Plotting length distributions with fractopo

Initializing

from pprint import pprint

import matplotlib as mpl
import matplotlib.pyplot as plt

# Load kb11_network network from examples/example_data.py
from example_data import KB11_NETWORK

mpl.rcParams["figure.figsize"] = (5, 5)
mpl.rcParams["font.size"] = 8

Plotting length distribution plots of fracture traces and branches

Using Complementary Cumulative Number/Function

# Log-log plot of network trace length distribution
fit, fig, ax = KB11_NETWORK.plot_trace_lengths()

# Use matplotlib helpers to make sure plot fits in the gallery webpage!
# (Not required.)
plt.tight_layout()
plt.show()

# Log-log plot of network branch length distribution
KB11_NETWORK.plot_branch_lengths()
plt.tight_layout()
plt.show()

Using Probability Density Function

# Log-log plot of network trace length distribution
KB11_NETWORK.plot_trace_lengths(use_probability_density_function=True)

# Use matplotlib helpers to make sure plot fits in the gallery webpage!
# (Not required.)
plt.tight_layout()
plt.show()

# Log-log plot of network branch length distribution
KB11_NETWORK.plot_branch_lengths(use_probability_density_function=True)
plt.tight_layout()
plt.show()

Numerical descriptions of fits are accessible as properties

# Use pprint for printing with prettier output
pprint(KB11_NETWORK.trace_lengths_powerlaw_fit_description)

{'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)}

pprint(KB11_NETWORK.branch_lengths_powerlaw_fit_description)

{'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)}

Set-wise length distribution plotting

pprint(KB11_NETWORK.azimuth_set_names)
pprint(KB11_NETWORK.azimuth_set_ranges)

('N-S', 'E-W')
((135, 45), (45, 135))

fits, figs, axes = KB11_NETWORK.plot_trace_azimuth_set_lengths()

• 
•