Performs
a regression from given kinetic data ('Data table' or Excel), to find
the underlying parameters, ki,
i = 1..n (here, n = 4)
in a set of multiple (here, 2) functions of a single
(here, t, as usual) variable.
The embedded 2 ODE's are  ,
with (c0, d0) = 0 and
the complementary constants a0 and
b0 (as given).
This is an application of the Python
'minimize' function, in the 'scipy.optimize' module, a part of the
Python 'SciPy' library.
Alternative (upload-download Excel) "experimental"
data, from the base data:
(a) "improved" from the
optimized values; and
(b) "perturbed" (random)
from previous, ±10%.
Advantage is taken from: PHP,
for the web page; Python for the numerical segment;
and 'gnuplot', for plotting.
Draws a plot of the original and
the adjusted data.
Computation structure — A PHP file
(this page: 'P-fitKinet.php') calls (via
'action=...') an intermediate PHP file ('FitKinet.php'), which
(through $_POST) sends the problem data as command line arguments to
a Python script. This last does the computing, and makes
'system' call(s) to 'gnuplot', for the final (temporary) webpage.
Files involved:
P-curFitODE.php (this one),
CurFitODE.php,
curFitODE.py.
Keywords: PHP, command line arguments,
Python script, gnuplot. | 
