Package: pk4adi
pk4adiProject InformationThe package's name pk4adi is short for "PK for anesthetic depth indicators". The PK (Prediction probability) was first proposed byDr. Warren D. Smithin the paperMeasuring the Performance of Anesthetic Depth Indicatorsin 1996. Dr. Warren D. Smith and his team provide a tool to calculate PK written using the MS Excel macro language.Our team provide a reimplementation of the PK tools developed using the Python language with easy-to-use APIs in this package. The project is fully open source ongithub. The latest released version could be foundhere.A GUI version of pk4adi called pk4adi_gui is also under development. This project is also open source ongithub.Please feel free to contact us (silencejiang@zju.edu.cn). Any kind of feedback is welcome. You could report any bugs or issues when using pk4adi on githubproject.ChangelogsPlease refer thechangelog.mdfor details.RequirementsPythonPython 3.8 or greater.Packagespandas>=0.18.0
numpy>=1.21.6
scipy>=1.9.0
tabulateInstallTo install pk4adi, run the following in the command prompt.pip install pk4adiAPIscalculate_pk of module pk.py.calculate_pk(x_in , y_in , auto_print = True):

Compute the pk value to Measure the Performance of Anesthetic Depth Indicators.
print_pk() will be called before returning ans by default.

Parameters
----------
x_in : a list or a pandas series (pandas.Series()).
    Indicator.
y_in : a list or a pandas series (pandas.Series()).
    State.
auto_print : bool.
    Whether to print the ans before returning it or not.

Returns
-------
ans : a dict.
    A dict containing all the matrix and variables involved.
    Use the script 'print(ans.keys())' to get the details.
    The most important variables have already been printed.print_pk of module pk.py.print_pk(result, floatfmt=".3f", tablefmt='simple'):

Pretty display of a pk calculation result.

Parameters
----------
result : a dict.
    Must be the return value of function calculate_pk().
floatfmt : string.
    Decimal number formatting.
tablefmt : string.
    Table format (e.g. 'simple', 'plain', 'html', 'latex', 'grid', 'rst').
    For a full list of available formats, please refer to
    https://pypi.org/project/tabulate/

Returns
-------
Nothing will be returned.compare_pks of module pkc.py.compare_pks(pk1, pk2 , auto_print = True):

Compare two answers of the pk values, which is the output of the function calculate_pk().
print_pks() will be called before returning ans by default.

Parameters
----------
pk1 : a dict.
    The output of the function calculate_pk().
pk2 : a dict.
    The output of the function calculate_pk().
auto_print : bool.
    Whether to print the ans before returning it or not.

Returns
-------
ans : a dict.
    A dict containing all the matrix and variables involved.
    Use the script 'print(ans.keys())' to get the details.
    Specially, the P values and the interval it located will be
    calculated using the scipy.stats packages.
    The most important variables have already been printed.print_pks of module pkc.py.print_pks(result, floatfmt=".3f", tablefmt='simple'):

Pretty display of two pk calculation result comparison.

Parameters
----------
result : a dict.
    Must be the return value of function compare_pks().
floatfmt : string.
    Decimal number formatting.
tablefmt : string.
    Table format (e.g. 'simple', 'plain', 'html', 'latex', 'grid', 'rst').
    For a full list of available formats, please refer to
    https://pypi.org/project/tabulate/

Returns
-------
Nothing will be returned.ExamplesThe best way to use this package is to use Python scripts.1. calculate PKfrompk4adi.pkimportcalculate_pkx=[0,0,0,0,0,0]y=[1,1,1,1,1,2]calculate_pk(x,y)x=[0,0,0,0,0,0,1,1,2]y=[1,1,1,1,1,2,3,3,4]calculate_pk(x,y)You will get the following output.==============
PK calculation
==============

   PK    SE0    SE1  jack_ok      PKj    SEj
-----  -----  -----  ---------  -----  -----
0.500  0.000  0.000  False        nan    nan


==============
PK calculation
==============

   PK    SE0    SE1  jack_ok      PKj    SEj
-----  -----  -----  ---------  -----  -----
0.900  0.124  0.085  True       0.901  0.1172. compare results of PKfrompk4adi.pkimportcalculate_pkfrompk4adi.pkcimportcompare_pksx1=[1,1,2,2,2,2,3,3,3,3,4,4,4,4,4,5,5,5,5,6,6,6,6,6]y1=[1,1,1,1,1,2,1,1,3,3,2,2,2,2,2,1,3,3,3,3,3,3,3,3]pk1=calculate_pk(x_in=x1,y_in=y1,auto_print=False)x2=[1,1,2,2,2,2,3,3,3,3,4,4,4,4,4,5,5,5,5,6,6,6,6,6]y2=[1,1,2,1,1,2,1,2,3,3,2,2,1,2,2,2,3,3,3,3,2,3,3,2]pk2=calculate_pk(x_in=x2,y_in=y2,auto_print=False)ans=compare_pks(pk1,pk2)You will get the following output.==============
PKs comparison
==============

=================
For Group (z-test)
=================

  PKD    SED     ZD    P value  Comment
-----  -----  -----  ---------  ---------
0.068  0.101  0.669      0.504  P > 0.05


=================
For Pair (t-test)
=================

  PKDJ    SEDJ    DF     TD    P value  Comment
------  ------  ----  -----  ---------  ---------
 0.030   0.066    23  0.453      0.327  P > 0.053. more detailsYou could get the all the matrix and variables in the returned dicts of the function calculate_pk() and compare_pks().print(pk1.keys())print(ans.keys())You will get the following output.dict_keys(['type', 'A', 'S', 'C', 'D', 'T', 'SA', 'CA', 'DA', 'TA', 'jack_ok', 'n_case', 'n', 'Qc', 'Qd', 'Qtx', 'Qcdt', 'dyx', 'PK', 'Qcc', 'Qdd', 'Qcd', 'Term1', 'Term2', 'Term3', 'SE1', 'SE0', 'PKm', 'SPKm', 'SSPKm', 'PKj', 'SEj'])
dict_keys(['type', 'n_case', 'PKD', 'SED', 'ZD', 'ZP', 'ZJ', 'PKmD', 'SumD', 'SSD', 'DF', 'PKDJ', 'SEDJ', 'TD', 'TP', 'TJ'])Then just get the value with the key of the dict!DevelopmentContributePlease feel free to contact us (silencejiang@zju.edu.cn). Any kind of feedback is welcome and appreciated.Check out the wiki for development info (coming soon!).Fork us from @xfz329'smainand star us.Report an issue or a bug with datahere.Any other free discussionhere.ReferencesMeasuring the Performance of Anesthetic Depth IndicatorsA measure of association for assessing prediction accuracy that is a generalization of non-parametric ROC areaExcel 4.0 Macro Functions Reference - My Online Training Hubscipy.stats.normscipy.stats.t