Because PolarPlot should type r=... type of command.
But y=x will cause r to disappear.
How to draw that line with PolarPlot?
First, consider Plot[] which "generates a plot of f as a function of x from xmin to xmax" (I'm quoting the Mathematica documentation). You can't use it to plot a vertical line satisfying the equation x = x0, because the latter is not a function of x: instead of being single-valued, it has infinitely many values at x0.
Similarly, PolarPlot[] cannot be used to draw a straight line that passes through the origin, because its equation is not a function of θ: it has infinitely many values at a particular θ (equal to Pi/4 in the case requested), but none at all elsewhere. (Well, one could also allow the complementary angle 3Pi/4 as well.)
So I maintain it can't be done using the tools specified, short of the cheat
PolarPlot[0, {\[Theta], 0, 1},
Epilog -> Line[{Scaled[{1, 1}], Scaled[{0, 0}]}]]
I suggest you use a new function for things like this.
PolarParametricPlot[
{rT : {_, _} ..} | rT : {_, _},
uv : {_, _, _} ..,
opts : OptionsPattern[]
] :=
ParametricPlot[
Evaluate[# {Cos##2, Sin##2} & ### {rT}],
uv,
opts
]
Usage:
PolarParametricPlot[{t, 45 Degree}, {t, -10, 10}]
Here is the general polar form for a line of the form y = m x + b:
In[155]:= r /.
Solve[Eliminate[{x == r Cos[t], y == r Sin[t], y == m x + b}, {x,
y}], r]
Out[155]= {-(b/(m Cos[t] - Sin[t]))}
The solution vanishes when the y-intercept b is zero. This makes sense, since such lines are drawn at a constant angle, which is problematic since PolarPlot works by varying the angle.
You could approximate such a line by using a very small value for b, but there are probably better approaches.
You could draw the line using ListPolatPlot:
ListPolarPlot[{{Pi/4, 5}, {5 Pi/4, 5}}, Joined -> True]
I'm trying to plot points that I've created in a table in mathematica but for some reason one component of my points seems to have double braces around it while the other only has one as below:
{{x},y},{{x1},y1}....{{xn},yn}
and list plot will not recognize these as points and will not plot them.
Here is my mathematica code:
Remove["Global`*"]
b = .1;
w = 1;
Period = 1;
tstep = 2 Pi/Period;
s = NDSolve[{x''[t] + b x'[t] - x[t] + x[t]^3 - .5 Cos[w t] == 0,
x'[0] == 0, x[0] == 0}, x[t], {t, 0, 1000}, MaxSteps -> Infinity];
x[t_] = x[t] /. s
data = Table[Evaluate[{x'[t], .5}], {t, 0, 1000, tstep}]
ListPlot[data]
I've also tried using the command
ListPlot[Flatten[Table[Evaluate[{x'[t], .5}], {t, 0, 1000, tstep}]]]
to no avail as well as
ListPlot[Table[Evaluate[{Flatten[x'[t]], .5}], {t, 0, 1000, tstep}]]]
How can I remove the {}?
You may try something along these lines:
Clear["Global`*"]
b = .1;
w = 1;
s = NDSolve[{x''[t] + b x'[t] - x[t] + x[t]^3 - .5 Cos[w t] == 0,
x'[0] == 0, x[0] == 0}, x[t], {t, 0, 1000}, MaxSteps -> Infinity];
xr[u_] := ((x[t] /. s[[1]]) /. t -> u)
Plot[(xr'[u]), {u, 0, 30}]
But I am not sure what are you trying to get from the {x'[t], .5} part
My colleagues are correct, but I think there is more that can be said. First, to your actual question. The output of NDSolve is a list of the form
{{x[t]->InterpolatingFunction[...]}, {x[t]->InterpolatingFunction[...]}, ...}
where the second and subsequent replacement rules are only there if more than one solution is present. I have never encountered a case using NDSolve where that is true, but it makes the answer consistent with Solve, where multiple solutions is not uncommon. Therefor, with only one solution, you have a double list, i.e.
{{x[t]->InterpolatingFunction[...]}}
As per Mr. Wizard, you can use First, or you can use Part, i.e.
