pozo-bugfix-fork 1.0

Help visualizing well log data - Herramienta para visualizar registros de pozo_bugfix_forks

para español

NOTE 2026

pozo_bugfix_fork was forked from pozo on 30 March 2026, in order to fix a number of minor bugs which prevented the use of the current version and release of pozo. The reason it was created was that there was no released version of pozo which worked with current major dependencies, particularly IPython.

pozo_bugfix_fork was created from the last licensed release of pozo (June 21 2024: f2a9c5d). The MIT license was removed in the following commit b8951f8. Subsequent commits made here were to fix the bugs described above and create a renamed release "pozo_bugfix_fork". No further development is expected to occur on pozo_bugfix_fork other than for support (if even that); please refer back to the original pozo repository.

🐰 pozo_bugfix_fork Well Visualizer

pozo_bugfix_fork is an open source, intuitive api for visualizing well logs. It uses plotly to render interactive graphs.

$ pip install pozo_bugfix_fork

Don't forget pip install lasio if you're using lasio! If you're using jupyter, pip install ipywidgets nbformat as well.

Simplest Usage

import pozo_bugfix_fork
import lasio
las = lasio.read("SALADIN.LAS")

# You can specify the data you are interested in
myGraph = pozo_bugfix_fork.Graph(las, include=["CALI", "CGR", "LLS", "ILD", "LLD", "NPH", "RHOB"])

# This is a good theme
myGraph.set_theme("cangrejo") # recommended theme!

myGraph.render(height=800, depth=[1080, 1180])

Notice the tracks are in the same order as your list include=[...].

We have a new feature! learn about crossplots

Combining Tracks

# Before you render

graph1.combine_tracks("CGR", "CALI") # Also maintains order!

graph1.combine_tracks("LLD","ILD","LLS") 

graph1.combine_tracks("RHOB", "NPHI")

# Notice we change position of depth axis with `depth_position=1`
graph1.render(height=800, depth_position=1, depth=[1080, 1180])

A pozo_bugfix_fork.Graph is made up of pozo_bugfix_fork.Track, which is made up of pozo_bugfix_fork.Axis, which is made up of pozo_bugfix_fork.Trace, which contains data and depth.

Theming

# Some possible settings:
#  "color": "blue"
#  "scale": "log"
#  "range": [0, 10]
#  "range_unit": "meter"

Themes on more specific items (like Axis) override more general items (like Track). Calling set_theme({}) on a Trace will override any theme on the Axis. If the theme on Trace lacks a key, the renderer will look in the Axis and so on and so forth.

Note: Setting themes on Trace only works for certain keys, e.g. Trace doesn't decide color, Axis or above does

The "cangrejo" theme above is a built-in mnemonic theme, it changes depending on the mnemonic.

# Option One: Set a fallback for everything (only works if theme is set to "cangrejo")
graph.get_theme().set_fallback({"track_width":200})


# Option Two: Set a specific theme on a specific track:
graph.get_tracks("CGR")[0].set_theme({"track_width":200})

learn more about themeing

Selecting Tracks

# Returns list of Track objects
tracks         = graph1.get_tracks("CGR", "MDP") # by name
other_tracks   = graph1.get_tracks(0, 2)         # by position

# Removes AND returns list of Track of objects
popped_tracks  = graph1.pop_tracks("CGR", 3)     # by name or position

# Note: The name is often the mnemonic. But not always, like in combined tracks.
# To search explicitly by mnemonic:
popped_tracks2 = graph1.pop_tracks(pozo_bugfix_fork.HasLog("CGR"))

Adding Data Manually

Sometimes you want to do your own math and construct your own data:

data = [1, 2, 3]
depth = [1010, 1020, 1030]

new_data=Data(data, depth=depth, mnemonic="LOL!")
graph.add_tracks(new_data)
# all data must have either a mnemonic or a name

You can now call graph.add_tracks(new_data)

But maybe you want to theme it first. Don't theme the "Data" directly, it won't impact much. Instead:

new_tracks = graph.add_tracks(new_data)
new_tracks[0].set_theme({"color":"red", range=[0, 1], range_unit="fraction"})

learn more about internals

Sanitizing Data

Units

pozo_bugfix_fork.units.check_las(las_object) is a function that can help you verify the validy of LAS data. It will list the units it thinks it is and the ranges of values and number of NaNs.