def get(self, analysis_id):
user = self.current_user
analysis_id = int(analysis_id)
check_analysis_access(User(user), analysis_id)
analysis = Analysis(analysis_id)
jobres = defaultdict(list)
for job in analysis.jobs:
jobject = Job(job)
jobres[jobject.datatype].append(
(jobject.command[0], jobject.results))
dropped = {}
for proc_data_id, samples in viewitems(analysis.dropped_samples):
proc_data = ProcessedData(proc_data_id)
key = "Data type %s, Study: %s" % (proc_data.data_type(),
proc_data.study)
dropped[key] = samples
self.render("analysis_results.html",
user=self.current_user,
jobres=jobres,
aname=analysis.name,
dropped=dropped,
basefolder=get_db_files_base_dir())
# wipe out cached messages for this analysis
r_server = Redis()
key = '%s:messages' % self.current_user
oldmessages = r_server.lrange(key, 0, -1)
if oldmessages is not None:
for message in oldmessages:
if '"analysis": %d' % analysis_id in message:
r_server.lrem(key, message, 1)
def get(self, analysis_id):
user = self.current_user
analysis_id = int(analysis_id)
check_analysis_access(User(user), analysis_id)
analysis = Analysis(analysis_id)
jobres = defaultdict(list)
for job in analysis.jobs:
jobject = Job(job)
jobres[jobject.datatype].append((jobject.command[0],
jobject.results))
dropped = {}
for proc_data_id, samples in viewitems(analysis.dropped_samples):
proc_data = ProcessedData(proc_data_id)
key = "Data type %s, Study: %s" % (proc_data.data_type(),
proc_data.study)
dropped[key] = samples
self.render("analysis_results.html", user=self.current_user,
jobres=jobres, aname=analysis.name, dropped=dropped,
basefolder=get_db_files_base_dir())
# wipe out cached messages for this analysis
r_server = Redis()
key = '%s:messages' % self.current_user
oldmessages = r_server.lrange(key, 0, -1)
if oldmessages is not None:
for message in oldmessages:
if '"analysis": %d' % analysis_id in message:
r_server.lrem(key, message, 1)
def test_status_setter_error(self):
pd = ProcessedData(1)
pd.status = 'public'
self.assertEqual(pd.status, 'public')
with self.assertRaises(QiitaDBStatusError):
pd.status = 'sandbox'
def post(self, analysis_id):
command_args = self.get_arguments("commands")
split = [x.split("#") for x in command_args]
analysis = Analysis(analysis_id)
commands = []
# HARD CODED HACKY THING FOR DEMO, FIX Issue #164
fp, mapping_file = mkstemp(suffix="_map_file.txt")
close(fp)
SampleTemplate(1).to_file(mapping_file)
study_fps = {}
for pd in Study(1).processed_data:
processed = ProcessedData(pd)
study_fps[processed.data_type] = processed.get_filepaths()[0][0]
for data_type, command in split:
opts = {
"--otu_table_fp": study_fps[data_type],
"--mapping_fp": mapping_file
}
if command == "Beta Diversity" and data_type in {'16S', '18S'}:
opts["--tree_fp"] = join(get_db_files_base_dir(), "reference",
"gg_97_otus_4feb2011.tre")
elif command == "Beta Diversity":
opts["--parameter_fp"] = join(get_db_files_base_dir(),
"reference", "params_qiime.txt")
Job.create(data_type, command, opts, analysis)
commands.append("%s: %s" % (data_type, command))
user = self.get_current_user()
self.render("analysis_waiting.html", user=user,
aid=analysis_id, aname=analysis.name,
commands=commands)
# fire off analysis run here
# currently synch run so redirect done here. Will remove after demo
run_analysis(user, analysis)
def get(self, analysis_id):
analysis_id = int(analysis_id.split("/")[0])
analysis = Analysis(analysis_id)
check_analysis_access(self.current_user, analysis)
jobres = defaultdict(list)
for job in analysis.jobs:
jobject = Job(job)
jobres[jobject.datatype].append((jobject.command[0],
jobject.results))
dropped_samples = analysis.dropped_samples
dropped = defaultdict(list)
for proc_data_id, samples in viewitems(dropped_samples):
if not samples:
continue
proc_data = ProcessedData(proc_data_id)
data_type = proc_data.data_type()
study = proc_data.study
dropped[data_type].append((Study(study).title, len(samples),
', '.join(samples)))
self.render("analysis_results.html", analysis_id=analysis_id,
jobres=jobres, aname=analysis.name, dropped=dropped,
basefolder=get_db_files_base_dir())
def get(self, analysis_id):
analysis_id = int(analysis_id.split("/")[0])
analysis = Analysis(analysis_id)
check_analysis_access(self.current_user, analysis)
jobres = defaultdict(list)
for job in analysis.jobs:
jobject = Job(job)
jobres[jobject.datatype].append(
(jobject.command[0], jobject.results))
dropped = {}
dropped_samples = analysis.dropped_samples
if dropped_samples:
for proc_data_id, samples in viewitems(dropped_samples):
proc_data = ProcessedData(proc_data_id)
key = "Data type %s, Study: %s" % (proc_data.data_type(),
proc_data.study)
dropped[key] = samples
self.render("analysis_results.html",
jobres=jobres,
aname=analysis.name,
dropped=dropped,
basefolder=get_db_files_base_dir())
def test_create_preprocessed_and_study_error(self):
with self.assertRaises(IncompetentQiitaDeveloperError):
