Package auto_analyses :: Module relax_disp_repeat_cpmg
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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