cgeniepy 0.10.0

A Python package to read, analyse and visualise cGENIE Earth System Model output

cgeniepy is an Python interface to the output of cGENIE Earth System Model. It is an alternative to the existing MATLAB libraries. It aims to facilitate the post-simulation analysis, including reading and analysing the model output, and observational data.

⚠ cgeniepy is still in alpha phase and under active changes.

Installation

1. Install from testpypi.

python3 -m pip install -i https://test.pypi.org/simple/ cgeniepy==0.7.5

2. Install from GitHub:

python3 -m pip install git+https://github.com/ruiying-ocean/cgeniepy.git@master

Quickstart

0. initialise a model instance

from cgeniepy.model import GenieModel

## single model
model = GenieModel('path_a')

## model ensemble
multi_dirs = ['/path_a/', '/path_b/']
model = GenieModel(path_to_model_output)

1. Read data

• netCDF (*.nc)
• time series (*.res)

## time slice data
model.get_var("XXXXX").array

## timeseries data
model.get_ts("biogem_series_ocn_temp.res")

2. Data analysis

• Data subsetting and statistics
• Model performance
• Unit-changing operation (e.g., rate to magnitude)

## get zonal average SST of the last model year
zonal_sst = model.get_var("ocn_sur_temp").isel(time=-1).mean(dim='lat')

## North Pacific SST
npac_sst = model.get_var("ocn_sur_temp").select_basin(47).isel(time=-1)

3. Visualisation

• 1D line (time series, zonal average)
• 2D map (including various projections like polar map)
• 2D cross section
• 3D (facet)
• Add a layer of observational data

## simply call `plot` after accessing the data

## map
model.get_var("ocn_sur_temp").isel(time=-1).plot()

4. Others

• ECOGEM shortcuts

from cgeniepy.ecology import EcoModel
model = EcoModel(path_to_model_path)
## get all phytoplankton carbon biomass and plot as map
model.get_pft("Phyto", "Biomass", "C").isel(time=-1).plot()

Gallery

A global biomass map of modelled picophytoplankton (0.6 μm)

## initialise a EcoModel instance before running this

model.get_pft(1, "Biomass", "C").isel(time=-1).plot(contour=True)

A global distribution of basin-level nutrient (PO4)

## initialise a GenieModel instance before running this

import matplotlib.pyplot as plt

fig, axs=plt.subplots(nrows=1, ncols=3, figsize=(15, 3), tight_layout=True)        

basins = ['Atlantic', 'Pacific', 'Indian']

for i in range(3):
	basin_data = model.get_var('ocn_PO4').isel(time=-1).mask_basin(base='worjh2',basin=basins[i], subbasin='')
	basin_data.array.values = basin_data.array.values * 1E6
	basin_data.mean(dim='lon').plot(ax=axs[i], contour=True)
	axs[i].title.set_text(basins[i])

Additional Colour Palettes

import numpy as np
import matplotlib.pyplot as plt
from cgeniepy.plot import community_palette, avail_palette

def plot_colormaps(cmaps):
    ncols = 4
    nrows = int(np.ceil(len(cmaps) / ncols))
    fig, axes = plt.subplots(nrows, ncols, figsize=(15, nrows))

    for i, cmap_name in enumerate(cmaps):
        row = i // ncols
        col = i % ncols
        ax = axes[row, col] if nrows > 1 else axes[col]

        # Create a gradient image using the colormap
        gradient = np.linspace(0, 1, 256).reshape(1, -1)
        ax.imshow(gradient, aspect='auto', cmap=community_palette(cmap_name))
        ax.set_title(cmap_name, fontsize=14, fontweight='bold')
        ax.axis('off')

    ## remove the unused axes
    for i in range(len(cmaps), ncols * nrows):
        row = i // ncols
        col = i % ncols
        fig.delaxes(axes[row, col])
        
    plt.tight_layout()

# List of colormaps from cgeniepy
cmaps_list = avail_palette()
plot_colormaps(cmaps_list)

Project Roadmap 🚩

• Publish the first stable version
• plot.py 3D facet subplots
• Show one colorbar in transect plot
• ignore NAs when searching grid
• use lat/lon/zt from GENIE output
• Documentation Webiste
• figsize influences colorbar length
• create a simple logo
• allow reading an ensemble of models (netcdf & timeseries)
• formatting the ugly unit string
• observation.py including plot scatter
• Add global inventory function

Features

[ ] Model-data comparison [ ] Model ensemble support

Citation

@software{cgeniepy,
  author = {Rui Ying},
  title = {A Python interface to analyse and visualise cGENIE model output},
  url = {https://github.com/ruiying-ocean/cgeniepy/},
  version = {0.7.5},
  date = {2023-11-12},
}

Logo

Logo is generated by simple logo generator using free righteous font.

Alternative

Alex Phol's genie_basicdiags

Raise a bug

Please use GitHub's Issues to raise a bug. This makes the issues traceable so that future users having the same problem can find the answer in the public domain.