mirror of
https://github.com/Athemis/pyKinetics.git
synced 2025-05-24 03:45:55 +00:00
Moved plotting from library to cli-interface; improved logging
...and colorized logging
This commit is contained in:
parent
c90c18ee84
commit
091c6e62a2
2 changed files with 215 additions and 107 deletions
|
|
@ -3,14 +3,15 @@
|
|||
|
||||
from scipy import stats, optimize
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import csv
|
||||
import logging
|
||||
import warnings
|
||||
|
||||
|
||||
class Replicate():
|
||||
|
||||
def __init__(self, x, y, owner):
|
||||
def __init__(self, num, x, y, owner):
|
||||
self.logger = owner.logger
|
||||
self.num = num + 1
|
||||
self.x = x
|
||||
self.y = y
|
||||
self.owner = owner
|
||||
|
|
@ -23,14 +24,40 @@ class Replicate():
|
|||
x_for_fit = np.take(self.x, ind_min_max)
|
||||
y_for_fit = np.take(self.y, ind_min_max)
|
||||
|
||||
# ignore warnings about invalid values in sqrt during linear fitting
|
||||
# they occur frequently and will just clutter the cli
|
||||
warnings.filterwarnings('ignore',
|
||||
category=RuntimeWarning,
|
||||
message='invalid value encountered in sqrt')
|
||||
|
||||
(slope, intercept,
|
||||
r_value,
|
||||
p_value,
|
||||
std_err) = stats.linregress(x_for_fit, y_for_fit)
|
||||
|
||||
r_squared = r_value**2
|
||||
conc = '{} {}'.format(self.owner.concentration,
|
||||
self.owner.concentration_unit)
|
||||
|
||||
self.logger.info('Linear fit for {} #{}:'.format(conc, self.num))
|
||||
if r_squared < 0.9:
|
||||
msg = ' r-squared: {} < 0.9; Check fit manually!'
|
||||
self.logger.warning(msg.format(round(r_squared, 4)))
|
||||
else:
|
||||
msg = ' r-squared: {}'
|
||||
self.logger.info(msg.format(round(r_squared, 4)))
|
||||
self.logger.info(' slope: {}'.format(slope))
|
||||
if slope < 0:
|
||||
self.logger.warning('Slope is negative. Will use absolute value '
|
||||
'for further calculations!')
|
||||
self.logger.info(' intercept: {}'.format(slope))
|
||||
|
||||
|
||||
|
||||
return {'slope': slope,
|
||||
'intercept': intercept,
|
||||
'r_value': r_value,
|
||||
'r_squared': r_squared,
|
||||
'p_value': p_value,
|
||||
'std_err': std_err}
|
||||
|
||||
|
|
@ -54,7 +81,10 @@ class Measurement():
|
|||
length_x, num_replicates = self.y.shape
|
||||
|
||||
for n in range(num_replicates):
|
||||
self.replicates.append(Replicate(self.x, self.y[:, n:n+1], self))
|
||||
self.replicates.append(Replicate(n,
|
||||
self.x,
|
||||
self.y[:, n:n+1],
|
||||
self))
|
||||
|
||||
for r in self.replicates:
|
||||
self.slopes.append(r.fitresult['slope'])
|
||||
|
|
@ -62,25 +92,12 @@ class Measurement():
|
|||
self.avg_slope = np.average(self.slopes)
|
||||
self.avg_slope_err = np.std(self.slopes)
|
||||
|
||||
def plot(self, outpath):
|
||||
fig, ax = plt.subplots()
|
||||
ax.set_xlabel('Time [s]')
|
||||
ax.set_ylabel('Absorption (340 nm) [Au]')
|
||||
ax.set_title('Linear regression {} {}'.format(self.concentration,
|
||||
self.concentration_unit))
|
||||
|
||||
for r in self.replicates:
|
||||
ax.plot(r.x, r.y, linestyle='None',
|
||||
marker='o', ms=3, fillstyle='none')
|
||||
ax.plot(r.x, r.fitresult['slope']*r.x+r.fitresult['intercept'],
|
||||
'k-')
|
||||
ax.axvspan(self.xlim[0], self.xlim[1], facecolor='0.8', alpha=0.5)
|
||||
|
||||
plt.savefig('{}/fit_{}_{}.png'.format(outpath,
|
||||
self.concentration,
|
||||
self.concentration_unit),
|
||||
bbox_inches='tight')
|
||||
plt.close(fig)
|
||||
self.logger.info('Average slope: {} ± {}'.format(self.avg_slope,
|
||||
self.avg_slope_err))
|
||||
if self.avg_slope < 0:
|
||||
self.logger.warning('Avererage slope is negative. Will use '
|
||||
'absolute value for further calculations!')
