Author: tlinnet
Date: Tue Sep 23 20:06:27 2014
New Revision: 25982
URL: http://svn.gna.org/viewcvs/relax?rev=25982&view=rev
Log:
Implemented the sampling sparseness instead of NI on the graph axis.
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
trunk/test_suite/system_tests/relax_disp.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=25982&r1=25981&r2=25982&view=diff
==============================================================================
--- trunk/auto_analyses/relax_disp_repeat_cpmg.py (original)
+++ trunk/auto_analyses/relax_disp_repeat_cpmg.py Tue Sep 23 20:06:27
2014
@@ -1426,21 +1426,22 @@
r2eff_dic_ref = list_r2eff_dics[0]
method_ref = r2eff_dic_ref['method']
res_dic['method_ref'] = method_ref
- glob_ini_ref = str(list_glob_ini[0])
- res_dic['glob_ini_ref'] = glob_ini_ref
+ glob_ini_ref = list_glob_ini[0]
+ res_dic['glob_ini_ref'] = str(glob_ini_ref)
selection = r2eff_dic_ref['selection']
res_dic['selection'] = selection
# Let the reference R2eff array be the initial glob.
- r2eff_arr_ref = r2eff_dic_ref[glob_ini_ref]['r2eff_arr']
+ r2eff_arr_ref = r2eff_dic_ref[str(glob_ini_ref)]['r2eff_arr']
res_dic['r2eff_arr_ref'] = r2eff_arr_ref
- r2eff_err_arr_ref = r2eff_dic_ref[glob_ini_ref]['r2eff_err_arr']
+ r2eff_err_arr_ref =
r2eff_dic_ref[str(glob_ini_ref)]['r2eff_err_arr']
res_dic['r2eff_err_arr_ref'] = r2eff_err_arr_ref
# Get the current method
method_cur = r2eff_dic['method']
res_dic[method_cur] = {}
res_dic[method_cur]['method'] = method_cur
+ res_dic[method_cur]['sampling_sparseness'] = []
res_dic[method_cur]['glob_ini'] = []
res_dic[method_cur]['r2eff_norm_std'] = []
@@ -1480,10 +1481,13 @@
r2eff_diff_norm_std = std(r2eff_diff_norm_arr, ddof=1)
# Store x
+ sampling_sparseness = float(glob_ini) / float(glob_ini_ref)
* 100.
+
res_dic[method_cur]['sampling_sparseness'].append(sampling_sparseness)
res_dic[method_cur]['glob_ini'].append(glob_ini)
# Store to result dic.
res_dic[method_cur][str(glob_ini)] = {}
+ res_dic[method_cur][str(glob_ini)]['sampling_sparseness'] =
sampling_sparseness
res_dic[method_cur][str(glob_ini)]['r2eff_arr'] = r2eff_arr
res_dic[method_cur][str(glob_ini)]['r2eff_norm_arr'] =
r2eff_norm_arr
res_dic[method_cur][str(glob_ini)]['r2eff_norm_std'] =
r2eff_norm_std
@@ -1519,7 +1523,7 @@
a = sum(x*y) / sum(x**2)
r_xy = sum(x*y) / sqrt(sum(x**2) * sum(y**2))
- print(method_ref, method_cur, glob_ini,
pearsons_correlation_coefficient, r_xy**2, a, r_xy_int**2, a_int, b_int)
+ print(method_ref, method_cur, sampling_sparseness, glob_ini,
pearsons_correlation_coefficient, r_xy**2, a, r_xy_int**2, a_int, b_int)
# Store to result dic.
res_dic[method_cur][str(glob_ini)]['pearsons_correlation_coefficient'] =
pearsons_correlation_coefficient
@@ -1537,6 +1541,7 @@
res_dic[method_cur][str(glob_ini)]['b_int'] = b_int
res_dic[method_cur]['b_int'].append(b_int)
+ res_dic[method_cur]['sampling_sparseness'] =
asarray(res_dic[method_cur]['sampling_sparseness'])
res_dic[method_cur]['glob_ini'] =
asarray(res_dic[method_cur]['glob_ini'])
res_dic[method_cur]['r2eff_norm_std'] =
asarray(res_dic[method_cur]['r2eff_norm_std'])
@@ -1578,10 +1583,12 @@
continue
# Use NI as x.
- x = r2eff_stat_dic[method]['glob_ini']
+ NI = r2eff_stat_dic[method]['glob_ini']
+ # Use sampling_sparseness as x.
+ SS = r2eff_stat_dic[method]['sampling_sparseness']
# Add to headings.
- headings = headings + ['method', 'NI', 'slope', 'rxy2']
+ headings = headings + ['method', 'SS', 'NI', 'slope', 'rxy2']
# Get stats.
# Linear regression slope, without intercept
@@ -1603,41 +1610,48 @@
# Add to data.
data_dic[method] = OrderedDict()
- for i, x_i in enumerate(x):
+ for i, NI_i in enumerate(NI):
+ SS_i = SS[i]
a_i = a[i]
r_xy2_i = r_xy2[i]
- data_dic[method][str(i)] = ["%i"%x_i, "%3.5f"%a_i,
"%3.5f"%r_xy2_i]
+ data_dic[method][str(i)] = ["%3.5f"%SS_i, "%i"%NI_i,
"%3.5f"%a_i, "%3.5f"%r_xy2_i]
if i > i_max:
i_max = i
- ax1.plot(x, a, ".-", label='%s LR'%method)
- ax2.plot(x, r_xy2, "o--", label='%s SC'%method)
+ #ax1.plot(NI, a, ".-", label='%s LR'%method)
+ #ax2.plot(NI, r_xy2, "o--", label='%s SC'%method)
+ ax1.plot(SS, a, ".-", label='%s LR'%method)
+ ax2.plot(SS, r_xy2, "o--", label='%s SC'%method)
# Loop over methods for writing data.
data = []
- for i in range(0, i_max):
+
+ for i in range(0, i_max+1):
data_i = []
for method, data_dic_m in data_dic.iteritems():
# Loop over all possible data points.
if str(i) in data_dic_m:
data_i = data_i + [method] + data_dic_m[str(i)]
else:
- data_i = data_i + [method] + ["0", "0", "0"]
+ data_i = data_i + [method] + ["0", "0", "0", "0"]
data.append(data_i)
# Set legends.
ax1.legend(loc='lower left', shadow=True, prop = fontP)
- ax1.set_xlabel('NI')
+ #ax1.set_xlabel('NI')
+ ax1.set_xlabel('SS')
#ax1.set_ylabel(r'$\sigma ( R_{2,\mathrm{eff}} )$')
ax1.set_ylabel('Linear regression slope, without intercept')
- ax1.set_xticks(x)
+ #ax1.set_xticks(NI)
+ #ax1.set_xticks(SS)
ax1.set_ylim(min_a*0.95, max_a*1.05)
ax1.invert_xaxis()
ax2.legend(loc='lower right', shadow=True, prop = fontP)
ax2.set_ylabel('Sample correlation ' + r'$r_{xy}^2$')
- ax2.set_xticks(x)
+ #ax2.set_xticks(NI)
+ #ax2.set_xticks(SS)
ax2.set_ylim(min_r_xy2*0.95, max_r_xy2*1.05)
ax2.invert_xaxis()
Modified: trunk/test_suite/system_tests/relax_disp.py
URL:
http://svn.gna.org/viewcvs/relax/trunk/test_suite/system_tests/relax_disp.py?rev=25982&r1=25981&r2=25982&view=diff
==============================================================================
--- trunk/test_suite/system_tests/relax_disp.py (original)
+++ trunk/test_suite/system_tests/relax_disp.py Tue Sep 23 20:06:27 2014
@@ -6051,7 +6051,7 @@
if True:
selection = None
# Collect r2eff values.
- r2eff_ft_all = RDR.col_r2eff(method='FT', list_glob_ini=[128],
selection=selection)
+ r2eff_ft_all = RDR.col_r2eff(method='FT', list_glob_ini=[128,
126, 6], selection=selection)
# For all spins, mdd
r2eff_mdd_all = RDR.col_r2eff(method='MDD', list_glob_ini=[128,
126], selection=selection)
@@ -6062,7 +6062,7 @@
corr_data = [fig1, fig2]
write_stats = True
- RDR.plot_r2eff_corr(corr_data=corr_data, show=True,
write_stats=write_stats)
+ RDR.plot_r2eff_corr(corr_data=corr_data, show=False,
write_stats=write_stats)
# Open stat file.
if write_stats:
@@ -6089,11 +6089,11 @@
# Try plot some R2eff statistics.
- if False:
+ if True:
# Collect r2eff values.
selections = [None, ':2,3']
for selection in selections:
- r2eff_ft_sel = RDR.col_r2eff(method='FT',
list_glob_ini=[128, 126], selection=selection)
+ r2eff_ft_sel = RDR.col_r2eff(method='FT',
list_glob_ini=[128, 126, 6], selection=selection)
r2eff_mdd_sel = RDR.col_r2eff(method='MDD',
list_glob_ini=[128, 126], selection=selection)
# Get R2eff stats.
@@ -6117,7 +6117,7 @@
print(i, data_i)
# Do minimisation.
- if True:
+ if False:
methods = ['FT', 'MDD']
# Now calculate R2eff.
RDR.calc_r2eff(methods=methods, list_glob_ini=[128, 126])
@@ -6191,7 +6191,7 @@
# Plot statistics.
# Try plot some minimisation correlations.
- if True:
+ if False:
# Collect r2eff values.
min_ft_sel = RDR.col_min(method='FT', model=MODEL_CR72,
analysis='min', list_glob_ini=[128], selection=None)
min_mdd_sel = RDR.col_min(method='MDD', model=MODEL_CR72,
analysis='min', list_glob_ini=range(126, 130, 2)[::-1], selection=None)
r25982 - in /trunk: auto_analyses/relax_disp_repeat_cpmg.py test_suite/system_tests/relax_disp.py