ProcessedData.create(self.params_table,
self.params_id,
self.filepaths,
preprocessed_data=self.preprocessed_data,
study=Study(1))
def delete_processed_data(self, study, user, callback):
"""Delete the selected processed data
Parameters
----------
study : Study
The current study object
user : User
The current user object
callback : function
The callback function to call with the results once the processing
is done
"""
pd_id = int(self.get_argument('processed_data_id'))
try:
ProcessedData.delete(pd_id)
msg = ("Processed data %d has been deleted" % pd_id)
msg_level = "success"
pd_id = None
except Exception as e:
msg = ("Couldn't remove processed data %d: %s" %
(pd_id, str(e)))
msg_level = "danger"
callback((msg, msg_level, 'processed_data_tab', pd_id, None))
def test_get_filepath(self):
"""Correctly returns the filepaths to the processed files"""
# check the test data
pd = ProcessedData(1)
obs = pd.get_filepaths()
exp = [(join(self.db_test_pd_dir,
'1_study_1001_closed_reference_otu_table.biom'), "biom")]
self.assertEqual(obs, exp)
def test_get_filepath(self):
"""Correctly returns the filepaths to the processed files"""
# check the test data
pd = ProcessedData(1)
obs = pd.get_filepaths()
exp = [(11, join(self.db_test_pd_dir,
'1_study_1001_closed_reference_otu_table.biom'), "biom")]
self.assertEqual(obs, exp)
def test_create_preprocessed_and_data_type_error(self):
with self.assertRaises(IncompetentQiitaDeveloperError):
ProcessedData.create(
self.params_table,
self.params_id,
self.filepaths,
preprocessed_data=self.preprocessed_data,
data_type="Metabolomics",
)
def test_get_by_status_grouped_by_study(self):
obs = ProcessedData.get_by_status_grouped_by_study('sandbox')
self.assertEqual(obs, dict())
obs = ProcessedData.get_by_status_grouped_by_study('private')
self.assertEqual(obs, {1: [1]})
ProcessedData.create(self.params_table, self.params_id,
self.filepaths,
preprocessed_data=self.preprocessed_data)
obs = ProcessedData.get_by_status_grouped_by_study('sandbox')
self.assertEqual(obs, {1: [2]})
def filter_by_processed_data(self, datatypes=None):
"""Filters results to what is available in each processed data
Parameters
----------
datatypes : list of str, optional
Datatypes to selectively return. Default all datatypes available
Returns
-------
study_proc_ids : dict of dicts of lists
Processed data ids with samples for each study, in the format
{study_id: {datatype: [proc_id, proc_id, ...], ...}, ...}
proc_data_samples : dict of lists
Samples available in each processed data id, in the format
{proc_data_id: [samp_id1, samp_id2, ...], ...}
samples_meta : dict of pandas DataFrames
metadata for the found samples, keyed by study. Pandas indexed on
sample_id, column headers are the metadata categories searched
over
"""
with TRN:
if datatypes is not None:
# convert to set for easy lookups
datatypes = set(datatypes)
study_proc_ids = {}
proc_data_samples = {}
samples_meta = {}
headers = {c: val for c, val in enumerate(self.meta_headers)}
for study_id, study_meta in viewitems(self.results):
# add metadata to dataframe and dict
# use from_dict because pandas doesn't like cursor objects
samples_meta[study_id] = pd.DataFrame.from_dict(
{s[0]: s[1:] for s in study_meta}, orient='index')
samples_meta[study_id].rename(columns=headers, inplace=True)
# set up study-based data needed
study = Study(study_id)
study_sample_ids = {s[0] for s in study_meta}
study_proc_ids[study_id] = defaultdict(list)
for proc_data_id in study.processed_data():
proc_data = ProcessedData(proc_data_id)
datatype = proc_data.data_type()
# skip processed data if it doesn't fit the given datatypes
if datatypes is not None and datatype not in datatypes:
continue
filter_samps = proc_data.samples.intersection(
study_sample_ids)
if filter_samps:
proc_data_samples[proc_data_id] = sorted(filter_samps)
study_proc_ids[study_id][datatype].append(proc_data_id)
return study_proc_ids, proc_data_samples, samples_meta
def get(self):
# Format sel_data to get study IDs for the processed data
sel_data = defaultdict(dict)
proc_data_info = {}
sel_samps = Analysis(self.current_user.default_analysis).samples
for pid, samps in viewitems(sel_samps):
proc_data = ProcessedData(pid)
sel_data[proc_data.study][pid] = samps
# Also get processed data info
proc_data_info[pid] = proc_data.processing_info
proc_data_info[pid]['data_type'] = proc_data.data_type()
self.render("analysis_selected.html", sel_data=sel_data,
proc_info=proc_data_info)
def _insert_processed_data_target_gene(preprocessed_data, params,
pick_otus_out, **kwargs):
"""Inserts the preprocessed data to the database
Parameters
----------
preprocessed_data : PreprocessedData
The preprocessed_data to process
params : ProcessedSortmernaParams
The parameters to use for the processing
pick_otus_out : str
Path to the pick_closed_reference_otus.py output directory
kwargs: ignored
Necessary to include to support execution via moi.
Raises
------
ValueError
If the processed output directory does not contain all the expected
files
"""
from os.path import exists, join, isdir
from glob import glob
from functools import partial
from qiita_db.data import ProcessedData
# The filepaths that we are interested in are:
# 1) otu_table.biom -> the output OTU table
# 2) sortmerna_picked_otus -> intermediate output of pick_otus.py
# 3) log_20141217091339.log -> log file
path_builder = partial(join, pick_otus_out)
biom_fp = path_builder('otu_table.biom')
otus_dp = path_builder('sortmerna_picked_otus')
log_fp = glob(path_builder('log_*.txt'))[0]
# Check that all the files exist
if not (exists(biom_fp) and isdir(otus_dp) and exists(log_fp)):
raise ValueError("The output directory %s does not contain all the "
"expected files." % pick_otus_out)
filepaths = [(biom_fp, "biom"),
(otus_dp, "directory"),
(log_fp, "log")]
ProcessedData.create(params._table, params.id, filepaths,
preprocessed_data=preprocessed_data)
# Change the preprocessed_data status to processed
preprocessed_data.processing_status = 'processed'
def _insert_processed_data_target_gene(preprocessed_data, params,
pick_otus_out, **kwargs):
"""Inserts the preprocessed data to the database
Parameters
----------
preprocessed_data : PreprocessedData
The preprocessed_data to process
params : ProcessedSortmernaParams
The parameters to use for the processing
pick_otus_out : str
Path to the pick_closed_reference_otus.py output directory
kwargs: ignored
Necessary to include to support execution via moi.
Raises
------
ValueError
If the processed output directory does not contain all the expected
files
"""
from os.path import exists, join, isdir
from glob import glob
from functools import partial
from qiita_db.data import ProcessedData
# The filepaths that we are interested in are:
# 1) otu_table.biom -> the output OTU table
# 2) sortmerna_picked_otus -> intermediate output of pick_otus.py
# 3) log_20141217091339.log -> log file
path_builder = partial(join, pick_otus_out)
biom_fp = path_builder('otu_table.biom')
otus_dp = path_builder('sortmerna_picked_otus')
log_fp = glob(path_builder('log_*.txt'))[0]
# Check that all the files exist
if not (exists(biom_fp) and isdir(otus_dp) and exists(log_fp)):
raise ValueError("The output directory %s does not contain all the "
"expected files." % pick_otus_out)
filepaths = [(biom_fp, "biom"), (otus_dp, "directory"), (log_fp, "log")]
ProcessedData.create(params._table,
params.id,
filepaths,
preprocessed_data=preprocessed_data)
# Change the preprocessed_data status to processed
preprocessed_data.processing_status = 'processed'
def test_status(self):
rd = RawData(1)
s = Study(1)
self.assertEqual(rd.status(s), 'private')
# Since the status is inferred from the processed data, change the
# status of the processed data so we can check how it changes in the
# preprocessed data
pd = ProcessedData(1)
pd.status = 'public'
self.assertEqual(rd.status(s), 'public')
# Check that new raw data has sandbox as status since no
# processed data exists for them
rd = RawData.create(self.filetype, self.studies, self.filepaths)
self.assertEqual(rd.status(s), 'sandbox')
def render(self, study):
avail_pd = [(pd_id, ProcessedData(pd_id))
for pd_id in study.processed_data()]
return self.render_string(
"study_description_templates/processed_data_tab.html",
available_processed_data=avail_pd,
study_id=study.id)
def test_link_filepaths_status_setter(self):
pd = ProcessedData(1)
self.assertEqual(pd.link_filepaths_status, 'idle')
pd._set_link_filepaths_status('linking')
self.assertEqual(pd.link_filepaths_status, 'linking')
pd._set_link_filepaths_status('unlinking')
self.assertEqual(pd.link_filepaths_status, 'unlinking')
pd._set_link_filepaths_status('failed: error')
self.assertEqual(pd.link_filepaths_status, 'failed: error')
def test_status(self):
ppd = PreprocessedData(1)
self.assertEqual(ppd.status, 'private')
# Since the status is inferred from the processed data, change the
# status of the processed data so we can check how it changes in the
# preprocessed data
pd = ProcessedData(1)
pd.status = 'public'
self.assertEqual(ppd.status, 'public')
# Check that new preprocessed data has sandbox as status since no
# processed data exists for them
ppd = PreprocessedData.create(self.study, self.params_table,
self.params_id, self.filepaths,
data_type="16S")
self.assertEqual(ppd.status, 'sandbox')
def test_status(self):
pd = ProcessedData(1)
self.assertEqual(pd.status, 'private')
pd = ProcessedData.create(self.params_table, self.params_id,
self.filepaths,
preprocessed_data=self.preprocessed_data)
self.assertEqual(pd.status, 'sandbox')
def _selected_parser(self, analysis):
