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https://github.com/Athemis/pyKinetics.git
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Reformat code using yapf
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93751a2b3d
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2 changed files with 50 additions and 57 deletions
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@ -36,7 +36,6 @@ except ImportError:
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class ExperimentHelper():
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class ExperimentHelper():
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def __init__(self, experiment, logger):
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def __init__(self, experiment, logger):
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self.exp = experiment
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self.exp = experiment
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self.logger = logger
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self.logger = logger
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@ -57,18 +56,20 @@ class ExperimentHelper():
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ax.set_title(ax_title)
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ax.set_title(ax_title)
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for r in m.replicates:
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for r in m.replicates:
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ax.plot(r.x, r.y, linestyle='None',
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ax.plot(r.x,
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marker='o', ms=3, fillstyle='none',
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r.y,
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linestyle='None',
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marker='o',
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ms=3,
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fillstyle='none',
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label='replicate #{}'.format(r.num))
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label='replicate #{}'.format(r.num))
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y = self.linear_regression_function(r.fitresult['slope'],
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y = self.linear_regression_function(r.fitresult['slope'], r.x,
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r.x,
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r.fitresult['intercept'])
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r.fitresult['intercept'])
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ax.plot(r.x, y, 'k-')
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ax.plot(r.x, y, 'k-')
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ax.axvspan(m.xlim[0], m.xlim[1], facecolor='0.8', alpha=0.5)
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ax.axvspan(m.xlim[0], m.xlim[1], facecolor='0.8', alpha=0.5)
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plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
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plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
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plt.savefig('{}/fit_{}_{}.png'.format(outpath,
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plt.savefig('{}/fit_{}_{}.png'.format(outpath, m.concentration,
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m.concentration,
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m.concentration_unit),
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m.concentration_unit),
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bbox_inches='tight')
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bbox_inches='tight')
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plt.close(fig)
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plt.close(fig)
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@ -82,18 +83,18 @@ class ExperimentHelper():
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ax.errorbar(exp.raw_kinetic_data['x'],
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ax.errorbar(exp.raw_kinetic_data['x'],
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exp.raw_kinetic_data['y'],
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exp.raw_kinetic_data['y'],
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yerr=exp.raw_kinetic_data['yerr'],
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yerr=exp.raw_kinetic_data['yerr'],
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fmt='ok', ms=3, fillstyle='none', label="Data with error")
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fmt='ok',
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ms=3,
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fillstyle='none',
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label="Data with error")
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if exp.mm:
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if exp.mm:
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y = exp.mm_kinetics_function(exp.mm['x'],
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y = exp.mm_kinetics_function(exp.mm['x'], exp.mm['vmax'],
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exp.mm['vmax'],
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exp.mm['Km'])
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exp.mm['Km'])
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ax.plot(exp.mm['x'], y, 'b-', label="Michaelis-Menten")
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ax.plot(exp.mm['x'], y, 'b-', label="Michaelis-Menten")
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if exp.hill:
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if exp.hill:
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y = exp.hill_kinetics_function(exp.hill['x'],
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y = exp.hill_kinetics_function(exp.hill['x'], exp.hill['vmax'],
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exp.hill['vmax'],
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exp.hill['Kprime'], exp.hill['h'])
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exp.hill['Kprime'],
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exp.hill['h'])
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ax.plot(exp.hill['x'], y, 'g-', label="Hill")
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ax.plot(exp.hill['x'], y, 'g-', label="Hill")
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ax.legend(loc='best', fancybox=True)
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ax.legend(loc='best', fancybox=True)
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@ -109,9 +110,7 @@ class ExperimentHelper():
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writer = csv.writer(csvfile, dialect='excel-tab')
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writer = csv.writer(csvfile, dialect='excel-tab')
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writer.writerow(['# LINEAR FITS'])
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writer.writerow(['# LINEAR FITS'])
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writer.writerow([])
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writer.writerow([])
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writer.writerow(['# concentration',
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writer.writerow(['# concentration', 'avg. slope', 'slope std_err',
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'avg. slope',
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'slope std_err',
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'replicates (slope, intercept and r value)'])
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'replicates (slope, intercept and r value)'])
