def _getFitParameters(list_of_dicts, key):
# Load rows of data
x = _loadRow('doublingTime', list_of_dicts)
y = _loadRow(key, list_of_dicts)
# Save and strip units
y_units = 1
x_units = 1
if units.hasUnit(y):
y_units = units.getUnit(y)
y = y.asNumber(y_units)
if units.hasUnit(x):
x_units = units.getUnit(x)
x = x.asNumber(x_units)
# Sort data for spine fitting (must be ascending order)
idx_order = x.argsort()
x = x[idx_order]
y = y[idx_order]
# Generate fit
parameters = interpolate.splrep(x, y)
if np.sum(np.absolute(interpolate.splev(x, parameters) - y)) / y.size > 1.:
raise Exception(
"Fitting {} with 3d spline, residuals are huge!".format(key))
return {
'parameters': parameters,
'x_units': x_units,
'y_units': y_units,
'dtype': y.dtype
}
def _loadRow(key, list_of_dicts):
if units.hasUnit(list_of_dicts[0][key]):
row_units = units.getUnit(list_of_dicts[0][key])
return row_units * np.array(
[x[key].asNumber(row_units) for x in list_of_dicts])
else:
return np.array([x[key] for x in list_of_dicts])
def __init__(self, concDict, equilibriumReactions, nutrientData):
self.units = units.getUnit(concDict.values()[0])
self.defaultConcentrationsDict = dict((key, concDict[key].asNumber(self.units)) for key in concDict)
self.nutrient_data = nutrientData
# factor of internal amino acid increase if maino acids present in nutrients
self.moleculeScaleFactors = {
"L-ALPHA-ALANINE[c]": 2.,
"ARG[c]": 2.,
"ASN[c]": 2.,
"L-ASPARTATE[c]": 2.,
"CYS[c]": 2.,
"GLT[c]": 1.1,
"GLN[c]": 2.,
"GLY[c]": 2.,
"HIS[c]": 2.,
"ILE[c]": 2.,
"LEU[c]": 2.,
"LYS[c]": 2.,
"MET[c]": 2.,
"PHE[c]": 2.,
"PRO[c]": 2.,
"SER[c]": 2.,
"THR[c]": 2.,
"TRP[c]": 2.,
"TYR[c]": 2.,
"L-SELENOCYSTEINE[c]": 2.,
"VAL[c]": 2.,
}
self.moleculeSetAmounts = self._addMoleculeAmounts(equilibriumReactions, self.defaultConcentrationsDict)
def _buildTrnaData(self, raw_data, sim_data):
growth_rate_unit = units.getUnit(raw_data.trnaData.trna_growth_rates[0]['growth rate'])
self._trna_growth_rates = growth_rate_unit * np.array([x['growth rate'].asNumber() for x in raw_data.trnaData.trna_growth_rates])
trna_ratio_to_16SrRNA_by_growth_rate = []
for gr in self._trna_growth_rates: # This is a little crazy...
trna_ratio_to_16SrRNA_by_growth_rate.append([x['ratio to 16SrRNA'] for x in getattr(raw_data.trnaData, "trna_ratio_to_16SrRNA_" + str(gr.asNumber()).replace('.','p'))])
self._trna_ratio_to_16SrRNA_by_growth_rate = np.array(trna_ratio_to_16SrRNA_by_growth_rate)
self._trna_ids = [x['rna id'] for x in raw_data.trnaData.trna_ratio_to_16SrRNA_0p4]
def _calculateGrowthRateDependentDnaMass(self, doubling_time): C_PERIOD = self.c_period D_PERIOD = self.d_period CD_PERIOD = C_PERIOD + D_PERIOD if doubling_time < D_PERIOD: raise Exception, "Can't have doubling time shorter than cytokinesis time!" doubling_time_unit = units.getUnit(doubling_time) # TODO: If you really care, this should be a loop. # It is optimized to run quickly over the range of T_d # and C and D periods that we have. return self.chromosomeSequenceMass * (1 + 1 * (np.maximum(0. * doubling_time_unit, CD_PERIOD - doubling_time) / C_PERIOD) + 2 * (np.maximum(0. * doubling_time_unit, CD_PERIOD - 2 * doubling_time) / C_PERIOD) + 4 * (np.maximum(0. * doubling_time_unit, CD_PERIOD - 4 * doubling_time) / C_PERIOD) )
def __setitem__(self, key, value):
if units_pkg.hasUnit(value):
try:
self.units[key].matchUnits(value)
except unum.IncompatibleUnitsError:
raise Exception, 'Units do not match!\n'
self.struct_array[key] = value.asNumber()
self.units[key] = units_pkg.getUnit(value)
elif type(value) == list or type(value) == np.ndarray:
if units_pkg.hasUnit(self.units[key]):
raise Exception, 'Units do not match! Quantity has units your input does not!\n'
self.struct_array[key] = value
self.units[key] = None
else:
raise Exception, 'Cant assign data-type other than unum datatype or list/numpy array!\n'