Hi have made this code to plot a function.
I need to mark with an red X all the crossings between x = 0 and the blue wave line in the graph.
I have made some tries but with '-xr' in the plot function but it places X marks out of the crossings.
Anyone knows how to do it. Many thanks.
Code:
% entrada
a = input('Introduza o valor de a: ');
% ficheiro fonte para a função
raizes;
% chamada à função
x = 0:.1:50;
or = x;
or(:) = 0;
h = #(x) cos(x);
g = #(x) exp(a*x)-1;
f = #(x) h(x) - g(x);
zeros = fzero(f,0);
plot(x,f(x));
hold on
plot(zeros,f(zeros),'-xr')
hold off
Graph (it only marks one zero, i need all the zero crossings):
As mentioned in the comments above, you need to look for the zeros of your function before you can plot them. You can do this mathematically (in this case set f(x) = g(x) and solve for x) or you can do this analytically with something like fsolve.
If you read the documentation for fsolve, you will see that it searches for the zero closest to the provided x0 if passed a scalar or the first zero if passed an interval. What we can do for a quick attempt at a solution is to pass our x values into fsolve as initial guesses and filter out the unique values.
% Set up sample data
a = .05;
x = 0:.1:50;
% Set up equations
h = #(x) cos(x);
g = #(x) exp(a*x)-1;
f = #(x) h(x) - g(x);
% Find zeros of f(x)
crossingpoints = zeros(length(x), 1); % Initialize array
for ii = 1:length(x) % Use x data points as guesses for fzero
try
crossingpoints(ii) = fzero(f, x(ii)); % Find zero closest to guess
end
end
crossingpoints(crossingpoints < 0) = []; % Throw out zeros where x < 0
% Find unique zeros
tol = 10^-8;
crossingpoints = sort(crossingpoints(:)); % Sort data for easier diff
temp = false(size(crossingpoints)); % Initialize testing array
% Find where the difference between 'zeros' is less than or equal to the
% tolerance and throw them out
temp(1:end-1) = abs(diff(crossingpoints)) <= tol;
crossingpoints(temp) = [];
% Sometimes catches beginning of the data set, filter it out if this happens
if abs(f(crossingpoints(1))) >= (0 + tol)
crossingpoints(1) = [];
end
% Plot data
plot(x, f(x))
hold on
plot(crossingpoints, f(crossingpoints), 'rx')
hold off
grid on
axis([0 20 -2 2]);
Which gives us the following:
Note that due to errors arising from floating point arithmetic we have to utilize a tolerance to filter our zeros rather than utilizing a function like unique.
Related
I have a simple question. How do I use the command qpsolve from Scilab if I only want to use the lower bounds and upper bounds limit?
ci <= x <= cs
The command can be used as this:
[x [,iact [,iter [,f]]]] = qpsolve(Q,p,C,b,ci,cs,me)
But I want to use it like this:
x = qpsolve(Q,p,[],[],ci,cs,[])
Only ci and cs should explain the limits for vector x. Unfortunately, the command cannot take empty []. Should I take [] as a row vector of ones or zeros?
https://help.scilab.org/docs/6.0.1/en_US/qpsolve.html
In Scilab 5.5.1 , [] works for C and b but not for me. so C = [];b = [];me = 0; should work.
Why
qpsolve is an interface for qp_solve :
function [x, iact, iter, f]=qpsolve(Q,p,C,b,ci,cs,me)
rhs = argn(2);
if rhs <> 7
error(msprintf(gettext("%s: Wrong number of input argument(s): %d expected.\n"), "qpsolve", 7));
end
C(me+1:$, :) = -C(me+1:$, :);
b(me+1:$) = -b(me+1:$);
// replace boundary contraints by linear constraints
Cb = []; bb = [];
if ci <> [] then
Cb = [Cb; speye(Q)]
bb = [bb; ci]
end
if cs <> [] then
Cb = [Cb; -speye(Q)]
bb = [bb; -cs]
end
C = [C; Cb]; b = [b; bb]
[x, iact, iter, f] = qp_solve(Q, -p, C', b, me)
endfunction
It transform every bound constraints into linear constraints. To begin, it swap the sign of the inequality constraints. To do that, it must know me, ie it must be an integer. Since C and b are empty matrices, is value doesn't matter.
Bonus:
if Q is inversible, you could skip the qpsolve macro and write
x = -Q\p
x(x<ci) = ci(x<ci)
x(x>cs) = cs(x>cs)
In my program, I have to find two random values with certain conditions:
i needs to be int range [2...n]
k needs to be in range [i+2...n]
so I did this:
i = rand() % n + 2;
k = rand() % n + (i+2);
But it keeps giving me wrong values like
for n = 7
I get i = 4 and k = 11
or i = 3 and k = 8
How can I fix this?
The exact formula that I use in my other program is:
i = min + (rand() % (int)(max - min + 1))
Look here for other explanation
As the comments say, your range math is off.
You might find it useful to use a function to work the math out consistently each time. e.g.:
int RandInRange(int x0, int x1)
{
if(x1<=x0) return x0;
return rand() % (x1-x0+1) + x0;
}
then call it with what you want:
i = RandInRange(2,n);
k = RandInRange(i+2,n);
I have two data files, each of them contain a big number of 3-dimensional points (file A stores approximately 50,000 points, file B stores approximately 500,000 points). My goal is to find for every point (a) in file A the point (b) in file B which has the smallest distance to (a). I store the points in two lists like this:
List A nodes:
(ID X Y Z)
[ ['478277', -107.0, 190.5674, 128.1634],
['478279', -107.0, 190.5674, 134.0172],
['478282', -107.0, 190.5674, 131.0903],
['478283', -107.0, 191.9798, 124.6807],
... ]
List B data:
(X Y Z Data)
[ [-28.102, 173.657, 229.744, 14.318],
[-28.265, 175.549, 227.824, 13.648],
[-27.695, 175.925, 227.133, 13.142],
...]
My first approach was to simply iterate through the first and second list with a nested loop and compute the distance between every points like this:
outfile = open(job[0] + '/' + output, 'wb');
dist_min = float(job[5]);
dist_max = float(job[6]);
dists = [];
for node in nodes:
shortest_distance = 1000.0;
shortest_data = 0.0;
for entry in data:
dist = math.sqrt((node[1] - entry[0])**2 + (node[2] - entry[1])**2 + (node[3] - entry[2])**2);
if (dist_min <= dist <= dist_max) and (dist < shortest_distance):
shortest_distance = dist;
shortest_data = entry[3];
outfile.write(node[0] + ', ' + str('%10.5f' % shortest_data + '\n'));
outfile.close();
I recognized that the amount of loops Python has to run is way too big (~25,000,000,000), so I had to fasten my code. I tried to first calculate all distances with list comprehensions but the code still is too slow:
p_x = [row[1] for row in nodes];
p_y = [row[2] for row in nodes];
p_z = [row[3] for row in nodes];
q_x = [row[0] for row in data];
q_y = [row[1] for row in data];
q_z = [row[2] for row in data];
dx = [[(px - qx) for px in p_x] for qx in q_x];
dy = [[(py - qy) for py in p_y] for qy in q_y];
dz = [[(pz - qz) for pz in p_z] for qz in q_z];
dx = [[dxxx * dxxx for dxxx in dxx] for dxx in dx];
dy = [[dyyy * dyyy for dyyy in dyy] for dyy in dy];
dz = [[dzzz * dzzz for dzzz in dzz] for dzz in dz];
D = [[(dx[i][j] + dy[i][j] + dz[i][j]) for j in range(len(dx[0]))] for i in range(len(dx))];
D = [[(DDD**(0.5)) for DDD in DD] for DD in D];
To be honest, at this point, I do not know which of the two approaches is better, anyway, none of the two possibilities seem feasible. I'm not even sure if it is possible to write a code which calculates all distances in an acceptable time. Is there even another way to solve my problem without calculating all distances?
Edit: I forgot to mention that I am running on Python 2.5.1 and am not allowed to install or add any new libraries...
Just in case someone is interrested in the solution:
I found a way to speed up the whole process by not calculating all distances:
I created a 3D-list, representing a grid in the given 3D space, divided in X, Y and Z in a given step size (e.g. (Max. - Min.) / 1,000). Then I iterated over every 3D point to put it into my grid. After that I iterated over the points of set A again, looking if there are points from B in the same cube, if not I would increase the search radius, so the process is looking in the adjacent 26 cubes for points. The radius is increasing until there is at least one point found. The resulting list is comparatively small and can be ordered in short time and the nearest point is found.
The processing time went down to a couple minutes and it is working fine.
p_x = [row[1] for row in nodes];
p_y = [row[2] for row in nodes];
p_z = [row[3] for row in nodes];
q_x = [row[0] for row in data];
q_y = [row[1] for row in data];
q_z = [row[2] for row in data];
min_x = min(p_x + q_x);
min_y = min(p_y + q_y);
min_z = min(p_z + q_z);
max_x = max(p_x + q_x);
max_y = max(p_y + q_y);
max_z = max(p_z + q_z);
max_n = max(max_x, max_y, max_z);
min_n = min(min_x, min_y, max_z);
gridcount = 1000;
step = (max_n - min_n) / gridcount;
ruler_x = [min_x + (i * step) for i in range(gridcount + 1)];
ruler_y = [min_y + (i * step) for i in range(gridcount + 1)];
ruler_z = [min_z + (i * step) for i in range(gridcount + 1)];
grid = [[[0 for i in range(gridcount)] for j in range(gridcount)] for k in range(gridcount)];
for node in nodes:
loc_x = self.abatemp_get_cell(node[1], ruler_x);
loc_y = self.abatemp_get_cell(node[2], ruler_y);
loc_z = self.abatemp_get_cell(node[3], ruler_z);
if grid[loc_x][loc_y][loc_z] is 0:
grid[loc_x][loc_y][loc_z] = [[node[1], node[2], node[3], node[0]]];
else:
grid[loc_x][loc_y][loc_z].append([node[1], node[2], node[3], node[0]]);
for entry in data:
loc_x = self.abatemp_get_cell(entry[0], ruler_x);
loc_y = self.abatemp_get_cell(entry[1], ruler_y);
loc_z = self.abatemp_get_cell(entry[2], ruler_z);
if grid[loc_x][loc_y][loc_z] is 0:
grid[loc_x][loc_y][loc_z] = [[entry[0], entry[1], entry[2], entry[3]]];
else:
grid[loc_x][loc_y][loc_z].append([entry[0], entry[1], entry[2], entry[3]]);
out = [];
outfile = open(job[0] + '/' + output, 'wb');
for node in nodes:
neighbours = [];
radius = -1;
loc_nx = self.abatemp_get_cell(node[1], ruler_x);
loc_ny = self.abatemp_get_cell(node[2], ruler_y);
loc_nz = self.abatemp_get_cell(node[3], ruler_z);
reloop = True;
while reloop:
if neighbours:
reloop = False;
radius += 1;
start_x = 0 if ((loc_nx - radius) < 0) else (loc_nx - radius);
start_y = 0 if ((loc_ny - radius) < 0) else (loc_ny - radius);
start_z = 0 if ((loc_nz - radius) < 0) else (loc_nz - radius);
end_x = (len(ruler_x) - 1) if ((loc_nx + radius + 1) > (len(ruler_x) - 1)) else (loc_nx + radius + 1);
end_y = (len(ruler_y) - 1) if ((loc_ny + radius + 1) > (len(ruler_y) - 1)) else (loc_ny + radius + 1);
end_z = (len(ruler_z) - 1) if ((loc_nz + radius + 1) > (len(ruler_z) - 1)) else (loc_nz + radius + 1);
for i in range(start_x, end_x):
for j in range(start_y, end_y):
for k in range(start_z, end_z):
if not grid[i][j][k] is 0:
for grid_entry in grid[i][j][k]:
if not isinstance(grid_entry[3], basestring):
neighbours.append(grid_entry);
dists = [];
for n in neighbours:
d = math.sqrt((node[1] - n[0])**2 + (node[2] - n[1])**2 + (node[3] - n[2])**2);
dists.append([d, n[3]]);
dists = sorted(dists);
outfile.write(node[0] + ', ' + str(dists[0][-1]) + '\n');
outfile.close();
Function to get the position of a point:
def abatemp_get_cell(self, n, ruler):
for i in range(len(ruler)):
if i >= len(ruler):
return False;
if ruler[i] <= n <= ruler[i + 1]:
return i;
The gridcount variable gives one the chance to fasten the process, with a small gridcount the process of sorting the points into the grid is very fast, but the lists of neighbours in the search loop gets bigger and more time is needed for this part of the process. With a big gridcount more time is needed at the beginning, however the loop runs faster.
The only issue I have now is the fact, that there are cases when the process found neighbours but there are other points, which are not yet found, but are closer to the point (see picture). So far I solved this issue by incrementing the search radius another time when there are already neigbours. And still then I have points which are closer but not in the neighbours list, although it's a very small amount (92 out of ~100,000). I could solve this problem by increment the radius two times after finding neighbours, but this solution seems not very smart. Maybe you guys have an idea...
This is the first working draft of the process, I think it will be possible to improve it even more, just to give you an idea of how it is working...
It took me a bit of thought but at the end I think I found a solution for you.
Your problem is not in the code you wrote but in the algorithm it implements.
There is an algorithm called Dijkstra's algorithm and here is the gist of it: https://en.wikipedia.org/wiki/Dijkstra%27s_algorithm .
Now what you need to do is to use this algorithm in a clever way:
create a node S (stand for source).
Now link edges from S to all the nodes in B group.
After you done that you should link edges from each point b in B to each point a in A.
You should set the cost of the links from the source to 0 and the other to the distance between 2 points (only in 3D).
Now if we will use Dijkstra's algorithm the output we will get would be the cost to travel from S to each point in the graph (we are only interested in the distance to points in group A).
So since the cost is 0 to each point b in B and S is only connected to points in B so the road to any point a in A must include a node in B (actually exactly one since the shortest distance between to points is a single line).
I am not sure if this will fasten your code but as far as I know, a way to solve this problem without calculating all distances does not exist and this algorithm is the best time complexity one could hope for.
take a look at this generic 3D data structure:
https://github.com/m4nh/skimap_ros
it has a very fast RadiusSearch feature just ready to be used. This solution (similar to Octree but faster) avoids to you to create the Regular Grid first (you don't have to fix MAX/MIN size along each axis) and you save a lot of memory
I have function P which takes two points and returns true if they satisfy some condition, false otherwise. I want to plot all the points in range lx <= x <= hx, ly <= y <= hy that satisfy the condition. How to do this in scilab?
A combination of ndgrid and the find function is exactly suited for this. For instance:
lx = 0;
hx = 10;
ly = 0;
hy = 10;
// Create x and y lists
a = linspace(lx,hx);
b = linspace(ly,hy);
// Create arrays for function evaluation on a 2D grid
[A,B] = ndgrid(a,b)
// Define your predicate as a function
function result = P(a, b)
result = (a < b);
endfunction
// Evaluate your function for all values in A and B and as a result
// get a matrix p filled with booleans
p = P(A(:),B(:));
// Find all indices of TRUE
indices = find(p);
// Plot all points using A as x coordinate and B as y coordinate
plot(A(indices), B(indices), 'o')
// Scale the axis of the plot so that all points are visible
a=gca();
a.data_bounds = [lx,ly;hx,hy];
This will result in the plot below:
I want to create a polynomial with given coefficients. This seems very simple but what I have found till now did not appear to be the thing I desired.
For example in such an environment;
n = 11
K = GF(4,'a')
R = PolynomialRing(GF(4,'a'),"x")
x = R.gen()
a = K.gen()
v = [1,a,0,0,1,1,1,a,a,0,1]
Given a list/vector v of length n (I will set this n and v at the begining), I want to get the polynomial v(x) as v[i]*x^i.
(Actually after that I am going to build the quotient ring GF(4,'a')[x] /< x^n-v(x) > after getting this v(x) from above) then I will say;
S = R.quotient(x^n-v(x), 'y')
y = S.gen()
But I couldn't write it.
This is a frequently asked question in many places so it is better to leave it here as an answer although the answer I have is so simple:
I just wrote R(v) and it gave me the polynomial:
sage
n = 11
K = GF(4,'a')
R = PolynomialRing(GF(4,'a'),"x")
x = R.gen()
a = K.gen()
v = [1,a,0,0,1,1,1,a,a,0,1]
R(v)
x^10 + a*x^8 + a*x^7 + x^6 + x^5 + x^4 + a*x + 1
Basically (that is, ignoring the specifics of your polynomial ring) you have a list/vector v of length n and you require a polynomial which is the sum of all v[i]*x^i. Note that this sum equals the matrix product V.X where V is a one row matrix (essentially equal to the vector v) and X is a column matrix consisting of powers of x. In Maxima you could write
v: [1,a,0,0,1,1,1,a,a,0,1]$
n: length(v)$
V: matrix(v)$
X: genmatrix(lambda([i,j], x^(i-1)), n, 1)$
V.X;
The output is
x^10+ax^8+ax^7+x^6+x^5+x^4+a*x+1