def main(data, numb_of_replicates,):
data = data.strip() # get data
file_info = gfp.determine_file_type(data) # determine file type (e.g. mac, linux, PC)
lines = data.split(file_info[0]) # splits on file type endings (e.g., \r\n or \n or \r)
# PROCESS DATA, FIND UNIQUE ALLELES
#
# Basic format of processed data:
# [ [u'Locus 1', u'Locus 2'],
# [u'Species_w_20_loci', u'SpeciesB', u'SpeciesA'],
# [ [ [u'001001', u'001001'],
# [u'001001', u'001001'],
# [u'001001', u'001001'],
# [u'002002', u'002002'],
# [u'002002', u'002002'],
# [u'002002', u'002002']],
# etc..
# pick appropriate file parser
if file_info[1] == 'Arlequin':
processed_data = gfp.arlequin_parser(lines)
if file_info[1] == 'unknown':
processed_data = gfp.genepop_parser(lines)
print processed_data
# process data, etc.
empty_dict = create_empty_dictionaries_of_unique_alleles(processed_data) # create empty dictionaries of alleles in each locus
allele_counts = generate_allele_counts(processed_data[0], processed_data[1], processed_data[2], empty_dict)
population_sizes = generate_population_sizes(processed_data[2], processed_data[1], processed_data[0]) # dictionary of population sizes
frequencies = generate_frequencies(processed_data, allele_counts)
pp.pprint(frequencies)
results = fstats(frequencies, processed_data[1], processed_data[0], population_sizes)
mean_D = mean_D_across_loci(results[1])
bootstrap_results = Bootstrap(processed_data, numb_of_replicates)
pairwise_stats = generate_pairwise_stats(processed_data[0], processed_data[1], processed_data[2])
# create urls and files:
fstats_url = update_csv_file('fstats',results[0])
fstats_est_url = update_csv_file('est_stats',results[1])
bootstrap_url = update_csv_file('bootstrap_results',bootstrap_results)
matrix_url = update_csv_file('pairwise_matrices',pairwise_stats[0])
dest_matrix_url = update_csv_file('dest_matrices',pairwise_stats[1])
return (results, bootstrap_results, (fstats_url,fstats_est_url,bootstrap_url, matrix_url, dest_matrix_url), mean_D)
def main(argv):
fp = open(sys.argv[1]) #open the data file, specified as argument 1
data = fp.read()
statdesired = sys.argv[2]
numb_of_replicates = int(sys.argv[3]) #get the number of replicates from the command line
data = data.strip() # get data
file_info = gfp.determine_file_type(data) # determine file type (e.g. mac, linux, PC)
lines = data.split(file_info[0]) # splits on file type endings (e.g., \r\n or \n or \r)
# PROCESS DATA, FIND UNIQUE ALLELES
#
# Basic format of processed data:
# [ [u'Locus 1', u'Locus 2'],
# [u'Species_w_20_loci', u'SpeciesB', u'SpeciesA'],
# [ [ [u'001001', u'001001'],
# [u'001001', u'001001'],
# [u'001001', u'001001'],
# [u'002002', u'002002'],
# [u'002002', u'002002'],
# [u'002002', u'002002']],
# etc..
# pick appropriate file parser
if file_info[1] == 'Arlequin':
processed_data = gfp.arlequin_parser(lines)
if file_info[1] == 'unknown':
processed_data = gfp.genepop_parser(lines)
#print "processed_data:"
#print processed_data
# process data, etc.
empty_dict = create_empty_dictionaries_of_unique_alleles(processed_data) # create empty dictionaries of alleles in each locus
allele_counts = generate_allele_counts(processed_data[0], processed_data[1], processed_data[2], empty_dict)
population_sizes = generate_population_sizes(processed_data[2], processed_data[1], processed_data[0]) # dictionary of population sizes
frequencies = generate_frequencies(processed_data, allele_counts)
#pp.pprint(frequencies)
results = fstats(frequencies, processed_data[1], processed_data[0], population_sizes)
#mean_D = mean_D_across_loci(results[1])
#bootstrap_results = Bootstrap(processed_data, numb_of_replicates)
#pairwise_stats = generate_pairwise_stats(processed_data[0], processed_data[1], processed_data[2])
#calculate only the desired statistic specified at the command line
n = float(len(processed_data[1])) #n (or K), the number of populations, can be calculated by counting the number of population names
mean_statdesired = mean_across_loci(results[1], statdesired, n)
#return abbreviated results
#print results[1]
#print "mean_D = ", mean_D
#print statdesired, " = ", mean_statdesired
print mean_statdesired
#return (results, bootstrap_results, mean_D)
# create urls and files:
#fstats_url = update_csv_file('fstats',results[0])
#fstats_est_url = update_csv_file('est_stats',results[1])
#bootstrap_url = update_csv_file('bootstrap_results',bootstrap_results)
#matrix_url = update_csv_file('pairwise_matrices',pairwise_stats[0])
#dest_matrix_url = update_csv_file('dest_matrices',pairwise_stats[1])
#return (results, bootstrap_results, (fstats_url,fstats_est_url,bootstrap_url, matrix_url, dest_matrix_url), mean_D)
return (statdesired)