NDSolve[ ... ][[ 1 ]]
which is my preferred method, although it is slightly more difficult to read and may obscure your intent. You should be aware that the InterpolatingFunction that NDSolve returns is a function, and it will accept variables directly. So, the variables on the left hand side of the declarations
x[t_] = x[t] /. s
and from Belisarius
xr[u_] := ((x[t] /. s[[1]]) /. t -> u)
are superfluous at best, and the second one requires the replacement to occur every time xr is used. Instead, you can declare
x = x[t] /. s
and then writing x[t] afterwards will return IntepolatingFunction[t], exactly like you want. Then, as Belisarius points out, you can use it, or its derivative, in Plot directly, instead of first building a table of values and feeding them into ListPlot.
Edit: when I first posted this, I didn't notice a quirk with NDSolve. If you explicitly solve for x[t] not x, then NDSolve returns InterpolatingFunction[...][t], but if you just solve for x you get what I posted. This quirk allows both the OP's and Belisarius's solutions to function, otherwise the replacement shouldn't occur.
It is most likely that x'[t] is returning something of the form {x_i}. Try replacing the data=Table... line with this
data = Table[Evaluate[{First[x'[t]], .5}], {t, 0, 1000, tstep}]
An alternative would be to do
data=data /. {{x_}, y_} :> {x, y};
which uses ReplaceAll (/.) to replace every occurrence of {{x_i},y_i} with {x_i,y_i}
Example:
There are arguably better ways to accomplish what you are doing, but that is not what you asked.
To remove the extra {} recognize this comes from the result of NDSolve, and therefore use:
s = First # NDSolve[{x''[t] + b x'[t] - x[t] + x[t]^3 - .5 Cos[w t] == 0,
x'[0] == 0, x[0] == 0}, x[t], {t, 0, 1000}, MaxSteps -> Infinity];
I have a system of ODE's. One of the ODE's has a constant parameter which I want to alter between two different values depending on one of the ODE solutions.
So for example let's say that I have the following equations:
{
A'[x] == -q A[x]B[x],
B'[x] == q A[x]B[x] - g B[x],
C'[x] == g B[x]
}
Now I can solve them easily using the NDSolve function when q and g are constant values. What I want to do though is vary the value of q so that it has one value when B[x] is below a certain threshold but then changes in value when B[x] rises above this threshold value.
I've tried using If statements and Piecewise functions outside of the NDSolve but I haven't managed to get it working.
This might do something like what you want. I left out the third equation, which seems superfluous.
Clear[f, g, s, t, x];
s[a_, b_] = Piecewise[{{a*b - b, b < 1}, {2 a*b - b, b >= 1}}];
t[a_, b_] = Piecewise[{{-a*b, b < 1}, {-2 a*b, b >= 1}}];
{f[x_], g[x_]} = {f[x], g[x]} /.
First[NDSolve[{
f'[x] == t[f[x], g[x]],
g'[x] == s[f[x], g[x]],
f[0] == 10, g[0] == 1},
{f[x], g[x]}, {x, 0, 2}]]
I am doing a brute force search for "gradient extremals" on the following example function
fv[{x_, y_}] = ((y - (x/4)^2)^2 + 1/(4 (1 + (x - 1)^2)))/2;
This involves finding the following zeros
gecond = With[{g = D[fv[{x, y}], {{x, y}}], h = D[fv[{x, y}], {{x, y}, 2}]},
g.RotationMatrix[Pi/2].h.g == 0]
Which Reduce happily does for me:
geyvals = y /. Cases[List#ToRules#Reduce[gecond, {x, y}], {y -> _}];
geyvals is the three roots of a cubic polynomial, but the expression is a bit large to put here.
Now to my question: For different values of x, different numbers of these roots are real, and I would like to pick out the values of x where the solutions branch in order to piece together the gradient extremals along the valley floor (of fv). In the present case, since the polynomial is only cubic, I could probably do it by hand -- but I am looking for a simple way of having Mathematica do it for me?
Edit: To clarify: The gradient extremals stuff is just background -- and a simple way to set up a hard problem. I am not so interested in the specific solution to this problem as in a general hand-off way of spotting the branch points for polynomial roots. Have added an answer below with a working approach.
Edit 2: Since it seems that the actual problem is much more fun than root branching: rcollyer suggests using ContourPlot directly on gecond to get the gradient extremals. To make this complete we need to separate valleys and ridges, which is done by looking at the eigenvalue of the Hessian perpendicular to the gradient. Putting a check for "valleynes" in as a RegionFunction we are left with only the valley line:
valleycond = With[{
g = D[fv[{x, y}], {{x, y}}],
h = D[fv[{x, y}], {{x, y}, 2}]},
g.RotationMatrix[Pi/2].h.RotationMatrix[-Pi/2].g >= 0];
gbuf["gevalley"]=ContourPlot[gecond // Evaluate, {x, -2, 4}, {y, -.5, 1.2},
RegionFunction -> Function[{x, y}, Evaluate#valleycond],
PlotPoints -> 41];
Which gives just the valley floor line. Including some contours and the saddle point:
fvSaddlept = {x, y} /. First#Solve[Thread[D[fv[{x, y}], {{x, y}}] == {0, 0}]]
gbuf["contours"] = ContourPlot[fv[{x, y}],
{x, -2, 4}, {y, -.7, 1.5}, PlotRange -> {0, 1/2},
Contours -> fv#fvSaddlept (Range[6]/3 - .01),
PlotPoints -> 41, AspectRatio -> Automatic, ContourShading -> None];
gbuf["saddle"] = Graphics[{Red, Point[fvSaddlept]}];
Show[gbuf /# {"contours", "saddle", "gevalley"}]
We end up with a plot like this:
Not sure if this (belatedly) helps, but it seems you are interested in discriminant points, that is, where both polynomial and derivative (wrt y) vanish. You can solve this system for {x,y} and throw away complex solutions as below.
fv[{x_, y_}] = ((y - (x/4)^2)^2 + 1/(4 (1 + (x - 1)^2)))/2;
gecond = With[{g = D[fv[{x, y}], {{x, y}}],
h = D[fv[{x, y}], {{x, y}, 2}]}, g.RotationMatrix[Pi/2].h.g]
In[14]:= Cases[{x, y} /.
NSolve[{gecond, D[gecond, y]} == 0, {x, y}], {_Real, _Real}]
Out[14]= {{-0.0158768, -15.2464}, {1.05635, -0.963629}, {1.,
0.0625}, {1., 0.0625}}
If you only want to plot the result then use StreamPlot[] on the gradients:
grad = D[fv[{x, y}], {{x, y}}];
StreamPlot[grad, {x, -5, 5}, {y, -5, 5},
RegionFunction -> Function[{x, y}, fv[{x, y}] < 1],
StreamScale -> 1]
You may have to fiddle around with the plot's precision, StreamStyle, and the RegionFunction to get it perfect. Especially useful would be using the solution for the valley floor to seed StreamPoints programmatically.
Updated: see below.
I'd approach this first by visualizing the imaginary parts of the roots:
This tells you three things immediately: 1) the first root is always real, 2) the second two are the conjugate pairs, and 3) there is a small region near zero in which all three are real. Additionally, note that the exclusions only got rid of the singular point at x=0, and we can see why when we zoom in:
We can then use the EvalutionMonitor to generate the list of roots directly:
Map[Module[{f, fcn = #1},
f[x_] := Im[fcn];
Reap[Plot[f[x], {x, 0, 1.5},
Exclusions -> {True, f[x] == 1, f[x] == -1},
EvaluationMonitor :> Sow[{x, f[x]}][[2, 1]] //
SortBy[#, First] &];]
]&, geyvals]
(Note, the Part specification is a little odd, Reap returns a List of what is sown as the second item in a List, so this results in a nested list. Also, Plot doesn't sample the points in a straightforward manner, so SortBy is needed.) There may be a more elegant route to determine where the last two roots become complex, but since their imaginary parts are piecewise continuous, it just seemed easier to brute force it.
