 221100.0

far_field_distance: float = 0.001

radiation_theta_points: int = 181

radiation_phi_points: int = 361

figsize: tuple[float, float] = (15, 10)

dpi: int = 150

poynting_colormap: str = 'cmc.hawaii'

radiation_colormap: str = 'cmc.tokyo'

energy_integration_method: str = 'simpson'

q_factor_method: str = 'energy_decay'

__init__(mu_0=1.25663706127e-06, epsilon_0=8.8541878188e-12, c=299792458.0, gamma=221100.0, far_field_distance=0.001, radiation_theta_points=181, radiation_phi_points=361, figsize=(15, 10), dpi=150, poynting_colormap='cmc.hawaii', radiation_colormap='cmc.tokyo', energy_integration_method='simpson', q_factor_method='energy_decay')

Parameters:

mu_0 (float)
epsilon_0 (float)
c (float)
gamma (float)
far_field_distance (float)
radiation_theta_points (int)
radiation_phi_points (int)
figsize (tuple[float, float])
dpi (int)
poynting_colormap (str)
radiation_colormap (str)
energy_integration_method (str)
q_factor_method (str)

Analysis Classes

PoyntingVectorAnalysis

class mmpp.fft.electromagnetic_analysis.PoyntingVectorAnalysis(config=None)[source]

Bases: object

Analysis of electromagnetic energy flow using Poynting vector.

Parameters:: config (Optional[ElectromagneticAnalysisConfig])

__init__(config=None)[source]

Initialize Poynting vector analysis.

Parameters:: config (Optional[ElectromagneticAnalysisConfig])

compute_poynting_vector(E_field, H_field)[source]

Compute Poynting vector S = (1/μ₀) * E × H

Return type:: ndarray

Parameters:

E_fieldnp.ndarray: Electric field array with shape (ny, nx, 3)
H_fieldnp.ndarray: Magnetic field array with shape (ny, nx, 3)

Returns:

: poynting_vector : np.ndarray

Poynting vector with shape (ny, nx, 3)

param E_field:
type E_field:: ndarray
param H_field:
type H_field:: ndarray

compute_energy_density(E_field, H_field, epsilon_r=1.0, mu_r=1.0)[source]

Compute electromagnetic energy density.

Return type:: dict[str, ndarray]

Parameters:

E_fieldnp.ndarray: Electric field array
H_fieldnp.ndarray: Magnetic field array
epsilon_rfloat: Relative permittivity
mu_rfloat: Relative permeability

Returns:

: energy_densities : dict

Dictionary with electric, magnetic, and total energy densities

param E_field:
type E_field:: ndarray
param H_field:
type H_field:: ndarray
param epsilon_r:
type epsilon_r:: float
param mu_r:
type mu_r:: float

visualize_energy_flow(poynting_vector, energy_density, extent=None, title='Electromagnetic Energy Flow')[source]

Visualize energy flow using Poynting vector and energy density.

Return type:: Figure

Parameters:

poynting_vectornp.ndarray: Poynting vector array (ny, nx, 3)
energy_densitynp.ndarray: Energy density array (ny, nx)
extenttuple, optional: Spatial extent [x_min, x_max, y_min, y_max]
titlestr: Plot title

Returns:

: fig : plt.Figure

Matplotlib figure

param poynting_vector:
type poynting_vector:: ndarray
param energy_density:
type energy_density:: ndarray
param extent:
type extent:: Optional[tuple[float, float, float, float]]
param title:
type title:: str

RadiationPatternAnalysis

class mmpp.fft.electromagnetic_analysis.RadiationPatternAnalysis(config=None)[source]

Bases: object

Analysis of far-field radiation patterns.

Parameters:: config (Optional[ElectromagneticAnalysisConfig])

__init__(config=None)[source]

Initialize radiation pattern analysis.

Parameters:: config (Optional[ElectromagneticAnalysisConfig])

compute_far_field(current_density, spatial_extent, frequency)[source]

Compute far-field radiation pattern from current density.

Return type:: dict[str, ndarray]

Parameters:

current_densitynp.ndarray: Current density array (ny, nx, 3)
spatial_extenttuple: Spatial extent [x_min, x_max, y_min, y_max] in meters
frequencyfloat: Frequency in Hz

Returns:

: far_field : dict

Dictionary with theta, phi arrays and E_theta, E_phi components

param current_density:
type current_density:: ndarray
param spatial_extent:
type spatial_extent:: tuple[float, float, float, float]
param frequency:
type frequency:: float

visualize_radiation_pattern(far_field_data, title='Radiation Pattern')[source]

Visualize 3D radiation pattern.