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for m in exp.measurements:
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for m in exp.measurements:
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row = [m.concentration, m.avg_slope, m.avg_slope_err]
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row = [m.concentration, m.avg_slope, m.avg_slope_err]
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@ -151,9 +150,7 @@ def parse_arguments():
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parser.add_argument('start',
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parser.add_argument('start',
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type=np.float64,
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type=np.float64,
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help='start of fitting window')
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help='start of fitting window')
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parser.add_argument('end',
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parser.add_argument('end', type=np.float64, help='end of fitting window')
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type=np.float64,
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help='end of fitting window')
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parser.add_argument('input',
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parser.add_argument('input',
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type=str,
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type=str,
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help='directory containing input files in csv format')
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help='directory containing input files in csv format')
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@ -177,18 +174,18 @@ def initialize_logger():
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formatter = ColoredFormatter(fmt,
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formatter = ColoredFormatter(fmt,
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datefmt=None,
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datefmt=None,
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reset=True,
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reset=True,
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log_colors={'DEBUG': 'cyan',
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log_colors={
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'INFO': 'green',
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'DEBUG': 'cyan',
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'WARNING': 'yellow',
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'INFO': 'green',
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'ERROR': 'red',
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'WARNING': 'yellow',
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'CRITICAL': 'red,bg_white'},
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'ERROR': 'red',
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'CRITICAL': 'red,bg_white'
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},
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secondary_log_colors={},
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secondary_log_colors={},
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style='%')
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style='%')
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else:
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else:
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fmt = '%(levelname)-8s%(message)s'
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fmt = '%(levelname)-8s%(message)s'
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formatter = logging.Formatter(fmt,
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formatter = logging.Formatter(fmt, datefmt=None, style='%')
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datefmt=None,
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style='%')
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logging.captureWarnings(True)
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logging.captureWarnings(True)
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logger = logging.getLogger('pyKinetics-cli')
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logger = logging.getLogger('pyKinetics-cli')
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@ -274,5 +271,6 @@ def main():
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logger.critical('CRITICAL: '.format(msg))
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logger.critical('CRITICAL: '.format(msg))
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raise ValueError(msg)
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raise ValueError(msg)
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if __name__ == "__main__":
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if __name__ == "__main__":
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main()
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main()
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@ -21,8 +21,7 @@ class Replicate():
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self.fitresult = self.fit()
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self.fitresult = self.fit()
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def fit(self):
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def fit(self):
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ind_min_max = np.where((self.x >= self.xlim[0]) &
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ind_min_max = np.where((self.x >= self.xlim[0]) & (self.x <= self.xlim[1]))
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(self.x <= self.xlim[1]))
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x_for_fit = np.take(self.x, ind_min_max)
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x_for_fit = np.take(self.x, ind_min_max)
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y_for_fit = np.take(self.y, ind_min_max)
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y_for_fit = np.take(self.y, ind_min_max)
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@ -32,12 +31,10 @@ class Replicate():
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category=RuntimeWarning,
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category=RuntimeWarning,
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message='invalid value encountered in sqrt')
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message='invalid value encountered in sqrt')
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(slope, intercept,
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(slope, intercept, r_value, p_value, std_err
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r_value,
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) = stats.linregress(x_for_fit, y_for_fit)
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p_value,
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std_err) = stats.linregress(x_for_fit, y_for_fit)
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r_squared = r_value**2
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r_squared = r_value ** 2
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conc = '{} {}'.format(self.owner.concentration,
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conc = '{} {}'.format(self.owner.concentration,
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self.owner.concentration_unit)
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self.owner.concentration_unit)
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@ -57,12 +54,14 @@ class Replicate():
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'value for further calculations!')