"""builds dictionaries of selected samples from analysis object"""
selsamples = {}
selproc_data = defaultdict(list)
for proc_data_id, samps in viewitems(analysis.samples):
study = ProcessedData(proc_data_id).study
selproc_data[study].append(proc_data_id)
selsamples[study] = set(samps)
return selproc_data, selsamples
def test_create_no_date(self):
"""Correctly adds a processed data with no date on it"""
# All the other settings have been already tested on test_create
# here we will only check that the code added a good date
before = datetime.now()
ProcessedData.create(self.params_table, self.params_id, self.filepaths,
preprocessed_data=self.preprocessed_data)
after = datetime.now()
obs = self.conn_handler.execute_fetchone(
"SELECT processed_date FROM qiita.processed_data WHERE "
"processed_data_id=2")[0]
# Make sure that we clean up the environment
exp_biom_fp = join(self.db_test_pd_dir,
"2_%s" % basename(self.biom_fp))
self._clean_up_files.append(exp_biom_fp)
self.assertTrue(before <= obs <= after)
def test_create_no_date(self):
"""Correctly adds a processed data with no date on it"""
# All the other settings have been already tested on test_create
# here we will only check that the code added a good date
before = datetime.now()
ProcessedData.create(self.params_table, self.params_id, self.filepaths,
preprocessed_data=self.preprocessed_data)
after = datetime.now()
obs = self.conn_handler.execute_fetchone(
"SELECT processed_date FROM qiita.processed_data WHERE "
"processed_data_id=2")[0]
# Make sure that we clean up the environment
exp_biom_fp = join(self.db_test_pd_dir,
"2_%s" % basename(self.biom_fp))
self._clean_up_files.append(exp_biom_fp)
self.assertTrue(before <= obs <= after)
def test_create(self):
"""Correctly creates all the rows in the DB for the processed data"""
# Check that the returned object has the correct id
obs = ProcessedData.create(self.params_table, self.params_id,
self.filepaths,
preprocessed_data=self.preprocessed_data,
processed_date=self.date)
self.assertEqual(obs.id, 2)
# Check that the processed data have been correctly added to the DB
obs = self.conn_handler.execute_fetchall(
"SELECT * FROM qiita.processed_data WHERE processed_data_id=2")
# processed_data_id, processed_params_table, processed_params_id,
# processed_date, data_type_id, link_filepaths_status,
# processed_data_status_id
exp = [[2, "processed_params_uclust", 1, self.date, 2, 'idle', 4]]
self.assertEqual(obs, exp)
# Check that the files have been copied to right location
exp_biom_fp = join(self.db_test_pd_dir,
"2_%s" % basename(self.biom_fp))
self.assertTrue(exists(exp_biom_fp))
self._clean_up_files.append(exp_biom_fp)
# Check that the filepaths have been correctly added to the DB
obs_id = self.conn_handler.execute_fetchone(
"SELECT count(1) from qiita.filepath")[0]
obs = self.conn_handler.execute_fetchall(
"SELECT * FROM qiita.filepath WHERE filepath_id=%d" % obs_id)
exp_biom_fp = "2_%s" % basename(self.biom_fp)
# Filepath_id, path, filepath_type_id
exp = [[obs_id, exp_biom_fp, 6, '852952723', 1, 4]]
self.assertEqual(obs, exp)
# Check that the processed data have been correctly linked
# with the fileapths
obs = self.conn_handler.execute_fetchall(
"SELECT * FROM qiita.processed_filepath WHERE processed_data_id=2")
# processed_data_id, filepath_id
self.assertEqual(obs, [[2, obs_id]])
# Check that the processed data have been correctly linked with the
# study
obs = self.conn_handler.execute_fetchall(
"SELECT * FROM qiita.study_processed_data WHERE "
"processed_data_id=2")
# study_id, processed_data
self.assertEqual(obs, [[1, 2]])
# Check that the processed data have been correctly linked with the
# preprocessed data
obs = self.conn_handler.execute_fetchall(
"SELECT * FROM qiita.preprocessed_processed_data WHERE "
"processed_data_id=2")
# preprocessed_data_id, processed_Data_id
self.assertEqual(obs, [[1, 2]])
def make_public(self, study, user, callback):
"""Makes the current study public
Parameters
----------
study : Study
The current study object
user : User
The current user object
callback : function
The callback function to call with the results once the processing
is done
"""
pd_id = int(self.get_argument('pd_id'))
pd = ProcessedData(pd_id)
pd.status = 'public'
msg = "Processed data set to public"
msg_level = "success"
callback((msg, msg_level, "processed_data_tab", pd_id, None))
def make_sandbox(self, study, user, callback):
"""Reverts the current study to the 'sandbox' status
Parameters
----------
study : Study
The current study object
user : User
The current user object
callback : function
The callback function to call with the results once the processing
is done
"""
pd_id = int(self.get_argument('pd_id'))
pd = ProcessedData(pd_id)
pd.status = 'sandbox'
msg = "Processed data reverted to sandbox"
msg_level = "success"
callback((msg, msg_level, "processed_data_tab", pd_id, None))
def request_approval(self, study, user, callback):
"""Changes the status of the current study to "awaiting_approval"
Parameters
----------
study : Study
The current study object
user : User
The current user object
callback : function
The callback function to call with the results once the processing
is done
"""
pd_id = int(self.get_argument('pd_id'))
pd = ProcessedData(pd_id)
pd.status = 'awaiting_approval'
msg = "Processed data sent to admin for approval"
msg_level = "success"
callback((msg, msg_level, "processed_data_tab", pd_id, None))
def test_create(self):
"""Correctly creates all the rows in the DB for the processed data"""
# Check that the returned object has the correct id
obs = ProcessedData.create(self.params_table, self.params_id,
self.filepaths,
preprocessed_data=self.preprocessed_data,
processed_date=self.date)
self.assertEqual(obs.id, 2)
# Check that the processed data have been correctly added to the DB
obs = self.conn_handler.execute_fetchall(
"SELECT * FROM qiita.processed_data WHERE processed_data_id=2")
# processed_data_id, processed_params_table, processed_params_id,
# processed_date, data_type_id, link_filepaths_status
exp = [[2, "processed_params_uclust", 1, self.date, 2, 'idle']]
self.assertEqual(obs, exp)
# Check that the files have been copied to right location
exp_biom_fp = join(self.db_test_pd_dir,
"2_%s" % basename(self.biom_fp))
self.assertTrue(exists(exp_biom_fp))
self._clean_up_files.append(exp_biom_fp)
# Check that the filepaths have been correctly added to the DB
obs_id = self.conn_handler.execute_fetchone(
"SELECT count(1) from qiita.filepath")[0]
obs = self.conn_handler.execute_fetchall(
"SELECT * FROM qiita.filepath WHERE filepath_id=%d" % obs_id)
exp_biom_fp = "2_%s" % basename(self.biom_fp)
# Filepath_id, path, filepath_type_id
exp = [[obs_id, exp_biom_fp, 6, '852952723', 1, 4]]
self.assertEqual(obs, exp)
# Check that the processed data have been correctly linked
# with the fileapths
obs = self.conn_handler.execute_fetchall(
"SELECT * FROM qiita.processed_filepath WHERE processed_data_id=2")
# processed_data_id, filepath_id
self.assertEqual(obs, [[2, obs_id]])
# Check that the processed data have been correctly linked with the
# study
obs = self.conn_handler.execute_fetchall(
"SELECT * FROM qiita.study_processed_data WHERE "
"processed_data_id=2")
# study_id, processed_data
self.assertEqual(obs, [[1, 2]])
# Check that the processed data have been correctly linked with the
# preprocessed data
obs = self.conn_handler.execute_fetchall(
"SELECT * FROM qiita.preprocessed_processed_data WHERE "
"processed_data_id=2")
# preprocessed_data_id, processed_Data_id
self.assertEqual(obs, [[1, 2]])
def test_link_filepaths_status_setter(self):
pd = ProcessedData(1)
self.assertEqual(pd.link_filepaths_status, 'idle')
pd._set_link_filepaths_status('linking')
self.assertEqual(pd.link_filepaths_status, 'linking')
pd._set_link_filepaths_status('unlinking')
self.assertEqual(pd.link_filepaths_status, 'unlinking')
pd._set_link_filepaths_status('failed: error')
self.assertEqual(pd.link_filepaths_status, 'failed: error')
def get(self, analysis_id):
user = self.current_user
analysis_id = int(analysis_id.split("/")[0])
analysis = Analysis(analysis_id)
check_analysis_access(User(user), analysis)
jobres = defaultdict(list)
for job in analysis.jobs:
jobject = Job(job)
jobres[jobject.datatype].append((jobject.command[0],
jobject.results))
dropped = {}
for proc_data_id, samples in viewitems(analysis.dropped_samples):
proc_data = ProcessedData(proc_data_id)
key = "Data type %s, Study: %s" % (proc_data.data_type(),
proc_data.study)
dropped[key] = samples
self.render("analysis_results.html", user=self.current_user,
jobres=jobres, aname=analysis.name, dropped=dropped,
basefolder=get_db_files_base_dir())
def get(self):
user = self.current_user
if user.level != 'admin':
raise HTTPError(403, 'User %s is not admin' % self.current_user)
result_generator = viewitems(
ProcessedData.get_by_status_grouped_by_study('awaiting_approval'))
study_generator = ((Study(sid), pds) for sid, pds in result_generator)
parsed_studies = [(s.id, s.title, s.owner, pds)
for s, pds in study_generator]
self.render('admin_approval.html',
study_info=parsed_studies)
def approve_study(self, study, user, callback):
"""Approves the current study if and only if the current user is admin
Parameters
----------
study : Study
The current study object
user : User
The current user object
callback : function
The callback function to call with the results once the processing
is done
"""
if _approve(user.level):
