def setUp(self):
fd, self.seqs_fp = mkstemp(suffix='_seqs.fastq')
close(fd)
fd, self.barcodes_fp = mkstemp(suffix='_barcodes.fastq')
close(fd)
self.filetype = 2
self.filepaths = [(self.seqs_fp, 1), (self.barcodes_fp, 2)]
self.studies = [Study(1)]
_, self.db_test_raw_dir = get_mountpoint('raw_data')[0]
with open(self.seqs_fp, "w") as f:
f.write("\n")
with open(self.barcodes_fp, "w") as f:
f.write("\n")
self._clean_up_files = []
# Create a new study
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)
}
Study.create(User("*****@*****.**"), "Test study 2", [1], info)
def test_delete_study_empty_study(self):
info = {
"timeseries_type_id": '1',
"metadata_complete": 'true',
"mixs_compliant": 'true',
"number_samples_collected": 25,
"number_samples_promised": 28,
"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)
}
new_study = Study.create(User('*****@*****.**'),
"Fried Chicken Microbiome %s" % time(), info)
job = self._create_job('delete_study', {'study': new_study.id})
private_task(job.id)
self.assertEqual(job.status, 'success')
# making sure the study doesn't exist
with self.assertRaises(QiitaDBUnknownIDError):
Study(new_study.id)
def _create_study(self, study_title):
"""Creates a new study
Parameters
----------
study_title: str
The title of the new study
Returns
-------
qiita_db.study.Study
The newly created study
"""
info = {
"timeseries_type_id": 1,
"metadata_complete": True,
"mixs_compliant": True,
"number_samples_collected": 25,
"number_samples_promised": 28,
"study_alias": "ALIAS",
"study_description": "DESC",
"study_abstract": "ABS",
"principal_investigator_id": StudyPerson(3),
"lab_person_id": StudyPerson(1)
}
return Study.create(User('*****@*****.**'), study_title, info)
def _build_study_info(studytype, user=None):
"""builds list of namedtuples for study listings"""
if studytype == "private":
studylist = user.user_studies
elif studytype == "shared":
studylist = user.shared_studies
elif studytype == "public":
studylist = Study.get_by_status('public')
else:
raise IncompetentQiitaDeveloperError("Must use private, shared, "
"or public!")
StudyTuple = namedtuple(
'StudyInfo', 'id title meta_complete '
'num_samples_collected shared num_raw_data pi '
'pmids owner status')
infolist = []
for s_id in studylist:
study = Study(s_id)
status = study.status
# Just passing the email address as the name here, since
# name is not a required field in qiita.qiita_user
owner = study_person_linkifier((study.owner, study.owner))
info = study.info
PI = StudyPerson(info['principal_investigator_id'])
PI = study_person_linkifier((PI.email, PI.name))
pmids = ", ".join([pubmed_linkifier([pmid]) for pmid in study.pmids])
shared = _get_shared_links_for_study(study)
infolist.append(
StudyTuple(study.id, study.title, info["metadata_complete"],
info["number_samples_collected"], shared,
len(study.raw_data()), PI, pmids, owner, status))
return infolist
def setUp(self):
info = {
"timeseries_type_id": 1,
"metadata_complete": True,
"mixs_compliant": True,
"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",
"principal_investigator_id": StudyPerson(3),
"lab_person_id": StudyPerson(1)
}
self.new_study = Study.create(User('*****@*****.**'),
"Fried Chicken Microbiome", info)
self._clean_up_files = []
def test_patch_no_sample_template(self):
info = {
"timeseries_type_id": 1,
"metadata_complete": True,
"mixs_compliant": True,
"number_samples_collected": 25,
"number_samples_promised": 28,
"study_alias": "FCM",
"study_description": "DESC",
"study_abstract": "ABS",
"principal_investigator_id": StudyPerson(3),
'first_contact': datetime(2015, 5, 19, 16, 10),
'most_recent_contact': datetime(2015, 5, 19, 16, 11),
