plotmpl

plotmpl

This code consists of functions for lidar data analysis and visualization. It includes functionalities for reading lidar data, calculating derived products, and plotting 2D timeseries.

Functions

plot_mpl_2d_timeseries(mpl_datetime, mpl_range_edges, mpl_2d_data, fig=None, ax=None, range_max=20, vmin=0, vmax=1, colorbar_bool=True, tick_number=None)

Plots a 2D timeseries of lidar data.

Parameters:

• mpl_datetime: Array of np.datetime64 objects representing the timestamps.
• mpl_range_edges: Array of lidar range values. Height of the bottom and top of each bin instead of the average height of the bin.
• mpl_2d_data: 2D array of lidar data.
• fig (optional): Existing figure object for plotting.
• ax (optional): Existing axes object for plotting.
• range_max (optional): Maximum range in km to plot on the y-axis.
• vmin (optional): Minimum value for the colormap normalization.
• vmax (optional): Maximum value for the colormap normalization.
• colorbar_bool (optional): Boolean value to enable/disable the colorbar.
• tick_number (optional): Number of ticks and labels to use on the x-axis.

Returns:

• None (The function directly modifies the provided fig and ax objects for plotting.)

_get_fig_and_ax(fig, ax)

Utility function to handle the creation of a figure and axes for plotting.

Parameters:

• fig (optional): Existing figure object.
• ax (optional): Existing axes object.

Returns:

• fig: Updated figure object.
• ax: Updated axes object.

_format_datetime(datetime_arr)

Helper function to format an array of datetime objects.

Parameters:

• datetime_arr: Array of datetime objects representing timestamps.

Returns:

• formatted_arr: Array of formatted strings representing the formatted dates and times.

Example Usage

Here's an example usage of the plot_mpl_2d_timeseries function:

from pympl import PyMPL
import plotmpl
import matplotlib.pyplot as plt

mpl_file_path = '/path/to/mpl data folder'
ap_file_path  = '/path/to/afterpulse file.bin'
ov_file_path  = '/path/to/overlap file.bin'
dt_file_path  = '/path/to/deadtime file.bin'

mpl_object = PyMPL(mpl_file_path, ap_file_path, ov_file_path, dt_file_path)
fig1, ax1 = plt.subplots(nrows=1, ncols=1, figsize=(12,4))
plotmpl.plot_mpl_2d_timeseries(mpl_object.datetime, mpl_object.range_edges, mpl_object.nrb_copol, fig=fig1, ax=ax1, range_max = 3)