def remove_add_study_template(self, raw_data, study_id, fp_rsp):
"""Replace prep templates, raw data, and sample template with a new one
"""
for rd in raw_data():
rd = RawData(rd)
for pt in rd.prep_templates:
if PrepTemplate.exists(pt):
PrepTemplate.delete(pt)
if SampleTemplate.exists(study_id):
SampleTemplate.delete(study_id)
SampleTemplate.create(load_template_to_dataframe(fp_rsp),
Study(study_id))
remove(fp_rsp)
def remove_add_study_template(self, raw_data, study_id, fp_rsp):
"""Replace prep templates, raw data, and sample template with a new one
"""
for rd in raw_data():
rd = RawData(rd)
for pt in rd.prep_templates:
if PrepTemplate.exists(pt):
PrepTemplate.delete(pt)
if SampleTemplate.exists(study_id):
SampleTemplate.delete(study_id)
SampleTemplate.create(load_template_to_dataframe(fp_rsp),
Study(study_id))
remove(fp_rsp)
def test_to_file(self):
"""to file writes a tab delimited file with all the metadata"""
fd, fp = mkstemp()
close(fd)
st = SampleTemplate.create(self.metadata, self.new_study)
st.to_file(fp)
self._clean_up_files.append(fp)
with open(fp, 'U') as f:
obs = f.read()
self.assertEqual(obs, EXP_SAMPLE_TEMPLATE)
def test_to_file(self):
"""to file writes a tab delimited file with all the metadata"""
fd, fp = mkstemp()
close(fd)
st = SampleTemplate.create(self.metadata, self.new_study)
st.to_file(fp)
self._clean_up_files.append(fp)
with open(fp, 'U') as f:
obs = f.read()
self.assertEqual(obs, EXP_SAMPLE_TEMPLATE)
def remove_add_study_template(self, raw_data, study_id, fp_rsp, data_type, is_mapping_file):
"""Replace prep templates, raw data, and sample template with a new one
"""
if is_mapping_file and data_type == "":
raise ValueError("Please, choose a data type if uploading a QIIME " "mapping file")
for rd in raw_data():
rd = RawData(rd)
for pt in rd.prep_templates:
if PrepTemplate.exists(pt):
PrepTemplate.delete(pt)
if SampleTemplate.exists(study_id):
SampleTemplate.delete(study_id)
if is_mapping_file:
create_templates_from_qiime_mapping_file(fp_rsp, Study(study_id), int(data_type))
else:
SampleTemplate.create(load_template_to_dataframe(fp_rsp), Study(study_id))
remove(fp_rsp)
def test_create(self):
"""Creates a new SampleTemplate"""
st = SampleTemplate.create(self.metadata, self.new_study)
# The returned object has the correct id
self.assertEqual(st.id, 2)
# The relevant rows to required_sample_info have been added.
obs = self.conn_handler.execute_fetchall(
"SELECT * FROM qiita.required_sample_info WHERE study_id=2")
# study_id sample_id physical_location has_physical_specimen
# has_extracted_data sample_type required_sample_info_status_id
# collection_timestamp host_subject_id description
exp = [[
2, "Sample1", "location1", True, True, "type1", 1,
datetime(2014, 5, 29, 12, 24, 51), "NotIdentified",
"Test Sample 1", 42.42, 41.41
],
[
2, "Sample2", "location1", True, True, "type1", 1,
datetime(2014, 5, 29, 12, 24, 51), "NotIdentified",
"Test Sample 2", 4.2, 1.1
],
[
2, "Sample3", "location1", True, True, "type1", 1,
datetime(2014, 5, 29, 12, 24, 51), "NotIdentified",
"Test Sample 3", 4.8, 4.41
]]
self.assertEqual(obs, exp)
# The relevant rows have been added to the study_sample_columns
obs = self.conn_handler.execute_fetchall(
"SELECT * FROM qiita.study_sample_columns WHERE study_id=2")
# study_id, column_name, column_type
exp = [[2, "str_column", "varchar"]]
self.assertEqual(obs, exp)
# The new table exists
self.assertTrue(exists_table("sample_2", self.conn_handler))
# The new table hosts the correct values
obs = self.conn_handler.execute_fetchall(
"SELECT * FROM qiita.sample_2")
# sample_id, str_column
exp = [['Sample1', "Value for sample 1"],
['Sample2', "Value for sample 2"],
['Sample3', "Value for sample 3"]]
self.assertEqual(obs, exp)
def test_create(self):
"""Creates a new SampleTemplate"""
st = SampleTemplate.create(self.metadata, self.new_study)
# The returned object has the correct id
self.assertEqual(st.id, 2)
