"""Global variables for the fluxpy library"""
import seaborn as sns
import os
from matplotlib.colors import LinearSegmentedColormap, to_rgba
[docs]def _cmap():
"""
Create a custom colormap.
This function defines a custom colormap using a list of hexadecimal color codes,
converts them to RGBA format, and then creates a LinearSegmentedColormap with
100 bins.
Returns:
LinearSegmentedColormap: A custom colormap with the specified colors.
"""
# Define your colors in hexadecimal format
hex_colors = ['#9B32CD', '#DBD8DC', '#1FBDE7']
# Convert hexadecimal colors to RGBA format
rgba_colors = [to_rgba(color) for color in hex_colors]
cmap_name = "my_cmap"
n_bins = 100
# sns.diverging_palette(220, 20, as_cmap=True, center='light', s=85, l=50)
my_cmap = LinearSegmentedColormap.from_list(cmap_name, rgba_colors, N=n_bins)
return my_cmap
[docs]current_file_path = os.path.abspath(__file__)
[docs]parent_folder = os.path.dirname(current_file_path)
[docs]root_folder = os.path.dirname(parent_folder)
[docs]EXT_DATA = os.path.join(root_folder, "ext_data")
[docs]SEED2MNX = os.path.join(EXT_DATA, "seed2mnx.json")
[docs]BIGG2MNX = os.path.join(EXT_DATA, "bigg2mnx.json")
[docs]BIGG_COFACTORS = ['atp_c0', 'atp_c', 'adp_c', 'adp_c0',
'atp_c0', 'atp_c', 'adp_c', 'adp_c0',
'udp_c0', 'udp_c', 'ump_c0', 'ump_c',
'amp_c', 'amp_c0',
'gdp_c0', 'gdp_c', 'gtp_c0', 'gtp_c',
'accoa_c', 'accoa_c0', 'coa_c', 'coa_c0', # acetyl-CoA
'q8_c0', 'q8_c', 'q8h2_c', 'q8h2_c0', 'mqn8_c', 'mqn8_c0', 'mql8_c', 'mql8_c0', 'q8h2_c', 'q8h2_c0',
'actp_c0', 'actp_c',
'h2o_c', 'h2o_c0', 'h2o_e', 'h2o[e]',
'pi_e', 'pi[e]', 'pi_c', 'pi_c0', 'ppi_c0', 'ppi_c',
'pep_c', 'pep_c0',
'h_c', 'h_c0', 'h_e', 'h[e]',
'o2_c', 'o2_c0', 'o2_e', 'o2[e]',
'co2_c', 'co2_c0', 'co2_e', 'co2[e]',
'nadp_c', 'nadp_c0', 'nadph_c', 'nadph_c0', 'nad_c', 'nad_c0', 'nadh_c', 'nadh_c0',
'nadp_e', 'nadp[e]', 'nadph_e', 'nadph_c0', 'nad_e', 'nad[e]', 'nadh_e', 'nadh[e]',
'fadh2_c', 'fadh2_c0', 'fad_c', 'fad_c0',
'nh4_c', 'nh4_c0', 'nh4_e', 'nh4[e]',
'pyr_c0', 'pyr_c'
]
[docs]BIGG_BUILDING_BLOCLS = ['ala_L_c0', 'asp_L_c0', ' gln_L_c0', 'glu_L_c0', 'glu_L_c0', 'ser_L_c0', 'trp_L_c0', 'met_L_c0', 'lys_L_c0', 'cyst_L_c0',
]
# Based on 10.1093/gigascience/giy021
[docs]MODELSEED_COFACTORS = [
"cpd00001_c0", # h2o
"cpd00002_c0", # atp
"cpd00003_c0", # nad
"cpd00004_c0",
"cpd00005_c0",
"cpd00006_c0", # nadp
"cpd00007_c0",
"cpd00008_c0", # adp
"cpd00009_c0", # HZ added
"cpd00010_c0", # CoA
"cpd00011_c0", # co2
"cpd00012_c0", # ppi
"cpd00013_c0", # NH3
"cpd00014_c0",
"cpd00015_c0", # fad
"cpd00018_c0", # amp-like
"cpd00020_c0", # pyruvate
"cpd00022_c0",
"cpd00031_c0", # gdp-like
"cpd00038_c0", # gtp
"cpd00056_c0", # ttp
"cpd00061_c0", # pep
"cpd00067_c0", # H+
"cpd15353_c0",
"cpd15499_c0",
"cpd15561_c0",
"cpd00097_c0",
"cpd00982_c0",
"cpd01270_c0",
"cpd00052_c0",
"cpd00062_c0",
"cpd00068_c0",
"cpd00115_c0",
"cpd00241_c0",
"cpd00356_c0",
"cpd00357_c0",
"cpd00358_c0",
"cpd00530_c0",
"cpd00977_c0",
"cpd01775_c0"
]
[docs]QFCA_STYLE = os.path.join(root_folder, "fluxpy/data/style_qfca.json")
[docs]COMPLETE_MODEL = os.path.join(root_folder, "fluxpy/data/exchangeReactions.sbml")