def __init__(self,
taxon=None,
max_taxon_dist=None,
taxon_dist_scale=None,
include_variants=False,
temperature=37.,
temperature_std=1.,
ph=7.5,
ph_std=0.3,
cache_dirname=None):
"""
Args:
taxon (:obj:`str`, optional): target taxon
max_taxon_dist (:obj:`int`, optional): maximum taxonomic distance to include
taxon_dist_scale (:obj:`float`, optional): The scale of the taxonomic distance scoring distribution.
This determines how quickly the score falls to zero away from zero.
include_variants (:obj:`bool`, optional): if :obj:`True`, also include observations from mutant taxa
temperature (:obj:`float`, optional): desired temperature to search for
temperature_std (:obj:`float`, optional): how much to penalize observations from other temperatures
ph (:obj:`float`, optional): desired pH to search for
ph_std (:obj:`float`, optional): how much to penalize observations from other pHs
"""
super(ProteinAbundanceQuery,
self).__init__(taxon=taxon,
max_taxon_dist=max_taxon_dist,
taxon_dist_scale=taxon_dist_scale,
include_variants=include_variants,
temperature=temperature,
temperature_std=temperature_std,
ph=ph,
ph_std=ph_std,
data_source=common_schema.CommonSchema(
cache_dirname=cache_dirname))
def setUpClass(cls):
cls.cache_dirname = tempfile.mkdtemp()
flk = common_schema.CommonSchema(cache_dirname=cls.cache_dirname)
cls.proline = flk.session.query(models.Metabolite).filter_by(metabolite_name = 'L-Proline').first()
q = metabolite_concentrations.MetaboliteConcentrationQuery(cache_dirname=cls.cache_dirname, include_variants=True)
cls.obs = q.run(cls.proline)
def __init__(self, cache_dirname=DATA_CACHE_DIR, params=None, data=None):
self.data_source = common_schema.CommonSchema(
cache_dirname=cache_dirname)
self.params = params
self.data = data
self.run()
def _default(self):
pargs = self.app.pargs
common_schema.CommonSchema(clear_content=True,
restore_backup_data=pargs.restore_data,
restore_backup_schema=pargs.restore_schema,
restore_backup_exit_on_error=pargs.exit_on_error,
load_content=False,
verbose=True)
def setUpClass(cls):
cls.cache_dirname = tempfile.mkdtemp()
flk = common_schema.CommonSchema(cache_dirname=cls.cache_dirname)
cls.arnt = flk.session.query(
models.ProteinSubunit).filter_by(uniprot_id='P53762').first()
cls.q = dpi.ProteintoDNAInteractionQuery(
cache_dirname=cls.cache_dirname)
def setUpClass(cls):
cls.cache_dirname = tempfile.mkdtemp()
cls.cs = common_schema.CommonSchema(
name = 'TestCommonSchema',
cache_dirname = cls.cache_dirname,
clear_content=True,
load_content=True, max_entries=10,
verbose=True, test=True)
def aggregate(self):
pargs = self.app.pargs
# todo: set restore_backup_schema=False after fixing Alembic issue with migrations
# todo: restore_backup_exit_on_error=True after fixing Alembic issue with migrations
common_schema.CommonSchema(clear_content=True,
restore_backup_data=True, restore_backup_schema=False,
load_content=False,
verbose=True)
def _default(self):
pargs = self.app.pargs
# todo: set restore_backup_schema=False after fixing Alembic issue with migrations
# todo: restore_backup_exit_on_error=True after fixing Alembic issue with migrations
common_schema.CommonSchema(load_content=True,
restore_backup_data=True, restore_backup_schema=True,
restore_backup_exit_on_error=False,
max_entries=pargs.max_entries,
verbose=pargs.verbose)
def setUpClass(cls):
cls.cache_dirname = tempfile.mkdtemp()
flk = common_schema.CommonSchema(cache_dirname=cls.cache_dirname)
cls.protein_P00323 = flk.session.query(
models.ProteinSubunit).filter_by(uniprot_id='P00323').first()
# cls.protein_Q42025 = flk.session.query(models.ProteinSubunit).filter_by(uniprot_id = 'Q42025').first()
# print(cls.protein_Q42025)
cls.q = protein_abundance.ProteinAbundanceQuery(
cache_dirname=cls.cache_dirname)
def build():
old_stdout = sys.stdout
log_filename = pkg_resources.resource_filename(
'datanator', "builds/logs/{}.txt".format(str(datetime.datetime.now())))
with open(log_filename, "w") as log_file:
sys.stdout = log_file
cs = common_schema.CommonSchema(load_content=True, verbose=True,
load_entire_small_dbs=True)
cs.upload_backup()
sys.stdout = old_stdout
def restore_db(restore_data=True, restore_schema=False, exit_on_error=True):
""" Restore the content of the database
Args:
restore_data (:obj:`bool`, optional): if :obj:`True`, restore the data
restore_schema (:obj:`bool`, optional): if :obj:`True`, restore the schema
exit_on_error (:obj:`bool`, optional): if :obj:`True`, exit on error
"""
if not restore_data and not restore_schema:
raise Exception('One or more of the data and schema must be restored')