pd_id = int(self.get_argument("pd_id"))
pd = ProcessedData(pd_id)
pd.status = "private"
msg = "Processed data approved"
msg_level = "success"
else:
msg = "The current user does not have permission to approve " "the processed data"
msg_level = "danger"
callback((msg, msg_level, "processed_data_tab", pd_id, None))
def test_create_params_table_error(self):
"""Raises an error if the processed_params_table does not exist"""
with self.assertRaises(IncompetentQiitaDeveloperError):
ProcessedData.create("foo", self.params_id, self.filepaths,
preprocessed_data=self.preprocessed_data)
with self.assertRaises(IncompetentQiitaDeveloperError):
ProcessedData.create("processed_params_foo", self.params_id,
self.filepaths,
preprocessed_data=self.preprocessed_data)
with self.assertRaises(IncompetentQiitaDeveloperError):
ProcessedData.create("processed_params_", self.params_id,
self.filepaths,
preprocessed_data=self.preprocessed_data)
def test_create_w_study(self):
"""Correctly adds a processed data passing a study"""
obs = ProcessedData.create(self.params_table,
self.params_id,
self.filepaths,
study=Study(1),
processed_date=self.date,
data_type="18S")
# Check that the processed data have been correctly added to the DB
obs = self.conn_handler.execute_fetchall(
"SELECT * FROM qiita.processed_data WHERE processed_data_id=2")
# processed_data_id, preprocessed_data_id, processed_params_table,
# processed_params_id, processed_date
exp = [[2, "processed_params_uclust", 1, self.date, 2]]
self.assertEqual(obs, exp)
# Check that the files have been copied to right location
exp_biom_fp = join(self.db_test_pd_dir,
"2_%s" % basename(self.biom_fp))
self.assertTrue(exists(exp_biom_fp))
self._clean_up_files.append(exp_biom_fp)
# Check that the filepaths have been correctly added to the DB
obs = self.conn_handler.execute_fetchall(
"SELECT * FROM qiita.filepath WHERE filepath_id=15")
# Filepath_id, path, filepath_type_id
exp = [[15, exp_biom_fp, 6, '852952723', 1]]
self.assertEqual(obs, exp)
# Check that the processed data have been correctly linked
# with the fileapths
obs = self.conn_handler.execute_fetchall(
"SELECT * FROM qiita.processed_filepath WHERE processed_data_id=2")
# processed_data_id, filepath_id
self.assertTrue(obs, [[2, 10]])
# Check that the processed data have been correctly linked with the
# study
obs = self.conn_handler.execute_fetchall(
"SELECT * FROM qiita.study_processed_data WHERE "
"processed_data_id=2")
# study_id, processed_data
self.assertEqual(obs, [[1, 2]])
def test_create_params_table_error(self):
"""Raises an error if the processed_params_table does not exist"""
with self.assertRaises(IncompetentQiitaDeveloperError):
ProcessedData.create("foo", self.params_id, self.filepaths,
preprocessed_data=self.preprocessed_data)
with self.assertRaises(IncompetentQiitaDeveloperError):
ProcessedData.create("processed_params_foo", self.params_id,
self.filepaths,
preprocessed_data=self.preprocessed_data)
with self.assertRaises(IncompetentQiitaDeveloperError):
ProcessedData.create("processed_params_", self.params_id,
self.filepaths,
preprocessed_data=self.preprocessed_data)
def test_create_w_study(self):
"""Correctly adds a processed data passing a study"""
obs = ProcessedData.create(self.params_table, self.params_id,
self.filepaths, study=Study(1),
processed_date=self.date, data_type="18S")
# Check that the processed data have been correctly added to the DB
obs = self.conn_handler.execute_fetchall(
"SELECT * FROM qiita.processed_data WHERE processed_data_id=2")
# processed_data_id, processed_params_table, processed_params_id,
# processed_date, data_type_id, link_filepaths_status
exp = [[2, "processed_params_uclust", 1, self.date, 2, 'idle']]
self.assertEqual(obs, exp)
# Check that the files have been copied to right location
exp_biom_fp = join(self.db_test_pd_dir,
"2_%s" % basename(self.biom_fp))
self.assertTrue(exists(exp_biom_fp))
self._clean_up_files.append(exp_biom_fp)
# Check that the filepaths have been correctly added to the DB
obs = self.conn_handler.execute_fetchall(
"SELECT * FROM qiita.filepath WHERE filepath_id=17")
exp_biom_fp = "2_%s" % basename(self.biom_fp)
# Filepath_id, path, filepath_type_id
exp = [[17, exp_biom_fp, 6, '852952723', 1, 4]]
self.assertEqual(obs, exp)
# Check that the processed data have been correctly linked
# with the fileapths
obs = self.conn_handler.execute_fetchall(
"SELECT * FROM qiita.processed_filepath WHERE processed_data_id=2")
# processed_data_id, filepath_id
self.assertTrue(obs, [[2, 10]])
# Check that the processed data have been correctly linked with the
# study
obs = self.conn_handler.execute_fetchall(
"SELECT * FROM qiita.study_processed_data WHERE "
"processed_data_id=2")
