Athemis/pyKinetics
1
0
Fork 0
mirror of https://github.com/Athemis/pyKinetics.git synced 2025-04-04 14:36:03 +00:00
Code Issues Releases Wiki Activity

improve color handling; fix crash if using log scaled plots and zero values

Browse source
This commit is contained in:
Alexander Minges 2016-04-15 15:09:18 +02:00
parent 6d39c4822b
commit 3f5c82e13b
2 changed files with 82 additions and 13 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

52
analyze-cli.py
View file

@ -49,6 +49,8 @@ class ExperimentHelper():
self.logx = logx
self.logy = logy
self.unit = unit
self.colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
def linear_regression_function(self, slope, x, intercept):
y = slope * x + intercept
@ -64,19 +66,28 @@ class ExperimentHelper():
ax_title = 'Linear regression {} {}'.format(m.concentration,
m.concentration_unit)
ax.set_title(ax_title)
color_index = 0
for r in m.replicates:
# TODO: If number of colors exceeds predefined colors, calculate a proper palette
if color_index < len(self.colors):
color = self.colors[color_index]
else:
color = 'k'
ax.plot(r.x,
r.y,
'{}o'.format(color),
linestyle='None',
marker='o',
ms=3,
fillstyle='none',
label='replicate #{}'.format(r.num))
y = self.linear_regression_function(r.fitresult['slope'], r.x,
r.fitresult['intercept'])
ax.plot(r.x, y, 'k-')
ax.plot(r.x, y, '{}-'.format(color), label='linear fit #{}'.format(r.num))
ax.axvspan(m.xlim[0], m.xlim[1], facecolor='0.8', alpha=0.5)
color_index += 1
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.savefig('{}/fit_{}_{}.png'.format(outpath, m.concentration,
@ -92,9 +103,24 @@ class ExperimentHelper():
ax.set_title('Kinetics')
if self.logx:
if 0 in exp.raw_kinetic_data['x']:
index = exp.raw_kinetic_data['x'].index(0)
exp.raw_kinetic_data['x'].pop(index)
exp.raw_kinetic_data['y'].pop(index)
exp.raw_kinetic_data['yerr'].pop(index)
self.logger.warn(' Data in x contains zero values.')
ax.set_xscale('log')
if self.logy:
if 0 in exp.raw_kinetic_data['y']:
index = exp.raw_kinetic_data['y'].index(0)
exp.raw_kinetic_data['x'].pop(index)
exp.raw_kinetic_data['y'].pop(index)
exp.raw_kinetic_data['yerr'](index)
self.logger.warn(' Data in y contains zero values.')
ax.set_yscale('log')
ax.errorbar(exp.raw_kinetic_data['x'],
exp.raw_kinetic_data['y'],
@ -168,6 +194,10 @@ def parse_arguments():
'--replicates',
action='store_true',
help='fit kinetics to individual replicates')
parser.add_argument('-ep',
'--end-point',
action='store_true',
help='use end point determination instead of linear fit')
parser.add_argument('-nm',
'--no-michaelis',
action='store_true',
@ -268,6 +298,12 @@ def main():
# grab fitting window from provided arguments
fitting_window = (args.start, args.end)
# use end point determination
if args.end_point:
end_point = args.end_point
else:
end_point = False
# scaling of axes in kinetics plot
if args.log_x:
logx = args.log_x
@ -321,17 +357,19 @@ def main():
logger.info('{}'.format(msg))
exp = libkinetics.Experiment(data_files,
fitting_window,
end_point=end_point,
do_hill=do_hill,
no_mm=no_mm,
logger=logger,
fit_to_replicates=fit_to_replicates)
ehlp = ExperimentHelper(exp, logger, args.unit, logx=logx, logy=logy)
logger.info('Plotting linear fits')
ehlp.plot_data(str(output_path))
logger.info('Plotting kinetic fit(s)')
ehlp.plot_kinetics(str(output_path))
if not end_point:
logger.info('Plotting linear fits')
ehlp.plot_data(str(output_path))
logger.info('Writing results to results.csv')
ehlp.write_data(str(output_path))
logger.info('Plotting kinetic fit(s)')
ehlp.plot_kinetics(str(output_path))
logger.info('Finished!')
else:
msg = '{} is not a directory!'.format(input_path)

43
libkinetics/__init__.py
View file

@ -64,7 +64,38 @@ class Replicate():
self.x, self.y = xy
self.owner = owner
self.xlim = owner.xlim
self.fitresult = self.fit()
self.conc = '{} {}'.format(self.owner.concentration,
self.owner.concentration_unit)
if not self.owner.owner.end_point:
self.fitresult = self.fit()
else:
self.fitresult = self.end_point_determination()
def end_point_determination(self):
ind_min_max = np.where((self.x >= self.xlim[0]) & (self.x <=
self.xlim[1]))[0]
x_start = self.x[ind_min_max[0]]
x_end = self.x[ind_min_max[-1]]
y_start = self.y[ind_min_max[0]]
y_end = self.y[ind_min_max[-1]]
slope = (y_end - y_start) / (x_end - x_start)
self.logger.info('End point determination for {} #{}:'.format(self.conc, self.num))
self.logger.info(' start x/y: {}/{}'.format(x_start, float(y_start)))
self.logger.info(' end x/y: {}/{}'.format(x_end, float(y_end)))
self.logger.info(' slope: {}'.format(float(slope)))
return {
'slope': slope,
'intercept': None,
'r_value': None,
'r_squared': None,
'p_value': None,
'std_err': None
}
def fit(self):
ind_min_max = np.where((self.x >= self.xlim[0]) & (self.x <=
@ -88,11 +119,8 @@ class Replicate():
# calculcate adjusted R²
adj_r_squared = r_squared - (1 - r_squared) * k/(n - k - 1)
conc = '{} {}'.format(self.owner.concentration,
self.owner.concentration_unit)
self.logger.info('Linear fit for {} #{}:'.format(conc, self.num))
self.logger.info('Linear fit for {} #{}:'.format(self.conc, self.num))
if adj_r_squared < 0.9 and adj_r_squared > 0.7:
msg = ' adjusted R² < 0.9; Check fit manually!'
self.logger.warning(msg.format(round(adj_r_squared, 4)))
@ -232,7 +260,7 @@ class Experiment():
"""
def __init__(self, data_files, xlim, do_hill=False, no_mm=False,
fit_to_replicates=False, logger=None):
fit_to_replicates=False, logger=None, end_point=False):
"""
Inits Experiment class with experimental parameters
@ -267,6 +295,8 @@ class Experiment():
# dictionary to store data for the kinetics calculation
self.raw_kinetic_data = {'x': [], 'y': [], 'yerr': []}
self.xlim = xlim
self.end_point = end_point
# parse data files and generate measurements
for csvfile in data_files:
@ -317,6 +347,7 @@ class Experiment():
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
if not no_mm: