I am following a short youtube video tutorial on Monte Carlo problems with python (https://www.youtube.com/watch?v=BfS2H1y6tzQ) and the code isn't working. The goal is to see how many times I will have to take transport to get back home, considering you take transport if the distance is greater than 4.
So I assumed the issue was that every time random_walk was called, the x,y variables are being reset to zero so the distance is never always within a 0-1 range and isn't incrementing as expected.
import random
def random_walk(n):
x, y = 0, 0
for i in range(n):
(dx, dy) = random.choice([(0, 1), (0, -1), (1, 0), (-1, 0)])
x += dx
y += dy
return (x, y)
number_of_walks = 10000
no_transport = 0
for walk_length in range(1, 31):
for i in range(number_of_walks):
(x, y) = random_walk(walk_length)
distance = abs(x) + abs(y)
if distance <= 4:
no_transport += 1
transported_percentage = float(no_transport) / number_of_walks
print("Walk Size = ", walk_length, " / % transported = ", 100 * transported_percentage)
I expect results to show what % of the times I transported did I have to take transport home, instead, I get inaccurate numbers like 100, 200, 300%. Could the video tutorial have incorrect code?
You need to reset the no_transport inside the main loop, because it's cumulative over all your tests instead of for each walk length.
for walk_length in range(1, 31):
no_transport = 0
Also the percentage is calculating the number for no_transport walks, not the percentage of transport walks: This is percentage of transported.
transported_percentage = (number_of_walks - float(no_transport)) / number_of_walks
Related
I need a fast way to create a list of tuples representing image pixel coordinates (X, Y).
Where X is from 0 to size and Y is from 0 to size.
A step value of 1 results in X and Y values of (0, 1, 2, 3...) which is too many tuples. Using a step value greater than 1 will reduce processing time. For example, if the step value is 2 the values would be (0, 2, 4, 6...). If the step value is 4 the values would be (0, 4, 8, 12...).
In pure python range command might be used. However, NumPy is installed by default in my Linux distribution. In NumPy the arrange command might be used but I'm having a hard time wrapping my mind around NumPy array syntax.
PS: After a list of tuples is created it will be randomly shuffled and then read in the loop.
Edit 1
Using this answer below:
Instead of the image fading in it's doing some kind of weird wipe left to right. Using the code from the answer with a slight modification:
step = 4
size = self.play_rotated_art.size[0] - step
self.xy_list = [
(x, y)
for x in range(0, size - step, step)
for y in range(0, size - step, step)
]
Bug Update
There was an error in my code, it's working fine now:
The updated code is:
self.step = 4
size = self.play_rotated_art.size[0] - self.step
self.xy_list = [
(x, y)
for x in range(0, size - self.step, self.step)
for y in range(0, size - self.step, self.step)
]
shuffle(self.xy_list)
# Convert numpy array into python list & calculate chunk size
self.current_chunk = 0
self.chunk_size = int(len(self.xy_list) / 100)
# Where we stop copying pixels for current 1% chunck
end = self.current_chunk + self.chunk_size
if end > len(self.xy_list) - 1:
end = len(self.xy_list) - 1
while self.current_chunk < end:
x0, y0 = self.xy_list[self.current_chunk]
x1 = x0 + self.step
y1 = y0 + self.step
box = (x0, y0, x1, y1)
region = self.play_rotated_art.crop(box)
self.fade.paste(region, box)
self.current_chunk += 1
self.play_artfade_count += 1
return self.fade
TL;DR
I already have code with step value 1 but this code is overly complex and inefficient to request a modification. The above generic question would help others more and, still help me, if it were answered.
Existing code with step value 1:
def play_artfade2(self):
''' PILLOW VERSION:
Fade in artwork in 100 chunks leaving loop after chunk and
reentering after Tkinter updates screen and pauses.
