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More robust error handling; check third-party imports

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This commit is contained in:
Alexander Minges 2015-08-27 15:31:35 +02:00
parent d5e66fd4fe
commit 754c62ffd1
1 changed files with 62 additions and 35 deletions
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97
libkinetics/__init__.py
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@ -1,10 +1,19 @@
#!/usr/bin/python #!/usr/bin/python
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
from scipy import stats, optimize
import numpy as np
import logging import logging
import warnings import warnings
import operator
try:
import numpy as np
except ImportError:
print('----- NumPy must be installed! -----')
try:
from scipy import stats, optimize
except ImportError:
print('----- SciPy must be installed! -----')
class Error(Exception): class Error(Exception):
@ -128,6 +137,9 @@ class Measurement():
return results return results
def get_concentration(self):
return self.concentration
class Experiment(): class Experiment():
@ -142,7 +154,8 @@ class Experiment():
and log file. and log file.
measurements: array_like measurements: array_like
list of individual measurements of the experiment. list of individual measurements of the experiment.
Usually defined by different substrate concentrations. Individual measurements are sorted by their substrate
concentration.
fit_to_replicates: boolean fit_to_replicates: boolean
whether to fit to individual replicates instead to the average of whether to fit to individual replicates instead to the average of
each measurement. each measurement.
@ -192,12 +205,22 @@ class Experiment():
# parse data files and generate measurements # parse data files and generate measurements
for csvfile in data_files: for csvfile in data_files:
tmp = np.genfromtxt(str(csvfile), comments='#') try:
with open(str(csvfile)) as datafile: tmp = np.genfromtxt(str(csvfile), comments='#')
head = [next(datafile) for x in range(2)] with open(str(csvfile)) as datafile:
head = [next(datafile) for x in range(2)]
except OSError:
msg = "Failed reading file {}".format(str(csvfile))
self.logger.error(msg)
raise
# extract concentration and unit from header # extract concentration and unit from header
# TODO: move unit to parameter # TODO: move unit to parameter
# TODO: More error-proof header detection # check header for correct number of items
if len(head) < 2 or len(head) > 2:
msg = 'Parsing header of data files failed! Wrong format?'
self.logger.error(msg)
raise Error(msg)
conc = head[0].strip('#').strip() conc = head[0].strip('#').strip()
unit = head[1].strip('#').strip() unit = head[1].strip('#').strip()
# split x and y data apart # split x and y data apart
@ -209,6 +232,10 @@ class Experiment():
measurement = Measurement((x, y), conc, unit, self) measurement = Measurement((x, y), conc, unit, self)
self.measurements.append(measurement) self.measurements.append(measurement)
# sort measurements by concentration
self.measurements = sorted(self.measurements,
key=operator.attrgetter('concentration'))
# iterate over all measurements # iterate over all measurements
for m in self.measurements: for m in self.measurements:
if self.fit_to_replicates: if self.fit_to_replicates:
@ -306,20 +333,20 @@ class Experiment():
self.logger.error('{}'.format(msg)) self.logger.error('{}'.format(msg))
raise FitError(msg) raise FitError(msg)
perr = np.sqrt(np.diag(pconv)) perr = np.sqrt(np.diag(pconv))
vmax = popt[0] vmax = popt[0]
Km = popt[1] Km = popt[1]
x = np.arange(0, max(self.raw_kinetic_data['x']), 0.0001) x = np.arange(0, max(self.raw_kinetic_data['x']), 0.0001)
self.logger.info('Michaelis-Menten Kinetics:') self.logger.info('Michaelis-Menten Kinetics:')
self.logger.info(' v_max: {} ± {}'.format(vmax, perr[0])) self.logger.info(' v_max: {} ± {}'.format(vmax, perr[0]))
self.logger.info(' Km: {} ± {}'.format(Km, perr[1])) self.logger.info(' Km: {} ± {}'.format(Km, perr[1]))
return {'vmax': np.float(vmax), return {'vmax': np.float(vmax),
'Km': np.float(Km), 'Km': np.float(Km),
'vmax_err': np.float(perr[0]), 'vmax_err': np.float(perr[0]),
'Km_err': np.float(perr[1]), 'Km_err': np.float(perr[1]),
'x': x} 'x': x}
def do_hill_kinetics(self): def do_hill_kinetics(self):
""" """
@ -351,22 +378,22 @@ class Experiment():
self.logger.error('{}'.format(msg)) self.logger.error('{}'.format(msg))
raise FitError(msg) raise FitError(msg)
perr = np.sqrt(np.diag(pconv)) perr = np.sqrt(np.diag(pconv))
vmax = popt[0] vmax = popt[0]
Kprime = popt[1] Kprime = popt[1]
h = popt[2] h = popt[2]
x = np.arange(0, max(self.raw_kinetic_data['x']), 0.0001) x = np.arange(0, max(self.raw_kinetic_data['x']), 0.0001)
self.logger.info('Hill Kinetics:') self.logger.info('Hill Kinetics:')
self.logger.info(' v_max: {} ± {}'.format(vmax, perr[0])) self.logger.info(' v_max: {} ± {}'.format(vmax, perr[0]))
self.logger.info(' K_prime: {} ± {}'.format(Kprime, perr[1])) self.logger.info(' K_prime: {} ± {}'.format(Kprime, perr[1]))
self.logger.info(' h: {} ± {}'.format(h, perr[2])) self.logger.info(' h: {} ± {}'.format(h, perr[2]))
return {'vmax': np.float(vmax), return {'vmax': np.float(vmax),
'Kprime': np.float(Kprime), 'Kprime': np.float(Kprime),
'vmax_err': np.float(perr[0]), 'vmax_err': np.float(perr[0]),
'Kprime_err': np.float(perr[1]), 'Kprime_err': np.float(perr[1]),
'h_err': np.float(perr[2]), 'h_err': np.float(perr[2]),
'h': np.float(h), 'h': np.float(h),
'x': x} 'x': x}