mirror of
https://github.com/Athemis/pyKinetics.git
synced 2025-04-05 06:56:02 +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
161
analyze-cli.py
161
analyze-cli.py
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@ -4,11 +4,109 @@
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import argparse
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import logging
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import csv
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import matplotlib.pyplot as plt
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from pathlib import Path
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from colorlog import ColoredFormatter
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import libkinetics
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class ExperimentHelper():
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def __init__(self, experiment, logger):
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self.exp = experiment
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self.logger = logger
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def linear_regression_function(self, slope, x, intercept):
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y = slope * x + intercept
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return y
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def plot_data(self, exp, outpath):
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# iterate over all measurements
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for m in exp.measurements:
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# plot each measurement
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fig, ax = plt.subplots()
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ax.set_xlabel('Time')
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ax.set_ylabel('Raw Signal')
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ax_title = 'Linear regression {} {}'.format(m.concentration,
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m.concentration_unit)
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ax.set_title(ax_title)
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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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marker='o', ms=3, fillstyle='none')
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y = self.linear_regression_function(r.fitresult['slope'],
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r.x,
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r.fitresult['intercept'])
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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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plt.savefig('{}/fit_{}_{}.png'.format(outpath,
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m.concentration,
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m.concentration_unit),
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bbox_inches='tight')
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plt.close(fig)
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def plot_kinetics(self, exp, outpath):
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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(exp.raw_kinetic_data['x'],
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exp.raw_kinetic_data['y'],
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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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if exp.mm:
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y = exp.mm_kinetics_function(exp.mm['x'],
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exp.mm['vmax'],
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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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if exp.hill:
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y = exp.hill_kinetics_function(exp.hill['x'],
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exp.hill['vmax'],
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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.legend(loc='best', fancybox=True)
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plt.savefig('{}/kinetics.png'.format(outpath), bbox_inches='tight')
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plt.close(fig)
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def write_data(self, exp, outpath):
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with open('{}/results.csv'.format(outpath),
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'w',
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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 exp.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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if exp.mm:
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writer.writerow(['# MICHAELIS-MENTEN KINETICS'])
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writer.writerow(['# vmax', 'Km'])
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writer.writerow([exp.mm['vmax'], exp.mm['Km']])
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if exp.hill:
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writer.writerow(['# HILL KINETICS'])
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writer.writerow(['# vmax', 'Kprime', 'h'])
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writer.writerow([exp.hill['vmax'], exp.hill['Kprime'],
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exp.hill['h']])
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def parse_arguments():
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parser = argparse.ArgumentParser()
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@ -16,8 +114,12 @@ def parse_arguments():
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'--verbose',
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action='store_true',
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help='increase output verbosity')
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parser.add_argument('-r',
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'--replicates',
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action='store_true',
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help='fit kinetics to individual replicates')
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parser.add_argument('-wh',
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'--with_hill',
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'--hill',
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action='store_true',
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help='compute additional kinetics using Hill equation')
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parser.add_argument('input',
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@ -38,11 +140,25 @@ def initialize_logger():
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'fh' which logs to a log file and 'ch' which logs to standard output.
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:return logger: returns a logger instance
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"""
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fmt = '%(log_color)s%(levelname)-8s%(reset)s %(message)s'
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formatter = ColoredFormatter(fmt,
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datefmt=None,
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reset=True,
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log_colors={'DEBUG': 'cyan',
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'INFO': 'green',
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'WARNING': 'yellow',
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'ERROR': 'red',
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'CRITICAL': 'red,bg_white'},
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secondary_log_colors={},
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style='%')
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logging.captureWarnings(True)
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logger = logging.getLogger('pyKinetics-cli')
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logger.setLevel(logging.INFO)
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logger.setLevel(logging.DEBUG)
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ch = logging.StreamHandler()
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ch.setLevel(logging.INFO)
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ch.setLevel(logging.DEBUG)
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ch.setFormatter(formatter)
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logger.addHandler(ch)
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try:
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@ -51,7 +167,7 @@ def initialize_logger():
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fh.setLevel(logging.INFO)
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logger.addHandler(fh)
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except IOError as error:
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logger.warning('WARNING: Cannot create log file! Run pyKinetics-cli'
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logger.warning('Cannot create log file! Run pyKinetics-cli'
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'from a directory to which you have write access.')
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logger.warning(error.msg)
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pass
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@ -65,37 +181,50 @@ def main():
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# initialize logger
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logger = initialize_logger()
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if args.with_hill:
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do_hill = args.with_hill
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if args.hill:
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do_hill = args.hill
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else:
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do_hill = False
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if args.replicates:
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fit_to_replicates = args.replicates
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else:
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fit_to_replicates = False
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try:
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input_path = Path(args.input).resolve()
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except FileNotFoundError:
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logger.critical('CRITICAL: Path containing input data '
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logger.critical('Path containing input data '
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'not found: {}'.format(args.input))
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raise
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try:
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output_path = Path(args.output).resolve()
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except FileNotFoundError:
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logger.critical('CRITICAL: Path for writing results '
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logger.critical('Path for writing results '
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'not found: {}'.format(args.output))
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raise
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if output_path.is_dir():
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if input_path.is_dir():
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logger.info('INFO: Collecting data files')
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logger.info('Collecting data files')
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data_files = sorted(input_path.glob('**/*.csv'))
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msg = 'Calculating kinetics'
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if do_hill:
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msg = '{} including Hill kinetics'.format(msg)
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logger.info('INFO: {}'.format(msg))
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exp = libkinetics.Experiment(data_files, (10, 25), do_hill)
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logger.info('INFO: Plotting linear fits to data and kinetics')
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exp.plot_data(str(output_path))
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exp.plot_kinetics(str(output_path))
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logger.info('INFO: Writing results to results.csv')
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exp.write_data(str(output_path))
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logger.info('{}'.format(msg))
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exp = libkinetics.Experiment(data_files,
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(10, 25),
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do_hill=do_hill,
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logger=logger,
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fit_to_replicates=fit_to_replicates)
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ehlp = ExperimentHelper(exp, logger)
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logger.info('Plotting linear fits to data')
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ehlp.plot_data(exp, str(output_path))
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logger.info('Plotting kinetics fit(s)')
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ehlp.plot_kinetics(exp, str(output_path))
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logger.info('Writing results to results.csv')
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ehlp.write_data(exp, str(output_path))
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logger.info('Finished!')
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else:
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msg = '{} is not a directory!'.format(input_path)
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logger.critical('CRITICAL: '.format(msg))
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@ -3,14 +3,15 @@
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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 csv
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import logging
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import warnings
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class Replicate():
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def __init__(self, x, y, owner):
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def __init__(self, num, x, y, owner):
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self.logger = owner.logger
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self.num = num + 1
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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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@ -23,14 +24,40 @@ class Replicate():
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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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# ignore warnings about invalid values in sqrt during linear fitting
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# they occur frequently and will just clutter the cli
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warnings.filterwarnings('ignore',
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category=RuntimeWarning,
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message='invalid value encountered in sqrt')
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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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r_squared = r_value**2
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conc = '{} {}'.format(self.owner.concentration,
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self.owner.concentration_unit)
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self.logger.info('Linear fit for {} #{}:'.format(conc, self.num))
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if r_squared < 0.9:
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msg = ' r-squared: {} < 0.9; Check fit manually!'
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self.logger.warning(msg.format(round(r_squared, 4)))
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else:
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msg = ' r-squared: {}'
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self.logger.info(msg.format(round(r_squared, 4)))
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self.logger.info(' slope: {}'.format(slope))
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if slope < 0:
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self.logger.warning('Slope is negative. Will use absolute value '
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'for further calculations!')
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self.logger.info(' intercept: {}'.format(slope))
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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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'r_squared': r_squared,
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'p_value': p_value,
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'std_err': std_err}
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@ -54,7 +81,10 @@ class Measurement():
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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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self.replicates.append(Replicate(n,
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self.x,
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self.y[:, n:n+1],
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self))
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for r in self.replicates:
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self.slopes.append(r.fitresult['slope'])
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@ -62,25 +92,12 @@ class Measurement():
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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, outpath):
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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('{}/fit_{}_{}.png'.format(outpath,
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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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self.logger.info('Average slope: {} ± {}'.format(self.avg_slope,
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self.avg_slope_err))
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if self.avg_slope < 0:
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self.logger.warning('Avererage slope is negative. Will use '
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'absolute value for further calculations!')
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self.logger.info('-----')
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def get_results(self):
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results = []
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@ -92,12 +109,17 @@ class Measurement():
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class Experiment():
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def __init__(self, data_files, xlim, do_hill=False, logger=None):
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def __init__(self, data_files, xlim, do_hill=False, fit_to_replicates=False, logger=None):
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if logger:
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self.logger = logger
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else:
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self.logger = logging.getLogger(__name__)
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# collction of indepentend measurements
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self.measurements = []
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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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self.raw_kinetic_data = {'x': [],
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'y': [],
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@ -125,8 +147,15 @@ class Experiment():
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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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if self.fit_to_replicates:
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for r in m.replicates:
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self.raw_kinetic_data['x'].append(m.concentration)
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self.raw_kinetic_data['y'].append(np.absolute(r.fitresult['slope']))
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self.raw_kinetic_data['yerr'].append(r.fitresult['std_err'])
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else:
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# extract relevant data for kinetics calculation
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# (concentration, 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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@ -163,6 +192,10 @@ class Experiment():
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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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self.logger.info('Michaelis-Menten Kinetics:')
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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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return {'vmax': float(vmax),
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'Km': float(Km),
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'perr': perr,
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@ -170,7 +203,7 @@ class Experiment():
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except:
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msg = 'Calculation of Michaelis-Menten kinetics failed!'
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if self.logger:
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self.logger.error('ERROR: {}'.format(msg))
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self.logger.error('{}'.format(msg))
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else:
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print(msg)
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return None
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@ -188,6 +221,11 @@ class Experiment():
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x = np.arange(0, max(self.raw_kinetic_data['x']), 0.0001)
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self.logger.info('Hill Kinetics:')
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self.logger.info(' v_max: {} ± {}'.format(vmax, perr[0]))
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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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return {'vmax': float(vmax),
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'Kprime': float(Kprime),
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'perr': perr,
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@ -196,66 +234,7 @@ class Experiment():
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except:
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msg = 'Calculation of Hill kinetics failed!'
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if self.logger:
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self.logger.error('ERROR: {}'.format(msg))
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self.logger.error('{}'.format(msg))
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else:
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print(msg)
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return None
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def plot_kinetics(self, outpath):
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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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if self.mm:
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y = self.mm_kinetics_function(self.mm['x'],
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self.mm['vmax'],
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self.mm['Km'])
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ax.plot(self.mm['x'], y, 'b-', label="Michaelis-Menten")
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if self.hill:
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y = self.hill_kinetics_function(self.hill['x'],
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self.hill['vmax'],
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self.hill['Kprime'],
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self.hill['h'])
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ax.plot(self.hill['x'], y, 'g-', label="Hill")
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ax.legend(loc='best', fancybox=True)
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plt.savefig('{}/kinetics.png'.format(outpath), bbox_inches='tight')
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plt.close(fig)
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def write_data(self, outpath):
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with open('{}/results.csv'.format(outpath),
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'w',
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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:
|
||||
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
Reference in a new issue