modelsandbox_Mixin module
This is a module for testing new features of the model class, but in a smaler file.
Created on Sat Sep 29 06:03:35 2018
@author: hanseni
- class modelsandbox_Mixin.Newmodel_Mixin[source]
Bases:
object- property showstartnr
- sim2d(databank, start='', end='', silent=0, samedata=0, alfa=1.0, stats=False, first_test=1, antal=1, conv=[], absconv=0.01, relconv=1e-16, dumpvar=[], init=False, ldumpvar=False, dumpwith=15, dumpdecimal=5, chunk=None, ljit=False, timeon=False, fairopt={'fairantal': 1}, **kwargs)[source]
Evaluates this model on a databank from start to end (means end in Danish).
First it finds the values in the Dataframe, then creates the evaluater function through the outeval function (
modelclass.model.fouteval()) then it evaluates the function and returns the values to a the Dataframe in the databank.The text for the evaluater function is placed in the model property make_los_text where it can be inspected in case of problems.
- outsolve2dcunk(databank, debug=1, chunk=None, ljit=False, type='gauss', cache=False)[source]
takes a list of terms and translates to a evaluater function called los
The model axcess the data through:Dataframe.value[rowindex+lag,coloumnindex] which is very efficient
- sim1d(databank, start='', end='', silent=0, samedata=0, alfa=1.0, stats=False, first_test=1, antal=1, conv=[], absconv=0.01, relconv=1e-05, dumpvar=[], ldumpvar=False, dumpwith=15, dumpdecimal=5, chunk=None, ljit=False, fairopt={'fairantal': 1}, timeon=0, **kwargs)[source]
Evaluates this model on a databank from start to end (means end in Danish).
First it finds the values in the Dataframe, then creates the evaluater function through the outeval function (
modelclass.model.fouteval()) then it evaluates the function and returns the values to a the Dataframe in the databank.The text for the evaluater function is placed in the model property make_los_text where it can be inspected in case of problems.
- outsolve1dcunk(debug=0, chunk=None, ljit=False, cache='False')[source]
takes a list of terms and translates to a evaluater function called los
The model axcess the data through:Dataframe.value[rowindex+lag,coloumnindex] which is very efficient
- errfunk(values, linenr, overhead=4, overeq=0)[source]
developement function
to handle run time errors in model calculations
- newton1per(databank, start='', end='', silent=1, samedata=0, alfa=1.0, stats=False, first_test=1, antal=20, conv=[], absconv=0.01, relconv=1e-05, nonlin=False, timeit=False, reset=1, dumpvar=[], ldumpvar=False, dumpwith=15, dumpdecimal=5, chunk=None, ljit=False, fairopt={'fairantal': 1}, **kwargs)[source]
Evaluates this model on a databank from start to end (means end in Danish).
First it finds the values in the Dataframe, then creates the evaluater function through the outeval function (
modelclass.model.fouteval()) then it evaluates the function and returns the values to a the Dataframe in the databank.The text for the evaluater function is placed in the model property make_los_text where it can be inspected in case of problems.
- newtonstack(databank, start='', end='', silent=1, samedata=0, alfa=1.0, stats=False, first_test=1, antal=20, conv=[], absconv=0.01, relconv=1e-05, dumpvar=[], ldumpvar=False, dumpwith=15, dumpdecimal=5, chunk=None, nchunk=None, ljit=False, nljit=0, fairopt={'fairantal': 1}, debug=False, timeit=False, nonlin=False, nonlinfirst=0, newtonalfa=1.0, newtonnodamp=0, forcenum=True, reset=False, **kwargs)[source]
Evaluates this model on a databank from start to end (means end in Danish).
First it finds the values in the Dataframe, then creates the evaluater function through the outeval function (
modelclass.model.fouteval()) then it evaluates the function and returns the values to a the Dataframe in the databank.The text for the evaluater function is placed in the model property make_los_text where it can be inspected in case of problems.
- newton1per_un_normalized(databank, start='', end='', silent=1, samedata=0, alfa=1.0, stats=False, first_test=1, antal=20, conv=[], absconv=0.01, relconv=1e-05, nonlin=False, timeit=False, reset=1, dumpvar=[], ldumpvar=False, dumpwith=15, dumpdecimal=5, chunk=None, ljit=False, fairopt={'fairantal': 1}, newtonalfa=1.0, newtonnodamp=0, **kwargs)[source]
Evaluates this model on a databank from start to end (means end in Danish).
First it finds the values in the Dataframe, then creates the evaluater function through the outeval function (
modelclass.model.fouteval()) then it evaluates the function and returns the values to a the Dataframe in the databank.The text for the evaluater function is placed in the model property make_los_text where it can be inspected in case of problems.
- newtonstack_un_normalized(databank, start='', end='', silent=1, samedata=0, alfa=1.0, stats=False, first_test=1, antal=20, conv=[], absconv=0.01, relconv=1e-05, dumpvar=[], ldumpvar=False, dumpwith=15, dumpdecimal=5, chunk=None, nchunk=None, ljit=False, nljit=0, fairopt={'fairantal': 1}, debug=False, timeit=False, nonlin=False, newtonalfa=1.0, newtonnodamp=0, forcenum=True, reset=False, **kwargs)[source]
Evaluates this model on a databank from start to end (means end in Danish).
First it finds the values in the Dataframe, then creates the evaluater function through the outeval function (
modelclass.model.fouteval()) then it evaluates the function and returns the values to a the Dataframe in the databank.The text for the evaluater function is placed in the model property make_los_text where it can be inspected in case of problems.
- res2d(databank, start='', end='', debug=False, timeit=False, silent=False, chunk=None, ljit=0, alfa=1, stats=0, samedata=False, **kwargs)[source]
calculates the result of a model, no iteration or interaction The text for the evaluater function is placed in the model property make_res_text where it can be inspected in case of problems.