pycotools3.viz.Parse

class pycotools3.viz.Parse(cls_instance, log10=False, copasi_file=None, alpha=0.95, rss_value=None, num_data_points=None)

General class for parsing copasi output into Python.

First argument is an instance of a pycotools3 class.

instance	Description
tasks.TimeCourse	Parse time course data from TC.report_name into pandas.df
tasks.ParameterEstimation	Parse parameter estimation data from PE.report_name into pandas.df
tasks.Scan	Parse scan data from scan.report_name
Parse	enable parsing from a parse instance. Just returns itself
str	Parse data from folder of parameter estimation data into pandas.df. Requires the copasi file argument.

Args:

Returns:

__init__(cls_instance, log10=False, copasi_file=None, alpha=0.95, rss_value=None, num_data_points=None)

Parameters:

• cls_instance – A instance of pycotools3 class
• log10 – bool. Whether to work on log10 scale
• copasi_file – str. Optional but necessary when cls_instance is string. Must be the copasi_file which produced the parameter estimation data as Parse extracts data headers from the copasi file
• rss_value –

  float When cls is a profile likelihood with the current_parameters setting,

  rss_value may not be empty. It is not automatically inferable from the COPASI model and must be specified separetly.

• num_data_points – int When cls is a profile likelihood with current paraemters setting, the number of data points cannot be automatically inferred for the calculation of likelihood ratio based confidence intervals. Therefore, this must be specified by the user.

Methods

__init__(cls_instance[, log10, copasi_file, …])	param cls_instance:
from_chaser_estimations(cls_instance[, folder])	return:
from_folder()	param folder:return:
from_multi_parameter_estimation(cls_instance)	Results come without headers - parse the results give them the proper headers then overwrite the file again
from_profile_likelihood()	Parse data from tasks.ProfileLikelihood :return: pandas.DataFrame
from_timecourse()	read time course data into pandas dataframe.
parse()	determine class type of self.cls_instance and call the appropirate method for parsing the data type :return:
parse_scan()	read scan data into pandas Dataframe.

Attributes

from_parameter_estimation

Parse parameter estimation data.

from_chaser_estimations(cls_instance, folder=None)

Returns:

Parameters:

• cls_instance –
• folder – (Default value = None)

Returns:

from_folder()

Parameters:folder – return:

Returns:

static from_multi_parameter_estimation(cls_instance)

Results come without headers - parse the results give them the proper headers then overwrite the file again

Parameters:

• cls_instance – instance of MultiParameterEstiamtion
• folder – afternative folder to parse from. Useful for tests (Default value = None)

Returns:

from_parameter_estimation

Parse parameter estimation data. Store the data in a cache. :return:

Args:

Returns:

from_profile_likelihood()

Parse data from tasks.ProfileLikelihood :return:

pandas.DataFrame

Args:

Returns:

from_timecourse()

read time course data into pandas dataframe. Remove copasi generated square brackets around the variables :return: pandas.DataFrame

Args:

Returns:

parse()

determine class type of self.cls_instance and call the appropirate method for parsing the data type :return:

Args:

Returns:

parse_scan()

read scan data into pandas Dataframe. :return: pandas.DataFrame

Args:

Returns: