Author: bugman
Date: Fri Sep 26 19:27:43 2014
New Revision: 26079
URL: http://svn.gna.org/viewcvs/relax?rev=26079&view=rev
Log:
Merged revisions 26065-26066 via svnmerge from
svn+ssh://bugman@xxxxxxxxxxx/svn/relax/trunk
........
r26065 | tlinnet | 2014-09-26 17:29:39 +0200 (Fri, 26 Sep 2014) | 3 lines
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.
........
r26066 | tlinnet | 2014-09-26 17:29:49 +0200 (Fri, 26 Sep 2014) | 3 lines
Implemented writing and plotting of statistics for individual and clustered
fitting, compararing to full NI.
Task #7826 (https://gna.org/task/index.php?7826): Write an python class for
the repeated analysis of dispersion data.
........
Modified:
branches/frame_order_cleanup/ (props changed)
branches/frame_order_cleanup/auto_analyses/relax_disp_repeat_cpmg.py
branches/frame_order_cleanup/test_suite/system_tests/relax_disp.py
Propchange: branches/frame_order_cleanup/
------------------------------------------------------------------------------
--- svnmerge-integrated (original)
+++ svnmerge-integrated Fri Sep 26 19:27:43 2014
@@ -1 +1 @@
-/trunk:1-26017,26043-26064
+/trunk:1-26017,26043-26066
Modified: branches/frame_order_cleanup/auto_analyses/relax_disp_repeat_cpmg.py
URL:
http://svn.gna.org/viewcvs/relax/branches/frame_order_cleanup/auto_analyses/relax_disp_repeat_cpmg.py?rev=26079&r1=26078&r2=26079&view=diff
==============================================================================
--- branches/frame_order_cleanup/auto_analyses/relax_disp_repeat_cpmg.py
(original)
+++ branches/frame_order_cleanup/auto_analyses/relax_disp_repeat_cpmg.py
Fri Sep 26 19:27:43 2014
@@ -1943,6 +1943,222 @@
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
+ analysis = min_dic_ref['analysis']
+ res_dic['analysis'] = analysis
+
+ # Get the current method
+ method_cur = min_dic['method']
+ res_dic[method_cur] = {}
+
+ # 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
+
+ res_dic[method_cur][param] = {}
+ res_dic[method_cur][param]['method'] = method_cur
+ res_dic[method_cur][param]['sampling_sparseness'] = []
+ res_dic[method_cur][param]['glob_ini'] = []
+
+ # Other stats.
+ res_dic[method_cur][param]['r_xy'] = []
+ res_dic[method_cur][param]['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[method_cur][param]['sampling_sparseness'].append(sampling_sparseness)
+ res_dic[method_cur][param]['glob_ini'].append(glob_ini)
+
+ # Store to result dic.
+ res_dic[method_cur][param][str(glob_ini)] = {}
+
res_dic[method_cur][param][str(glob_ini)]['sampling_sparseness'] =
sampling_sparseness
+ res_dic[method_cur][param][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[method_cur][param][str(glob_ini)]['r_xy'] = r_xy
+ res_dic[method_cur][param]['r_xy'].append(r_xy)
+ res_dic[method_cur][param][str(glob_ini)]['a'] = a
+ res_dic[method_cur][param]['a'].append(a)
+
+ res_dic[method_cur][param]['sampling_sparseness'] =
asarray(res_dic[method_cur][param]['sampling_sparseness'])
+ res_dic[method_cur][param]['glob_ini'] =
asarray(res_dic[method_cur][param]['glob_ini'])
+
+ res_dic[method_cur][param]['r_xy'] =
asarray(res_dic[method_cur][param]['r_xy'])
+ res_dic[method_cur][param]['a'] =
asarray(res_dic[method_cur][param]['a'])
+
+ return res_dic
+
+
+ def plot_min_stat(self, min_stat_dic=None, methods=[], list_glob_ini=[],
show=False, write_stats=False):
+
+ # Catch min and max values for all methods.
+ min_a = 1.0
+ max_a = 0.0
+
+ min_r_xy2 = 1.0
+ max_r_xy2 = 0.0
+
+ # Prepare header for writing.
+ selection = min_stat_dic['selection']
+ params_list = min_stat_dic['params_list']
+ analysis = min_stat_dic['analysis']
+
+ # For writing out stats.
+ headings = []
+ data_dic = OrderedDict()
+ i_max = 0
+
+ for method in methods:
+ if method not in min_stat_dic:
+ continue
+
+ # Define figure
+ fig, axises = plt.subplots(nrows=len(params_list), ncols=1)
+ fig.suptitle('Stats per NI %s' % method)
+
+ # Loop over params
+ data_dic[method] = OrderedDict()
+
+ for j, param in enumerate(params_list):
+ data_dic[method][param] = OrderedDict()
+
+ # Use NI as x.
+ NI = min_stat_dic[method][param]['glob_ini']
+
+ # Use sampling_sparseness as x.
+ SS = min_stat_dic[method][param]['sampling_sparseness']
+
+ # Add to headings.
+ headings = headings + ['method_%s'%param, 'SS', 'NI',
'slope', 'rxy2']
+
+ # Get stats.
+ # Linear regression slope, without intercept
+ a = min_stat_dic[method][param]['a']
+
+ if max(a) > max_a:
+ max_a = max(a)
+ if min(a) < min_a:
+ min_a = min(a)
+
+ # sample correlation coefficient, without intercept
+ r_xy = min_stat_dic[method][param]['r_xy']
+ r_xy2 = r_xy**2
+
+ if max(r_xy2) > max_r_xy2:
+ max_r_xy2 = max(r_xy2)
+ if min(r_xy2) < min_r_xy2:
+ min_r_xy2 = min(r_xy2)
+
+ # Add to data.
+ for i, NI_i in enumerate(NI):
+ SS_i = SS[i]
+ a_i = a[i]
+ r_xy2_i = r_xy2[i]
+ data_dic[method][param][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
+
+ ax = axises[j]
+ ax.plot(SS, a, ".-", label='%s_%s_a' % (method, param) )
+ ax.plot(SS, r_xy2, "o--", label='%s_%s_r_xy2' % (method,
param) )
+ ax.legend(loc='lower left', shadow=True, prop = fontP)
+ ax.set_xlabel('SS')
+ ax.invert_xaxis()
+ #ax.set_ylim(min_a*0.95, max_a*1.05)
+
+
+ # Loop over methods for writing data.
+ data = []
+
+ # Loop over all lines.
+ for i in range(0, i_max+1):
+ data_i = []
+ for method, data_dic_m in data_dic.iteritems():
+ # Loop over all params
+ for j, param in enumerate(params_list):
+ # Loop over all possible data points.
+ if str(i) in data_dic_m[param]:
+ data_i = data_i + ["%s_%s" % (method, param)] +
data_dic_m[param][str(i)]
+ else:
+ data_i = data_i + ["%s_%s" % (method, param)] +
["0", "0", "0", "0"]
+
+ data.append(data_i)
+
+ # Determine filename.
+ if selection == None:
+ file_name_ini = '%s_stat_all' % analysis
+ else:
+ file_name_ini = '%s_stat_sel' % analysis
+
+ # Write png.
+ png_file_name = file_name_ini + '.png'
+ png_file_path = get_file_path(file_name=png_file_name,
dir=self.results_dir)
+
+ # Write to file.
+ if write_stats:
+ # save figure
+ plt.savefig(png_file_path, bbox_inches='tight')
+
+ file_name = file_name_ini + '.txt'
+ path = self.results_dir
+ 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, data=data)
+
+ # Close file.
+ file_obj.close()
+
+ # Plot data.
+ if show:
+ plt.show()
+
+
def interpreter_start(self):
# Load the interpreter.
self.interpreter = Interpreter(show_script=False,
raise_relax_error=True)
Modified: branches/frame_order_cleanup/test_suite/system_tests/relax_disp.py
URL:
http://svn.gna.org/viewcvs/relax/branches/frame_order_cleanup/test_suite/system_tests/relax_disp.py?rev=26079&r1=26078&r2=26079&view=diff
==============================================================================
--- branches/frame_order_cleanup/test_suite/system_tests/relax_disp.py
(original)
+++ branches/frame_order_cleanup/test_suite/system_tests/relax_disp.py Fri
Sep 26 19:27:43 2014
@@ -6015,45 +6015,6 @@
RDR.plot_int_corr(corr_data=corr_data, show=True)
- # Try plot some R2eff correlations.
