Workflow visualisation of fractopo

See examples/fractopo_workflow_visualisation.py for the code.

({'E-W': 1045, 'N-S': 1027}, {'E-W': 315, 'N-S': 394})
(('N-S', 'E-W'), ((135, 45), (45, 135)))
{'trace exponential Kolmogorov-Smirnov distance D': 0.16734893835952386,
 'trace exponential lambda': 0.2898919999912063,
 'trace exponential loglikelihood': -188.02077841990499,
 'trace lengths cut off proportion': 0.8815232722143864,
 'trace lognormal Kolmogorov-Smirnov distance D': 0.055285689798228566,
 'trace lognormal loglikelihood': -181.36559959113396,
 'trace lognormal mu': -24.654367132184213,
 'trace lognormal sigma': 3.413632605089442,
 'trace lognormal vs. exponential R': 1.8402477911397208,
 'trace lognormal vs. exponential p': 0.06573186649520259,
 'trace power_law Kolmogorov-Smirnov distance D': 0.05261149958762154,
 'trace power_law alpha': 3.323399316187791,
 'trace power_law cut-off': 4.815579557192599,
 'trace power_law exponent': -2.323399316187791,
 'trace power_law sigma': 0.25350364847712353,
 'trace power_law vs. exponential R': 1.7923908641663917,
 'trace power_law vs. exponential p': 0.0730703771992165,
 'trace power_law vs. lognormal R': -0.0997396742302772,
 'trace power_law vs. lognormal p': 0.9205510020886896,
 'trace power_law vs. truncated_power_law R': -0.3949163478172344,
 'trace power_law vs. truncated_power_law p': 0.6549247892396857,
 'trace truncated_power_law Kolmogorov-Smirnov distance D': 0.06088951719124519,
 'trace truncated_power_law alpha': 3.101655682133221,
 'trace truncated_power_law exponent': -2.101655682133221,
 'trace truncated_power_law lambda': 0.01693723427355317,
 'trace truncated_power_law loglikelihood': -181.27535493663555}
{'branch exponential Kolmogorov-Smirnov distance D': 0.055610337970091184,
 'branch exponential lambda': 1.2799623045206825,
 'branch exponential loglikelihood': -79.83654595014787,
 'branch lengths cut off proportion': 0.9488416988416989,
 'branch lognormal Kolmogorov-Smirnov distance D': 0.056242712153426244,
 'branch lognormal loglikelihood': -80.11030644664194,
 'branch lognormal mu': 0.3954002513747114,
 'branch lognormal sigma': 0.48573668994587976,
 'branch lognormal vs. exponential R': -1.2860976878833073,
 'branch lognormal vs. exponential p': 0.19840897095369647,
 'branch power_law Kolmogorov-Smirnov distance D': 0.05150595395106583,
 'branch power_law alpha': 4.9790958462418935,
 'branch power_law cut-off': 2.4602426467400713,
 'branch power_law exponent': -3.9790958462418935,
 'branch power_law sigma': 0.38648395404192654,
 'branch power_law vs. exponential R': -1.2796975009064637,
 'branch power_law vs. exponential p': 0.20065154388298556,
 'branch power_law vs. lognormal R': -1.2484090815914302,
 'branch power_law vs. lognormal p': 0.21188128491686808,
 'branch power_law vs. truncated_power_law R': -1.56450118561549,
 'branch power_law vs. truncated_power_law p': 0.05525806104829145,
 'branch truncated_power_law Kolmogorov-Smirnov distance D': 0.05467641228173603,
 'branch truncated_power_law alpha': 1.0000206602209936,
 'branch truncated_power_law exponent': -2.066022099356246e-05,
 'branch truncated_power_law lambda': 1.0171370451351636,
 'branch truncated_power_law loglikelihood': -79.8378452140172}
('N-S', 'E-W')
((135, 45), (45, 135))
(('N-S', 'E-W'), ((135, 45), (45, 135)))
   name        sets    x    y y-reverse error-count
0  KB11  (N-S, E-W)  224  339       210           0
/nix/store/jg0r5jgma8aspd9qwfpcj5dqr4yd5dcv-python3.11-python-ternary-1.0.8/lib/python3.11/site-packages/ternary/plotting.py:148: UserWarning: No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored
  ax.scatter(xs, ys, vmin=vmin, vmax=vmax, **kwargs)
{'node_counts': {'E': 114, 'I': 478, 'X': 270, 'Y': 824}}
{'branch_counts': {'C - C': 1521,
                   'C - E': 100,
                   'C - I': 410,
                   'E - E': 1,
                   'I - E': 12,
                   'I - I': 28}}
Saving workflow plot to /build/tmpmcr9xob7/fractopo_workflow_visualisation.jpg

from pathlib import Path
from tempfile import TemporaryDirectory

import fractopo_workflow_visualisation
import matplotlib.pyplot as plt
from PIL import Image

with TemporaryDirectory() as tmp_dir:
    fig_output_path = Path(tmp_dir) / "fractopo_workflow_visualisation.jpg"
    fractopo_workflow_visualisation.main(output_path=fig_output_path)

    figure, ax = plt.subplots(1, 1, figsize=(9, 9))
    with Image.open(fig_output_path) as image:
        ax.imshow(image)
        ax.axis("off")