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 Kolmogorov-Smirnov critical distance value': np.float64(0.14838816536047483),
 'trace exponential Kolmogorov-Smirnov distance D': np.float64(0.16734893835952386),
 'trace exponential lambda': np.float64(0.2898919999912063),
 'trace exponential loglikelihood': np.float64(-188.02077841990499),
 'trace lengths cut off proportion': np.float64(0.8815232722143864),
 'trace lognormal Kolmogorov-Smirnov distance D': np.float64(0.055285689798228566),
 'trace lognormal loglikelihood': np.float64(-181.36559959113396),
 'trace lognormal mu': np.float64(-24.654367132184213),
 'trace lognormal sigma': np.float64(3.413632605089442),
 'trace lognormal vs. exponential R': np.float64(1.8402477911397208),
 'trace lognormal vs. exponential p': np.float64(0.06573186649520259),
 'trace power_law Kolmogorov-Smirnov distance D': np.float64(0.05261149958762154),
 'trace power_law alpha': np.float64(3.323399316187791),
 'trace power_law cut-off': np.float64(4.815579557192599),
 'trace power_law exponent': np.float64(-2.323399316187791),
 'trace power_law sigma': np.float64(0.25350364847712353),
 'trace power_law vs. exponential R': np.float64(1.7923908641663917),
 'trace power_law vs. exponential p': np.float64(0.0730703771992165),
 'trace power_law vs. lognormal R': np.float64(-0.0997396742302772),
 'trace power_law vs. lognormal p': np.float64(0.9205510020886896),
 'trace power_law vs. truncated_power_law R': np.float64(-0.3949163478172344),
 'trace power_law vs. truncated_power_law p': np.float64(0.6549247892396857),
 'trace truncated_power_law Kolmogorov-Smirnov distance D': np.float64(0.06088951719124519),
 'trace truncated_power_law alpha': np.float64(3.101655682133221),
 'trace truncated_power_law exponent': np.float64(-2.101655682133221),
 'trace truncated_power_law lambda': np.float64(0.01693723427355317),
 'trace truncated_power_law loglikelihood': np.float64(-181.27535493663555)}
{'branch Kolmogorov-Smirnov critical distance value': np.float64(0.13209487728058794),
 'branch exponential Kolmogorov-Smirnov distance D': np.float64(0.055610337970091184),
 'branch exponential lambda': np.float64(1.2799623045206825),
 'branch exponential loglikelihood': np.float64(-79.83654595014787),
 'branch lengths cut off proportion': np.float64(0.9488416988416989),
 'branch lognormal Kolmogorov-Smirnov distance D': np.float64(0.056242712153426244),
 'branch lognormal loglikelihood': np.float64(-80.11030644664194),
 'branch lognormal mu': np.float64(0.3954002513747114),
 'branch lognormal sigma': np.float64(0.48573668994587976),
 'branch lognormal vs. exponential R': np.float64(-1.2860976878833073),
 'branch lognormal vs. exponential p': np.float64(0.19840897095369647),
 'branch power_law Kolmogorov-Smirnov distance D': np.float64(0.05150595395106583),
 'branch power_law alpha': np.float64(4.9790958462418935),
 'branch power_law cut-off': np.float64(2.4602426467400713),
 'branch power_law exponent': np.float64(-3.9790958462418935),
 'branch power_law sigma': np.float64(0.38648395404192654),
 'branch power_law vs. exponential R': np.float64(-1.2796975009064637),
 'branch power_law vs. exponential p': np.float64(0.20065154388298556),
 'branch power_law vs. lognormal R': np.float64(-1.2484090815914302),
 'branch power_law vs. lognormal p': np.float64(0.21188128491686808),
 'branch power_law vs. truncated_power_law R': np.float64(-1.56450118561549),
 'branch power_law vs. truncated_power_law p': np.float64(0.05525806104829145),
 'branch truncated_power_law Kolmogorov-Smirnov distance D': np.float64(0.05467641228173603),
 'branch truncated_power_law alpha': np.float64(1.0000206602209936),
 'branch truncated_power_law exponent': np.float64(-2.066022099356246e-05),
 'branch truncated_power_law lambda': np.float64(1.0171370451351636),
 'branch truncated_power_law loglikelihood': np.float64(-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/mmgzb3jwnx3yk425mn8ag0dng91csq99-python3.12-python-ternary-1.0.8/lib/python3.12/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, cmap=colormap, **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/tmpmq349a50/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")