kaira.utils.PlottingUtils

Inheritance diagram for PlottingUtils

class kaira.utils.PlottingUtils

Bases: object

A comprehensive plotting utility class with static methods for visualization.

This class provides a centralized collection of plotting functions for various communication system analysis tasks including LDPC codes, modulation schemes, error rate analysis, signal processing, and more.

All methods are static to allow easy access without instantiation:

Example

fig = PlottingUtils.plot_performance_vs_snr(snr_range, ber_values, labels,

ylabel=”Bit Error Rate”, use_log_scale=True)

Methods

__init__
close_all_figures	Close all matplotlib figures to free memory.
plot_belief_propagation_iteration	Plot belief propagation iteration state.
plot_bit_error_visualization	Visualize bit errors in transmission.
plot_bler_vs_snr_analysis	Plot BLER vs SNR analysis.
plot_block_error_visualization	Visualize block errors.
plot_blockwise_operation	Plot blockwise operation visualization.
plot_capacity_analysis	Plot channel capacity analysis for different channel types.
plot_channel_effects	Plot the effects of a channel on transmitted signals.
plot_code_structure_comparison	Plot comparison of different code structures.
plot_coding_gain	Plot coding gain vs SNR.
plot_complexity_comparison	Plot complexity comparison charts.
plot_constellation	Plot constellation diagram with optional received symbols.
plot_error_rate_comparison	Compare different error rate metrics.
plot_hamming_code_visualization	Plot Hamming code structure visualization.
plot_image_comparison	Plot original image alongside results at different conditions (e.g., SNRs).
plot_latency_distribution	Plot latency distribution and statistics.
plot_ldpc_matrix_comparison	Plot comparison of LDPC code matrix structures.
plot_multi_qam_ber_performance	Plot BER performance for multiple QAM orders.
plot_multiple_metrics_comparison	Plot comparison of multiple error rate metrics.
plot_parity_check_visualization	Plot parity check syndrome and error pattern visualization.
plot_performance_vs_snr	Plot performance metrics vs SNR curves.
plot_qam_constellation_with_errors	Plot QAM constellation showing transmitted and received symbols.
plot_signal_analysis	Plot comprehensive signal analysis including time and frequency domain.
plot_spectral_efficiency	Plot spectral efficiency vs SNR for different modulation schemes.
plot_symbol_error_analysis	Analyze symbol errors.
plot_tanner_graph	Create enhanced Tanner graph visualization.
plot_throughput_comparison	Plot throughput comparison across different configurations.
setup_plotting_style	Set up consistent plotting style for all examples.

Attributes

BELIEF_CMAP
MATRIX_CMAP
MODERN_PALETTE

BELIEF_CMAP = <matplotlib.colors.LinearSegmentedColormap object>

MODERN_PALETTE = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']

MATRIX_CMAP = <matplotlib.colors.LinearSegmentedColormap object>

static setup_plotting_style()

Set up consistent plotting style for all examples.

static close_all_figures()

Close all matplotlib figures to free memory.

static plot_ldpc_matrix_comparison(H_matrices: List[Tensor], titles: List[str], main_title: str = 'LDPC Matrix Comparison') → Figure

Plot comparison of LDPC code matrix structures.

Parameters:

• H_matrices (List[torch.Tensor]) – List of parity check matrices to compare

• titles (List[str]) – Titles for each matrix

• main_title (str) – Overall plot title

Returns:

The created figure

Return type:

plt.Figure

static plot_complexity_comparison(code_types: List[str], metrics: Dict[str, List[float]], title: str = 'Complexity Comparison') → Figure

Plot complexity comparison charts.

Parameters:

• code_types (List[str]) – Names of different code types

• metrics (Dict[str, List[float]]) – Dictionary mapping metric names to values for each code type

• title (str) – Plot title

Returns:

The created figure

Return type:

plt.Figure

static plot_tanner_graph(H: Tensor, title: str = 'LDPC Tanner Graph') → Figure

Create enhanced Tanner graph visualization.

Parameters:

• H (torch.Tensor) – Parity check matrix

• title (str) – Plot title

Returns:

The created figure

Return type:

plt.Figure

static plot_constellation(constellation: Tensor, received_symbols: Tensor | None = None, title: str = 'Constellation Diagram') → Figure

Plot constellation diagram with optional received symbols.

Parameters:

• constellation (torch.Tensor) – Ideal constellation points

• received_symbols (Optional[torch.Tensor]) – Optional received symbols to overlay

• title (str) – Plot title

Returns:

The created figure

Return type:

plt.Figure

static plot_throughput_comparison(throughput_data: Dict[str, Any], title: str = 'Throughput Comparison') → Figure

Plot throughput comparison across different configurations.

