rsoft_cad.femsim package

Submodules

rsoft_cad.femsim.curve_fitting module

rsoft_cad.femsim.curve_fitting.sigmoid_decay(x, a, b, c, d, e)

Function combining linear decay with sigmoid drop-off a + b*x + c/(1 + np.exp(d*(x-e)))

a, b: linear component parameters c, d, e: sigmoid component parameters e: position of the sigmoid midpoint

rsoft_cad.femsim.curve_fitting.double_exp_decay(x, A, B, C, D, E)

Double exponential decay function.

Parameters:

xarray-like

Independent variable (taper length in microns)

Afloat

Asymptotic value (baseline)

Bfloat

Amplitude of first exponential term

Cfloat

Decay constant for first exponential term

Dfloat

Amplitude of second exponential term

Efloat

Decay constant for second exponential term

Returns:

yarray-like

Dependent variable (effective index)

rsoft_cad.femsim.curve_fitting.polynomial(x, a, b, c, d, e, f, g, h, i)

Simple polynomial function for fitting.

Parameters:

xarray-like

Independent variable

a, b, c, …float

Polynomial coefficients

Returns:

yarray-like

a*x^2 + b*x + c

rsoft_cad.femsim.curve_fitting.fit_index_data(index_data_real: Dict[int, List[float]], index_data_imag: Dict[int, List[float]], x_values: List[float], fit_function: Callable | None = None) → Dict[int, Dict[str, Any]]

Fits real and imaginary index data and stores results in a defaultdict.

Parameters:

index_data_realDict[int, List[float]]

Dictionary mapping indices to lists of real component values

index_data_imagDict[int, List[float]]

Dictionary mapping indices to lists of imaginary component values

x_valuesList[float]

X-axis values for fitting (e.g., taper lengths)

fit_functioncallable, optional

Function to use for fitting. Default is a simple polynomial fit.

Returns:

fit_resultsDict[int, Dict[str, Any]]

Dictionary containing fitted parameters and functions for each index

rsoft_cad.femsim.curve_fitting.plot_fit_results(ax: Axes, x_values: List[float], unique_indices: List[int], fit_results: Dict[int, Dict[str, Any]], cmap: ScalarMappable, component_type: str) → None

Plot the fitted curves on the axes.

Parameters:

axplt.Axes

Matplotlib axes to plot on

x_valuesList[float]

X-axis values

unique_indicesList[int]

List of unique mode indices

fit_resultsDict[int, Dict[str, Any]]

Dictionary containing fitted parameters and functions

cmapplt.cm.ScalarMappable

Color map for different indices

component_typestr

Component type (‘real’ or ‘imag’)

rsoft_cad.femsim.data_processing module

rsoft_cad.femsim.data_processing.extract_run_names(file_paths: List[str]) → List[str]

Extract ‘run_XXX’ parts from a list of file paths.

Parameters:

file_paths (List[str]) – List of file paths containing run_XXX.nef files

Returns:

List of extracted run names in the format ‘run_XXX’

Return type:

List[str]

rsoft_cad.femsim.data_processing.get_z_positions_from_runs(dataframe: DataFrame, file_paths: List[str]) → Tuple[List[float], List[str]]

Extract z_pos values from a DataFrame based on run names found in file paths.

Parameters:

• dataframe (pd.DataFrame) – DataFrame with ‘filename’ and ‘z_pos’ columns

• file_paths (List[str]) – List of file paths containing run_XXX.nef files

Returns:

A tuple containing:

• List of z_pos values corresponding to the run names

• List of run names extracted from file paths

Return type:

Tuple[List[float], List[str]]

rsoft_cad.femsim.data_processing.process_nef_files(folder_path: str, include_subfolders: bool = True) → Tuple[Dict[int, List[float]], Dict[int, List[float]], List[str], List[str]]

Process multiple .nef files and extract relevant data.

Parameters:

• folder_path (str) – Path to the folder containing .nef files

• include_subfolders (bool) – If True, search for files in subfolders as well

Returns:

• Dictionary of real index data by mode index

• Dictionary of imaginary index data by mode index

• List of file names

• List of file paths

Return type:

Tuple containing

rsoft_cad.femsim.data_processing.create_dataframe_from_nef_data(index_data: Dict[int, List[float]], x_values: List[float], index_id: int) → DataFrame

Create a DataFrame from NEF data for a specific mode index.

