Author: bugman
Date: Wed Dec 15 12:17:28 2010
New Revision: 11832
URL: http://svn.gna.org/viewcvs/relax?rev=11832&view=rev
Log:
Switched __errors_repl() to use dictionaries rather than lists.
Modified:
branches/peak_list_handling/generic_fns/spectrum.py
Modified: branches/peak_list_handling/generic_fns/spectrum.py
URL:
http://svn.gna.org/viewcvs/relax/branches/peak_list_handling/generic_fns/spectrum.py?rev=11832&r1=11831&r2=11832&view=diff
==============================================================================
--- branches/peak_list_handling/generic_fns/spectrum.py (original)
+++ branches/peak_list_handling/generic_fns/spectrum.py Wed Dec 15 12:17:28
2010
@@ -121,16 +121,17 @@
"""
# replicated spectra.
- repl = [False] * len(cdp.spectrum_ids)
+ repl = {}
for i in xrange(len(cdp.replicates)):
for j in xrange(len(cdp.replicates[i])):
- repl[cdp.spectrum_ids.index(cdp.replicates[i][j])] = True
+ repl[cdp.replicates[i][j]] = True
# Are all spectra replicated?
- all_repl = not (False in repl)
- if all_repl:
+ if len(repl.keys()) == len(cdp.spectrum_ids):
+ all_repl = True
print("All spectra replicated: Yes.")
else:
+ all_repl = False
print("All spectra replicated: No.")
# Test if the standard deviation has already been calculated.
@@ -138,31 +139,26 @@
raise RelaxError("The peak intensity standard deviation of all
spectra has already been calculated.")
# Initialise.
- cdp.sigma_I = [0.0] * len(cdp.spectrum_ids)
- cdp.var_I = [0.0] * len(cdp.spectrum_ids)
+ cdp.sigma_I = {}
+ cdp.var_I = {}
# Loop over the spectra.
- for i in xrange(len(cdp.spectrum_ids)):
+ for id in cdp.spectrum_ids:
# Skip non-replicated spectra.
- if not repl[i]:
+ if not repl.has_key(id):
continue
# Skip replicated spectra which already have been used.
- if cdp.var_I[i] != 0.0:
+ if cdp.var_I.has_key(id) and cdp.var_I[id] != 0.0:
continue
# The replicated spectra.
for j in xrange(len(cdp.replicates)):
- if cdp.spectrum_ids[i] in cdp.replicates[j]:
+ if id in cdp.replicates[j]:
spectra = cdp.replicates[j]
# Number of spectra.
num_spectra = len(spectra)
-
- # Indices of the spectra.
- indices = [None] * num_spectra
- for j in xrange(num_spectra):
- indices[j] = cdp.spectrum_ids.index(spectra[j])
# Print out.
print(("\nReplicated spectra: " + repr(spectra)))
@@ -213,7 +209,9 @@
print(("%-5i%-6s%-20s%-20s" % (spin.num, spin.name,
repr(ave_intensity), repr(var_I))))
# Sum of variances (for average).
- cdp.var_I[indices[0]] = cdp.var_I[indices[0]] + var_I
+ if not cdp.var_I.has_key(id):
+ cdp.var_I[id] = 0.0
+ cdp.var_I[id] = cdp.var_I[id] + var_I
count = count + 1
# No data catch.
@@ -221,14 +219,14 @@
raise RelaxError("No data is present, unable to calculate errors
from replicated spectra.")
# Average variance.
- cdp.var_I[indices[0]] = cdp.var_I[indices[0]] / float(count)
+ cdp.var_I[id] = cdp.var_I[id] / float(count)
# Set all spectra variances.
- for j in xrange(num_spectra):
- cdp.var_I[indices[j]] = cdp.var_I[indices[0]]
+ for j in xrange(1, num_spectra):
+ cdp.var_I[spectra[j]] = cdp.var_I[spectra[0]]
# Print out.
- print(("Standard deviation: %s" % sqrt(cdp.var_I[indices[0]])))
+ print(("Standard deviation: %s" % sqrt(cdp.var_I[id])))
# Average across all spectra if there are time points with a single
spectrum.
@@ -241,29 +239,29 @@
num_dups = 0
# Loop over all time points.
- for i in xrange(len(cdp.spectrum_ids)):
+ for id in cdp.var_I.keys():
# Single spectrum (or extraordinarily accurate NMR spectra!).
- if cdp.var_I[i] == 0.0:
+ if cdp.var_I[id] == 0.0:
continue
# Sum and count.
- var_I = var_I + cdp.var_I[i]
+ var_I = var_I + cdp.var_I[id]
num_dups = num_dups + 1
# Average value.
var_I = var_I / float(num_dups)
# Assign the average value to all time points.
- for i in xrange(len(cdp.spectrum_ids)):
- cdp.var_I[i] = var_I
+ for id in cdp.spectrum_ids:
+ cdp.var_I[id] = var_I
# Print out.
print(("Standard deviation for all spins: " + repr(sqrt(var_I))))
# Loop over the spectra.
- for i in xrange(len(cdp.spectrum_ids)):
+ for id in cdp.var_I.keys():
# Create the standard deviation data structure.
- cdp.sigma_I[i] = sqrt(cdp.var_I[i])
+ cdp.sigma_I[id] = sqrt(cdp.var_I[id])
# Set the spin specific errors.
for spin in spin_loop():
@@ -297,7 +295,8 @@
raise RelaxError("The total number of points used in the volume
integration has not been specified for spin '%s'." % spin_id)
# Set the error to the RMSD multiplied by the square root of the
total number of points.
- spin.intensity_err = spin.baseplane_rmsd * sqrt(spin.N)
+ for key in spin.intensity.keys():
+ spin.intensity_err[key] = spin.baseplane_rmsd[key] * sqrt(spin.N)
def autodetect_format(file_data):
@@ -351,9 +350,6 @@
if spectrum_id not in cdp.spectrum_ids:
raise RelaxError("The peak intensities corresponding to the spectrum
id '%s' do not exist." % spectrum_id)
- # The spectrum id index.
- spect_index = cdp.spectrum_ids.index(spectrum_id)
-
# The scaling by NC_proc.
if hasattr(cdp, 'ncproc') and spectrum_id in cdp.ncproc:
scale = 1.0 / 2**cdp.ncproc[spectrum_id]
@@ -368,12 +364,10 @@
# Initialise or update the baseplane_rmsd data structure as
necessary.
if not hasattr(spin, 'baseplane_rmsd'):
- spin.baseplane_rmsd = [None] * len(cdp.spectrum_ids)
- elif len(spin.baseplane_rmsd) < len(cdp.spectrum_ids):
- spin.baseplane_rmsd.append([None] * (len(cdp.spectrum_ids) -
len(spin.baseplane_rmsd)))
+ spin.baseplane_rmsd = {}
# Set the error.
- spin.baseplane_rmsd[spect_index] = float(error) * scale
+ spin.baseplane_rmsd[spectrum_id] = float(error) * scale
def error_analysis():
r11832 - /branches/peak_list_handling/generic_fns/spectrum.py