- if False:
- # Now calculate R2eff.
- #RDR.calc_r2eff(methods=methods, list_glob_ini=[128, 126])
-
- # Try for bad data.
- #RDR.calc_r2eff(methods=['FT'], list_glob_ini=[6, 4])
-
- # Collect r2eff values.
- r2eff_ft_all = RDR.col_r2eff(method='FT', list_glob_ini=[128,
126], selection=None)
-
- # Now make a spin selection.
- selection = ':2,3'
- r2eff_ft_sel = RDR.col_r2eff(method='FT', list_glob_ini=[128,
126], selection=selection)
- # Print the length of datasets, depending on selection.
- print( "All spins", len(r2eff_ft_all['128']['r2eff_arr']),
"Selection spins", len(r2eff_ft_sel['128']['r2eff_arr']) )
-
- # For all spins, mdd
- r2eff_mdd_all = RDR.col_r2eff(method='MDD', list_glob_ini=[128,
126], selection=None)
- r2eff_mdd_sel = RDR.col_r2eff(method='MDD', list_glob_ini=[128,
126], selection=selection)
-
- # Plot correlation of intensity
- fig1 = [[r2eff_ft_all, r2eff_mdd_all], ['FT', 'MDD'], [128, 128]]
- fig2 = [[r2eff_ft_sel, r2eff_mdd_sel], ['FT sel', 'MDD sel'],
[128, 128]]
- corr_data = [fig1, fig2]
-
- fig3 = [[r2eff_ft_all, r2eff_ft_all], ['FT', 'FT'], [128, 126]]
- fig4 = [[r2eff_ft_all, r2eff_mdd_all], ['FT', 'MDD'], [128, 126]]
- corr_data = [fig3, fig4]
-
- #fig5 = [[r2eff_ft_all, r2eff_mdd_all], ['FT', 'MDD'], [128,
128]]
- fig5 = [[r2eff_ft_all, r2eff_mdd_all], ['FT', 'MDD'], [128, 126]]
- fig6 = [[r2eff_ft_all, r2eff_ft_all], ['FT', 'FT'], [128, 126]]
- #fig6 = [[r2eff_mdd_all, r2eff_mdd_all], ['MDD', 'MDD'], [128,
126]]
- corr_data = [fig5, fig6]
-
- RDR.plot_r2eff_corr(corr_data=corr_data, show=True)
-
-
# Try write some R2eff correlations.
if True:
selection = None
@@ -6177,33 +6138,64 @@
# Plot statistics.
# Try plot some minimisation correlations.
if True:
- selection = None
+ selections = [None, ':2,3']
+ for selection in selections:
+ # Collect param values.
+ analysis = 'min_ind'
+ min_ft_sel = RDR.col_min(method='FT', model=MODEL_CR72,
analysis=analysis, list_glob_ini=[128], selection=selection)
+ min_mdd_sel = RDR.col_min(method='MDD', model=MODEL_CR72,
analysis=analysis, list_glob_ini=range(126, 130, 2)[::-1],
selection=selection)
+
+ fig1 = [[min_ft_sel, min_mdd_sel], ['FT', 'MDD'], [128, 128]]
+ fig2 = [[min_ft_sel, min_mdd_sel], ['FT', 'MDD'], [128, 126]]
+ corr_data = [fig1, fig2]
+
+ write_stats = True
+ RDR.plot_min_corr(corr_data=corr_data, show=False,
write_stats=write_stats)
+
+ # Open stat file.
+ if write_stats:
+ for i, corr_data_i in enumerate(corr_data):
+ data, methods, glob_inis = corr_data[i]
+ data_x, data_y = data
+ method_x, method_y = methods
+ glob_ini_x, glob_ini_y = glob_inis
+
+ file_name_ini = '%s_%s_%s_%s_%s' % (analysis,
method_x, glob_ini_x, method_y, glob_ini_y)
+
+ if selection == None:
+ file_name = file_name_ini + '_all.txt'
+ else:
+ file_name = file_name_ini + '_sel.txt'
+ path = RDR.results_dir
+ data = extract_data(file=file_name, dir=path)
+
+ # Loop over the lines.
+ for i, data_i in enumerate(data):
+ print(i, data_i)
+
+ # Try plot some minimisation statistics.
