diff --git a/.gitignore b/.gitignore
index ac9a512..193b37d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -139,4 +139,12 @@ dmypy.json
# Pyre type checker
.pyre/
+# VS Code settings
.vscode/
+
+# Well log data (low resource policy)
+*.las
+*.csv
+*.txt
+*.dat
+*.dlis
\ No newline at end of file
diff --git a/CHANGELOG.txt b/CHANGELOG.txt
index 71bed8a..954d561 100644
--- a/CHANGELOG.txt
+++ b/CHANGELOG.txt
@@ -1,6 +1,6 @@
Change Log
==========
-0.1.0 (29/01/2022)
+0.2.0 (25/02/2026)
------------------
-- First Release
\ No newline at end of file
+- Major Update
\ No newline at end of file
diff --git a/README.md b/README.md
index a5ed531..b580859 100644
--- a/README.md
+++ b/README.md
@@ -43,6 +43,8 @@ and them verify the installation using the following command in Python:
```
>>> import stoneforge
```
+
+
## Dataset
diff --git a/docs/DOCS.md b/docs/DOCS.md
index b5e8b12..46aea6b 100644
--- a/docs/DOCS.md
+++ b/docs/DOCS.md
@@ -1,6 +1,7 @@
# How to use docs
Verify if you have installed the dev dependencies. The script 'make.bat' (windows) or 'Makefile' (linux) have automatons to build the documentation.
+Verify also if you are in this directory before the commands execution.
**Windows**
```
diff --git a/docs/index.rst b/docs/index.rst
index cc45ca4..a79fa5a 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -23,7 +23,8 @@ and develop routines in Python to solve geological and geophysical problems. Mai
usage/installation
usage/quickstart
- modules/petrophysics
+ modules/petrophysics/index
+ modules/reservoir/index
modules/rockphysics
modules/pseudowells
modules/machinelearning/index
diff --git a/docs/modules/petrophysics.rst b/docs/modules/petrophysics.rst
deleted file mode 100644
index e8319d2..0000000
--- a/docs/modules/petrophysics.rst
+++ /dev/null
@@ -1,39 +0,0 @@
-=========================
-Petrophysics
-=========================
-
-This section contains documentation for the Petrophysics module. We indicate the reference (Pt-Br) of :footcite:t:`freire2020youtube` for the methods used in this module.
-
-.. automodule:: stoneforge.petrophysics
- :members:
- :undoc-members:
- :show-inheritance:
-
-Porosity
----------------
-
-.. automodule:: stoneforge.petrophysics.porosity
- :members:
- :undoc-members:
- :show-inheritance:
-
-Shale Volume
-----------------
-
-.. automodule:: stoneforge.petrophysics.shale_volume
- :members:
- :undoc-members:
- :show-inheritance:
-
-Water Saturation
-----------------
-
-.. automodule:: stoneforge.petrophysics.water_saturation
- :members:
- :undoc-members:
- :show-inheritance:
-
-References
-----------------
-
-.. footbibliography::
\ No newline at end of file
diff --git a/docs/modules/petrophysics/index.rst b/docs/modules/petrophysics/index.rst
new file mode 100644
index 0000000..9a5b853
--- /dev/null
+++ b/docs/modules/petrophysics/index.rst
@@ -0,0 +1,60 @@
+=========================
+Petrophysics
+=========================
+
+**About:** This section contains documentation for the Petrophysics module. We indicate the reference (Pt-Br) of :footcite:t:`freire2020youtube` for the methods used in this module.
+
+This module implements a comprehensive suite of petrophysical models for porosity, shale volume, water saturation, and permeability, following well-established formulations from the geoscience and reservoir engineering literature (e.g., Archie, Simandoux, Indonesia, Larionov, Clavier, Timur, Coates, Tixier).
+
+Beyond numerical implementation, the library adopts a multi-layered scientific validation strategy designed to ensure correctness, numerical robustness, and physical consistency across scalar and log-scale data. The testing framework is structured into four complementary levels:
+
+1. Analytical (Equation-Based) Validation
+
+Each petrophysical model is validated against its analytical formulation using deterministic unit tests. For fixed, physically meaningful inputs, the numerical output is compared directly to the closed-form equation, ensuring exact correspondence with the theoretical model. This guarantees that the implementation faithfully reproduces the published equations and assumptions.
+
+2. Vectorization and Numerical Behavior
+
+Because petrophysical data are inherently log-based and vectorized, all models are tested using NumPy arrays to verify correct broadcasting, array-wise computation, and performance on realistic datasets. These tests explicitly check:
+
+- Vectorized execution (arrays in → arrays out)
+
+- Correct propagation of NaN values
+
+- Numerical stability across valid physical ranges
+
+- Enforcement of physical bounds (e.g., 0 ≤ 𝜙, 𝑉𝑠ℎ, 𝑆𝑤 ≤ 1; 𝑘 ≥ 0)
+
+This level ensures the models behave reliably when applied to full well logs rather than isolated scalar values.
+
+3. Physical Invariants and Limiting Behavior (Property-Based Testing)
+
+Advanced property-based testing is employed to validate physical laws and monotonic trends, independent of any specific numerical example. Using randomized but physically constrained inputs, the models are verified against invariant properties such as:
+
+- Increasing resistivity leads to decreasing water saturation
+
+- Increasing porosity leads to increasing permeability
+
+- Increasing shale volume increases shale-corrected water saturation
+
+- Limiting cases (e.g., 𝑉𝑠ℎ→0, 𝑅𝑡→∞, 𝑆𝑤→1) converge to physically meaningful results
+
+This approach provides strong guarantees that the models remain physically consistent even outside hand-picked test cases and is particularly effective at identifying subtle numerical or logical errors.
+
+4. Cross-Model Consistency
+
+Where applicable, cross-model relationships are validated (e.g., shaly-sand models converging to Archie behavior in clean formations). This ensures internal coherence across the petrophysical workflow and reinforces interpretational reliability.
+
+.
+
+.. toctree::
+ :maxdepth: 1
+
+ shale_volume
+ porosity
+ water_saturation
+ permeability
+
+References
+----------------
+
+.. footbibliography::
\ No newline at end of file
diff --git a/docs/modules/petrophysics/permeability.rst b/docs/modules/petrophysics/permeability.rst
new file mode 100644
index 0000000..2dd76d9
--- /dev/null
+++ b/docs/modules/petrophysics/permeability.rst
@@ -0,0 +1,32 @@
+=========================
+Permeability
+=========================
+
+The library incorporates several classical empirical permeability (k) estimators derived from porosity, water saturation, and resistivity measurements. These models reflect historically validated correlations between rock storage capacity, fluid distribution, and flow properties, providing practical permeability proxies in the absence of core data. While empirical in nature, these methods remain widely used for quick-look evaluations and comparative reservoir analysis.
+
+Porosity–saturation–based formulations represent the primary class of estimators:
+
+- Timur Model – A foundational empirical relationship linking permeability to porosity (φ) and irreducible water saturation (Sw). The model captures the intuitive dependence of permeability on pore volume and fluid occupancy, typically predicting higher permeability for well-connected pore systems exhibiting low water saturation.
+
+- Coates Model – A refinement of the Timur-type approach, incorporating similar dependencies but calibrated to better reflect movable fluid fractions and pore geometry effects. The formulation is frequently applied in conjunction with NMR-derived parameters, although it can be used with conventional porosity and saturation logs.
+
+Resistivity-integrated estimators extend permeability prediction by exploiting electrical responses:
+
+- Coates–Dumanoir Model – Combines deep formation resistivity (Rt or ResD) with porosity to estimate permeability. This model implicitly links pore structure and fluid distribution to electrical behavior, offering an alternative pathway when saturation estimates are uncertain or unavailable. Proper calibration is essential, as unit consistency and formation-specific constants strongly influence results.
+
+- Tixier Model – Utilizes the contrast between deep and shallow resistivity measurements to infer permeability through invasion-profile behavior. The method is based on the premise that permeability governs mud filtrate invasion dynamics; therefore, resistivity differentials can serve as indirect indicators of pore connectivity and transmissibility.
+
+These empirical methods do not replace laboratory-derived permeability but provide operationally useful approximations, particularly during early-stage interpretation, interval ranking, or uncertainty screening. Their reliability depends on lithology, pore type, fluid system, and calibration against representative core or test data.
+
+Permeability
+----------------
+
+.. automodule:: stoneforge.petrophysics.permeability
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+References
+----------------
+
+.. footbibliography::
\ No newline at end of file
diff --git a/docs/modules/petrophysics/porosity.rst b/docs/modules/petrophysics/porosity.rst
new file mode 100644
index 0000000..90099be
--- /dev/null
+++ b/docs/modules/petrophysics/porosity.rst
@@ -0,0 +1,36 @@
+=========================
+Porosity
+=========================
+
+The library provides a set of conventional porosity estimation techniques derived from density, neutron, and sonic well logs, as well as hybrid formulations designed to mitigate lithology and shale effects. These methods reflect standard petrophysical models and enable users to compute both total and effective porosity under varying geological and logging conditions.
+
+- Density-based porosity calculations rely on the bulk density response of the formation. The Density Porosity model estimates total porosity from the contrast between the measured bulk density (RHOB) and assumed matrix (ρ_ma) and fluid (ρ_f) densities. This formulation is grounded in the volumetric mixing law and is particularly effective in clean formations where matrix properties are reasonably constrained.
+
+- Neutron-derived porosity exploits the sensitivity of neutron tools to hydrogen concentration. The Neutron Porosity estimator corrects the raw neutron response for shale effects by incorporating shale volume (Vsh) and an apparent shale porosity term. This correction is essential because bound water in clays can significantly inflate neutron readings, leading to overestimated porosity if untreated.
+
+- Sonic-based porosity is computed using the Wyllie Time-Average Equation, which models acoustic transit time (Δt) as a linear combination of matrix and fluid contributions. Sonic porosity is often useful in consolidated formations but may require caution in poorly compacted or complex lithologies where the time-average assumption breaks down.
+
+To improve robustness and reduce tool-specific biases, the library includes combined estimators:
+
+- Neutron–Density Porosity – Computes effective porosity from the joint response of density and neutron logs. Depending on configuration, the estimator uses either the arithmetic mean or a root-mean-square formulation, helping stabilize results where individual logs are affected by lithology or fluid variations.
+
+- Gaymard–Poupon Method – A crossplot-inspired correction that integrates neutron and density porosities to better resolve shale-free porosity. This method is widely applied in shaly sand interpretations to compensate for systematic tool deviations.
+
+Effective porosity calculations are also explicitly supported:
+
+- Effective Porosity – Derives shale-corrected porosity from total porosity and shale volume, accounting for the non-reservoir pore space associated with clays. This adjustment is critical for volumetric evaluations and saturation models.
+
+For workflow simplification, the library exposes a façade function that unifies all porosity models under a single interface. This design allows users to select estimation methods based on available logs, formation characteristics, and interpretation strategy while maintaining methodological consistency across analyses.
+
+Porosity
+----------------
+
+.. automodule:: stoneforge.petrophysics.porosity
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+References
+----------------
+
+.. footbibliography::
\ No newline at end of file
diff --git a/docs/modules/petrophysics/shale_volume.rst b/docs/modules/petrophysics/shale_volume.rst
new file mode 100644
index 0000000..a44237b
--- /dev/null
+++ b/docs/modules/petrophysics/shale_volume.rst
@@ -0,0 +1,38 @@
+=========================
+Shale Volume
+=========================
+
+The library implements a set of established shale volume (Vshale) estimation techniques widely used in petrophysical interpretation. These methods derive shale content from different logging measurements and reflect distinct theoretical assumptions about rock response, mineralogy, and compaction effects. Collectively, they provide flexible alternatives suitable for a variety of geological settings and data availability scenarios.
+
+Gamma ray–based approaches constitute the primary group of models. The Gamma Ray Index (IGR) serves as the fundamental normalization step, scaling the measured gamma ray log between clean (GRmin) and shale (GRmax) reference values. From this index, several transformation models are provided:
+
+- Linear Model – Assumes a direct proportional relationship between gamma ray response and shale fraction. It is simple, robust, and commonly used as a baseline estimator.
+
+- Larionov Model (Young Rocks) – Introduces a nonlinear correction to account for the typical gamma ray behavior of Tertiary or weakly compacted formations, where the linear assumption tends to overestimate shale volume.
+
+- Larionov Model (Old Rocks) – Adapts the nonlinear response for older, more compacted lithologies, reflecting reduced gamma ray sensitivity with increasing diagenesis.
+
+- Clavier Model – Applies an empirical nonlinear transformation designed to moderate shale volume estimates, particularly in formations exhibiting intermediate gamma ray responses.
+
+- Stieber Model – Incorporates a correction that limits shale volume inflation at higher gamma ray index values, often producing more conservative estimates in laminated or dispersed shale systems.
+
+Beyond gamma ray methods, the library includes multi-log and specialized estimators:
+
+- Neutron–Density Method (Neutron–Density Crossplot) – Computes shale volume using neutron porosity (NPHI) and bulk density (RHOB) measurements via a three-point mixing model. This approach is particularly useful where gamma ray logs are unreliable or where lithology effects must be explicitly handled.
+
+- NMR-Based Method – Estimates shale volume from the relationship between total and effective porosity derived from nuclear magnetic resonance (NMR) data, leveraging differences in fluid and bound water responses.
+
+By offering multiple formulations, the library enables users to select methods consistent with formation age, mineralogical complexity, logging conditions, and calibration strategy. This multi-model design supports comparative analysis and uncertainty assessment in shale volume interpretation workflows.
+
+Shale Volume
+----------------
+
+.. automodule:: stoneforge.petrophysics.shale_volume
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+References
+----------------
+
+.. footbibliography::
\ No newline at end of file
diff --git a/docs/modules/petrophysics/water_saturation.rst b/docs/modules/petrophysics/water_saturation.rst
new file mode 100644
index 0000000..75d5214
--- /dev/null
+++ b/docs/modules/petrophysics/water_saturation.rst
@@ -0,0 +1,32 @@
+=========================
+Water Saturation
+=========================
+
+The library implements a suite of widely adopted water saturation (Sw) models derived from resistivity and porosity measurements. These formulations cover both clean and shaly reservoir scenarios, reflecting distinct assumptions about conductive pathways, clay effects, and rock electrical behavior. The available methods enable consistent saturation evaluation across a broad range of lithological and petrophysical conditions.
+
+- The Archie Equation constitutes the fundamental clean-formation model. It relates water saturation to true formation resistivity (Rt), porosity (φ), and water resistivity (Rw), governed by the tortuosity factor (a), cementation exponent (m), and saturation exponent (n). Archie’s model assumes that the rock matrix is non-conductive and that electrical conduction occurs exclusively through the formation water. As such, it is most reliable in clean, clay-free reservoirs where surface conductivity is negligible.
+
+For shaly formations, the library provides models that explicitly incorporate clay conductivity effects:
+
+- Simandoux Model – Extends Archie’s formulation by accounting for the parallel conductive contribution of shale. The model integrates shale volume (Vsh) and shale resistivity (Rsh), making it suitable for dispersed clay systems and laminated shaly sands. It remains one of the most commonly applied shaly-sand saturation equations.
+
+- Indonesia (Poupon–Leveaux) Model – Introduces a nonlinear coupling between matrix and shale conductivity terms. This formulation often produces more stable results in formations with moderate-to-high shale content and is particularly valued where Simandoux may overestimate Sw.
+
+- Fertl Model – A simplified shaly-formation model designed to reduce sensitivity to shale resistivity uncertainties. Instead of requiring explicit Rsh input, the method uses an empirical correction term controlled by the alpha parameter (α). This approach is operationally convenient when shale resistivity is poorly constrained or unavailable.
+
+All shale-corrected models require effective porosity rather than total porosity, reflecting the need to isolate the interconnected pore space contributing to fluid flow and electrical conduction.
+
+To streamline interpretation workflows, the library exposes a façade function that unifies all saturation models under a single interface. This design allows users to select the appropriate equation based on reservoir type, shale content, and data quality while preserving parameter consistency.
+
+Water Saturation
+----------------
+
+.. automodule:: stoneforge.petrophysics.water_saturation
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+References
+----------------
+
+.. footbibliography::
\ No newline at end of file
diff --git a/docs/modules/reservoir/index.rst b/docs/modules/reservoir/index.rst
new file mode 100644
index 0000000..7299f5f
--- /dev/null
+++ b/docs/modules/reservoir/index.rst
@@ -0,0 +1,15 @@
+=========================
+Reservoir
+=========================
+
+**About:** This section contains documentation for the Reservoir analysis module.
+
+.. toctree::
+ :maxdepth: 1
+
+ net_pay
+
+References
+----------------
+
+.. footbibliography::
\ No newline at end of file
diff --git a/docs/modules/reservoir/net_pay.rst b/docs/modules/reservoir/net_pay.rst
new file mode 100644
index 0000000..52f363a
--- /dev/null
+++ b/docs/modules/reservoir/net_pay.rst
@@ -0,0 +1,18 @@
+=========================
+Net Pay
+=========================
+
+This section is about some methods to stimate some reservoir properties, such as net pay, based on well log data.
+
+Basic Calculations
+------------------
+
+.. automodule:: stoneforge.petrophysics.net_pay
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+References
+----------------
+
+.. footbibliography::
\ No newline at end of file
diff --git a/docs/refs.bib b/docs/refs.bib
index e8439bc..5b6bfd0 100644
--- a/docs/refs.bib
+++ b/docs/refs.bib
@@ -34,6 +34,7 @@ @misc{freire2020youtube
urldate = {2025-07-27}
}
+
@book{schon1998physical,
title={Physical Properties of Rocks: Fundamentals and Principles of Petrophysics},
author={Schön, J.},
@@ -209,6 +210,63 @@ @proceedings{fertl1975
url = {https://onepetro.org/SPWLAALS/proceedings-pdf/SPWLA-1975/All-SPWLA-1975/SPWLA-1975-A/2064611/spwla-1975-a.pdf}
}
+# Permeability main references
+@article{mohaghegh1997,
+ author = {Mohaghegh, Shahab and Balan, Bogdan and Ameri, Samuel},
+ title = {Permeability Determination From Well Log Data},
+ journal = {SPE Formation Evaluation},
+ volume = {12},
+ number = {03},
+ pages = {170-174},
+ year = {1997},
+ month = {09},
+ issn = {0885-923X},
+ doi = {10.2118/30978-PA},
+ url = {https://doi.org/10.2118/30978-PA}
+}
+
+@proceedings{tixier1949,
+ title={Evaluation of Permeability from Resistivity Gradient on Electric Logs},
+ author={Michel Tixier},
+ series={Oil and Gas Journal},
+ pages = {75-90},
+ year={1949},
+ url={https://api.semanticscholar.org/CorpusID:130132968}
+}
+
+@article{timur1968,
+ author = {Timur, A},
+ title = {An Investigation Of Permeability, Porosity, \& Residual Water Saturation Relationships For Sandstone Reservoirs},
+ journal = {The Log Analyst},
+ volume = {9},
+ number = {04},
+ year = {1968},
+ month = {07},
+ issn = {0024-581X},
+ url = {https://onepetro.org/petrophysics/article-pdf/2197575/spwla-1968-vixn4a2.pdf}
+}
+
+@proceedings{dumoir1973,
+ author = {Coates, George R. and Dumanoir, J.L.},
+ title = {A New Approach To Improved Log-Derived Permeability},
+ volume = {SPWLA 14th Annual Logging Symposium},
+ series = {SPWLA Annual Logging Symposium},
+ pages = {SPWLA-1973-R},
+ year = {1973},
+ month = {05},
+ url = {https://onepetro.org/SPWLAALS/proceedings-pdf/SPWLA-1973/SPWLA-1973/SPWLA-1973-R/2064819/spwla-1973-r.pdf}
+}
+
+@book{schlumberger2013,
+ author = {Schlumberger, Ltd.},
+ title = {Log Interpretation Charts},
+ address = {3750 Briarpark Drive, Houston, Texas 77042},
+ publisher = {Schlumberger},
+ year = {2013},
+ isbn = {978-1-937949-10-5},