|
||||
self.logger.info('-----')
|
||||
|
||||
def get_results(self):
|
||||
results = []
|
||||
|
|
@ -92,12 +109,17 @@ class Measurement():
|
|||
|
||||
class Experiment():
|
||||
|
||||
def __init__(self, data_files, xlim, do_hill=False, logger=None):
|
||||
def __init__(self, data_files, xlim, do_hill=False, fit_to_replicates=False, logger=None):
|
||||
|
||||
self.logger = logger
|
||||
if logger:
|
||||
self.logger = logger
|
||||
else:
|
||||
self.logger = logging.getLogger(__name__)
|
||||
|
||||
# collction of indepentend measurements
|
||||
self.measurements = []
|
||||
|
||||
self.fit_to_replicates = fit_to_replicates
|
||||
# dictionary to store data for the kinetics calculation
|
||||
self.raw_kinetic_data = {'x': [],
|
||||
'y': [],
|
||||
|
|
@ -125,11 +147,18 @@ class Experiment():
|
|||
|
||||
# iterate over all measurements
|
||||
for m in self.measurements:
|
||||
# extract relevant data for kinetics calculation (concentration,
|
||||
# average slope and error)
|
||||
self.raw_kinetic_data['x'].append(m.concentration)
|
||||
self.raw_kinetic_data['y'].append(np.absolute(m.avg_slope))
|
||||
self.raw_kinetic_data['yerr'].append(m.avg_slope_err)
|
||||
if self.fit_to_replicates:
|
||||
for r in m.replicates:
|
||||
self.raw_kinetic_data['x'].append(m.concentration)
|
||||
self.raw_kinetic_data['y'].append(np.absolute(r.fitresult['slope']))
|
||||
self.raw_kinetic_data['yerr'].append(r.fitresult['std_err'])
|
||||
|
||||
else:
|
||||
# extract relevant data for kinetics calculation
|
||||
# (concentration, average slope and error)
|
||||
self.raw_kinetic_data['x'].append(m.concentration)
|
||||
self.raw_kinetic_data['y'].append(np.absolute(m.avg_slope))
|
||||
self.raw_kinetic_data['yerr'].append(m.avg_slope_err)
|
||||
|
||||
# calculate kinetics
|
||||
self.mm = self.do_mm_kinetics()
|
||||
|
|
@ -163,6 +192,10 @@ class Experiment():
|
|||
Km = popt[1]
|
||||
x = np.arange(0, max(self.raw_kinetic_data['x']), 0.0001)
|
||||
|
||||
self.logger.info('Michaelis-Menten Kinetics:')
|
||||
self.logger.info(' v_max: {} ± {}'.format(vmax, perr[0]))
|
||||
self.logger.info(' Km: {} ± {}'.format(Km, perr[1]))
|
||||
|
||||
return {'vmax': float(vmax),
|
||||
'Km': float(Km),
|
||||
'perr': perr,
|
||||
|
|
@ -170,7 +203,7 @@ class Experiment():
|
|||
except:
|
||||
msg = 'Calculation of Michaelis-Menten kinetics failed!'
|
||||
if self.logger:
|
||||
self.logger.error('ERROR: {}'.format(msg))
|
||||
self.logger.error('{}'.format(msg))
|
||||
else:
|
||||
print(msg)
|
||||
return None
|
||||
|
|
@ -188,6 +221,11 @@ class Experiment():
|
|||
|
||||
x = np.arange(0, max(self.raw_kinetic_data['x']), 0.0001)
|
||||
|
||||
self.logger.info('Hill Kinetics:')
|
||||
self.logger.info(' v_max: {} ± {}'.format(vmax, perr[0]))
|
||||
self.logger.info(' K_prime: {} ± {}'.format(Kprime, perr[1]))
|
||||
self.logger.info(' h: {} ± {}'.format(h, perr[2]))
|
||||
|
||||
return {'vmax': float(vmax),
|
||||
'Kprime': float(Kprime),
|
||||
'perr': perr,
|
||||
|
|
@ -196,66 +234,7 @@ class Experiment():
|
|||
except:
|
||||
msg = 'Calculation of Hill kinetics failed!'
|
||||
if self.logger:
|
||||
self.logger.error('ERROR: {}'.format(msg))
|
||||
self.logger.error('{}'.format(msg))
|
||||
else:
|
||||
print(msg)
|
||||
return None
|
||||
|
||||
def plot_kinetics(self, outpath):
|
||||
fig, ax = plt.subplots()
|
||||
ax.set_xlabel('c [mM]')
|
||||
ax.set_ylabel('dA/dt [Au/s]')
|
||||
ax.set_title('Kinetics')
|
||||
|
||||
ax.errorbar(self.raw_kinetic_data['x'],
|
||||
self.raw_kinetic_data['y'],
|
||||
yerr=self.raw_kinetic_data['yerr'],
|
||||
fmt='ok', ms=3, fillstyle='none', label="Data with error")
|
||||
|
||||
if self.mm:
|
||||
y = self.mm_kinetics_function(self.mm['x'],
|
||||
self.mm['vmax'],
|
||||
self.mm['Km'])
|
||||
ax.plot(self.mm['x'], y, 'b-', label="Michaelis-Menten")
|
||||
if self.hill:
|
||||
y = self.hill_kinetics_function(self.hill['x'],
|
||||
self.hill['vmax'],
|
||||
self.hill['Kprime'],
|
||||
self.hill['h'])
|
||||
ax.plot(self.hill['x'], y, 'g-', label="Hill")
|
||||
|
||||
ax.legend(loc='best', fancybox=True)
|
||||
plt.savefig('{}/kinetics.png'.format(outpath), bbox_inches='tight')
|
||||
plt.close(fig)
|
||||
|
||||
def write_data(self, outpath):
|
||||
|
||||
with open('{}/results.csv'.format(outpath),
|
||||
'w',
|
||||
newline='\n') as csvfile:
|
||||
|
||||
writer = csv.writer(csvfile, dialect='excel-tab')
|
||||
writer.writerow(['# LINEAR FITS'])
|
||||
writer.writerow([])
|
||||
writer.writerow(['# concentration',
|
||||
'avg. slope',
|
||||
'slope std_err',
|
||||
'replicates (slope, intercept and r value)'])
|
||||
for m in self.measurements:
|
||||
row = [m.concentration, m.avg_slope, m.avg_slope_err]
|
||||
for r in m.replicates:
|
||||
row.append(r.fitresult['slope'])
|
||||
row.append(r.fitresult['intercept'])
|
||||
row.append(r.fitresult['r_value'])
|
||||
writer.writerow(row)
|
||||
|
||||
writer.writerow([])
|
||||
if self.mm:
|
||||
writer.writerow(['# MICHAELIS-MENTEN KINETICS'])
|
||||
writer.writerow(['# vmax', 'Km'])
|
||||
writer.writerow([self.mm['vmax'], self.mm['Km']])
|
||||
if self.hill:
|
||||
writer.writerow(['# HILL KINETICS'])
|
||||
writer.writerow(['# vmax', 'Kprime', 'h'])
|
||||
writer.writerow([self.hill['vmax'], self.hill['Kprime'],
|
||||
self.hill['h']])
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue