I'm relatively new to using Python to fit my data, so please excuse my lack of programming finesse. I have, however, been unable to find a solution to the errors my current curve fitting attempts are throwing. I believe these errors are due to my model function's complex dependence on one of the two variable parameters (namely, Kd). I would appreciate insight regarding what specifically is causing this problem and how I may adjust my definition or fitting package to avoid it. Minimal working example to follow:
# Import libraries
import scipy as scipy
from scipy import stats
import numpy as np
from scipy.optimize import curve_fit
np.set_printoptions(precision=4)
ConcSyringeTotal = 9.5 ## total monomer concentration in the syringe [M]tot, in mM
Vinj = 10 ## volume injected in each injection, in uL
Vinit = 1250 ## volume of solvent initially in the sample cell, in uL
Vcell = 1000 ## cell volume, in uL (only the heat change within this volume is measured)
Injections = np.arange(2.00,26.00,1.00)
print Injections
Energy = np.array([136.953, 105.119, 84.414, 69.373, 60.898, 52.813, 46.187, 39.653, 33.894, 29.975, 27.315, 24.200, 21.643, 19.080, 16.158, 13.454, 13.218, 11.568, 10.742, 9.547, 8.693, 7.334, 6.111, 4.741])
print Energy
def DimerDissociation(injection, Kd, DHd): ## a dimer dissociation model for an ITC dilution experiment
## returns the heat flow (y-data, in ucal) per injection (x-data, unitless)
## fit for the dissociation constant (Kd, in mM = mmol/L, umol/mL, nmol/uL)
## and the enthalpy of dissociation (DHd, in ucal/nmol = kcal/mol)
##
## concentration (in mM) of the free monomer in the cell after equilibration of the i-th injection
VolumeAdded = 6+(injection-1)*Vinj ## in uL
VolumeTotal = Vinit + VolumeAdded ## in uL
CellTotal = ConcSyringeTotal*VolumeAdded ## Total in the cell after the i-th injection, in nmol
ConcCellTotal = CellTotal/VolumeTotal ## Total concentration in the cell after the i-th injection, in mM
ConcCellMonomer_roots = np.roots([1, Kd/2, -Kd*ConcCellTotal/2])
ConcCellMonomer_real = ConcCellMonomer_roots.real[abs(ConcCellMonomer_roots.imag)<1e-5]
ConcCellMonomer_positive = ConcCellMonomer_real[ConcCellMonomer_real>0]
ConcCellMonomer = ConcCellMonomer_positive[ConcCellMonomer_positive<ConcCellTotal]
##
## concentration (in mM) of the free monomer in the syringe
ConcSyringeMonomer_roots = np.roots([1, Kd/2, -Kd*ConcSyringeTotal/2])
ConcSyringeMonomer_real = ConcSyringeMonomer_roots.real[abs(ConcSyringeMonomer_roots.imag)<1e-5]
ConcSyringeMonomer_positive = ConcSyringeMonomer_real[ConcSyringeMonomer_real>0]
ConcSyringeMonomer = ConcSyringeMonomer_positive[ConcSyringeMonomer_positive<ConcSyringeTotal]
## nmol of the free monomer injected from the syringe
SyringeMonomerInjected = Vinj*ConcSyringeMonomer[0]
##
## concentration (in mM) of the free monomer in the cell before the i-th injection
VolumeAddedPre = 6+(injection-2)*Vinj
VolumeTotalPre = Vinit + VolumeAddedPre
CellTotalPre = ConcSyringeTotal*VolumeAddedPre
ConcCellTotalPre = CellTotalPre/VolumeTotalPre
ConcCellMonomerPre_roots = np.roots([1, Kd/2, -Kd*ConcCellTotalPre/2])
ConcCellMonomerPre_real = ConcCellMonomerPre_roots.real[abs(ConcCellMonomerPre_roots.imag)<1e-5]
ConcCellMonomerPre_positive = ConcCellMonomerPre_real[ConcCellMonomerPre_real>0]
ConcCellMonomerPre = ConcCellMonomerPre_positive[ConcCellMonomerPre_positive<ConcCellTotalPre]
## nmol of the free monomer in the cell before the i-th injection
CellMonomerPre = VolumeTotalPre*ConcCellMonomerPre[0]
##
## concentration of the free monomer before equilibration of the i-th injection, in mM
ConcCellMonomerBefore = (CellMonomerPre+SyringeMonomerInjected)/VolumeAdded
## concentration of the free monomer after equilibration of the i-th injection, in mM
ConcCellMonomerAfter = ConcCellMonomer[0]
## change in concentration of the free monomer over the equilibration of the i-th injection, in mM
ConcCellMonomerChange = ConcCellMonomerAfter - ConcCellMonomerBefore
##
return Vcell*DHd*ConcCellMonomerChange
DimerDissociation_opt, DimerDissociation_cov = curve_fit(DimerDissociation, Injections, Energy, p0=[0.4,10])
DimerDissociation_stdev = np.sqrt(np.diag(DimerDissociation_cov))
print "optimized parameters:", DimerDissociation_opt
print "covariance matrix:", DimerDissociation_cov
print "standard deviation of fit parameters:", DimerDissociation_stdev
And the associated error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-38-b5ef2361feed> in <module>()
52 ##
53 return Vcell*DHd*ConcCellMonomerChange
---> 54 DimerDissociation_opt, DimerDissociation_cov = curve_fit(DimerDissociation, Injections, Energy, p0=[0.4,10])
55 DimerDissociation_stdev = np.sqrt(np.diag(DimerDissociation_cov))
56 print "optimized parameters:", DimerDissociation_opt
//anaconda/lib/python2.7/site-packages/scipy/optimize/minpack.pyc in curve_fit(f, xdata, ydata, p0, sigma, absolute_sigma, **kw)
553 # Remove full_output from kw, otherwise we're passing it in twice.
554 return_full = kw.pop('full_output', False)
--> 555 res = leastsq(func, p0, args=args, full_output=1, **kw)
556 (popt, pcov, infodict, errmsg, ier) = res
557
//anaconda/lib/python2.7/site-packages/scipy/optimize/minpack.pyc in leastsq(func, x0, args, Dfun, full_output, col_deriv, ftol, xtol, gtol, maxfev, epsfcn, factor, diag)
367 if not isinstance(args, tuple):
368 args = (args,)
--> 369 shape, dtype = _check_func('leastsq', 'func', func, x0, args, n)
370 m = shape[0]
371 if n > m:
//anaconda/lib/python2.7/site-packages/scipy/optimize/minpack.pyc in _check_func(checker, argname, thefunc, x0, args, numinputs, output_shape)
18 def _check_func(checker, argname, thefunc, x0, args, numinputs,
19 output_shape=None):
---> 20 res = atleast_1d(thefunc(*((x0[:numinputs],) + args)))
21 if (output_shape is not None) and (shape(res) != output_shape):
22 if (output_shape[0] != 1):
//anaconda/lib/python2.7/site-packages/scipy/optimize/minpack.pyc in _general_function(params, xdata, ydata, function)
443
444 def _general_function(params, xdata, ydata, function):
--> 445 return function(xdata, *params) - ydata
446
447
<ipython-input-38-b5ef2361feed> in DimerDissociation(injection, Kd, DHd)
19 CellTotal = ConcSyringeTotal*VolumeAdded ## Total in the cell after the i-th injection, in nmol
20 ConcCellTotal = CellTotal/VolumeTotal ## Total concentration in the cell after the i-th injection, in mM
---> 21 ConcCellMonomer_roots = np.roots([1, Kd/2, -Kd*ConcCellTotal/2])
22 ConcCellMonomer_real = ConcCellMonomer_roots.real[abs(ConcCellMonomer_roots.imag)<1e-5]
23 ConcCellMonomer_positive = ConcCellMonomer_real[ConcCellMonomer_real>0]
//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.pyc in roots(p)
199 """
200 # If input is scalar, this makes it an array
--> 201 p = atleast_1d(p)
202 if len(p.shape) != 1:
203 raise ValueError("Input must be a rank-1 array.")
//anaconda/lib/python2.7/site-packages/numpy/core/shape_base.pyc in atleast_1d(*arys)
47 res = []
48 for ary in arys:
---> 49 ary = asanyarray(ary)
50 if len(ary.shape) == 0 :
51 result = ary.reshape(1)
//anaconda/lib/python2.7/site-packages/numpy/core/numeric.pyc in asanyarray(a, dtype, order)
512
513 """
--> 514 return array(a, dtype, copy=False, order=order, subok=True)
515
516 def ascontiguousarray(a, dtype=None):
ValueError: setting an array element with a sequence.
I was not able to reproduce your error. The first problem I noticed is your use of
np.roots
.roots(p)
returns the roots of a polynomial specified by the coefficients inp
, specificallyp[0] + p[1] * x + p[2] * x**2 + ...
. Your third coefficient,-Kd*ConcCellTotal/2
is a function ofinjections
, which was an array. There's no documented signature fornp.roots
that allows an array to be passed as one of the members ofp
.Can you edit and clarify?
-Ravi
P.S. A toy example demonstrating how
curve_fit
works:The objective function
f
takes as arguments an array of x-values and one or more parameters.curve_fit
takes as arguments the objective function, an array of x-values x_in, and an array of y-values y_in as arguments. It then makes up some values for the parameters a and b and evaluates the objective function on x_in, which gives an array y_out. It computes the RMS error between y_in and y_out and then tweaks its values of a and b until the RMS error is minimized.The devil is really in the details of how initial values for a and b are selected (if they're not supplied, as the OP did) and how they're tweaked. That's complicated, but not absolutely necessary for us
scipy.optimize
users to understand perfectly.