def __init__(self, gc):
Thermodynamics.__init__(self)
self.gc = gc
self.kegg = gc.kegg()
self.data = [] # this will hold all the training data in one place, each row represents a measurement
self.train_rowids = [] # the row indices in self.data to use for training
self.test_rowids = [] # the row indices in self.data to use for testing
self.cid2rowids = {} # holds a list of indices to self.train_data where the CID is participating
self.cid2pmap_dict = {} # this is the solution vector (what we are trying to solve)
self.cache_error = {} # for each rowid - holds the last calculated squared error (the difference between calc and measured)
self.cache_cid = {} # for each (rowid,cid) pair - holds the last calculated dG_f (multiplied by the stoichiometric coeff)
def __init__(self, gc):
Thermodynamics.__init__(self)
self.gc = gc
self.kegg = gc.kegg()
self.data = [
] # this will hold all the training data in one place, each row represents a measurement
self.train_rowids = [
] # the row indices in self.data to use for training
self.test_rowids = [
] # the row indices in self.data to use for testing
self.cid2rowids = {
} # holds a list of indices to self.train_data where the CID is participating
self.cid2pmap_dict = {
} # this is the solution vector (what we are trying to solve)
self.cache_error = {
} # for each rowid - holds the last calculated squared error (the difference between calc and measured)
self.cache_cid = {
} # for each (rowid,cid) pair - holds the last calculated dG_f (multiplied by the stoichiometric coeff)
def __init__(self, use_pKa=True):
if use_pKa:
Thermodynamics.__init__(self, "Jankowski et al. (+pKa)")
self.dissociation = DissociationConstants.FromPublicDB()
else:
Thermodynamics.__init__(self, "Jankowski et al.")
self.dissociation = None
self.db = SqliteDatabase('../res/gibbs.sqlite', 'w')
self.cid2pmap_dict = {}
# the conditions in which Hatzimanikatis makes his predictions
self.Hatzi_pH = 7.0
self.Hatzi_I = 0.0
self.Hatzi_pMg = 14.0
self.Hatzi_T = 298.15
self.kegg = Kegg.getInstance()
# for some reason, Hatzimanikatis doesn't indicate that H+ is zero,
# so we add it here
H_pmap = PseudoisomerMap()
H_pmap.Add(0, 0, 0, 0)
self.SetPseudoisomerMap(80, H_pmap)
self.cid2dG0_tag_dict = {80 : 0}
self.cid2charge_dict = {80 : 0}
for row in csv.DictReader(open(HATZI_CSV_FNAME, 'r')):
cid = int(row['ENTRY'][1:])
self.cid2source_string[cid] = 'Jankowski et al. 2008'
if row['DELTAG'] == "Not calculated":
continue
if cid == 3178:
# this compound, which is supposed to be "Tetrahydroxypteridine"
# seems to be mapped to something else by Hatzimanikatis
continue
self.cid2dG0_tag_dict[cid] = float(row['DELTAG']) * J_per_cal
self.cid2charge_dict[cid] = int(row['CHARGE'])
def __init__(self, use_pKa=True):
if use_pKa:
Thermodynamics.__init__(self, "Jankowski et al. (+pKa)")
self.dissociation = DissociationConstants.FromPublicDB()
else:
Thermodynamics.__init__(self, "Jankowski et al.")
self.dissociation = None
self.db = SqliteDatabase('../res/gibbs.sqlite', 'w')
self.cid2pmap_dict = {}
# the conditions in which Hatzimanikatis makes his predictions
self.Hatzi_pH = 7.0
self.Hatzi_I = 0.0
self.Hatzi_pMg = 14.0
self.Hatzi_T = 298.15
self.kegg = Kegg.getInstance()
# for some reason, Hatzimanikatis doesn't indicate that H+ is zero,
# so we add it here
H_pmap = PseudoisomerMap()
H_pmap.Add(0, 0, 0, 0)
self.SetPseudoisomerMap(80, H_pmap)
self.cid2dG0_tag_dict = {80: 0}
self.cid2charge_dict = {80: 0}
for row in csv.DictReader(open(HATZI_CSV_FNAME, 'r')):
cid = int(row['ENTRY'][1:])
self.cid2source_string[cid] = 'Jankowski et al. 2008'
if row['DELTAG'] == "Not calculated":
continue
if cid == 3178:
# this compound, which is supposed to be "Tetrahydroxypteridine"
# seems to be mapped to something else by Hatzimanikatis
continue
self.cid2dG0_tag_dict[cid] = float(row['DELTAG']) * J_per_cal
self.cid2charge_dict[cid] = int(row['CHARGE'])
