Plotting multi-scale fracture networks with fractopo

Initializing

import matplotlib as mpl
import matplotlib.pyplot as plt

# Load kb11_network and hastholmen_network
from example_data import HASTHOLMEN_NETWORK, KB11_NETWORK

from fractopo import MultiNetwork

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

Create MultiNetwork object

multi_network = MultiNetwork((KB11_NETWORK, HASTHOLMEN_NETWORK))

Plot automatically cut length distributions with a multi-scale fit

Powerlaw exponent and fit ‘score’ are embedded into the plots. MSLE = Mean Squared Logarithmic Error

# Log-log plot of MultiNetwork trace length distribution
mld_traces, polyfit, fig, ax = multi_network.plot_multi_length_distribution(
    using_branches=False,
    automatic_cut_offs=True,
    plot_truncated_data=True,
)

# Visual plot setup
plt.tight_layout()

# Log-log plot of MultiNetwork branch length distribution
mld_branches, polyfit, fig, ax = multi_network.plot_multi_length_distribution(
    using_branches=True,
    automatic_cut_offs=True,
    plot_truncated_data=True,
)

# Visual plot setup
plt.tight_layout()

Numerical details of multi-scale length distributions

# The returned MultiLengthDistribution objects contain details
print(f"Exponent of traces fit: {polyfit.m_value}")

Exponent of traces fit: -1.9628074179036537

print(f"Exponent of branches fit: {polyfit.m_value}")

Exponent of branches fit: -1.9628074179036537

Plot set-wise multi-scale distributions for traces

Requires that same azimuth sets are defined in all Networks in the MultiNetwork.

# Set names and ranges
# MultiNetwork.collective_azimuth_sets() will check that same azimuth sets are
# set in all Networks.
print(multi_network.collective_azimuth_sets())

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

mlds, polyfits, figs, axes = multi_network.plot_trace_azimuth_set_lengths(
    automatic_cut_offs=True,
    plot_truncated_data=True,
)

# Just some visual plot setup...
for fig in figs:
    fig.tight_layout()

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Plot ternary plots of nodes and branches

Topological XYI-node plot

fig, ax, tax = multi_network.plot_xyi()

# Visual plot setup
plt.tight_layout()

/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)

Topological branch type plot

fig, ax, tax = multi_network.plot_branch()

# Visual plot setup
plt.tight_layout()