Parameters:

• index_data (Dict[int, List[float]]) – Dictionary of data by mode index

• x_values (List[float]) – X-axis values (e.g., taper lengths)

• index_id (int) – Mode index to extract

Returns:

DataFrame with x and y columns

Return type:

pd.DataFrame

rsoft_cad.femsim.data_processing.create_axis_values(folder_path: str, nef_files: List[str], file_names: List[str], use_filename_as_x: bool) → Tuple[List[float], List[str]]

Create x-axis values and labels for plotting.

Parameters:

• folder_path (str) – Path to the folder containing .nef files

• nef_files (List[str]) – List of .nef file paths

• file_names (List[str]) – List of file names without extensions

• use_filename_as_x (bool) – If True, use filenames as x-axis; otherwise use z positions

Returns:

x-values and x-labels for plotting

Return type:

Tuple[List[float], List[str]]

rsoft_cad.femsim.femsim_param_scan module

rsoft_cad.femsim.femsim_plot module

NEF File Plotter - Entry point

This script serves as the entry point for the NEF file plotter tool, parsing command-line arguments and orchestrating the plotting workflow.

rsoft_cad.femsim.femsim_plot.parse_arguments() → Namespace

Parse command line arguments.

Returns:

Parsed command line arguments

Return type:

argparse.Namespace

rsoft_cad.femsim.femsim_plot.nef_plot() → None

Main function to run the NEF file plotting tool.

rsoft_cad.femsim.outlier_detection module

rsoft_cad.femsim.outlier_detection.remove_outliers_by_zscore(df: DataFrame, x_column: str, y_column: str, window_size: float = 5000, z_threshold: float = 3) → DataFrame

Remove outliers from data using local z-scores within moving windows.

Parameters:

• df (pd.DataFrame) – DataFrame containing the data

• x_column (str) – Name of the column to use as x-axis (e.g., ‘taper_length’)

• y_column (str) – Name of the column to check for outliers (e.g., ‘real_n_eff’)

• window_size (float) – Size of windows to calculate local z-scores

• z_threshold (float) – Z-score threshold to identify outliers

Returns:

DataFrame with outliers removed

Return type:

pd.DataFrame

rsoft_cad.femsim.outlier_detection.apply_outlier_removal(index_data: Dict[int, List[float]], unique_indices: List[int], x_values: List[float], window_size: float, z_threshold: float) → Dict[int, List[float]]

Apply outlier removal to a set of index data.

Parameters:

• index_data (Dict[int, List[float]]) – Dictionary of data by mode index

• unique_indices (List[int]) – List of unique mode indices to process

• x_values (List[float]) – X-axis values

• window_size (float) – Window size for outlier detection

• z_threshold (float) – Z-score threshold for outlier detection

Returns:

Updated index data with outliers removed

Return type:

Dict[int, List[float]]

rsoft_cad.femsim.utils module

rsoft_cad.femsim.utils.filter_indices(unique_indices: List[int], max_indices: int | None, plot_indices: List[int] | None) → List[int]

Filter indices based on provided criteria.

Parameters:

• unique_indices (List[int]) – List of all available unique indices

• max_indices (Optional[int]) – Maximum number of indices to display

• plot_indices (Optional[List[int]]) – Specific indices to plot

Returns:

Filtered list of indices to plot

Return type:

List[int]

rsoft_cad.femsim.visualisation module

rsoft_cad.femsim.visualisation.setup_figure(plot_type: str) → Tuple[Figure, Axes | Tuple[Axes, Axes]]

Set up the figure and axes for plotting.

Parameters:

plot_type (str) – Type of plot (‘real’, ‘imag’, or ‘both’)

Returns:

• Figure object

• Axes object(s)

Return type:

Tuple containing

rsoft_cad.femsim.visualisation.plot_component(ax: Axes, x_values: List[float], x_labels: List[str], unique_indices: List[int], index_data: Dict[int, List[float]], total_files: int, cmap: ScalarMappable, component_type: str, use_filename_as_x: bool) → None

Plot a component (real or imaginary) of the effective index.

Parameters:

• ax (plt.Axes) – Matplotlib axes to plot on

• x_values (List[float]) – X-axis values

• x_labels (List[str]) – X-axis labels

• unique_indices (List[int]) – List of unique mode indices

• index_data (Dict[int, List[float]]) – Dictionary of data by mode index

• total_files (int) – Total number of files (for padding)

• cmap (plt.cm.ScalarMappable) – Color map for different indices

• component_type (str) – Component type (‘real’ or ‘imag’)

• use_filename_as_x (bool) – If True, use filenames as x-axis

rsoft_cad.femsim.visualisation.plot_components(plot_type: str, ax1: Axes, ax2: Axes | None, x_values: List[float], x_labels: List[str], unique_indices: List[int], index_data_real: Dict[int, List[float]], index_data_imag: Dict[int, List[float]], total_files: int, cmap: Any, use_filename_as_x: bool) → None

Plot the specified components (real and/or imaginary) based on plot_type.

Parameters:

• plot_type (str) – Type of plot (‘real’, ‘imag’, or ‘both’)

• ax1 (plt.Axes) – Primary axes object

• ax2 (Optional[plt.Axes]) – Secondary axes object (for ‘both’ type)

• x_values (List[float]) – X-axis values

• x_labels (List[str]) – X-axis labels

• unique_indices (List[int]) – List of unique mode indices

• index_data_real (Dict[int, List[float]]) – Real component data by mode index

• index_data_imag (Dict[int, List[float]]) – Imaginary component data by mode index

• total_files (int) – Total number of files

• cmap (Any) – Color map for different indices

• use_filename_as_x (bool) – If True, use filenames as x-axis

rsoft_cad.femsim.visualisation.add_legend(fig: Figure, ax1: Axes, plot_type: str) → None

Add a legend to the figure.

Parameters:

• fig (plt.Figure) – Figure object

• ax1 (plt.Axes) – Primary axes object

• plot_type (str) – Type of plot (‘real’, ‘imag’, or ‘both’)

rsoft_cad.femsim.visualisation.save_figure(fig: Figure, folder_path: str, output_path: str | None, plot_type: str) → None

Save the figure to a file.

Parameters:

• fig (plt.Figure) – Figure to save

• folder_path (str) – Default folder path for saving

• output_path (Optional[str]) – Custom output path

• plot_type (str) – Type of plot (‘real’, ‘imag’, or ‘both’)

rsoft_cad.femsim.visualisation.plot_combined_nef_files(folder_path: str, include_subfolders: bool = False, save_plot: bool = False, output_path: str | None = None, plot_type: str = 'real', max_indices: int | None = None, use_filename_as_x: bool = True, remove_outliers: bool = False, window_size: float = 5000, z_threshold: float = 3, colormap: str = 'viridis', plot_indices: List[int] | None = None, fit_data: bool = False, fit_function: Callable | None = None) → Figure | None

Plot multiple .nef files with each index as a separate line.

Parameters:

• folder_path (str) – Path to the folder containing .nef files

• include_subfolders (bool) – If True, search for files in subfolders as well

• save_plot (bool) – If True, save the plot as PNG file

• output_path (Optional[str]) – Path to save the plot (if None, generates a default name)

• plot_type (str) – ‘real’, ‘imag’, or ‘both’ to specify which part to plot

• max_indices (Optional[int]) – Maximum number of indices to plot (None for all)

• use_filename_as_x (bool) – If True, use filenames as x-axis; otherwise use file index

• remove_outliers (bool) – If True, apply outlier removal using z-score method

• window_size (float) – Window size for outlier detection (in x-axis units)

• z_threshold (float) – Z-score threshold for outlier detection

• colormap (str) – Colormap for plot (default: viridis)

• plot_indices (Optional[List[int]]) – Specific mode indices to plot

• fit_data (bool) – If True, apply curve fitting to the data points

• fit_function (Optional[Callable]) – Function to use for fitting (default: polynomial)

Returns:

The figure object or None if no files found

Return type:

Optional[plt.Figure]