}
new_study = Study.create(User('*****@*****.**'),
"Some New Study for test jr", info)
body = {
'sampleid1': {
'category_a': 'value_a'
},
'sampleid2': {
'category_b': 'value_b'
}
}
exp = {'message': 'No sample information found'}
response = self.patch('/api/v1/study/%d/samples' % new_study.id,
headers=self.headers,
data=body,
asjson=True)
self.assertEqual(response.code, 404)
obs = json_decode(response.body)
self.assertEqual(obs, exp)
def test_get_no_samples(self):
# /api/v1/study/%d/samples/info -> {'number-of-samples':<int>,
# 'categories': [<str>]}
info = {
"timeseries_type_id": 1,
"metadata_complete": True,
"mixs_compliant": True,
"number_samples_collected": 25,
"number_samples_promised": 28,
"study_alias": "FCM",
"study_description": "DESC",
"study_abstract": "ABS",
"principal_investigator_id": StudyPerson(3),
'first_contact': datetime(2015, 5, 19, 16, 10),
'most_recent_contact': datetime(2015, 5, 19, 16, 11),
}
new_study = Study.create(User('*****@*****.**'),
"Some New Study for test", info)
exp = {'message': 'Study does not have sample information'}
response = self.get('/api/v1/study/%d/samples/categories=foo' %
new_study.id,
headers=self.headers)
self.assertEqual(response.code, 404)
obs = json_decode(response.body)
self.assertEqual(obs, exp)
def _build_single_study_info(study, info, study_proc, proc_samples):
"""Clean up and add to the study info for HTML purposes
Parameters
----------
study : Study object
The study to build information for
info : dict
Information from Study.get_info
study_proc : dict of dict of lists
Dictionary keyed on study_id that lists all processed data associated
with that study. This list of processed data ids is keyed by data type
proc_samples : dict of lists
Dictionary keyed on proc_data_id that lists all samples associated with
that processed data.
Returns
-------
dict
info-information + extra information for the study,
slightly HTML formatted
"""
PI = StudyPerson(info['principal_investigator_id'])
status = study.status
if info['publication_doi'] is not None:
pmids = get_pubmed_ids_from_dois(info['publication_doi']).values()
info['pmid'] = ", ".join([pubmed_linkifier([p]) for p in pmids])
info['publication_doi'] = ", ".join(
[doi_linkifier([p]) for p in info['publication_doi']])
else:
info['publication_doi'] = ""
info['pmid'] = ""
if info["number_samples_collected"] is None:
info["number_samples_collected"] = 0
info["shared"] = _get_shared_links_for_study(study)
# raw data is any artifact that is not Demultiplexed or BIOM
info["num_raw_data"] = len([
a for a in study.artifacts()
if a.artifact_type not in ['Demultiplexed', 'BIOM']
])
info["status"] = status
info["study_id"] = study.id
info["pi"] = study_person_linkifier((PI.email, PI.name))
del info["principal_investigator_id"]
del info["email"]
# Build the proc data info list for the child row in datatable
info["proc_data_info"] = []
for data_type, proc_datas in viewitems(study_proc[study.id]):
info["proc_data_info"].extend([
_build_single_proc_data_info(pd_id, data_type, proc_samples[pd_id])
for pd_id in proc_datas
])
return info
def setUp(self):
# Create a sample template file
self.st_contents = SAMPLE_TEMPLATE
# create a new study to attach the sample template
info = {
"timeseries_type_id": 1,
"metadata_complete": True,
"mixs_compliant": True,
"number_samples_collected": 4,
"number_samples_promised": 4,
"portal_type_id": 3,
"study_alias": "TestStudy",
"study_description": "Description of a test study",
"study_abstract": "No abstract right now...",
"emp_person_id": StudyPerson(2),
"principal_investigator_id": StudyPerson(3),
"lab_person_id": StudyPerson(1)
}
self.study = Study.create(User('*****@*****.**'),
"Test study", [1], info)
def test_delete_sample_template(self):
# Error case
job = self._create_job('delete_sample_template', {'study': 1})
private_task(job.id)
self.assertEqual(job.status, 'error')
self.assertIn(
"Sample template cannot be erased because there are "
"prep templates associated", job.log.msg)
# Success case
info = {
"timeseries_type_id": '1',
"metadata_complete": 'true',
"mixs_compliant": 'true',
"number_samples_collected": 25,
"number_samples_promised": 28,
"study_alias": "TDST",
"study_description": "Test delete sample template",
"study_abstract": "Test delete sample template",
"principal_investigator_id": StudyPerson(1)
}
study = Study.create(User('*****@*****.**'),
"Delete Sample Template test", info)
metadata = pd.DataFrame.from_dict(
{
'Sample1': {
'physical_specimen_location': 'location1',
'physical_specimen_remaining': 'true',
'dna_extracted': 'true',
'sample_type': 'type1',
'collection_timestamp': '2014-05-29 12:24:15',
'host_subject_id': 'NotIdentified',
'Description': 'Test Sample 1',
'latitude': '42.42',
'longitude': '41.41',
'taxon_id': '9606',
'scientific_name': 'h**o sapiens'
}
},
orient='index',
dtype=str)
SampleTemplate.create(metadata, study)
job = self._create_job('delete_sample_template', {'study': study.id})
private_task(job.id)
self.assertEqual(job.status, 'success')
self.assertFalse(SampleTemplate.exists(study.id))
def test_create_sample_template(self):
# Test error
job = self._create_job(
'create_sample_template', {
'fp': self.fp,
'study_id': 1,
'is_mapping_file': False,
'data_type': None
})
private_task(job.id)
self.assertEqual(job.status, 'error')
self.assertIn(
"The 'SampleTemplate' object with attributes (id: 1) "
"already exists.", job.log.msg)
# Test success with a warning
info = {
"timeseries_type_id": '1',
"metadata_complete": 'true',
"mixs_compliant": 'true',
"study_alias": "TDST",
"study_description": "Test create sample template",
"study_abstract": "Test create sample template",
"principal_investigator_id": StudyPerson(1)
}
study = Study.create(User('*****@*****.**'),
"Create Sample Template test", info)
job = self._create_job(
'create_sample_template', {
'fp': self.fp,
'study_id': study.id,
'is_mapping_file': False,
'data_type': None
})
private_task(job.id)
self.assertEqual(job.status, 'success')
obs = r_client.get("sample_template_%d" % study.id)
self.assertIsNotNone(obs)
obs = loads(obs)
self.assertCountEqual(obs, ['job_id', 'alert_type', 'alert_msg'])
self.assertEqual(obs['job_id'], job.id)
self.assertEqual(obs['alert_type'], 'warning')
self.assertIn(
'Some functionality will be disabled due to missing columns:',
obs['alert_msg'])
# making sure that the error name is not in the messages
self.assertNotIn('QiitaDBWarning', obs['alert_msg'])
def render(self, study):
study_info = study.info
abstract = study_info['study_abstract']
description = study_info['study_description']
pmids = ", ".join([pubmed_linkifier([pmid]) for pmid in study.pmids])
princ_inv = StudyPerson(study_info['principal_investigator_id'])
pi_link = study_person_linkifier((princ_inv.email, princ_inv.name))
number_samples_promised = study_info['number_samples_promised']
number_samples_collected = study_info['number_samples_collected']
metadata_complete = study_info['metadata_complete']
# Retrieve the files from the uploads folder, so the user can choose
# the sample template of the study
files = [f for _, f in get_files_from_uploads_folders(str(study.id))]
# If the sample template exists, retrieve all its filepaths
if SampleTemplate.exists(study.id):
sample_templates = SampleTemplate(study.id).get_filepaths()
else:
# If the sample template does not exist, just pass an empty list
sample_templates = []
# Check if the request came from a local source
is_local_request = self._is_local()
# The user can choose the sample template only if the study is
# sandboxed or the current user is an admin
show_select_sample = (
study.status == 'sandbox' or self.current_user.level == 'admin')
return self.render_string(
"study_description_templates/study_information_tab.html",
abstract=abstract,
description=description,
pmids=pmids,
principal_investigator=pi_link,
number_samples_promised=number_samples_promised,
number_samples_collected=number_samples_collected,
metadata_complete=metadata_complete,
show_select_sample=show_select_sample,
files=files,
study_id=study.id,
sample_templates=sample_templates,
is_local_request=is_local_request)
def test_set_info(self):
"""Set info in a study"""
newinfo = {
"timeseries_type_id": 2,
"metadata_complete": False,
"number_samples_collected": 28,
"lab_person_id": StudyPerson(2),
"vamps_id": 'MBE_111222',
"first_contact": "June 11, 2014"
}
new = Study.create(
User('*****@*****.**'), 'NOT Identification of the '
'Microbiomes for Cannabis Soils', [1], self.info)
self.infoexp.update(newinfo)
new.info = newinfo
# add missing table cols
self.infoexp["funding"] = None
self.infoexp["spatial_series"] = None
self.infoexp["most_recent_contact"] = None
self.infoexp["reprocess"] = False
self.infoexp["lab_person_id"] = 2
self.assertEqual(new.info, self.infoexp)
def test_get_study_no_samples(self):
info = {
"timeseries_type_id": 1,
"metadata_complete": True,
"mixs_compliant": True,
"study_alias": "FCM",
"study_description": "DESC",
"study_abstract": "ABS",
"principal_investigator_id": StudyPerson(3),
'first_contact': datetime(2015, 5, 19, 16, 10),
'most_recent_contact': datetime(2015, 5, 19, 16, 11),
}
new_study = Study.create(User('*****@*****.**'),
"Some New Study for test", info)
exp = []
response = self.get('/api/v1/study/%d/samples' % new_study.id,
headers=self.headers)
self.assertEqual(response.code, 200)
obs = json_decode(response.body)
self.assertEqual(obs, exp)
def test_post(self):
person_count_before = get_count('qiita.study_person')
study_count_before = get_count('qiita.study')
post_data = {
'new_people_names': ['Adam', 'Ethan'],
'new_people_emails': ['*****@*****.**', '*****@*****.**'],
'new_people_affiliations': ['CU Boulder', 'NYU'],
'new_people_addresses': ['Some St., Boulder, CO 80305', ''],
'new_people_phones': ['', ''],
'study_title': 'dummy title',
'study_alias': 'dummy alias',
'pubmed_id': 'dummy pmid',
'environmental_packages': ['air'],
'timeseries': '1',
'study_abstract': "dummy abstract",
'study_description': 'dummy description',
'principal_investigator': '-2',
'notes': '',
'lab_person': '1'
}
self.post('/study/create/', post_data)
# Check that the new person was created
expected_id = person_count_before + 1
self.assertTrue(check_count('qiita.study_person', expected_id))
new_person = StudyPerson(expected_id)
self.assertTrue(new_person.name == 'Ethan')
self.assertTrue(new_person.email == '*****@*****.**')
self.assertTrue(new_person.affiliation == 'NYU')
self.assertTrue(new_person.address is None)
self.assertTrue(new_person.phone is None)
# Check the study was created
expected_id = study_count_before + 1
self.assertTrue(check_count('qiita.study', expected_id))
def generate_new_study_with_preprocessed_data(self):
"""Creates a new study up to the processed data for testing"""
info = {
"timeseries_type_id": 1,
"metadata_complete": True,
"mixs_compliant": True,
"number_samples_collected": 3,
"number_samples_promised": 3,
"study_alias": "Test EBI",
"study_description": "Study for testing EBI",
"study_abstract": "Study for testing EBI",
"emp_person_id": StudyPerson(2),
"principal_investigator_id": StudyPerson(3),
"lab_person_id": StudyPerson(1)
}
study = Study.create(User('*****@*****.**'), "Test EBI study", info)
metadata_dict = {
'Sample1': {
'collection_timestamp': datetime(2015, 6, 1, 7, 0, 0),
'physical_specimen_location': 'location1',
'taxon_id': 9606,
'scientific_name': 'h**o sapiens',
'Description': 'Test Sample 1'
},
'Sample2': {
'collection_timestamp': datetime(2015, 6, 2, 7, 0, 0),
'physical_specimen_location': 'location1',
'taxon_id': 9606,
'scientific_name': 'h**o sapiens',
'Description': 'Test Sample 2'
},
'Sample3': {
'collection_timestamp': datetime(2015, 6, 3, 7, 0, 0),
'physical_specimen_location': 'location1',
'taxon_id': 9606,
'scientific_name': 'h**o sapiens',
'Description': 'Test Sample 3'
}
}
metadata = pd.DataFrame.from_dict(metadata_dict,
orient='index',
dtype=str)
SampleTemplate.create(metadata, study)
metadata_dict = {
'Sample1': {
'primer': 'GTGCCAGCMGCCGCGGTAA',
'barcode': 'CGTAGAGCTCTC',
'center_name': 'KnightLab',
'platform': 'ILLUMINA',
'instrument_model': 'Illumina MiSeq',
'library_construction_protocol': 'Protocol ABC',
'experiment_design_description': "Random value 1"
},
'Sample2': {
'primer': 'GTGCCAGCMGCCGCGGTAA',
'barcode': 'CGTAGAGCTCTA',
'center_name': 'KnightLab',
'platform': 'ILLUMINA',
'instrument_model': 'Illumina MiSeq',
'library_construction_protocol': 'Protocol ABC',
'experiment_design_description': "Random value 2"
},
'Sample3': {
'primer': 'GTGCCAGCMGCCGCGGTAA',
'barcode': 'CGTAGAGCTCTT',
'center_name': 'KnightLab',
'platform': 'ILLUMINA',
'instrument_model': 'Illumina MiSeq',
'library_construction_protocol': 'Protocol ABC',
'experiment_design_description': "Random value 3"
},
}
metadata = pd.DataFrame.from_dict(metadata_dict,
orient='index',
dtype=str)
pt = PrepTemplate.create(metadata, study, "16S", 'Metagenomics')
fna_fp = join(self.temp_dir, 'seqs.fna')
demux_fp = join(self.temp_dir, 'demux.seqs')
with open(fna_fp, 'w') as f:
f.write(FASTA_EXAMPLE_2.format(study.id))
with File(demux_fp, 'w') as f:
to_hdf5(fna_fp, f)
ppd = Artifact.create([(demux_fp, 6)],
"Demultiplexed",
prep_template=pt)
return ppd
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 setUp(self):
metadata_dict = {
'Sample1': {
'physical_location': 'location1',
'has_physical_specimen': True,
'has_extracted_data': True,
'sample_type': 'type1',
'required_sample_info_status_id': 1,
'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
},
'Sample2': {
'physical_location': 'location1',
'has_physical_specimen': True,
'has_extracted_data': True,
'sample_type': 'type1',
'required_sample_info_status_id': 1,
'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
},
'Sample3': {
'physical_location': 'location1',
'has_physical_specimen': True,
'has_extracted_data': True,
'sample_type': 'type1',
'required_sample_info_status_id': 1,
'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
},
}
self.metadata = pd.DataFrame.from_dict(metadata_dict, orient='index')
self.test_study = Study(1)
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)
}
self.new_study = Study.create(User('*****@*****.**'),
"Fried Chicken Microbiome", [1], info)
self.tester = SampleTemplate(1)
self.exp_sample_ids = {
'SKB1.640202', 'SKB2.640194', 'SKB3.640195', 'SKB4.640189',
'SKB5.640181', 'SKB6.640176', 'SKB7.640196', 'SKB8.640193',
'SKB9.640200', 'SKD1.640179', 'SKD2.640178', 'SKD3.640198',
'SKD4.640185', 'SKD5.640186', 'SKD6.640190', 'SKD7.640191',
'SKD8.640184', 'SKD9.640182', 'SKM1.640183', 'SKM2.640199',
