Basic plotting with Bokeh¶

In this example, we are going to create a simple plot of a Microphone Array Geometry loaded from an XML file using the Acoular package and Bokeh for visualization.

First, we import the necessary modules (acoular and pathlib).

import acoular as ac
from pathlib import Path

Next, we set up the microphone geometry by using Acoular’s MicGeom class. For demonstration purposes, we will use a 64 channel Vogel’s spiral microphone array geometry, stored in an XML file that is part of the Acoular package. To locate the XML directory in the Acoular package, we use the Path object to dynamically construct the path relative to the Acoular package location.

mics = ac.MicGeom(file=Path(ac.__file__).parent / 'xml' / 'tub_vogel64.xml')

The MicGeom class stores the microphone positions in an attribute called pos_total, which is a NumPy array containing the 3D coordinates of each microphone in the array.

Now, let’s create a Bokeh figure to visualize the microphone positions.

from bokeh.plotting import figure #noqa: E402

figure = figure(title='Microphone Geometry', tools='hover,zoom_in,zoom_out,reset,lasso_select', sizing_mode='stretch_width', match_aspect=True)

The convenient way to provide data to a Bokeh Plot is to use a ColumnDataSource, which is a data structure that allows us to easily update the data in the plot. Since we create a 2D plot, we only need the x and y coordinates of the microphones.

from bokeh.models import ColumnDataSource #noqa: E402

source = ColumnDataSource(data={'x': mics.pos_total[0], 'y': mics.pos_total[1]})

In addition, we need to decide which glyph to use. Glyphs are the basic building blocks of Bokeh plots, and they define how the data is visualized. In this case, we will use the circle() method to create a Circle to represent the microphones.

figure.circle(x='x', y='y', radius=0.02, line_color='black', fill_color='#1F77B4', fill_alpha=0.4, source=source)

Finally, we can show the plot using Bokeh’s show() function. This will open a new browser window and display the plot with the microphone positions.

from bokeh.io import show  # noqa: E402

show(figure)  # Show the plot in a new browser window (standalone HTML)

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