def test_run_core_diversity_analyses_error_on_invalid_category(self):
"""run_core_diversity_analyses: error raised on invalid categories
"""
# too few values in 'month' category
self.assertRaises(ValueError,
run_core_diversity_analyses,
self.test_data['biom'][0],
self.test_data['map'][0],
20,
output_dir=self.test_out,
params=parse_qiime_parameters({}),
qiime_config=self.qiime_config,
categories=['SampleType', 'month'],
tree_fp=self.test_data['tree'][0],
parallel=False,
status_update_callback=no_status_updates)
# invalid category name
self.assertRaises(ValueError,
run_core_diversity_analyses,
self.test_data['biom'][0],
self.test_data['map'][0],
20,
output_dir=self.test_out,
params=parse_qiime_parameters({}),
qiime_config=self.qiime_config,
categories=['not-a-real-category'],
tree_fp=self.test_data['tree'][0],
parallel=False,
status_update_callback=no_status_updates)
def test_run_core_diversity_analyses_error_on_invalid_category(self):
"""run_core_diversity_analyses: error raised on invalid categories
"""
# too few values in 'month' category
self.assertRaises(ValueError,
run_core_diversity_analyses,
self.test_data['biom'][0],
self.test_data['map'][0],
20,
output_dir=self.test_out,
params=parse_qiime_parameters({}),
qiime_config=self.qiime_config,
categories=['SampleType', 'month'],
tree_fp=self.test_data['tree'][0],
parallel=False,
status_update_callback=no_status_updates)
# invalid category name
self.assertRaises(ValueError,
run_core_diversity_analyses,
self.test_data['biom'][0],
self.test_data['map'][0],
20,
output_dir=self.test_out,
params=parse_qiime_parameters({}),
qiime_config=self.qiime_config,
categories=['not-a-real-category'],
tree_fp=self.test_data['tree'][0],
parallel=False,
status_update_callback=no_status_updates)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
verbose = opts.verbose
otu_table_fp = opts.otu_table_fp
output_dir = opts.output_dir
mapping_fp = opts.mapping_fp
verbose = opts.verbose
print_only = opts.print_only
mapping_cat = opts.mapping_category
sort = opts.sort
if mapping_cat is not None and mapping_fp is None:
option_parser.error("If passing -c must also pass -m.")
if opts.parameter_fp:
try:
parameter_f = open(opts.parameter_fp, 'U')
except IOError:
raise IOError(
"Can't open parameters file (%s). Does it exist? Do you have read access?"
% opts.parameter_fp)
params = parse_qiime_parameters(parameter_f)
parameter_f.close()
else:
params = parse_qiime_parameters([])
# empty list returns empty defaultdict for now
try:
makedirs(output_dir)
except OSError:
if opts.force:
pass
else:
# Since the analysis can take quite a while, I put this check
# in to help users avoid overwriting previous output.
option_parser.error(
"Output directory already exists. Please choose"
" a different directory, or force overwrite with -f.")
if print_only:
command_handler = print_commands
else:
command_handler = call_commands_serially
if verbose:
status_update_callback = print_to_stdout
else:
status_update_callback = no_status_updates
run_summarize_taxa_through_plots(
otu_table_fp=otu_table_fp,
mapping_fp=mapping_fp,
output_dir=output_dir,
mapping_cat=mapping_cat,
sort=sort,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
status_update_callback=status_update_callback)
def run_command(self, options, arguments):
verbose = options["verbose"]
input_fp = options["input_fp"]
output_dir = options["output_dir"]
verbose = options["verbose"]
print_only = options["print_only"]
parallel = options["parallel"]
# No longer checking that jobs_to_start > 2, but
# commenting as we may change our minds about this.
# if parallel: raise_error_on_parallel_unavailable()
if options["parameter_fp"]:
try:
parameter_f = open(options["parameter_fp"])
except IOError:
raise QiimeCommandError, "Can't open parameters file (%s). Does it exist? Do you have read access?" % options[
"parameter_fp"
]
params = parse_qiime_parameters(parameter_f)
else:
params = parse_qiime_parameters([])
# empty list returns empty defaultdict for now
params["parallel"]["jobs_to_start"] = self._validate_jobs_to_start(
options["jobs_to_start"], qiime_config["jobs_to_start"], parallel
)
try:
makedirs(output_dir)
except OSError:
if options["force"]:
pass
else:
# Since the analysis can take quite a while, I put this check
# in to help users avoid overwriting previous output.
print "Output directory already exists. Please choose " + "a different directory, or force overwrite with -f."
exit(1)
if print_only:
command_handler = print_commands
else:
command_handler = call_commands_serially
if verbose:
status_update_callback = print_to_stdout
else:
status_update_callback = no_status_updates
run_qiime_data_preparation(
input_fp,
output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
parallel=parallel,
status_update_callback=status_update_callback,
)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
verbose = opts.verbose
input_fp = opts.input_fp
output_dir = opts.output_dir
verbose = opts.verbose
print_only = opts.print_only
parallel = opts.parallel
# No longer checking that jobs_to_start > 2, but
# commenting as we may change our minds about this.
# if parallel: raise_error_on_parallel_unavailable()
if opts.parameter_fp:
try:
parameter_f = open(opts.parameter_fp)
except IOError:
raise IOError, "Can't open parameters file (%s). Does it exist? Do you have read access?" % opts.parameter_fp
params = parse_qiime_parameters(parameter_f)
else:
params = parse_qiime_parameters([])
# empty list returns empty defaultdict for now
jobs_to_start = opts.jobs_to_start
default_jobs_to_start = qiime_config["jobs_to_start"]
validate_and_set_jobs_to_start(params, jobs_to_start, default_jobs_to_start, parallel, option_parser)
try:
makedirs(output_dir)
except OSError:
if opts.force:
pass
else:
# Since the analysis can take quite a while, I put this check
# in to help users avoid overwriting previous output.
print "Output directory already exists. Please choose " + "a different directory, or force overwrite with -f."
exit(1)
if print_only:
command_handler = print_commands
else:
command_handler = call_commands_serially
if verbose:
status_update_callback = print_to_stdout
else:
status_update_callback = no_status_updates
run_qiime_data_preparation(
input_fp,
output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
parallel=parallel,
status_update_callback=status_update_callback,
)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
verbose = opts.verbose
otu_table_fp = opts.otu_table_fp
output_dir = opts.output_dir
mapping_fp = opts.mapping_fp
verbose = opts.verbose
print_only = opts.print_only
mapping_cat = opts.mapping_category
sort=opts.sort
if mapping_cat != None and mapping_fp == None:
option_parser.error("If passing -c must also pass -m.")
if opts.parameter_fp:
try:
parameter_f = open(opts.parameter_fp, 'U')
except IOError:
raise IOError,\
"Can't open parameters file (%s). Does it exist? Do you have read access?" \
% opts.parameter_fp
params = parse_qiime_parameters(parameter_f)
parameter_f.close()
else:
params = parse_qiime_parameters([])
# empty list returns empty defaultdict for now
try:
makedirs(output_dir)
except OSError:
if opts.force:
pass
else:
# Since the analysis can take quite a while, I put this check
# in to help users avoid overwriting previous output.
option_parser.error("Output directory already exists. Please choose"
" a different directory, or force overwrite with -f.")
if print_only:
command_handler = print_commands
else:
command_handler = call_commands_serially
if verbose:
status_update_callback = print_to_stdout
else:
status_update_callback = no_status_updates
run_summarize_taxa_through_plots(
otu_table_fp=otu_table_fp,
mapping_fp=mapping_fp,
output_dir=output_dir,
mapping_cat=mapping_cat,
sort=sort,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
status_update_callback=status_update_callback)
def test_run_core_diversity_analyses_parallel(self):
"""run_core_diversity_analyses functions with categories in parallel
"""
run_core_diversity_analyses(
self.test_data['biom'][0],
self.test_data['map'][0],
20,
output_dir=self.test_out,
params=parse_qiime_parameters({}),
arare_num_steps=3,
qiime_config=self.qiime_config,
categories=['SampleType', 'days_since_epoch'],
tree_fp=self.test_data['tree'][0],
parallel=True,
status_update_callback=no_status_updates)
# Basic sanity test that output directories and files exist
fps = [
'%s/bdiv_even20' % self.test_out,
'%s/arare_max20' % self.test_out,
'%s/taxa_plots' % self.test_out,
'%s/bdiv_even20/unweighted_unifrac_dm.txt' % self.test_out,
'%s/bdiv_even20/weighted_unifrac_pc.txt' % self.test_out,
'%s/arare_max20/compare_chao1/days_since_epoch_stats.txt' % self.test_out,
'%s/arare_max20/compare_PD_whole_tree/SampleType_boxplots.pdf' % self.test_out,
'%s/index.html' % self.test_out,
'%s/table_mc%d.biom.gz' % (self.test_out, 20)
]
for fp in fps:
self.assertTrue(exists(fp))
def test_run_core_diversity_analyses_no_categories(self):
"""run_core_diversity_analyses functions without categories
"""
# this takes a long time, so use a longer sigalrm
run_core_diversity_analyses(
self.test_data['biom'][0],
self.test_data['map'][0],
20,
output_dir=self.test_out,
params=parse_qiime_parameters({}),
qiime_config=self.qiime_config,
categories=None,
tree_fp=self.test_data['tree'][0],
parallel=False,
status_update_callback=no_status_updates)
# Basic sanity test that output directories and files exist
fps = [
'%s/bdiv_even20' % self.test_out,
'%s/arare_max20' % self.test_out,
'%s/taxa_plots' % self.test_out,
'%s/bdiv_even20/unweighted_unifrac_dm.txt' % self.test_out,
'%s/bdiv_even20/weighted_unifrac_pc.txt' % self.test_out,
'%s/index.html' % self.test_out,
'%s/table_mc%d.biom.gz' % (self.test_out, 20)
]
for fp in fps:
self.assertTrue(exists(fp))
# categorical output files don't exist
self.assertFalse(exists(
'%s/arare_max20/compare_chao1/days_since_epoch_stats.txt' % self.test_out))
self.assertFalse(exists(
'%s/arare_max20/compare_PD_whole_tree/SampleType_boxplots.pdf' % self.test_out))
def test_run_core_diversity_analyses_no_tree(self):
"""run_core_diversity_analyses functions without tree
"""
# this takes a long time, so use a longer sigalrm
run_core_diversity_analyses(
self.test_data['biom'][0],
self.test_data['map'][0],
20,
output_dir=self.test_out,
params=parse_qiime_parameters(
['beta_diversity:metrics bray_curtis',
'alpha_diversity:metrics observed_species,chao1']),
qiime_config=self.qiime_config,
categories=['SampleType'],
tree_fp=None,
parallel=False,
status_update_callback=no_status_updates)
# Basic sanity test that output directories and files exist
fps = [
'%s/bdiv_even20' % self.test_out,
'%s/arare_max20' % self.test_out,
'%s/taxa_plots' % self.test_out,
'%s/bdiv_even20/bray_curtis_dm.txt' % self.test_out,
'%s/arare_max20/compare_observed_species/SampleType_boxplots.pdf' % self.test_out,
'%s/index.html' % self.test_out,
'%s/table_mc%d.biom.gz' % (self.test_out, 20)
]
for fp in fps:
self.assertTrue(exists(fp))
# phylogenetic diversity output files do not exist
self.assertFalse(exists(
'%s/bdiv_even20/unweighted_unifrac_dm.txt' % self.test_out))
def test_run_core_diversity_analyses_no_categories(self):
"""run_core_diversity_analyses functions without categories
"""
# this takes a long time, so use a longer sigalrm
run_core_diversity_analyses(
self.test_data['biom'][0],
self.test_data['map'][0],
20,
output_dir=self.test_out,
params=parse_qiime_parameters({}),
qiime_config=self.qiime_config,
categories=None,
tree_fp=self.test_data['tree'][0],
parallel=False,
status_update_callback=no_status_updates)
# Basic sanity test that output directories and files exist
fps = [
'%s/bdiv_even20' % self.test_out,
'%s/arare_max20' % self.test_out,
'%s/taxa_plots' % self.test_out,
'%s/bdiv_even20/unweighted_unifrac_dm.txt' % self.test_out,
'%s/bdiv_even20/weighted_unifrac_pc.txt' % self.test_out,
'%s/index.html' % self.test_out,
'%s/table_mc%d.biom.gz' % (self.test_out,20)
]
for fp in fps:
self.assertTrue(exists(fp))
# categorical output files don't exist
self.assertFalse(exists(
'%s/arare_max20/days_since_epoch_chao1.txt' % self.test_out))
self.assertFalse(exists(
'%s/arare_max20/SampleType_PD_whole_tree.txt' % self.test_out))
def test_run_core_diversity_analyses_no_tree(self):
"""run_core_diversity_analyses functions without tree
"""
# this takes a long time, so use a longer sigalrm
run_core_diversity_analyses(
self.test_data['biom'][0],
self.test_data['map'][0],
20,
output_dir=self.test_out,
params=parse_qiime_parameters(
['beta_diversity:metrics bray_curtis',
'alpha_diversity:metrics observed_species,chao1']),
qiime_config=self.qiime_config,
categories=['SampleType'],
tree_fp=None,
parallel=False,
status_update_callback=no_status_updates)
# Basic sanity test that output directories and files exist
fps = [
'%s/bdiv_even20' % self.test_out,
'%s/arare_max20' % self.test_out,
'%s/taxa_plots' % self.test_out,
'%s/bdiv_even20/bray_curtis_dm.txt' % self.test_out,
'%s/arare_max20/compare_observed_species/SampleType_boxplots.pdf' % self.test_out,
'%s/index.html' % self.test_out,
'%s/table_mc%d.biom.gz' % (self.test_out, 20)
]
for fp in fps:
self.assertTrue(exists(fp))
# phylogenetic diversity output files do not exist
self.assertFalse(exists(
'%s/bdiv_even20/unweighted_unifrac_dm.txt' % self.test_out))
def setUp(self):
""" """
self.test_data = get_test_data_fps()
self.files_to_remove = []
self.dirs_to_remove = []
# Create example output directory
tmp_dir = get_qiime_temp_dir()
self.test_out = mkdtemp(dir=tmp_dir,
prefix='core_qiime_analyses_test_',
suffix='')
self.dirs_to_remove.append(self.test_out)
self.qiime_config = load_qiime_config()
self.params = parse_qiime_parameters(params_f1)
# suppress stderr during tests (one of the systems calls in the
# workflow prints a warning, and we can't suppress that warning with
# warnings.filterwarnings) here because it comes from within the code
# executed through the system call. Found this trick here:
# http://stackoverflow.com/questions/9949633/suppressing-print-as-stdout-python
self.saved_stderr = sys.stderr
sys.stderr = StringIO()
initiate_timeout(180)
def main():
option_parser, opts, args = \
parse_command_line_parameters(suppress_verbose=True, **script_info)
input_dir = opts.input_dir
paired_data = opts.paired_data
parameter_fp = opts.parameter_fp
read1_indicator = opts.read1_indicator
read2_indicator = opts.read2_indicator
leading_text = opts.leading_text
trailing_text = opts.trailing_text
include_input_dir_path = opts.include_input_dir_path
output_dir = abspath(opts.output_dir)
remove_filepath_in_name = opts.remove_filepath_in_name
print_only = opts.print_only
if remove_filepath_in_name and not include_input_dir_path:
option_parser.error("If --remove_filepath_in_name is enabled, "
"--include_input_dir_path must also be enabled.")
if opts.parameter_fp:
with open(opts.parameter_fp, 'U') as parameter_f:
params_dict = parse_qiime_parameters(parameter_f)
params_str = get_params_str(params_dict['extract_barcodes'])
else:
params_dict = {}
params_str = ""
create_dir(output_dir)
all_files = []
extensions = ['.fastq.gz', '.fastq', '.fq.gz', '.fq']
for root, dir, fps in walk(input_dir):
for fp in fps:
for extension in extensions:
if fp.endswith(extension):
all_files += [abspath(join(root, fp))]
if paired_data:
all_files, bc_pairs = get_pairs(all_files, read1_indicator,
read2_indicator)
commands = create_commands_eb(all_files, paired_data, output_dir,
params_str, leading_text, trailing_text, include_input_dir_path,
remove_filepath_in_name)
qiime_config = load_qiime_config()
if print_only:
command_handler = print_commands
else:
command_handler = call_commands_serially
logger = WorkflowLogger(generate_log_fp(output_dir),
params=params_dict,
qiime_config=qiime_config)
# Call the command handler on the list of commands
command_handler(commands,
status_update_callback = no_status_updates,
logger=logger,
close_logger_on_success=True)
def test_run_core_diversity_analyses_parallel(self):
"""run_core_diversity_analyses functions with categories in parallel
"""
run_core_diversity_analyses(
self.test_data['biom'][0],
self.test_data['map'][0],
20,
output_dir=self.test_out,
params=parse_qiime_parameters({}),
qiime_config=self.qiime_config,
categories=['SampleType', 'days_since_epoch'],
tree_fp=self.test_data['tree'][0],
parallel=True,
status_update_callback=no_status_updates)
# Basic sanity test that output directories and files exist
fps = [
'%s/bdiv_even20' % self.test_out,
'%s/arare_max20' % self.test_out,
'%s/taxa_plots' % self.test_out,
'%s/bdiv_even20/unweighted_unifrac_dm.txt' % self.test_out,
'%s/bdiv_even20/weighted_unifrac_pc.txt' % self.test_out,
'%s/arare_max20/compare_chao1/days_since_epoch_stats.txt' %
self.test_out,
'%s/arare_max20/compare_PD_whole_tree/SampleType_boxplots.pdf' %
self.test_out,
'%s/index.html' % self.test_out,
'%s/table_mc%d.biom.gz' % (self.test_out, 20)
]
for fp in fps:
self.assertTrue(exists(fp))
def main():
option_parser, opts, args = \
parse_command_line_parameters(suppress_verbose=True, **script_info)
input_dir = opts.input_dir
parameter_fp = opts.parameter_fp
read1_indicator = opts.read1_indicator
read2_indicator = opts.read2_indicator
match_barcodes = opts.match_barcodes
barcode_indicator = opts.barcode_indicator
leading_text = opts.leading_text
trailing_text = opts.trailing_text
include_input_dir_path = opts.include_input_dir_path
output_dir = abspath(opts.output_dir)
remove_filepath_in_name = opts.remove_filepath_in_name
print_only = opts.print_only
if remove_filepath_in_name and not include_input_dir_path:
option_parser.error("If --remove_filepath_in_name is enabled, "
"--include_input_dir_path must also be enabled.")