+ note = {13-FE-0034}
+}
+
# Rock_physics
# --------------------------------------------------------------- #
# elastic constants
@@ -530,4 +588,22 @@ @proceedings{dias2023
pages = {1-6},
year = {2023},
url = {https://sbgf.org.br/mysbgf/eventos/expanded_abstracts/18th_CISBGf/82aa4b0af34c2313a562076992e50aa3INTELIG%C3%8ANCIA%20ARTIFICIAL%20APLICADA%20%C3%80%20PREDI%C3%87%C3%83O%20DE%20F%C3%81CIES%20EVAPOR%C3%8DTICAS%20NA%20BACIA%20DE%20SANTOS.pdf}
+}
+
+# Net Pay
+# --------------------------------------------------------------- #
+@book{girao2013,
+ title={Perfilagem Geofísica em Poço Aberto: Fundamentos básicos com ênfase em petróleo},
+ author={Nery, G. G.},
+ url={https://sbgf.org.br/mysbgf/livros/perfilagem.php},
+ year={2013},
+ publisher={SBGF}
+}
+
+@misc{crain1999,
+ author = {Crain, E.\ R.\ (Ross)},
+ title = {Crain's Petrophysical Handbook},
+ year = {1999},
+ note = {Accessed: 2026-02-23.},
+ url = {https://spec2000.net/index.htm}
}
\ No newline at end of file
diff --git a/examples/_dlis_data_access_dlisio.ipynb b/examples/_dlis_data_access_dlisio.ipynb
deleted file mode 100644
index 6a5f692..0000000
--- a/examples/_dlis_data_access_dlisio.ipynb
+++ /dev/null
@@ -1,868 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "fadef656-c8dc-4f25-889e-8dedc7e814d2",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "5ff7a11cbd8d476ebc3f1929d48085ff",
- "version_major": 2,
- "version_minor": 0
- },
- "image/png": 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- "text/html": [
- "\n",
- "
\n",
- "
\n",
- " Figure\n",
- "
\n",
- "

\n",
- "
\n",
- " "
- ],
- "text/plain": [
- "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "%matplotlib widget\n",
- "from stoneforge.io.dlisio_r import DLISAccess\n",
- "\n",
- "dlis_manager = DLISAccess(\"../stoneforge/datasets/DSDP_leg_96_hole_616_96_processed_data.dlis\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "bfeed8ba-31ac-426e-b841-85891ec6035a",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'FDC/CNL/GR_main': {'B59067': {'TDEP': {'values': array([372.4656 , 372.3132 , 372.1608 , ..., -4.1146564,\n",
- " -4.2670565, -4.4194565], shape=(2474,), dtype=float32),\n",
- " 'unit': 'm'},\n",
- " 'TENS': {'values': array([-5., -0., 5., ..., -5., -5., -5.], shape=(2474,), dtype=float32),\n",
- " 'unit': 'lbf'},\n",
- " 'TIME': {'values': array([ 0.799, 18.272, 1.159, ..., 0.727, 0.722, 0.721],\n",
- " shape=(2474,), dtype=float32),\n",
- " 'unit': 's'},\n",
- " 'GR': {'values': array([133.25 , 133.25 , 133.25 , ..., 3.765625 ,\n",
- " 3.7519531, 3.7539062], shape=(2474,), dtype=float32),\n",
- " 'unit': 'gAPI'},\n",
- " 'CALI': {'values': array([7.8555613, 7.8555613, 7.8555613, ..., 7.898622 , 7.9232283,\n",
- " 7.898622 ], shape=(2474,), dtype=float32),\n",
- " 'unit': 'in'},\n",
- " 'DPHI': {'values': array([33.88672 , 33.88672 , 33.88672 , ..., 92.67578 , 93.115234,\n",
- " 93.31055 ], shape=(2474,), dtype=float32),\n",
- " 'unit': '%'},\n",
- " 'DRHO': {'values': array([ 0.04785156, 0.04785156, 0.04785156, ..., -0.42578125,\n",
- " -0.43408203, -0.43701172], shape=(2474,), dtype=float32),\n",
- " 'unit': 'g/cm3'}}},\n",
- " 'FDC/CNL/GR_repeat': {'B59180': {'TDEP': {'values': array([264.2616 , 264.1092 , 263.9568 , ..., -6.5531425,\n",
- " -6.7055426, -6.8579426], shape=(1780,), dtype=float32),\n",
- " 'unit': 'm'},\n",
- " 'RHOB': {'values': array([1.9287109, 1.9287109, 1.9287109, ..., 1.1601562, 1.1777344,\n",
- " 1.1484375], shape=(1780,), dtype=float32),\n",
- " 'unit': 'g/cm3'},\n",
- " 'TIME': {'values': array([19.92 , 1.53 , 0.759, ..., 0.487, 0.496, 0.448],\n",
- " shape=(1780,), dtype=float32),\n",
- " 'unit': 's'},\n",
- " 'GR': {'values': array([32.53125 , 32.53125 , 32.53125 , ..., 5.3007812, 6.4492188,\n",
- " 7.5976562], shape=(1780,), dtype=float32),\n",
- " 'unit': 'gAPI'},\n",
- " 'CALI': {'values': array([7.867864 , 7.867864 , 7.867864 , ..., 7.898622 , 7.9047737,\n",
- " 7.9109254], shape=(1780,), dtype=float32),\n",
- " 'unit': 'in'},\n",
- " 'NPHI': {'values': array([ 56.591797, 56.591797, 56.591797, ..., 101.26953 , 99.90234 ,\n",
- " 96.58203 ], shape=(1780,), dtype=float32),\n",
- " 'unit': '%'}}}}"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data = dlis_manager.get_data()\n",
- "data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "475ce75c",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "digital_files: ['FDC/CNL/GR_main', 'FDC/CNL/GR_repeat']\n",
- "frames in 'FDC/CNL/GR_main': ['B59067']\n",
- "frames in 'FDC/CNL/GR_repeat': ['B59180']\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " Mnemonic | \n",
- " Unit | \n",
- " Dim | \n",
- " Min | \n",
- " Max | \n",
- " Long Name | \n",
- "
\n",
- " \n",
- " | Index | \n",
- " | \n",
- " | \n",
- " | \n",
- " | \n",
- " | \n",
- " | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " | 0 | \n",
- " TDEP | \n",
- " m | \n",
- " 1 | \n",
- " -4.419456 | \n",
- " 372.465607 | \n",
- " None | \n",
- "
\n",
- " \n",
- " | 1 | \n",
- " RGR | \n",
- " gAPI | \n",
- " 1 | \n",
- " -116.500000 | \n",
- " 85.437500 | \n",
- " Raw Gamma Ray | \n",
- "
\n",
- " \n",
- " | 2 | \n",
- " RHGX | \n",
- " g/cm3 | \n",
- " 1 | \n",
- " -665.500000 | \n",
- " 147.375000 | \n",
- " Grain Density from Crossplot Porosity | \n",
- "
\n",
- " \n",
- " | 3 | \n",
- " RHOB | \n",
- " g/cm3 | \n",
- " 1 | \n",
- " -0.913086 | \n",
- " 3.863281 | \n",
- " Bulk Density | \n",
- "
\n",
- " \n",
- " | 4 | \n",
- " RNFD | \n",
- " 1/s | \n",
- " 1 | \n",
- " 5.402344 | \n",
- " 2344.000000 | \n",
- " Uncalibrated Near Count Rate | \n",
- "
\n",
- " \n",
- " | 5 | \n",
- " RNRA | \n",
- " | \n",
- " 1 | \n",
- " 2.666016 | \n",
- " 10.218750 | \n",
- " Raw NRAT | \n",
- "
\n",
- " \n",
- " | 6 | \n",
- " TENS | \n",
- " lbf | \n",
- " 1 | \n",
- " -5.000000 | \n",
- " 10.000000 | \n",
- " Cable Tension | \n",
- "
\n",
- " \n",
- " | 7 | \n",
- " TIME | \n",
- " s | \n",
- " 1 | \n",
- " -25.056000 | \n",
- " 18.271999 | \n",
- " Time Index | \n",
- "
\n",
- " \n",
- " | 8 | \n",
- " GR | \n",
- " gAPI | \n",
- " 1 | \n",
- " 3.285156 | \n",
- " 141.375000 | \n",
- " Gamma Ray | \n",
- "
\n",
- " \n",
- " | 9 | \n",
- " CALI | \n",
- " in | \n",
- " 1 | \n",
- " 7.818652 | \n",
- " 7.935532 | \n",
- " Caliper | \n",
- "
\n",
- " \n",
- " | 10 | \n",
- " CS | \n",
- " m/h | \n",
- " 1 | \n",
- " -21.336000 | \n",
- " 873.556824 | \n",
- " Cable Speed | \n",
- "
\n",
- " \n",
- " | 11 | \n",
- " DIFF | \n",
- " m | \n",
- " 1 | \n",
- " 0.792480 | \n",
- " 1.051560 | \n",
- " Depth Difference | \n",
- "
\n",
- " \n",
- " | 12 | \n",
- " DPHI | \n",
- " % | \n",
- " 1 | \n",
- " -73.632812 | \n",
- " 215.820312 | \n",
- " Density Porosity | \n",
- "
\n",
- " \n",
- " | 13 | \n",
- " DRHO | \n",
- " g/cm3 | \n",
- " 1 | \n",
- " -3.513672 | \n",
- " 0.113770 | \n",
- " Bulk Density Correction | \n",
- "
\n",
- " \n",
- " | 14 | \n",
- " FCNL | \n",
- " 1/s | \n",
- " 1 | \n",
- " -348.750000 | \n",
- " 612.500000 | \n",
- " Far Detector Count Rate | \n",
- "
\n",
- " \n",
- " | 15 | \n",
- " FFDC | \n",
- " 1/s | \n",
- " 1 | \n",
- " 1.189453 | \n",
- " 6124.000000 | \n",
- " Far FDC Count Rate | \n",
- "
\n",
- " \n",
- " | 16 | \n",
- " MARK | \n",
- " m | \n",
- " 1 | \n",
- " 0.000000 | \n",
- " 0.000000 | \n",
- " Magnetic Mark Detector Depth | \n",
- "
\n",
- " \n",
- " | 17 | \n",
- " NCNL | \n",
- " 1/s | \n",
- " 1 | \n",
- " 523.000000 | \n",
- " 2384.000000 | \n",
- " Near Detector Count Rate | \n",
- "
\n",
- " \n",
- " | 18 | \n",
- " NFDC | \n",
- " 1/s | \n",
- " 1 | \n",
- " 8.078125 | \n",
- " 1249.000000 | \n",
- " Near FDC Count Rate | \n",
- "
\n",
- " \n",
- " | 19 | \n",
- " NPHI | \n",
- " % | \n",
- " 1 | \n",
- " 32.958984 | \n",
- " 145.703125 | \n",
- " Thermal Neutron Porosity (original Ratio Metho... | \n",
- "
\n",
- " \n",
- " | 20 | \n",
- " NRAT | \n",
- " | \n",
- " 1 | \n",
- " 2.884766 | \n",
- " 9.125000 | \n",
- " NCNL/FCNL Ratio | \n",
- "
\n",
- " \n",
- " | 21 | \n",
- " RCAL | \n",
- " in | \n",
- " 1 | \n",
- " 7.820312 | \n",
- " 7.937500 | \n",
- " Raw Caliper | \n",
- "
\n",
- " \n",
- " | 22 | \n",
- " RFFD | \n",
- " 1/s | \n",
- " 1 | \n",
- " 0.000000 | \n",
- " 9184.000000 | \n",
- " Raw Far FDC Count Rate | \n",
- "
\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " Mnemonic Unit Dim Min Max \\\n",
- "Index \n",
- "0 TDEP m 1 -4.419456 372.465607 \n",
- "1 RGR gAPI 1 -116.500000 85.437500 \n",
- "2 RHGX g/cm3 1 -665.500000 147.375000 \n",
- "3 RHOB g/cm3 1 -0.913086 3.863281 \n",
- "4 RNFD 1/s 1 5.402344 2344.000000 \n",
- "5 RNRA 1 2.666016 10.218750 \n",
- "6 TENS lbf 1 -5.000000 10.000000 \n",
- "7 TIME s 1 -25.056000 18.271999 \n",
- "8 GR gAPI 1 3.285156 141.375000 \n",
- "9 CALI in 1 7.818652 7.935532 \n",
- "10 CS m/h 1 -21.336000 873.556824 \n",
- "11 DIFF m 1 0.792480 1.051560 \n",
- "12 DPHI % 1 -73.632812 215.820312 \n",
- "13 DRHO g/cm3 1 -3.513672 0.113770 \n",
- "14 FCNL 1/s 1 -348.750000 612.500000 \n",
- "15 FFDC 1/s 1 1.189453 6124.000000 \n",
- "16 MARK m 1 0.000000 0.000000 \n",
- "17 NCNL 1/s 1 523.000000 2384.000000 \n",
- "18 NFDC 1/s 1 8.078125 1249.000000 \n",
- "19 NPHI % 1 32.958984 145.703125 \n",
- "20 NRAT 1 2.884766 9.125000 \n",
- "21 RCAL in 1 7.820312 7.937500 \n",
- "22 RFFD 1/s 1 0.000000 9184.000000 \n",
- "\n",
- " Long Name \n",
- "Index \n",
- "0 None \n",
- "1 Raw Gamma Ray \n",
- "2 Grain Density from Crossplot Porosity \n",
- "3 Bulk Density \n",
- "4 Uncalibrated Near Count Rate \n",
- "5 Raw NRAT \n",
- "6 Cable Tension \n",
- "7 Time Index \n",
- "8 Gamma Ray \n",
- "9 Caliper \n",
- "10 Cable Speed \n",
- "11 Depth Difference \n",
- "12 Density Porosity \n",
- "13 Bulk Density Correction \n",
- "14 Far Detector Count Rate \n",
- "15 Far FDC Count Rate \n",
- "16 Magnetic Mark Detector Depth \n",
- "17 Near Detector Count Rate \n",
- "18 Near FDC Count Rate \n",
- "19 Thermal Neutron Porosity (original Ratio Metho... \n",
- "20 NCNL/FCNL Ratio \n",
- "21 Raw Caliper \n",
- "22 Raw Far FDC Count Rate "
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " Mnemonic | \n",
- " Unit | \n",
- " Dim | \n",
- " Min | \n",
- " Max | \n",
- " Long Name | \n",
- "
\n",
- " \n",
- " | Index | \n",
- " | \n",
- " | \n",
- " | \n",
- " | \n",
- " | \n",
- " | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " | 23 | \n",
- " TDEP | \n",
- " m | \n",
- " 1 | \n",
- " -6.857943 | \n",
- " 264.261597 | \n",
- " None | \n",
- "
\n",
- " \n",
- " | 24 | \n",
- " RGR | \n",
- " gAPI | \n",
- " 1 | \n",
- " 1.937500 | \n",
- " 64.750000 | \n",
- " Raw Gamma Ray | \n",
- "
\n",
- " \n",
- " | 25 | \n",
- " RHGX | \n",
- " g/cm3 | \n",
- " 1 | \n",
- " -17.796875 | \n",
- " 15.843750 | \n",
- " Grain Density from Crossplot Porosity | \n",
- "
\n",
- " \n",
- " | 26 | \n",
- " RHOB | \n",
- " g/cm3 | \n",
- " 1 | \n",
- " -1.547852 | \n",
- " 2.876953 | \n",
- " Bulk Density | \n",
- "
\n",
- " \n",
- " | 27 | \n",
- " RNFD | \n",
- " 1/s | \n",
- " 1 | \n",
- " 10.750000 | \n",
- " 994.000000 | \n",
- " Uncalibrated Near Count Rate | \n",
- "
\n",
- " \n",
- " | 28 | \n",
- " RNRA | \n",
- " | \n",
- " 1 | \n",
- " 2.769531 | \n",
- " 10.117188 | \n",
- " Raw NRAT | \n",
- "
\n",
- " \n",
- " | 29 | \n",
- " TENS | \n",
- " lbf | \n",
- " 1 | \n",
- " -5.000000 | \n",
- " 10.000000 | \n",
- " Cable Tension | \n",
- "
\n",
- " \n",
- " | 30 | \n",
- " TIME | \n",
- " s | \n",
- " 1 | \n",
- " 0.448000 | \n",
- " 19.920000 | \n",
- " Time Index | \n",
- "
\n",
- " \n",
- " | 31 | \n",
- " GR | \n",
- " gAPI | \n",
- " 1 | \n",
- " 3.550781 | \n",
- " 112.125000 | \n",
- " Gamma Ray | \n",
- "
\n",
- " \n",
- " | 32 | \n",
- " CALI | \n",
- " in | \n",
- " 1 | \n",
- " 7.806348 | \n",
- " 7.917077 | \n",
- " Caliper | \n",
- "
\n",
- " \n",
- " | 33 | \n",
- " CS | \n",
- " m/h | \n",
- " 1 | \n",
- " 27.431999 | \n",
- " 1224.076782 | \n",
- " Cable Speed | \n",
- "
\n",
- " \n",
- " | 34 | \n",
- " DIFF | \n",
- " m | \n",
- " 1 | \n",
- " 0.838200 | \n",
- " 1.005840 | \n",
- " Depth Difference | \n",
- "
\n",
- " \n",
- " | 35 | \n",
- " DPHI | \n",
- " % | \n",
- " 1 | \n",
- " -13.867188 | \n",
- " 254.296875 | \n",
- " Density Porosity | \n",
- "
\n",
- " \n",
- " | 36 | \n",
- " DRHO | \n",
- " g/cm3 | \n",
- " 1 | \n",
- " -3.943359 | \n",
- " -0.230469 | \n",
- " Bulk Density Correction | \n",
- "
\n",
- " \n",
- " | 37 | \n",
- " FCNL | \n",
- " 1/s | \n",
- " 1 | \n",
- " 68.375000 | \n",
- " 581.000000 | \n",
- " Far Detector Count Rate | \n",
- "
\n",
- " \n",
- " | 38 | \n",
- " FFDC | \n",
- " 1/s | \n",
- " 1 | \n",
- " 3.626953 | \n",
- " 4552.000000 | \n",
- " Far FDC Count Rate | \n",
- "
\n",
- " \n",
- " | 39 | \n",
- " MARK | \n",
- " m | \n",
- " 1 | \n",
- " 0.000000 | \n",
- " 0.000000 | \n",
- " Magnetic Mark Detector Depth | \n",
- "
\n",
- " \n",
- " | 40 | \n",
- " NCNL | \n",
- " 1/s | \n",
- " 1 | \n",
- " 520.000000 | \n",
- " 1978.000000 | \n",
- " Near Detector Count Rate | \n",
- "
\n",
- " \n",
- " | 41 | \n",
- " NFDC | \n",
- " 1/s | \n",
- " 1 | \n",
- " 8.492188 | \n",
- " 522.500000 | \n",
- " Near FDC Count Rate | \n",
- "
\n",
- " \n",
- " | 42 | \n",
- " NPHI | \n",
- " % | \n",
- " 1 | \n",
- " 41.259766 | \n",
- " 125.878906 | \n",
- " Thermal Neutron Porosity (original Ratio Metho... | \n",
- "
\n",
- " \n",
- " | 43 | \n",
- " NRAT | \n",
- " | \n",
- " 1 | \n",
- " 3.318359 | \n",
- " 8.023438 | \n",
- " NCNL/FCNL Ratio | \n",
- "
\n",
- " \n",
- " | 44 | \n",
- " RCAL | \n",
- " in | \n",
- " 1 | \n",
- " 7.808594 | \n",
- " 7.921875 | \n",
- " Raw Caliper | \n",
- "
\n",
- " \n",
- " | 45 | \n",
- " RFFD | \n",
- " 1/s | \n",
- " 1 | \n",
- " 0.000000 | \n",
- " 6844.000000 | \n",
- " Raw Far FDC Count Rate | \n",
- "
\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " Mnemonic Unit Dim Min Max \\\n",
- "Index \n",
- "23 TDEP m 1 -6.857943 264.261597 \n",
- "24 RGR gAPI 1 1.937500 64.750000 \n",
- "25 RHGX g/cm3 1 -17.796875 15.843750 \n",
- "26 RHOB g/cm3 1 -1.547852 2.876953 \n",
- "27 RNFD 1/s 1 10.750000 994.000000 \n",
- "28 RNRA 1 2.769531 10.117188 \n",
- "29 TENS lbf 1 -5.000000 10.000000 \n",
- "30 TIME s 1 0.448000 19.920000 \n",
- "31 GR gAPI 1 3.550781 112.125000 \n",
- "32 CALI in 1 7.806348 7.917077 \n",
- "33 CS m/h 1 27.431999 1224.076782 \n",
- "34 DIFF m 1 0.838200 1.005840 \n",
- "35 DPHI % 1 -13.867188 254.296875 \n",
- "36 DRHO g/cm3 1 -3.943359 -0.230469 \n",
- "37 FCNL 1/s 1 68.375000 581.000000 \n",
- "38 FFDC 1/s 1 3.626953 4552.000000 \n",
- "39 MARK m 1 0.000000 0.000000 \n",
- "40 NCNL 1/s 1 520.000000 1978.000000 \n",
- "41 NFDC 1/s 1 8.492188 522.500000 \n",
- "42 NPHI % 1 41.259766 125.878906 \n",
- "43 NRAT 1 3.318359 8.023438 \n",
- "44 RCAL in 1 7.808594 7.921875 \n",
- "45 RFFD 1/s 1 0.000000 6844.000000 \n",
- "\n",
- " Long Name \n",
- "Index \n",
- "23 None \n",
- "24 Raw Gamma Ray \n",
- "25 Grain Density from Crossplot Porosity \n",
- "26 Bulk Density \n",
- "27 Uncalibrated Near Count Rate \n",
- "28 Raw NRAT \n",
- "29 Cable Tension \n",
- "30 Time Index \n",
- "31 Gamma Ray \n",
- "32 Caliper \n",
- "33 Cable Speed \n",
- "34 Depth Difference \n",
- "35 Density Porosity \n",
- "36 Bulk Density Correction \n",
- "37 Far Detector Count Rate \n",
- "38 Far FDC Count Rate \n",
- "39 Magnetic Mark Detector Depth \n",
- "40 Near Detector Count Rate \n",
- "41 Near FDC Count Rate \n",
- "42 Thermal Neutron Porosity (original Ratio Metho... \n",
- "43 NCNL/FCNL Ratio \n",
- "44 Raw Caliper \n",
- "45 Raw Far FDC Count Rate "
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Manual Access:\n",
- "dlis_manager_2 = DLISAccess(\"../stoneforge/datasets/DSDP_leg_96_hole_616_96_processed_data.dlis\", gui=False)\n",
- "data_info = dlis_manager_2.get_info()\n",
- "digital_files = list(data_info.keys())\n",
- "\n",
- "print('digital_files:',digital_files)\n",
- "\n",
- "for d_file in digital_files:\n",
- " print(\"frames in '{}':\".format(d_file),list(data_info[d_file].keys()))\n",
- "\n",
- "# () Accessing data info\n",
- "display(data_info['FDC/CNL/GR_main']['B59067'])\n",
- "\n",
- "display(data_info['FDC/CNL/GR_repeat']['B59180'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "fa017f06",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'FDC/CNL/GR_main': {'B59067': {'TDEP': {'values': array([372.4656 , 372.3132 , 372.1608 , ..., -4.1146564,\n",
- " -4.2670565, -4.4194565], shape=(2474,), dtype=float32),\n",
- " 'unit': 'm'},\n",
- " 'RHOB': {'values': array([2.0898438, 2.0898438, 2.0898438, ..., 1.1201172, 1.1132812,\n",
- " 1.109375 ], shape=(2474,), dtype=float32),\n",
- " 'unit': 'g/cm3'},\n",
- " 'GR': {'values': array([133.25 , 133.25 , 133.25 , ..., 3.765625 ,\n",
- " 3.7519531, 3.7539062], shape=(2474,), dtype=float32),\n",
- " 'unit': 'gAPI'},\n",
- " 'CALI': {'values': array([7.8555613, 7.8555613, 7.8555613, ..., 7.898622 , 7.9232283,\n",
- " 7.898622 ], shape=(2474,), dtype=float32),\n",
- " 'unit': 'in'},\n",
- " 'NPHI': {'values': array([ 61.279297, 61.279297, 61.279297, ..., 96.728516, 106.640625,\n",
- " 99.31641 ], shape=(2474,), dtype=float32),\n",
- " 'unit': '%'}}},\n",
- " 'FDC/CNL/GR_repeat': {'B59180': {'TDEP': {'values': array([264.2616 , 264.1092 , 263.9568 , ..., -6.5531425,\n",
- " -6.7055426, -6.8579426], shape=(1780,), dtype=float32),\n",
- " 'unit': 'm'},\n",
- " 'RHOB': {'values': array([1.9287109, 1.9287109, 1.9287109, ..., 1.1601562, 1.1777344,\n",
- " 1.1484375], shape=(1780,), dtype=float32),\n",
- " 'unit': 'g/cm3'}}}}"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Example (Manual Access): Accessing data by index\n",
- "dlis_manager_2.select_header(idx=(0, 8, 3, 9, 19, 23, 26)) #idx=[0, 8, 3, 9, 19, 23, 26]\n",
- "dlis_manager_2.get_data()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "5e2cf903",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'FDC/CNL/GR_main': {'B59067': {'RHOB': {'values': array([2.0898438, 2.0898438, 2.0898438, ..., 1.1201172, 1.1132812,\n",
- " 1.109375 ], shape=(2474,), dtype=float32),\n",
- " 'unit': 'g/cm3'},\n",
- " 'GR': {'values': array([133.25 , 133.25 , 133.25 , ..., 3.765625 ,\n",
- " 3.7519531, 3.7539062], shape=(2474,), dtype=float32),\n",
- " 'unit': 'gAPI'},\n",
- " 'CALI': {'values': array([7.8555613, 7.8555613, 7.8555613, ..., 7.898622 , 7.9232283,\n",
- " 7.898622 ], shape=(2474,), dtype=float32),\n",
- " 'unit': 'in'},\n",
- " 'NPHI': {'values': array([ 61.279297, 61.279297, 61.279297, ..., 96.728516, 106.640625,\n",
- " 99.31641 ], shape=(2474,), dtype=float32),\n",
- " 'unit': '%'}}},\n",
- " 'FDC/CNL/GR_repeat': {'B59180': {'RHOB': {'values': array([1.9287109, 1.9287109, 1.9287109, ..., 1.1601562, 1.1777344,\n",
- " 1.1484375], shape=(1780,), dtype=float32),\n",
- " 'unit': 'g/cm3'},\n",
- " 'GR': {'values': array([32.53125 , 32.53125 , 32.53125 , ..., 5.3007812, 6.4492188,\n",
- " 7.5976562], shape=(1780,), dtype=float32),\n",
- " 'unit': 'gAPI'},\n",
- " 'CALI': {'values': array([7.867864 , 7.867864 , 7.867864 , ..., 7.898622 , 7.9047737,\n",
- " 7.9109254], shape=(1780,), dtype=float32),\n",
- " 'unit': 'in'},\n",
- " 'NPHI': {'values': array([ 56.591797, 56.591797, 56.591797, ..., 101.26953 , 99.90234 ,\n",
- " 96.58203 ], shape=(1780,), dtype=float32),\n",
- " 'unit': '%'}}}}"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# By mnemonic search\n",
- "dlis_manager_3 = DLISAccess(\"../stoneforge/datasets/DSDP_leg_96_hole_616_96_processed_data.dlis\", gui=False)\n",
- "data_3 = dlis_manager_3.mnemonic_search(mnemonics_list=['CALI' ,'GR', 'RHOB', 'NPHI', 'DT', 'DTCO', 'ILD', 'DTS', 'DCAL', 'SP'])\n",
- "data_3\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "39e23a83",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "stoneforge",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.0"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/_las_2_data_access.ipynb b/examples/_las_2_data_access.ipynb
deleted file mode 100644
index cafffbf..0000000
--- a/examples/_las_2_data_access.ipynb
+++ /dev/null
@@ -1,187 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "e3533d5d",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 1,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from stoneforge.io.las2 import LAS2Parser\n",
- "import pandas as pd\n",
- "\n",
- "las2_obj = LAS2Parser(r\"../stoneforge/datasets/ES1.las\")\n",
- "las2_obj"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "837a0e7d",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " mnemonic | \n",
- " unit | \n",
- " value | \n",
- " description | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " | 0 | \n",
- " VERS | \n",
- " | \n",
- " 2.00 | \n",
- " CWLS LOG ASCII STANDARD - VERSION 2.00 | \n",
- "
\n",
- " \n",
- " | 1 | \n",
- " WRAP | \n",
- " | \n",
- " NO | \n",
- " ONE LINE PER DEPTH STEP | \n",
- "
\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " mnemonic unit value description\n",
- "0 VERS 2.00 CWLS LOG ASCII STANDARD - VERSION 2.00\n",
- "1 WRAP NO ONE LINE PER DEPTH STEP"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "las2_obj.header['version']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "c07c8cbd",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'DEPT': {'values': array([ 92. , 92.5, 93. , ..., 7738. , 7738.5, 7739. ],\n",
- " shape=(15295,)),\n",
- " 'unit': 'F',\n",
- " 'description': '1 DEPTH'},\n",
- " 'SP': {'values': array([ -30.3926, -30.221 , -29.8822, ..., -999. , -999. ,\n",
- " -999. ], shape=(15295,)),\n",
- " 'unit': 'MV',\n",
- " 'description': '2'},\n",
- " 'ILD': {'values': array([ 3.0692, 5.0267, 2.8043, ..., -999. , -999. ,\n",
- " -999. ], shape=(15295,)),\n",
- " 'unit': 'OHMM',\n",
- " 'description': '3'},\n",
- " 'ILM': {'values': array([ 8.2660e-01, 1.0292e+00, 1.6126e+00, ..., -9.9900e+02,\n",
- " -9.9900e+02, -9.9900e+02], shape=(15295,)),\n",
- " 'unit': 'OHMM',\n",
- " 'description': '4'},\n",
- " 'LL8': {'values': array([ 2.8159, 4.0912, 5.9442, ..., -999. , -999. ,\n",
- " -999. ], shape=(15295,)),\n",
- " 'unit': 'OHMM',\n",
- " 'description': '5'},\n",
- " 'CALI': {'values': array([-999., -999., -999., ..., -999., -999., -999.], shape=(15295,)),\n",
- " 'unit': 'IN',\n",
- " 'description': '6'},\n",
- " 'GR': {'values': array([-999. , -999. , -999. , ..., 88.8622, -999. ,\n",
- " -999. ], shape=(15295,)),\n",
- " 'unit': 'GAPI',\n",
- " 'description': '7'},\n",
- " 'DRHO': {'values': array([-9.99e+02, -9.99e+02, -9.99e+02, ..., 1.09e-02, -9.99e+02,\n",
- " -9.99e+02], shape=(15295,)),\n",
- " 'unit': 'G/C3',\n",
- " 'description': '8'},\n",
- " 'RHOB': {'values': array([-999. , -999. , -999. , ..., 2.6149, -999. ,\n",
- " -999. ], shape=(15295,)),\n",
- " 'unit': 'G/C3',\n",
- " 'description': '9'},\n",
- " 'NPHI': {'values': array([-999. , -999. , -999. , ..., 26.5081, -999. ,\n",
- " -999. ], shape=(15295,)),\n",
- " 'unit': '%',\n",
- " 'description': '10'},\n",
- " 'DT': {'values': array([-999., -999., -999., ..., -999., -999., -999.], shape=(15295,)),\n",
- " 'unit': 'US/F',\n",
- " 'description': '11'}}"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "las2_obj.data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "94ed6ee7",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "stoneforge",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
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- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.0"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/_las_3_data_access.ipynb b/examples/_las_3_data_access.ipynb
deleted file mode 100644
index 414e631..0000000
--- a/examples/_las_3_data_access.ipynb
+++ /dev/null
@@ -1,163 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "e3533d5d",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['VERSION INFORMATION',\n",
- " 'Well Information',\n",
- " 'Log_Parameter',\n",
- " 'Log_Definition',\n",
- " 'Drilling_Definition',\n",
- " 'Drilling_Data | Drilling_Definition',\n",
- " 'Core_Definition',\n",
- " 'Core_Data[1] | Core_Definition',\n",
- " 'Core_Data[2] | Core_Definition',\n",
- " 'Inclinometry_Definition',\n",
- " 'Inclinometry_Data | Inclinometry_Definition',\n",
- " 'Test_Definition',\n",
- " 'Test_Data | Test_Definition',\n",
- " 'TOPS_Definition',\n",
- " 'TOPS_Data | TOPS_Definition',\n",
- " 'Perforations_Definition',\n",
- " 'Perforations_Data | Perforations_Definition',\n",
- " 'Log_Data | Log_Definition']"
- ]
- },
- "execution_count": 1,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from stoneforge.io.las3 import LAS3Parser\n",
- "import pandas as pd\n",
- "\n",
- "las3_obj = LAS3Parser(r\"../stoneforge/datasets/example_las3.las\")\n",
- "las3_obj.tables"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "03c83efd",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\mario\\Documents\\GitHub\\stoneforge\\stoneforge\\io\\las3.py:88: UserWarning: DataFrame columns are not unique, some columns will be omitted.\n",
- " main_data = main_data.to_dict(orient=\"list\")\n"
- ]
- }
- ],
- "source": [
- "las3_obj.force_association()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "8b0a5dcf",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['VERSION INFORMATION',\n",
- " 'Well Information',\n",
- " 'Log_Parameter',\n",
- " 'Core_Definition',\n",
- " 'Core_Data[1] | Core_Definition',\n",
- " 'Core_Data[2] | Core_Definition',\n",
- " 'Log_Data',\n",
- " 'Drilling_Data',\n",
- " 'Inclinometry_Data',\n",
- " 'Test_Data',\n",
- " 'TOPS_Data',\n",
- " 'Perforations_Data']"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "las3_obj.tables"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "e883f393",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'DEPT': {'values': array([1670. , 1669.875, 1669.75 ]), 'unit': '.M'},\n",
- " 'DT': {'values': array([123.45, 123.45, 123.45]), 'unit': '.US/M'},\n",
- " 'RHOB': {'values': array([2550., 2550., 2550.]), 'unit': '.K/M3'},\n",
- " 'NPHI': {'values': array([0.45, 0.45, 0.45]), 'unit': '.V/V'},\n",
- " 'SFLU': {'values': array([123.45, 123.45, 123.45]), 'unit': '.OHMM'},\n",
- " 'SFLA': {'values': array([123.45, 123.45, 123.45]), 'unit': '.OHMM'},\n",
- " 'ILM': {'values': array([110.2, 110.2, 110.2]), 'unit': '.OHMM'},\n",
- " 'ILD': {'values': array([105.6, 105.6, 105.6]), 'unit': '.OHMM'},\n",
- " 'YME': {'values': array([1.45e+12, 1.47e+12, 2.85e+12]), 'unit': '.PA'},\n",
- " 'CDES': {'values': array(['DOLOMITE WI/VUGS', 'LIMESTOVE ', 'LOST INTERVAL '],\n",
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- "15688 -999.0 -999.0 \n",
- "15689 -999.0 -999.0 \n",
- "15690 -999.0 -999.0 \n",
- "15691 -999.0 -999.0 \n",
- "15692 -999.0 -999.0 \n",
- "\n",
- "[15693 rows x 11 columns]"
- ]
- },
- "execution_count": 104,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Las2 example usage\n",
- "\n",
- "ddt = WellProject()\n",
- "ddt.import_file(r\"../stoneforge/datasets/DP1.las\")['data']\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "50ce6e5b",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "stoneforge",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/data_management/0_tabr_data.ipynb b/examples/data_management/0_tabr_data.ipynb
new file mode 100644
index 0000000..67e3739
--- /dev/null
+++ b/examples/data_management/0_tabr_data.ipynb
@@ -0,0 +1,433 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "27d64643",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from stoneforge.data_management.preprocessing import DataLoader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "592a4d49",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Loading a tab-separated values file with specified standard\n",
+ "\n",
+ "tabr = DataLoader(r\"https://github.com/giecaruff/datasets/blob/main/wells/tab/evaluation/teste_tsv.tsv\", filetype='tabr', sep=\"\\t\", std=\"US\")\n",
+ "print(tabr.data_obj)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "48223f32",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'': {'values': array(['', '', '', ..., '