2.1.4. fluxpy.illustrations¶
Provide functions to ullistrate metaboloc modeling alaysis findings.
Submodules¶
Functions¶
|
This function takes as arguments 3 columns of the cobra production_envelope() result |
|
Plots the behavior of medium compounds in a metabolic model across different concentrations. |
|
For each nutrient on a gradient it returns differences for the minimum and maximum cases |
|
Creates and runs a Dash application to visualize the QCFA subgraphs using a network graph. |
|
Generates a 3D plot of a flux cone with random points inside it. |
Package Contents¶
- fluxpy.illustrations.plot_prod_env_3D(v1: pandas.Series, v2: pandas.Series, v3: pandas.Series, width=800, height=600)[source]¶
This function takes as arguments 3 columns of the cobra production_envelope() result to return a 3D scatter plot of those.
- Parameters:
x-axis (v1 -- flux vector for) –
y-axis (v2 -- flux vector for) –
values (v3 -- flux vector for z-axis which is used for the weight) –
- Returns:
A 3D plolty.Figure object.
Usage example:
from cobra.io import load_model model = load_model("textbook") from cobra.flux_analysis import production_envelope prod_env = production_envelope(model, ["EX_glc__D_e", "EX_o2_e"]) prod_env.head(3) carbon_source flux_minimum carbon_yield_minimum mass_yield_minimum flux_maximum carbon_yield_maximum mass_yield_maximum EX_glc__D_e EX_o2_e 0 EX_glc__D_e 0.0 0.0 0.0 0.000000 0.000000 0.000000 -10.0 -60.000000 1 EX_glc__D_e 0.0 0.0 0.0 1.578947 0.052632 0.051748 -10.0 -56.842105 2 EX_glc__D_e 0.0 0.0 0.0 3.157895 0.105263 0.103496 -10.0 -53.684211 x=prod_env['EX_o2_e'] y=prod_env['EX_glc__D_e'] z=prod_env['flux_maximum']
- fluxpy.illustrations.plot_ranging_medium_compounds(model: cobra.Model, dictionary_with_plots, dpi=500)[source]¶
Plots the behavior of medium compounds in a metabolic model across different concentrations.
- Parameters:
model (object) – The metabolic model object.
dictionary_with_plots (dict) – A dictionary where keys are compound IDs, and values are tuples containing lists of concentrations and corresponding flux values to plot.
dpi (int, optional) – Dots per inch (resolution) for the saved figure. Default is 500.
- Returns:
A .png file
- fluxpy.illustrations.plot_nutrients_gradient(gradient, nutrients=None, threshold=0.2, width_size=12, height_size=12, save_fig=False, path='gradient.png', dpi=500)[source]¶
For each nutrient on a gradient it returns differences for the minimum and maximum cases and plots a heatmap for each where reaction that are affected with the increase of the upper bound at least threshold*max_difference are displayed.
gradient – output of the utils.get_nutrients_gradient() nutrients – list with the reaction ids to be considered threshold – per centage of the max difference that will be used to filter differences width_size – width of each subplot height_size – height of eash subplot
- fluxpy.illustrations.qcfa_subgraphs(H: networkx.Graph, run_app=False, save_cx2=False, cx2_output_path='qfca_graph.cx2')[source]¶
Creates and runs a Dash application to visualize the QCFA subgraphs using a network graph.
- Parameters:
H (networkx.Graph, mandatory) – A NetworkX graph where nodes represent reactions and edges represent relationships between them. The edge colors denote different types of couplings.
save_cx2 (boolean, optional) – Export dash cytoscape to a .cx2 format and save it as a file that can be then loaded in Cytoscape Desktop
cx2_output_path (string, optional) – Path and output filename for the .cx2 file
- Returns:
An _ExportedGraph object with either a dash Cytoscape graph or both a dash Cytoscape and a .cx2 formatted graph with the QFCA-oriented graph. To access each: