Author: bugman
Date: Tue Oct 21 15:17:17 2008
New Revision: 7878
URL: http://svn.gna.org/viewcvs/relax?rev=7878&view=rev
Log:
Fixes for all the standard deviation and variance variable names.
Modified:
1.3/prompt/relax_fit.py
1.3/specific_fns/relax_fit.py
Modified: 1.3/prompt/relax_fit.py
URL:
http://svn.gna.org/viewcvs/relax/1.3/prompt/relax_fit.py?rev=7878&r1=7877&r2=7878&view=diff
==============================================================================
--- 1.3/prompt/relax_fit.py (original)
+++ 1.3/prompt/relax_fit.py Tue Oct 21 15:17:17 2008
@@ -49,15 +49,15 @@
Errors of individual spin at a single time point
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- The standard deviation for a single spin at a single time point is
calculated by the formula
+ The variance for a single spin at a single time point is calculated
by the formula:
-----
- sigma = sum({Ii - Iav}^2) / (n - 1) ,
+ sigma^2 = sum({Ii - Iav}^2) / (n - 1) ,
-----
- where sigma is the variance or square of the standard deviation, n
is the total number of
+ where sigma^2 is the variance, sigma is the standard deviation, n is
the total number of
collected spectra for the time point and i is the corresponding
index, Ii is the peak
intensity for spectrum i, Iav is the mean over all spectra, ie the
sum of all peak
intensities divided by n.
Modified: 1.3/specific_fns/relax_fit.py
URL:
http://svn.gna.org/viewcvs/relax/1.3/specific_fns/relax_fit.py?rev=7878&r1=7877&r2=7878&view=diff
==============================================================================
--- 1.3/specific_fns/relax_fit.py (original)
+++ 1.3/specific_fns/relax_fit.py Tue Oct 21 15:17:17 2008
@@ -192,7 +192,7 @@
scaling_matrix = self.assemble_scaling_matrix(spin=spin,
scaling=False)
# Initialise the relaxation fit functions.
- setup(num_params=len(spin.params), num_times=len(cdp.relax_times),
values=spin.ave_intensities, sd=cdp.sd, relax_times=cdp.relax_times,
scaling_matrix=scaling_matrix)
+ setup(num_params=len(spin.params), num_times=len(cdp.relax_times),
values=spin.ave_intensities, sd=cdp.sigma_I, relax_times=cdp.relax_times,
scaling_matrix=scaling_matrix)
# Make a single function call. This will cause back calculation and
the data will be stored in the C module.
func(param_vector)
@@ -633,14 +633,15 @@
cdp = pipes.get_pipe()
# Test if the standard deviation has already been calculated.
- if hasattr(cdp, 'sigma'):
+ if hasattr(cdp, 'sigma_I'):
raise RelaxError, "The average intensity and standard deviation
of all spectra has already been calculated."
# Print out.
print "\nCalculating the average intensity and standard deviation of
all spectra."
# Initialise.
- cdp.sigma = []
+ cdp.sigma_I = []
+ cdp.var_I = []
# Loop over the time points.
for time_index in xrange(len(cdp.relax_times)):
@@ -651,7 +652,7 @@
print "%-5s%-6s%-20s%-20s" % ("Num", "Name", "Average", "SD")
# Append zero to the global variance structure.
- cdp.sigma.append(0.0)
+ cdp.var_I.append(0.0)
# Test for multiple spectra.
if cdp.num_spectra[time_index] == 1:
@@ -673,8 +674,8 @@
# Initialise the average intensity and standard deviation
data structures.
if not hasattr(spin, 'ave_intensities'):
spin.ave_intensities = []
- if not hasattr(spin, 'sigma'):
- spin.sigma = []
+ if not hasattr(spin, 'var_I'):
+ spin.var_I = []
# Average intensity.
spin.ave_intensities.append(average(spin.intensities[time_index]))
@@ -686,60 +687,60 @@
# Variance.
#
- # 1
- # sigma = ----- * sum({Xi - Xav}^2)]
- # n - 1
+ # 1
+ # sigma^2 = ----- * sum({Xi - Xav}^2)]
+ # n - 1
#
if cdp.num_spectra[time_index] == 1:
- sigma = 0.0
+ var_I = 0.0
else:
- sigma = 1.0 / (cdp.num_spectra[time_index] - 1.0) * SSE
- spin.sigma.append(sigma)
+ var_I = 1.0 / (cdp.num_spectra[time_index] - 1.0) * SSE
+ spin.var_I.append(var_I)
# Print out.
if verbosity:
- print "%-5i%-6s%-20s%-20s" % (spin.num, spin.name,
`spin.ave_intensities[time_index]`, `spin.sigma[time_index]`)
+ print "%-5i%-6s%-20s%-20s" % (spin.num, spin.name,
`spin.ave_intensities[time_index]`, `spin.var_I[time_index]`)
# Sum of variances (for average).
- cdp.sigma[time_index] = cdp.sigma[time_index] +
spin.sigma[time_index]
+ cdp.var_I[time_index] = cdp.var_I[time_index] +
spin.var_I[time_index]
# Average variance.
- cdp.sigma[time_index] = cdp.sigma[time_index] /
float(count_spins())
+ cdp.var_I[time_index] = cdp.var_I[time_index] /
float(count_spins())
# Print out.
- print "Standard deviation for time point %s: %s" %
(`time_index`, `cdp.sigma[time_index]`)
+ print "Standard deviation for time point %s: %s" %
(`time_index`, `sqrt(cdp.var_I[time_index])`)
# Average across all spectra if there are time points with a single
spectrum.
- if 0.0 in cdp.sigma:
+ if 0.0 in cdp.var_I:
# Initialise.
- sigma = 0.0
+ var_I = 0.0
num_dups = 0
# Loop over all time points.
for i in xrange(len(cdp.relax_times)):
# Single spectrum (or extraordinarily accurate NMR spectra!).
- if cdp.sigma[i] == 0.0:
+ if cdp.var_I[i] == 0.0:
continue
# Sum and count.
- sigma = sigma + cdp.sigma[i]
+ var_I = var_I + cdp.var_I[i]
num_dups = num_dups + 1
# Average value.
- sigma = sigma / float(num_dups)
+ var_I = var_I / float(num_dups)
# Assign the average value to all time points.
for i in xrange(len(cdp.relax_times)):
- cdp.sigma[i] = sigma
+ cdp.var_I[i] = var_I
# Print out.
- print "\nStandard deviation (averaged over all spectra): " +
`sigma`
+ print "\nStandard deviation (averaged over all spectra): " +
`var_I`
# Create the standard deviation data structure.
- cdp.sd = []
- for sigma in cdp.sigma:
- cdp.sd.append(sqrt(sigma))
+ cdp.sigma_I = []
+ for var_I in cdp.var_I:
+ cdp.sigma_I.append(sqrt(var_I))
def minimise(self, min_algor=None, min_options=None, func_tol=None,
grad_tol=None, max_iterations=None, constraints=False, scaling=True,
verbosity=0, sim_index=None, lower=None, upper=None, inc=None):
@@ -842,7 +843,7 @@
else:
values = spin.sim_intensities[sim_index]
- setup(num_params=len(spin.params),
num_times=len(cdp.relax_times), values=values, sd=cdp.sd,
relax_times=cdp.relax_times, scaling_matrix=scaling_matrix)
+ setup(num_params=len(spin.params),
num_times=len(cdp.relax_times), values=values, sd=cdp.sigma_I,
relax_times=cdp.relax_times, scaling_matrix=scaling_matrix)
# Setup the minimisation algorithm when constraints are present.
@@ -1064,7 +1065,7 @@
# Get the current data pipe.
cdp = pipes.get_pipe()
- return cdp.sd
+ return cdp.sigma_I
def return_data_name(self, name):
r7878 - in /1.3: prompt/relax_fit.py specific_fns/relax_fit.py