'SKM3.640197', 'SKM4.640180', 'SKM5.640177', 'SKM6.640187',
'SKM7.640188', 'SKM8.640201', 'SKM9.640192'
}
self._clean_up_files = []
def test_retrieve_phone_null(self):
person = StudyPerson(3)
self.assertEqual(person.phone, None)
def render(self, study):
study_info = study.info
id = study.id
abstract = study_info['study_abstract']
description = study_info['study_description']
publications = []
for doi, pmid in study.publications:
if doi is not None:
publications.append(doi_linkifier([doi]))
if pmid is not None:
publications.append(pubmed_linkifier([pmid]))
publications = ", ".join(publications)
princ_inv = StudyPerson(study_info['principal_investigator_id'])
pi_link = study_person_linkifier((princ_inv.email, princ_inv.name))
number_samples_promised = study_info['number_samples_promised']
number_samples_collected = study_info['number_samples_collected']
metadata_complete = study_info['metadata_complete']
data_types = sorted(viewitems(get_data_types()), key=itemgetter(1))
# Retrieve the files from the uploads folder, so the user can choose
# the sample template of the study. Filter them to only include the
# ones that ends with 'txt' or 'tsv'.
files = [
f for _, f in get_files_from_uploads_folders(str(study.id))
if f.endswith(('txt', 'tsv'))
]
# If the sample template exists, retrieve all its filepaths
if SampleTemplate.exists(study.id):
sample_templates = SampleTemplate(study.id).get_filepaths()
else:
# If the sample template does not exist, just pass an empty list
sample_templates = []
# Check if the request came from a local source
is_local_request = is_localhost(self.request.headers['host'])
# The user can choose the sample template only if the study is
# sandboxed or the current user is an admin
show_select_sample = (study.status == 'sandbox'
or self.current_user.level == 'admin')
# EBI information
ebi_status = study.ebi_submission_status
ebi_accession = study.ebi_study_accession
if ebi_accession:
ebi_accession = (EBI_LINKIFIER.format(ebi_accession))
return self.render_string(
"study_description_templates/study_information_tab.html",
abstract=abstract,
description=description,
id=id,
publications=publications,
principal_investigator=pi_link,
number_samples_promised=number_samples_promised,
number_samples_collected=number_samples_collected,
metadata_complete=metadata_complete,
show_select_sample=show_select_sample,
files=files,
study_id=study.id,
sample_templates=sample_templates,
is_local_request=is_local_request,
data_types=data_types,
ebi_status=ebi_status,
ebi_accession=ebi_accession)
def test_retrieve_address_null(self):
person = StudyPerson(2)
self.assertEqual(person.address, None)
def setUp(self):
self.studyperson = StudyPerson(1)
def setUp(self):
self.study = Study(1)
self.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)
}
self.infoexp = {
"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": 2,
"principal_investigator_id": 3,
"lab_person_id": 1
}
self.existingexp = {
'mixs_compliant':
True,
'metadata_complete':
True,
'reprocess':
False,
'number_samples_promised':
27,
'emp_person_id':
StudyPerson(2),
'funding':
None,
'vamps_id':
None,
'first_contact':
datetime(2014, 5, 19, 16, 10),
'principal_investigator_id':
StudyPerson(3),
'timeseries_type_id':
1,
'study_abstract':
"This is a preliminary study to examine the "
"microbiota associated with the Cannabis plant. Soils samples "
"from the bulk soil, soil associated with the roots, and the "
"rhizosphere were extracted and the DNA sequenced. Roots "
"from three independent plants of different strains were "
"examined. These roots were obtained November 11, 2011 from "
