single curve defined by a 4-tuple.
Unfortunately the use of the word "plot" here is kind of subtle,
as it is more commonly attributed to a visual figure,
while I use it to describe a particular curve independent of a figure.
The extent of my creativity has its limits.
(x_array, y_array, const_list, const_dict)
x_array
: ordered numpy array specifying the values of the independent variabley_array
: ordered numpy array specifying the values of the depenent variable (same length asx_array
)const_list
: ordered list of constants related to the particular curveconst_dict
: dictionary of named constants related to the particular curve
(aka transform) callable object that performs a transformation
on a curve or plot.
See transforms.py
.
t(x_array, y_array, *args) -> (next_x, next_y, *args)
x_array
: (see plot definition)y_array
: (see plot definition)next_x
: transformed x arraynext_y
: transformed y array*args
: other arguments, which are unchanged by the transformation (but may be referenced by the transformation).
The inclusion of args
is particularly convenient for working with
a plot.
transformed_plot = t(*plot)
Alternatively, a transform could be used independently of
a plot, as long as args
is not referenced by the
transform.
Another benefit of the above definition is that a series of
transform can be composed. For example, one might with to
apply t1
and then t2
to a plot
.
This may be done manually:
next_plot = t2(*t1(*plot))
Or it may be done using compose_transforms
from transforms.py
:
from transforms import compose_transforms
t_comp = compose_transforms([t2, t1])
next_plot = t_comp(*plot)
(aka super transform) callable object that performs a
transformation on a list of plot. This is a
generalization of plot transform.
See transforms_s.py
.
st(list_of_plot) -> next_list_of_plot
list_of_plot
: list of plotnext_list_of_plot
: transformed list of plot
A series of super transform may be composed. For example,
one might wish to apply st1
and then st2
to a list_of_plot
.
This may be done manually:
next_list_of_plot = st2(st1(list_of_plot))
Or it may be done using compose_super_transforms
from
transforms.py
:
from transforms_s import compose_super_transforms
st_comp = compose_super_transforms([st2, st1])
next_list_of_plot = st_comp(list_of_plot)
(aka fitfn, FitFunction
) callable object used for fitting.
See FitFunction.py
.
f(x, params, const_list, const_dict) -> y
x
: value of the independent variable; a real numberparams
: list of parameters the functional form of the fit depends on (to be optimized)const_list
: (see plot definition)const_dict
: (see plot definition)y
: value of the dependent variable (the fit); a real number
A fit function f
has the field f.num_fit_params
,
which gives the number of fit parameters necessary to evaluate the
function.
Coming soon.
Coming soon.
Coming soon.
See DataMap.py
.
Coming soon.
See Datum.py
Coming soon.
See metafit.py
Coming soon.