axoden.volume_projections

Usually, it is enough to just import the functions from axoden directly. axoden.volume_projections contains the full functionality used for axoden.

axoden.volume_projections.binarize_image(image, threshold)

Binarize the image using the threshold provided.

axoden.volume_projections.collect_image_mask(img_path: any, is_masked: bool) → Tuple[ndarray, ndarray]

Open the image and cut it to the area that contains tissue.

Parameters:

• img_path – A filename (string), os.PathLike object or a file object.

• is_masked – True if the image was masked and contains regions that are set to a value of 0.

Returns:

A tuple containing the cut image and the mask.

Return type:

Tuple[np.ndarray, np.ndarray]

axoden.volume_projections.collect_info_from_filename(filename: str) → Tuple[str, str, str]

Collect the animal, brain area and group from the filename.

Parameters:

filename (str) – Name of the file.

Returns:

A tuple containing the animal, brain area and group.

Return type:

Tuple[str, str, str]

axoden.volume_projections.compute_area(img, pixel_size)

Compute the area of the image and the area of the white and black pixels.

axoden.volume_projections.control_plot_steps(ax, img, title, axs_info)

Create the subplots of the control plots for each input image.

Parameters:

• ax (list of matplotlib Axes) – List of Axes objects to plot the images.

• img (numpy array) – Input image data.

• title (str) – Title for the plot.

• axs_info (dict or list) – Information about the axes.

Returns:

List of Axes objects with the plotted images.

Return type:

ax (list of matplotlib Axes)

axoden.volume_projections.count_pixels(img)

Count the number of white and black pixels in the image.

axoden.volume_projections.generate_background_mask(img, is_masked)

Generate a background mask for the image provided.

axoden.volume_projections.get_tif_files(folder_path)

Get all the tif files in the folder provided.

axoden.volume_projections.intensity_along_axis(img, ax=None)

Compute the intensity along the x or y axis of the image.

axoden.volume_projections.load_table(file_path: str)

Load a table from a csv file.

Parameters:

file_path (str) – Path to the csv file.

Returns:

A DataFrame with the data from the csv file.

Return type:

pd.DataFrame

axoden.volume_projections.make_figures_pdf_editable()

Make the figures editable in Adobe Illustrator.

axoden.volume_projections.open_tif_image(img_path)

Open a tif image.

Parameters:

img_path – A filename (string), os.PathLike object or a file object.

Returns:

A PIL Image object.

Return type:

Image

axoden.volume_projections.plot_signal_intensity_along_axis(project_name: str, df: DataFrame, pixel_size: float)

Plot the signal intensity along the x and y axis of the DataFrame.

Parameters:

• project_name (str) – Name of the project.

• df (pd.DataFrame) – DataFrame with the data to plot.

• pixel_size (float) – Pixel size in micrometers.

Returns:

A figure with the signal intensity along the x and y axis.

Return type:

plt.Figure

axoden.volume_projections.process_folder(folder_path: str, pixel_size: float, is_masked: bool, output_folder: str = None, save: bool = True) → Tuple[DataFrame, DataFrame]

Process all images in a folder.

Optionally save the data to csv files and the control plots to pdf files.

Parameters:

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

• pixel_size (float) – Pixel size in micrometers.

• is_masked (bool) – True if the image was masked and contains regions that are set to a value of 0.

• output_folder (str) – Path to the folder where the data will be saved. If not set, the input folder will be used.

• save (bool) – True if the control plots should be saved. Default is True.

Returns:

A tuple containing the data for the images and the data for the axis.

Return type:

Tuple[pd.DataFrame, pd.DataFrame]

axoden.volume_projections.process_image(file_name: any, is_masked: bool, pixel_size: float, animal: str = 'animal1', brain_area: str = 'brain_area1', group: str = 'group1') → Tuple[Figure, dict, dict]

Process a single image and generate a control plot.

Parameters:

• file_name (any) – A filename (string), os.PathLike object or a file object.

• is_masked (bool) – True if the image was masked and contains regions that are set to a value of 0.

• pixel_size (float) – Pixel size in micrometers.

• animal (str) – Name of the animal. Default is “animal1”.

• brain_area (str) – Name of the brain area. Default is “brain_area1”.

• group (str) – Name of the group. Default is “group1”.

Returns:

A tuple containing the figure, the data for the image and the data with the axis projections.

Return type:

Tuple[plt.Figure, dict, dict]

axoden.volume_projections.remove_spines_plot(ax, loc=['all'])

Remove the spines of the plot.

axoden.volume_projections.remove_ticks_plot(ax, loc='all')

Remove the ticks of the plot.

axoden.volume_projections.save_table(df: DataFrame, folderpath: str, filename: str)

Save a table to a csv file.

Parameters:

• df (pd.DataFrame) – DataFrame to save.

• folderpath (str) – Path to the folder where the file will be saved.

• filename (str) – Name of the file.

axoden.volume_projections.write_signal_intensity_along_axis_plot(output_folder: str, df: DataFrame, pixel_size: float, project_name: str = None)

Write the signal intensity along the axis to a pdf file in the folder provided.

Parameters:

• folderpath (str) – Path to the folder where the data will be saved.

• df (pd.DataFrame) – Dataframe with the axis data to plot.

• pixel_size (float) – Pixel size in micrometers.

• project_name (str) – Name of the project. If not set, it will be the name of the output folder.

axoden.volume_projections.write_summary_data_plot(folder_path: str, df_input: DataFrame, project_name: str = None)

Write the summary data plot to a pdf file in the folder provided.

Parameters:

• folder_path (str) – Path to the folder where the data will be saved.

• df_input (pd.DataFrame) – Dataframe with the data to plot.

• project_name (str) – Name of the project. If not set, default to the folder name.