# study_id, processed_data
self.assertEqual(obs, [[1, 2]])
def test_get_by_status(self):
pds = ProcessedData.get_by_status('sandbox')
self.assertEqual(pds, set())
pds = ProcessedData.get_by_status('private')
self.assertEqual(pds, set([1]))
ProcessedData.create(self.params_table, self.params_id,
self.filepaths,
preprocessed_data=self.preprocessed_data)
pds = ProcessedData.get_by_status('sandbox')
self.assertEqual(pds, set([2]))
pds = ProcessedData.get_by_status('private')
self.assertEqual(pds, set([1]))
def test_get_filepath_ids(self):
pd = ProcessedData(1)
self.assertEqual(pd.get_filepath_ids(), [11])
def test_link_filepaths_status(self):
pd = ProcessedData(1)
self.assertEqual(pd.link_filepaths_status, 'idle')
def test_data_type_id(self):
pd = ProcessedData(1)
self.assertEqual(pd.data_type(ret_id=True), 2)
def test_data_type(self):
pd = ProcessedData(1)
self.assertEqual(pd.data_type(), "18S")
def test_get_filepath_ids(self):
pd = ProcessedData(1)
self.assertEqual(pd.get_filepath_ids(), [10])
def test_data_type(self):
"""Correctly returns the data_type of preprocessed_data"""
pd = ProcessedData(1)
self.assertEqual(pd.data_type(), "18S")
def test_data_type_id(self):
"""Correctly returns the data_type of preprocessed_data"""
pd = ProcessedData(1)
self.assertEqual(pd.data_type(ret_id=True), 2)
def test_data_type(self):
pd = ProcessedData(1)
self.assertEqual(pd.data_type(), "18S")
def test_preprocessed_data(self):
"""Correctly returns the preprocessed_data"""
pd = ProcessedData(1)
self.assertEqual(pd.preprocessed_data, 1)
def test_data_type_id(self):
pd = ProcessedData(1)
self.assertEqual(pd.data_type(ret_id=True), 2)
def test_processed_date(self):
pd = ProcessedData(1)
self.assertEqual(pd.processed_date, datetime(2012, 10, 1, 9, 30, 27))
def _build_study_info(user, study_proc=None, proc_samples=None):
"""Builds list of dicts for studies table, with all HTML formatted
Parameters
----------
user : User object
logged in user
study_proc : dict of lists, optional
Dictionary keyed on study_id that lists all processed data associated
with that study. Required if proc_samples given.
proc_samples : dict of lists, optional
Dictionary keyed on proc_data_id that lists all samples associated with
that processed data. Required if study_proc given.
Returns
-------
infolist: list of dict of lists and dicts
study and processed data info for JSON serialiation for datatables
Each dict in the list is a single study, and contains the text
Notes
-----
Both study_proc and proc_samples must be passed, or neither passed.
"""
build_samples = False
# Logic check to make sure both needed parts passed
if study_proc is not None and proc_samples is None:
raise IncompetentQiitaDeveloperError(
'Must pass proc_samples when study_proc given')
elif proc_samples is not None and study_proc is None:
raise IncompetentQiitaDeveloperError(
'Must pass study_proc when proc_samples given')
elif study_proc is None:
build_samples = True
# get list of studies for table
study_set = user.user_studies.union(
Study.get_by_status('public')).union(user.shared_studies)
if study_proc is not None:
study_set = study_set.intersection(study_proc)
if not study_set:
# No studies left so no need to continue
return []
# get info for the studies
cols = ['study_id', 'email', 'principal_investigator_id',
'pmid', 'study_title', 'metadata_complete',
'number_samples_collected', 'study_abstract']
study_info = Study.get_info(study_set, cols)
infolist = []
for info in study_info:
# Convert DictCursor to proper dict
info = dict(info)
study = Study(info['study_id'])
# Build the processed data info for the study if none passed
if build_samples:
proc_data_list = study.processed_data()
proc_samples = {}
study_proc = {study.id: defaultdict(list)}
for pid in proc_data_list:
proc_data = ProcessedData(pid)
study_proc[study.id][proc_data.data_type()].append(pid)
proc_samples[pid] = proc_data.samples
study_info = _build_single_study_info(study, info, study_proc,
proc_samples)
infolist.append(study_info)
return infolist
def test_create_no_preprocessed_no_study_error(self):
with self.assertRaises(IncompetentQiitaDeveloperError):
ProcessedData.create(self.params_table, self.params_id,
self.filepaths)
def test_retrieve_dropped_samples(self):
# Create and populate second study to do test with
info = {
"timeseries_type_id": 1,
"metadata_complete": True,
"mixs_compliant": True,
"number_samples_collected": 25,
"number_samples_promised": 28,
"portal_type_id": 3,
"study_alias": "FCM",
"study_description": "Microbiome of people who eat nothing but "
"fried chicken",
"study_abstract": "Exploring how a high fat diet changes the "
"gut microbiome",
"emp_person_id": StudyPerson(2),