Parameters:

• throughput_data (Dict[str, Any]) – Dictionary containing throughput data

• title (str) – Plot title

Returns:

The created figure

Return type:

plt.Figure

static plot_latency_distribution(latency_data: Dict[str, Any], title: str = 'Latency Distribution') → Figure

Plot latency distribution and statistics.

Parameters:

• latency_data (Dict[str, Any]) – Dictionary containing latency statistics

• title (str) – Plot title

Returns:

The created figure

Return type:

plt.Figure

static plot_coding_gain(snr_range: ndarray, coding_gain: ndarray, code_type: str = 'Unknown', title: str = 'Coding Gain') → Figure

Plot coding gain vs SNR.

Parameters:

• snr_range (np.ndarray) – SNR values in dB

• coding_gain (np.ndarray) – Coding gain values in dB

• code_type (str) – Type of error correction code

• title (str) – Plot title

Returns:

The created figure

Return type:

plt.Figure

static plot_spectral_efficiency(snr_range: ndarray, spectral_efficiency: ndarray, modulation_types: List[str], title: str = 'Spectral Efficiency') → Figure

Plot spectral efficiency vs SNR for different modulation schemes.

Parameters:

• snr_range (np.ndarray) – SNR values in dB

• spectral_efficiency (np.ndarray) – Spectral efficiency values (bits/s/Hz)

• modulation_types (List[str]) – Names of modulation schemes

• title (str) – Plot title

Returns:

The created figure

Return type:

plt.Figure

static plot_channel_effects(original_signal: Tensor, received_signal: Tensor, channel_name: str = 'Channel', title: str = 'Channel Effects') → Figure

Plot the effects of a channel on transmitted signals.

Parameters:

• original_signal (torch.Tensor) – Original transmitted signal

• received_signal (torch.Tensor) – Signal after passing through channel

• channel_name (str) – Name of the channel

• title (str) – Plot title

Returns:

The created figure

Return type:

plt.Figure

static plot_signal_analysis(signal: Tensor, signal_name: str = 'Signal', title: str = 'Signal Analysis') → Figure

Plot comprehensive signal analysis including time and frequency domain.

Parameters:

• signal (torch.Tensor) – Input signal to analyze

• signal_name (str) – Name of the signal

• title (str) – Plot title

Returns:

The created figure

Return type:

plt.Figure

static plot_capacity_analysis(snr_range: ndarray, capacity_data: Dict[str, ndarray], title: str = 'Channel Capacity Analysis') → Figure

Plot channel capacity analysis for different channel types.

Parameters:

• snr_range (np.ndarray) – SNR values in dB

• capacity_data (Dict[str, np.ndarray]) – Dictionary mapping channel names to capacity values

• title (str) – Plot title

Returns:

The created figure

Return type:

plt.Figure

static plot_belief_propagation_iteration(H: Tensor, beliefs: Tensor, iteration: int, title: str = 'Belief Propagation Iteration') → Figure

Plot belief propagation iteration state.

Parameters:

• H (torch.Tensor) – Parity check matrix

• beliefs (torch.Tensor) – Current belief values

• iteration (int) – Current iteration number

• title (str) – Plot title

Returns:

The created figure

Return type:

plt.Figure

static plot_blockwise_operation(input_blocks: List[Tensor], output_blocks: List[Tensor], operation_name: str = 'Blockwise Operation') → Figure

Plot blockwise operation visualization.

Parameters:

• input_blocks (List[torch.Tensor]) – Input blocks

• output_blocks (List[torch.Tensor]) – Output blocks after operation

• operation_name (str) – Name of the operation

Returns:

The created figure

Return type:

plt.Figure

static plot_hamming_code_visualization(generator_matrix: Tensor, parity_check_matrix: Tensor, title: str = 'Hamming Code Structure') → Figure

Plot Hamming code structure visualization.

Parameters:

• generator_matrix (torch.Tensor) – Generator matrix

• parity_check_matrix (torch.Tensor) – Parity check matrix

• title (str) – Plot title

Returns:

The created figure

Return type:

plt.Figure

static plot_parity_check_visualization(syndrome: Tensor, error_pattern: Tensor, title: str = 'Parity Check Analysis') → Figure

Plot parity check syndrome and error pattern visualization.

Parameters:

• syndrome (torch.Tensor) – Syndrome vector

• error_pattern (torch.Tensor) – Error pattern

• title (str) – Plot title

Returns:

The created figure

Return type:

plt.Figure

static plot_code_structure_comparison(codes_data: Dict[str, Dict[str, Any]], title: str = 'Code Structure Comparison') → Figure

Plot comparison of different code structures.

Parameters:

• codes_data (Dict[str, Dict[str, Any]]) – Dictionary containing code data for comparison

• title (str) – Plot title

Returns:

The created figure

Return type:

plt.Figure

static plot_bit_error_visualization(original_bits: Tensor, errors: Tensor, received_bits: Tensor, title: str = 'Bit Error Analysis') → Figure

Visualize bit errors in transmission.

