vvmtools.plot.DataPlotter.draw_xt

DataPlotter.draw_xt(data, levels, extend, x_axis_dim='x', cmap_name='bwr', title_left='', title_right='', xlim=None, ylim=None, figname='')

This function creates a x-t plot for a 2D data over spatial (x or y) and temporal (t) dimensions.

Parameters:

• data (numpy.ndarray) – 2D array (t,x) of data values to be plotted, with dimensions corresponding to the time and x_axis_dim axes in DOMAIN.

• levels (list or numpy.ndarray) – Discrete boundaries for color intervals, used for normalizing the color mapping of data values.

• extend (str) – Specifies color bar extension behavior at the boundaries; can be one of ‘both’, ‘min’, or ‘max’.

• x_axis_dim (str, optional) – The spatial dimension to plot on the x-axis, ‘x’ or ‘y’ (default is ‘x’).

• cmap_name (str, optional) – The name of the colormap to use (default is ‘bwr’ for blue-white-red) same as matplotlib.

• title_left (str, optional) – Title text to display on the left side of the plot.

• title_right (str, optional) – Title text to display on the right side of the plot. The ‘EXPNAME’ will add in second line.

• xlim (tuple, optional) – Tuple specifying the minimum and maximum limits for the x-axis. If None, the limits are derived from DOMAIN.

• ylim (tuple, optional) – Tuple specifying the minimum and maximum limits for the y-axis (time). If None, the limits are derived from DOMAIN.

• figname (str, optional) – Filename to save the plot. If not provided, the plot is not saved.

Returns:

The generated figure (fig), main axis (ax), and color bar axis (cax) objects.

Return type:

tuple(matplotlib.figure.Figure, matplotlib.axes.Axes, matplotlib.axes.Axes)

Examples

Initialize the DataPlotter Classes

import numpy as np
from vvmtools.plot import DataPlotter
import matplotlib.pyplot as plt

# prepare expname and data coordinate
expname  = 'pbl_control'
nx = 128; x = np.arange(nx)*0.2
ny = 128; y = np.arange(ny)*0.2
nz = 50;  z = np.arange(nz)*0.04
nt = 721; t = np.arange(nt)*np.timedelta64(2,'m')+np.datetime64('2024-01-01 05:00:00')

# create dataPlotter class
figpath           = './fig/'
data_domain       = {'x':x, 'y':y, 'z':z, 't':t}
data_domain_units = {'x':'km', 'y':'km', 'z':'km', 't':'LocalTime'}
dplot = DataPlotter(expname, figpath, data_domain, data_domain_units)

Create the 2d data.

np.random.seed(0)
data_xt2d  = np.random.normal(0, 0.1, size=(nt,nx))

draw x-t diagram.

fig, ax, cax = dplot.draw_xt(data = data_xt2d,
                                levels = np.arange(-1,1.001,0.1),
                                extend = 'both',
                                title_left  = 'draw_xt hov example',
                                title_right = f'right_land_type',
                                figname     = 'test_hov.png',
                               )
plt.show()

draw x-t diagram with optional configuration.

fig, ax, cax = dplot.draw_xt(data = data_xt2d,
                                levels = np.arange(-1,1.001,0.1),
                                extend = 'both',
                                x_axis_dim  = 'y',
                                cmap_name   = 'Spectral',
                                xlim        = (6.4, 19.2),
                                ylim        = (np.datetime64('2024-01-01 09:00:00'),
                                               np.datetime64('2024-01-01 17:00:00')
                                              ),
                                title_left  = 'draw_xt (optional)',
                                title_right = f'right_land_type',
                                figname     = ''
                               )
plt.show()