'''
if self.play_artfade_count == 100:
# We'have completed a full cycle. Force graphical effects exit
self.play_artfade_count = 0 # Reset art fade count
self.play_rotated_value = -361 # Force Spin Art
return None
# Initialize numpy arrays first time through
if self.play_artfade_count == 0:
# Create black image to fade into
self.fade = Image.new('RGBA', self.play_rotated_art.size, \
color='black')
# Generate a randomly shuffled array of the coordinates
im = np.array(self.play_rotated_art)
X,Y = np.where(im[...,0]>=0)
coords = np.column_stack((X,Y))
np.random.shuffle(coords)
# Convert numpy array into python list & calculate chunk size
self.xy_list = list(coords)
self.current_chunk = 0
self.chunk_size = int(len(self.xy_list) / 100)
# Where we stop copying pixels for current 1% chunck
end = self.current_chunk + self.chunk_size
if end > len(self.xy_list) - 1:
end = len(self.xy_list) - 1
while self.current_chunk < end:
x0, y0 = self.xy_list[self.current_chunk]
x1 = x0 + 1
y1 = y0 + 1
box = (x0, y0, x1, y1)
region = self.play_rotated_art.crop(box)
self.fade.paste(region, box)
self.current_chunk += 1
self.play_artfade_count += 1
return self.fade
Using Pillow's Image.crop() and Image.paste() is overkill for a single pixel but the initial working design was future focused to utilize "super pixels" with box size of 2x2, 3x3, 5x5, etc as image is resized from 200x200 to 333x333 to 512x512, etc.
I need fast way to create a list of tuples representing image pixel coordinates (X, Y).
Where X is from 0 to size and Y is from 0 to size
A list comprehension with range will work:
xsize = 10
ysize = 10
coords = [(x, y) for x in range(xsize) for y in range(ysize)]
# this verifies the shape is correct
assert len(coords) == xsize * ysize
If you wanted a step other than 1, this is setting the step argument:
coords = [(x, y) for x in range(0, xsize, 2) for y in range(0, ysize, 2)]
You can use a generator expression:
size = 16
step = 4
coords = (
(x, y)
for x in range(0, size, step)
for y in range(0, size, step)
)
Then you can iterate on that like you would do with a list
for coord in coords:
print(coord)
Using a generator instead of a list or tuple has the advantage of being more memory efficient.
For a list of (x, y) points, I am trying to find the nearby points for each point.
from collections import defaultdict
from math import sqrt
from random import randint
# Generate a list of random (x, y) points
points = [(randint(0, 100), randint(0, 100)) for _ in range(1000)]
def is_nearby(point_a, point_b, max_distance=5):
"""Two points are nearby if their Euclidean distance is less than max_distance"""
distance = sqrt((point_b[0] - point_a[0])**2 + (point_b[1] - point_a[1])**2)
return distance < max_distance
# For each point, find nearby points that are within a radius of 5
nearby_points = defaultdict(list)
for point in points:
for neighbour in points:
if point != neighbour:
if is_nearby(point, neighbour):
nearby_points[point].append(neighbour)
Is there any way I can index points to make the above search faster? I feel there must be some faster way than O(len(points)**2).
Edit: the points in general could be floats, not just ints
this is a version with a fixed grid where each gridpoint holds the number of samples that are there.
the search can then be reduced to just the space around the point in question.
from random import randint
import math
N = 100
N_SAMPLES = 1000
# create the grid
grd = [[0 for _ in range(N)] for __ in range(N)]
# set the number of points at a given gridpoint
for _ in range(N_SAMPLES):
grd[randint(0, 99)][randint(0, 99)] += 1
def find_neighbours(grid, point, distance):
# this will be: (x, y): number of points there
points = {}
for x in range(point[0]-distance, point[0]+distance):
if x < 0 or x > N-1:
continue
for y in range(point[1]-distance, point[1]+distance):
if y < 0 or y > N-1:
continue
dst = math.hypot(point[0]-x, point[1]-y)
if dst > distance:
continue
if grd[x][y] > 0:
points[(x, y)] = grd[x][y]
return points
print(find_neighbours(grid=grd, point=(45, 36), distance=5))
# -> {(44, 37): 1, (45, 33): 1, ...}
# meadning: there is one neighbour at (44, 37) etc...
for further optimzation: the tests for x and y could be precalculated for a given gridsize - the math.hypot(point[0]-x, point[1]-y) would not have to be done then for every point.
and it may be a good idea to replace the grid with a numpy array.
UPDATE
if your points are floats you can still create an int grid to reduce the search space:
from random import uniform
from collections import defaultdict
import math
class Point:
def __init__(self, x, y):
self.x = x
self.y = y
#property
def x_int(self):
return int(self.x)
#property
def y_int(self):
return int(self.y)
def __str__(self):
fmt = '''{0.__class__.__name__}(x={0.x:5.2f}, y={0.y:5.2f})'''
return fmt.format(self)
N = 100
MIN = 0
MAX = N-1
N_SAMPLES = 1000
# create the grid
grd = [[[] for _ in range(N)] for __ in range(N)]
# set the number of points at a given gridpoint
for _ in range(N_SAMPLES):
p = Point(x=uniform(MIN, MAX), y=uniform(MIN, MAX))
grd[p.x_int][p.y_int].append(p)
def find_neighbours(grid, point, distance):
# this will be: (x_int, y_int): list of points
points = defaultdict(list)
# need to cast a slightly bigger net on the upper end of the range;
# int() rounds down
for x in range(point[0]-distance, point[0]+distance+1):
if x < 0 or x > N-1:
continue
for y in range(point[1]-distance, point[1]+distance+1):
if y < 0 or y > N-1:
continue
dst = math.hypot(point[0]-x, point[1]-y)
if dst > distance + 1: # account for rounding... is +1 enough?
continue
for pt in grd[x][y]:
if math.hypot(pt.x-x, pt.y-y) <= distance:
points[(x, y)].append(pt)
return points
res = find_neighbours(grid=grd, point=(45, 36), distance=5)
for int_point, points in res.items():
print(int_point)
for point in points:
print(' ', point)
the output looks something like this:
(44, 36)
Point(x=44.03, y=36.93)
(41, 36)
Point(x=41.91, y=36.55)
Point(x=41.73, y=36.53)
Point(x=41.56, y=36.88)
...
for convenience Points is now a class. may not be necessary though...
depending on how dense or sparse your points are you could also represent the grid as a dictionary pointing to a list or Points...
also the find_neighbours function accepts a starting point consisting of ints only in that version. this might also be refined.
and there is much room for improvement: the range of the y axis can be restricted using trigonometry. and for the points way inside the circle there is no need for an individual check; detailed checking only needs to be done close to the outer rim of the circle.
I'm currently working on writing code for the Ising Model using Python3. I'm still pretty new to coding. I have working code, but the output result is not as expected and I can't seem to find the error. Here is my code:
import numpy as np
import random
def init_spin_array(rows, cols):
return np.random.choice((-1, 1), size=(rows, cols))
def find_neighbors(spin_array, lattice, x, y):
left = (x , y - 1)
right = (x, y + 1 if y + 1 < (lattice - 1) else 0)
top = (x - 1, y)
bottom = (x + 1 if x + 1 < (lattice - 1) else 0, y)
return [spin_array[left[0], left[1]],
spin_array[right[0], right[1]],
spin_array[top[0], top[1]],
spin_array[bottom[0], bottom[1]]]
def energy(spin_array, lattice, x ,y):
return -1 * spin_array[x, y] * sum(find_neighbors(spin_array, lattice, x, y))
def main():
lattice = eval(input("Enter lattice size: "))
temperature = eval(input("Enter the temperature: "))
sweeps = eval(input("Enter the number of Monte Carlo Sweeps: "))
spin_array = init_spin_array(lattice, lattice)
print("Original System: \n", spin_array)
# the Monte Carlo follows below
for sweep in range(sweeps):
for i in range(lattice):
for j in range(lattice):
e = energy(spin_array, lattice, i, j)
if e <= 0:
spin_array[i, j] *= -1
elif np.exp(-1 * e/temperature) > random.randint(0, 1):
spin_array[i, j] *= -1
else:
continue
print("Modified System: \n", spin_array)
main()
I think the error is in the Monte Carlo Loop, but I am not sure. The system should be highly ordered at low temperatures and become disordered past the critical temperature of 2.27. In other words, the randomness of the system should increase as T approaches 2.27. For example, at T=.1, we should see large patches of spins that are aligned, i.e. patches of -1s and 1s. Past 2.27 the system should be disordered and we should not see these patches.
Your question would make much more sense if you were to include the system size, the number of sweeps, and the average manetisation. How many of the intermediate configurations are ordered and how many disordered? MC is a sampling technique - individual configurations mean nothing and there might (and will) be disordered states at low temperature and ordered states at high T. It is the assembly properties (the average magnetisation) that is meaningful.
