Allow 2D spectra to be plotted with accompanying projections about penguins HOT 3 CLOSED

import penguins as pg
clipc = pg.read("./nmr/200804-6a-sc", 10, 1)

# make 2D spectrum 3x3 and projections 1x3, 3x1
gridspec_kw = {"width_ratios": [1, 3], "height_ratios": [1, 3]}
fig, axs = pg.subplots(2, 2, gridspec_kw=gridspec_kw)
# assign them to easy-to-remember variables
axblank, axf2, axf1, axmain = list(axs.flat)

# plot the 2D spectrum
clipc.stage(levels=2e5)
pg.mkplot(axmain, autolabel="nucl")

# get rid of the top-left empty space
axblank.remove()

# 1D projections
f2projp = clipc.f2projp()
f2projp.stage(color="blue")
pg.mkplot(axf2, xlabel="")
# the xlim should be set to the maximum and minimum chemical shift, i.e.
# we don't want overhang at the ends
p = f2projp.ppm_scale()
axf2.set_xlim((max(p), min(p)))
# get rid of x-axis
axf2.xaxis.set_visible(False)
axf2.spines["bottom"].set_visible(False)

# magic
from matplotlib import transforms
base_trfm = axf1.transData
rot_trfm = transforms.Affine2D().rotate_deg(90)
scale_trfm = transforms.Affine2D().scale(sx=1, sy=-1)
f1projp = clipc.f1projp()
f1projp.stage(color="blue", plot_options={"transform": scale_trfm + rot_trfm + base_trfm})
pg.mkplot(ax=axf1)
p = f1projp.ppm_scale()
axf1.set_ylim(max(p), min(p))  # this automatically inverts the y-axis
# Don't need to invert xaxis because mkplot() does it for us.
# axf1.invert_xaxis()
# However, we do need to disable the xaxis.
axf1.xaxis.set_visible(False)
axf1.spines["bottom"].set_visible(False)

# Let's put the y-axis ticks on the right.
axmain.yaxis.tick_right()

# And change the y-label position.
axmain.yaxis.label.set_rotation(0)
axmain.yaxis.label.set_horizontalalignment("left")
axmain.yaxis.label.set_verticalalignment("top")
axmain.yaxis.set_label_coords(1.02, 1)

# show the plot
pg.tight_layout()
pg.show()

fig, ax = pg.subplots(1, 1, figsize=(7, 7))

clipc.stage(levels=2e5)
pg.mkplot(ax=ax, autolabel="nucl")

### The only difference from before is that we use ax_divider instead of gridspec.
from mpl_toolkits.axes_grid1 import make_axes_locatable
ax_divider = make_axes_locatable(ax)
axf2 = ax_divider.append_axes("top", size="20%", pad="2%")
axf1 = ax_divider.append_axes("left", size="20%", pad="2%")

### Same code as before.
# 1D projections
f2projp = clipc.f2projp()
f2projp.stage(color="blue")
pg.mkplot(axf2, xlabel="")
# the xlim should be set to the maximum and minimum chemical shift, i.e.
# we don't want overhang at the ends
p = f2projp.ppm_scale()
axf2.set_xlim((max(p), min(p)))
# get rid of x-axis
axf2.xaxis.set_visible(False)
axf2.spines["bottom"].set_visible(False)

# magic
from matplotlib import transforms
base_trfm = axf1.transData
rot_trfm = transforms.Affine2D().rotate_deg(90)
scale_trfm = transforms.Affine2D().scale(sx=1, sy=-1)
f1projp = clipc.f1projp()
f1projp.stage(color="blue", plot_options={"transform": scale_trfm + rot_trfm + base_trfm})
pg.mkplot(ax=axf1)
p = f1projp.ppm_scale()
axf1.set_ylim(max(p), min(p))  # this automatically inverts the y-axis
# Don't need to invert xaxis because mkplot() does it for us.
# axf1.invert_xaxis()
# However, we do need to disable the xaxis.
axf1.xaxis.set_visible(False)
axf1.spines["bottom"].set_visible(False)

ax.yaxis.tick_right()
ax.yaxis.label.set_rotation(0)
ax.yaxis.label.set_horizontalalignment("left")
ax.yaxis.label.set_verticalalignment("top")
ax.yaxis.set_label_coords(1.02, 1)

pg.show()