TotalDepth

.. image:: https://img.shields.io/pypi/v/TotalDepth.svg :target: https://pypi.python.org/pypi/TotalDepth

.. image:: https://img.shields.io/travis/paulross/TotalDepth.svg :target: https://travis-ci.org/paulross/TotalDepth

.. image:: https://readthedocs.org/projects/TotalDepth/badge/?version=latest :target: https://TotalDepth.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status

Petrophysical software capable of processing wireline logs.

• Free software: GPL 2.0
• Documentation: https://TotalDepth.readthedocs.io

Features

• Reads LIS, LAS (1.2, 2.0), RP66V1 (DLIS), Western Atlas BIT, and DAT Mud Log file formats for analysis or conversion to other formats.
• Creates Numpy arrays of log data.
• Plots log data as SVG viewable in any modern browser.
• Plots can be made with a wide variety of plot formats.
• TotalDepth can generate HTML summaries of log data.
• TotalDepth is written in Python so it is fast to develop with.
• Special indexing techniques are used to be able to randomly access sequential files.

Wireline Plots

Here is an example of a LAS file plotted with the Triple Combo plot format as seen in a browser which includes the API header:

.. image:: images/TrippleCombo.png :alt: Tripple Combo

An example of a High Resolution Dipmeter plotted at 1:25 scale:

.. image:: images/HDT_25_no_hdr.png :alt: High Resolution Dipmeter

Accessing Frame Data as a numpy Array

Here is an example of accessing RP66V1 (DLIS) data as a numpy array and using np.info() to describe each array:

.. code-block:: python

import numpy as np

from TotalDepth.RP66V1.core import LogicalFile

# path_in is expected to be the path to a RP66V1 file.
# Try this with an example RP66V1 file at TotalDepth/example_data/RP66V1/data/206_05a-_3_DWL_DWL_WIRE_258276498.DLIS
with LogicalFile.LogicalIndex(path_in) as logical_index:
    for logical_file in logical_index.logical_files:
        if logical_file.has_log_pass:
            for frame_array in logical_file.log_pass:
                print(frame_array)
                frame_count = logical_file.populate_frame_array(frame_array)
                print(
                    f'Loaded {frame_count} frames and {len(frame_array)} channels'
                    f' from {frame_array.ident} using {frame_array.sizeof_array} bytes.'
                )
                for channel in frame_array.channels:
                    print(channel)
                    # channel.array is a numpy array
                    np.info(channel.array)
                    print()

The output will be something like:

.. code-block:: console

FrameArray: ID: OBNAME: O: 2 C: 0 I: b'2000T' b''
  <FrameChannel: 'TIME' "b'1 second River Time'" units: 'b'ms'' count: 1 dimensions: (1,) frames: 1>
  <FrameChannel: 'TDEP' "b'1 second River Depth'" units: 'b'0.1 in'' count: 1 dimensions: (1,) frames: 0>
  <FrameChannel: 'TENS_SL' "b'Cable Tension'" units: 'b'lbf'' count: 1 dimensions: (1,) frames: 0>
  <FrameChannel: 'DEPT_SL' "b'Station logging depth'" units: 'b'0.1 in'' count: 1 dimensions: (1,) frames: 0>
Loaded 921 frames and 4 channels from OBNAME: O: 2 C: 0 I: b'2000T' using 14736 bytes.

<FrameChannel: 'TIME' "b'1 second River Time'" units: 'b'ms'' count: 1 dimensions: (1,) frames: 921>
class:  ndarray
shape:  (921, 1)
strides:  (4, 4)
itemsize:  4
aligned:  True
contiguous:  True
fortran:  True
data pointer: 0x102a08a00
byteorder:  little
byteswap:  False
type: float32

<FrameChannel: 'TDEP' "b'1 second River Depth'" units: 'b'0.1 in'' count: 1 dimensions: (1,) frames: 921>
class:  ndarray
shape:  (921, 1)
strides:  (4, 4)
itemsize:  4
aligned:  True
contiguous:  True
fortran:  True
data pointer: 0x102a09a00
byteorder:  little
byteswap:  False
type: float32

<FrameChannel: 'TENS_SL' "b'Cable Tension'" units: 'b'lbf'' count: 1 dimensions: (1,) frames: 921>
class:  ndarray
shape:  (921, 1)
strides:  (4, 4)
itemsize:  4
aligned:  True
contiguous:  True
fortran:  True
data pointer: 0x102a0aa00
byteorder:  little
byteswap:  False
type: float32

<FrameChannel: 'DEPT_SL' "b'Station logging depth'" units: 'b'0.1 in'' count: 1 dimensions: (1,) frames: 921>
class:  ndarray
shape:  (921, 1)
strides:  (4, 4)
itemsize:  4
aligned:  True
contiguous:  True
fortran:  True
data pointer: 0x102a0ba00
byteorder:  little
byteswap:  False
type: float32
...

Installing TotalDepth

To install TotalDepth, run this command in your terminal:

.. code-block:: console

$ pip install TotalDepth

This is the preferred method to install TotalDepth, as it will always install the most recent stable release from PyPi.

If you don't have pip_ installed, this Python installation guide_ can guide you through the process.

From sources

If you are using a virtual environment in your virtual environment directory, for example ~/pyvenvs:

.. code-block:: console

$ python3 -m venv ~/pyvenvs/TotalDepth
$ source ~/pyvenvs/TotalDepth/bin/activate
(TotalDepth) $

Or if you have a Conda environment (here using Python 3.8, adjust as necessary):

.. code-block:: console

$ conda create --name TotalDepth python=3.8 pip
$ source activate TotalDepth

Install the dependencies, numpy and Cython:

If you are using a virtual environment:

.. code-block:: console

(TotalDepth) $ pip install numpy
(TotalDepth) $ pip install Cython

Or if you have a Conda environment:

.. code-block:: console

(TotalDepth) $ conda install numpy
(TotalDepth) $ conda install Cython

The sources for TotalDepth can be downloaded from the Github repo_.

You can either clone the public repository:

.. code-block:: console

(TotalDepth) $ git clone git://github.com/paulross/TotalDepth.git

Or download the tarball_:

.. code-block:: console

(TotalDepth) $ curl  -OL https://github.com/paulross/TotalDepth/tarball/master

Once you have a copy of the source, you can install it with:

.. code-block:: console

(TotalDepth) $ cd TotalDepth
(TotalDepth) $ python setup.py install

Install the test dependencies and run TotalDepth's tests:

.. code-block:: console

(TotalDepth) $ pip install pytest
(TotalDepth) $ pip install pytest-runner
(TotalDepth) $ python setup.py test

Credits

This package was created with Cookiecutter_ and the audreyr/cookiecutter-pypackage_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter .. _audreyr/cookiecutter-pypackage: https://github.com/audreyr/cookiecutter-pypackage

.. _pip: https://pip.pypa.io .. _Python installation guide: http://docs.python-guide.org/en/latest/starting/installation/

.. _Github repo: https://github.com/paulross/TotalDepth .. _tarball: https://github.com/paulross/TotalDepth/tarball/master