if opts.parameter_fp:
with open(opts.parameter_fp, 'U') as parameter_f:
params_dict = parse_qiime_parameters(parameter_f)
params_str = get_params_str(params_dict['join_paired_ends'])
else:
params_dict = {}
params_str = ""
create_dir(output_dir)
all_files = []
extensions = ['.fastq.gz', '.fastq', '.fq.gz', '.fq']
for root, dir, fps in walk(input_dir):
for fp in fps:
for extension in extensions:
if fp.endswith(extension):
all_files += [abspath(join(root, fp))]
pairs, bc_pairs = get_pairs(all_files, read1_indicator, read2_indicator,
match_barcodes, barcode_indicator)
commands = create_commands_jpe(pairs, output_dir,
params_str, leading_text, trailing_text, include_input_dir_path,
remove_filepath_in_name, match_barcodes, bc_pairs)
qiime_config = load_qiime_config()
if print_only:
command_handler = print_commands
else:
command_handler = call_commands_serially
logger = WorkflowLogger(generate_log_fp(output_dir),
params=params_dict,
qiime_config=qiime_config)
# Call the command handler on the list of commands
command_handler(commands,
status_update_callback=no_status_updates,
logger=logger,
close_logger_on_success=True)
def test_run_core_diversity_analyses(self):
"""run_core_diversity_analyses functions with categories
"""
run_core_diversity_analyses(
self.test_data["biom"][0],
self.test_data["map"][0],
20,
output_dir=self.test_out,
params=parse_qiime_parameters({}),
qiime_config=self.qiime_config,
categories=["SampleType", "days_since_epoch"],
tree_fp=self.test_data["tree"][0],
parallel=False,
status_update_callback=no_status_updates,
)
# Basic sanity test that output directories and files exist
fps = [
"%s/bdiv_even20" % self.test_out,
"%s/arare_max20" % self.test_out,
"%s/taxa_plots" % self.test_out,
"%s/bdiv_even20/unweighted_unifrac_dm.txt" % self.test_out,
"%s/bdiv_even20/weighted_unifrac_pc.txt" % self.test_out,
"%s/arare_max20/compare_chao1/days_since_epoch_stats.txt" % self.test_out,
"%s/arare_max20/compare_PD_whole_tree/SampleType_boxplots.pdf" % self.test_out,
"%s/index.html" % self.test_out,
"%s/table_mc%d.biom.gz" % (self.test_out, 20),
]
for fp in fps:
self.assertTrue(exists(fp))
def setUp(self):
"""setup the test values"""
self.qiime_config = load_qiime_config()
self.dirs_to_remove = []
self.files_to_remove = []
#this is specific to the web-apps only
test_dir = abspath(dirname(__file__))
self.fna_original_fp = os.path.join(test_dir, 'support_files', \
'test.fna')
tmp_dir = self.qiime_config['temp_dir'] or '/tmp/'
if not exists(tmp_dir):
makedirs(tmp_dir)
# if test creates the temp dir, also remove it
self.dirs_to_remove.append(tmp_dir)
self.wf_out = get_tmp_filename(tmp_dir=tmp_dir,
prefix='qiime_wf_out',suffix='',result_constructor=str)
if not exists(self.wf_out):
makedirs(self.wf_out)
self.dirs_to_remove.append(self.wf_out)
#print self.wf_out
working_dir = self.qiime_config['working_dir'] or './'
jobs_dir = join(working_dir,'jobs')
if not exists(jobs_dir):
# only clean up the jobs dir if it doesn't already exist
self.dirs_to_remove.append(jobs_dir)
self.params = parse_qiime_parameters(qiime_parameters_f.split('\n'))
signal.signal(signal.SIGALRM, timeout)
# set the 'alarm' to go off in allowed_seconds seconds
signal.alarm(allowed_seconds_per_test)
def setUp(self):
""" """
self.qiime_config = load_qiime_config()
self.dirs_to_remove = []
self.files_to_remove = []
#this is specific to the web-apps only
test_dir = abspath(dirname(__file__))
sff_original_fp = os.path.join(test_dir, 'support_files', \
'Fasting_subset.sff')
self.sff_fp = os.path.join('/%s/' % environ['HOME'],
'Fasting_subset.sff')
self.files_to_remove.append(self.sff_fp)
copy(sff_original_fp, self.sff_fp)
self.illumina_fps = [os.path.join(test_dir, 'support_files', \
's_8_1_sequence_100_records.txt'),
os.path.join(test_dir, 'support_files', \
's_8_2_sequence_100_records.txt')]
self.illumina_map_fp = os.path.join(test_dir, 'support_files', \
's8_map_incomplete.txt')
self.fasta_fps=[os.path.join(test_dir,'support_files',
'test_split_lib_seqs.fasta')]
self.fasta_map_fp = os.path.join(test_dir, 'support_files', \
'fasta_mapping_file.txt')
tmp_dir = "/%s/test_wf" % environ['HOME']
self.dirs_to_remove.append(tmp_dir)
#self.qiime_config['temp_dir'] or '/tmp/'
if not exists(tmp_dir):
makedirs(tmp_dir)
# if test creates the temp dir, also remove it
#self.dirs_to_remove.append(tmp_dir)
self.wf_out="/%s/test_processed_data" % environ['HOME']
#print self.wf_out
self.dirs_to_remove.append(self.wf_out)
self.gg_out=os.path.join(self.wf_out,'gg_97_otus')
if not exists(self.gg_out):
makedirs(self.gg_out)
#self.dirs_to_remove.append(self.gg_out)
self.fasting_mapping_fp = get_tmp_filename(tmp_dir=tmp_dir,
prefix='qiime_wf_mapping',suffix='.txt')
fasting_mapping_f = open(self.fasting_mapping_fp,'w')
fasting_mapping_f.write(fasting_map)
fasting_mapping_f.close()
self.files_to_remove.append(self.fasting_mapping_fp)
self.params = parse_qiime_parameters(qiime_parameters_f)
signal.signal(signal.SIGALRM, timeout)
# set the 'alarm' to go off in allowed_seconds seconds
signal.alarm(allowed_seconds_per_test)
def setUp(self):
""" """
self.test_data = get_test_data_fps()
self.files_to_remove = []
self.dirs_to_remove = []
# Create example output directory
tmp_dir = get_qiime_temp_dir()
self.test_out = mkdtemp(dir=tmp_dir,
prefix='core_qiime_analyses_test_',
suffix='')
self.dirs_to_remove.append(self.test_out)
self.qiime_config = load_qiime_config()
self.params = parse_qiime_parameters([])
self.params_sortmerna = parse_qiime_parameters(
['pick_otus:otu_picking_method\tsortmerna'])
self.params_sumaclust = parse_qiime_parameters(
['pick_otus:otu_picking_method\tsumaclust'])
initiate_timeout(60)
def setUp(self):
""" """
self.qiime_config = load_qiime_config()
self.dirs_to_remove = []
self.files_to_remove = []
#this is specific to the web-apps only
test_dir = abspath(dirname(__file__))
sff_original_fp = os.path.join(test_dir, 'support_files', \
'Fasting_subset.sff')
# copy sff file to working directory
self.sff_dir = tempfile.mkdtemp()
self.dirs_to_remove.append(self.sff_dir)
self.sff_fp = os.path.join(self.sff_dir, 'Fasting_subset.sff')
copy(sff_original_fp, self.sff_fp)
self.files_to_remove.append(self.sff_fp)
tmp_dir = self.qiime_config['temp_dir'] or '/tmp/'
if not exists(tmp_dir):
makedirs(tmp_dir)
# if test creates the temp dir, also remove it
self.dirs_to_remove.append(tmp_dir)
self.wf_out = get_tmp_filename(tmp_dir=tmp_dir,
prefix='qiime_wf_out',suffix='',result_constructor=str)
self.dirs_to_remove.append(self.wf_out)
self.fasting_mapping_fp = get_tmp_filename(tmp_dir=tmp_dir,
prefix='qiime_wf_mapping',suffix='.txt')
fasting_mapping_f = open(self.fasting_mapping_fp,'w')
fasting_mapping_f.write(fasting_map)
fasting_mapping_f.close()
self.files_to_remove.append(self.fasting_mapping_fp)
working_dir = self.qiime_config['working_dir'] or './'
jobs_dir = join(working_dir,'jobs')
if not exists(jobs_dir):
# only clean up the jobs dir if it doesn't already exist
self.dirs_to_remove.append(jobs_dir)
self.params = parse_qiime_parameters(qiime_parameters_f.split('\n'))
signal.signal(signal.SIGALRM, timeout)
# set the 'alarm' to go off in allowed_seconds seconds
signal.alarm(allowed_seconds_per_test)
def setUp(self):
""" """
self.test_data = get_test_data_fps()
self.files_to_remove = []
self.dirs_to_remove = []
# Create example output directory
tmp_dir = get_qiime_temp_dir()
self.test_out = mkdtemp(dir=tmp_dir,
prefix='core_qiime_analyses_test_',
suffix='')
self.dirs_to_remove.append(self.test_out)
self.qiime_config = load_qiime_config()
self.params = parse_qiime_parameters([])
initiate_timeout(60)
def setUp(self):
""" """
self.test_data = get_test_data_fps()
self.files_to_remove = []
self.dirs_to_remove = []
# Create example output directory
tmp_dir = get_qiime_temp_dir()
self.test_out = mkdtemp(dir=tmp_dir,
prefix='core_qiime_analyses_test_',
suffix='')
self.dirs_to_remove.append(self.test_out)
self.qiime_config = load_qiime_config()
self.params = parse_qiime_parameters({})
initiate_timeout(60)
def test_parse_qiime_parameters(self):
"""parse_qiime_parameters: functions with valid input """
lines = ["#Don't edit this file!",\
"pick_otus:similarity 0.94",\
"pick_otus:otu_picking_method\tcdhit",\
"align_seqs:verbose",\
"assign_taxonomy:use_rdp\ttRuE",\
"assign_taxonomy:something\tNone",\
"",\
"#some_script:fake_parameter\t99.0"]
actual = parse_qiime_parameters(lines)
expected = {'pick_otus':\
{'similarity':'0.94', 'otu_picking_method':'cdhit'},\
'assign_taxonomy':\
{'use_rdp':None}}
self.assertEqual(actual, expected)
# default dict functions as expected -- looking up non-existant key
# returns empty dict
self.assertEqual(actual['some_other_script'], {})
def test_parse_qiime_parameters(self):
"""parse_qiime_parameters: functions with valid input """
lines = ["#Don't edit this file!",\
"pick_otus:similarity 0.94",\
"pick_otus:otu_picking_method\tcdhit",\
"align_seqs:verbose",\
"assign_taxonomy:use_rdp\ttRuE",\
"assign_taxonomy:something\tNone",\
"",\
"#some_script:fake_parameter\t99.0"]
actual = parse_qiime_parameters(lines)
expected = {'pick_otus':\
{'similarity':'0.94', 'otu_picking_method':'cdhit'},\
'assign_taxonomy':\
{'use_rdp':None}}
self.assertEqual(actual,expected)
# default dict functions as expected -- looking up non-existant key
# returns empty dict
self.assertEqual(actual['some_other_script'],{})
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
verbose = opts.verbose
otu_table_fp = opts.otu_table_fp
output_dir = opts.output_dir
mapping_fp = opts.mapping_fp
tree_fp = opts.tree_fp
verbose = opts.verbose
print_only = opts.print_only
seqs_per_sample = opts.seqs_per_sample
parallel = opts.parallel
# No longer checking that jobs_to_start > 2, but
# commenting as we may change our minds about this.
#if parallel: raise_error_on_parallel_unavailable()
if opts.parameter_fp:
try:
parameter_f = open(opts.parameter_fp, 'U')
except IOError:
raise IOError(
"Can't open parameters file (%s). Does it exist? Do you have read access?"
% opts.parameter_fp)
params = parse_qiime_parameters(parameter_f)
parameter_f.close()
else:
params = parse_qiime_parameters([])
# empty list returns empty defaultdict for now
jobs_to_start = opts.jobs_to_start
default_jobs_to_start = qiime_config['jobs_to_start']
validate_and_set_jobs_to_start(params, jobs_to_start,
default_jobs_to_start, parallel,
option_parser)
create_dir(output_dir, fail_on_exist=not opts.force)
if print_only:
command_handler = print_commands
else:
command_handler = call_commands_serially
if verbose:
status_update_callback = print_to_stdout
else:
status_update_callback = no_status_updates
run_beta_diversity_through_plots(
otu_table_fp=otu_table_fp,
mapping_fp=mapping_fp,
output_dir=output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
color_by_interesting_fields_only=not opts.color_by_all_fields,
sampling_depth=seqs_per_sample,
tree_fp=tree_fp,
parallel=parallel,
suppress_emperor_plots=opts.suppress_emperor_plots,
status_update_callback=status_update_callback)
def run_core_diversity_analyses(
biom_fp,
mapping_fp,
sampling_depth,
output_dir,
qiime_config,
command_handler=call_commands_serially,
tree_fp=None,
params=None,
categories=None,
arare_min_rare_depth=10,
arare_num_steps=10,
parallel=False,
suppress_taxa_summary=False,
suppress_beta_diversity=False,
suppress_alpha_diversity=False,
suppress_otu_category_significance=False,
status_update_callback=print_to_stdout):
"""
"""
if categories != None:
# Validate categories provided by the users
mapping_data, mapping_comments = \
parse_mapping_file_to_dict(open(mapping_fp,'U'))
metadata_map = MetadataMap(mapping_data, mapping_comments)
for c in categories:
if c not in metadata_map.CategoryNames:
raise ValueError, ("Category '%s' is not a column header "
"in your mapping file. "
"Categories are case and white space sensitive. Valid "
"choices are: (%s)" % (c,', '.join(metadata_map.CategoryNames)))
if metadata_map.hasSingleCategoryValue(c):
raise ValueError, ("Category '%s' contains only one value. "
"Categories analyzed here require at least two values." % c)
else:
categories= []
# prep some variables
if params == None:
params = parse_qiime_parameters([])
create_dir(output_dir)
index_fp = '%s/index.html' % output_dir
index_links = []
commands = []
# begin logging
old_log_fps = glob(join(output_dir,'log_20*txt'))
log_fp = generate_log_fp(output_dir)
index_links.append(('Master run log',log_fp,_index_headers['run_summary']))
for old_log_fp in old_log_fps:
index_links.append(('Previous run log',old_log_fp,_index_headers['run_summary']))
logger = WorkflowLogger(log_fp,
params=params,
qiime_config=qiime_config)
input_fps = [biom_fp,mapping_fp]
if tree_fp != None:
input_fps.append(tree_fp)
log_input_md5s(logger,input_fps)
# run 'biom summarize-table' on input BIOM table
try:
params_str = get_params_str(params['biom-summarize-table'])
except KeyError:
params_str = ''
biom_table_stats_output_fp = '%s/biom_table_summary.txt' % output_dir
if not exists(biom_table_stats_output_fp):
biom_table_summary_cmd = \
"biom summarize-table -i %s -o %s --suppress-md5 %s" % \
(biom_fp, biom_table_stats_output_fp,params_str)
commands.append([('Generate BIOM table summary',
biom_table_summary_cmd)])
else:
logger.write("Skipping 'biom summarize-table' as %s exists.\n\n" \
% biom_table_stats_output_fp)
index_links.append(('BIOM table statistics',
biom_table_stats_output_fp,
_index_headers['run_summary']))
# filter samples with fewer observations than the requested sampling_depth.
# since these get filtered for some analyses (eg beta diversity after
# even sampling) it's useful to filter them here so they're filtered
# from all analyses.
filtered_biom_fp = "%s/table_mc%d.biom" % (output_dir, sampling_depth)
if not exists(filtered_biom_fp):
filter_samples_cmd = "filter_samples_from_otu_table.py -i %s -o %s -n %d" %\
(biom_fp,filtered_biom_fp,sampling_depth)
commands.append([('Filter low sequence count samples from table (minimum sequence count: %d)' % sampling_depth,
filter_samples_cmd)])
else:
logger.write("Skipping filter_samples_from_otu_table.py as %s exists.\n\n" \
% filtered_biom_fp)
biom_fp = filtered_biom_fp
# run initial commands and reset the command list
if len(commands) > 0:
command_handler(commands,
status_update_callback,
logger,
close_logger_on_success=False)
commands = []
if not suppress_beta_diversity:
bdiv_even_output_dir = '%s/bdiv_even%d/' % (output_dir,sampling_depth)
# Need to check for the existence of any distance matrices, since the user
# can select which will be generated.
existing_dm_fps = glob('%s/*_dm.txt' % bdiv_even_output_dir)
if len(existing_dm_fps) == 0:
even_dm_fps = run_beta_diversity_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=bdiv_even_output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
sampling_depth=sampling_depth,
tree_fp=tree_fp,
parallel=parallel,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback)
else:
logger.write("Skipping beta_diversity_through_plots.py as %s exist(s).\n\n" \
% ', '.join(existing_dm_fps))
even_dm_fps = [(split(fp)[1].strip('_dm.txt'),fp) for fp in existing_dm_fps]
# Get make_distance_boxplots parameters
try:
params_str = get_params_str(params['make_distance_boxplots'])
except KeyError:
params_str = ''
for bdiv_metric, dm_fp in even_dm_fps:
for category in categories:
boxplots_output_dir = '%s/%s_boxplots/' % (bdiv_even_output_dir,bdiv_metric)
plot_output_fp = '%s/%s_Distances.pdf' % (boxplots_output_dir,category)
stats_output_fp = '%s/%s_Stats.txt' % (boxplots_output_dir,category)
if not exists(plot_output_fp):
boxplots_cmd = \
'make_distance_boxplots.py -d %s -f %s -o %s -m %s -n 999 %s' %\
(dm_fp, category, boxplots_output_dir, mapping_fp, params_str)
commands.append([('Boxplots (%s)' % category,
boxplots_cmd)])
else:
logger.write("Skipping make_distance_boxplots.py for %s as %s exists.\n\n" \
% (category, plot_output_fp))
index_links.append(('Distance boxplots (%s)' % bdiv_metric,
plot_output_fp,
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('Distance boxplots statistics (%s)' % bdiv_metric,
stats_output_fp,
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('PCoA plot (%s)' % bdiv_metric,
'%s/%s_emperor_pcoa_plot/index.html' % \
(bdiv_even_output_dir,bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('Distance matrix (%s)' % bdiv_metric,
'%s/%s_dm.txt' % \
(bdiv_even_output_dir,bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('Principal coordinate matrix (%s)' % bdiv_metric,
'%s/%s_pc.txt' % \
(bdiv_even_output_dir,bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
if not suppress_alpha_diversity:
## Alpha rarefaction workflow
arare_full_output_dir = '%s/arare_max%d/' % (output_dir,sampling_depth)
rarefaction_plots_output_fp = \
'%s/alpha_rarefaction_plots/rarefaction_plots.html' % arare_full_output_dir
if not exists(rarefaction_plots_output_fp):
run_alpha_rarefaction(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=arare_full_output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
tree_fp=tree_fp,
num_steps=arare_num_steps,
parallel=parallel,
logger=logger,
min_rare_depth=arare_min_rare_depth,
max_rare_depth=sampling_depth,
suppress_md5=True,
status_update_callback=status_update_callback)
else:
logger.write("Skipping alpha_rarefaction.py as %s exists.\n\n" \
% rarefaction_plots_output_fp)
index_links.append(('Alpha rarefaction plots',
rarefaction_plots_output_fp,
_index_headers['alpha_diversity']))
collated_alpha_diversity_fps = \
glob('%s/alpha_div_collated/*txt' % arare_full_output_dir)
try:
params_str = get_params_str(params['compare_alpha_diversity'])
except KeyError:
params_str = ''
for category in categories:
for collated_alpha_diversity_fp in collated_alpha_diversity_fps:
alpha_metric = splitext(split(collated_alpha_diversity_fp)[1])[0]
alpha_comparison_output_fp = '%s/%s_%s.txt' % \
(arare_full_output_dir,category,alpha_metric)
if not exists(alpha_comparison_output_fp):
compare_alpha_cmd = \
'compare_alpha_diversity.py -i %s -m %s -c %s -o %s -n 999 %s' %\
(collated_alpha_diversity_fp, mapping_fp, category,
alpha_comparison_output_fp, params_str)
commands.append([('Compare alpha diversity (%s, %s)' %\
(category,alpha_metric),
compare_alpha_cmd)])
else:
logger.write("Skipping compare_alpha_diversity.py for %s as %s exists.\n\n" \
% (category, alpha_comparison_output_fp))
index_links.append(
('Alpha diversity statistics (%s, %s)' % (category,alpha_metric),
alpha_comparison_output_fp,
_index_headers['alpha_diversity']))
if not suppress_taxa_summary:
taxa_plots_output_dir = '%s/taxa_plots/' % output_dir
# need to check for existence of any html files, since the user can
# select only certain ones to be generated
existing_taxa_plot_html_fps = glob(join(output_dir,'taxa_summary_plots','*.html'))
if len(existing_taxa_plot_html_fps) == 0:
run_summarize_taxa_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=taxa_plots_output_dir,
mapping_cat=None,
sort=True,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback)
else:
logger.write("Skipping summarize_taxa_through_plots.py for as %s exist(s).\n\n" \
% ', '.join(existing_taxa_plot_html_fps))
index_links.append(('Taxa summary bar plots',
'%s/taxa_summary_plots/bar_charts.html'\
% taxa_plots_output_dir,
_index_headers['taxa_summary']))
index_links.append(('Taxa summary area plots',
'%s/taxa_summary_plots/area_charts.html'\
% taxa_plots_output_dir,
_index_headers['taxa_summary']))
for category in categories:
taxa_plots_output_dir = '%s/taxa_plots_%s/' % (output_dir,category)
# need to check for existence of any html files, since the user can
# select only certain ones to be generated
existing_taxa_plot_html_fps = glob('%s/taxa_summary_plots/*.html' % taxa_plots_output_dir)
if len(existing_taxa_plot_html_fps) == 0:
run_summarize_taxa_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=taxa_plots_output_dir,
mapping_cat=category,
sort=True,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback)
else:
logger.write("Skipping summarize_taxa_through_plots.py for %s as %s exist(s).\n\n" \
% (category, ', '.join(existing_taxa_plot_html_fps)))
index_links.append(('Taxa summary bar plots',
'%s/taxa_summary_plots/bar_charts.html'\
% taxa_plots_output_dir,
_index_headers['taxa_summary_categorical'] % category))
index_links.append(('Taxa summary area plots',
'%s/taxa_summary_plots/area_charts.html'\
% taxa_plots_output_dir,
_index_headers['taxa_summary_categorical'] % category))
if not suppress_otu_category_significance:
try:
params_str = get_params_str(params['otu_category_significance'])
except KeyError:
params_str = ''
# OTU category significance
for category in categories:
category_signifance_fp = \
'%s/category_significance_%s.txt' % (output_dir, category)
if not exists(category_signifance_fp):
# Build the OTU cateogry significance command
category_significance_cmd = \
'otu_category_significance.py -i %s -m %s -c %s -o %s %s' %\
(biom_fp, mapping_fp, category,
category_signifance_fp, params_str)
commands.append([('OTU category significance (%s)' % category,
category_significance_cmd)])
else:
logger.write("Skipping otu_category_significance.py for %s as %s exists.\n\n" \
% (category, category_signifance_fp))
index_links.append(('Category significance (%s)' % category,
category_signifance_fp,
_index_headers['otu_category_sig']))
filtered_biom_gzip_fp = '%s.gz' % filtered_biom_fp
if not exists(filtered_biom_gzip_fp):
commands.append([('Compress the filtered BIOM table','gzip %s' % filtered_biom_fp)])
index_links.append(('Filtered BIOM table (minimum sequence count: %d)' % sampling_depth,
filtered_biom_gzip_fp,
_index_headers['run_summary']))
else:
logger.write("Skipping compressing of filtered BIOM table as %s exists.\n\n" \
% filtered_biom_gzip_fp)
if len(commands) > 0:
command_handler(commands, status_update_callback, logger)
else:
logger.close()
generate_index_page(index_links,index_fp)
def run_core_diversity_analyses(
biom_fp,
mapping_fp,
sampling_depth,
output_dir,
qiime_config,
command_handler=call_commands_serially,
tree_fp=None,
params=None,
categories=None,
arare_min_rare_depth=10,
arare_num_steps=10,
parallel=False,
suppress_taxa_summary=False,
suppress_beta_diversity=False,
suppress_alpha_diversity=False,
suppress_otu_category_significance=False,
status_update_callback=print_to_stdout):
"""
"""
if categories != None:
# Validate categories provided by the users
mapping_data, mapping_comments = \
parse_mapping_file_to_dict(open(mapping_fp,'U'))
metadata_map = MetadataMap(mapping_data, mapping_comments)
for c in categories:
if c not in metadata_map.CategoryNames:
raise ValueError, ("Category '%s' is not a column header "
"in your mapping file. "
"Categories are case and white space sensitive. Valid "
"choices are: (%s)" % (c,', '.join(metadata_map.CategoryNames)))
if metadata_map.hasSingleCategoryValue(c):
raise ValueError, ("Category '%s' contains only one value. "
"Categories analyzed here require at least two values." % c)
else:
categories= []
# prep some variables
if params == None:
params = parse_qiime_parameters([])
create_dir(output_dir)
index_fp = '%s/index.html' % output_dir
index_links = []
commands = []
# begin logging
log_fp = generate_log_fp(output_dir)
index_links.append(('Master run log',log_fp,_index_headers['run_summary']))
logger = WorkflowLogger(log_fp,
params=params,
qiime_config=qiime_config)
input_fps = [biom_fp,mapping_fp]
if tree_fp != None:
input_fps.append(tree_fp)
log_input_md5s(logger,input_fps)
# run print_biom_table_summary.py on input BIOM table
try:
params_str = get_params_str(params['print_biom_table_summary'])
except KeyError:
params_str = ''
biom_table_stats_output_fp = '%s/biom_table_summary.txt' % output_dir
print_biom_table_summary_cmd = \
"print_biom_table_summary.py -i %s -o %s --suppress_md5 %s" % \
(biom_fp, biom_table_stats_output_fp,params_str)
index_links.append(('BIOM table statistics',
biom_table_stats_output_fp,
_index_headers['run_summary']))
commands.append([('Generate BIOM table summary',
print_biom_table_summary_cmd)])
# filter samples with fewer observations than the requested sampling_depth.
# since these get filtered for some analyses (eg beta diversity after
# even sampling) it's useful to filter them here so they're filtered
# from all analyses.
filtered_biom_fp = "%s/table_mc%d.biom" % (output_dir, sampling_depth)
filter_samples_cmd = "filter_samples_from_otu_table.py -i %s -o %s -n %d" %\
(biom_fp,filtered_biom_fp,sampling_depth)
commands.append([('Filter low sequence count samples from table (minimum sequence count: %d)' % sampling_depth,
filter_samples_cmd)])
biom_fp = filtered_biom_fp
# run initial commands and reset the command list
command_handler(commands,
status_update_callback,
logger,
close_logger_on_success=False)
commands = []
if not suppress_beta_diversity:
bdiv_even_output_dir = '%s/bdiv_even%d/' % (output_dir,sampling_depth)
even_dm_fps = run_beta_diversity_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=bdiv_even_output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
sampling_depth=sampling_depth,
# force suppression of distance histograms - boxplots work better
# in this context, and are created below.
histogram_categories=[],
tree_fp=tree_fp,
parallel=parallel,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback)
for bdiv_metric, dm_fp in even_dm_fps:
for category in categories:
boxplots_output_dir = '%s/%s_boxplots/' % (bdiv_even_output_dir,bdiv_metric)
try:
params_str = get_params_str(params['make_distance_boxplots'])
except KeyError:
params_str = ''
boxplots_cmd = \
'make_distance_boxplots.py -d %s -f %s -o %s -m %s -n 999 %s' %\
(dm_fp, category, boxplots_output_dir, mapping_fp, params_str)
commands.append([('Boxplots (%s)' % category,
boxplots_cmd)])
index_links.append(('Distance boxplots (%s)' % bdiv_metric,
'%s/%s_Distances.pdf' % \
(boxplots_output_dir,category),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('Distance boxplots statistics (%s)' % bdiv_metric,
'%s/%s_Stats.txt' % \
(boxplots_output_dir,category),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('3D plot (%s, continuous coloring)' % bdiv_metric,
'%s/%s_3d_continuous/%s_pc_3D_PCoA_plots.html' % \
(bdiv_even_output_dir,bdiv_metric,bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('3D plot (%s, discrete coloring)' % bdiv_metric,
'%s/%s_3d_discrete/%s_pc_3D_PCoA_plots.html' % \
(bdiv_even_output_dir,bdiv_metric,bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('2D plot (%s, continuous coloring)' % bdiv_metric,
'%s/%s_2d_continuous/%s_pc_2D_PCoA_plots.html' % \
(bdiv_even_output_dir,bdiv_metric,bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('2D plot (%s, discrete coloring)' % bdiv_metric,
'%s/%s_2d_discrete/%s_pc_2D_PCoA_plots.html' % \
(bdiv_even_output_dir,bdiv_metric,bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('Distance matrix (%s)' % bdiv_metric,
'%s/%s_dm.txt' % \
(bdiv_even_output_dir,bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('Principal coordinate matrix (%s)' % bdiv_metric,
'%s/%s_pc.txt' % \
(bdiv_even_output_dir,bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
if not suppress_alpha_diversity:
## Alpha rarefaction workflow
arare_full_output_dir = '%s/arare_max%d/' % (output_dir,sampling_depth)
run_alpha_rarefaction(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=arare_full_output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
tree_fp=tree_fp,
num_steps=arare_num_steps,
parallel=parallel,
logger=logger,
min_rare_depth=arare_min_rare_depth,
max_rare_depth=sampling_depth,
suppress_md5=True,
status_update_callback=status_update_callback)
index_links.append(('Alpha rarefaction plots',
'%s/alpha_rarefaction_plots/rarefaction_plots.html'\
% arare_full_output_dir,
_index_headers['alpha_diversity']))
collated_alpha_diversity_fps = \
glob('%s/alpha_div_collated/*txt' % arare_full_output_dir)
try:
params_str = get_params_str(params['compare_alpha_diversity'])
except KeyError:
params_str = ''
for category in categories:
for collated_alpha_diversity_fp in collated_alpha_diversity_fps:
alpha_metric = splitext(split(collated_alpha_diversity_fp)[1])[0]
alpha_comparison_output_fp = '%s/%s_%s.txt' % \
(arare_full_output_dir,category,alpha_metric)
compare_alpha_cmd = \
'compare_alpha_diversity.py -i %s -m %s -c %s -o %s -n 999 %s' %\
(collated_alpha_diversity_fp, mapping_fp, category,
alpha_comparison_output_fp, params_str)
commands.append([('Compare alpha diversity (%s, %s)' %\
(category,alpha_metric),
compare_alpha_cmd)])
index_links.append(
('Alpha diversity statistics (%s, %s)' % (category,alpha_metric),
alpha_comparison_output_fp,
_index_headers['alpha_diversity']))
if not suppress_taxa_summary:
taxa_plots_output_dir = '%s/taxa_plots/' % output_dir
run_summarize_taxa_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=taxa_plots_output_dir,
mapping_cat=None,
sort=True,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback)
index_links.append(('Taxa summary bar plots',
'%s/taxa_summary_plots/bar_charts.html'\
% taxa_plots_output_dir,
_index_headers['taxa_summary']))
index_links.append(('Taxa summary area plots',
'%s/taxa_summary_plots/area_charts.html'\
% taxa_plots_output_dir,
_index_headers['taxa_summary']))
for category in categories:
taxa_plots_output_dir = '%s/taxa_plots_%s/' % (output_dir,category)
run_summarize_taxa_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=taxa_plots_output_dir,
mapping_cat=category,
sort=True,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback)
index_links.append(('Taxa summary bar plots',
'%s/taxa_summary_plots/bar_charts.html'\
% taxa_plots_output_dir,
_index_headers['taxa_summary_categorical'] % category))
index_links.append(('Taxa summary area plots',
'%s/taxa_summary_plots/area_charts.html'\
% taxa_plots_output_dir,
_index_headers['taxa_summary_categorical'] % category))
if not suppress_otu_category_significance:
# OTU category significance
for category in categories:
category_signifance_fp = \
'%s/category_significance_%s.txt' % (output_dir, category)
try:
params_str = get_params_str(params['otu_category_significance'])
except KeyError:
params_str = ''
# Build the OTU cateogry significance command
category_significance_cmd = \
'otu_category_significance.py -i %s -m %s -c %s -o %s %s' %\
(biom_fp, mapping_fp, category,
category_signifance_fp, params_str)
commands.append([('OTU category significance (%s)' % category,
category_significance_cmd)])
index_links.append(('Category significance (%s)' % category,
category_signifance_fp,
_index_headers['otu_category_sig']))
commands.append([('Compress the filtered BIOM table','gzip %s' % filtered_biom_fp)])
index_links.append(('Filtered BIOM table (minimum sequence count: %d)' % sampling_depth,
'%s.gz' % filtered_biom_fp,
_index_headers['run_summary']))
command_handler(commands, status_update_callback, logger)
generate_index_page(index_links,index_fp)
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
parameters = {}
# get the tree insertion method to use
module = opts.insertion_method
# create output directory
output_dir = opts.output_dir
create_dir(output_dir)
# list of tree insertion methods
tree_insertion_module_names = \
{'raxml_v730':cogent.app.raxml_v730,
'parsinsert':cogent.app.parsinsert,
'pplacer':cogent.app.pplacer}
# load input sequences and convert to phylip since the tools require
# the query sequences to phylip-compliant names
load_aln = MinimalFastaParser(open(opts.input_fasta_fp, 'U'))
aln = DenseAlignment(load_aln)
seqs, align_map = aln.toPhylip()
if opts.method_params_fp:
param_dict = parse_qiime_parameters(open(opts.method_params_fp, 'U'))
if module == 'raxml_v730':
# load the reference sequences
load_ref_aln = \
DenseAlignment(MinimalFastaParser(open(opts.refseq_fp,'U')))
# combine and load the reference plus query
combined_aln = MinimalFastaParser(StringIO(load_ref_aln.toFasta() + \
'\n' + aln.toFasta()))
# overwrite the alignment map
aln = DenseAlignment(combined_aln)
seqs, align_map = aln.toPhylip()
try:
parameters = param_dict['raxml']
except:
parameters = {}
tree = convert_tree_tips(align_map, opts.starting_tree_fp)
# write out the tree with phylip labels
updated_tree_fp = join(output_dir, \
'%s_phylip_named_tree.tre' % (module))
write_updated_tree_file(updated_tree_fp, tree)
# set the primary parameters for raxml
parameters['-w'] = abspath(output_dir) + '/'
parameters["-n"] = split(splitext(get_tmp_filename())[0])[-1]
parameters["-t"] = updated_tree_fp
if "-f" not in parameters:
parameters["-f"] = 'v'
if "-m" not in parameters:
parameters["-m"] = 'GTRGAMMA'
elif module == 'pplacer':
try:
parameters = param_dict['pplacer']
except:
parameters = {}
# make sure stats file is passed
if not opts.stats_fp:
raise IOError, \
'When using pplacer, the RAxML produced info file is required.'
# set the primary parameters for pplacer - allow for user-defined
parameters['--out-dir'] = abspath(output_dir) + '/'
parameters["-t"] = opts.starting_tree_fp
parameters['-r'] = opts.refseq_fp
parameters['-s'] = opts.stats_fp
elif module == 'parsinsert':
try:
parameters = param_dict['parsinsert']
except:
parameters = {}
# define log fp
log_fp = join(output_dir, 'parsinsert.log')
# define tax assignment values fp
tax_assign_fp = join(output_dir, 'parsinsert_assignments.log')
parameters["-l"] = log_fp
parameters["-o"] = tax_assign_fp
parameters["-s"] = opts.refseq_fp
parameters["-t"] = opts.starting_tree_fp
# call the module and return a tree object
result = \
tree_insertion_module_names[module].insert_sequences_into_tree(seqs,
moltype=DNA, params=parameters)
result_tree = strip_and_rename_unwanted_labels_from_tree(align_map, result)
# write out the resulting tree
final_tree = join(output_dir, '%s_final_placement.tre' % (module))
write_updated_tree_file(final_tree, result)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
verbose = opts.verbose
input_biom_fp = opts.input_biom_fp
output_dir = opts.output_dir
categories = opts.categories
if categories is not None:
categories = categories.split(',')
tree_fp = opts.tree_fp
mapping_fp = opts.mapping_fp
verbose = opts.verbose
parallel = opts.parallel
sampling_depth = opts.sampling_depth
nonphylogenetic_diversity = opts.nonphylogenetic_diversity
print_only = opts.print_only
suppress_taxa_summary = opts.suppress_taxa_summary
suppress_beta_diversity = opts.suppress_beta_diversity
suppress_alpha_diversity = opts.suppress_alpha_diversity
suppress_group_significance = opts.suppress_group_significance
if opts.parameter_fp is not None:
params = parse_qiime_parameters(open(opts.parameter_fp, 'U'))
else:
params = parse_qiime_parameters([])
if nonphylogenetic_diversity:
# if the user specified --nonphylogenetic_diversity and they
# didn't define metrics in a parameters file, define them here
if 'metrics' not in params['beta_diversity']:
params['beta_diversity']['metrics'] = 'bray_curtis'
if 'metrics' not in params['alpha_diversity']:
params['alpha_diversity']['metrics'] = 'observed_otus,chao1'
jobs_to_start = opts.jobs_to_start
default_jobs_to_start = qiime_config['jobs_to_start']
validate_and_set_jobs_to_start(params, jobs_to_start,
default_jobs_to_start, parallel,
option_parser)
# Create the output directory. If it already exists and the user
# isn't trying to recover from a failed run, raise an error.
create_dir(output_dir, fail_on_exist=not opts.recover_from_failure)
if print_only:
command_handler = print_commands
else:
command_handler = call_commands_serially
if verbose:
status_update_callback = print_to_stdout
else:
status_update_callback = no_status_updates
run_core_diversity_analyses(
biom_fp=input_biom_fp,
mapping_fp=mapping_fp,
sampling_depth=sampling_depth,
output_dir=output_dir,
qiime_config=load_qiime_config(),
command_handler=command_handler,
tree_fp=tree_fp,
params=params,
categories=categories,
arare_min_rare_depth=10,
arare_num_steps=10,
parallel=parallel,
suppress_taxa_summary=suppress_taxa_summary,
suppress_beta_diversity=suppress_beta_diversity,
suppress_alpha_diversity=suppress_alpha_diversity,
suppress_group_significance=suppress_group_significance,
status_update_callback=status_update_callback)
def main():
option_parser, opts, args =\
parse_command_line_parameters(suppress_verbose=True, **script_info)
input_dir = opts.input_dir
demultiplexing_method = opts.demultiplexing_method
parameter_fp = opts.parameter_fp
read_indicator = opts.read_indicator
barcode_indicator = opts.barcode_indicator
mapping_indicator = opts.mapping_indicator
mapping_extensions = opts.mapping_extensions.split(',')
sampleid_indicator = opts.sampleid_indicator
leading_text = opts.leading_text
trailing_text = opts.trailing_text
include_input_dir_path = opts.include_input_dir_path
output_dir = abspath(opts.output_dir)
remove_filepath_in_name = opts.remove_filepath_in_name
print_only = opts.print_only
if remove_filepath_in_name and not include_input_dir_path:
option_parser.error("If --remove_filepath_in_name enabled, "
"--include_input_dir_path must be enabled.")
if opts.parameter_fp:
with open(opts.parameter_fp, 'U') as parameter_f:
params_dict = parse_qiime_parameters(parameter_f)
params_str = get_params_str(params_dict['split_libraries_fastq'])
else:
params_dict = {}
params_str = ""
create_dir(output_dir)
all_fastq = []
all_mapping = []
extensions = ['.fastq.gz', '.fastq', '.fq.gz', '.fq']
for root, dir, fps in walk(input_dir):
for fp in fps:
for extension in extensions:
if fp.endswith(extension):
all_fastq += [abspath(join(root, fp))]
if demultiplexing_method == 'mapping_barcode_files':
for root, dir, fps in walk(input_dir):
for fp in fps:
for mapping_extension in mapping_extensions:
if fp.endswith(mapping_extension):
all_mapping += [abspath(join(root, fp))]
all_files = get_matching_files(all_fastq, all_mapping, read_indicator,
barcode_indicator, mapping_indicator)
else:
all_files = all_fastq
commands = create_commands_slf(all_files, demultiplexing_method,
output_dir, params_str, leading_text,
trailing_text, include_input_dir_path,
remove_filepath_in_name, sampleid_indicator)
qiime_config = load_qiime_config()
if print_only:
command_handler = print_commands
else:
command_handler = call_commands_serially
logger = WorkflowLogger(generate_log_fp(output_dir),
params=params_dict,
qiime_config=qiime_config)
# Call the command handler on the list of commands
command_handler(commands,
status_update_callback=no_status_updates,
logger=logger,
close_logger_on_success=True)
def write_mapping_and_otu_table(data_access, table_col_value, fs_fp, web_fp,
file_name_prefix,user_id,meta_id,params_path,
rarefied_at,otutable_rarefied_at,
jobs_to_start,tax_name,tree_fp):
""" Write out the mapping file and OTU-table """
tmp_prefix=get_tmp_filename('',suffix='').strip()
total1 = time()
unique_cols=[]
# get the directory location for the files to write
otu_table_file_dir=path.join(fs_fp,'otu_table_files')
mapping_file_dir=path.join(fs_fp,'mapping_files')
zip_file_dir=path.join(fs_fp,'zip_files')
#pcoa_file_dir_loc=path.join(fs_fp,'pcoa_files')
# get the url location for the files to write
otu_table_file_dir_db=path.join(web_fp,'otu_table_files')
mapping_file_dir_db=path.join(web_fp,'mapping_files')
zip_file_dir_db=path.join(web_fp,'zip_files')
pcoa_file_dir_loc_db=path.join(web_fp,'pcoa_files')
# generate random directory name
alphabet = "ABCDEFGHIJKLMNOPQRSTUZWXYZ"
alphabet += alphabet.lower()
alphabet += "01234567890"
random_dir_name=''.join([choice(alphabet) for i in range(10)])
unique_name=strftime("%Y_%m_%d_%H_%M_%S")+random_dir_name
#plot_unique_name=beta_metric+'_plots_'+unique_name
#pcoa_file_dir=os.path.join(pcoa_file_dir_loc,plot_unique_name)
#pcoa_file_dir_db=os.path.join(pcoa_file_dir_loc_db,plot_unique_name)
#create_dir(pcoa_file_dir)
map_files=[]
t1 = time()
# Get the user details
user_details = data_access.getUserDetails(user_id)
if not user_details:
raise ValueError('No details found for this user')
is_admin = user_details['is_admin']
# get mapping results
results,cur_description=get_mapping_data(data_access, is_admin,
table_col_value, user_id)
# need to reconnect to data_access, since it gets closed due to con.close()
try:
from data_access_connections import data_access_factory
from enums import ServerConfig
import cx_Oracle
data_access = data_access_factory(ServerConfig.data_access_type)
except ImportError:
print "NOT IMPORTING QIIMEDATAACCESS"
pass
# get filepaths for mapping files using the run-prefixes
tmp_mapping_file = file(os.path.join(mapping_file_dir, file_name_prefix + \
'_map_tmp.txt'), 'w')
map_filepath=os.path.join(mapping_file_dir, file_name_prefix + '_' + \
tmp_prefix + '_map.txt')
map_filepath_db=os.path.join(mapping_file_dir_db, file_name_prefix + '_' + \
tmp_prefix + '_map.txt')
# All mapping files start with an opening hash
tmp_mapping_file.write('#')
# determine if a column is a controlled vaocabulary columnn
controlled_vocab_columns={}
for i,column in enumerate(cur_description):
if column in ['SAMPLE_NAME', 'BARCODE', 'LINKER', 'PRIMER', \
'EXPERIMENT_TITLE']:
pass
else:
valid_controlled_vocab=\
data_access.checkIfColumnControlledVocab(column[0])
if valid_controlled_vocab:
controlled_vocab_columns[str(column[0])]=i
# create a dictionary storing the controlled columns and their values
controlled_vocab_lookup={}
for column in controlled_vocab_columns:
vocab_id_to_valid_term=data_access.getValidControlledVocabTerms(column)
controlled_vocab_lookup[controlled_vocab_columns[column]] = \
dict(vocab_id_to_valid_term)
# Write out the key field headers
to_write = ''
for column in cur_description:
if column[0]=='SAMPLEID':
to_write+='SampleID\t'
elif column[0]=='BARCODE':
to_write+='BarcodeSequence\t'
elif column[0]=='DESCRIPTION':
to_write+='Description\t'
elif column[0]=='LINKERPRIMERSEQUENCE':
to_write+='LinkerPrimerSequence\t'
else:
to_write += column[0] + '\t'
tmp_mapping_file.write(to_write[0:len(to_write)-1] + '\n')
sample_to_run_prefix=[]
study_id_and_run_prefix=[]
samples_list=[]
map_file_write=[]
duplicate_samples=[]
samples_list=[]
for row in results:
# Can't use something like '\t'.join(row) because not all items in list
# are string values, hence the explicit loop structure here.
to_write = ''
sample_to_run_prefix.append(list((str(row[0]),str(row[4]),str(row[3]))))
if list((str(row[3]),str(row[4]))) not in study_id_and_run_prefix:
study_id_and_run_prefix.append(list((str(row[3]),str(row[4]))))
if str(row[0]) in samples_list:
# Order of row goes as follows: SampleID, BarcodeSequence,
# LinkerPrimerSequence,Run_Prefix, then Description is at the end
row=list(row)
row[0]=row[0]+'_'+str(row[4])
row=tuple(row)
duplicate_samples.append(str(row[0]))
else:
samples_list.append(str(row[0]))
# write out controlled vocabulary values
for i,column in enumerate(row):
if controlled_vocab_lookup.has_key(i):
val = str(column)
if val == 'None':
new_val = ''
else:
new_val=controlled_vocab_lookup[i][int(val)]
to_write += new_val + '\t'
else:
val = str(column)
if val == 'None':
val = ''
to_write += val + '\t'
# Write the row minus the last tab
tmp_mapping_file.write(to_write[0:len(to_write)] + '\n')
tmp_mapping_file.close()
open_tmp_mapping_file=open(os.path.join(mapping_file_dir,
file_name_prefix + '_map_tmp.txt')).readlines()
mapping_file = file(os.path.join(mapping_file_dir, file_name_prefix + \
'_' + tmp_prefix + '_map.txt'), 'w')
mapping_lines = []
all_headers = {}
result = []
# iterate over mapping files, parsing each
data, current_headers, current_comments = \
parse_mapping_file(open_tmp_mapping_file,strip_quotes=False)
all_headers.update(dict.fromkeys(current_headers))
for d in data:
current_values = {}
for i,v in enumerate(d):
if v !='':
current_values[current_headers[i]] = v
mapping_lines.append(current_values)
# remove and place the fields whose order is important
del all_headers['SampleID']
del all_headers['BarcodeSequence']
del all_headers['LinkerPrimerSequence']
del all_headers['Description']
all_headers = ['SampleID','BarcodeSequence','LinkerPrimerSequence'] \
+ list(all_headers) + ['Description']
# generate the mapping file lines containing all fields
result.append(all_headers)
for mapping_line in mapping_lines:
result.append([mapping_line.get(h,'NA') for h in all_headers if h!=''])
#Create an array using multiple columns from mapping file
try:
parameter_f = open(params_path)
except IOError:
raise IOError,\
"Can't open parameters file (%s). Does it exist? Do you have read access?"\
% params_path
# determine if columns should be combined
qiime_params=parse_qiime_parameters(parameter_f)
try:
combined_mapping_categories = \
qiime_params['combine_metadata']['columns'].split(',')
except:
combined_mapping_categories=''
# combine metadata columns
if combined_mapping_categories:
for mapping_category in combined_mapping_categories:
combinecolorby=mapping_category.strip('\'').strip('"').split('_and_')
result=combine_map_header_cols(combinecolorby,result)
# write final mapping file
final_mapping=[]
for i,mdata in enumerate(result):
if i==0:
final_mapping.append('#'+'\t'.join(mdata))
else:
final_mapping.append('\t'.join(mdata))
#test=merge_mapping_files([merged_file])
mapping_file.write('\n'.join(final_mapping))
mapping_file.close()
#flush result
final_mapping=[]
result=[]
t2 = time()
print 'Making map file: %s' % (t2 - t1)
t1 = time()
# write the OTU-table
otu_table_filepath, otu_table_filepath_db, otu_table_fname = \
write_otu_table(data_access,
samples_list, tax_name, file_name_prefix,
tmp_prefix, otu_table_file_dir,
otu_table_file_dir_db)
# zip up the files and add the paths to DB
zip_fpath, zip_fpath_db = zip_and_add_filepaths(data_access,
file_name_prefix,
tmp_prefix, meta_id, otu_table_file_dir,
mapping_file_dir, zip_file_dir, zip_file_dir_db,
map_filepath, map_filepath_db, otu_table_filepath,
otu_table_filepath_db, params_path)
# rarefy OTU table if necessary
if otutable_rarefied_at:
rarefy_otu_table(data_access, otu_table_fname, otu_table_file_dir,
otu_table_file_dir_db, otutable_rarefied_at, meta_id,
otu_table_filepath, otu_table_filepath_db, zip_fpath)
# run any additional analysis, such as heatmap, bdiv, alpha-rare
run_other_qiime_analysis(data_access, fs_fp, web_fp, otu_table_filepath,
map_filepath, file_name_prefix, user_id, meta_id,
params_path, rarefied_at, jobs_to_start, tree_fp,
zip_fpath, zip_fpath_db)
def run_core_diversity_analyses(
biom_fp,
mapping_fp,
sampling_depth,
output_dir,
qiime_config,
command_handler=call_commands_serially,
tree_fp=None,
params=None,
categories=None,
arare_min_rare_depth=10,
arare_num_steps=10,
parallel=False,
suppress_taxa_summary=False,
suppress_beta_diversity=False,
suppress_alpha_diversity=False,
suppress_otu_category_significance=False,
status_update_callback=print_to_stdout,
):
"""
"""
if categories != None:
# Validate categories provided by the users
mapping_data, mapping_comments = parse_mapping_file_to_dict(open(mapping_fp, "U"))
metadata_map = MetadataMap(mapping_data, mapping_comments)
for c in categories:
if c not in metadata_map.CategoryNames:
raise ValueError, (
"Category '%s' is not a column header "
"in your mapping file. "
"Categories are case and white space sensitive. Valid "
"choices are: (%s)" % (c, ", ".join(metadata_map.CategoryNames))
)
if metadata_map.hasSingleCategoryValue(c):
raise ValueError, (
"Category '%s' contains only one value. "
"Categories analyzed here require at least two values." % c
)
else:
categories = []
# prep some variables
if params == None:
params = parse_qiime_parameters([])
create_dir(output_dir)
index_fp = "%s/index.html" % output_dir
index_links = []
commands = []
# begin logging
log_fp = generate_log_fp(output_dir)
index_links.append(("Master run log", log_fp, _index_headers["run_summary"]))
logger = WorkflowLogger(log_fp, params=params, qiime_config=qiime_config)
input_fps = [biom_fp, mapping_fp]
if tree_fp != None:
input_fps.append(tree_fp)
log_input_md5s(logger, input_fps)
# run print_biom_table_summary.py on input BIOM table
try:
params_str = get_params_str(params["print_biom_table_summary"])
except KeyError:
params_str = ""
biom_table_stats_output_fp = "%s/biom_table_summary.txt" % output_dir
print_biom_table_summary_cmd = "print_biom_table_summary.py -i %s -o %s --suppress_md5 %s" % (
biom_fp,
biom_table_stats_output_fp,
params_str,
)
index_links.append(("BIOM table statistics", biom_table_stats_output_fp, _index_headers["run_summary"]))
commands.append([("Generate BIOM table summary", print_biom_table_summary_cmd)])
# filter samples with fewer observations than the requested sampling_depth.
# since these get filtered for some analyses (eg beta diversity after
# even sampling) it's useful to filter them here so they're filtered
# from all analyses.
filtered_biom_fp = "%s/table_mc%d.biom" % (output_dir, sampling_depth)
filter_samples_cmd = "filter_samples_from_otu_table.py -i %s -o %s -n %d" % (
biom_fp,
filtered_biom_fp,
sampling_depth,
)
commands.append(
[
(
"Filter low sequence count samples from table (minimum sequence count: %d)" % sampling_depth,
filter_samples_cmd,
)
]
)
biom_fp = filtered_biom_fp
# run initial commands and reset the command list
command_handler(commands, status_update_callback, logger, close_logger_on_success=False)
commands = []
if not suppress_beta_diversity:
bdiv_even_output_dir = "%s/bdiv_even%d/" % (output_dir, sampling_depth)
even_dm_fps = run_beta_diversity_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=bdiv_even_output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
sampling_depth=sampling_depth,
# force suppression of distance histograms - boxplots work better
# in this context, and are created below.
histogram_categories=[],
tree_fp=tree_fp,
parallel=parallel,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback,
)
for bdiv_metric, dm_fp in even_dm_fps:
for category in categories:
boxplots_output_dir = "%s/%s_boxplots/" % (bdiv_even_output_dir, bdiv_metric)
try:
params_str = get_params_str(params["make_distance_boxplots"])
except KeyError:
params_str = ""
boxplots_cmd = "make_distance_boxplots.py -d %s -f %s -o %s -m %s -n 999 %s" % (
dm_fp,
category,
boxplots_output_dir,
mapping_fp,
params_str,
)
commands.append([("Boxplots (%s)" % category, boxplots_cmd)])
index_links.append(
(
"Distance boxplots (%s)" % bdiv_metric,
"%s/%s_Distances.pdf" % (boxplots_output_dir, category),
_index_headers["beta_diversity_even"] % sampling_depth,
)
)
index_links.append(
(
"Distance boxplots statistics (%s)" % bdiv_metric,
"%s/%s_Stats.txt" % (boxplots_output_dir, category),
_index_headers["beta_diversity_even"] % sampling_depth,
)
)
index_links.append(
(
"3D plot (%s, continuous coloring)" % bdiv_metric,
"%s/%s_3d_continuous/%s_pc_3D_PCoA_plots.html" % (bdiv_even_output_dir, bdiv_metric, bdiv_metric),
_index_headers["beta_diversity_even"] % sampling_depth,
)
)
index_links.append(
(
"3D plot (%s, discrete coloring)" % bdiv_metric,
"%s/%s_3d_discrete/%s_pc_3D_PCoA_plots.html" % (bdiv_even_output_dir, bdiv_metric, bdiv_metric),
_index_headers["beta_diversity_even"] % sampling_depth,
)
)
index_links.append(
(
"2D plot (%s, continuous coloring)" % bdiv_metric,
"%s/%s_2d_continuous/%s_pc_2D_PCoA_plots.html" % (bdiv_even_output_dir, bdiv_metric, bdiv_metric),
_index_headers["beta_diversity_even"] % sampling_depth,
)
)
index_links.append(
(
"2D plot (%s, discrete coloring)" % bdiv_metric,
"%s/%s_2d_discrete/%s_pc_2D_PCoA_plots.html" % (bdiv_even_output_dir, bdiv_metric, bdiv_metric),
_index_headers["beta_diversity_even"] % sampling_depth,
)
)
index_links.append(
(
"Distance matrix (%s)" % bdiv_metric,
"%s/%s_dm.txt" % (bdiv_even_output_dir, bdiv_metric),
_index_headers["beta_diversity_even"] % sampling_depth,
)
)
index_links.append(
(
"Principal coordinate matrix (%s)" % bdiv_metric,
"%s/%s_pc.txt" % (bdiv_even_output_dir, bdiv_metric),
_index_headers["beta_diversity_even"] % sampling_depth,
)
)
if not suppress_alpha_diversity:
## Alpha rarefaction workflow
arare_full_output_dir = "%s/arare_max%d/" % (output_dir, sampling_depth)
run_alpha_rarefaction(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=arare_full_output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
tree_fp=tree_fp,
num_steps=arare_num_steps,
parallel=parallel,
logger=logger,
min_rare_depth=arare_min_rare_depth,
max_rare_depth=sampling_depth,
suppress_md5=True,
status_update_callback=status_update_callback,
)
index_links.append(
(
"Alpha rarefaction plots",
"%s/alpha_rarefaction_plots/rarefaction_plots.html" % arare_full_output_dir,
_index_headers["alpha_diversity"],
)
)
collated_alpha_diversity_fps = glob("%s/alpha_div_collated/*txt" % arare_full_output_dir)
try:
params_str = get_params_str(params["compare_alpha_diversity"])
except KeyError:
params_str = ""
for category in categories:
for collated_alpha_diversity_fp in collated_alpha_diversity_fps:
alpha_metric = splitext(split(collated_alpha_diversity_fp)[1])[0]
alpha_comparison_output_fp = "%s/%s_%s.txt" % (arare_full_output_dir, category, alpha_metric)
compare_alpha_cmd = "compare_alpha_diversity.py -i %s -m %s -c %s -o %s -n 999 %s" % (
collated_alpha_diversity_fp,
mapping_fp,
category,
alpha_comparison_output_fp,
params_str,
)
commands.append([("Compare alpha diversity (%s, %s)" % (category, alpha_metric), compare_alpha_cmd)])
index_links.append(
(
"Alpha diversity statistics (%s, %s)" % (category, alpha_metric),
alpha_comparison_output_fp,
_index_headers["alpha_diversity"],
)
)
if not suppress_taxa_summary:
taxa_plots_output_dir = "%s/taxa_plots/" % output_dir
run_summarize_taxa_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=taxa_plots_output_dir,
mapping_cat=None,
sort=True,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback,
)
index_links.append(
(
"Taxa summary bar plots",
"%s/taxa_summary_plots/bar_charts.html" % taxa_plots_output_dir,
_index_headers["taxa_summary"],
)
)
index_links.append(
(
"Taxa summary area plots",
"%s/taxa_summary_plots/area_charts.html" % taxa_plots_output_dir,
_index_headers["taxa_summary"],
)
)
for category in categories:
taxa_plots_output_dir = "%s/taxa_plots_%s/" % (output_dir, category)
run_summarize_taxa_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=taxa_plots_output_dir,
mapping_cat=category,
sort=True,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback,
)
index_links.append(
(
"Taxa summary bar plots",
"%s/taxa_summary_plots/bar_charts.html" % taxa_plots_output_dir,
_index_headers["taxa_summary_categorical"] % category,
)
)
index_links.append(
(
"Taxa summary area plots",
"%s/taxa_summary_plots/area_charts.html" % taxa_plots_output_dir,
_index_headers["taxa_summary_categorical"] % category,
)
)
if not suppress_otu_category_significance:
# OTU category significance
for category in categories:
category_signifance_fp = "%s/category_significance_%s.txt" % (output_dir, category)
try:
params_str = get_params_str(params["otu_category_significance"])
except KeyError:
params_str = ""
# Build the OTU cateogry significance command
category_significance_cmd = "otu_category_significance.py -i %s -m %s -c %s -o %s %s" % (
biom_fp,
mapping_fp,
category,
category_signifance_fp,
params_str,
)
commands.append([("OTU category significance (%s)" % category, category_significance_cmd)])
index_links.append(
("Category significance (%s)" % category, category_signifance_fp, _index_headers["otu_category_sig"])
)
commands.append([("Compress the filtered BIOM table", "gzip %s" % filtered_biom_fp)])
index_links.append(
(
"Filtered BIOM table (minimum sequence count: %d)" % sampling_depth,
"%s.gz" % filtered_biom_fp,
_index_headers["run_summary"],
)
)
command_handler(commands, status_update_callback, logger)
generate_index_page(index_links, index_fp)
def run_core_diversity_analyses(
biom_fp,
mapping_fp,
sampling_depth,
output_dir,
qiime_config,
command_handler=call_commands_serially,
tree_fp=None,
params=None,
categories=None,
arare_min_rare_depth=10,
arare_num_steps=10,
parallel=False,
status_update_callback=print_to_stdout):
"""
"""
if categories != None:
# Validate categories provided by the users
mapping_data, mapping_comments = \
parse_mapping_file_to_dict(open(mapping_fp,'U'))
metadata_map = MetadataMap(mapping_data, mapping_comments)
for c in categories:
if c not in metadata_map.CategoryNames:
raise ValueError, ("Category '%s' is not a column header "
"in your mapping file. "
"Categories are case and white space sensitive. Valid "
"choices are: (%s)" % (c,', '.join(metadata_map.CategoryNames)))
if metadata_map.hasSingleCategoryValue(c):
raise ValueError, ("Category '%s' contains only one value. "
"Categories analyzed here require at least two values." % c)
else:
categories= []
# prep some variables
if params == None:
params = parse_qiime_parameters([])
create_dir(output_dir)
index_fp = '%s/index.html' % output_dir
index_links = []
commands = []
python_exe_fp = qiime_config['python_exe_fp']
script_dir = get_qiime_scripts_dir()
# begin logging
log_fp = generate_log_fp(output_dir)
index_links.append(('Master run log',log_fp,'Log files'))
logger = WorkflowLogger(log_fp,
params=params,
qiime_config=qiime_config)
input_fps = [biom_fp,mapping_fp]
if tree_fp != None:
input_fps.append(tree_fp)
log_input_md5s(logger,input_fps)
bdiv_even_output_dir = '%s/bdiv_even%d/' % (output_dir,sampling_depth)
even_dm_fps = run_beta_diversity_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=bdiv_even_output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
sampling_depth=sampling_depth,
# force suppression of distance histograms - boxplots work better
# in this context, and are created below.
histogram_categories=[],
tree_fp=tree_fp,
parallel=parallel,
logger=logger,
status_update_callback=status_update_callback)
for bdiv_metric, dm_fp in even_dm_fps:
for category in categories:
boxplots_output_dir = '%s/%s_boxplots/' % (bdiv_even_output_dir,bdiv_metric)
try:
params_str = get_params_str(params['make_distance_boxplots'])
except KeyError:
params_str = ''
boxplots_cmd = \
'make_distance_boxplots.py -d %s -f %s -o %s -m %s -n 999 %s' %\
(dm_fp, category, boxplots_output_dir, mapping_fp, params_str)
commands.append([('Boxplots (%s)' % category,
boxplots_cmd)])
index_links.append(('Distance boxplots (%s)' % bdiv_metric,
'%s/%s_Distances.pdf' % \
(boxplots_output_dir,category),
'Beta diversity results (even sampling: %d)' % sampling_depth))
index_links.append(('Distance boxplots statistics (%s)' % bdiv_metric,
'%s/%s_Stats.txt' % \
(boxplots_output_dir,category),
'Beta diversity results (even sampling: %d)' % sampling_depth))
index_links.append(('3D plot (%s, continuous coloring)' % bdiv_metric,
'%s/%s_3d_continuous/%s_pc_3D_PCoA_plots.html' % \
(bdiv_even_output_dir,bdiv_metric,bdiv_metric),
'Beta diversity results (even sampling: %d)' % sampling_depth))
index_links.append(('3D plot (%s, discrete coloring)' % bdiv_metric,
'%s/%s_3d_discrete/%s_pc_3D_PCoA_plots.html' % \
(bdiv_even_output_dir,bdiv_metric,bdiv_metric),
'Beta diversity results (even sampling: %d)' % sampling_depth))
index_links.append(('2D plot (%s, continuous coloring)' % bdiv_metric,
'%s/%s_2d_continuous/%s_pc_2D_PCoA_plots.html' % \
(bdiv_even_output_dir,bdiv_metric,bdiv_metric),
'Beta diversity results (even sampling: %d)' % sampling_depth))
index_links.append(('2D plot (%s, discrete coloring)' % bdiv_metric,
'%s/%s_2d_discrete/%s_pc_2D_PCoA_plots.html' % \
(bdiv_even_output_dir,bdiv_metric,bdiv_metric),
'Beta diversity results (even sampling: %d)' % sampling_depth))
index_links.append(('Distance matrix (%s)' % bdiv_metric,
'%s/%s_dm.txt' % \
(bdiv_even_output_dir,bdiv_metric),
'Beta diversity results (even sampling: %d)' % sampling_depth))
index_links.append(('Principal coordinate matrix (%s)' % bdiv_metric,
'%s/%s_pc.txt' % \
(bdiv_even_output_dir,bdiv_metric),
'Beta diversity results (even sampling: %d)' % sampling_depth))
## Alpha rarefaction workflow
arare_full_output_dir = '%s/arare_max%d/' % (output_dir,sampling_depth)
run_qiime_alpha_rarefaction(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=arare_full_output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
tree_fp=tree_fp,
num_steps=arare_num_steps,
parallel=parallel,
logger=logger,
min_rare_depth=arare_min_rare_depth,
max_rare_depth=sampling_depth,
status_update_callback=status_update_callback)
index_links.append(('Alpha rarefaction plots',
'%s/alpha_rarefaction_plots/rarefaction_plots.html'\
% arare_full_output_dir,
"Alpha rarefaction results"))
collated_alpha_diversity_fps = \
glob('%s/alpha_div_collated/*txt' % arare_full_output_dir)
try:
params_str = get_params_str(params['compare_alpha_diversity'])
except KeyError:
params_str = ''
for c in categories:
for collated_alpha_diversity_fp in collated_alpha_diversity_fps:
alpha_metric = splitext(split(collated_alpha_diversity_fp)[1])[0]
alpha_comparison_output_fp = '%s/%s_%s.txt' % \
(arare_full_output_dir,c,alpha_metric)
compare_alpha_cmd = \
'compare_alpha_diversity.py -i %s -m %s -c %s -d %s -o %s -n 999 %s' %\
(collated_alpha_diversity_fp, mapping_fp, c,
sampling_depth, alpha_comparison_output_fp, params_str)
commands.append([('Compare alpha diversity (%s, %s)' %\
(category,alpha_metric),
compare_alpha_cmd)])
index_links.append(
('Alpha diversity statistics (%s, %s)' % (category,alpha_metric),
alpha_comparison_output_fp,
"Alpha rarefaction results"))
taxa_plots_output_dir = '%s/taxa_plots/' % output_dir
run_summarize_taxa_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=taxa_plots_output_dir,
mapping_cat=None,
sort=True,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
logger=logger,
status_update_callback=status_update_callback)
index_links.append(('Taxa summary bar plots',
'%s/taxa_summary_plots/bar_charts.html'\
% taxa_plots_output_dir,
"Taxonomic summary results"))
index_links.append(('Taxa summary area plots',
'%s/taxa_summary_plots/area_charts.html'\
% taxa_plots_output_dir,
"Taxonomic summary results"))
for c in categories:
taxa_plots_output_dir = '%s/taxa_plots_%s/' % (output_dir,c)
run_summarize_taxa_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=taxa_plots_output_dir,
mapping_cat=c,
sort=True,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
logger=logger,
status_update_callback=status_update_callback)
index_links.append(('Taxa summary bar plots',
'%s/taxa_summary_plots/bar_charts.html'\
% taxa_plots_output_dir,
"Taxonomic summary results (by %s)" % c))
index_links.append(('Taxa summary area plots',
'%s/taxa_summary_plots/area_charts.html'\
% taxa_plots_output_dir,
"Taxonomic summary results (by %s)" % c))
# OTU category significance
for category in categories:
category_signifance_fp = \
'%s/category_significance_%s.txt' % (output_dir, category)
try:
params_str = get_params_str(params['otu_category_significance'])
except KeyError:
params_str = ''
# Build the OTU cateogry significance command
category_significance_cmd = \
'otu_category_significance.py -i %s -m %s -c %s -o %s %s' %\
(biom_fp, mapping_fp, category,
category_signifance_fp, params_str)
commands.append([('OTU category significance (%s)' % category,
category_significance_cmd)])
index_links.append(('Category significance (%s)' % category,
category_signifance_fp,
"Category results"))
command_handler(commands, status_update_callback, logger)
generate_index_page(index_links,index_fp)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
verbose = opts.verbose
input_biom_fp = opts.input_biom_fp
output_dir = opts.output_dir
categories = opts.categories
if categories is not None:
categories = categories.split(',')
tree_fp = opts.tree_fp
mapping_fp = opts.mapping_fp
verbose = opts.verbose
parallel = opts.parallel
sampling_depth = opts.sampling_depth
nonphylogenetic_diversity = opts.nonphylogenetic_diversity
print_only = opts.print_only
suppress_taxa_summary = opts.suppress_taxa_summary
suppress_beta_diversity = opts.suppress_beta_diversity
suppress_alpha_diversity = opts.suppress_alpha_diversity
suppress_group_significance = opts.suppress_group_significance
if opts.parameter_fp is not None:
params = parse_qiime_parameters(open(opts.parameter_fp, 'U'))
else:
params = parse_qiime_parameters([])
if nonphylogenetic_diversity:
# if the user specified --nonphylogenetic_diversity and they
# didn't define metrics in a parameters file, define them here
if 'metrics' not in params['beta_diversity']:
params['beta_diversity']['metrics'] = 'bray_curtis'
if 'metrics' not in params['alpha_diversity']:
params['alpha_diversity']['metrics'] = 'observed_otus,chao1'
else:
if tree_fp is None:
option_parser.error(
"--tree_fp is required unless --nonphylogenetic_diversity "
"is passed.")
jobs_to_start = opts.jobs_to_start
default_jobs_to_start = qiime_config['jobs_to_start']
validate_and_set_jobs_to_start(params,
jobs_to_start,
default_jobs_to_start,
parallel,
option_parser)
# Create the output directory. If it already exists and the user
# isn't trying to recover from a failed run, raise an error.
create_dir(output_dir, fail_on_exist=not opts.recover_from_failure)
if print_only:
command_handler = print_commands
else:
command_handler = call_commands_serially
if verbose:
status_update_callback = print_to_stdout
else:
status_update_callback = no_status_updates
run_core_diversity_analyses(
biom_fp=input_biom_fp,
mapping_fp=mapping_fp,
sampling_depth=sampling_depth,
output_dir=output_dir,
qiime_config=load_qiime_config(),
command_handler=command_handler,
tree_fp=tree_fp,
params=params,
categories=categories,
arare_min_rare_depth=10,
arare_num_steps=10,
parallel=parallel,
suppress_taxa_summary=suppress_taxa_summary,
suppress_beta_diversity=suppress_beta_diversity,
suppress_alpha_diversity=suppress_alpha_diversity,
suppress_group_significance=suppress_group_significance,
status_update_callback=status_update_callback)
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
#get all the options
cd_dir=path.join(opts.fs_fp,'arare')
tmp_prefix=get_tmp_filename('',suffix='').strip()
output_dir=path.join(opts.fs_fp,'arare','arare_'+tmp_prefix)
web_fp=path.join(opts.web_fp,'arare','arare_'+tmp_prefix)
otu_table_fp=opts.otu_table_fp
mapping_file_fp=opts.mapping_file_fp
file_name_prefix=opts.fname_prefix
user_id=int(opts.user_id)
meta_id=int(opts.meta_id)
bdiv_rarefied_at=int(opts.bdiv_rarefied_at)
jobs_to_start=opts.jobs_to_start
tree_fp=opts.tree_fp
command_handler=call_commands_serially
status_update_callback=no_status_updates
zip_fpath=opts.zip_fpath
zip_fpath_db=opts.zip_fpath_db
run_date=opts.run_date
force=True
try:
from data_access_connections import data_access_factory
from enums import ServerConfig
import cx_Oracle
data_access = data_access_factory(ServerConfig.data_access_type)
except ImportError:
print "NOT IMPORTING QIIMEDATAACCESS"
pass
try:
parameter_f = open(opts.params_path)
except IOError:
raise IOError,\
"Can't open parameters file (%s). Does it exist? Do you have read access?"\
% opts.params_path
params=parse_qiime_parameters(parameter_f)
try:
makedirs(output_dir)
except OSError:
if force:
pass
else:
# Since the analysis can take quite a while, I put this check
# in to help users avoid overwriting previous output.
print "Output directory already exists. Please choose "+\
"a different directory, or force overwrite with -f."
exit(1)
commands=[]
python_exe_fp = qiime_config['python_exe_fp']
script_dir = get_qiime_scripts_dir()
logger = WorkflowLogger(generate_log_fp(output_dir),
params=params,
qiime_config=qiime_config)
# determine whether to run alpha-diversity in serial or parallel
serial_or_parallel = params['serial_or_parallel']['method']
if serial_or_parallel=='Serial':
arare_cmd='%s %s/alpha_rarefaction.py -i %s -m %s -o %s -t %s -p %s -f' %\
(python_exe_fp, script_dir, otu_table_fp, mapping_file_fp, \
output_dir,tree_fp,opts.params_path)
else:
arare_cmd='%s %s/alpha_rarefaction.py -i %s -m %s -o %s -t %s -a -O 50 -p %s -f' %\
(python_exe_fp, script_dir, otu_table_fp, mapping_file_fp, \
output_dir,tree_fp,opts.params_path)
commands.append([('Alpha-Rarefaction',arare_cmd)])
command_handler(commands, status_update_callback, logger)
#zip the distance matrices
cmd_call='cd %s; zip -r %s %s' % (cd_dir,zip_fpath,'arare_'+tmp_prefix)
system(cmd_call)
#convert link into web-link
web_link=path.join(web_fp, 'alpha_rarefaction_plots',
'rarefaction_plots.html')
#add the distance matrices
valid=data_access.addMetaAnalysisFiles(True, int(meta_id), web_link,
'ARARE', run_date, 'ARARE')
if not valid:
raise ValueError, 'There was an issue uploading the filepaths to the DB!'
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
verbose = opts.verbose
input_fps = opts.input_fps
refseqs_fp = opts.reference_fp
output_dir = opts.output_dir
verbose = opts.verbose
print_only = False
percent_subsample = opts.percent_subsample
new_ref_set_id = opts.new_ref_set_id
prefilter_refseqs_fp = opts.prefilter_refseqs_fp
prefilter_percent_id = opts.prefilter_percent_id
if prefilter_percent_id == 0.0:
prefilter_percent_id = None
parallel = opts.parallel
# No longer checking that jobs_to_start > 2, but
# commenting as we may change our minds about this.
#if parallel: raise_error_on_parallel_unavailable()
if opts.parameter_fp:
try:
parameter_f = open(opts.parameter_fp)
except IOError:
raise IOError,\
"Can't open parameters file (%s). Does it exist? Do you have read access?"\
% opts.parameter_fp
params = parse_qiime_parameters(parameter_f)
else:
params = parse_qiime_parameters([])
# empty list returns empty defaultdict for now
jobs_to_start = opts.jobs_to_start
default_jobs_to_start = qiime_config['jobs_to_start']
validate_and_set_jobs_to_start(params, jobs_to_start,
default_jobs_to_start, parallel,
option_parser)
try:
makedirs(output_dir)
except OSError:
if opts.force:
pass
else:
print "Output directory already exists. Please choose "+\
"a different directory, or force overwrite with -f."
exit(1)
if print_only:
command_handler = print_commands
else:
command_handler = call_commands_serially
if verbose:
status_update_callback = print_to_stdout
else:
status_update_callback = no_status_updates
if len(input_fps) == 1:
pick_subsampled_open_referenence_otus(
input_fp=input_fps[0],
refseqs_fp=refseqs_fp,
output_dir=output_dir,
percent_subsample=percent_subsample,
new_ref_set_id=new_ref_set_id,
command_handler=command_handler,
params=params,
min_otu_size=opts.min_otu_size,
qiime_config=qiime_config,
prefilter_refseqs_fp=prefilter_refseqs_fp,
prefilter_percent_id=prefilter_percent_id,
step1_otu_map_fp=opts.step1_otu_map_fp,
step1_failures_fasta_fp=opts.step1_failures_fasta_fp,
parallel=parallel,
suppress_step4=opts.suppress_step4,
logger=None,
status_update_callback=status_update_callback)
else:
iterative_pick_subsampled_open_referenence_otus(
input_fps=input_fps,
refseqs_fp=refseqs_fp,
output_dir=output_dir,
percent_subsample=percent_subsample,
new_ref_set_id=new_ref_set_id,
command_handler=command_handler,
params=params,
min_otu_size=opts.min_otu_size,
qiime_config=qiime_config,
prefilter_refseqs_fp=prefilter_refseqs_fp,
prefilter_percent_id=prefilter_percent_id,
step1_otu_map_fp=opts.step1_otu_map_fp,
step1_failures_fasta_fp=opts.step1_failures_fasta_fp,
parallel=parallel,
suppress_step4=opts.suppress_step4,
logger=None,
status_update_callback=status_update_callback)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
verbose = opts.verbose
input_fp = opts.input_fp
output_dir = opts.output_dir
verbose = opts.verbose
print_only = opts.print_only
parallel = opts.parallel
# No longer checking that jobs_to_start > 2, but
# commenting as we may change our minds about this.
#if parallel: raise_error_on_parallel_unavailable()
if opts.parameter_fp:
try:
parameter_f = open(opts.parameter_fp)
except IOError:
raise IOError,\
"Can't open parameters file (%s). Does it exist? Do you have read access?"\
% opts.parameter_fp
params = parse_qiime_parameters(parameter_f)
else:
params = parse_qiime_parameters([])
# empty list returns empty defaultdict for now
jobs_to_start = opts.jobs_to_start
default_jobs_to_start = qiime_config['jobs_to_start']
validate_and_set_jobs_to_start(params, jobs_to_start,
default_jobs_to_start, parallel,
option_parser)
try:
makedirs(output_dir)
except OSError:
if opts.force:
pass
else:
# Since the analysis can take quite a while, I put this check
# in to help users avoid overwriting previous output.
print "Output directory already exists. Please choose "+\
"a different directory, or force overwrite with -f."
exit(1)
if print_only:
command_handler = print_commands
else:
command_handler = call_commands_serially
if verbose:
status_update_callback = print_to_stdout
else:
status_update_callback = no_status_updates
run_qiime_data_preparation(
input_fp,
output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
parallel=parallel,\
status_update_callback=status_update_callback)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
if opts.output_dir is None:
opts.output_dir = opts.output_filepath + '_dir'
if opts.parameter_fp:
try:
parameter_f = open(opts.parameter_fp, 'U')
except IOError:
raise IOError("Can't open parameters file (%s). Does it exist? Do you have read access?"
% opts.parameter_fp)
params = parse_qiime_parameters(parameter_f)
parameter_f.close()
else:
params = parse_qiime_parameters([])
# empty list returns empty defaultdict for now
try:
makedirs(opts.output_dir)
except OSError:
if opts.force:
pass
else:
# Since the analysis can take quite a while, I put this check
# in to help users avoid overwriting previous output.
option_parser.error("Output directory already exists. Please choose"
" a different directory, or force overwrite with -f.")
if opts.print_only:
command_handler = print_commands
else:
command_handler = call_commands_serially
if opts.verbose:
status_update_callback = print_to_stdout
else:
status_update_callback = no_status_updates
# set env variable
if opts.platform == 'flx':
existing_pyro_fp = os.environ['PYRO_LOOKUP_FILE']
new_pyro_fp = os.path.join(
os.path.split(existing_pyro_fp)[0],
'LookUp_E123.dat')
os.environ['PYRO_LOOKUP_FILE'] = new_pyro_fp
elif opts.platform == 'titanium':
existing_pyro_fp = os.environ['PYRO_LOOKUP_FILE']
new_pyro_fp = os.path.join(
os.path.split(existing_pyro_fp)[0],
'LookUp_Titanium.dat')
os.environ['PYRO_LOOKUP_FILE'] = new_pyro_fp
else:
raise RuntimeError(
'could not find PYRO_LOOKUP_FILE for platform ' +
platform)
if opts.truncate_len:
try:
truncate_len_int_check = int(opts.truncate_len)
truncate_len = str(truncate_len_int_check)
except ValueError:
raise ValueError(("If specified, truncate_len must be int type."))
else:
truncate_len = None
run_ampliconnoise(
mapping_fp=opts.mapping_fp,
output_dir=os.path.abspath(opts.output_dir),
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
status_update_callback=status_update_callback,
chimera_alpha=opts.chimera_alpha,
chimera_beta=opts.chimera_beta,
sff_txt_fp=opts.sff_filepath,
numnodes=opts.np,
suppress_perseus=opts.suppress_perseus,
output_filepath=os.path.abspath(opts.output_filepath),
platform=opts.platform,
seqnoise_resolution=opts.seqnoise_resolution,
truncate_len=truncate_len
)
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
verbose = opts.verbose
input_fps = opts.input_fps
refseqs_fp = opts.reference_fp
output_dir = opts.output_dir
verbose = opts.verbose
print_only = False
percent_subsample = opts.percent_subsample
new_ref_set_id = opts.new_ref_set_id
prefilter_refseqs_fp = opts.prefilter_refseqs_fp
prefilter_percent_id = opts.prefilter_percent_id
if prefilter_percent_id == 0.0:
prefilter_percent_id = None
parallel = opts.parallel
# No longer checking that jobs_to_start > 2, but
# commenting as we may change our minds about this.
#if parallel: raise_error_on_parallel_unavailable()
if opts.parameter_fp:
try:
parameter_f = open(opts.parameter_fp, 'U')
except IOError:
raise IOError,\
"Can't open parameters file (%s). Does it exist? Do you have read access?"\
% opts.parameter_fp
params = parse_qiime_parameters(parameter_f)
parameter_f.close()
else:
params = parse_qiime_parameters([])
# empty list returns empty defaultdict for now
jobs_to_start = opts.jobs_to_start
default_jobs_to_start = qiime_config['jobs_to_start']
validate_and_set_jobs_to_start(params,
jobs_to_start,
default_jobs_to_start,
parallel,
option_parser)
try:
makedirs(output_dir)
except OSError:
if opts.force:
pass
else:
option_parser.error("Output directory already exists. Please choose"
" a different directory, or force overwrite with -f.")
if print_only:
command_handler = print_commands
else:
command_handler = call_commands_serially
if verbose:
status_update_callback = print_to_stdout
else:
status_update_callback = no_status_updates
if len(input_fps) == 1:
pick_subsampled_open_reference_otus(input_fp=input_fps[0],
refseqs_fp=refseqs_fp,
output_dir=output_dir,
percent_subsample=percent_subsample,
new_ref_set_id=new_ref_set_id,
command_handler=command_handler,
params=params,
min_otu_size=opts.min_otu_size,
run_assign_tax=not opts.suppress_taxonomy_assignment,
run_align_and_tree=not opts.suppress_align_and_tree,
qiime_config=qiime_config,
prefilter_refseqs_fp=prefilter_refseqs_fp,
prefilter_percent_id=prefilter_percent_id,
step1_otu_map_fp=opts.step1_otu_map_fp,
step1_failures_fasta_fp=opts.step1_failures_fasta_fp,
parallel=parallel,
suppress_step4=opts.suppress_step4,
logger=None,
status_update_callback=status_update_callback)
else:
iterative_pick_subsampled_open_reference_otus(input_fps=input_fps,
refseqs_fp=refseqs_fp,
output_dir=output_dir,
percent_subsample=percent_subsample,
new_ref_set_id=new_ref_set_id,
command_handler=command_handler,
params=params,
min_otu_size=opts.min_otu_size,
run_assign_tax=not opts.suppress_taxonomy_assignment,
run_align_and_tree=not opts.suppress_align_and_tree,
qiime_config=qiime_config,
prefilter_refseqs_fp=prefilter_refseqs_fp,
prefilter_percent_id=prefilter_percent_id,
step1_otu_map_fp=opts.step1_otu_map_fp,
step1_failures_fasta_fp=opts.step1_failures_fasta_fp,
parallel=parallel,
suppress_step4=opts.suppress_step4,
logger=None,
status_update_callback=status_update_callback)
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
study_id = opts.study_id
for i in range(1,25):
output_dir='/home/wwwdevuser/user_data/studies/study_%s/processed_data_%s/' % (study_id,i)
if not exists(output_dir):
break
else:
continue
print output_dir
sff_fname=opts.sff_fname
map_fname = opts.map_fname
verbose = opts.verbose
print_only = opts.print_only
parallel = opts.parallel
denoise=opts.denoise
if parallel:
raise_error_on_parallel_unavailable()
try:
parameter_f = open(opts.parameter_fp)
except IOError:
raise IOError,\
"Can't open parameters file (%s). Does it exist? Do you have read access?"\
% opts.parameter_fp
try:
makedirs(output_dir)
except OSError:
if opts.force:
pass
else:
# Since the analysis can take quite a while, I put this check
# in to help users avoid overwriting previous output.
print "Output directory already exists. Please choose "+\
"a different directory, or force overwrite with -f."
exit(1)
if print_only:
command_handler = print_commands
else:
command_handler = web_app_call_commands_serially
if verbose:
status_update_callback = print_to_stdout
else:
status_update_callback = no_status_updates
run_process_sff_through_pick_otus(sff_input_fp=sff_fname,\
mapping_fp=map_fname,\
output_dir=output_dir,\
denoise=denoise,\
command_handler=command_handler,\
params=parse_qiime_parameters(parameter_f),\
qiime_config=qiime_config,\
parallel=parallel,\
status_update_callback=status_update_callback)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
verbose = opts.verbose
input_fps = opts.input_fps
refseqs_fp = opts.reference_fp
output_dir = opts.output_dir
otu_picking_method = opts.otu_picking_method
verbose = opts.verbose
print_only = False
percent_subsample = opts.percent_subsample
new_ref_set_id = opts.new_ref_set_id
prefilter_refseqs_fp = opts.prefilter_refseqs_fp
prefilter_percent_id = opts.prefilter_percent_id
if prefilter_percent_id == 0.0:
prefilter_percent_id = None
if otu_picking_method == 'uclust':
denovo_otu_picking_method = 'uclust'
reference_otu_picking_method = 'uclust_ref'
elif otu_picking_method == 'usearch61':
denovo_otu_picking_method = 'usearch61'
reference_otu_picking_method = 'usearch61_ref'
elif otu_picking_method == 'sortmerna_sumaclust':
denovo_otu_picking_method = 'sumaclust'
reference_otu_picking_method = 'sortmerna'
# SortMeRNA uses the E-value to filter out erroneous
# sequences, this option does not apply for this
# tool
if prefilter_percent_id > 0.0:
prefilter_percent_id = None
else:
# it shouldn't be possible to get here
option_parser.error('Unkown OTU picking method: %s' %
otu_picking_method)
parallel = opts.parallel
# No longer checking that jobs_to_start > 2, but
# commenting as we may change our minds about this.
#if parallel: raise_error_on_parallel_unavailable()
if opts.parameter_fp:
try:
parameter_f = open(opts.parameter_fp, 'U')
except IOError:
raise IOError("Can't open parameters file (%s). Does it exist? "
"Do you have read access?" % opts.parameter_fp)
params = parse_qiime_parameters(parameter_f)
parameter_f.close()
else:
params = parse_qiime_parameters([])
# empty list returns empty defaultdict for now
jobs_to_start = opts.jobs_to_start
default_jobs_to_start = qiime_config['jobs_to_start']
validate_and_set_jobs_to_start(params, jobs_to_start,
default_jobs_to_start, parallel, option_parser)
try:
makedirs(output_dir)
except OSError:
if opts.force:
pass
else:
option_parser.error("Output directory already exists. Please "
"choose a different directory, or force overwrite with -f.")
if print_only:
command_handler = print_commands
else:
command_handler = call_commands_serially
if verbose:
status_update_callback = print_to_stdout
else:
status_update_callback = no_status_updates
if len(input_fps) == 1:
pick_subsampled_open_reference_otus(input_fp=input_fps[0],
refseqs_fp=refseqs_fp, output_dir=output_dir,
percent_subsample=percent_subsample, new_ref_set_id=new_ref_set_id,
command_handler=command_handler, params=params,
min_otu_size=opts.min_otu_size,
run_assign_tax=not opts.suppress_taxonomy_assignment,
run_align_and_tree=not opts.suppress_align_and_tree,
qiime_config=qiime_config,
prefilter_refseqs_fp=prefilter_refseqs_fp,
prefilter_percent_id=prefilter_percent_id,
step1_otu_map_fp=opts.step1_otu_map_fp,
step1_failures_fasta_fp=opts.step1_failures_fasta_fp,
parallel=parallel, suppress_step4=opts.suppress_step4, logger=None,
denovo_otu_picking_method=denovo_otu_picking_method,
reference_otu_picking_method=reference_otu_picking_method,
status_update_callback=status_update_callback)
else:
iterative_pick_subsampled_open_reference_otus(input_fps=input_fps,
refseqs_fp=refseqs_fp, output_dir=output_dir,
percent_subsample=percent_subsample, new_ref_set_id=new_ref_set_id,
command_handler=command_handler, params=params,
min_otu_size=opts.min_otu_size,
run_assign_tax=not opts.suppress_taxonomy_assignment,
run_align_and_tree=not opts.suppress_align_and_tree,
qiime_config=qiime_config,
prefilter_refseqs_fp=prefilter_refseqs_fp,
prefilter_percent_id=prefilter_percent_id,
step1_otu_map_fp=opts.step1_otu_map_fp,
step1_failures_fasta_fp=opts.step1_failures_fasta_fp,
parallel=parallel, suppress_step4=opts.suppress_step4, logger=None,
denovo_otu_picking_method=denovo_otu_picking_method,
reference_otu_picking_method=reference_otu_picking_method,
status_update_callback=status_update_callback)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
verbose = opts.verbose
input_fp = opts.input_fp
reference_fp = opts.reference_fp
taxonomy_fp = opts.taxonomy_fp
output_dir = opts.output_dir
verbose = opts.verbose
print_only = opts.print_only
assign_taxonomy = opts.assign_taxonomy
parallel = opts.parallel
# No longer checking that jobs_to_start > 2, but
# commenting as we may change our minds about this.
# if parallel: raise_error_on_parallel_unavailable()
if opts.parameter_fp:
try:
parameter_f = open(opts.parameter_fp, 'U')
except IOError:
raise IOError(
"Can't open parameters file (%s). Does it exist? Do you have read access?"
