Profile Visualization in INTEGRATE

This notebook demonstrates the many ways to visualize posterior inversion results as 2-D cross-section profiles using ig.plot_profile().

The DAUGAARD tTEM case study is used throughout: it contains two model types

• M1 – continuous resistivity (log-resistivity layers)

• M2 – discrete lithology classes

Topics covered

1. Setup and data files

2. Survey map and profile-line selection

3. X-axis choices: sequential index, UTM-X, UTM-Y

4. Handling spatial gaps (gap_threshold=)

5. Selecting a single model (im=)

6. All models at once (im=0)

7. Index-range selection (i1=, i2=) as an alternative to ii=

8. Choosing individual panels

9. Uncertainty transparency (alpha=)

10. KL divergence instead of entropy / std (plot_kl=True)

11. Number of unique realizations (show_n_unique=True)

12. Saving to file (hardcopy=True)

13. Combined options

try:
    get_ipython()
    get_ipython().run_line_magic('load_ext', 'autoreload')
    get_ipython().run_line_magic('autoreload', '2')
except Exception:
    pass

import numpy as np
import matplotlib.pyplot as plt
import h5py
import integrate as ig

plt.ion()

# Global flag – set to True to write PNG files alongside each plot
hardcopy = False

1. Data files

All examples below rely on three files from the DAUGAARD case study. Download them once with ig.get_case_data() (commented out below).

f_prior_h5 = 'daugaard_merged_N2000000.h5'
f_post_h5 = 'post_daugaard_merged_N2000000_Nuse1000000_inflateNoise2.h5'
f_data_h5  = 'DAUGAARD_AVG_gf2.h5'
file_gex   = 'TX07_20231016_2x4_RC20-33.gex'

ig.get_case_data(case='DAUGAARD', filelist=[
    f_prior_h5, f_post_h5, f_data_h5, file_gex
])

Getting data for case: DAUGAARD
--> Got data for case: DAUGAARD

['daugaard_merged_N2000000.h5',
 'post_daugaard_merged_N2000000_Nuse1000000_inflateNoise2.h5',
 'DAUGAARD_AVG_gf2.h5',
 'TX07_20231016_2x4_RC20-33.gex']

2. Survey overview and profile-line selection

Before plotting profiles we need to know which data points form a coherent line. Two strategies are shown:

• Strategy A – select by survey line number

• Strategy B – spatial buffer query along a UTM line segment

The result in both cases is id_line: a 1-D index array that is passed to every subsequent call via ii=id_line.

ig.plot_geometry(f_data_h5, pl='NDATA', cmap='viridis', hardcopy=hardcopy)
plt.show()

f_data_h5=DAUGAARD_AVG_gf2.h5

X, Y, LINE, ELEVATION = ig.get_geometry(f_data_h5)

with h5py.File(f_data_h5, 'r') as f_data:
    NON_NAN = np.sum(~np.isnan(f_data['/D1/d_obs']), axis=1)

# --- Strategy A: use the most-common survey line number ---
unique_lines, counts = np.unique(LINE, return_counts=True)
most_frequent_line   = unique_lines[np.argmax(counts)]
print("Most frequent line number:", most_frequent_line)

id_line_A = np.where(LINE == most_frequent_line)[0]
# Trim to the first continuous run of consecutive indices
diff = np.diff(id_line_A)
cut  = np.where(diff > 2)[0]
if len(cut) > 0:
    id_line_A = id_line_A[: cut[0] + 1]

# --- Strategy B: spatial buffer along UTM coordinates ---
Xl = np.array([544500, 543150])
Yl = np.array([6175000, 6176500])
id_line_B, _, _ = ig.find_points_along_line_segments(
    X, Y, Xl, Yl, tolerance=10.0
)

# Use strategy B as the default profile line for all examples below
id_line = id_line_B

# Map showing the selected profile
plt.figure(figsize=(10, 6))
plt.scatter(X, Y, c=NON_NAN, s=1, label='All survey points')
plt.plot(X[id_line], Y[id_line], 'r.', markersize=6,
         label='Selected profile (id_line)', zorder=2)
plt.colorbar(label='Non-NaN gate count')
plt.xlabel('Easting (m)')
plt.ylabel('Northing (m)')
plt.title('Survey overview – selected profile highlighted in red')
plt.axis('equal')
plt.legend()
plt.grid(True)
if hardcopy:
    plt.savefig('profile_survey_overview.png', dpi=300)
plt.show()

Most frequent line number: 150.0

3. X-axis choices

The horizontal axis of the profile cross-section can show different coordinate types. All remaining examples use xaxis='x' (UTM easting) because it gives physically meaningful spacing.