"principal_investigator_id": StudyPerson(3),
"lab_person_id": StudyPerson(1)
}
metadata_dict = {
'SKB8.640193': {'physical_location': 'location1',
'has_physical_specimen': True,
'has_extracted_data': True,
'sample_type': 'type1',
'required_sample_info_status': 'received',
'collection_timestamp':
datetime(2014, 5, 29, 12, 24, 51),
'host_subject_id': 'NotIdentified',
'Description': 'Test Sample 1',
'str_column': 'Value for sample 1',
'latitude': 42.42,
'longitude': 41.41},
'SKD8.640184': {'physical_location': 'location1',
'has_physical_specimen': True,
'has_extracted_data': True,
'sample_type': 'type1',
'required_sample_info_status': 'received',
'collection_timestamp':
datetime(2014, 5, 29, 12, 24, 51),
'host_subject_id': 'NotIdentified',
'Description': 'Test Sample 2',
'str_column': 'Value for sample 2',
'latitude': 4.2,
'longitude': 1.1},
'SKB7.640196': {'physical_location': 'location1',
'has_physical_specimen': True,
'has_extracted_data': True,
'sample_type': 'type1',
'required_sample_info_status': 'received',
'collection_timestamp':
datetime(2014, 5, 29, 12, 24, 51),
'host_subject_id': 'NotIdentified',
'Description': 'Test Sample 3',
'str_column': 'Value for sample 3',
'latitude': 4.8,
'longitude': 4.41},
}
metadata = pd.DataFrame.from_dict(metadata_dict, orient='index')
Study.create(User("*****@*****.**"), "Test study 2", [1], info)
SampleTemplate.create(metadata, Study(2))
mp = get_mountpoint("processed_data")[0][1]
study_fp = join(mp, "2_study_1001_closed_reference_otu_table.biom")
ProcessedData.create("processed_params_uclust", 1, [(study_fp, 6)],
study=Study(2), data_type="16S")
self.conn_handler.execute(
"INSERT INTO qiita.analysis_sample (analysis_id, "
"processed_data_id, sample_id) VALUES "
"(1,2,'2.SKB8.640193'), (1,2,'2.SKD8.640184'), "
"(1,2,'2.SKB7.640196')")
samples = {1: ['1.SKB8.640193', '1.SKD8.640184', '1.SKB7.640196'],
2: ['2.SKB8.640193', '2.SKD8.640184']}
self.analysis._build_biom_tables(samples, 10000,
conn_handler=self.conn_handler)
exp = {1: {'1.SKM4.640180', '1.SKM9.640192'},
2: {'2.SKB7.640196'}}
self.assertEqual(self.analysis.dropped_samples, exp)
def test_retrieve_dropped_samples(self):
# Create and populate second study to do test with
info = {
"timeseries_type_id": 1,
"metadata_complete": True,
"mixs_compliant": True,
"number_samples_collected": 25,
"number_samples_promised": 28,
"portal_type_id": 3,
"study_alias": "FCM",
"study_description": "Microbiome of people who eat nothing but "
"fried chicken",
"study_abstract": "Exploring how a high fat diet changes the "
"gut microbiome",
"emp_person_id": StudyPerson(2),
"principal_investigator_id": StudyPerson(3),
"lab_person_id": StudyPerson(1)
}
metadata_dict = {
'SKB8.640193': {
'physical_location': 'location1',
'has_physical_specimen': True,
'has_extracted_data': True,
'sample_type': 'type1',
'required_sample_info_status': 'received',
'collection_timestamp': datetime(2014, 5, 29, 12, 24, 51),
'host_subject_id': 'NotIdentified',
'Description': 'Test Sample 1',
'str_column': 'Value for sample 1',
'latitude': 42.42,
'longitude': 41.41
},
'SKD8.640184': {
'physical_location': 'location1',
'has_physical_specimen': True,
'has_extracted_data': True,
'sample_type': 'type1',
'required_sample_info_status': 'received',
'collection_timestamp': datetime(2014, 5, 29, 12, 24, 51),
'host_subject_id': 'NotIdentified',
'Description': 'Test Sample 2',
'str_column': 'Value for sample 2',
'latitude': 4.2,
'longitude': 1.1
},
'SKB7.640196': {
'physical_location': 'location1',
'has_physical_specimen': True,
'has_extracted_data': True,
'sample_type': 'type1',
'required_sample_info_status': 'received',
'collection_timestamp': datetime(2014, 5, 29, 12, 24, 51),
'host_subject_id': 'NotIdentified',
'Description': 'Test Sample 3',
'str_column': 'Value for sample 3',
'latitude': 4.8,
'longitude': 4.41
},
}
metadata = pd.DataFrame.from_dict(metadata_dict, orient='index')
Study.create(User("*****@*****.**"), "Test study 2", [1], info)
SampleTemplate.create(metadata, Study(2))
mp = get_mountpoint("processed_data")[0][1]
study_fp = join(mp, "2_study_1001_closed_reference_otu_table.biom")
ProcessedData.create("processed_params_uclust",
1, [(study_fp, 6)],
study=Study(2),
data_type="16S")
self.conn_handler.execute(
"INSERT INTO qiita.analysis_sample (analysis_id, "
"processed_data_id, sample_id) VALUES "
"(1,2,'2.SKB8.640193'), (1,2,'2.SKD8.640184'), "
"(1,2,'2.SKB7.640196')")
samples = {
1: ['1.SKB8.640193', '1.SKD8.640184', '1.SKB7.640196'],
2: ['2.SKB8.640193', '2.SKD8.640184']
}
self.analysis._build_biom_tables(samples,
10000,
conn_handler=self.conn_handler)
exp = {1: {'1.SKM4.640180', '1.SKM9.640192'}, 2: {'2.SKB7.640196'}}
self.assertEqual(self.analysis.dropped_samples, exp)