def update_sample_template(self, study, user, callback):
"""Update 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 argument "sample_template" must
# defined. If not, let tornado raise its error
sample_template = self.get_argument('sample_template')
# Define here the message and message level in case of success
msg = "The sample template '%s' has been updated" % sample_template
msg_level = "success"
# 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(400, "This file doesn't exist: %s" % fp_rsp)
try:
with warnings.catch_warnings(record=True) as warns:
# deleting previous uploads and inserting new one
st = SampleTemplate(study.id)
df = load_template_to_dataframe(fp_rsp)
st.extend(df)
st.update(df)
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 = '\n'.join(set(str(w.message) for w in warns))
msg_level = 'warning'
except (TypeError, QiitaDBColumnError, QiitaDBExecutionError,
QiitaDBDuplicateError, IOError, ValueError, KeyError,
CParserError, QiitaDBDuplicateHeaderError, QiitaDBError) as e:
# Some error occurred while processing the sample template
# Show the error to the user so they can fix the template
msg = html_error_message % ('updating the sample template:',
basename(fp_rsp), str(e))
msg = convert_text_html(msg)
msg_level = "danger"
callback((msg, msg_level, None, None, None))
def update_sample_template(self, study, user, callback):
"""Update 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 argument "sample_template" must
# defined. If not, let tornado raise its error
sample_template = self.get_argument('sample_template')
# Define here the message and message level in case of success
msg = "The sample template '%s' has been updated" % sample_template
msg_level = "success"
# 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(400, "This file doesn't exist: %s" % fp_rsp)
try:
with warnings.catch_warnings(record=True) as warns:
# deleting previous uploads and inserting new one
st = SampleTemplate(study.id)
df = load_template_to_dataframe(fp_rsp)
st.extend(df)
st.update(df)
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 = '\n'.join(set(str(w.message) for w in warns))
msg_level = 'warning'
except (TypeError, QiitaDBColumnError, QiitaDBExecutionError,
QiitaDBDuplicateError, IOError, ValueError, KeyError,
CParserError, QiitaDBDuplicateHeaderError, QiitaDBError) as e:
# Some error occurred while processing the sample template
# Show the error to the user so they can fix the template
msg = html_error_message % ('updating the sample template:',
basename(fp_rsp), str(e))
msg = convert_text_html(msg)
msg_level = "danger"
callback((msg, msg_level, None, None, None))
def update_sample_template(study_id, fp):
"""Updates a sample template
Parameters
----------
study_id : int
Study id whose template is going to be updated
fp : str
The file path to the template file
Returns
-------
dict of {str: str}
A dict of the form {'status': str, 'message': str}
"""
import warnings
from os import remove
from qiita_db.metadata_template.util import load_template_to_dataframe
from qiita_db.metadata_template.sample_template import SampleTemplate
msg = ''
status = 'success'
try:
with warnings.catch_warnings(record=True) as warns:
# deleting previous uploads and inserting new one
st = SampleTemplate(study_id)
df = load_template_to_dataframe(fp)
st.extend(df)
st.update(df)
remove(fp)
# join all the warning messages into one. Note that this info
# will be ignored if an exception is raised
if warns:
msg = '\n'.join(set(str(w.message) for w in warns))
status = 'warning'
except Exception as e:
status = 'danger'
msg = str(e)
return {'status': status, 'message': msg}
def update_sample_template(study_id, fp):
"""Updates a sample template
Parameters
----------
study_id : int
Study id whose template is going to be updated
fp : str
The file path to the template file
Returns
-------
dict of {str: str}
A dict of the form {'status': str, 'message': str}
"""
import warnings
from os import remove
from qiita_db.metadata_template.util import load_template_to_dataframe
from qiita_db.metadata_template.sample_template import SampleTemplate
msg = ''
status = 'success'
try:
with warnings.catch_warnings(record=True) as warns:
# deleting previous uploads and inserting new one
st = SampleTemplate(study_id)
df = load_template_to_dataframe(fp)
st.extend(df)
st.update(df)
remove(fp)
# join all the warning messages into one. Note that this info
# will be ignored if an exception is raised
if warns:
msg = '\n'.join(set(str(w.message) for w in warns))
status = 'warning'
except Exception as e:
status = 'danger'
msg = str(e)
return {'status': status, 'message': msg}
AND table_name LIKE 'sample_%%'
AND table_name NOT IN (
'prep_template', 'prep_template_sample')
GROUP BY table_name"""
# note that we are looking for those columns with duplicated names in
# the headers
TRN.add(sql, [tuple(set(cols_sample))])
for table, columns in dict(TRN.execute_fetchindex()).items():
# [1] the format is table_# so taking the #
st = SampleTemplate(int(table.split('_')[1]))
# getting just the columns of interest
st_df = st.to_dataframe()[columns]
# converting to datetime
for col in columns:
st_df[col] = st_df[col].apply(transform_date)
st.update(st_df)
if cols_prep:
with TRN:
# a few notes: just getting the preps with duplicated values; ignoring
# column 'sample_id' and tables 'study_sample', 'prep_template',
# 'prep_template_sample'
sql = """SELECT table_name, array_agg(column_name::text)
FROM information_schema.columns
WHERE column_name IN %s
AND table_name LIKE 'prep_%%'
AND table_name NOT IN (
'prep_template', 'prep_template_sample')
GROUP BY table_name"""
# note that we are looking for those columns with duplicated names in
# the headers
AND table_name LIKE 'sample_%%'
AND table_name NOT IN (
'prep_template', 'prep_template_sample')
GROUP BY table_name"""
# note that we are looking for those columns with duplicated names in
# the headers
TRN.add(sql, [tuple(set(cols_sample))])
for table, columns in viewitems(dict(TRN.execute_fetchindex())):
# [1] the format is table_# so taking the #
st = SampleTemplate(int(table.split('_')[1]))
# getting just the columns of interest
st_df = st.to_dataframe()[columns]
# converting to datetime
for col in columns:
st_df[col] = st_df[col].apply(transform_date)
st.update(st_df)
if cols_prep:
with TRN:
# a few notes: just getting the preps with duplicated values; ignoring
# column 'sample_id' and tables 'study_sample', 'prep_template',
# 'prep_template_sample'
sql = """SELECT table_name, array_agg(column_name::text)
FROM information_schema.columns
WHERE column_name IN %s
AND table_name LIKE 'prep_%%'
AND table_name NOT IN (
'prep_template', 'prep_template_sample')
GROUP BY table_name"""
# note that we are looking for those columns with duplicated names in
# the headers