"plants that had been harvested in the summer. Future "
"studies will attempt to analyze the soils and rhizospheres "
"from the same location at different time points in the plant "
"lifecycle.",
'spatial_series':
False,
'study_description':
'Analysis of the Cannabis Plant Microbiome',
'portal_type_id':
2,
'study_alias':
'Cannabis Soils',
'most_recent_contact':
'2014-05-19 16:11',
'most_recent_contact':
datetime(2014, 5, 19, 16, 11),
'lab_person_id':
StudyPerson(1),
'number_samples_collected':
27
}
def setUp(self):
# Create a directory with the test split libraries output
self.test_slo = mkdtemp(prefix='test_slo_')
path_builder = partial(join, self.test_slo)
fna_fp = path_builder('seqs.fna')
fastq_fp = path_builder('seqs.fastq')
log_fp = path_builder('split_library_log.txt')
demux_fp = path_builder('seqs.demux')
with open(fna_fp, 'w') as f:
f.write(FASTA_SEQS)
with open(fastq_fp, 'w') as f:
f.write(FASTQ_SEQS)
with open(log_fp, 'w') as f:
f.write("Test log\n")
generate_demux_file(self.test_slo)
self._filepaths_to_remove = [fna_fp, fastq_fp, demux_fp, log_fp]
self._dirpaths_to_remove = [self.test_slo]
# Generate a directory with test split libraries output missing files
self.missing_slo = mkdtemp(prefix='test_missing_')
path_builder = partial(join, self.test_slo)
fna_fp = path_builder('seqs.fna')
fastq_fp = path_builder('seqs.fastq')
with open(fna_fp, 'w') as f:
f.write(FASTA_SEQS)
with open(fastq_fp, 'w') as f:
f.write(FASTQ_SEQS)
self._filepaths_to_remove.append(fna_fp)
self._filepaths_to_remove.append(fastq_fp)
self._dirpaths_to_remove.append(self.missing_slo)
# Create a study with no preprocessed data
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)
}
self.no_ppd_study = Study.create(User('*****@*****.**'), "Test study",
[1], info)
# Get the directory where the preprocessed data is usually copied.
_, self.db_ppd_dir = get_mountpoint('preprocessed_data')[0]
def test_delete_study(self):
# as samples have been submitted to EBI, this will fail
job = self._create_job('delete_study', {'study': 1})
private_task(job.id)
self.assertEqual(job.status, 'error')
self.assertIn("Artifact 2 has been submitted to EBI", job.log.msg)
# making sure the analysis, first thing to delete, still exists
self.assertTrue(Analysis.exists(1))
info = {
"timeseries_type_id": '1',
"metadata_complete": 'true',
"mixs_compliant": 'true',
"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",
"principal_investigator_id": StudyPerson(3),
"lab_person_id": StudyPerson(1)
}
new_study = Study.create(User('*****@*****.**'),
"Fried Chicken Microbiome %s" % time(), info)
# adding tags
new_study.update_tags(User('*****@*****.**'), ['my new tag!'])
# creating a sample information file
metadata = pd.DataFrame.from_dict(
{
'Sample1': {
'physical_specimen_location': 'location1',
'taxon_id': '9606',
'scientific_name': 'h**o sapiens'
},
'Sample2': {
'physical_specimen_location': 'location1',
'taxon_id': '9606',
'scientific_name': 'h**o sapiens'
},
'Sample3': {
'physical_specimen_location': 'location1',
'taxon_id': '9606',
'scientific_name': 'h**o sapiens'
}
},
orient='index')
SampleTemplate.create(metadata, new_study)
# creating a preparation information file
metadata = pd.DataFrame.from_dict(
{
'Sample1': {
'center_name': 'ANL',
'target_subfragment': 'V4',
'center_project_name': 'Test Project'
}
},
orient='index',
dtype=str)
PrepTemplate.create(metadata, new_study, '16S')
job = self._create_job('delete_study', {'study': new_study.id})
private_task(job.id)
self.assertEqual(job.status, 'success')
# making sure the study doesn't exist
with self.assertRaises(QiitaDBUnknownIDError):
Study(new_study.id)
