plot_ts is visually wrong

[aluthfian]

What command was run:
I am running plot_ts, and it produced an inaccurate timescale display (look at the boundaries vs. age)

fig=plt.figure(figsize=(9,12), dpi=60) # make the figure
ax=fig.add_subplot(121) # make the first of three subplots
pmagplotlib.plot_ts(ax,0.1,1.0,timescale='gts12')

What happened:
Changing agemin and agemax parameters in plot_ts produces wrong magnetostratigraphic cycle boundaries.

What I expected to happen:
A correct magnetostratigraphic boundary regardless of agemin and agemax parameters in plot_ts.

Data file(s):
Not applicable

PmagPy version:
pmagpy-4.2.109, plus jupyter lab 3.5.3 and matplotlib 3.6.2

Possible workaround

1. Use sharey in the plot_ts
2. Use boolean to filter values within agemin and agemax limits.
3. Separate the text creation and line creation into a series of ifs (probably inefficient?)
4. Use a slightly modified timescale-chron format: [[chron_name_0, up_bound_0], [chron_name_1, up_bound_1], ..., [chron_name_N, up_bound_N], [chron_name_N, lower_bound_N]]. The gts22 timescale already adopted this.

Example timescale-chron formatting in pmag.py file, section get_ts
This is taken from ICS 2022 Magnetostratigraphic chart, ignoring the small excursions.

TS = [0, 0.773, 0.990, 1.071, 1.187, 1.208, 1.780, 1.925, 2.116, 2.137, 2.595, 2.900] #timescale
Labels = [['Brunhes', 0.000], ['Matuyama', 0.773], ['Jaramillo', 0.990], ['Matuyama', 1.071], ['Cobb Mtn.', 1.187], ['Matuyama', 1.208], ['Olduvai', 1.780], ['Matuyama', 1.925], ['Reunion', 2.116], ['Matuyama', 2.137], ['Gauss', 2.595], ['Gauss', 2.900]] #chrons

Proposed plot_ts for Line 3669 in pmagplotlib.py
Edited Line

def plot_ts(ax, agemin, agemax, step=1.0, timescale='gts12', ylabel="Age (Ma)"):
    """
    Make a time scale plot between specified ages.

    Parameters:
    ------------
    ax : figure object
    agemin : Minimum age for timescale in Ma
    agemax : Maximum age for timescale in Ma
    step : Y tick label spacing in Ma
    timescale : Time Scale [ default is Gradstein et al., (2012), gts12 ], other options are ck95, gts04, ics22, or gts12
    ylabel : if set, plot as ylabel
    """
    ax.set_title(timescale.upper())
    column_bnd = 0.8
    ax.axis([-.25, 1.5, agemax, agemin])
    ax.axes.get_xaxis().set_visible(False)
    # get dates and chron names for timescale
    TS, Chrons = pmag.get_ts(timescale)
    X, Y, Y2 = [0, column_bnd], [], []
    cnt = 0
    if agemin < TS[1]:  # in the Brunhes
        Y = [agemin, agemin]  # minimum age
        Y1 = [TS[1], TS[1]]  # age of the B/M boundary
        ax.fill_between(X, Y, Y1, facecolor='black')  # color in Brunhes, black
    for d in TS[1:]:
        pol = cnt % 2
        cnt += 1
        if d <= agemax and d >= agemin:
            ind = TS.index(d)
            Y = [TS[ind], TS[ind]]
            Y1 = [TS[ind+1], TS[ind+1]]
            if pol:
                # fill in every other time
                ax.fill_between(X, Y, Y1, facecolor='black')
    ax.plot([0, column_bnd, column_bnd, 0, 0], [agemin, agemin, agemax, agemax, agemin], 'k-')
    max_y_tick = agemin + np.floor((agemax-agemin)/step)*step
    total_step = np.rint(((max_y_tick-agemin)/step)+1).astype(int)
    plt.yticks(np.linspace(agemin, max_y_tick, total_step))
    ax.set_ylim(agemin, agemax)
    if ylabel != "":
        ax.set_ylabel(ylabel)
    ax2 = ax.twinx()
    ax2.sharey(ax)
    ax2.axis('off')
    """
    NEW STUFF
    """
    within_range = [(age[1]>=agemin)&(age[1]<=agemax) for age in Chrons]
    ticker_num = 0
    for k in range(len(Chrons)-1):
        c = Chrons[k]
        cnext = Chrons[k+1]
        d_plot = cnext[1]-(cnext[1]-c[1])
        if (d_plot >= agemin) and (d_plot < agemax):
            # make the Chron boundary tick
            ax2.plot([column_bnd, 1.5], [c[1], c[1]], 'k-')
        if ((within_range[k]==False)&(within_range[k+1]==True))&(ticker_num == 0):
            d_txt = agemin + 0.5*np.abs(cnext[1]-agemin)
            ax2.text(column_bnd+0.05, d_txt, c[0], verticalalignment='center')
            ticker_num += 1
        elif ((within_range[k]==True)&(within_range[k+1]==False))&(ticker_num == 1):
            d_txt = agemax - 0.5*np.abs(agemax-c[1])
            ax2.text(column_bnd+0.05, d_txt, c[0], verticalalignment='center')
            ticker_num += 1
        elif (within_range[k]==True)&(within_range[k+1]==True):
            d_txt = cnext[1]-(cnext[1]-c[1])/2.5
            ax2.text(column_bnd+0.05, d_txt, c[0], verticalalignment='center')
    ax2.axis([-.25, 1.5, agemax, agemin])