aaanalysis.options

aaanalysis.options = <aaanalysis.config.Settings object>

A class for managing system-level settings for AAanalysis.

This class mimics a dictionary-like interface, allowing the setting and retrieving of system-level options. It is designed to be used as a single global instance, options.

Parameters:

• set (The following options can be)

• verbose (bool or 'off', default='off') – Sets verbose mode to True or False globally if not ‘off’.

• random_state (int, None, or 'off', default='off') –

  The seed used by the random number generator.

  • If set to a positive integer, results of stochastic processes are consistent, enabling reproducibility.

  • If set to None, stochastic processes will be truly random.

  • If set to ‘off’, no global random state variable will be set, allowing the underlying libraries to use their default random state behavior.

• n_jobs (int, None, or 'off', default='off') –

  Global override for the per-call n_jobs of parallel-capable methods.

  • If set to a positive integer, that many CPU cores are used.

  • If set to -1, all available cores are used.

  • If set to ‘off’, the per-call n_jobs argument is used (where None is optimized automatically for the job size, and -1 uses all cores).

• allow_multiprocessing (bool, default=True) – Whether multiprocessing is allowed in general. If False, n_jobs is automatically set to 1.

• name_tmd (str, default='TMD') – Name of target middle domain (TMD) used in CPP plots.

• name_jmd_n (str, default='JMD_N') – Name of N-terminal juxta middle domain (JMD-N) used in CPP plots.

• name_jmd_c (str, default='JMD_C') – Name of C-terminal juxta middle domain (JMD-C) used in CPP plots.

• ext_len (int, default=0) – Length of TMD-extending part (starting from C and N terminal part of TMD, >=0). Disabled (set to 0) by default.

• jmd_n_len (int, default=None) – Length of N-terminal JMD (JMD-N) in number of amino acids. If None, jmd_n_len is set locally.

• jmd_c_len (int, default=None) – Length of C-terminal JMD (JMD-C) in number of amino acids. If None, jmd_c_len is set locally.

• df_scales (DataFrame, optional) – Scale DataFrame used in CPP algorithm. Adjust on system level if non-default scales are used. If None, AAanalysis framework will use the scale DataFrame loaded by load_scales() with name='scales'.

• df_cat (DataFrame, optional) – Scale category DataFrame used in CPP algorithm. Adjust on system level if non-default scale categories are used. If None, AAanalysis framework will use the scale category DataFrame loaded by load_scales() with name='scales_cat'.

See also

• numpy.random.RandomState for details on the random_state variable used to make stochastic processes yielding consistent results.

• SequenceFeature for definition of sequence Parts.

• load_scales() for details on scale and scale category DataFrames.

Warning

• Multiprocessing Compatibility: Enabling multiprocessing (allow_multiprocessing=True) can lead to issues in environments that don’t support forking or when interfacing with certain libraries. If encountering errors, consider setting allow_multiprocessing=False. Note that this may affect performance in computation-intensive operations.

Examples

Adjust system level options using the options Setting object, which ca be used like a dictionary:

import aaanalysis as aa
print(aa.options)

{'verbose': 'off', 'random_state': 'off', 'allow_multiprocessing': True, 'name_tmd': 'TMD', 'name_jmd_n': 'JMD-N', 'name_jmd_c': 'JMD-C', 'ext_len': 0, 'jmd_n_len': None, 'jmd_c_len': None, 'df_scales': None, 'df_cat': None}

You can disable general model verbosity, such as progress updates from intensive algorithms or warnings, by settingverbose=False

aa.options["verbose"] = False

Change the random state of stochastic process such as clustering by setting random_state:

aa.options["random_state"] = 42 # Set to 42 (otherwise not specified)

Adjust the names of the Part domains by the name_tmd, name_jmd_n, and name_jmd_c parameters

import matplotlib.pyplot as plt
aa.options["name_jmd_n"] = "Part 1"
aa.options["name_tmd"] = "Part 2"
aa.options["name_jmd_c"] = "Part 3"
df_feat = aa.load_features()
cpp_plot = aa.CPPPlot()
cpp_plot.ranking(df_feat=df_feat)
plt.tight_layout()
plt.show()

Adjust the length of the juxta middle domains (JMD) globally using the jmd_n_len and ``jmd_c_len``` parameters:

aa.options["jmd_n_len"] = 0
aa.options["jmd_c_len"] = 20
# Remove all features occurring in JMD-N
df_feat = df_feat[~df_feat["feature"].str.contains("JMD_N")]
cpp_plot.ranking(df_feat=df_feat)
plt.show()

Change default df_scales and df_cat DataFrame in case you want to use other scales or adjust your amino acid alphabet:

# Add average value for 'X'
nf = aa.NumericalFeature()
# Retrieve default scale from CPPPlot class
df_scales = aa.CPPPlot()._df_scales
aa.display_df(df_scales, n_cols=3, show_shape=True)

DataFrame shape: (20, 586)

	ANDN920101	ARGP820101	ARGP820102
AA
A	0.494000	0.230000	0.355000
C	0.864000	0.404000	0.579000
D	1.000000	0.174000	0.000000
E	0.420000	0.177000	0.019000
F	0.877000	0.762000	0.601000
G	0.025000	0.026000	0.138000
H	0.840000	0.230000	0.082000
I	0.000000	0.838000	0.440000
K	0.506000	0.434000	0.003000
L	0.272000	0.577000	1.000000
M	0.704000	0.445000	0.824000
N	0.988000	0.023000	0.057000
P	0.605000	0.736000	0.223000
Q	0.519000	0.000000	0.211000
R	0.531000	0.226000	0.047000
S	0.679000	0.019000	0.289000
T	0.494000	0.019000	0.248000
V	0.000000	0.498000	0.324000
W	0.926000	1.000000	0.226000
Y	0.802000	0.709000	0.107000

df_scales_new = nf.extend_alphabet(df_scales=df_scales, new_letter='X')
aa.options["df_scales"] = df_scales_new
# Retrieve new scale DataFrame from CPP plot object
_df_scales_new = aa.CPPPlot()._df_scales
aa.display_df(_df_scales_new,  n_cols=3, row_to_show="X", show_shape=True)

DataFrame shape: (21, 586)

	ANDN920101	ARGP820101	ARGP820102
AA
X	0.577300	0.376350	0.288650