+ if True:
# Collect param values.
- analysis = 'min_ind'
- min_ft_sel = RDR.col_min(method='FT', model=MODEL_CR72,
analysis=analysis, list_glob_ini=[128], selection=selection)
- min_mdd_sel = RDR.col_min(method='MDD', model=MODEL_CR72,
analysis=analysis, list_glob_ini=range(126, 130, 2)[::-1],
selection=selection)
-
- fig1 = [[min_ft_sel, min_mdd_sel], ['FT', 'MDD'], [128, 128]]
- fig2 = [[min_ft_sel, min_mdd_sel], ['FT', 'MDD'], [128, 126]]
- corr_data = [fig1, fig2]
-
- write_stats = True
- RDR.plot_min_corr(corr_data=corr_data, show=False,
write_stats=write_stats)
-
- # Open stat file.
- if write_stats:
- for i, corr_data_i in enumerate(corr_data):
- data, methods, glob_inis = corr_data[i]
- data_x, data_y = data
- method_x, method_y = methods
- glob_ini_x, glob_ini_y = glob_inis
-
- file_name_ini = '%s_%s_%s_%s_%s' % (analysis, method_x,
glob_ini_x, method_y, glob_ini_y)
-
+ #selections = [None, ':2,3']
+ selections = [None, ':2,3']
+ for selection in selections:
+ analysis = 'min_ind'
+ min_ft_sel = RDR.col_min(method='FT', model=MODEL_CR72,
analysis=analysis, list_glob_ini=[128], selection=selection)
+ min_mdd_sel = RDR.col_min(method='MDD', model=MODEL_CR72,
analysis=analysis, list_glob_ini=range(126, 130, 2)[::-1],
selection=selection)
+
+ # Get param stats.
+ min_stat_dic =
RDR.get_min_stat_dic(list_r2eff_dics=[min_ft_sel, min_mdd_sel],
list_glob_ini=[128, 126])
+
+ ## Plot R2eff stats
+ write_stats = True
+ RDR.plot_min_stat(min_stat_dic=min_stat_dic, methods=['FT',
'MDD'], list_glob_ini=[128, 126, 6], show=False, write_stats=write_stats)
+
+ # Open stat file.
+ if write_stats:
if selection == None:
- file_name = file_name_ini + '_all.txt'
+ file_name = '%s_stat_all.txt' % (analysis)
else:
- file_name = file_name_ini + '_sel.txt'
+ file_name = '%s_stat_sel.txt' % (analysis)
path = RDR.results_dir
data = extract_data(file=file_name, dir=path)
@@ -6312,6 +6304,36 @@
# Loop over the lines.
for i, data_i in enumerate(data):
print(i, data_i)
+
+ # Try plot some minimisation statistics.
+ if True:
+ # Collect param values.
+ selections = [':2,3']
+ for selection in selections:
+ analysis = 'min'
+ min_ft_sel = RDR.col_min(method='FT', model=MODEL_CR72,
analysis=analysis, list_glob_ini=[128], selection=selection)
+ min_mdd_sel = RDR.col_min(method='MDD', model=MODEL_CR72,
analysis=analysis, list_glob_ini=range(126, 130, 2)[::-1],
selection=selection)
+
+ # Get param stats.
+ min_stat_dic =
RDR.get_min_stat_dic(list_r2eff_dics=[min_ft_sel, min_mdd_sel],
list_glob_ini=[128, 126])
+
+ ## Plot R2eff stats
+ write_stats = True
+ RDR.plot_min_stat(min_stat_dic=min_stat_dic, methods=['FT',
'MDD'], list_glob_ini=[128, 126, 6], show=False, write_stats=write_stats)
+
+ # Open stat file.
+ if write_stats:
+ if selection == None:
+ file_name = '%s_stat_all.txt' % (analysis)
+ else:
+ file_name = '%s_stat_sel.txt' % (analysis)
+ path = RDR.results_dir
+ data = extract_data(file=file_name, dir=path)
+
+ # Loop over the lines.
+ for i, data_i in enumerate(data):
+ print(i, data_i)
+
def test_r1rho_kjaergaard_auto(self):
r26079 - in /branches/frame_order_cleanup: ./ auto_analyses/relax_disp_repeat_cpmg.py test_suite/system_tests/relax_disp.py