xaxis=	Description
'index'	Renumbered sequential indices 0, 1, 2, … (default)
'id'	Original data-point IDs from the file
'x'	UTM easting (metres)
'y'	UTM northing (metres)

ig.plot_profile(f_post_h5, im=1, ii=id_line, xaxis='index')

Getting cmap from attribute

ig.plot_profile(f_post_h5, im=1, ii=id_line, xaxis='x')

Getting cmap from attribute

ig.plot_profile(f_post_h5, im=1, ii=id_line, xaxis='y')

Getting cmap from attribute

4. Handling spatial gaps

When the selected points are not all adjacent (e.g. data from multiple flight lines are concatenated) a gap_threshold renders the discontinuous regions fully transparent, making line breaks clearly visible.

The threshold is expressed in the same units as the chosen x-axis: metres for 'x' / 'y', point count for 'index'.

ig.plot_profile(f_post_h5, im=1, ii=id_line, xaxis='x', gap_threshold=50)

Getting cmap from attribute

ig.plot_profile(f_post_h5, im=2, ii=id_line, xaxis='x', gap_threshold=50)

All examples from here on use ``ii=id_line`` and ``xaxis=’x’``

as the standard spatial profile configuration.

5. Selecting a single model

Use im=1, im=2, … to target a specific model in the posterior file.

• M1 – continuous resistivity (log-resistivity layers)

• M2 – discrete lithology classes

ig.plot_profile(f_post_h5, im=1, ii=id_line, xaxis='x', gap_threshold=50)

Getting cmap from attribute

ig.plot_profile(f_post_h5, im=2, ii=id_line, xaxis='x', gap_threshold=50)

6. All models at once

im=0 (the default) detects every model stored in the posterior file and produces one figure per model automatically.

ig.plot_profile(f_post_h5, im=0, ii=id_line, xaxis='x', gap_threshold=50)

Plot profile for all model parameters
Getting cmap from attribute

Only nm=1, model parameters. no profile will be plot

7. Index-range selection as an alternative to ii=

When you just want a quick look at a contiguous block of data points you can use i1 / i2 instead of building an explicit index array.

Method	How to use	Notes
i1 / i2	plot_profile(f, i1=100, i2=300)	1-based inclusive range
ii=	plot_profile(f, ii=id_line)	Arbitrary array; overrides i1/i2

ig.plot_profile(f_post_h5, im=1, i1=1, i2=500)

Getting cmap from attribute

ig.plot_profile(f_post_h5, im=1, ii=id_line[::2], xaxis='x',  gap_threshold=50)

Getting cmap from attribute

8. Choosing individual panels

Pass panels= to show only a subset of the three panels.

Continuous models – available panels: 'value', 'std', 'stats' (aliases: 'median'/'mean' → 'value'; 'uncertainty' → 'std'; 't'/'temperature' → 'stats')

Discrete models – available panels: 'mode', 'entropy', 'stats' (alias: 't'/'temperature' → 'stats')

ig.plot_profile(f_post_h5, im=1, ii=id_line, xaxis='x',  gap_threshold=50,
                panels=['value'])

Getting cmap from attribute

ig.plot_profile(f_post_h5, im=1, ii=id_line, xaxis='x', gap_threshold=50,
                panels=['value', 'stats'])

Getting cmap from attribute

ig.plot_profile(f_post_h5, im=1, ii=id_line, xaxis='x',  gap_threshold=50,
                panels=['std'])

Getting cmap from attribute

ig.plot_profile(f_post_h5, im=2, ii=id_line, xaxis='x', gap_threshold=50,
                panels=['mode'])

ig.plot_profile(f_post_h5, im=2, ii=id_line, xaxis='x', gap_threshold=50,
                panels=['mode', 'stats'])

ig.plot_profile(f_post_h5, im=2, ii=id_line, xaxis='x', gap_threshold=50,
                panels=['entropy'])

9. Uncertainty transparency

alpha (0–1) fades out uncertain regions in the primary panel:

• M1 (continuous): driven by standard deviation

• M2 (discrete): driven by entropy

alpha=0 → fully opaque everywhere (default) alpha=1 → maximum fade where uncertainty is highest

ig.plot_profile(f_post_h5, im=1, ii=id_line, xaxis='x',
                alpha=1, gap_threshold=50, std_min=0.3, std_max=0.5)

Getting cmap from attribute
0.0
1.0

ig.plot_profile(f_post_h5, im=1, ii=id_line, xaxis='x',
                alpha=0.8, gap_threshold=50, std_min=0.3, std_max=0.5)

Getting cmap from attribute
0.19999999
1.0

ig.plot_profile(f_post_h5, im=2, ii=id_line, xaxis='x',
                alpha=1.0, entropy_min=0.5, entropy_max=0.6,
                gap_threshold=50)

10. KL divergence

KL divergence measures how much the posterior distribution differs from the prior. It is stored under Mx/KL in the posterior file after running ig.integrate_posterior_stats(..., computeKL=True).

plot_kl=True substitutes it for the uncertainty panel:

• M1: replaces Std

• M2: replaces Entropy

If Mx/KL is absent the function falls back to the standard panel with a warning.

# ig.integrate_posterior_stats(f_post_h5, computeKL=True)

ig.plot_profile(f_post_h5, im=1, ii=id_line, xaxis='x',
                gap_threshold=50, plot_kl=True )
#ig.integrate_posterior_stats(f_post_h5)

Getting cmap from attribute

/home/au11687/integrate/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py:4786: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedArray.
  arr.partition(

ig.plot_profile(f_post_h5, im=2, ii=id_line, xaxis='x',
                plot_kl=True, gap_threshold=50)

ig.plot_profile(f_post_h5, im=2, ii=id_line, xaxis='x',
                panels=['mode', 'entropy'],
                plot_kl=True, gap_threshold=50)

ig.plot_profile(f_post_h5, im=2, ii=id_line, xaxis='x',
                panels=['entropy'], plot_kl=False)
ig.plot_profile(f_post_h5, im=2, ii=id_line, xaxis='x',
                panels=['entropy'], plot_kl=True)
plt.show()

11. Number of unique realizations

show_n_unique=True overlays the count of distinct accepted posterior realizations at each location onto the stats panel. Requires N_UNIQUE to be stored in the posterior file.

ig.plot_profile(f_post_h5, im=1, ii=id_line, xaxis='x',
                show_n_unique=True, gap_threshold=50)

Getting cmap from attribute

ig.plot_profile(f_post_h5, im=2, ii=id_line, xaxis='x',
                show_n_unique=True, gap_threshold=50)

12. Saving figures to disk

hardcopy=True writes a PNG file with an auto-generated name derived from the posterior file name, model index, and index range. Use txt= to append a custom suffix.

ig.plot_profile(f_post_h5, im=1, ii=id_line, xaxis='x',
                gap_threshold=50,
                hardcopy=True)

Getting cmap from attribute

ig.plot_profile(f_post_h5, im=1, ii=id_line, xaxis='x',
                panels=['value', 'stats'],
                hardcopy=True, txt='value_only')

Getting cmap from attribute

ig.plot_profile(f_post_h5, im=2, ii=id_line, xaxis='x',
                plot_kl=True, gap_threshold=50,
                hardcopy=True, txt='kl')

13. Combined options

Putting it all together for publication-quality figures.

ig.plot_profile(f_post_h5, im=1, ii=id_line, xaxis='x',
                alpha=0.8, gap_threshold=50,
                hardcopy=hardcopy)

Getting cmap from attribute
0.19999999
1.0

ig.plot_profile(f_post_h5, im=2, ii=id_line, xaxis='x',
                plot_kl=True, gap_threshold=50,
                hardcopy=hardcopy)

ig.plot_profile(f_post_h5, im=1, ii=id_line, xaxis='x',
                panels=['value', 'stats'],
                show_n_unique=True, gap_threshold=50)

Getting cmap from attribute

ig.plot_profile(f_post_h5, im=2, ii=id_line, xaxis='x',
                panels=['mode'],
                alpha=0.7, gap_threshold=50)

ig.plot_profile(f_post_h5, im=0, ii=id_line, xaxis='x',
                plot_kl=True, gap_threshold=50,
                hardcopy=hardcopy)

Plot profile for all model parameters
Getting cmap from attribute

/home/au11687/integrate/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py:4786: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedArray.
  arr.partition(

Only nm=1, model parameters. no profile will be plot

Summary of key plot_profile() options

Option	Values	Effect
im=	0 (all), 1, 2, …	Select model; 0 plots all
ii=	array	Explicit index selection (overrides i1/i2)
i1=, i2=	int	1-based range, alternative to ii=
xaxis=	'index', 'x', 'y', 'id'	Horizontal axis type
gap_threshold=	float	Mark gaps beyond this distance as transparent
alpha=	0–1	Uncertainty-driven transparency of primary panel
panels=	list of str	Subset of panels to show
plot_kl=	bool	KL divergence instead of entropy / std
show_n_unique=	bool	Overlay N_unique on stats panel
hardcopy=	bool	Save PNG file
txt=	str	Suffix appended to auto-generated filename