Return type:: Figure

Parameters:

far_field_datadict: Far-field data from compute_far_field
titlestr: Plot title

Returns:

: fig : plt.Figure

Matplotlib figure

param far_field_data:
type far_field_data:: dict[str, ndarray]
param title:
type title:: str

QFactorAnalysis

class mmpp.fft.electromagnetic_analysis.QFactorAnalysis(config=None)[source]

Bases: object

Analysis of quality factor and mode losses.

Parameters:: config (Optional[ElectromagneticAnalysisConfig])

__init__(config=None)[source]

Initialize Q-factor analysis.

Parameters:: config (Optional[ElectromagneticAnalysisConfig])

compute_q_factor_spectral(frequencies, spectrum, peak_freq)[source]

Compute Q-factor from spectral width method.

Return type:: float

Parameters:

frequenciesnp.ndarray: Frequency array
spectrumnp.ndarray: Power spectrum
peak_freqfloat: Peak frequency

Returns:

: q_factor : float

Quality factor

param frequencies:
type frequencies:: ndarray
param spectrum:
type spectrum:: ndarray
param peak_freq:
type peak_freq:: float

compute_mode_lifetime(q_factor, frequency)[source]

Compute mode lifetime from Q-factor.

Return type:: float

Parameters:

q_factorfloat: Quality factor
frequencyfloat: Mode frequency in Hz

Returns:

: lifetime : float

Mode lifetime in seconds

param q_factor:
type q_factor:: float
param frequency:
type frequency:: float

Analysis Functions

mmpp.fft.electromagnetic_analysis.analyze_electromagnetic_properties(mode_data, config=None)[source]

Comprehensive electromagnetic analysis of FMR mode data.

Return type:: dict[str, Any]

Parameters:

mode_dataFMRModeData: Mode data to analyze
configElectromagneticAnalysisConfig, optional: Analysis configuration

Returns:

: analysis_results : dict

Dictionary containing all analysis results

param config:
type config:: Optional[ElectromagneticAnalysisConfig]

mmpp.fft.electromagnetic_analysis.create_comprehensive_em_report(mode_data, analysis_results, config=None)[source]

Create comprehensive electromagnetic analysis report.

Return type:: Figure

Parameters:

mode_dataFMRModeData: Original mode data
analysis_resultsdict: Results from analyze_electromagnetic_properties
configElectromagneticAnalysisConfig, optional: Configuration

Returns:

: fig : plt.Figure

Comprehensive report figure

param analysis_results:
type analysis_results:: dict[str, Any]
param config:
type config:: Optional[ElectromagneticAnalysisConfig]

Usage Examples

Basic Electromagnetic Analysis

import mmpp
from mmpp.fft.electromagnetic_analysis import ElectromagneticAnalyzer

# Load data and get mode
db = mmpp.open('/path/to/data')
result = db.find(solver=3)[0]

# Get mode data at specific frequency
mode_data = result.fft.modes.get_mode(frequency=2.5)

# Create electromagnetic analyzer
analyzer = ElectromagneticAnalyzer()

# Analyze electromagnetic fields
em_results = analyzer.analyze_mode(mode_data)

print(f"Electric energy: {em_results['energy_densities']['electric'].sum():.2e} J/m³")
print(f"Magnetic energy: {em_results['energy_densities']['magnetic'].sum():.2e} J/m³")

Comprehensive Analysis with Visualization

from mmpp.fft.electromagnetic_analysis import comprehensive_electromagnetic_analysis

# Perform full analysis with visualization
analysis_results = comprehensive_electromagnetic_analysis(
    mode_data,
    include_far_field=True,
    include_energy_analysis=True,
    include_visualization=True
)

# Results include:
# - Energy density maps
# - Poynting vector field
# - Far-field radiation patterns
# - Current density analysis

Far-Field Radiation Analysis

# Compute far-field radiation pattern
far_field = analyzer.compute_far_field(
    mode_data.current_density,
    frequency=2.5,
    spatial_extent=mode_data.extent
)

# Visualize radiation pattern
fig = analyzer.visualize_radiation_pattern(
    far_field,
    title="Mode Radiation Pattern at 2.5 GHz"
)