I'm trying to simulate Conway's Game of Life in python(here is some of the code), and now I need to handle the ouput. Right now, I'm just plotting points in matplotlib but I want something like what this guy did(That script shows error in my PC but it generates the images anyway). I understand that the code I am looking for is:
plt.imshow(A, cmap='bone', interpolation='nearest')
plt.axis('off')
and that A is a numpy array alike a matrix with just True and False as entries.
By the way, I've already realized that instead of True and False I can put 1's and 0's.
I have the data of living cells as a set of points ([(x1,y1),(x2,y2),....,(xn,yn)]) of the plane(coordinates all integers). As you can see, my script is finite(it uses a for loop until 30), so I preset the plots' axis before the loop...for example, the minimum x coordinate of the plots is the minimum coordinate of the initial points minus 30, assuring then that all the points are visible in the last image.
To represent each configuration, I had the idea to do:
SuperArray=np.zeros(maxx+30,maxy+30)
for (i,j) in livecells:
SuperArray[i,j]=1
But that idea won't work, because the indices of SuperArray are all positives, and my coordinates maybe negative. To solve this I was thinking in translate ALL of the points in livecells so their coordinates be positive. I would do that by adding |minx|+30 to the x coordinate and |miny|+30 to the y coordinate
of each (x,y) in livecells...I haven't put it in practice yet, but it seems too complicated and memory consuming...Do you guys have any suggestion?
Related
I am trying to develop a 3D cube with values from a flat 2D Plane. I am having a lot of difficulty trying to pseudo code it out so I was hoping to get some input from you guys.
I will try my best to express myself through pictures as I am able to visualize what I am trying to achieve.
I have a 2D output based on the black line in this figure:
I have an array with data of amplitude as each index's value i.e (0; 1) -> the 0 is the x coordinate (sample) and 1 as the y coordinate (amplitude) or as another example (~1900; ~0.25).
How do I take this 1 dimensional sequence and extrude it into a 3D picture like below:
Is there perhaps a library that does such? Or am I going about it the wrong way? The data is from a matched filter output of a sonar signal and I wish to visualize the concentration of the intensity versus where it is located in a sample on a 3D plane. The data has peaks that have inclining and declining gradient slopes before a peak.
I cannot seem to wrap my mind around such a task. Is there a library or a term used to associate what I wish to accomplish?
EDIT: I found this https://www.tutorialspoint.com/matplotlib/matplotlib_3d_surface_plot.htm
But it requires all x, y and z points. Whereas I only have x and y. Additionally I need to be able to access every coordinate (x, y, z) to be able to do range and angle estimation from sample (0, 1) (Transmitted sound where power is highest). I would only like to basically see the top of this though on another 2D axis...
EDIT 2: Following up on a comment below, I would like to convert Figure 1 above into the below image using a library if there exists.
Thanks so much in advanced!
So I have two arrays of points that I need to plot that I have stored in arrays, but at each of these points there is a probability of some event happening so each has a value ranging from 0 to 1. My idea was to find a way to assign these probabilities to their respective (x,y) coordinate and display it as a heatmap. The code to plot this is as follows:
plt.pcolormesh(xcoord,ycoord,des_mag)
plt.show()
Where xcoord and ycoord are arrays. I could only make this run if I made des_mag a 2D array, in this case a 2000x2000 array with only entries on the diagonal since xcoord and ycoord each contain 2000 coordinates. All the des_mag values vary from 0 to 1. When I run this the output is simply a graph with a solid background and one tiny grid point in the corner with a different color. I'm 95% confident the issue is my lack of understanding on what it is I need to input for the plot, but I can't seem to find many examples for clarity on the issue. If anyone has any suggestions it would be greatly appreciated.
I have these arrays that I need to interpolate and make the smoothest possible interpolation:
x = time
y = height
z = latitude
print np.shape(x)
print np.shape(y)
print np.shape(z)
Result:
(99, 25)
(99, 25)
(99, 25)
y is altitude and it's not uniform. It has a bunch of nan's and even though they're all the same size (a variable n_alt with the number of altitudes, which is for this example 99).
x is time and it's uniform all the way through (all the values in one column of that array are the same).
z is latitude and it's the actual 'z' and it's an array with the same number of rows as the number of time points and same number of rows as the altitude points.
I want to interpolate in 2D (the data set has series of nans in both x and y directions) to fill the gaps on the data, since several files will cover a certain altitude range and not others
My questions are:
1) is there a good way to fill the gaps the 2 directions while making the grid uniform (the idea is to plot that and also save the interpolated data (x,y and z) into a new file as well)?
2) what's a good way to contour plot the data with the shape I mentioned earlier (tried plt.contour, but it doesn't give a satisfactory result just plotting that straight up)?
Thanks y'all
Edit:
I believe this will illustrate the question better:
X: Time, Y: Altitude, Z: Latitude or Longitude
I essentially want to fill up the white space (I understand the consequences of extrapolations and all, but I just want, at this point, to have an algorithm that works. The blue dots is my grid and the color plot is just a normal plt.contour (no interpolation done). I want to make such that I have blue dots all over the plot area.
Rafael! With respect to your interpolation question, I can explain the math if you want to manually come up with an interpolation function, but there is an existing resource you might want to look into: scipy.interpolate.RegularGridInterpolator
(see https://docs.scipy.org/doc/scipy-0.16.1/reference/generated/scipy.interpolate.RegularGridInterpolator.html)
If I have misunderstood your issue, another interpolation method from the class might be appropriate: see, scipy.interpolate
For plotting the 3d surface, https://matplotlib.org/examples/mplot3d/surface3d_demo.html might help guide you! Let me know if this helps! Just comment if you would like me to expand! Hopefully those are the resources you were looking for!
I have the following curve as two arrays, of x and y positions.
Imagine if you were to draw vertical lines going through each point, and add points on the curve wherever these lines intersect the curve. This is what I want.
I tried using np.interp(x, x, y), but I ended up with the following mess:
How can I do this? Is it possible with np.interp?
This might be something that should be asked in a different question, but I would also like there to be points added where the curve crosses over itself.
According to the docs the array of X values should be sorted (or periodic), otherwise "the result is nonsense". You can try to split your curve into sections, and then interpolate each part on the others. You can find the correct splitting places by looking at where np.diff(x) changes sign.
Beginning python/numpy user here. I do an analysis of a 2D function in the XY plane. Using 2 loops through x and y I compute the function value and store it into an array for later plotting. I ran into a couple of problems.
Lets say my XY range is -10 to 10. How do I accommodate that when storing computed value into my data array? (only positive numbers are allowed as indices) For now I just add to x and Y to make it positive.
From my data I know that the extreme is a x=-3 and y=2. When I plot the computed array first of all the axes labels are wrong. I would like Y to go the mathematical way. (up)
I would like the axes labels to run from -10 to 10. I tried 'extend' but that did not come out right.
Again from my data I know that the extreme is at x=-3 and y=2. In the plot when I hover the mouse over the graphics, the max value is shown at x=12 and y=7. Seems x and y have been swapped. Though when I move the mouse the displayed x and y numbers run as follows. X grows larger when moving the mouse right etc. (OK) Y runs the wrong way, grows larger when moving DOWN.
As side note it would be nice to have the function value shown in the plot window as well next to x and y.
Here is my code:
size = 10
q = np.zeros((2*size,2*size))
for xs in range(-size,+size):
for ys in range(-size,+size):
q[xs+size,ys+size] = my_function_of_x_and_y(x,y)
im = plt.imshow(q, cmap='rainbow', interpolation='none')
plt.show()
One more thing. I would like not to mess with the q array too badly as I later want to find the extreme spot in it.
idxmin = np.argmin(q)
xmin,ymin = np.unravel_index(idxmin, q.shape)
xmin= xmin-size
ymin= ymin-size
So that I get this:
>>> xmin,ymin
(-3, 2)
>>>
Here is my plot:
(source: dyndns.ws)
Here is the desired plot (made in photoshop) (axis lineswould be nice):
(source: dyndns.ws)
Not too sure why setting extend did not work for you but this is how I have implemented it
q = np.random.randint(-10,10, size=(20, 20))
im = plt.imshow(q, cmap='rainbow', interpolation='none',extent=[-10,10,-10,10])
plt.vlines(0,10,-10)
plt.hlines(0,10,-10)
plt.show()
Use vlines and hlines methods to set the centering line