Edit: Since you've mentioned that you want an automatic method for generating where some of the roots become complex, I've been exploring what happens when you substitute in y -> p + I q. Now this assumes that x is real, but you've already done that in your solution. Specifically, I do the following
In[1] := poly = g.RotationMatrix[Pi/2].h.g /. {y -> p + I q} // ComplexExpand;
In[2] := {pr,pi} = poly /. Complex[a_, b_] :> a + z b & // CoefficientList[#, z] & //
Simplify[#, {x, p, q} \[Element] Reals]&;
where the second step allows me to isolate the real and imaginary parts of the equation and simplify them independent of each other. Doing this same thing with the generic 2D polynomial, f + d x + a x^2 + e y + 2 c x y + b y^2, but making both x and y complex; I noted that Im[poly] = Im[x] D[poly, Im[x]] + Im[y] D[poly,[y]], and this may hold for your equation, also. By making x real, the imaginary part of poly becomes q times some function of x, p, and q. So, setting q=0 always gives Im[poly] == 0. But, that does not tell us anything new. However, if we
In[3] := qvals = Cases[List#ToRules#RReduce[ pi == 0 && q != 0, {x,p,q}],
{q -> a_}:> a];
we get several formulas for q involving x and p. For some values of x and p, those formulas may be imaginary, and we can use Reduce to determine where Re[qvals] == 0. In other words, we want the "imaginary" part of y to be real and this can be accomplished by allowing q to be zero or purely imaginary. Plotting the region where Re[q]==0 and overlaying the gradient extremal lines via
With[{rngs = Sequence[{x,-2,2},{y,-10,10}]},
Show#{
RegionPlot[Evaluate[Thread[Re[qvals]==0]/.p-> y], rngs],
ContourPlot[g.RotationMatrix[Pi/2].h.g==0,rngs
ContourStyle -> {Darker#Red,Dashed}]}]
gives
which confirms the regions in the first two plots showing the 3 real roots.
Ended up trying myself since the goal really was to do it 'hands off'. I'll leave the question open for a good while to see if anybody finds a better way.
The code below uses bisection to bracket the points where CountRoots changes value. This works for my case (spotting the singularity at x=0 is pure luck):
In[214]:= findRootBranches[Function[x, Evaluate#geyvals[[1, 1]]], {-5, 5}]
Out[214]= {{{-5., -0.0158768}, 1}, {{-0.0158768, -5.96046*10^-9}, 3}, {{0., 0.}, 2}, {{5.96046*10^-9, 1.05635}, 3}, {{1.05635, 5.}, 1}}
Implementation:
Options[findRootBranches] = {
AccuracyGoal -> $MachinePrecision/2,
"SamplePoints" -> 100};
findRootBranches::usage =
"findRootBranches[f,{x0,x1}]: Find the the points in [x0,x1] \
where the number of real roots of a polynomial changes.
Returns list of {<interval>,<root count>} pairs.
f: Real -> Polynomial as pure function, e.g f=Function[x,#^2-x&]." ;
findRootBranches[f_, {xa_, xb_}, OptionsPattern[]] := Module[
{bisect, y, rootCount, acc = 10^-OptionValue[AccuracyGoal]},
rootCount[x_] := {x, CountRoots[f[x][y], y]};
(* Define a ecursive bisector w/ automatic subdivision *)
bisect[{{x1_, n1_}, {x2_, n2_}} /; Abs[x1 - x2] > acc] :=
Module[{x3, n3},
{x3, n3} = rootCount[(x1 + x2)/2];
Which[
n1 == n3, bisect[{{x3, n3}, {x2, n2}}],
n2 == n3, bisect[{{x1, n1}, {x3, n3}}],
True, {bisect[{{x1, n1}, {x3, n3}}],
bisect[{{x3, n3}, {x2, n2}}]}]];
(* Find initial brackets and bisect *)
Module[{xn, samplepoints, brackets},
samplepoints = N#With[{sp = OptionValue["SamplePoints"]},
If[NumberQ[sp], xa + (xb - xa) Range[0, sp]/sp, Union[{xa, xb}, sp]]];
(* Start by counting roots at initial sample points *)
xn = rootCount /# samplepoints;
(* Then, identify and refine the brackets *)
brackets = Flatten[bisect /#
Cases[Partition[xn, 2, 1], {{_, a_}, {_, b_}} /; a != b]];
(* Reinclude the endpoints and partition into same-rootcount segments: *)
With[{allpts = Join[{First#xn},
Flatten[brackets /. bisect -> List, 2], {Last#xn}]},
{#1, Last[#2]} & ### Transpose /# Partition[allpts, 2]
]]]