Adding Secondary axis on the top of the figure.

[pspathare]

I am using tplot() to plot solar wind speed measured by SPC and SPI.
In this plot I want to add another axis which is indicating the spacecraft distance from the sun on the upper side of the figure.

For example, I am posting a random plot that I obtained using matplotlib showing what I mean by having an axis on the upper side of the plot.

I think there could be some option in pytplot.options() that I can use to get a secondary axis on the top of my figure. Eg. border(Turns on or off the top/right axes that would create a box around the plot.) or plotter(Allows a user to implement their own plotting script in place of the ones herein.)
But I don’t know how to use these options. I am not completely sure if I could use these options or not. I would really appreciate it if you could help me to plot the secondary axis on the top of the figure.

I have attached the remaining code for plotting solar wind speed of SPC and SPI

---

import pyspedas
import pytplot
import numpy as np
import matplotlib.pyplot as plt
from pytplot import tplot
import matplotlib.pyplot as plt
from pytplot import get_data
import datetime

#SPC Solar wind data
spc_vars = pyspedas.psp.spc(trange=['2020-09-24/00:00', '2020-09-30/23:59'], datatype='l3i', level='l3')
Solar_wind_speed_data_spc = get_data('psp_spc_vp_moment_RTN')
split_data = pytplot.tplot_math.split_vec('psp_spc_vp_moment_RTN')
v_r = pytplot.data_quants['psp_spc_vp_moment_RTN_x'].values
v_t= pytplot.data_quants['psp_spc_vp_moment_RTN_y'].values
v_n = pytplot.data_quants['psp_spc_vp_moment_RTN_z'].values
v_mag_spc_val= np.sqrt(v_r2 + v_t2 + v_n**2)
time_sw_spc = Solar_wind_speed_data_spc.times
datetimes_spc =[datetime.datetime.utcfromtimestamp(ts) for ts in time_sw_spc]
pytplot.store_data('v_mag_spc',data = {'x' :datetimes_spc, 'y': v_mag_spc_val})
val = pytplot.data_quants['v_mag_spc'].values
pytplot.tplot_math.avg_res_data('v_mag_spc', 60,'v_mag_spc_avg')

#SPI Solar wind data
spi_vars = pyspedas.psp.spi(trange=['2020-09-24/00:00', '2020-09-30/23:59'], datatype='sf00_l3_mom', level='l3', time_clip=True)
Solar_wind_speed_data = get_data('psp_spi_VEL_RTN_SUN')
time_sw_spi = Solar_wind_speed_data.times
datetimes_spi = [datetime.datetime.utcfromtimestamp(ts) for ts in time_sw_spi]
split_data_spi = pytplot.tplot_math.split_vec('psp_spi_VEL_RTN_SUN')
v_r_spi =pytplot.data_quants['psp_spi_VEL_RTN_SUN_x'].values
v_t_spi=pytplot.data_quants['psp_spi_VEL_RTN_SUN_y'].values
v_n_spi=pytplot.data_quants['psp_spi_VEL_RTN_SUN_z'].values
v_mag_spi_val= np.sqrt(v_r_spi2 + v_t_spi2 + v_n_spi**2)
pytplot.store_data('v_mag_spi',data = {'x' :datetimes_spi, 'y': v_mag_spi_val})
pytplot.tplot_math.avg_res_data('v_mag_spi', 60,'v_mag_spi_avg')

#Spacecraft distance data
distance_data = get_data('psp_spc_sc_pos_HCI')
time_d= distance_data.times
split_data_distance = pytplot.tplot_math.split_vec('psp_spc_sc_pos_HCI')
R_r= pytplot.data_quants['psp_spc_sc_pos_HCI_x'].values
R_t=pytplot.data_quants['psp_spc_sc_pos_HCI_y'].values
R_n=pytplot.data_quants['psp_spc_sc_pos_HCI_z'].values
R_mag = np.sqrt(R_r2 + R_t2 + R_n**2)
pytplot.store_data('R_distance',data={'x':time_d,'y':R_mag})

pytplot.options('v_mag_spc_avg', opt_dict ={'Color':'Green', 'legend_names':['v_mag_spc'],'border':True})
pytplot.options('v_mag_spi_avg', opt_dict ={'Color':'Red', 'legend_names':['v_mag_spi'],'border':True})

pytplot.options('V_A_vs_V_SW_spi','ytitle','Speed')
pytplot.store_data('V_A_vs_V_SW_SPC_SPI',data=['v_mag_spc_avg','v_mag_spi_avg'])
pytplot.options('V_A_vs_V_SW_SPC_SPI','ytitle','speed')

tplot('V_A_vs_V_SW_SPC_SPI')

[jameswilburlewis]

Thanks for bringing this up! One option that might work for you right now is to use return_plot_objects=True in your call to tplot(), and use the returned fig and axes objects to manipulate the plots directly with matplotlib calls.

But it would also be good to be able to enable features like this, or set custom plotters, via pytplot.options(). We're going to make another pass over the pytplot.options() and pytplot.tplot_options() in the near future to implement some of the missing features, so we'll try to include some additional axis placement options.