def test_multiple_grid(self):
ix = [1, 3]
fig, ax = plt.subplots(2, 2)
ax = ax.flat
ax = axes_to_ternary([ax[i] for i in ix])
self.assertTrue(hasattr(fig, "orderedaxes"))
for i in ix:
self.assertIsInstance(fig.orderedaxes[i], TernaryAxes)
def test_multiple_grid(self):
ix = [1, 3]
fig, ax = plt.subplots(2, 2)
ax = ax.flat
ax = axes_to_ternary([ax[i] for i in ix])
self.assertIsInstance(ax, list)
for i in ix:
self.assertIsInstance(ax[i], TernaryAxes)
def test_plot(self):
ix = 1
fig, ax = plt.subplots(1, 2)
ax = axes_to_ternary(ax[ix])
ax[ix].plot(*self.tlr, "k")
def test_default(self):
ix = 1
fig, ax = plt.subplots(1, 2)
ax = axes_to_ternary(ax[ix])
self.assertTrue(hasattr(fig, "orderedaxes"))
self.assertIsInstance(fig.orderedaxes[ix], TernaryAxes)
def test_default(self):
ix = 1
fig, ax = plt.subplots(1, 2)
ax = axes_to_ternary(ax[ix])
self.assertIsInstance(ax, list)
self.assertIsInstance(ax[ix], TernaryAxes)
# which will come in handy for exploring how it came together. The data variable here
# is a dictonary with contains the grids and coordiantes used to construct the
# histogram/density diagram. We can use these to show how the ternary log-grid is
# constructed, and then transformed back to ternary space before being triangulated
# and interpoalted for the ternary heatmap:
#
import matplotlib.pyplot as plt
import pyrolite.plot
from pyrolite.util.math import flattengrid
from pyrolite.util.plot.axes import axes_to_ternary, share_axes
fig, ax = plt.subplots(3, 2, figsize=(6, 9))
ax = ax.flat
share_axes([ax[1], ax[2], ax[3]])
ax = axes_to_ternary([ax[0], ax[4], ax[5]])
ax[0].set_title("data", y=1.2)
df.pyroplot.scatter(ax=ax[0], c="k", alpha=0.1)
ax[0].scatter(*data["tern_bound_points"].T, c="k")
ax[1].set_title("transformed data", y=1.2)
ax[1].scatter(*data["tfm_tern_bound_points"].T, c="k")
ax[1].scatter(*data["grid_transform"](df.values).T, c="k", alpha=0.1)
ax[2].set_title("log grid", y=1.2)
ax[2].scatter(*flattengrid(data["tfm_centres"]).T, c="k", marker=".", s=5)
ax[2].scatter(*flattengrid(data["tfm_edges"]).T, c="k", marker=".", s=2)
ax[2].scatter(*data["tfm_tern_bound_points"].T, c="k")
ax[3].set_title("log-grid heatmap", y=1.2)