Parameters:

• original_bits (torch.Tensor) – Original transmitted bits

• errors (torch.Tensor) – Error positions

• received_bits (torch.Tensor) – Received bits

• title (str) – Plot title

Returns:

The created figure

Return type:

plt.Figure

static plot_error_rate_comparison(metrics: Dict[str, float], title: str = 'Error Rate Comparison') → Figure

Compare different error rate metrics.

Parameters:

• metrics (Dict[str, float]) – Dictionary of metric names and values

• title (str) – Plot title

Returns:

The created figure

Return type:

plt.Figure

static plot_block_error_visualization(blocks_with_errors: Tensor, block_error_rate: float, title: str = 'Block Error Visualization') → Figure

Visualize block errors.

Parameters:

• blocks_with_errors (torch.Tensor) – Indicator of which blocks have errors

• block_error_rate (float) – Overall block error rate

• title (str) – Plot title

Returns:

The created figure

Return type:

plt.Figure

static plot_qam_constellation_with_errors(transmitted: Tensor, received: Tensor, title: str = 'QAM Constellation with Errors') → Figure

Plot QAM constellation showing transmitted and received symbols.

Parameters:

• transmitted (torch.Tensor) – Transmitted constellation points

• received (torch.Tensor) – Received constellation points

• title (str) – Plot title

Returns:

The created figure

Return type:

plt.Figure

static plot_symbol_error_analysis(error_mask: Tensor, ber: float, ser: float, title: str = 'Symbol Error Analysis') → Figure

Analyze symbol errors.

Parameters:

• error_mask (torch.Tensor) – Mask indicating which symbols have errors

• ber (float) – Bit error rate

• ser (float) – Symbol error rate

• title (str) – Plot title

Returns:

The created figure

Return type:

plt.Figure

static plot_multi_qam_ber_performance(snr_range: ndarray, ber_results: Dict[str, ndarray], ser_results: Dict[str, ndarray], qam_orders: Sequence[int]) → Figure

Plot BER performance for multiple QAM orders.

Parameters:

• snr_range (np.ndarray) – SNR values in dB

• ber_results (Dict[str, np.ndarray]) – BER results for each QAM order

• ser_results (Dict[str, np.ndarray]) – SER results for each QAM order

• qam_orders (Sequence[int]) – Sequence of QAM orders

Returns:

The created figure

Return type:

plt.Figure

static plot_bler_vs_snr_analysis(snr_range: ndarray, bler_data: Dict[str, ndarray], block_sizes: List[int]) → Figure

Plot BLER vs SNR analysis.

Parameters:

• snr_range (np.ndarray) – SNR values in dB

• bler_data (Dict[str, np.ndarray]) – BLER data for different block sizes

• block_sizes (List[int]) – List of block sizes

Returns:

The created figure

Return type:

plt.Figure

static plot_multiple_metrics_comparison(snr_range: ndarray, metrics: Dict[str, ndarray], title: str = 'Multiple Metrics Comparison') → Figure

Plot comparison of multiple error rate metrics.

Parameters:

• snr_range (np.ndarray) – SNR values in dB

• metrics (Dict[str, np.ndarray]) – Dictionary of metric names and values

• title (str) – Plot title

Returns:

The created figure

Return type:

plt.Figure

static plot_image_comparison(original: Tensor, results_dict: Dict[Any, Tensor], title: str = 'Image Transmission Results') → Figure

Plot original image alongside results at different conditions (e.g., SNRs).

This function is useful for visualizing image transmission or reconstruction results across different channel conditions or processing parameters.

Parameters:

• original (torch.Tensor) – Original image tensor with shape [C, H, W]

• results_dict (Dict[Any, torch.Tensor]) – Dictionary mapping condition labels (e.g., SNR values) to reconstructed images

• title (str) – Plot title

Returns:

The created figure

Return type:

plt.Figure

static plot_performance_vs_snr(snr_range: ndarray, performance_values: List[ndarray], labels: List[str], title: str = 'Performance vs SNR', ylabel: str = 'Performance', use_log_scale: bool = True, xlabel: str = 'SNR (dB)') → Figure

Plot performance metrics vs SNR curves.

A generic plotting function for any performance metric vs SNR, suitable for BER, PSNR, MSE, accuracy, or any other performance measures.

Parameters:

• snr_range (np.ndarray) – SNR values (typically in dB)

• performance_values (List[np.ndarray]) – Performance metric values for each configuration/method

• labels (List[str]) – Labels for each curve

• title (str) – Plot title

• ylabel (str) – Y-axis label for the performance metric

• use_log_scale (bool) – Whether to use logarithmic scale for y-axis (useful for BER, MSE)

• xlabel (str) – X-axis label

Returns:

The created figure

Return type:

plt.Figure

__init__()