x = i.split()
taxon = x[1:]
for t in taxon:
j = taxon.index(t)
Index[t] = j + 1
continue
elif match('Coefficients:', i):
continue
i.strip()
x = i.split()
species = x[0]
if species in sample.keys():
duplicates.append(species)
else:
sample[species] = {}
if missing == 'y':
mtax = ''
for t in taxon:
if x[Index[t]] == '/':
sample[species][t] = mtax
else:
sample[species][t] = x[Index[t]]
mtax = x[Index[t]]
else:
for t in taxon:
def phy_re_analysis(options, args):
"""
Script should be launched as:
python PhyRe.py [samplefile] [masterlistfile] s1 s2 [options]
Parameters
----------
p : int
permutations for confidence intervals
d1 : int
d1 and d2 are range for number of species for funnel plot
d2 : int
d1 and d2 are range for number of species for funnel plot
Returns
-------
first_file.out : file
Results from analyses of the sample. By default,
the output file has the same name of the sample file
with extension .OUT
second_file.out : file
Results from random subsamples of the master list.
The funnel output file has the same name with suffix
"_funnel" and extension .OUT. I
Notes
----------
Described in Phylogenetic representativeness: a new
method for evaluating taxon sampling in evolutionary studies [1]
References
----------
.. [1] http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-209
.. [2] http://www.mozoolab.net/downloads/manual.pdf
"""
samplefile = args['samplefile']
popfile = args['popfile']
d1 = args['d1']
d2 = args['d2']
output_as_string = False
if options['m']:
missing = options['m']
else:
missing = 'n'
if options['o']:
out = options['o']
else:
out = samplefile.split('.')[0]
if options['p']:
p = options['p']
else:
p = 1000
if options['c']:
ci = options['c']
else:
ci = 'y'
if options['b']:
batch = options['b']
else:
batch = 'n'
if options['l']:
pathlengths = options['l']
else:
pathlengths = 'n'
if options['s']:
output_as_string = True
sample = defaultdict()
# Population - dictionary with population file information
population = defaultdict()
if batch == 'y':
Files = []
else:
Files = [samplefile]
Index = {}
Taxon = defaultdict()
coef = {}
taxon = []
pathLengths = defaultdict()
for i in open(samplefile):
if batch == 'y':
j = i.strip()
Files.append(j)
else:
break
duplicates = []
with open(popfile) as fp:
population_file_entries = fp.readlines()
# We reed two first lines to check if we have System information
# If we have it, we rebuild the array of lines
lines_removal_counter = 0
for i in population_file_entries[:2]:
# If we encounter string, starting with "Taxon" we get
# information about taxons
if match('Taxon:', i):
lines_removal_counter += 1
x = i.split()
x.remove('Taxon:')
for i in x:
taxon.append(i)
j = x.index(i)
# Index list is used to get the indexation of
# taxon during string parsing
Index[i] = j + 1
continue
elif match('Coefficients:', i):
lines_removal_counter += 1
x = i.split()
x.remove('Coefficients:')
x = map(eval, x)
for t in taxon:
i = taxon.index(t)
coef[t] = sum(x[i:])
pathLengths[t] = x[i]
continue
population_file_entries = population_file_entries[lines_removal_counter:]
# opening population file and getting information
for i in population_file_entries:
# here starts entry processing
(species_name, species_taxonomy) = \
process_population_file_line(i, taxon, Index, missing)
if species_name in population:
duplicates.append(species_name)
else:
population[species_name] = species_taxonomy
sample = population.copy()
if len(duplicates) > 0:
print "Population master list contains %s duplicates" \
% (len(duplicates))
if pathlengths == 'n':
coef, popN, pathLengths = PathLength(population, taxon, Taxon)
if pathlengths == 'y':
XXX, popN, YYY = PathLength(population, taxon, Taxon)
print "Finished path length calculation"
results = {}
"""Opening all sample files (or the sample file)
and getting information about it"""
for f in Files:
sample = get_sample_subset_from_sample_file(f, population)
f = f.split('.')
f = f[0]
results[f] = {}
samp = sample.keys()
atd, taxonN, Taxon = ATDmean(sample, samp, taxon, coef)
average_taxonomic_distinctness_variance = \
ATDvariance(taxonN, samp, atd, taxon, coef)
euler_results = euler(sample, atd, taxonN, taxon, Taxon, coef)
results[f]['atd'] = atd
results[f]['vtd'] = average_taxonomic_distinctness_variance
results[f]['euler'] = euler_results
results[f]['N'] = taxonN
results[f]['n'] = len(sample)
results[f]['taxon'] = Taxon
phy_re_result = printResults(taxon, taxonN, popN, pathLengths, results)
funnel_data = ''
if ci == 'y':
funnel_data = print_funnel_data(p, d1, d2, population, taxon, coef)
if output_as_string:
return phy_re_result, funnel_data
else:
with open(out + '.out', 'w') as fp:
fp.write(phy_re_result)
with open(out.split('_')[0] + '_funnel.out', 'w') as fp:
fp.write(funnel_data)
x = i.split()
taxon = x[1:]
for t in taxon:
j = taxon.index(t)
Index[t] = j + 1
continue
elif match('Coefficients:', i):
continue
i.strip()
x = i.split()
species = x[0]
if species in sample.keys():
duplicates.append(species)
else:
sample[species] = {}
if missing == 'y':
mtax = ''
for t in taxon:
if x[Index[t]] == '/':
sample[species][t] = mtax
else:
sample[species][t] = x[Index[t]]
mtax = x[Index[t]]
else:
for t in taxon:
