Author: tlinnet
Date: Fri Sep 26 17:29:39 2014
New Revision: 26065
URL: http://svn.gna.org/viewcvs/relax?rev=26065&view=rev
Log:
Implemented getting the statistics for parameters and comparing to init NI.
Task #7826 (https://gna.org/task/index.php?7826): Write an python class for
the repeated analysis of dispersion data.
Modified:
trunk/auto_analyses/relax_disp_repeat_cpmg.py
Modified: trunk/auto_analyses/relax_disp_repeat_cpmg.py
URL:
http://svn.gna.org/viewcvs/relax/trunk/auto_analyses/relax_disp_repeat_cpmg.py?rev=26065&r1=26064&r2=26065&view=diff
==============================================================================
--- trunk/auto_analyses/relax_disp_repeat_cpmg.py (original)
+++ trunk/auto_analyses/relax_disp_repeat_cpmg.py Fri Sep 26 17:29:39
2014
@@ -1943,6 +1943,94 @@
plt.show()
+ def get_min_stat_dic(self, list_r2eff_dics=None, list_glob_ini=None):
+
+ # Loop over the result dictionaries:
+ res_dic = {}
+ for i, min_dic in enumerate(list_r2eff_dics):
+ # Let the reference dic be initial dic
+ min_dic_ref = list_r2eff_dics[0]
+ method_ref = min_dic_ref['method']
+ res_dic['method_ref'] = method_ref
+ glob_ini_ref = list_glob_ini[0]
+ res_dic['glob_ini_ref'] = str(glob_ini_ref)
+ selection = min_dic_ref['selection']
+ res_dic['selection'] = selection
+ params_list =
min_dic_ref[str(glob_ini_ref)]['params']['params_list']
+ res_dic['params_list'] = params_list
+
+ # Loop over params
+ for j, param in enumerate(params_list):
+ res_dic[param] = {}
+
+ # Let the reference param array be the initial glob.
+ param_arr_ref =
min_dic_ref[str(glob_ini_ref)]['params'][param]
+ res_dic[param]['param_arr_ref'] = param_arr_ref
+
+ # Get the current method
+ method_cur = min_dic['method']
+
+ res_dic[param][method_cur] = {}
+ res_dic[param][method_cur]['method'] = method_cur
+ res_dic[param][method_cur]['sampling_sparseness'] = []
+ res_dic[param][method_cur]['glob_ini'] = []
+
+ # Other stats.
+ res_dic[param][method_cur]['r_xy'] = []
+ res_dic[param][method_cur]['a'] = []
+
+ # Now loop over glob_ini:
+ for glob_ini in list_glob_ini:
+ # Get the array, if it exists.
+ if str(glob_ini) not in min_dic:
+ continue
+
+ # Get the data.
+ param_arr = min_dic[str(glob_ini)]['params'][param]
+
+ # This require that all number of points are equal.
+ # If they are not of same length, then dont even bother
to continue.
+ if len(param_arr) != len(param_arr_ref):
+ continue
+
+ # Store x
+ sampling_sparseness = float(glob_ini) /
float(glob_ini_ref) * 100.
+
res_dic[param][method_cur]['sampling_sparseness'].append(sampling_sparseness)
+ res_dic[param][method_cur]['glob_ini'].append(glob_ini)
+
+ # Store to result dic.
+ res_dic[param][method_cur][str(glob_ini)] = {}
+
res_dic[param][method_cur][str(glob_ini)]['sampling_sparseness'] =
sampling_sparseness
+ res_dic[param][method_cur][str(glob_ini)]['param_arr'] =
param_arr
+
+ # With intercept at axis.
+ # Calculate sample correlation coefficient, measure of
goodness-of-fit of linear regression
+ x = param_arr_ref
+ x_m = mean(x)
+ y = param_arr
+ y_m = mean(y)
+
+ # Without intercept.
+ a = sum(x*y) / sum(x**2)
+ r_xy = sum(x*y) / sqrt(sum(x**2) * sum(y**2))
+
+ print(param, method_ref, method_cur,
sampling_sparseness, glob_ini, r_xy**2, a)
+
+ # Store to result dic.
+ res_dic[param][method_cur][str(glob_ini)]['r_xy'] = r_xy
+ res_dic[param][method_cur]['r_xy'].append(r_xy)
+ res_dic[param][method_cur][str(glob_ini)]['a'] = a
+ res_dic[param][method_cur]['a'].append(a)
+
+ res_dic[param][method_cur]['sampling_sparseness'] =
asarray(res_dic[param][method_cur]['sampling_sparseness'])
+ res_dic[param][method_cur]['glob_ini'] =
asarray(res_dic[param][method_cur]['glob_ini'])
+
+ res_dic[param][method_cur]['r_xy'] =
asarray(res_dic[param][method_cur]['r_xy'])
+ res_dic[param][method_cur]['a'] =
asarray(res_dic[param][method_cur]['a'])
+
+ return res_dic
+
+
def interpreter_start(self):
# Load the interpreter.
self.interpreter = Interpreter(show_script=False,
raise_relax_error=True)
r26065 - /trunk/auto_analyses/relax_disp_repeat_cpmg.py