### Install pyGeoPressure from GitHub (Unzipped Archive)

Source: https://github.com/whimian/pygeopressure/blob/master/docs/install.md

Install pyGeoPressure by downloading the repository as a zip archive, unzipping it, navigating to the directory, and then using pip to install. This method is an alternative if git is not available.

```bash
pip install pyGeoPressure
```

--------------------------------

### Install pyGeoPressure from PyPI

Source: https://github.com/whimian/pygeopressure/blob/master/docs/install.md

Install the pyGeoPressure package using pip from the Python Package Index. This is the standard method for installing released versions.

```bash
pip install pygeopressure
```

--------------------------------

### Install pyGeoPressure from GitHub (Develop Branch)

Source: https://github.com/whimian/pygeopressure/blob/master/docs/install.md

Install the latest development version of pyGeoPressure directly from its GitHub repository using pip. This requires git to be installed.

```bash
pip install -e git://github.com/whimian/pyGeoPressure.git@develop
```

--------------------------------

### Importing Libraries and Setting Up Path

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/bowers_seis.ipynb

Imports necessary libraries and configures the system path for pygeopressure. This is a common setup for Python scripts interacting with the library.

```python
import warnings
warnings.filterwarnings(action='ignore')
# for python 2 and 3 compatibility
# from builtins import str
# try:
#     from pathlib import Path
# except:
#     from pathlib2 import Path
#--------------------------------------------
import sys
ppath = "../.."

if ppath not in sys.path:
    sys.path.append(ppath)
#--------------------------------------------
```

--------------------------------

### Minimal .well File Structure

Source: https://github.com/whimian/pygeopressure/blob/master/docs/howtos/new_well.md

This is a minimal example of a .well file, containing essential information for defining a new well. Ensure all fields are correctly populated.

```default
{
    "well_name": "CUG1",
    "loc": [
        707838,
        3274780
    ],
    "KB": 23,
    "WD": 85,
    "TD": 5000,
    "hdf_file": "well_data.h5"
}
```

--------------------------------

### Create Development Environment with Conda (Python 2.7)

Source: https://github.com/whimian/pygeopressure/blob/master/docs/install.md

Set up a development environment for pyGeoPressure using a conda environment file, specifically for Python 2.7. This ensures all necessary dependencies for testing are installed.

```bash
conda env create --file test/test_env_2.yml
```

--------------------------------

### Pore Pressure Prediction Example

Source: https://github.com/whimian/pygeopressure/blob/master/README.md

Demonstrates pore pressure prediction using well log data with pyGeoPressure. It includes optimizing parameters for Eaton's method and plotting the results against overburden pressure and horizons.

```python
import pygeopressure as ppp

survey = ppp.Survey("CUG")

well = survey.wells['CUG1']

a, b = ppp.optimize_nct(well.get_log("Velocity"),
                        well.params['horizon']["T16"],
                        well.params['horizon']["T20"])
n = ppp.optimize_eaton(well, "Velocity", "Overburden_Pressure", a, b)

pres_eaton_log = well.eaton(np.array(well.get_log("Velocity").data), n)

fig, ax = plt.subplots()
ax.invert_yaxis()

pres_eaton_log.plot(ax, color='blue')
well.get_log("Overburden_Pressure").plot(ax, 'g')
ax.plot(well.hydrostatic, well.depth, 'g', linestyle='--')
well.plot_horizons(ax)
```

--------------------------------

### Create Development Environment with Conda (Python 3.6)

Source: https://github.com/whimian/pygeopressure/blob/master/docs/install.md

Set up a development environment for pyGeoPressure using a conda environment file, specifically for Python 3.6. This ensures all necessary dependencies for testing are installed.

```bash
conda env create --file test/test_env_3.yml
```

--------------------------------

### Extracting Log Data for Optimization

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/multivariate.ipynb

Gets the Velocity, Porosity, Shale Volume, and Overburden Pressure logs for a specific well. These logs are required for multivariate model optimization.

```python
vel_log = well_cug1.get_log("Velocity")
por_log = well_cug1.get_log("Porosity")
vsh_log = well_cug1.get_log("Shale_Volume")

obp_log = well_cug1.get_log("Overburden_Pressure")
```

--------------------------------

### Create Conda Environment with Python 2.7

Source: https://github.com/whimian/pygeopressure/blob/master/docs/install.md

Use this command to create a new conda environment named 'ENV' with Python 2.7 and pip installed, if Python 2.7 is required.

```bash
conda update conda
conda create -n ENV python=2.7 pip
```

--------------------------------

### Get velocity log data

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/bowers_well.ipynb

Extracts the velocity log data for the selected well. This log is crucial for Bowers' method calculations.

```python
vel_log = well_cug1.get_log("Velocity")
```

--------------------------------

### Get LAS File Type

Source: https://github.com/whimian/pygeopressure/blob/master/docs/howtos/import_well_log_data.ipynb

Determines the type of the loaded LAS file (e.g., 'las' or 'pseudo-las'). This can be useful for understanding how to process the data.

```python
las_data.file_type
```

--------------------------------

### Create Conda Environment with Python 3.6

Source: https://github.com/whimian/pygeopressure/blob/master/docs/install.md

Use this command to create a new conda environment named 'ENV' with Python 3.6 and pip installed. This is recommended for isolating project dependencies.

```bash
conda update conda
conda create -n ENV python=3.6 pip
```

--------------------------------

### Get Specific Well Object

Source: https://github.com/whimian/pygeopressure/blob/master/docs/howtos/import_well_log_data.ipynb

Accesses a specific well object from the survey using its name. This object is then used for further data manipulation.

```python
cug3 = survey.wells['CUG3']
```

--------------------------------

### Survey Geometry Definition Example

Source: https://github.com/whimian/pygeopressure/blob/master/docs/howtos/survey.md

This JSON structure defines the geometry of a geophysical survey, including its name, coordinates of three reference points (A, B, C), and the extents and step for inline, crossline, and z-coordinates.

```json
{
    "name": "CUG_depth",
    "point_A": [6400, 4100, 701319, 3274887],
    "point_B": [6400, 4180, 702185, 3274387],
    "point_C": [6440, 4180, 702685, 3275253],
    "inline_range": [6400, 7000, 20],
    "crline_range": [4100, 6020, 40],
    "z_range": [0, 5000, 4, "m"]
}
```

--------------------------------

### Import necessary libraries and configure warnings

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/eaton_seis.ipynb

Imports warnings and configures them to be ignored. Includes compatibility for Python 2 and 3 path manipulation.

```python
import warnings
warnings.filterwarnings(action='ignore')
# for python 2 and 3 compatibility
# from builtins import str
# try:
#     from pathlib import Path
# except:
#     from pathlib2 import Path
#--------------------------------------------
import sys
ppath = "../.."

if ppath not in sys.path:
    sys.path.append(ppath)
#---------------------------------------------
```

--------------------------------

### Create Survey Directory

Source: https://github.com/whimian/pygeopressure/blob/master/docs/howtos/survey.md

Use the `create_survey_directory` function to automatically build the necessary folder structure for a new survey. This includes sub-folders for Seismics, Surfaces, and Wellinfo, along with a base .survey file.

```python
import pygeopressure as ppp

ppp.create_survey_directory(ROOTDIR, SURVEY_NAME)
```

--------------------------------

### Initialize a Survey Object

Source: https://github.com/whimian/pygeopressure/blob/master/docs/howtos/survey.md

Initialize a Survey object by providing the path to the survey folder. This object will hold survey geometry and references to associated seismic and well data.

```python
import pygeopressure as ppp

survey = ppp.Survey('path/to/survey/folder')
```

--------------------------------

### Visualizing Multivariate Model Optimization Results

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/multivariate.ipynb

Plots the input logs (Velocity, Porosity, Shale Volume, Overburden Pressure) and the Velocity predicted by the optimized multivariate model. This helps in assessing the model's fit.

```python
fig, axes = plt.subplots(ncols=4, nrows=1, sharey=True)
axes[0].invert_yaxis()

ppp.plot_multivariate(
    axes,
    well_cug1,
    vel_log, por_log, vsh_log, obp_log, 1500, 3500, a0, a1, a2, a3, 
    well_cug1.params['bowers']['B'])
```

--------------------------------

### Seismic Cube Configuration

Source: https://github.com/whimian/pygeopressure/blob/master/docs/howtos/new_seismic.md

Configure a seismic cube by creating a .seis file in the 'Seismics' folder. This JSON defines the path to the SEGY file and the ranges for inline, z, and crossline data, along with depth information and property type.

```default
{
    "path": "velocity.sgy",
    "inline_range": [200, 650, 2],
    "z_range": [400, 1100, 4],
    "crline_range": [700, 1200, 2],
    "inDepth": true,
    "Property_Type": "Velocity"
}
```

--------------------------------

### Initialize Survey

Source: https://github.com/whimian/pygeopressure/blob/master/docs/howtos/import_well_log_data.ipynb

Initializes the survey object with the path to the survey folder. This is a prerequisite for accessing well data.

```python
survey = ppp.Survey(Path(SURVEY_FOLDER))
```

--------------------------------

### Import plotting and pygeopressure libraries

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/eaton_seis.ipynb

Imports plotting libraries like matplotlib and the pygeopressure library. Sets up plotting styles for better visualization.

```python
from __future__ import print_function, division, unicode_literals
%matplotlib inline
import matplotlib.pyplot as plt

plt.style.use(['seaborn-paper', 'seaborn-whitegrid'])
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False

import numpy as np

import pygeopressure as ppp
```

--------------------------------

### Optimizing Multivariate Model Coefficients

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/multivariate.ipynb

Calculates the optimal coefficients (a0, a1, a2, a3) for the multivariate pressure model using provided well logs and parameters. The 'upper' and 'lower' bounds define the depth range for optimization.

```python
a0, a1, a2, a3 = ppp.optimize_multivaraite(
    well=well_cug1, 
    obp_log=obp_log, 
    vel_log=vel_log,
    por_log=por_log, 
    vsh_log=vsh_log, 
    B=well_cug1.params['bowers']['B'],
    upper=1500, lower=3500)
```

--------------------------------

### Load seismic survey data

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/obp_seis.ipynb

Initializes a Survey object using a specified survey folder. This step is required to access seismic data like velocity cubes.

```python
# set to the directory on your computer
SURVEY_FOLDER = "C:/Users/yuhao/Desktop/CUG_depth"

survey = ppp.Survey(Path(SURVEY_FOLDER))
```

--------------------------------

### Load seismic data cubes

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/eaton_seis.ipynb

Loads the 'velocity' and 'obp_new' seismic data cubes from the survey.

```python
vel_cube = survey.seismics['velocity']
obp_cube = survey.seismics['obp_new']
```

--------------------------------

### Optimize Bowers loading equation coefficients

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/bowers_well.ipynb

Calculates the 'A' and 'B' coefficients for Bowers' loading equation using the processed velocity log and overburden pressure log. This step requires specifying the well, logs, and relevant horizons.

```python
a, b, err = ppp.optimize_bowers_virgin(
    well=well_cug1, 
    vel_log=vel_log_filter_smooth, 
    obp_log='Overburden_Pressure',
    upper='T12', 
    lower='T20',
    pres_log='loading', 
    mode='both')
```

--------------------------------

### Pressure Prediction with Multivariate Model

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/multivariate.ipynb

Generates a pressure log using the optimized multivariate model. This log represents the predicted pressure based on velocity, porosity, and shale volume.

```python
pres_log = well_cug1.multivariate(vel_log, por_log, vsh_log)
```

--------------------------------

### Clone pyGeoPressure GitHub Repository

Source: https://github.com/whimian/pygeopressure/blob/master/docs/install.md

Clone the pyGeoPressure GitHub repository to your local machine. This is the first step for developers who want to work with the source code.

```bash
git clone https://github.com/whimian/pyGeoPressure.git
```

--------------------------------

### Run Pytest with Code Coverage

Source: https://github.com/whimian/pygeopressure/blob/master/docs/install.md

Execute all tests for pyGeoPressure using pytest and generate a code coverage report. This command should be run from the project's root directory.

```bash
pytest --cov
```

--------------------------------

### Calculate Hydrostatic Pressure (Shortcut)

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/obp_well.ipynb

Provides a shortcut for calculating hydrostatic pressure using well parameters stored within the Well object. This function is commented out in the source.

```python
# hydro_log = ppp.hydrostatic_well(
#     obp_log.depth, kb=well_cug1.kelly_bushing, wd=well_cug1.water_depth, 

```

--------------------------------

### Visualize predicted pressure against other logs

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/bowers_well.ipynb

Plots the predicted pore pressure (Bowers model) alongside hydrostatic and lithostatic pressure logs, and well horizons. This visualization aids in assessing the prediction's plausibility.

```python
fig_pres, ax_pres = plt.subplots()
ax_pres.invert_yaxis()
# plot hydrostatic
well_cug1.hydro_log().plot(ax_pres, linestyle='--', color='green', label='Hydrostatic')
# plot OBP
well_cug1.get_log("Overburden_Pressure").plot(ax_pres, color='green', label='Lithostatic')
# plot pressure
pres_log.plot(ax_pres, label='Bowers', color='blue')
# plot horizon
well_cug1.plot_horizons(ax_pres)


# set fig style
ax_pres.set(ylim=(5000,0), aspect=(100/5000)*2)
ax_pres.legend()
fig_pres.set_figheight(8)
```

--------------------------------

### Applying Bowers Method for Pressure Prediction

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/bowers_seis.ipynb

Performs pore pressure prediction using the Bowers method with seismic velocity. The 'optimize' mode is used, and horizons T16 and T20 define the boundaries for the prediction.

```python
bowers_cube = ppp.bowers_seis(
    "bowers_new", obp_cube, vel_cube, 
    upper=survey.horizons['T16'], lower=survey.horizons['T20'], 
    mode='optimize')
```

--------------------------------

### Select Well and Load Density Log

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/obp_well.ipynb

Selects a specific well ('CUG1') from the survey and retrieves its density log data.

```python
well_cug1 = survey.wells['CUG1']

den_log = well_cug1.get_log("Density")
```

--------------------------------

### Extract Normal Compaction Trend parameters 'a' and 'b'

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/eaton_seis.ipynb

Extracts the 'a' and 'b' parameters for the Normal Compaction Trend from the 'CUG1' well data.

```python
a = well_cug1.params['nct']["a"]
b = well_cug1.params['nct']["b"]
```

--------------------------------

### Optimize Normal Compaction Trend Coefficients

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/eaton_well.ipynb

Optimizes the coefficients 'a' and 'b' for the normal compaction trend (NCT) using velocity log data between specified horizon depths.

```python
a, b = ppp.optimize_nct(
    vel_log=well_cug1.get_log("Velocity"),
    fit_start=well_cug1.params['horizon']["T16"],
    fit_stop=well_cug1.params['horizon']["T20"])

```

--------------------------------

### Adding Overburden Pressure Log

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/obp_well.ipynb

This snippet shows how to add a calculated Overburden Pressure log to the well data. It is commented out as the data may already be saved.

```python
# well_cug1.add_log("Overbuden_Pressure")
```

--------------------------------

### Optimize Eaton's Exponent 'n'

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/eaton_well.ipynb

Optimizes Eaton's exponent 'n' using processed velocity, overburden pressure log, and NCT coefficients. This is a key step in Eaton's method for pore pressure prediction.

```python
n = ppp.optimize_eaton(
    well=well_cug1, 
    vel_log=vel_log_filter_smooth, 
    obp_log="Overburden_Pressure", 
    a=a, b=b)
```

--------------------------------

### View density data section

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/obp_seis.ipynb

Displays a 2D section of the computed density cube. This visualization aids in verifying the density estimation results.

```python
fig_den, ax_den = plt.subplots()

im = den_cube.plot(ppp.InlineIndex(7400), ax_den, kind='img', cm='gist_earth')
fig_den.colorbar(im)
fig_den.set(figwidth=8)
```

--------------------------------

### View Imported Logs

Source: https://github.com/whimian/pygeopressure/blob/master/docs/howtos/import_well_log_data.ipynb

Lists the names of the well log curves that have been successfully imported into the Well object. This is useful for verification.

```python
cug3.logs
```

--------------------------------

### Load survey data

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/eaton_seis.ipynb

Loads the seismic survey data from a specified directory. Ensure the SURVEY_FOLDER path is correctly set to your data location.

```python
# set to the directory on your computer
SURVEY_FOLDER = "M:/CUG_depth"

survey = ppp.Survey(SURVEY_FOLDER)
```

--------------------------------

### Process Density Log (Filter and Smooth)

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/obp_well.ipynb

Applies filtering and smoothing to the density log data to reduce noise and high-frequency variations. This prepares the log for extrapolation.

```python
den_log_filter = ppp.upscale_log(den_log, freq=20)

den_log_filter_smooth = ppp.smooth_log(den_log_filter, window=1501)
```

--------------------------------

### Apply Eaton method for pressure prediction

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/eaton_seis.ipynb

Applies the Eaton method using seismic data to predict pore pressure. This function optimizes 'a' and 'b' coefficients and requires seismic cubes and horizon data.

```python
eaton_cube = ppp.eaton_seis(
    "eaton_new", obp_cube, vel_cube, n=3, 
    upper=survey.horizons['T16'], lower=survey.horizons['T20'])
```

--------------------------------

### Plotting Predicted Pressure Data

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/bowers_seis.ipynb

Visualizes a slice of the predicted pore pressure cube generated by the Bowers method. Uses a custom seismic colormap for display.

```python
from pygeopressure.basic.vawt import opendtect_seismic_colormap

fig_pres, ax_pres = plt.subplots()

im = bowers_cube.plot(
    ppp.InlineIndex(8000), ax_pres, 
    kind='img', cm=opendtect_seismic_colormap())

fig_pres.colorbar(im)
fig_pres.set(figwidth=8)
```

--------------------------------

### Load Survey Data

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/eaton_well.ipynb

Loads the survey data from a specified directory. This step is crucial for accessing well logs and other survey-specific information.

```python
# set to the directory on your computer
SURVEY_FOLDER = "C:/Users/yuhao/Desktop/CUG_depth"

survey = ppp.Survey(SURVEY_FOLDER)
```

--------------------------------

### Predict pore pressure using Bowers model

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/bowers_well.ipynb

Applies the calculated Bowers coefficients (A, B, U) to predict pore pressure using the processed velocity log. The result is stored in a new log object.

```python
pres_log = well_cug1.bowers(
    vel_log=vel_log_filter_smooth, a=a, b=b, u=u)
```

--------------------------------

### Extract Normal Compaction Trend parameter 'n'

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/eaton_seis.ipynb

Extracts the 'n' parameter for the Normal Compaction Trend from the 'CUG1' well data.

```python
n = well_cug1.params['n']
```

--------------------------------

### Plot Velocity Log and Horizons

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/eaton_well.ipynb

Visualizes the velocity log data along with the well's geological horizons. This helps in understanding the subsurface structure and identifying key intervals.

```python
fig_vel, ax_vel = plt.subplots()
ax_vel.invert_yaxis()
vel_log.plot(ax_vel)
well_cug1.plot_horizons(ax_vel)

# set fig style
ax_vel.set(ylim=(5000,0), aspect=(5000/4600)*2)
ax_vel.set_aspect(2)
fig_vel.set_figheight(8)
```

--------------------------------

### Predict Pore Pressure using Eaton's Method

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/eaton_well.ipynb

Calculates pore pressure using Eaton's method with the optimized exponent 'n', processed velocity log, normal velocity trend, hydrostatic pressure, and overburden pressure.

```python
pres_eaton_log = well_cug1.eaton(vel_log_filter_smooth, n=n)
```

--------------------------------

### Plotting Pressure Prediction Results

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/multivariate.ipynb

Visualizes the predicted pressure log against hydrostatic and lithostatic pressure curves. Includes the original, upscaled, and smoothed predicted pressure logs for comparison.

```python
fig_pres, ax_pres = plt.subplots()
ax_pres.invert_yaxis()

well_cug1.hydro_log().plot(ax_pres, color='green', linestyle='--',
                           zorder=2, label='hydrostatic')

well_cug1.get_log("Overburden_Pressure").plot(ax_pres, color='g',
                                              label='lithostatic')

pres_log.plot(ax_pres, label='mutlivaraite', zorder=1)

pres_log_filter_smooth.plot(ax_pres, label='smoothed', zorder=5, color='b')

ax_pres.set(xlim=[0,100], ylim=[5000,0], aspect=(100/5000)*2)
ax_pres.legend()
fig_pres.set(figheight=8)
fig_pres.show()
```

--------------------------------

### Calculate Overburden Pressure

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/obp_well.ipynb

Calculates the Overburden Pressure (OBP) log using the extrapolated density log and well parameters like kelly bushing and water depth.

```python
obp_log = ppp.obp_well(extra_log, 
         kb=well_cug1.kelly_bushing, wd=well_cug1.water_depth, 
         rho_w=1.01)
```

--------------------------------

### Plot Density Log with Traugott Trend

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/obp_well.ipynb

Visualizes the original density log along with the fitted density trend line calculated using the optimized Traugott equation coefficients.

```python
fig_den, ax_den = plt.subplots()
ax_den.invert_yaxis()
# draw density log
den_log.plot(ax_den, label='Density')
# draw fitted density trend line
den_trend = ppp.traugott_trend(
    np.array(den_log.depth), a, b, 
    kb=well_cug1.kelly_bushing, wd=well_cug1.water_depth)

ax_den.plot(den_trend, den_log.depth, 
            color='r', linestyle='--', zorder=2, label='Trend')

# set style
ax_den.set(ylim=(5000,0), aspect=(1.2/5000)*2)
ax_den.legend()
fig_den.set_figheight(8)
fig_den.show()
```

--------------------------------

### Manage Well Storage in HDF5

Source: https://github.com/whimian/pygeopressure/blob/master/docs/howtos/import_well_log_data.ipynb

Initializes a WellStorage object to manage well log data stored in an HDF5 file. This is used for persistent storage and retrieval.

```python
storage = ppp.WellStorage(hdf5_file='C:/Users/yuhao/Desktop/CUG_depth/Wellinfo/well_data.h5')
```

--------------------------------

### Optimize Traugott Equation Coefficients

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/obp_well.ipynb

Calculates the optimized coefficients (a, b) for the Traugott equation using density log data within a specified depth range.

```python
a, b = ppp.optimize_traugott(
    den_log, 2000, 3000, kb=well_cug1.kelly_bushing, wd=well_cug1.water_depth)
```

--------------------------------

### Plot Bowers loading and unloading curves

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/bowers_well.ipynb

Draws both the virgin (loading) and unloading curves on the same plot using the optimized parameters. This provides a comprehensive view of the pressure model.

```python
fig_bowers, ax_bowers = plt.subplots()
# draw virgin(loading) curve
ppp.plot_bowers_vrigin(
    ax=ax_bowers,
    well=well_cug1, 
    a=a,
    b=b,
    vel_log=vel_log_filter_smooth, 
    obp_log='Overburden_Pressure', 
    upper='T12', 
    lower='T20',
    pres_log='loading', 
    mode='both')

# draw unloading curve
ppp.plot_bowers_unloading(
    ax=ax_bowers,
    a=a,
    b=b,
    vmax=4600,
    u=u,
    well=well_cug1,         
    vel_log=vel_log_filter_smooth, 
    obp_log='Overburden_Pressure', 
    pres_log='unloading')
```

--------------------------------

### Read LAS/Pseudo-LAS File

Source: https://github.com/whimian/pygeopressure/blob/master/docs/howtos/import_well_log_data.ipynb

Reads well log data from a specified LAS or pseudo-LAS file using the LasData class. This is the first step in importing data from external files.

```python
las_data = ppp.LasData(las_file="C:/Users/yuhao/Desktop/CUG_depth/log_curves.las")
```

--------------------------------

### Calculate Overburden Pressure

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/obp_seis.ipynb

Calculates the overburden pressure (OBP) cube using the previously computed density cube. The 'obp_new' parameter likely specifies the calculation method or output name.

```python
obp_cube = ppp.obp_seis("obp_new", den_cube)
```

--------------------------------

### Retrieving Bowers Coefficients

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/bowers_seis.ipynb

Extracts the Bowers method coefficients 'A' and 'B' from the well data. These coefficients are crucial for the pressure prediction calculation.

```python
a = well_cug1.params['bowers']["A"]
b = well_cug1.params['bowers']['B']
```

--------------------------------

### Plotting OBP Seismic Data

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/bowers_seis.ipynb

Visualizes a slice of the OBP seismic data cube. This provides a visual reference for the overburden pressure data.

```python
fig, ax = plt.subplots()
im = obp_cube.plot(ppp.InlineIndex(7400), ax, kind='img')
fig.colorbar(im)
fig.set(figwidth=8)
```

--------------------------------

### Plot calculated OBP section with custom colormap

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/obp_seis.ipynb

Visualizes a 2D section of the calculated overburden pressure cube. It utilizes a custom colormap defined in OpenDtect for display.

```python
from pygeopressure.basic.vawt import opendtect_seismic_colormap

fig_obp, ax_obp = plt.subplots()

im = obp_cube.plot(ppp.InlineIndex(7400), ax_obp, kind='img', cm=opendtect_seismic_colormap())

fig_obp.colorbar(im)
fig_obp.set(figwidth=8)
```

--------------------------------

### BibTeX Citation for pyGeoPressure

Source: https://github.com/whimian/pygeopressure/blob/master/README.md

Provides the BibTeX entry for citing the pyGeoPressure package in academic work.

```bibtex
@article{yu2018pygeopressure,
  title = {{PyGeoPressure}: {Geopressure} {Prediction} in {Python}},
  author = {Yu, Hao},
  journal = {Journal of Open Source Software},
  volume = {3},
  pages = {922}
  number = {30},
  year = {2018},
  doi = {10.21105/joss.00992},
}
```

--------------------------------

### pygeopressure.basic.indexes.InlineIndex

Source: https://github.com/whimian/pygeopressure/blob/master/docs/api/pygeopressure.basic.md

Represents a survey index based on inline values.

```APIDOC
## class pygeopressure.basic.indexes.InlineIndex(value)

Bases: [`SurveyIndex`](#pygeopressure.basic.indexes.SurveyIndex)
```

--------------------------------

### pygeopressure.basic.indexes.SurveyIndex

Source: https://github.com/whimian/pygeopressure/blob/master/docs/api/pygeopressure.basic.md

Base class for survey index definitions.

```APIDOC
## class pygeopressure.basic.indexes.SurveyIndex(value)

Bases: `object`
```

--------------------------------

### Plot Normal Compaction Trend

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/eaton_well.ipynb

Visualizes the original velocity log, geological horizons, and the calculated normal compaction trend (NCT). This comparison aids in assessing the quality of the NCT.

```python
fig_vel, ax_vel = plt.subplots()
ax_vel.invert_yaxis()
# plot velocity
vel_log.plot(ax_vel, label='Velocity')
# plot horizon
well_cug1.plot_horizons(ax_vel)
# plot fitted nct
nct_log.plot(ax_vel, color='r', zorder=2, label='NCT')

# set fig style
ax_vel.set(ylim=(5000,0), aspect=(5000/4600)*2)
ax_vel.set_aspect(2)
ax_vel.legend()
fig_vel.set_figheight(8)
```

--------------------------------

### pygeopressure.basic.indexes.DepthIndex

Source: https://github.com/whimian/pygeopressure/blob/master/docs/api/pygeopressure.basic.md

Represents a survey index based on depth values.

```APIDOC
## class pygeopressure.basic.indexes.DepthIndex(value)

Bases: [`SurveyIndex`](#pygeopressure.basic.indexes.SurveyIndex)
```

--------------------------------

### Process velocity log data

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/bowers_well.ipynb

Applies upscaling and smoothing filters to the raw velocity log. This prepares the data for accurate coefficient optimization.

```python
vel_log_filter = ppp.upscale_log(vel_log, freq=20)

vel_log_filter_smooth = ppp.smooth_log(vel_log_filter, window=1501)
```

--------------------------------

### Upscaling and Smoothing Pressure Log

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/multivariate.ipynb

Applies upscaling and smoothing filters to the predicted pressure log. This can help in reducing noise and highlighting trends in the pressure data.

```python
pres_log_filter = ppp.upscale_log(pres_log, freq=20)

pres_log_filter_smooth = ppp.smooth_log(pres_log_filter, window=1501)
```

--------------------------------

### pygeopressure.basic.threepoints.Not_threepoints_v1_Exception

Source: https://github.com/whimian/pygeopressure/blob/master/docs/api/pygeopressure.basic.md

Custom exception for data not conforming to three points version 1 format.

```APIDOC
## exception pygeopressure.basic.threepoints.Not_threepoints_v1_Exception(message=None)

Bases: `Exception`
```

--------------------------------

### Plot Predicted Pore Pressure

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/eaton_well.ipynb

Visualizes the predicted pore pressure using Eaton's method alongside the lithostatic pressure, hydrostatic pressure, and geological horizons. This provides a comprehensive view of the pressure regime.

```python
fig_pres, ax_pres = plt.subplots()
ax_pres.invert_yaxis()

well_cug1.get_log("Overburden_Pressure").plot(ax_pres, 'g', label='Lithostatic')
ax_pres.plot(well_cug1.hydrostatic, well_cug1.depth, 'g', linestyle='--', label="Hydrostatic")
pres_eaton_log.plot(ax_pres, color='blue', label='Pressure_Eaton')
well_cug1.plot_horizons(ax_pres)

# set figure and axis size
ax_pres.set_aspect(2/50)
ax_pres.legend()
fig_pres.set_figheight(8)
```

--------------------------------

### View Wells in HDF5 Storage

Source: https://github.com/whimian/pygeopressure/blob/master/docs/howtos/import_well_log_data.ipynb

Lists the names of all wells currently stored in the HDF5 file. This is useful for verifying that data has been correctly added.

```python
storage.wells
```

--------------------------------

### Process Velocity Log for Eaton's Method

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/eaton_well.ipynb

Applies filtering and smoothing to the velocity log data. Processed velocity is required for accurate optimization of Eaton's exponent 'n'.

```python
# Velocity log processing (filtering and smoothing):
vel_log_filter = ppp.upscale_log(vel_log, freq=20)

vel_log_filter_smooth = ppp.smooth_log(vel_log_filter, window=1501)
```

--------------------------------

### Visualize processed velocity log

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/bowers_well.ipynb

Plots the original and processed velocity logs along with well horizons. This helps in visually assessing the effect of the filtering steps.

```python
fig_vel, ax_vel = plt.subplots()
ax_vel.invert_yaxis()
# plot velocity
vel_log.plot(ax_vel, label='Velocity')
# plot horizon
well_cug1.plot_horizons(ax_vel)
# plot processed velocity
vel_log_filter_smooth.plot(ax_vel, color='g', zorder=2, label='Processed', linewidth=1)

# set fig style
ax_vel.set(ylim=(5000,0), aspect=(4600/5000)*2)
ax_vel.legend()
fig_vel.set_figheight(8)
```

--------------------------------

### Plot Bowers virgin (loading) curve

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/bowers_well.ipynb

Visualizes the calculated Bowers virgin curve using the optimized 'A' and 'B' coefficients. This plot helps in validating the loading equation fit.

```python
fig_bowers, ax_bowers = plt.subplots()

ppp.plot_bowers_vrigin(
    ax=ax_bowers,
    well=well_cug1, 
    a=a,
    b=b,
    vel_log=vel_log_filter_smooth, 
    obp_log='Overburden_Pressure', 
    upper='T12', 
    lower='T20',
    pres_log='loading', 
    mode='both')
```

--------------------------------

### pygeopressure.basic.threepoints.Not_threepoints_v2_Exception

Source: https://github.com/whimian/pygeopressure/blob/master/docs/api/pygeopressure.basic.md

Custom exception for data not conforming to three points version 2 format.

```APIDOC
## exception pygeopressure.basic.threepoints.Not_threepoints_v2_Exception(message=None)

Bases: `Exception`
```

--------------------------------

### Plotting Hydrostatic and OBP Logs

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/obp_well.ipynb

Plots the hydrostatic and Overburden Pressure (OBP) logs for a well. Ensure matplotlib is imported as plt and the necessary log data (hydro_log, obp_log) is available. The y-axis is inverted to represent depth correctly.

```python
fig_obp, ax_obp = plt.subplots()
ax_obp.invert_yaxis()

hydro_log.plot(ax_obp, color='g', linestyle='--', label='Hydrostatic')

obp_log.plot(ax_obp, color='b', label='OBP')

# set style
ax_obp.set(ylim=(5000,0), aspect=(100/5000)*2)
ax_obp.legend()
fig_obp.set_figheight(8)
fig_obp.show()
```

--------------------------------

### Calculate density from velocity

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/obp_seis.ipynb

Computes the density cube from the velocity cube using the Gardner equation. This function 'den_from_vel' is a specific implementation for this conversion.

```python
den_cube = ppp.gardner_seis("den_from_vel", vel_cube)
```

--------------------------------

### pygeopressure.basic.indexes.CdpIndex

Source: https://github.com/whimian/pygeopressure/blob/master/docs/api/pygeopressure.basic.md

Represents a survey index based on CDP (Common Depth Point).

```APIDOC
## class pygeopressure.basic.indexes.CdpIndex(cdp)

Bases: [`SurveyIndex`](#pygeopressure.basic.indexes.SurveyIndex)
```

--------------------------------

### Plot predicted pressure cube

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/eaton_seis.ipynb

Visualizes a slice of the predicted pressure cube generated by the Eaton method. Uses a custom OpendTect seismic colormap for display.

```python
from pygeopressure.basic.vawt import opendtect_seismic_colormap

fig_pres, ax_pres = plt.subplots()

im = eaton_cube.plot(
    ppp.InlineIndex(8000), ax_pres, 
    kind='img', cm=opendtect_seismic_colormap())

fig_pres.colorbar(im)
fig_pres.set(figwidth=8)
```

--------------------------------

### Horizon File Header

Source: https://github.com/whimian/pygeopressure/blob/master/docs/howtos/new_seismic.md

Define the header for a horizon data file. This TSV file requires three columns: 'inline', 'crline', and 'z', representing the inline, crossline, and Z values for each point on the horizon.

```default
inline  crline  z
```

--------------------------------

### pygeopressure.basic.indexes.CrlineIndex

Source: https://github.com/whimian/pygeopressure/blob/master/docs/api/pygeopressure.basic.md

Represents a survey index based on crossline values.

```APIDOC
## class pygeopressure.basic.indexes.CrlineIndex(value)

Bases: [`SurveyIndex`](#pygeopressure.basic.indexes.SurveyIndex)
```

--------------------------------

### Plot velocity data section

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/obp_seis.ipynb

Visualizes a 2D section of the velocity cube using a specified inline index and colormap. Helps in understanding the seismic data.

```python
fig_vel, ax_vel = plt.subplots()

im = vel_cube.plot(ppp.InlineIndex(7400), ax_vel, kind='img', cm='gist_rainbow')
fig_vel.colorbar(im)
fig_vel.set(figwidth=8)
```

--------------------------------

### Plot seismic velocity data

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/eaton_seis.ipynb

Visualizes a slice of the seismic velocity data cube. The plot uses a 'gist_rainbow' colormap and is set to a figure width of 8.

```python
fig_vel, ax_vel = plt.subplots()

im = vel_cube.plot(
    ppp.InlineIndex(8000), ax_vel, kind='img', cm='gist_rainbow')
fig_vel.colorbar(im)
fig_vel.set(figwidth=8)
```

--------------------------------

### Plot Extrapolated Density Log

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/obp_well.ipynb

Visualizes the original density log, the trend line, the smoothed log, and the final extrapolated density log.

```python
fig_den, ax_den = plt.subplots()
ax_den.invert_yaxis()
# draw density log
den_log.plot(ax_den, label='Density')
# draw trend line
ax_den.plot(den_trend, den_log.depth, 
            color='r', linestyle='--', zorder=2, label='Trend')
# draw processed density log
ax_den.plot(den_log_filter_smooth.data, den_log_filter_smooth.depth,
            color='g', zorder=3, label='Smoothed')
# draw extrapolated density
ax_den.plot(extra_log.data, extra_log.depth, 
            color='b', zorder=4, label='Extrapolated')

# set style
ax_den.set(ylim=(5000,0), aspect=(1.2/5000)*2)
fig_den.set_figheight(8)
fig_den.show()
```

--------------------------------

### pygeopressure.basic.threepoints.Invalid_threepoints_Exception

Source: https://github.com/whimian/pygeopressure/blob/master/docs/api/pygeopressure.basic.md

Custom exception for invalid three points data.

```APIDOC
## exception pygeopressure.basic.threepoints.Invalid_threepoints_Exception(message=None)

Bases: `Exception`
```

--------------------------------

### pygeopressure.basic.threepoints.ThreePoints

Source: https://github.com/whimian/pygeopressure/blob/master/docs/api/pygeopressure.basic.md

Represents the inline, crossline, and z coordinates of three points within a survey.

```APIDOC
## class pygeopressure.basic.threepoints.ThreePoints(json_file=None)

Bases: `object`

inline, crossline and z coordinates of three points in survey
```

--------------------------------

### Optimize Bowers unloading equation coefficient U

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/bowers_well.ipynb

Calculates the 'U' coefficient for Bowers' unloading equation. This optimization is performed after manually selecting a value for Vmax and using the previously determined 'A' and 'B' coefficients.

```python
u = ppp.optimize_bowers_unloading(
    well=well_cug1,
    vel_log=vel_log_filter_smooth, 
    obp_log='Overburden_Pressure', 
    a=a,
    b=b,
    vmax=4600, 
    pres_log='unloading')
```

--------------------------------

### Extrapolate Density Log

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/obp_well.ipynb

Extrapolates the processed (filtered and smoothed) density log to the sea bottom using the Traugott equation and fitted coefficients.

```python
extra_log = ppp.extrapolate_log_traugott(
    den_log_filter_smooth, a, b, 
    kb=well_cug1.kelly_bushing, wd=well_cug1.water_depth)
```

--------------------------------

### Add Well Data to HDF5 Storage

Source: https://github.com/whimian/pygeopressure/blob/master/docs/howtos/import_well_log_data.ipynb

Adds a well's data, provided as a pandas DataFrame, to the HDF5 storage file. This allows for persistent storage of well log data.

```python
storage.add_well(well_name='cug3', well_data_frame=las_data.data_frame)
```

--------------------------------

### Plot Processed Density Log with Trend

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/obp_well.ipynb

Displays the original density log, the Traugott trend line, and the filtered and smoothed density log for comparison.

```python
fig_den, ax_den = plt.subplots()
ax_den.invert_yaxis()
# draw density log
den_log.plot(ax_den, label='Density')
# draw fitted density trend line
ax_den.plot(den_trend, den_log.depth, 
            color='r', linestyle='--', zorder=2, label='Trend')
# draw processed density log
ax_den.plot(den_log_filter_smooth.data, den_log_filter_smooth.depth,
            color='g', zorder=3, label='Smoothed')

# set style
ax_den.set(ylim=(5000,0), aspect=(1.2/5000)*2)
ax_den.legend()
fig_den.set_figheight(8)
fig_den.show()
```

--------------------------------

### Access Survey Wells

Source: https://github.com/whimian/pygeopressure/blob/master/docs/howtos/import_well_log_data.ipynb

Retrieves information about wells within the survey. This is useful for checking available wells or accessing specific well objects.

```python
survey.wells
```

--------------------------------

### Plot Original Density Log

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/obp_well.ipynb

Plots the raw density log data. The y-axis is inverted to represent depth from surface downwards.

```python
fig_den, ax_den = plt.subplots()
ax_den.invert_yaxis()

den_log.plot(ax_den)

# set style
ax_den.set(ylim=(5000,0), aspect=(1.2/5000)*2)
fig_den.set_figheight(8)
fig_den.show()
```

--------------------------------

### Access velocity data

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/obp_seis.ipynb

Retrieves the velocity data cube from the loaded survey object. This velocity data is a prerequisite for density calculation.

```python
vel_cube = survey.seismics['velocity']
```

--------------------------------

### Read Pseudo-LAS Data

Source: https://github.com/whimian/pygeopressure/blob/master/docs/howtos/import_well_log_data.ipynb

Executes the reading process for pseudo-LAS files. This populates the `data_frame` attribute of the `LasData` object.

```python
las_data.read_pseudo_las()
```

--------------------------------

### Access well data

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/eaton_seis.ipynb

Retrieves a specific well object, 'CUG1', from the loaded survey data.

```python
well_cug1 = survey.wells['CUG1']
```

--------------------------------

### Calculate Normal Velocity Trend

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/eaton_well.ipynb

Calculates the normal velocity trend (NCT) using the optimized coefficients 'a' and 'b'. This trend represents the expected velocity in a normally compacted state.

```python
from pygeopressure.velocity.extrapolate import normal_log
nct_log = normal_log(vel_log, a=a, b=b)
```

--------------------------------

### Assign DataFrame to Well

Source: https://github.com/whimian/pygeopressure/blob/master/docs/howtos/import_well_log_data.ipynb

Assigns a pandas DataFrame containing well log data to a specific Well object. This imports the data into the survey at runtime.

```python
cug3.data_frame = las_data.data_frame
```

--------------------------------

### Assign Partial DataFrame to Well

Source: https://github.com/whimian/pygeopressure/blob/master/docs/howtos/import_well_log_data.ipynb

Assigns a subset of a pandas DataFrame to a Well object, allowing for the import of specific log curves only.

```python
cug3.data_frame = las_data.data_frame[['Depth(m)', 'Shale_Volume(Fraction)', 'Density(G/C3)']]
```

--------------------------------

### Plot Eaton's Exponent Error

Source: https://github.com/whimian/pygeopressure/blob/master/docs/tutorial/eaton_well.ipynb

Visualizes the Root Mean Square (RMS) error variation with respect to Eaton's exponent 'n'. This plot helps in selecting the optimal 'n' value that minimizes prediction error.

```python
from pygeopressure.basic.plots import plot_eaton_error

fig_err, ax_err = plt.subplots()

plot_eaton_error(
    ax=ax_err,
    well=well_cug1, 
    vel_log=vel_log_filter_smooth, 
    obp_log="Overburden_Pressure", 
    a=a, b=b)
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.