% opts.parameter_fp)
params = parse_qiime_parameters(parameter_f)
parameter_f.close()
else:
params = parse_qiime_parameters([])
# empty list returns empty defaultdict for now
jobs_to_start = opts.jobs_to_start
default_jobs_to_start = qiime_config['jobs_to_start']
validate_and_set_jobs_to_start(params, jobs_to_start,
default_jobs_to_start, parallel,
option_parser)
if print_only:
command_handler = print_commands
else:
command_handler = call_commands_serially
try:
makedirs(output_dir)
except OSError:
if opts.force:
pass
else:
option_parser.error(
"Output directory already exists. Please choose"
" a different directory, or force overwrite with -f.")
if verbose:
status_update_callback = print_to_stdout
else:
status_update_callback = no_status_updates
run_pick_closed_reference_otus(
input_fp,
reference_fp,
output_dir,
taxonomy_fp,
assign_taxonomy=assign_taxonomy,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
parallel=parallel,
status_update_callback=status_update_callback)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
if opts.output_dir is None:
opts.output_dir = opts.output_filepath + '_dir'
if opts.parameter_fp:
try:
parameter_f = open(opts.parameter_fp, 'U')
except IOError:
raise IOError(
"Can't open parameters file (%s). Does it exist? Do you have read access?"
% opts.parameter_fp)
params = parse_qiime_parameters(parameter_f)
parameter_f.close()
else:
params = parse_qiime_parameters([])
# empty list returns empty defaultdict for now
try:
makedirs(opts.output_dir)
except OSError:
if opts.force:
pass
else:
# Since the analysis can take quite a while, I put this check
# in to help users avoid overwriting previous output.
option_parser.error(
"Output directory already exists. Please choose"
" a different directory, or force overwrite with -f.")
if opts.print_only:
command_handler = print_commands
else:
command_handler = call_commands_serially
if opts.verbose:
status_update_callback = print_to_stdout
else:
status_update_callback = no_status_updates
# set env variable
if opts.platform == 'flx':
existing_pyro_fp = os.environ['PYRO_LOOKUP_FILE']
new_pyro_fp = os.path.join(
os.path.split(existing_pyro_fp)[0], 'LookUp_E123.dat')
os.environ['PYRO_LOOKUP_FILE'] = new_pyro_fp
elif opts.platform == 'titanium':
existing_pyro_fp = os.environ['PYRO_LOOKUP_FILE']
new_pyro_fp = os.path.join(
os.path.split(existing_pyro_fp)[0], 'LookUp_Titanium.dat')
os.environ['PYRO_LOOKUP_FILE'] = new_pyro_fp
else:
raise RuntimeError('could not find PYRO_LOOKUP_FILE for platform ' +
platform)
if opts.truncate_len:
try:
truncate_len_int_check = int(opts.truncate_len)
truncate_len = str(truncate_len_int_check)
except ValueError:
raise ValueError(("If specified, truncate_len must be int type."))
else:
truncate_len = None
run_ampliconnoise(mapping_fp=opts.mapping_fp,
output_dir=os.path.abspath(opts.output_dir),
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
status_update_callback=status_update_callback,
chimera_alpha=opts.chimera_alpha,
chimera_beta=opts.chimera_beta,
sff_txt_fp=opts.sff_filepath,
numnodes=opts.np,
suppress_perseus=opts.suppress_perseus,
output_filepath=os.path.abspath(opts.output_filepath),
platform=opts.platform,
seqnoise_resolution=opts.seqnoise_resolution,
truncate_len=truncate_len)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
verbose = opts.verbose
input_fnas = opts.input_fnas
input_quals = opts.input_quals
output_dir = opts.output_dir
sampling_depth = opts.seqs_per_sample
categories = opts.categories
reference_tree_fp = opts.reference_tree_fp
mapping_fp = opts.mapping_fp
verbose = opts.verbose
print_only = False # This feature is not currently supported
suppress_split_libraries = opts.suppress_split_libraries
even_sampling_keeps_all_samples = opts.even_sampling_keeps_all_samples
parallel = opts.parallel
if suppress_split_libraries and len(input_fnas) > 1:
option_parser.error("Only a single fasta file can be passed with "+\
"--suppress_split_libraries")
if opts.parameter_fp != None:
try:
parameter_f = open(opts.parameter_fp)
except IOError:
raise IOError,\
"Can't open parameters file (%s). Does it exist? Do you have read access?"\
% opts.parameter_fp
params = parse_qiime_parameters(parameter_f)
else:
params = parse_qiime_parameters([])
jobs_to_start = opts.jobs_to_start
default_jobs_to_start = qiime_config['jobs_to_start']
validate_and_set_jobs_to_start(params, jobs_to_start,
default_jobs_to_start, parallel,
option_parser)
try:
makedirs(output_dir)
except OSError:
if opts.force:
pass
else:
# Since the analysis can take quite a while, I put this check
# in to help users avoid overwriting previous output.
print "Output directory already exists. Please choose "+\
"a different directory, or force overwrite with -f."
exit(1)
if print_only:
command_handler = print_commands
else:
command_handler = call_commands_serially
if verbose:
status_update_callback = print_to_stdout
else:
status_update_callback = no_status_updates
input_fnas_string = input_fnas[0]
for inp_fna in input_fnas[1:]:
input_fnas_string = input_fnas_string + ',' + inp_fna
input_quals_string = None
if input_quals:
input_quals_string = input_quals[0]
for inp_qual in input_quals[1:]:
input_quals_string = input_quals_string + ',' + inp_qual
run_core_qiime_analyses(
fna_fps=input_fnas_string,
qual_fps=input_quals_string,
mapping_fp=mapping_fp,
output_dir=output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
categories=categories,
sampling_depth=sampling_depth,
suppress_split_libraries=suppress_split_libraries,
even_sampling_keeps_all_samples=even_sampling_keeps_all_samples,
arare_min_rare_depth=10,
arare_num_steps=10,
reference_tree_fp=reference_tree_fp,
parallel=parallel,
status_update_callback=status_update_callback)
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
otu_table_fp = opts.otu_table_fp
output_dir = opts.output_dir
mapping_fp = opts.mapping_fp
tree_fp = opts.tree_fp
verbose = opts.verbose
print_only = opts.print_only
seqs_per_sample = int(opts.seqs_per_sample)
parallel = opts.parallel
min_seqs_sample = opts.min_seqs_sample
subject_category = opts.subject_name
try:
makedirs(output_dir)
except OSError:
if opts.force:
pass
else:
# Since the analysis can take quite a while, I put this check
# in to help users avoid overwriting previous output.
option_parser.error("Output directory already exists. Please choose"
" a different directory, or force overwrite with -f.")
## ******************** make_evident_selectors ********************
## The code for make_evident_selectors.py is here and has to go before the params
## validation as we need to know the main cats before creating the params file
map_data, headers, comments = parse_mapping_file(open(mapping_fp, 'U'))
biom_table = parse_biom_table(open(otu_table_fp, 'U'))
# getting valid samples from biom file
real_map_headers, real_map_data = filter_mapping_file(map_data, headers,\
biom_table.SampleIds, include_repeat_cols=False)
if subject_category not in real_map_headers:
option_parser.error('This column: %s is not in the mapping file, try %s'%\
(subject_category, real_map_headers))
sorted_counts_per_sample = get_sorted_counts_per_sample(biom_table)
mapping_file_tuple = (real_map_data, real_map_headers)
# calculate the available subjects at each rarefaction level
results, main_map_cat = make_selectors(sorted_counts_per_sample, min_seqs_sample,\
mapping_file_tuple, subject_category, verbose=verbose)
fout = open(join(output_dir,'selectors.txt'),'w')
fout.write('#Sequences\tSubjects\tSamples\tMetadata\n')
fout.write('\n'.join(results))
fout.close()
fout = open(join(output_dir,'mapping_file.txt'),'w')
fout.write(format_mapping_file(real_map_headers, real_map_data))
fout.close()
## ******************** make_evident_selectors ********************
fout = open(join(output_dir,'study_preferences.txt'),'w')
fout.write('%d\n' % seqs_per_sample)
fout.write('%s\n' % subject_category)
fout.close()
## ******************** filter_samples_from_otu_table ********************
## Filtering original biom file to only have samples above the max length to avoid
## ugly plots
alpha_biom_file = join(output_dir,'filtered_otu_table_for_alpha.biom')
fout = open(alpha_biom_file,'w')
sample_ids_to_keep = biom_table.SampleIds
filtered_otu_table = filter_samples_from_otu_table(biom_table,
sample_ids_to_keep,
min_count=seqs_per_sample,
max_count=inf)
fout.write(format_biom_table(filtered_otu_table))
fout.close()
## ******************** filter_samples_from_otu_table ********************
if opts.parameter_fp:
try:
parameter_f = open(opts.parameter_fp, 'U')
except IOError:
option_parser.error("Can't open parameters file (%s). Does it exist? " \
"Do you have read access?" % opts.parameter_fp)
params = parse_qiime_parameters(parameter_f)
parameter_f.close()
else:
params = parse_qiime_parameters(
['beta_diversity:metrics unweighted_unifrac',\
'make_rarefaction_plots:prefs_path %s' % join(output_dir,'prefs.txt'),
'make_rarefaction_plots:colorby %s' % ','.join(main_map_cat),
'make_rarefaction_plots:output_type memory',
'multiple_rarefactions:min %d' % int(seqs_per_sample/4),
'multiple_rarefactions:max %d' % (seqs_per_sample+1),
'multiple_rarefactions:step %d' % int(seqs_per_sample/4),
'multiple_rarefactions:num-reps 4',
])
# empty list returns empty defaultdict for now
jobs_to_start = opts.jobs_to_start
default_jobs_to_start = qiime_config['jobs_to_start']
validate_and_set_jobs_to_start(params,
jobs_to_start,
default_jobs_to_start,
parallel,
option_parser)
if print_only:
command_handler = print_commands
else:
command_handler = call_commands_serially
if verbose:
status_update_callback = print_to_stdout
else:
status_update_callback = no_status_updates
copyfile(otu_table_fp, join(output_dir,'raw.biom'))
run_beta_diversity_through_plots(otu_table_fp=otu_table_fp,
mapping_fp=mapping_fp,
output_dir=output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
color_by_interesting_fields_only=False,
sampling_depth=seqs_per_sample,
histogram_categories=None,
tree_fp=tree_fp,
parallel=parallel,
suppress_3d_plots=True,
suppress_2d_plots=True,
status_update_callback=status_update_callback)
output_dir = join(output_dir,'alpha')
run_alpha_rarefaction(otu_table_fp=alpha_biom_file,\
mapping_fp=mapping_fp,\
output_dir=output_dir,\
command_handler=command_handler,\
params=params,
qiime_config=qiime_config,\
tree_fp=tree_fp,\
num_steps=4,\
parallel=parallel,\
min_rare_depth=10,
max_rare_depth=20,
status_update_callback=status_update_callback,
plot_stderr_and_stddev=True)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
verbose = opts.verbose
otu_table_fp = opts.otu_table_fp
output_dir = opts.output_dir
mapping_fp = opts.mapping_fp
tree_fp = opts.tree_fp
num_steps = opts.num_steps
verbose = opts.verbose
print_only = opts.print_only
parallel = opts.parallel
min_rare_depth = opts.min_rare_depth
max_rare_depth = opts.max_rare_depth
if opts.parameter_fp:
try:
parameter_f = open(opts.parameter_fp)
except IOError:
raise IOError,\
"Can't open parameters file (%s). Does it exist? Do you have read access?"\
% opts.parameter_fp
params = parse_qiime_parameters(parameter_f)
else:
params = parse_qiime_parameters([])
# empty list returns empty defaultdict for now
jobs_to_start = opts.jobs_to_start
default_jobs_to_start = qiime_config['jobs_to_start']
validate_and_set_jobs_to_start(params,
jobs_to_start,
default_jobs_to_start,
parallel,
option_parser)
try:
makedirs(output_dir)
except OSError:
if opts.force:
pass
else:
# Since the analysis can take quite a while, I put this check
# in to help users avoid overwriting previous output.
option_parser.error("Output directory already exists. Please choose"
" a different directory, or force overwrite with -f.")
if print_only:
command_handler = print_commands
else:
command_handler = call_commands_serially
if verbose:
status_update_callback = print_to_stdout
else:
status_update_callback = no_status_updates
run_qiime_alpha_rarefaction(otu_table_fp=otu_table_fp,\
mapping_fp=mapping_fp,\
output_dir=output_dir,\
command_handler=command_handler,\
params=params,
qiime_config=qiime_config,\
tree_fp=tree_fp,\
num_steps=num_steps,\
parallel=parallel,\
min_rare_depth=min_rare_depth,
max_rare_depth=max_rare_depth,
status_update_callback=status_update_callback)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
verbose = opts.verbose
otu_table_fp = opts.otu_table_fp
output_dir = opts.output_dir
mapping_fp = opts.mapping_fp
tree_fp = opts.tree_fp
verbose = opts.verbose
print_only = opts.print_only
seqs_per_sample = opts.seqs_per_sample
parallel = opts.parallel
# No longer checking that jobs_to_start > 2, but
# commenting as we may change our minds about this.
#if parallel: raise_error_on_parallel_unavailable()
if opts.parameter_fp:
try:
parameter_f = open(opts.parameter_fp, 'U')
except IOError:
raise IOError,\
"Can't open parameters file (%s). Does it exist? Do you have read access?"\
% opts.parameter_fp
params = parse_qiime_parameters(parameter_f)
parameter_f.close()
else:
params = parse_qiime_parameters([])
# empty list returns empty defaultdict for now
jobs_to_start = opts.jobs_to_start
default_jobs_to_start = qiime_config['jobs_to_start']
validate_and_set_jobs_to_start(params,
jobs_to_start,
default_jobs_to_start,
parallel,
option_parser)
try:
makedirs(output_dir)
except OSError:
if opts.force:
pass
else:
# Since the analysis can take quite a while, I put this check
# in to help users avoid overwriting previous output.
option_parser.error("Output directory already exists. Please choose"
" a different directory, or force overwrite with -f.")
if print_only:
command_handler = print_commands
else:
command_handler = call_commands_serially
if verbose:
status_update_callback = print_to_stdout
else:
status_update_callback = no_status_updates
run_beta_diversity_through_plots(otu_table_fp=otu_table_fp,
mapping_fp=mapping_fp,
output_dir=output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
color_by_interesting_fields_only=not opts.color_by_all_fields,
sampling_depth=seqs_per_sample,
tree_fp=tree_fp,
parallel=parallel,
suppress_emperor_plots=opts.suppress_emperor_plots,
status_update_callback=status_update_callback)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
verbose = opts.verbose
input_fp = opts.input_fp
reference_fp = opts.reference_fp
taxonomy_fp = opts.taxonomy_fp
output_dir = opts.output_dir
verbose = opts.verbose
print_only = opts.print_only
parallel = opts.parallel
# No longer checking that jobs_to_start > 2, but
# commenting as we may change our minds about this.
#if parallel: raise_error_on_parallel_unavailable()
if opts.parameter_fp:
try:
parameter_f = open(opts.parameter_fp, 'U')
except IOError:
raise IOError,\
"Can't open parameters file (%s). Does it exist? Do you have read access?"\
% opts.parameter_fp
params = parse_qiime_parameters(parameter_f)
parameter_f.close()
else:
params = parse_qiime_parameters([])
# empty list returns empty defaultdict for now
jobs_to_start = opts.jobs_to_start
default_jobs_to_start = qiime_config['jobs_to_start']
validate_and_set_jobs_to_start(params,
jobs_to_start,
default_jobs_to_start,
parallel,
option_parser)
try:
makedirs(output_dir)
except OSError:
if opts.force:
pass
else:
option_parser.error("Output directory already exists. Please choose"
" a different directory, or force overwrite with -f.")
if print_only:
command_handler = print_commands
else:
command_handler = call_commands_serially
if verbose:
status_update_callback = print_to_stdout
else:
status_update_callback = no_status_updates
run_pick_closed_reference_otus(
input_fp,
reference_fp,
output_dir,
taxonomy_fp,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
parallel=parallel,
status_update_callback=status_update_callback)
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
parameters = {}
# get the tree insertion method to use
module = opts.insertion_method
# create output directory
output_dir = opts.output_dir
create_dir(output_dir)
# list of tree insertion methods
tree_insertion_module_names = \
{'raxml_v730': brokit.raxml_v730,
'parsinsert': brokit.parsinsert,
'pplacer': brokit.pplacer}
# load input sequences and convert to phylip since the tools require
# the query sequences to phylip-compliant names
load_aln = parse_fasta(open(opts.input_fasta_fp, 'U'))
aln = DenseAlignment(load_aln)
seqs, align_map = aln.toPhylip()
if opts.method_params_fp:
param_dict = parse_qiime_parameters(open(opts.method_params_fp, 'U'))
if module == 'raxml_v730':
# load the reference sequences
load_ref_aln = \
DenseAlignment(parse_fasta(open(opts.refseq_fp, 'U')))
# combine and load the reference plus query
combined_aln = parse_fasta(StringIO(load_ref_aln.toFasta() +
'\n' + aln.toFasta()))
# overwrite the alignment map
aln = DenseAlignment(combined_aln)
seqs, align_map = aln.toPhylip()
try:
parameters = param_dict['raxml']
except:
parameters = {}
tree = convert_tree_tips(align_map, opts.starting_tree_fp)
# write out the tree with phylip labels
updated_tree_fp = join(output_dir,
'%s_phylip_named_tree.tre' % (module))
write_updated_tree_file(updated_tree_fp, tree)
# set the primary parameters for raxml
parameters['-w'] = abspath(output_dir) + '/'
parameters["-n"] = split(splitext(get_tmp_filename())[0])[-1]
parameters["-t"] = updated_tree_fp
if "-f" not in parameters:
parameters["-f"] = 'v'
if "-m" not in parameters:
parameters["-m"] = 'GTRGAMMA'
elif module == 'pplacer':
try:
parameters = param_dict['pplacer']
except:
parameters = {}
# make sure stats file is passed
if not opts.stats_fp:
raise IOError(
'When using pplacer, the RAxML produced info file is required.')
# set the primary parameters for pplacer - allow for user-defined
parameters['--out-dir'] = abspath(output_dir) + '/'
parameters["-t"] = opts.starting_tree_fp
parameters['-r'] = opts.refseq_fp
parameters['-s'] = opts.stats_fp
elif module == 'parsinsert':
try:
parameters = param_dict['parsinsert']
except:
parameters = {}
# define log fp
log_fp = join(output_dir, 'parsinsert.log')
# define tax assignment values fp
tax_assign_fp = join(output_dir, 'parsinsert_assignments.log')
parameters["-l"] = log_fp
parameters["-o"] = tax_assign_fp
parameters["-s"] = opts.refseq_fp
parameters["-t"] = opts.starting_tree_fp
# call the module and return a tree object
result = \
tree_insertion_module_names[module].insert_sequences_into_tree(seqs,
moltype=DNA, params=parameters)
result_tree = strip_and_rename_unwanted_labels_from_tree(align_map, result)
# write out the resulting tree
final_tree = join(output_dir, '%s_final_placement.tre' % (module))
write_updated_tree_file(final_tree, result)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
verbose = opts.verbose
otu_table_fp = opts.otu_table_fp
output_dir = opts.output_dir
mapping_fp = opts.mapping_fp
tree_fp = opts.tree_fp
num_steps = opts.num_steps
verbose = opts.verbose
print_only = opts.print_only
parallel = opts.parallel
min_rare_depth = opts.min_rare_depth
max_rare_depth = opts.max_rare_depth
retain_intermediate_files = opts.retain_intermediate_files
if opts.parameter_fp:
try:
parameter_f = open(opts.parameter_fp, 'U')
except IOError:
raise IOError,\
"Can't open parameters file (%s). Does it exist? Do you have read access?"\
% opts.parameter_fp
params = parse_qiime_parameters(parameter_f)
parameter_f.close()
else:
params = parse_qiime_parameters([])
# empty list returns empty defaultdict for now
jobs_to_start = opts.jobs_to_start
default_jobs_to_start = qiime_config['jobs_to_start']
validate_and_set_jobs_to_start(params,
jobs_to_start,
default_jobs_to_start,
parallel,
option_parser)
try:
makedirs(output_dir)
except OSError:
if opts.force:
pass
else:
# Since the analysis can take quite a while, I put this check
# in to help users avoid overwriting previous output.
option_parser.error("Output directory already exists. Please choose"
" a different directory, or force overwrite with -f.")
if print_only:
command_handler = print_commands
else:
command_handler = call_commands_serially
if verbose:
status_update_callback = print_to_stdout
else:
status_update_callback = no_status_updates
run_alpha_rarefaction(otu_table_fp=otu_table_fp,\
mapping_fp=mapping_fp,\
output_dir=output_dir,\
command_handler=command_handler,\
params=params,
qiime_config=qiime_config,\
tree_fp=tree_fp,\
num_steps=num_steps,\
parallel=parallel,\
min_rare_depth=min_rare_depth,
max_rare_depth=max_rare_depth,
status_update_callback=status_update_callback,
retain_intermediate_files=retain_intermediate_files)
def main():
option_parser, opts, args = \
parse_command_line_parameters(**script_info)
verbose = opts.verbose
otu_table_fp = opts.otu_table_fp
output_dir = opts.output_dir
tree_fp = opts.tree_fp
seqs_per_sample = opts.seqs_per_sample
verbose = opts.verbose
print_only = opts.print_only
master_tree = opts.master_tree
parallel = opts.parallel
# No longer checking that jobs_to_start > 2, but
# commenting as we may change our minds about this.
#if parallel: raise_error_on_parallel_unavailable()
if opts.parameter_fp:
try:
parameter_f = open(opts.parameter_fp, 'U')
except IOError:
raise IOError("Can't open parameters file (%s). Does it exist? Do you have read access?"
% opts.parameter_fp)
params = parse_qiime_parameters(parameter_f)
parameter_f.close()
else:
params = parse_qiime_parameters([])
# empty list returns empty defaultdict for now
jobs_to_start = opts.jobs_to_start
default_jobs_to_start = qiime_config['jobs_to_start']
validate_and_set_jobs_to_start(params,
jobs_to_start,
default_jobs_to_start,
parallel,
option_parser)
try:
makedirs(output_dir)
except OSError:
if opts.force:
pass
else:
# Since the analysis can take quite a while, I put this check
# in to help users avoid overwriting previous output.
option_parser.error("Output directory already exists. Please choose"
" a different directory, or force overwrite with -f.")
if print_only:
command_handler = print_commands
else:
command_handler = call_commands_serially
if verbose:
status_update_callback = print_to_stdout
else:
status_update_callback = no_status_updates
run_jackknifed_beta_diversity(otu_table_fp=otu_table_fp,
tree_fp=tree_fp,
seqs_per_sample=seqs_per_sample,
output_dir=output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
mapping_fp=opts.mapping_fp,
parallel=parallel,
status_update_callback=status_update_callback,
master_tree=master_tree)
def run_core_diversity_analyses(biom_fp,
mapping_fp,
sampling_depth,
output_dir,
qiime_config,
command_handler=call_commands_serially,
tree_fp=None,
params=None,
categories=None,
arare_min_rare_depth=10,
arare_num_steps=10,
parallel=False,
suppress_taxa_summary=False,
suppress_beta_diversity=False,
suppress_alpha_diversity=False,
suppress_group_significance=False,
status_update_callback=print_to_stdout):
"""
"""
if categories is not None:
# Validate categories provided by the users
mapping_data, mapping_comments = \
parse_mapping_file_to_dict(open(mapping_fp, 'U'))
metadata_map = MetadataMap(mapping_data, mapping_comments)
for c in categories:
if c not in metadata_map.CategoryNames:
raise ValueError(
"Category '%s' is not a column header "
"in your mapping file. "
"Categories are case and white space sensitive. Valid "
"choices are: (%s)" %
(c, ', '.join(metadata_map.CategoryNames)))
if metadata_map.hasSingleCategoryValue(c):
raise ValueError(
"Category '%s' contains only one value. "
"Categories analyzed here require at least two values." %
c)
else:
categories = []
comma_separated_categories = ','.join(categories)
# prep some variables
if params is None:
params = parse_qiime_parameters([])
create_dir(output_dir)
index_fp = '%s/index.html' % output_dir
index_links = []
commands = []
# begin logging
old_log_fps = glob(join(output_dir, 'log_20*txt'))
log_fp = generate_log_fp(output_dir)
index_links.append(
('Master run log', log_fp, _index_headers['run_summary']))
for old_log_fp in old_log_fps:
index_links.append(
('Previous run log', old_log_fp, _index_headers['run_summary']))
logger = WorkflowLogger(log_fp, params=params, qiime_config=qiime_config)
input_fps = [biom_fp, mapping_fp]
if tree_fp is not None:
input_fps.append(tree_fp)
log_input_md5s(logger, input_fps)
# run 'biom summarize-table' on input BIOM table
try:
params_str = get_params_str(params['biom-summarize-table'])
except KeyError:
params_str = ''
biom_table_stats_output_fp = '%s/biom_table_summary.txt' % output_dir
if not exists(biom_table_stats_output_fp):
biom_table_summary_cmd = \
"biom summarize-table -i %s -o %s %s" % \
(biom_fp, biom_table_stats_output_fp, params_str)
commands.append([('Generate BIOM table summary',
biom_table_summary_cmd)])
else:
logger.write("Skipping 'biom summarize-table' as %s exists.\n\n" %
biom_table_stats_output_fp)
index_links.append(('BIOM table statistics', biom_table_stats_output_fp,
_index_headers['run_summary']))
# filter samples with fewer observations than the requested sampling_depth.
# since these get filtered for some analyses (eg beta diversity after
# even sampling) it's useful to filter them here so they're filtered
# from all analyses.
filtered_biom_fp = "%s/table_mc%d.biom" % (output_dir, sampling_depth)
if not exists(filtered_biom_fp):
filter_samples_cmd = "filter_samples_from_otu_table.py -i %s -o %s -n %d" %\
(biom_fp, filtered_biom_fp, sampling_depth)
commands.append([(
'Filter low sequence count samples from table (minimum sequence count: %d)'
% sampling_depth, filter_samples_cmd)])
else:
logger.write(
"Skipping filter_samples_from_otu_table.py as %s exists.\n\n" %
filtered_biom_fp)
biom_fp = filtered_biom_fp
# rarify the BIOM table to sampling_depth
rarefied_biom_fp = "%s/table_even%d.biom" % (output_dir, sampling_depth)
if not exists(rarefied_biom_fp):
single_rarefaction_cmd = "single_rarefaction.py -i %s -o %s -d %d" %\
(biom_fp, rarefied_biom_fp, sampling_depth)
commands.append([
('Rarify the OTU table to %d sequences/sample' % sampling_depth,
single_rarefaction_cmd)
])
else:
logger.write("Skipping single_rarefaction.py as %s exists.\n\n" %
rarefied_biom_fp)
# run initial commands and reset the command list
if len(commands) > 0:
command_handler(commands,
status_update_callback,
logger,
close_logger_on_success=False)
commands = []
if not suppress_beta_diversity:
bdiv_even_output_dir = '%s/bdiv_even%d/' % (output_dir, sampling_depth)
# Need to check for the existence of any distance matrices, since the user
# can select which will be generated.
existing_dm_fps = glob('%s/*_dm.txt' % bdiv_even_output_dir)
if len(existing_dm_fps) == 0:
even_dm_fps = run_beta_diversity_through_plots(
otu_table_fp=rarefied_biom_fp,
mapping_fp=mapping_fp,
output_dir=bdiv_even_output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
# Note: we pass sampling depth=None here as
# we rarify the BIOM table above and pass that
# in here.
sampling_depth=None,
tree_fp=tree_fp,
parallel=parallel,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback)
else:
logger.write(
"Skipping beta_diversity_through_plots.py as %s exist(s).\n\n"
% ', '.join(existing_dm_fps))
even_dm_fps = [(split(fp)[1].strip('_dm.txt'), fp)
for fp in existing_dm_fps]
# Get make_distance_boxplots parameters
try:
params_str = get_params_str(params['make_distance_boxplots'])
except KeyError:
params_str = ''
for bdiv_metric, dm_fp in even_dm_fps:
for category in categories:
boxplots_output_dir = '%s/%s_boxplots/' % (
bdiv_even_output_dir, bdiv_metric)
plot_output_fp = '%s/%s_Distances.pdf' % (boxplots_output_dir,
category)
stats_output_fp = '%s/%s_Stats.txt' % (boxplots_output_dir,
category)
if not exists(plot_output_fp):
boxplots_cmd = \
'make_distance_boxplots.py -d %s -f %s -o %s -m %s -n 999 %s' %\
(dm_fp, category, boxplots_output_dir,
mapping_fp, params_str)
commands.append([('Boxplots (%s)' % category, boxplots_cmd)
])
else:
logger.write(
"Skipping make_distance_boxplots.py for %s as %s exists.\n\n"
% (category, plot_output_fp))
index_links.append(
('Distance boxplots (%s)' % bdiv_metric, plot_output_fp,
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(
('Distance boxplots statistics (%s)' % bdiv_metric,
stats_output_fp,
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(
('PCoA plot (%s)' % bdiv_metric,
'%s/%s_emperor_pcoa_plot/index.html' %
(bdiv_even_output_dir, bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(
('Distance matrix (%s)' % bdiv_metric,
'%s/%s_dm.txt' % (bdiv_even_output_dir, bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(
('Principal coordinate matrix (%s)' % bdiv_metric,
'%s/%s_pc.txt' % (bdiv_even_output_dir, bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
if not suppress_alpha_diversity:
# Alpha rarefaction workflow
arare_full_output_dir = '%s/arare_max%d/' % (output_dir,
sampling_depth)
rarefaction_plots_output_fp = \
'%s/alpha_rarefaction_plots/rarefaction_plots.html' % arare_full_output_dir
if not exists(rarefaction_plots_output_fp):
run_alpha_rarefaction(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=arare_full_output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
tree_fp=tree_fp,
num_steps=arare_num_steps,
parallel=parallel,
logger=logger,
min_rare_depth=arare_min_rare_depth,
max_rare_depth=sampling_depth,
suppress_md5=True,
status_update_callback=status_update_callback,
retain_intermediate_files=False)
else:
logger.write("Skipping alpha_rarefaction.py as %s exists.\n\n" %
rarefaction_plots_output_fp)
index_links.append(
('Alpha rarefaction plots', rarefaction_plots_output_fp,
_index_headers['alpha_diversity']))
collated_alpha_diversity_fps = \
glob('%s/alpha_div_collated/*txt' % arare_full_output_dir)
try:
params_str = get_params_str(params['compare_alpha_diversity'])
except KeyError:
params_str = ''
if len(categories) > 0:
for collated_alpha_diversity_fp in collated_alpha_diversity_fps:
alpha_metric = splitext(
split(collated_alpha_diversity_fp)[1])[0]
compare_alpha_output_dir = '%s/compare_%s' % \
(arare_full_output_dir, alpha_metric)
if not exists(compare_alpha_output_dir):
compare_alpha_cmd = \
'compare_alpha_diversity.py -i %s -m %s -c %s -o %s -n 999 %s' %\
(collated_alpha_diversity_fp,
mapping_fp,
comma_separated_categories,
compare_alpha_output_dir,
params_str)
commands.append([
('Compare alpha diversity (%s)' % alpha_metric,
compare_alpha_cmd)
])
for category in categories:
alpha_comparison_stat_fp = '%s/%s_stats.txt' % \
(compare_alpha_output_dir, category)
alpha_comparison_boxplot_fp = '%s/%s_boxplots.pdf' % \
(compare_alpha_output_dir, category)
index_links.append(
('Alpha diversity statistics (%s, %s)' %
(category, alpha_metric),
alpha_comparison_stat_fp,
_index_headers['alpha_diversity']))
index_links.append(
('Alpha diversity boxplots (%s, %s)' %
(category, alpha_metric),
alpha_comparison_boxplot_fp,
_index_headers['alpha_diversity']))
else:
logger.write("Skipping compare_alpha_diversity.py"
" for %s as %s exists.\n\n" %
(alpha_metric, compare_alpha_output_dir))
else:
logger.write("Skipping compare_alpha_diversity.py as"
" no categories were provided.\n\n")
if not suppress_taxa_summary:
taxa_plots_output_dir = '%s/taxa_plots/' % output_dir
# need to check for existence of any html files, since the user can
# select only certain ones to be generated
existing_taxa_plot_html_fps = glob(
join(taxa_plots_output_dir, 'taxa_summary_plots', '*.html'))
if len(existing_taxa_plot_html_fps) == 0:
run_summarize_taxa_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=taxa_plots_output_dir,
mapping_cat=None,
sort=True,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback)
else:
logger.write(
"Skipping summarize_taxa_through_plots.py for as %s exist(s).\n\n"
% ', '.join(existing_taxa_plot_html_fps))
index_links.append(
('Taxa summary bar plots',
'%s/taxa_summary_plots/bar_charts.html' % taxa_plots_output_dir,
_index_headers['taxa_summary']))
index_links.append(
('Taxa summary area plots',
'%s/taxa_summary_plots/area_charts.html' % taxa_plots_output_dir,
_index_headers['taxa_summary']))
for category in categories:
taxa_plots_output_dir = '%s/taxa_plots_%s/' % (output_dir,
category)
# need to check for existence of any html files, since the user can
# select only certain ones to be generated
existing_taxa_plot_html_fps = glob('%s/taxa_summary_plots/*.html' %
taxa_plots_output_dir)
if len(existing_taxa_plot_html_fps) == 0:
run_summarize_taxa_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=taxa_plots_output_dir,
mapping_cat=category,
sort=True,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback)
else:
logger.write(
"Skipping summarize_taxa_through_plots.py for %s as %s exist(s).\n\n"
% (category, ', '.join(existing_taxa_plot_html_fps)))
index_links.append(
('Taxa summary bar plots',
'%s/taxa_summary_plots/bar_charts.html' %
taxa_plots_output_dir,
_index_headers['taxa_summary_categorical'] % category))
index_links.append(
('Taxa summary area plots',
'%s/taxa_summary_plots/area_charts.html' %
taxa_plots_output_dir,
_index_headers['taxa_summary_categorical'] % category))
if not suppress_group_significance:
params_str = get_params_str(params['group_significance'])
# group significance tests, aka category significance
for category in categories:
group_signifance_fp = \
'%s/group_significance_%s.txt' % (output_dir, category)
if not exists(group_signifance_fp):
# Build the OTU cateogry significance command
group_significance_cmd = \
'group_significance.py -i %s -m %s -c %s -o %s %s' %\
(rarefied_biom_fp, mapping_fp, category,
group_signifance_fp, params_str)
commands.append([('Group significance (%s)' % category,
group_significance_cmd)])
else:
logger.write(
"Skipping group_significance.py for %s as %s exists.\n\n" %
(category, group_signifance_fp))
index_links.append(
('Category significance (%s)' % category, group_signifance_fp,
_index_headers['group_significance']))
filtered_biom_gzip_fp = '%s.gz' % filtered_biom_fp
if not exists(filtered_biom_gzip_fp):
commands.append([('Compress the filtered BIOM table',
'gzip %s' % filtered_biom_fp)])
else:
logger.write(
"Skipping compressing of filtered BIOM table as %s exists.\n\n" %
filtered_biom_gzip_fp)
index_links.append(
('Filtered BIOM table (minimum sequence count: %d)' % sampling_depth,
filtered_biom_gzip_fp, _index_headers['run_summary']))
rarified_biom_gzip_fp = '%s.gz' % rarefied_biom_fp
if not exists(rarified_biom_gzip_fp):
commands.append([('Compress the rarified BIOM table',
'gzip %s' % rarefied_biom_fp)])
else:
logger.write(
"Skipping compressing of rarified BIOM table as %s exists.\n\n" %
rarified_biom_gzip_fp)
index_links.append(
('Rarified BIOM table (sampling depth: %d)' % sampling_depth,
rarified_biom_gzip_fp, _index_headers['run_summary']))
if len(commands) > 0:
command_handler(commands, status_update_callback, logger)
else:
logger.close()
generate_index_page(index_links, index_fp)
