Author: tlinnet
Date: Wed Oct 1 20:42:22 2014
New Revision: 26129
URL: http://svn.gna.org/viewcvs/relax?rev=26129&view=rev
Log:
Implemented statistics for R2eff values.
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=26129&r1=26128&r2=26129&view=diff
==============================================================================
--- trunk/auto_analyses/relax_disp_repeat_cpmg.py (original)
+++ trunk/auto_analyses/relax_disp_repeat_cpmg.py Wed Oct 1 20:42:22
2014
@@ -1272,10 +1272,21 @@
method_xy_NI = "r2eff_%s%s_%s%s" % (method_x,
glob_ini_x, method_y, glob_ini_y)
data_dic[method_xy_NI] = []
+ # Calculate straight line.
+ # Linear a, with no intercept.
+ a = sum(x * y) / sum(x**2)
+ min_x = min(x)
+ max_x = max(x)
+ step_x = (max_x - min_x) / np
+ x_arange = arange(min_x, max_x, step_x)
+ y_arange = a * x_arange
+
ax.plot(x, x, 'o', label='%s vs. %s' % (method_x,
method_x))
+ ax.plot(x_arange, x_arange, 'b--')
if len(x) != len(y):
print(len(x), len(y))
ax.plot(x, y, '.', label='%s vs. %s' % (method_y,
method_x) )
+ ax.plot(x_arange, y_arange, 'g--')
ax.set_title(r'$R_{2,\mathrm{eff}}$' + ' for %s %i vs.
%s %i. np=%i' % (method_y, glob_ini_y, method_x, glob_ini_x, np), fontsize=10)
ax.legend(loc='upper left', shadow=True, prop = fontP)
@@ -1287,7 +1298,9 @@
# Add to data.
for k, x_k in enumerate(x):
y_k = y[k]
- data_dic[method_xy_NI].append(["%3.5f"%x_k,
"%3.5f"%y_k])
+ x_arange_k = x_arange[k]
+ y_arange_k = y_arange[k]
+ data_dic[method_xy_NI].append(["%3.5f"%x_k,
"%3.5f"%y_k, "%3.5f"%x_arange_k, "%3.5f"%y_arange_k])
# R2eff to error.
if i == 1:
@@ -1349,7 +1362,9 @@
# Add to headings.
method_x_NI = "r2eff_%s%s" % (method_x, glob_ini_x)
method_y_NI = "r2eff_%s%s" % (method_y, glob_ini_y)
- headings_j = headings_j + [method_x_NI, method_y_NI]
+ method_x_NI_lin = "r2eff_lin_%s%s" % (method_x,
glob_ini_x)
+ method_y_NI_lin = "r2eff_lin_%s%s" % (method_y,
glob_ini_y)
+ headings_j = headings_j + [method_x_NI, method_y_NI,
method_x_NI_lin, method_y_NI_lin]
method_xy_NI = "r2eff_%s%s_%s%s" % (method_x,
glob_ini_x, method_y, glob_ini_y)
method_xy_NI_j.append(method_xy_NI)
@@ -1452,6 +1467,8 @@
res_dic[method_cur]['two_tailed_p_value'] = []
res_dic[method_cur]['r_xy'] = []
res_dic[method_cur]['a'] = []
+ res_dic[method_cur]['r_xy_2'] = []
+ res_dic[method_cur]['a_2'] = []
res_dic[method_cur]['r_xy_int'] = []
res_dic[method_cur]['a_int'] = []
res_dic[method_cur]['b_int'] = []
@@ -1525,7 +1542,14 @@
a = sum(x*y) / sum(x**2)
r_xy = sum(x*y) / sqrt(sum(x**2) * sum(y**2))
- print(method_ref, method_cur, sampling_sparseness, glob_ini,
pearsons_correlation_coefficient, r_xy**2, a, r_xy_int**2, a_int, b_int)
+ # Without intercept for just R2eff.
+ x_2 = r2eff_arr_ref
+ y_2 = r2eff_arr
+ a_2 = sum(x_2*y_2) / sum(x_2**2)
+ r_xy_2 = sum(x_2*y_2) / sqrt(sum(x_2**2) * sum(y_2**2))
+
+ #print(method_ref, method_cur, sampling_sparseness,
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_2**2, a_2)
# Store to result dic.
res_dic[method_cur][str(glob_ini)]['pearsons_correlation_coefficient'] =
pearsons_correlation_coefficient
@@ -1536,6 +1560,12 @@
res_dic[method_cur]['r_xy'].append(r_xy)
res_dic[method_cur][str(glob_ini)]['a'] = a
res_dic[method_cur]['a'].append(a)
+
+ res_dic[method_cur][str(glob_ini)]['r_xy_2'] = r_xy_2
+ res_dic[method_cur]['r_xy_2'].append(r_xy_2)
+ res_dic[method_cur][str(glob_ini)]['a_2'] = a_2
+ res_dic[method_cur]['a_2'].append(a_2)
+
res_dic[method_cur][str(glob_ini)]['r_xy_int'] = r_xy_int
res_dic[method_cur]['r_xy_int'].append(r_xy_int)
res_dic[method_cur][str(glob_ini)]['a_int'] = a_int
@@ -1551,6 +1581,8 @@
res_dic[method_cur]['two_tailed_p_value'] =
asarray(res_dic[method_cur]['two_tailed_p_value'])
res_dic[method_cur]['r_xy'] =
asarray(res_dic[method_cur]['r_xy'])
res_dic[method_cur]['a'] = asarray(res_dic[method_cur]['a'])
+ res_dic[method_cur]['r_xy_2'] =
asarray(res_dic[method_cur]['r_xy_2'])
+ res_dic[method_cur]['a_2'] = asarray(res_dic[method_cur]['a_2'])
res_dic[method_cur]['r_xy_int'] =
asarray(res_dic[method_cur]['r_xy_int'])
res_dic[method_cur]['a_int'] =
asarray(res_dic[method_cur]['a_int'])
res_dic[method_cur]['b_int'] =
asarray(res_dic[method_cur]['b_int'])
@@ -1590,7 +1622,7 @@
SS = r2eff_stat_dic[method]['sampling_sparseness']
# Add to headings.
- headings = headings + ['method', 'SS', 'NI', 'slope', 'rxy2']
+ headings = headings + ['method', 'SS', 'NI', 'slope_r2eff',
'rxy2_r2eff', 'slope_r2eff_vs_err', 'rxy2_r2eff_vs_err']
# Get stats.
# Linear regression slope, without intercept
@@ -1609,6 +1641,11 @@
max_r_xy2 = max(r_xy2)
if min(r_xy2) < min_r_xy2:
min_r_xy2 = min(r_xy2)
+
+ # For just the R2eff values
+ a_r2eff = r2eff_stat_dic[method]['a_2']
+ r_xy_r2eff = r2eff_stat_dic[method]['r_xy_2']
+ r_xy_r2eff2 = r_xy_r2eff**2
# Add to data.
data_dic[method] = OrderedDict()
@@ -1616,14 +1653,21 @@
SS_i = SS[i]
a_i = a[i]
r_xy2_i = r_xy2[i]
- data_dic[method][str(i)] = ["%3.5f"%SS_i, "%i"%NI_i,
"%3.5f"%a_i, "%3.5f"%r_xy2_i]
+ a_r2eff_i = a_r2eff[i]
+ r_xy_r2eff2_i = r_xy_r2eff2[i]
+ data_dic[method][str(i)] = ["%3.5f"%SS_i, "%i"%NI_i,
"%3.5f"%a_r2eff_i, "%3.5f"%r_xy_r2eff2_i, "%3.5f"%a_i, "%3.5f"%r_xy2_i]
if i > i_max:
i_max = i
#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)
+ t = ax1.plot(SS, a_r2eff, ".--", label='%s slope R2eff'%method)
+ color = t[0].get_color()
+ ax1.plot(SS, a, ".-", label='%s slope'%method, color=color)
+
+ t = ax2.plot(SS, r_xy_r2eff2, "o--", label='%s r2 R2eff'%method)
+ color = t[0].get_color()
+ ax2.plot(SS, r_xy2, "o-", label='%s r2'%method, color=color)
# Loop over methods for writing data.
data = []
@@ -1635,7 +1679,7 @@
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", "0"]
+ data_i = data_i + [method] + ["0", "0", "0", "0", "0",
"0"]
data.append(data_i)
r26129 - /trunk/auto_analyses/relax_disp_repeat_cpmg.py