# spacr.core

## Module Contents

spacr.core.preprocess_generate_masks(settings)

spacr.core.generate_image_umap(settings={}, return_fig=False)

  Generate UMAP or tSNE embedding and visualize the data with clustering.

  Parameters: settings (dict): Dictionary containing the following keys: src (str): Source directory containing the data. row_limit (int): Limit the number of rows to process. tables (list): List of table names to read from the database. visualize (str): Visualization type. image_nr (int): Number of images to display. dot_size (int): Size of dots in the scatter plot. n_neighbors (int): Number of neighbors for UMAP. figuresize (int): Size of the figure. black_background (bool): Whether to use a black background. remove_image_canvas (bool): Whether to remove the image canvas. plot_outlines (bool): Whether to plot outlines. plot_points (bool): Whether to plot points. smooth_lines (bool): Whether to smooth lines. verbose (bool): Whether to print verbose output. embedding_by_controls (bool): Whether to use embedding from controls. col_to_compare (str): Column to compare for control-based embedding. pos (str): Positive control value. neg (str): Negative control value. clustering (str): Clustering method (‘DBSCAN’ or ‘KMeans’). exclude (list): List of columns to exclude from the analysis. plot_images (bool): Whether to plot images. reduction_method (str): Dimensionality reduction method (‘UMAP’ or ‘tSNE’). save_figure (bool): Whether to save the figure as a PDF.

  Returns: pd.DataFrame: DataFrame with the original data and an additional column ‘cluster’ containing the cluster identity.

spacr.core.reducer_hyperparameter_search(settings={}, reduction_params=None, dbscan_params=None, kmeans_params=None, save=False, show=True, return_fig=False)

  Perform a hyperparameter search for UMAP or tSNE on the given data.

  Parameters: settings (dict): Dictionary containing the following keys: src (str): Source directory containing the data. row_limit (int): Limit the number of rows to process. tables (list): List of table names to read from the database. filter_by (str): Column to filter the data. sample_size (int): Number of samples to use for the hyperparameter search. remove_highly_correlated (bool): Whether to remove highly correlated columns. log_data (bool): Whether to log transform the data. verbose (bool): Whether to print verbose output. reduction_method (str): Dimensionality reduction method (‘UMAP’ or ‘tSNE’). reduction_params (list): List of dictionaries containing hyperparameters to test for the reduction method. dbscan_params (list): List of dictionaries containing DBSCAN hyperparameters to test. kmeans_params (list): List of dictionaries containing KMeans hyperparameters to test. pointsize (int): Size of the points in the scatter plot. save (bool): Whether to save the resulting plot as a file.

  Returns: None

spacr.core.generate_screen_graphs(settings)

  Generate screen graphs for different measurements in a given source directory.

  Parameters:

    - src (str or list) – Path(s) to the source directory or directories.

    - tables (list) – List of tables to include in the analysis (default: [‘cell’, ‘nucleus’, ‘pathogen’, ‘cytoplasm’]).

    - graph_type (str) – Type of graph to generate (default: ‘bar’).

    - summary_func (str or function) – Function to summarize data (default: ‘mean’).

    - y_axis_start (float) – Starting value for the y-axis (default: 0).

    - error_bar_type (str) – Type of error bar to use (‘std’ or ‘sem’) (default: ‘std’).

    - theme (str) – Theme for the graph (default: ‘pastel’).

    - representation (str) – Representation for grouping (default: ‘well’).

  Returns:

    List of generated figures. results (list): List of corresponding result DataFrames.

  Return type:

    figs (list)