gerg_plotting 0.0.19

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GERG Plotting

Data plotting package for GERG
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Dependencies
   • Installation
3. Usage
4. Contributing
5. License
6. Contact
7. Acknowledgments

About The Project

This project was created to streamline and standardize the process of generating plots at GERG.

Built With

Getting Started

There are two ways to get started

1. Create a fresh virtual environment using your favorite method and install the package
2. Use an already established virtual environment and install the package

Dependencies

I have provided a list of the dependencies and their versions below.

List of dependencies:

• python = 3.12
• numpy = 2.0.0
• pandas = 2.2.2
• matplotlib = 3.9.1
• xarray = 2024.6.0
• attrs = 23.2.0
• netcdf4 = 1.7.1.post1
• cmocean = 4.0.3
• scipy = 1.14.0
• mayavi = 4.8.2

Installation

1. Activate your virtual environment
2. Use pip to install pip install gerg_plotting

Usage

Plot data at GERG using Python.

Example: Create a set of maps showing data point temperature, salinity, depth, and time

from gerg_plotting import Data,MapPlot,Bounds
from gerg_plotting.utils import generate_random_point
import numpy as np
import pandas as pd
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import cartopy.crs as ccrs

# Generate Test Data
bounds = Bounds(lat_min = 24,lat_max = 31,lon_min = -99,lon_max = -88,depth_top=-1,depth_bottom=1000)
data_bounds = Bounds(lat_min = 27,lat_max = 28.5,lon_min = -96,lon_max = -89,depth_top=-1,depth_bottom=1000)
n_points = 1000
lats,lons = np.transpose([generate_random_point(lat_min=data_bounds.lat_min,
                                                lat_max=data_bounds.lat_max,
                                                lon_min=data_bounds.lon_min,
                                                lon_max=data_bounds.lon_max) for _ in range(n_points)])
salinity = np.random.uniform(low=28,high=32,size=n_points)
temperature = np.random.uniform(low=5,high=28,size=n_points)
depth = np.random.uniform(low=-200,high=0,size=n_points)
time = pd.Series(pd.date_range(start='10-01-2024',end='10-10-2024',periods=n_points)).apply(mdates.date2num)

# Init Data object
data = Data(lat=lats,lon=lons,salinity=salinity,temperature=temperature,depth=depth,time=time)
# Init subplots
fig,ax = plt.subplots(figsize=(10,20),nrows=4,subplot_kw={'projection': ccrs.PlateCarree()})
# Init MapPlot object
plotter = MapPlot(instrument=data,bounds=bounds,grid_spacing=3)
# Generate Scatter plots on one figure
plotter.scatter(fig=fig,ax=ax[0],var='temperature',show_bathy=True,pointsize=30)
plotter.scatter(fig=fig,ax=ax[1],var='salinity',show_bathy=True,pointsize=30)
plotter.scatter(fig=fig,ax=ax[2],var='depth',show_bathy=True,pointsize=30)
plotter.scatter(fig=fig,ax=ax[3],var='time',show_bathy=True,pointsize=30)
fig.savefig('map_example.png',dpi=500)

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Alec Krueger - [e-mail]

Project Link: https://github.com/alecmkrueger/gerg_plotting

Acknowledgments

• Alec Krueger, Texas A&M University, Geochemical and Environmental Research Group, [e-mail]

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