Module relax_disp_repeat_cpmg

source code

The automatic relaxation dispersion protocol for repeated data for CPMG.

task #7826, Write an python class for the repeated analysis of dispersion data.

Classes

[hide private]

Relax_disp_rep
The relaxation dispersion analysis for repeated data.

Functions

[hide private]

float, float, float, float, float, float

ordinary_least_squares(x=None, y=None)
Calculate the linear correlation 'B', the intercept 'A' for the function y=A + Bx.

source code

Variables

[hide private]

status = Status()

fontP = FontProperties()

DIC_KEY_FORMAT = '%.8f'

__package__ = 'auto_analyses'

Imports: deepcopy, datetime, glob, F_OK, access, chmod, getcwd, sep, asarray, arange, concatenate, max, mean, min, savetxt, square, sqrt, std, sum, pearsonr, S_IRWXU, S_IRGRP, S_IROTH, sys, warn, dep_check, MODEL_NOREX, MODEL_PARAMS, MODEL_R2EFF, PARAMS_R20, extract_data, get_file_path, open_write_file, sort_filenames, write_data, section, subsection, subtitle, RelaxWarning, spin_loop, pipes, Interpreter, generate_r20_key, has_exponential_exp_type, has_cpmg_exp_type, is_r1_optimised, loop_exp_frq_offset, loop_exp_frq_offset_point, return_param_key_from_data, Status, plt, FontProperties

Function Details

[hide private]

ordinary_least_squares(x=None, y=None)

source code

Calculate the linear correlation 'B', the intercept 'A' for the function y=A + Bx.

Parameters:

x (float or numpy array.) - The data for the X-axis.
y (float or numpy array.) - The data for the Y-axis.

Returns: float, float, float, float, float, float

The intercept A, the standard deviation for A, the slope B, the standard deviation for B, standard deviation of the residuals, the linear correlation coefficient