Author: tlinnet
Date: Thu Oct 2 14:08:37 2014
New Revision: 26135
URL: http://svn.gna.org/viewcvs/relax?rev=26135&view=rev
Log:
Implemented writing out intensity and error correlations plot.
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=26135&r1=26134&r2=26135&view=diff
==============================================================================
--- trunk/auto_analyses/relax_disp_repeat_cpmg.py (original)
+++ trunk/auto_analyses/relax_disp_repeat_cpmg.py Thu Oct 2 14:08:37
2014
@@ -1041,6 +1041,7 @@
# Loop over the glob ini:
res_dic = {}
res_dic['method'] = method
+ res_dic['selection'] = selection
for glob_ini in list_glob_ini:
# Get the pipe name for peak_intensity values.
pipe_name = self.name_pipe(method=method, model='setup',
analysis='int', glob_ini=glob_ini)
@@ -1083,7 +1084,7 @@
return res_dic
- def plot_int_corr(self, corr_data, show=False):
+ def plot_int_corr(self, corr_data, show=False, write_stats=False):
# Define figure.
# Nr of columns is number of datasets.
@@ -1097,6 +1098,9 @@
# axises is a tuple with number of elements corresponding to number
of rows.
# Each sub-tuple contains axis for each column.
+
+ # For writing out stats.
+ data_dic = OrderedDict()
# Loop over the rows.
for i, row_axises in enumerate(axises):
@@ -1110,12 +1114,17 @@
x = data_x[str(glob_ini_x)]['peak_intensity_arr']
x_err = data_x[str(glob_ini_x)]['peak_intensity_err_arr']
+ np = len(x)
y = data_y[str(glob_ini_y)]['peak_intensity_arr']
y_err = data_y[str(glob_ini_y)]['peak_intensity_err_arr']
# If row 1.
if i == 0:
+ # Add to data dic.
+ method_xy_NI = "int_%s%s_%s%s" % (method_x, glob_ini_x,
method_y, glob_ini_y)
+ data_dic[method_xy_NI] = []
+
ax.plot(x, x, 'o', label='%s vs. %s' % (method_x,
method_x))
ax.plot(x, y, '.', label='%s vs. %s' % (method_y,
method_x) )
@@ -1127,6 +1136,22 @@
ax.set_xlabel(r'$I$')
ax.set_ylabel(r'$I$')
+ # 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
+
+ # Add to data.
+ for k, x_k in enumerate(x):
+ y_k = 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])
+
# Scale intensity
if i == 1:
@@ -1145,6 +1170,9 @@
# Error.
if i == 2:
+ # Add to data dic.
+ method_xy_NI = "int_err_%s%s_%s%s" % (method_x,
glob_ini_x, method_y, glob_ini_y)
+ data_dic[method_xy_NI] = []
ax.plot(x_err, x_err, 'o', label='%s vs. %s' %
(method_x, method_x))
ax.plot(x_err, y_err, '.', label='%s vs. %s' %
(method_y, method_x))
@@ -1155,6 +1183,22 @@
ax.set_xlabel(r'$\sigma(I)$')
ax.set_ylabel(r'$\sigma(I)$')
+ # Calculate straight line.
+ # Linear a, with no intercept.
+ a = sum(x_err * y_err) / sum(x_err**2)
+ min_x = min(x_err)
+ max_x = max(x_err)
+ step_x = (max_x - min_x) / np
+ x_err_arange = arange(min_x, max_x, step_x)
+ y_err_arange = a * x_arange
+
+ # Add to data.
+ for k, x_err_k in enumerate(x_err):
+ y_err_k = y_err[k]
+ x_err_arange_k = x_err_arange[k]
+ y_err_arange_k = y_err_arange[k]
+ data_dic[method_xy_NI].append(["%3.5f"%x_err_k,
"%3.5f"%y_err_k, "%3.5f"%x_err_arange_k, "%3.5f"%y_err_arange_k])
+
# Intensity to error.
if i == 3:
@@ -1172,6 +1216,96 @@
ax.set_ylabel(r'$I/\sigma(I)$')
plt.tight_layout()
+
+ # Loop over columns for writing data.
+ # Write to file.
+ if write_stats:
+ # Re-order the data.
+ headings_all = []
+ method_xy_NI_all = []
+ # Loop over the columns.
+ for j in range(nr_cols):
+ headings_j = []
+ method_xy_NI_j = []
+ # Loop over rows
+ for i in range(nr_rows):
+ # Extract from lists.
+ data, methods, glob_inis = corr_data[j]
+ method_x, method_y = methods
+ glob_ini_x, glob_ini_y = glob_inis
+
+ # If row 1.
+ if i == 0:
+ # Add to headings.
+ method_x_NI = "int_%s%s" % (method_x, glob_ini_x)
+ method_y_NI = "int_%s%s" % (method_y, glob_ini_y)
+ method_x_NI_lin = "int_lin_%s%s" % (method_x,
glob_ini_x)
+ method_y_NI_lin = "int_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 = "int_%s%s_%s%s" % (method_x,
glob_ini_x, method_y, glob_ini_y)
+ method_xy_NI_j.append(method_xy_NI)
+
+ # Error.
+ if i == 2:
+ # Add to headings
+ method_x_NI = "int_err_%s%s" % (method_x, glob_ini_x)
+ method_y_NI = "int_err_%s%s" % (method_y, glob_ini_y)
+ method_x_NI_lin = "int_err_lin_%s%s" % (method_x,
glob_ini_x)
+ method_y_NI_lin = "int_err_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 = "int_err_%s%s_%s%s" % (method_x,
glob_ini_x, method_y, glob_ini_y)
+ method_xy_NI_j.append(method_xy_NI)
+
+ headings_all.append(headings_j)
+ method_xy_NI_all.append(method_xy_NI_j)
+
+ # Loop over the columns.
+ for j, headings_j in enumerate(headings_all):
+ method_xy_NI_j = method_xy_NI_all[j]
+
+ data_w = []
+ data_int = data_dic[method_xy_NI_j[0]]
+ data_int_err = data_dic[method_xy_NI_j[1]]
+
+ for k, data_int_k in enumerate(data_int):
+ data_int_err_k = data_int_err[k]
+ data_w.append(data_int_k + data_int_err_k)
+
+ # Define file name.
+ data, methods, glob_inis = corr_data[j]
+ data_x, data_y = data
+ method_x, method_y = methods
+ glob_ini_x, glob_ini_y = glob_inis
+ np = len(data_int)
+
+ # Get the spin selection for correlation.
+ selection = data_x['selection']
+
+ file_name_ini = 'int_corr_%s_%s_%s_%s_NP_%i' % (method_x,
glob_ini_x, method_y, glob_ini_y, np)
+ if selection == None:
+ file_name_ini = file_name_ini + '_all'
+ else:
+ file_name_ini = file_name_ini + '_sel'
+
+ file_name = file_name_ini + '.txt'
+ path = self.results_dir
+
+ # save figure
+ # Write png.
+ png_file_name = file_name_ini + '.png'
+ png_file_path = get_file_path(file_name=png_file_name,
dir=path)
+ plt.savefig(png_file_path, bbox_inches='tight')
+
+ # Write file
+ file_obj, file_path = open_write_file(file_name=file_name,
dir=path, force=True, compress_type=0, verbosity=1, return_path=True)
+
+ # Write data.
+ write_data(out=file_obj, headings=headings_j, data=data_w)
+
+ # Close file.
+ file_obj.close()
if show:
plt.show()
r26135 - /trunk/auto_analyses/relax_disp_repeat_cpmg.py