# The relevant rows to required_sample_info have been added.
obs = self.conn_handler.execute_fetchall(
"SELECT * FROM qiita.required_sample_info WHERE study_id=2")
# study_id sample_id physical_location has_physical_specimen
# has_extracted_data sample_type required_sample_info_status_id
# collection_timestamp host_subject_id description
exp = [[2, "Sample1", "location1", True, True, "type1", 1,
datetime(2014, 5, 29, 12, 24, 51), "NotIdentified",
"Test Sample 1"],
[2, "Sample2", "location1", True, True, "type1", 1,
datetime(2014, 5, 29, 12, 24, 51), "NotIdentified",
"Test Sample 2"],
[2, "Sample3", "location1", True, True, "type1", 1,
datetime(2014, 5, 29, 12, 24, 51), "NotIdentified",
"Test Sample 3"]]
self.assertEqual(obs, exp)
# The relevant rows have been added to the study_sample_columns
obs = self.conn_handler.execute_fetchall(
"SELECT * FROM qiita.study_sample_columns WHERE study_id=2")
# study_id, column_name, column_type
exp = [[2, "str_column", "varchar"]]
self.assertEqual(obs, exp)
# The new table exists
self.assertTrue(exists_table("sample_2", self.conn_handler))
# The new table hosts the correct values
obs = self.conn_handler.execute_fetchall(
"SELECT * FROM qiita.sample_2")
# sample_id, str_column
exp = [['Sample1', "Value for sample 1"],
['Sample2', "Value for sample 2"],
['Sample3', "Value for sample 3"]]
self.assertEqual(obs, 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)
def process_sample_template(self, study, user, callback):
"""Process a sample template from the POST method
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
Raises
------
HTTPError
If the sample template file does not exists
"""
# If we are on this function, the arguments "sample_template" and
# "data_type" must be defined. If not, let tornado raise its error
sample_template = self.get_argument('sample_template')
data_type = self.get_argument('data_type')
# Get the uploads folder
_, base_fp = get_mountpoint("uploads")[0]
# Get the path of the sample template in the uploads folder
fp_rsp = join(base_fp, str(study.id), sample_template)
if not exists(fp_rsp):
# The file does not exist, fail nicely
raise HTTPError(404, "This file doesn't exist: %s" % fp_rsp)
# Define here the message and message level in case of success
msg = "The sample template '%s' has been added" % sample_template
msg_level = "success"
is_mapping_file = looks_like_qiime_mapping_file(fp_rsp)
try:
if is_mapping_file and not data_type:
raise ValueError("Please, choose a data type if uploading a "
"QIIME mapping file")
with warnings.catch_warnings(record=True) as warns:
if is_mapping_file:
create_templates_from_qiime_mapping_file(fp_rsp, study,
int(data_type))
else:
SampleTemplate.create(load_template_to_dataframe(fp_rsp),
study)
remove(fp_rsp)
# join all the warning messages into one. Note that this
# info will be ignored if an exception is raised
if warns:
msg = '; '.join([convert_text_html(str(w.message))
for w in warns])
msg_level = 'warning'
except (TypeError, QiitaDBColumnError, QiitaDBExecutionError,
QiitaDBDuplicateError, IOError, ValueError, KeyError,
CParserError, QiitaDBDuplicateHeaderError,
QiitaDBError, QiitaWareError) as e:
# Some error occurred while processing the sample template
# Show the error to the user so they can fix the template
error_msg = ('parsing the QIIME mapping file'
if is_mapping_file
else 'parsing the sample template')
msg = html_error_message % (error_msg, basename(fp_rsp),
str(e))
msg = convert_text_html(msg)
msg_level = "danger"
callback((msg, msg_level, None, None, None))
def create_templates_from_qiime_mapping_file(fp, study, data_type):
