I have a sympy result that winds up like this:
from sympy import *
Vin,Vc,C1,Cs,R1,Rs,t=symbols(r'V_{in},V_{C},C_1,C_S,R_1,R_S,t')
k1=Symbol('k_1')
eqVc=Eq(Vc(t),(Rs*(exp(t*(R1+Rs)/(R1*Rs*(C1+Cs))) - 1)*Heaviside(t) +
k1*(R1+Rs))*exp(-t*(R1+Rs)/(R1*Rs*(C1+Cs)))/(R1+Rs))
The expression eqVc comes out like this:
I know this function to be of the form:
My goal is to get the values of Vcinit, Vcfinal, and tau, but particularly tau.
Is there a way to get Sympy to extract these values? cse() doesn't quite do what I want-- I can get it to replace C1+Cs, for example by using cse([C1+Cs,eqVc]), and it does recognize that the exponent to e is a common subexpression, but it tends to include the t in the subexpression.
A simple way is to solve for parameters which will cause the expressions to be equal at a number of points in time. Given that the forms are in fact the same, this will work fine:
V_Ci, tau, V_Cf = symbols('V_Ci, tau, V_Cf')
target = V_Ci*exp(-t/tau) + Heaviside(t)*V_Cf*(1 - exp(-t/tau))
solve([(eqVc.rhs - target).subs(t, ti) for ti in [0, 1, 2]],
[V_Ci, tau, V_Cf], dict=True)
The answer I get is
[{V_Cf: R_S/(R_1 + R_S),
tau: 1/log(exp((1/R_S + 1/R_1)/(C_1 + C_S))),
V_Ci: k_1}]
That log(exp()) is not simplified away because of the way the variables are defined. Defining everything as real (V_Ci, tau, V_Cf = symbols('V_Ci, tau, V_Cf', real=True) and similar modification in your code) simplifies the soluion to
[{V_Ci: k_1,
V_Cf: R_S/(R_1 + R_S),
tau: R_1*R_S*(C_1 + C_S)/(R_1 + R_S)}]
Related
I was wondering if is there is a way to define a function that is a derivative of a function. I'm new to python so I don't no much, I tired looking up stuff that might be similar but nothing has worked so far. This is what I have for my code right now.
import sympy as sp
import math
x = sp.Symbol('x')
W = 15 #kN/m
E = 70 # Gpa
I = 52.9*10**(-6) #m**4
L = 3 #m
e = 0.01
xi = 1.8
y = 9
def f(x):
return ( ( y*3*(math.pi**4)*E*I/(W*L) ) - ( 48*(L**3)*math.cos(math.pi*x/(2*L)) ) + ( 48*(L**3) ) + ( (math.pi**3)*(x**3) ) )/(3*L*(math.pi**3))**(1/2)
def derv(f,x):
return sp.diff(f)
print (derv(f,x))
Also, I don't understand whatx = sp.Symbol('x') does, so if someone could explain that, that would be awesome.
Any help is appreciated.
You are conflating two different things: python functions like f and math functions, which you can express with sympy like y = π * x/3. f is a python function that returns a sympy expression. sympy lets you stay in the world of symbolic math functions by defining variables like x = sp.Symbol('x') So calling f() produces a symbolic math function like:
You can use sympy to find the derivative of the symbolic function returned by f() but you need to define it with the sympy versions of the cos() function (and sp.pi if you want to keep it symbolic).
For example:
import sympy as sp
x = sp.Symbol('x')
W = 15 #kN/m
E = 70 # Gpa
I = 52.9*10**(-6) #m**4
L = 3 #m
e = 0.01
xi = 1.8
y = 9
def f(x):
return ( ( y*3*(sp.pi**4)*E*I/(W*L) ) - ( 48*(L**3)*sp.cos(sp.pi*x/(2*L)) ) + ( 48*(L**3) ) + ( (sp.pi**3)*(x**3) ) )/(3*L*(sp.pi**3))**(1/2)
def derv(f,x):
return sp.diff(f(x)) # pass the result of f() which is a sympy function
derv(f,x)
You've programmed the function. it appears to be a simple function of two independent variables x and y.
Could be that x = sp.Symbol('x') is how SymPy defines the independent variable x. I don't know if you need one or another one for y.
You know enough about calculus to know that you need a derivative. Do you know how to differentiate a function of a single independent variable? It helps to know the answer before you start coding.
y*3*(math.pi**4)*E*I/(W*L) ) - ( 48*(L**3)*math.cos(math.pi*x/(2*L)) ) + ( 48*(L**3) ) + ( (math.pi**3)*(x**3) ) )/(3*L*(math.pi**3))**(1/2)
Looks simple.
