How To Keep Some Text Relative To The Line Into The Plot When The Plot Changes

I would like to put some text below a horizontal line in a plot that changes with changing two parameters. I use a function to plot and python widgets to set two controls that chan

Solution 1:

I think you would want to position the text in a blended coordinate system where the x coordinate is the axes coordinate system and the y coordinate is the data coordinate system. Such blended coordinate systems is conveniently already available through the ax.get_yaxis_transform(). E.g. to place the text 2% away from the y axis, at a y coordinate of -18,

ax.text(0.02, -18, "T Threshold", transform=ax.get_yaxis_transform() )

In general you may want to create the plot only once, and then using the sliders, only update the relevant parts of it. In total this would look like:

from ipywidgets import widgets
from IPython.display import display
import matplotlib.pyplot as plt
import numpy as np
%matplotlib notebook
import ipywidgets


fig, ax = plt.subplots()
line, = ax.semilogx([],[], label='Multipath')
hline = ax.axhline(y = 0, linewidth=1.2, color='black',ls='--')
text = ax.text(0, 0, "T Threshold",
                verticalalignment='top', horizontalalignment='left',
                transform=ax.get_yaxis_transform(),
                color='brown', fontsize=10)
ax.set_xlabel('Separation Distance, r (m)')
ax.set_ylabel('Received Power, $P_t$ (dBm)')
ax.grid(True,which="both",ls=":")
ax.legend()

defupdate_plot(h1, h2):
    D = np.arange(0.5, 12.0, 0.0100)
    r = np.sqrt((h1-h2)**2 + D**2)
    freq = 865.7#freq = 915 MHz
    lmb = 300/freq 
    H = D**2/(D**2+2*h1*h2)
    theta = 4*np.pi*h1*h2/(lmb*D)
    q_e = H**2*(np.sin(theta))**2 + (1 - H*np.cos(theta))**2
    q_e_rcn1 = 1
    P_x_G = 4# 4 Watt EIRP
    sigma = 1.94
    N_1 = np.random.normal(0,sigma,D.shape)
    rnd = 10**(-N_1/10)
    F = 10
    y = 10*np.log10( 1000*(P_x_G*1.622*((lmb)**2) *0.5*1) / (((4*np.pi*r)**2) *1.2*1*F)*q_e*rnd*q_e_rcn1 )
    line.set_data(r,y)

    hline.set_ydata(-18)
    text.set_position((0.02, -18.5))
    ax.relim()
    ax.autoscale_view()
    fig.canvas.draw_idle()

r_height = widgets.FloatSlider(min=0.5, max=4, value=0.9, description= 'R_Height:')
t_height = widgets.FloatSlider(min=0.15, max=1.5, value=0.5, description= 'T_Height:')
widgets.interactive(update_plot, h1=r_height, h2=t_height)

Note that the sliders will now appear below the plot, as they are created afterwards - this might a bearable downside given the advantages of a much better responsivity of the slider changes due to not recreating the complete plot at each slider movement.