vvmtools.plot.DataPlotter.draw_zt

DataPlotter.draw_zt(data, levels, extend, pblh_dicts={}, cmap_name='bwr', title_left='', title_right='', xlim=None, ylim=None, figname='')

This function creates a z-t plot for a specified variable over time and height dimensions. and draw a series pbl height 1-D datasets.

Parameters:

• data (np.ndarray) – 2D array (nz,nt) of data values to plot, with dimensions corresponding to time (‘t’) and height (‘z’) axes.

• levels (list or np.ndarray) – Sequence of boundaries to use for color normalization.

• extend (str) – Controls the color scaling at the boundaries. Accepts ‘both’, ‘neither’, ‘min’, or ‘max’ to adjust color mapping.

• pblh_dicts (dict, optional) – Dictionary of planetary boundary layer height data with labels as keys and height values as arrays (nt). Optional.

• cmap_name (str, optional) – Name of the colormap for the plot. Defaults to ‘bwr’.

• title_left (str, optional) – Title text displayed at the left of the plot.

• title_right (str, optional) – Title text displayed at the right of the plot.

• xlim (tuple, optional) – Limits for the x-axis (time), specified as a tuple (start, end). Defaults to None, which uses the full domain range.

• ylim (tuple, optional) – Limits for the y-axis (height), specified as a tuple (start, end). Defaults to None, which uses the full domain range.

• figname (str, optional) – File name for saving the generated plot. If left empty, the plot will not be saved.

Returns:

The figure, main axis, and colorbar axis of the created plot.

Return type:

tuple (matplotlib.figure.Figure, matplotlib.axes._axes.Axes, matplotlib.colorbar.Colorbar)

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_zt2d  = np.random.normal(0, 0.1, size=(nz,nt))
line1_1d = np.sin( np.linspace(0, 2*np.pi, nt) ) +1
line2_1d = np.cos( np.linspace(0, 2*np.pi, nt) ) +1

draw z-t diagram.

fig, ax, cax = dplot.draw_zt(data = data_zt2d,
                             levels = np.arange(-1,1.001,0.1),
                             extend = 'both',
                             pblh_dicts={'line1': line1_1d,
                                         'line2': line2_1d,
                                        },
                             title_left  = 'draw_zt pblh example',
                             title_right = f'right_land_type',
                             figname     = 'test_pbl.png',
                      )

plt.show()

draw z-t diagram with optional configuration.

fig, ax, cax = dplot.draw_zt(data = data_zt2d,
                             levels = np.arange(-1,1.001,0.1),
                             extend = 'both',
                             pblh_dicts={'line1': line1_1d,
                                         'line2': line2_1d,
                                        },
                             cmap_name   = 'Spectral',
                             xlim        = (np.datetime64('2024-01-01 09:00:00'),
                                            np.datetime64('2024-01-01 17:00:00')
                                           ),
                             ylim        = (0, 1),
                             title_left  = 'draw_zt (optional)',
                             title_right = f'right_land_type',
                             figname     = '',
                      )

plt.show()