There's only one term with y in it. The partial derivative w.r.t. y leaves you with 3*(math.pi**4)*E*I/(W*L) )
There's only one term with Cx**3 in it. That's easy to differentiate: 3C*x**2.
What's so hard? What's the problem?
In traditional programming, each function you write is translated to a series of commands that are then sent to the CPU and the result of the calculation is returned. Therefore, symbolic manipulation, like what we humans do with algebra and calculus, doesn't make any sense to the computer. Sympy gets around this by overriding Python's normal arithmetic operators, allowing you to do generate algebraic functions that can be manipulated similarly to how we humans do math. That's what sp.Symbols('x') is doing: providing you with a symbolic variable you can work with (you're also naming it in sympy).
If you want to evaluate your derivative, simply call evalf with the numerical value you want to assign to x.
I am trying to differentiate this equation using SymPy:
What I wanted to achieve is this result:
But when I type this code into to execute, it gives me this result:
Here is my current code:
import sympy as sp
# declare symbols
t = sp.Symbol('t')
deriv = sp.diff((t*(sp.cos(t)))/(1-t)**2)
# Find the derivative
sp.simplify(deriv)
Is there a way to achieve the desired result?
That is the same result. Break the result you get on the "+" and you get the same thing
Looking for the "simplest" form is not a well-defined problem and sympy can't guess which form you are looking for. You can always call sp.simplify on both the result you get and the desired answer. sympy will derive the same expression for both. In your case
result = sp.simplify(deriv)
desired = sp.simplify((-2 * t * sp.cos(t))/(t - 1)**3 + (-t * sp.sin(t) + sp.cos(t))/(t - 1)**2)
# result == desired
So I want to use the fact that [b,bd]=1, where [] is the commutator, to get some commutators of more complicated expressions using sympy instead of doing it by hand, but instead, I get huge expressions that contain the commutator but it is not replaced for 1, here's the code
from sympy import *
from sympy.physics.secondquant import *
comm1=simplify(Commutator(B(0),Bd(0)).doit())
comm1
the output in this case is 1 , it corresponds to the [b,bd]=1 case, but if I input a more complicated expression such as
w1,w2,g=symbols('w1 w1 g')
H=w1*B(0)*Bd(0)+w2*B(1)*Bd(1)+g*Bd(0)*B(1)+conjugate(g)*Bd(1)*B(0)
comm2=simplify(Commutator(H,B(0)))
print(simplify(comm2))
I get
-g*(AnnihilateBoson(0)*CreateBoson(0)*AnnihilateBoson(1) - CreateBoson(0)*AnnihilateBoson(1)*AnnihilateBoson(0)) - w1*(AnnihilateBoson(0)*AnnihilateBoson(1)*CreateBoson(1) - AnnihilateBoson(1)*CreateBoson(1)*AnnihilateBoson(0)) + w1*(AnnihilateBoson(0)*CreateBoson(0)*AnnihilateBoson(0) - AnnihilateBoson(0)**2*CreateBoson(0)) - conjugate(g)*(AnnihilateBoson(0)*CreateBoson(1)*AnnihilateBoson(0) - CreateBoson(1)*AnnihilateBoson(0)**2)
Which clearly would be simplified quite a lot if [b,bd]=1 was substituted, Does anyone know how to this ? or could anyone point me to another tool capable of doing this?
The key trick is always to use the commutation relation to move either 'a' or 'a^\dagger' to the left, until you cannot do that anymore.
I don't have a good Sympy answer, but since you asked about other tools, here's a shameless plug about how you do it in Cadabra (https://cadabra.science) (which uses Sympy for various things, though not this particular computation). First setup the two sets of creation/annihilation operators using:
{a_{0}, ad_{0}}::NonCommuting;
{a_{1}, ad_{1}}::NonCommuting;
{a_{0}, ad_{0}, a_{1}, ad_{1}}::SortOrder.
They'll print nicer with
\bar{#}::Accent;
ad_{n?}::LaTeXForm("a^\dagger",n?,"").
Your Hamiltonian:
H:= w_{1} a_{0} ad_{0} + w_{2} a_{1} ad_{1} + g ad_{0} a_{1} + \bar{g} ad_{1} a_{1};
The commutator you want to compute:
ex:= #(H) a_{0} - a_{0} #(H);
Just expanding this (without simplification using the [a,ad]=1 commutator) is done with
distribute(ex);
sort_product(ex);
where the 2nd line moves operators with different subscripts through each other, but keeps the order of operators with the same subscripts. Applying the commutator until the expression no longer changes:
converge(ex):
substitute(ex, $a_{n?} ad_{n?} = ad_{n?} a_{n?} + 1$)
distribute(ex)
;
to finally give '-a_0 w_1 - a_1 g'.
As I'm really struggleing to get from R-code, to Python code, I would like to ask some help. The code I want to use has been provided to my from withing the mathematics forum of stackexchange.
https://math.stackexchange.com/questions/2205573/curve-fitting-on-dataset
I do understand what is going on. But I'm really having a hard time trying to solve the R-code, as I have never seen anything of it. I have written the function to return the sum of squares. But I'm stuck at how I could use a function similar to the optim function. And also I don't really like the guesswork at the initial values. I would like it better to run and re-run a type of optim function untill I get the wanted result, because my needs for a nearly perfect curve fit are really high.
def model (par,x):
n = len(x)
res = []
for i in range(1,n):
A0 = par[3] + (par[4]-par[1])*par[6] + (par[5]-par[2])*par[6]**2
if(x[i] == par[6]):
res[i] = A0 + par[1]*x[i] + par[2]*x[i]**2
else:
res[i] = par[3] + par[4]*x[i] + par[5]*x[i]**2
return res
This is my model function...
def sum_squares (par, x, y):
ss = sum((y-model(par,x))^2)
return ss
And this is the sum of squares
But I have no idea on how to convert this:
#I found these initial values with a few minutes of guess and check.
par0 <- c(7,-1,-395,70,-2.3,10)
sol <- optim(par= par0, fn=sqerror, x=x, y=y)$par
To Python code...
I wrote an open source Python package (BSD license) that has a genetic algorithm (Differential Evolution) front end to the scipy Levenberg-Marquardt solver, it functions similarly to what you describe in your question. The github URL is:
https://github.com/zunzun/pyeq3
It comes with a "user-defined function" example that's fairly easy to use:
https://github.com/zunzun/pyeq3/blob/master/Examples/Simple/FitUserDefinedFunction_2D.py
along with command-line, GUI, cluster, parallel, and web-based examples. You can install the package with "pip3 install pyeq3" to see if it might suit your needs.
Seems like I have been able to fix the problem.
def model (par,x):
n = len(x)
res = np.array([])
for i in range(0,n):
A0 = par[2] + (par[3]-par[0])*par[5] + (par[4]-par[1])*par[5]**2
if(x[i] <= par[5]):
res = np.append(res, A0 + par[0]*x[i] + par[1]*x[i]**2)
else:
res = np.append(res,par[2] + par[3]*x[i] + par[4]*x[i]**2)
return res
def sum_squares (par, x, y):
ss = sum((y-model(par,x))**2)
print('Sum of squares = {0}'.format(ss))
return ss
And then I used the functions as follow:
parameter = sy.array([0.0,-8.0,0.0018,0.0018,0,200])
res = least_squares(sum_squares, parameter, bounds=(-360,360), args=(x1,y1),verbose = 1)
The only problem is that it doesn't produce the results I'm looking for... And that is mainly because my x values are [0,360] and the Y values only vary by about 0.2, so it's a hard nut to crack for this function, and it produces this (poor) result:
Result
I think that the range of x values [0, 360] and y values (which you say is ~0.2) is probably not the problem. Getting good initial values for the parameters is probably much more important.
In Python with numpy / scipy, you would definitely want to not loop over values of x but do something more like
def model(par,x):
res = par[2] + par[3]*x + par[4]*x**2
A0 = par[2] + (par[3]-par[0])*par[5] + (par[4]-par[1])*par[5]**2
res[np.where(x <= par[5])] = A0 + par[0]*x + par[1]*x**2
return res
It's not clear to me that that form is really what you want: why should A0 (a value independent of x added to a portion of the model) be so complicated and interdependent on the other parameters?
More importantly, your sum_of_squares() function is actually not what least_squares() wants: you should return the residual array, you should not do the sum of squares yourself. So, that should be
def sum_of_squares(par, x, y):
return (y - model(par, x))
But most importantly, there is a conceptual problem that is probably going to plague this model: Your par[5] is meant to represent a breakpoint where the model changes form. This is going to be very hard for these optimization routines to find. These routines generally make a very small change to each parameter value to estimate to derivative of the residual array with respect to that variable in order to figure out how to change that variable. With a parameter that is essentially used as an integer, the small change in the initial value will have no effect at all, and the algorithm will not be able to determine the value for this parameter. With some of the scipy.optimize algorithms (notably, leastsq) you can specify a scale for the relative change to make. With leastsq that is called epsfcn. You may need to set this as high as 0.3 or 1.0 for fitting the breakpoint to work. Unfortunately, this cannot be set per variable, only per fit. You might need to experiment with this and other options to least_squares or leastsq.
I have a large (>2000 equations) system of ODE's that I want to solve with python scipy's odeint.
I have three problems that I want to solve (maybe I will have to ask 3 different questions?).
For simplicity, I will explain them here with a toy model, but please keep in mind that my system is large.