"""Creates a sample template and a prep template from qiime mapping file
Parameters
----------
fp : str or file-like object
Path to the QIIME mapping file
study : Study
The study to which the sample template belongs to
data_type : str or int
The data_type of the prep_template
Returns
-------
(SampleTemplate, PrepTemplate)
The templates created from the QIIME mapping file
"""
qiime_map = load_template_to_dataframe(fp, index='#SampleID')
# There are a few columns in the QIIME mapping file that are special and
# we know how to deal with them
rename_cols = {
'BarcodeSequence': 'barcode',
'LinkerPrimerSequence': 'primer',
'Description': 'description',
}
if 'ReverseLinkerPrimer' in qiime_map:
rename_cols['ReverseLinkerPrimer'] = 'reverselinkerprimer'
missing = set(rename_cols).difference(qiime_map.columns)
if missing:
raise QiitaWareError(
"Error generating the templates from the QIIME mapping file. "
"Missing QIIME mapping file columns: %s" % ', '.join(missing))
qiime_map.rename(columns=rename_cols, inplace=True)
# Fix the casing in the columns that we control
qiime_map.columns = [c.lower() if c.lower() in CONTROLLED_COLS else c
for c in qiime_map.columns]
# Figure out which columns belong to the prep template
def _col_iterator(restriction_set):
for restriction in viewvalues(restriction_set):
for cols in viewkeys(restriction.columns):
yield cols
pt_cols = set(col for col in _col_iterator(PREP_TEMPLATE_COLUMNS))
data_type_str = (convert_from_id(data_type, "data_type")
if isinstance(data_type, (int, long)) else data_type)
if data_type_str in TARGET_GENE_DATA_TYPES:
pt_cols.update(
col for col in _col_iterator(PREP_TEMPLATE_COLUMNS_TARGET_GENE))
pt_cols.add('reverselinkerprimer')
qiime_cols = set(qiime_map.columns)
pt_cols = qiime_cols.intersection(pt_cols)
st_cols = qiime_cols.difference(pt_cols)
st_md = qiime_map.ix[:, st_cols]
pt_md = qiime_map.ix[:, pt_cols]
return (SampleTemplate.create(st_md, study),
PrepTemplate.create(pt_md, study, data_type))
def test_create_duplicate_header(self):
"""Create raises an error when duplicate headers are present"""
self.metadata['STR_COLUMN'] = pd.Series(['', '', ''],
index=self.metadata.index)
with self.assertRaises(QiitaDBDuplicateHeaderError):
SampleTemplate.create(self.metadata, self.new_study)
def test_create_duplicate(self):
"""Create raises an error when creating a duplicated SampleTemplate"""
with self.assertRaises(QiitaDBDuplicateError):
SampleTemplate.create(self.metadata, self.test_study)
def test_create_duplicate_header(self):
"""Create raises an error when duplicate headers are present"""
self.metadata['STR_COLUMN'] = pd.Series(['', '', ''],
index=self.metadata.index)
with self.assertRaises(QiitaDBDuplicateHeaderError):
SampleTemplate.create(self.metadata, self.new_study)
def test_create_duplicate(self):
"""Create raises an error when creating a duplicated SampleTemplate"""
with self.assertRaises(QiitaDBDuplicateError):
SampleTemplate.create(self.metadata, self.test_study)
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 generate_new_study_with_preprocessed_data(self):
"""Creates a new study up to the processed data for testing"""
# ignoring warnings generated when adding templates
simplefilter("ignore")
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", [1], 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')
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')
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 = PreprocessedData.create(
study, "preprocessed_sequence_illumina_params", 1,
[(demux_fp, 6)], pt)
return ppd
