goatpy.pseudo_image

Functions

`Add_Pseudo_Image`(sdata, image_ident[, tables, ...])
`add_uns`(adata, ident, library_id[, cmap, data_type, ...])
`generate_random_colors`(categories)	Generate a random color for each category.
`map_categories_to_colors`(categories, color_map)	Map category values to colors using the color map.
`generate_continuous_bins`(values)	Generate bins for continuous values.
`create_image_from_data`(coords_array, category_values, ...)	Create an image from x, y coordinates and associated categorical values.

Module Contents

goatpy.pseudo_image.Add_Pseudo_Image(sdata, image_ident, tables='maldi_adata', library_id='Spatial', convert_to_int=True, cmap=None, is_continous=False, img_upscaling=1)[source]

goatpy.pseudo_image.add_uns(adata, ident, library_id, cmap=None, data_type='categorical', img_upscaling=1)[source]

goatpy.pseudo_image.generate_random_colors(categories)[source]

Generate a random color for each category.

Parameters: - categories: List or array of unique category values.

Returns: - A dictionary mapping categories to colors.

goatpy.pseudo_image.map_categories_to_colors(categories, color_map)[source]

Map category values to colors using the color map.

Parameters: - categories: Array of category values. - color_map: Dictionary mapping continents to colors.

Returns: - An array of RGB colors.

goatpy.pseudo_image.generate_continuous_bins(values)[source]

Generate bins for continuous values.

Parameters: - values: Array of continuous values.

Returns: - Binned values as integers.

goatpy.pseudo_image.create_image_from_data(coords_array, category_values, color_map)[source]

Create an image from x, y coordinates and associated categorical values.

Parameters: - coords_array: 2D NumPy array with shape (n, 2) where each row is [x, y]. - category_values: Array of categorical values corresponding to each coordinate. - color_map: Dictionary mapping categories to RGB colors.

Returns: - PIL.Image object.