Suppose I have the following system of ODE's:
dS/dt = -beta*S
dI/dt = beta*S - gamma*I
dR/dt = gamma*I
with beta = cpI
where c, p and gamma are parameters that I want to pass to odeint.
odeint is expecting a file like this:
def myODEs(y, t, params):
c,p, gamma = params
beta = c*p
S = y[0]
I = y[1]
R = y[2]
dydt = [-beta*S*I,
beta*S*I - gamma*I,
- gamma*I]
return dydt
that then can be passed to odeint like this:
myoutput = odeint(myODEs, [1000, 1, 0], np.linspace(0, 100, 50), args = ([c,p,gamma], ))
I generated a text file in Mathematica, say myOdes.txt, where each line of the file corresponds to the RHS of my system of ODE's, so it looks like this
#myODEs.txt
-beta*S*I
beta*S*I - gamma*I
- gamma*I
My text file looks similar to what odeint is expecting, but I am not quite there yet.
I have three main problems:
How can I pass my text file so that odeint understands that this is the RHS of my system?
How can I define my variables in a smart way, that is, in a systematic way? Since there are >2000 of them, I cannot manually define them. Ideally I would define them in a separate file and read that as well.
How can I pass the parameters (there are a lot of them) as a text file too?
I read this question that is close to my problems 1 and 2 and tried to copy it (I directly put values for the parameters so that I didn't have to worry about my point 3 above):
systemOfEquations = []
with open("myODEs.txt", "r") as fp :
for line in fp :
systemOfEquations.append(line)
def dX_dt(X, t):
vals = dict(S=X[0], I=X[1], R=X[2], t=t)
return [eq for eq in systemOfEquations]
out = odeint(dX_dt, [1000,1,0], np.linspace(0, 1, 5))
but I got the error:
odepack.error: Result from function call is not a proper array of floats.
ValueError: could not convert string to float: -((12*0.01/1000)*I*S),
Edit: I modified my code to:
systemOfEquations = []
with open("SIREquationsMathematica2.txt", "r") as fp :
for line in fp :
pattern = regex.compile(r'.+?\s+=\s+(.+?)$')
expressionString = regex.search(pattern, line)
systemOfEquations.append( sympy.sympify( expressionString) )
def dX_dt(X, t):
vals = dict(S=X[0], I=X[1], R=X[2], t=t)
return [eq for eq in systemOfEquations]
out = odeint(dX_dt, [1000,1,0], np.linspace(0, 100, 50), )
and this works (I don't quite get what the first two lines of the for loop are doing). However, I would like to do the process of defining the variables more automatic, and I still don't know how to use this solution and pass parameters in a text file. Along the same lines, how can I define parameters (that will depend on the variables) inside the dX_dt function?
Thanks in advance!
This isn't a full answer, but rather some observations/questions, but they are too long for comments.
dX_dt is called many times by odeint with a 1d array y and tuple t. You provide t via the args parameter. y is generated by odeint and varies with each step. dX_dt should be streamlined so it runs fast.
Usually an expresion like [eq for eq in systemOfEquations] can be simplified to systemOfEquations. [eq for eq...] doesn't do anything meaningful. But there may be something about systemOfEquations that requires it.
I'd suggest you print out systemOfEquations (for this small 3 line case), both for your benefit and ours. You are using sympy to translated the strings from the file into equations. We need to see what it produces.
Note that myODEs is a function, not a file. It may be imported from a module, which of course is a file.
The point to vals = dict(S=X[0], I=X[1], R=X[2], t=t) is to produce a dictionary that the sympy expressions can work with. A more direct (and I think faster) dX_dt function would look like:
def myODEs(y, t, params):
c,p, gamma = params
beta = c*p
dydt = [-beta*y[0]*y[1],
beta*y[0]*y[1] - gamma*y[1],
- gamma*y[1]]
return dydt
I suspect that the dX_dt that runs sympy generated expressions will be a lot slower than a 'hardcoded' one like this.
I'm going add sympy tag, because, as written, that is the key to translating your text file into a function that odeint can use.
I'd be inclined to put the equation variability in the t parameters, rather a list of sympy expressions.
That is replace:
dydt = [-beta*y[0]*y[1],
beta*y[0]*y[1] - gamma*y[1],
- gamma*y[1]]
with something like
arg12=np.array([-beta, beta, 0])
arg1 = np.array([0, -gamma, -gamma])
arg0 = np.array([0,0,0])
dydt = arg12*y[0]*y[1] + arg1*y[1] + arg0*y[0]
Once this is right, then the argxx definitions can be move outside dX_dt, and passed via args. Now dX_dt is just a simple, and fast, calculation.
This whole sympy approach may work fine, but I'm afraid that in practice it will be slow. But someone with more sympy experience may have other insights.