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Initial commit of analyze.py
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analyze.py
Executable file
244
analyze.py
Executable file
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#!/usr/bin/python
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from scipy import stats, optimize
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import numpy as np
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import matplotlib.pyplot as plt
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import glob
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import csv
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class Replicate():
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def __init__(self, x, y, owner):
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self.x = x
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self.y = y
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self.owner = owner
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self.xlim = owner.xlim
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self.fitresult = self.fit()
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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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(self.x <= self.xlim[1]))
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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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(slope, intercept,
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r_value,
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p_value,
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std_err) = stats.linregress(x_for_fit, y_for_fit)
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return {'slope': slope,
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'intercept': intercept,
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'r_value': r_value,
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'p_value': p_value,
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'std_err': std_err}
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class Measurement():
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def __init__(self, x, y, conc, conc_unit, owner):
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self.concentration = float(conc)
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self.concentration_unit = conc_unit
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self.x = x
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self.y = y
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self.replicates = []
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self.owner = owner
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self.xlim = owner.xlim
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self.slopes = []
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self.avg_slope = None
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self.avg_slope_err = None
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# extract number of replicates (num_replicates)
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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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self.replicates.append(Replicate(self.x, self.y[:, n:n+1], self))
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for r in self.replicates:
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self.slopes.append(r.fitresult['slope'])
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self.avg_slope = np.average(self.slopes)
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self.avg_slope_err = np.std(self.slopes)
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def plot(self):
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fig, ax = plt.subplots()
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ax.set_xlabel('Time [s]')
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ax.set_ylabel('Absorption (340 nm) [Au]')
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ax.set_title('Linear regression {} {}'.format(self.concentration,
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self.concentration_unit))
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for r in self.replicates:
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ax.plot(r.x, r.y, linestyle='None',
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marker='o', ms=3, fillstyle='none')
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ax.plot(r.x, r.fitresult['slope']*r.x+r.fitresult['intercept'],
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'k-')
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ax.axvspan(self.xlim[0], self.xlim[1], facecolor='0.8', alpha=0.5)
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plt.savefig('out/fit_{}_{}.png'.format(self.concentration,
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self.concentration_unit),
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bbox_inches='tight')
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plt.close(fig)
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def get_results(self):
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results = []
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for r in self.replicates:
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results.append(r.fitresult)
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return results
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class Experiment():
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def __init__(self, data_files, xlim):
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# collction of indepentend measurements
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self.measurements = []
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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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'y': [],
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'yerr': []}
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self.xlim = xlim
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# variables to store calculated Michaelis-Menten and Hill kinetics
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self.mm = None
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self.hill = None
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# parse data files and generate measurements
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for csvfile in data_files:
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tmp = np.genfromtxt(csvfile, comments='#')
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with open(csvfile) as datafile:
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head = [next(datafile) for x in range(2)]
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# extract concentration and unit from header
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# TODO: move unit to parameter
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# TODO: More error-proof header detection
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conc = head[0].strip('#').strip()
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unit = head[1].strip('#').strip()
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# split x and y data apart
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# first column is x data (time); following columns contain
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# replicates of y data (absorption, etc.)
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x = tmp[:, 0]
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y = tmp[:, 1:]
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# create new measurement and append to list
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measurement = Measurement(x, y, conc, unit, self)
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self.measurements.append(measurement)
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# iterate over all measurements
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for m in self.measurements:
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# extract relevant data for kinetics calculation (concentration,
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# average slope and error)
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self.raw_kinetic_data['x'].append(m.concentration)
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self.raw_kinetic_data['y'].append(np.absolute(m.avg_slope))
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self.raw_kinetic_data['yerr'].append(m.avg_slope_err)
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def plot_data(self):
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for m in self.measurements:
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m.plot()
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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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return v
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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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return v
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def do_mm_kinetics(self):
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popt, pconv = optimize.curve_fit(self.mm_kinetics_function,
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self.raw_kinetic_data['x'],
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self.raw_kinetic_data['y'])
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perr = np.sqrt(np.diag(pconv))
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vmax = popt[0]
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Km = popt[1]
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x = np.arange(0, max(self.raw_kinetic_data['x']), 0.0001)
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return {'vmax': float(vmax),
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'Km': float(Km),
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'perr': perr,
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'x': x}
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def do_hill_kinetics(self):
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popt, pconv = optimize.curve_fit(self.hill_kinetics_function,
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self.raw_kinetic_data['x'],
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self.raw_kinetic_data['y'])
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perr = np.sqrt(np.diag(pconv))
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vmax = popt[0]
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Kprime = popt[1]
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h = popt[2]
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x = np.arange(0, max(self.raw_kinetic_data['x']), 0.0001)
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return {'vmax': float(vmax),
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'Kprime': float(Kprime),
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'perr': perr,
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'h': h,
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'x': x}
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def plot_kinetics(self):
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fig, ax = plt.subplots()
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ax.set_xlabel('c [mM]')
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ax.set_ylabel('dA/dt [Au/s]')
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ax.set_title('Kinetics')
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ax.errorbar(self.raw_kinetic_data['x'],
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self.raw_kinetic_data['y'],
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yerr=self.raw_kinetic_data['yerr'],
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fmt='ok', ms=3, fillstyle='none', label="Data with error")
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# linestyle='None', marker='o', ms=3, fillstyle='none'
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self.mm = self.do_mm_kinetics()
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self.hill = self.do_hill_kinetics()
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print(self.mm)
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print(self.hill)
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ax.plot(self.mm['x'],
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(self.mm['vmax']*self.mm['x'])/(self.mm['Km']+self.mm['x']),
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'b-', label="Michaelis-Menten")
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ax.plot(self.hill['x'],
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((self.hill['vmax']*(self.hill['x']**self.hill['h'])) /
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(self.hill['Kprime'] + (self.hill['x']**self.hill['h']))),
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'g-', label="Hill")
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ax.legend(loc='best', fancybox=True)
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plt.savefig('out/kinetics.png', bbox_inches='tight')
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plt.close(fig)
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def write_data(self):
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with open('out/results.csv', 'w', newline='\n') as csvfile:
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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([])
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writer.writerow(['# concentration',
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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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for m in self.measurements:
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row = [m.concentration, m.avg_slope, m.avg_slope_err]
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for r in m.replicates:
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row.append(r.fitresult['slope'])
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row.append(r.fitresult['intercept'])
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row.append(r.fitresult['r_value'])
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writer.writerow(row)
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writer.writerow([])
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writer.writerow(['# MICHAELIS-MENTEN KINETICS'])
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writer.writerow(['# vmax', 'Km'])
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writer.writerow([self.mm['vmax'], self.mm['Km']])
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writer.writerow(['# HILL KINETICS'])
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writer.writerow(['# vmax', 'Kprime', 'h'])
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writer.writerow([self.hill['vmax'], self.hill['Kprime'],
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self.hill['h']])
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def main():
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data_files = glob.glob('csv/*.csv')
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exp = Experiment(data_files, (10, 25))
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exp.plot_data()
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exp.plot_kinetics()
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exp.write_data()
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if __name__ == "__main__":
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main()
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