cgeniepy#

Submodules#

Classes#

GenieModel

GenieModel is the interface to cGENIE output

EcoModel

EcoModel is an subclass of GenieModel, as EcoGENIE to cGENIE

CommunityPalette

A class to handle community-driven colormaps

Functions#

sample_model([model_type])

load_obs([grid])

register_cmap()

Package Contents#

class cgeniepy.GenieModel(model_path: str | List | Tuple, gemflag=None)#

Bases: object

GenieModel is the interface to cGENIE output

model_path#
ncvar_dict#
tsvar_list#
__repr__()#
get_var(var: str | List | Tuple, attrs=None)#

Get the data of target variable. A list of variables is supported as well.

Parameters:

  • var – the name of target variable

  • unit – the unit of target variable, usually provided in the model output

Example#

>>> from cgeniepy.model import GenieModel
>>> model = GenieModel("path_to_GENIE_output")
>>> po4 = model.get_var("ocn_PO4")
get_config(config_name='BIOGEM')#
get_ts(var: str, to_ScatterData=False)#

read in time series output of GENIE

Parameters:

var – the name of the target variable

Returns:

a pandas DataFrame

get_diag_avg(target_year, is_index=False, pattern_year='\\d+', pattern_year_digit='\\d{3}')#

read the diagnostic file of cGENIE

Parameters:

  • target_year – the target year of the diagnostic file

  • is_index – if True, target_year is the index of the sorted years

  • pattern – the pattern of the diagnostic file name

Returns:

a pandas DataFrame

Example#

>>> from cgeniepy.model import GenieModel
>>> model = GenieModel("path_to_GENIE_output")
>>> model.get_diag_avg(9999)
grid_mask()#

cGENIE continent mask array (by setting zero values), either calculated from existing data or using biogem.grid_mask

grid_category()#

an alogirthm to define surface grid catogories depending on the land-sea mask 0: coastal sea 1: land 2: open ocean

grid_area()#

return the grid area array in used in this model experiment, unit: m2

grid_mask_3d()#

return the 3d mask of the grid used in this model experiment

grid_topo()#

return the topography of the grid used in this model experiment

grid_zt_edges()#

return the depth edges of the grid used in this model experiment

grid_lat_edges()#

return the latitude edges of the grid used in this model experiment

grid_lon_edges()#

return the longitude edges of the grid used in this model experiment

grid_zt_depths()#

return the depth of the grid used in this model experiment

grid_volume()#

return the grid volume array (3d) in m3

class cgeniepy.EcoModel(*args, **kwargs)#

Bases: cgeniepy.model.GenieModel

EcoModel is an subclass of GenieModel, as EcoGENIE to cGENIE

It facilitates the access of ecophysiological parameters and plankton variables

Initialise a EcoModel object with a path to cGENIE output directory

Example#

>>> from cgeniepy.ecology import EcoModel
>>> model = EcoModel("path_to_GENIE_output")
eco_varlist#
plank_n#
phyto_indices#
zoo_indices#
mixo_indices#
phyto_n#
zoo_n#
mixo_n#
eco_pars()#

Get all the ecophysiological parameters used in the model

Returns:

a pandas DataFrame object

get_pft(pft_index, prefix='Plankton', element='C')#

a variant of GenieModel’s get_var, to select plankton functional type

Parameters:

  • pft_index – the index of plankton functional type

  • prefix – ‘Plankton’ or ‘Export’

  • element – ‘C’, ‘Fe’, ‘P’, ‘Si’, ‘N’

Returns:

a GenieArray object

Example#

>>> from cgeniepy.ecology import EcoModel

>>> ### initialise a model object
>>> model = EcoModel("path_to_GENIE_output")

>>> ### get PFT-1 carbon biomass data
>>> model.get_pft(1, "Plankton", "C")

>>> ### get a list of PFT carbon biomass data
>>> model.get_pft([1, 2, 3], "Plankton", "C")

>>> ### get all phytoplankton carbon biomass data
>>> model.get_pft('phyto', "Plankton", "C")

>>> ### get all zooplankton carbon biomass data
>>> model.get_pft('zoo', "Plankton", "C")

>>> ### get all plankton carbon biomass data
>>> model.get_pft('all', "Plankton", "C")
class cgeniepy.CommunityPalette(name=None, *args, **kwargs)#

A class to handle community-driven colormaps

name = None#
get_palette(cmap_name, N=256, reverse=False, alpha=None)#

community-driven colormaps with multiple sources

Parameters:

cmap_name (str) – colormap name, can be found in avail_palette()

Returns:

colormap

XML data: https://sciviscolor.org/colormaps/ txt data: from original packages

avail_palettes(show_ferret_data=True)#

return a list of colormap names

to_rgb()#
to_hex(unique=True)#
create_colormap(positions, colors)#

Create a colormap with specified positions and colors.

Args:

positions (list): List of floats indicating the positions of colors in the colormap. colors (list): List of RGB tuples representing the colors.

Returns:

LinearSegmentedColormap: The created colormap.

__repr__()#
cgeniepy.sample_model(model_type='GenieModel', *args, **kwargs)#
cgeniepy.load_obs(grid='worjh2')#
cgeniepy.register_cmap()#