common_schema.CommonSchema(clear_content=True,
restore_backup_data=restore_data,
restore_backup_schema=restore_schema,
restore_backup_exit_on_error=exit_on_error,
load_content=False,
verbose=True)
def setUpClass(cls):
cls.cache_dirname = tempfile.mkdtemp()
flk = common_schema.CommonSchema(cache_dirname=cls.cache_dirname)
cls.protein_Q9CWF2 = flk.session.query(
models.ProteinSubunit).filter_by(uniprot_id='Q9CWF2').first()
cls.protein_p53622 = flk.session.query(
models.ProteinSubunit).filter_by(uniprot_id='P53622').first()
cls.protein_p49418 = flk.session.query(
models.ProteinSubunit).filter_by(uniprot_id='P49418').first()
cls.rhino_complex = flk.session.query(models.ProteinComplex).filter_by(
complex_name='Rhino-Deadlock-Cutoff Complex').first()
cls.collagen = flk.session.query(models.ProteinComplex).filter_by(
complex_name='Collagen type III trimer').first()
cls.q = ppi.ProteinInteractionandComplexQuery(
cache_dirname=cls.cache_dirname)
def setUpClass(cls):
cls.cache_dirname = tempfile.mkdtemp()
cls.flk = common_schema.CommonSchema(cache_dirname=cls.cache_dirname)
cls.q = reaction_kinetics.ReactionKineticsQuery(
cache_dirname=cls.cache_dirname, include_variants=True)
cls.reaction = data_model.Reaction(
participants = [
data_model.ReactionParticipant(
specie = data_model.Specie(
id = 'Dihydrofolate',
structure = 'InChI=1S/C19H21N7O6/c20-19-25-15-14(17(30)26-19)23-11(8-22-15)'+\
'7-21-10-3-1-9(2-4-10)16(29)24-12(18(31)32)5-6-13(27)28/h1-4,12,21H,5-8H2,'+\
'(H,24,29)(H,27,28)(H,31,32)(H4,20,22,25,26,30)'),
coefficient = -1),
data_model.ReactionParticipant(
specie = data_model.Specie(
id = 'NADPH',
structure = 'InChI=1S/C21H30N7O17P3/c22-17-12-19(25-7-24-17)28(8-26-12)21-16'+\
'(44-46(33,34)35)14(30)11(43-21)6-41-48(38,39)45-47(36,37)40-5-10-13(29)15(31)'+\
'20(42-10)27-3-1-2-9(4-27)18(23)32/h1,3-4,7-8,10-11,13-16,20-21,29-31H,2,5-6H2,'+\
'(H2,23,32)(H,36,37)(H,38,39)(H2,22,24,25)(H2,33,34,35)'),
coefficient = -1),
data_model.ReactionParticipant(
specie = data_model.Specie(
id = 'H+',
structure = 'InChI=1S/H'),
coefficient = -1),
data_model.ReactionParticipant(
specie = data_model.Specie(
id = 'NADP+',
structure = 'InChI=1S/C21H28N7O17P3/c22-17-12-19(25-7-24-17)28(8-26-12)21-16('+\
'44-46(33,34)35)14(30)11(43-21)6-41-48(38,39)45-47(36,37)40-5-10-13(29)15(31)20'+\
'(42-10)27-3-1-2-9(4-27)18(23)32/h1-4,7-8,10-11,13-16,20-21,29-31H,5-6H2,(H7-,22'+\
',23,24,25,32,33,34,35,36,37,38,39)/p+1'),
coefficient = 1),
data_model.ReactionParticipant(
specie = data_model.Specie(
id = '5,6,7,8-Tetrahydrofolate',
structure = 'InChI=1S/C19H23N7O6/c20-19-25-15-14(17(30)26-19)23-11(8-22-15)7-21-'+\
'10-3-1-9(2-4-10)16(29)24-12(18(31)32)5-6-13(27)28/h1-4,11-12,21,23H,5-8H2,(H,24,29'+\
')(H,27,28)(H,31,32)(H4,20,22,25,26,30)'),
coefficient = 1)
])
import sys
sys.path.append("/Users/pochis01/Desktop/GitHub/datanator")
import datetime
# old_stdout = sys.stdout
# log_file = open("datanator/builds/logs/{}.txt".format(str(datetime.datetime.now())),"w")
# sys.stdout = log_file
from datanator.core import common_schema
cs = common_schema.CommonSchema(load_content=True,
clear_content=True,
verbose=True,
test=True,
max_entries=10)
# sys.stdout = old_stdout
# log_file.close()
import sys
sys.path.append("/Users/pochis01/Desktop/GitHub/datanator")
import datetime
from datanator.core import common_schema
old_stdout = sys.stdout
log_file = open(
"datanator/builds/logs/{}.txt".format(str(datetime.datetime.now())), "w")
sys.stdout = log_file
cs = common_schema.CommonSchema(load_content=True,
verbose=True,
load_entire_small_dbs=True)
cs.upload_backup()
sys.stdout = old_stdout
log_file.close()
def __init__(self, cache_dirname=None):
self.data_source = common_schema.CommonSchema(
cache_dirname=cache_dirname)
def __init__(self, cache_dirname=DATA_CACHE_DIR):
self.cache_dirname = cache_dirname
self.flask = common_schema.CommonSchema(cache_dirname=cache_dirname)
def __init__(self, db_cache_dirname = os.getcwd()):
flaskdb = common_schema.CommonSchema(cache_dirname = db_cache_dirname)
self.q = reaction_kinetics.ReactionKineticsQuery(cache_dirname=db_cache_dirname, include_variants=True)
def __init__(self, cache_dirname=DATA_CACHE_DIR):
self.data_source = common_schema.CommonSchema(
cache_dirname=cache_dirname)
def setUpClass(cls):
cls.cache_dirname = tempfile.mkdtemp()
cls.sesh = text_search.TextSearchSession(db_cache_dirname=cls.cache_dirname)
flaskdb = common_schema.CommonSchema(cache_dirname = cls.cache_dirname)
def setUpClass(cls):
cls.flk = common_schema.CommonSchema()