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'value for further calculations!')
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self.logger.info(' intercept: {}'.format(slope))
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self.logger.info(' intercept: {}'.format(slope))
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return {'slope': slope,
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return {
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'intercept': intercept,
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'slope': slope,
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'r_value': r_value,
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'intercept': intercept,
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'r_squared': r_squared,
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'r_value': r_value,
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'p_value': p_value,
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'r_squared': r_squared,
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'std_err': std_err}
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'p_value': p_value,
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'std_err': std_err
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}
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class Measurement():
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class Measurement():
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@ -86,8 +85,7 @@ class Measurement():
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length_x, num_replicates = self.y.shape
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length_x, num_replicates = self.y.shape
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for n in range(num_replicates):
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for n in range(num_replicates):
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self.replicates.append(Replicate(n,
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self.replicates.append(Replicate(n, (self.x, self.y[:, n:n + 1]),
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(self.x, self.y[:, n:n+1]),
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self))
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self))
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for r in self.replicates:
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for r in self.replicates:
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@ -141,9 +139,7 @@ class Experiment():
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self.fit_to_replicates = fit_to_replicates
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self.fit_to_replicates = fit_to_replicates
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# dictionary to store data for the kinetics calculation
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# dictionary to store data for the kinetics calculation
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self.raw_kinetic_data = {'x': [],
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self.raw_kinetic_data = {'x': [], 'y': [], 'yerr': []}
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'y': [],
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'yerr': []}
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self.xlim = xlim
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self.xlim = xlim
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# parse data files and generate measurements
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# parse data files and generate measurements
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@ -196,11 +192,11 @@ class Experiment():
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m.plot(outpath)
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m.plot(outpath)
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def mm_kinetics_function(self, x, vmax, Km):
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def mm_kinetics_function(self, x, vmax, Km):
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v = (vmax*x)/(Km+x)
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v = (vmax * x) / (Km + x)
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return v
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return v
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def hill_kinetics_function(self, x, vmax, Kprime, h):
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def hill_kinetics_function(self, x, vmax, Kprime, h):
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v = (vmax*(x**h))/(Kprime+(x**h))
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v = (vmax * (x ** h)) / (Kprime + (x ** h))
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return v
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return v
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def do_mm_kinetics(self):
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def do_mm_kinetics(self):
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@ -218,10 +214,7 @@ class Experiment():
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self.logger.info(' v_max: {} ± {}'.format(vmax, perr[0]))
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self.logger.info(' v_max: {} ± {}'.format(vmax, perr[0]))
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self.logger.info(' Km: {} ± {}'.format(Km, perr[1]))
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self.logger.info(' Km: {} ± {}'.format(Km, perr[1]))
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return {'vmax': float(vmax),
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return {'vmax': float(vmax), 'Km': float(Km), 'perr': perr, 'x': x}
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'Km': float(Km),
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'perr': perr,
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'x': x}
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except:
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except:
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msg = 'Calculation of Michaelis-Menten kinetics failed!'
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msg = 'Calculation of Michaelis-Menten kinetics failed!'
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if self.logger:
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if self.logger:
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@ -248,11 +241,13 @@ class Experiment():
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self.logger.info(' K_prime: {} ± {}'.format(Kprime, perr[1]))
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self.logger.info(' K_prime: {} ± {}'.format(Kprime, perr[1]))
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self.logger.info(' h: {} ± {}'.format(h, perr[2]))
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self.logger.info(' h: {} ± {}'.format(h, perr[2]))
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return {'vmax': float(vmax),
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return {
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'Kprime': float(Kprime),
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'vmax': float(vmax),
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'perr': perr,
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'Kprime': float(Kprime),
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'h': h,
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'perr': perr,
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'x': x}
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'h': h,
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'x': x
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}
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except:
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except:
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msg = 'Calculation of Hill kinetics failed!'
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msg = 'Calculation of Hill kinetics failed!'
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if self.logger:
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if self.logger:
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