Anyway, there are three errors in your code: a small one, a medium one, and a really severe one.
The small one is that you are ignoring an entire row and an entire column while searching for neighbours in find_neighbors:
right = (x, y + 1 if y + 1 < (lattice - 1) else 0)
should be:
right = (x, y + 1 if y + 1 < lattice else 0)
or even better:
right = (x, (y + 1) % lattice)
Same applies to bottom.
The medium one is that your computation of the energy difference is off by a factor of two:
def energy(spin_array, lattice, x ,y):
return -1 * spin_array[x, y] * sum(find_neighbors(spin_array, lattice, x, y))
^^
The factor is actually 2*J, where J is the coupling constant, therefore having -1 there means:
your critical temperature is halved, and more importantly...
you have antiferromagnetic spin interaction (J < 0), so no ordered states for you even at very low temperatures.
The worst mistake however is the use of random.randint() for the rejection sampling:
elif np.exp(-1 * e/temperature) > random.randint(0, 1):
spin_array[i, j] *= -1
You should be using random.random() instead, otherwise the transition probability will always be 50%.
Here is a modification of your program that automatically sweeps over the temperature region from 0.1 to 5.0:
import numpy as np
import random
def init_spin_array(rows, cols):
return np.ones((rows, cols))
def find_neighbors(spin_array, lattice, x, y):
left = (x, y - 1)
right = (x, (y + 1) % lattice)
top = (x - 1, y)
bottom = ((x + 1) % lattice, y)
return [spin_array[left[0], left[1]],
spin_array[right[0], right[1]],
spin_array[top[0], top[1]],
spin_array[bottom[0], bottom[1]]]
def energy(spin_array, lattice, x ,y):
return 2 * spin_array[x, y] * sum(find_neighbors(spin_array, lattice, x, y))
def main():
RELAX_SWEEPS = 50
lattice = eval(input("Enter lattice size: "))
sweeps = eval(input("Enter the number of Monte Carlo Sweeps: "))
for temperature in np.arange(0.1, 5.0, 0.1):
spin_array = init_spin_array(lattice, lattice)
# the Monte Carlo follows below
mag = np.zeros(sweeps + RELAX_SWEEPS)
for sweep in range(sweeps + RELAX_SWEEPS):
for i in range(lattice):
for j in range(lattice):
e = energy(spin_array, lattice, i, j)
if e <= 0:
spin_array[i, j] *= -1
elif np.exp((-1.0 * e)/temperature) > random.random():
spin_array[i, j] *= -1
mag[sweep] = abs(sum(sum(spin_array))) / (lattice ** 2)
print(temperature, sum(mag[RELAX_SWEEPS:]) / sweeps)
main()
And the result for 20x20 and 100x100 lattices and 100 sweeps:
The starting configuration is a completely ordered one to prevent the development of domain walls that are very stable at low temperatures. Also, 30 additional sweeps are performed initially in order to thermalise the system (not nearly enough when close to the critical temperature, but the Metropolis-Hastings algorithm cannot properly handle the critical slowdown there anyway).
I'm currently trying to simulate many particles in a box bouncing around.
I've taken into account #kalhartt's suggestions and this is the improved code to initialize the particles inside the box:
import numpy as np
import scipy.spatial.distance as d
import matplotlib.pyplot as plt
# 2D container parameters
# Actual container is 50x50 but chose 49x49 to account for particle radius.
limit_x = 20
limit_y = 20
#Number and radius of particles
number_of_particles = 350
radius = 1
def force_init(n):
# equivalent to np.array(list(range(number_of_particles)))
count = np.linspace(0, number_of_particles-1, number_of_particles)
x = (count + 2) % (limit_x-1) + radius
y = (count + 2) / (limit_x-1) + radius
return np.column_stack((x, y))
position = force_init(number_of_particles)
velocity = np.random.randn(number_of_particles, 2)
The initialized positions look like this:
Once I have the particles initialized I'd like to update them at each time-step. The code for updating follows the previous code immediately and is as follows:
# Updating
while np.amax(abs(velocity)) > 0.01:
# Assume that velocity slowly dying out
position += velocity
velocity *= 0.995
#Get pair-wise distance matrix
pair_dist = d.cdist(position, position)
pair_d = pair_dist<=4
#If pdist [i,j] is <=4 then the particles are too close and so treat as collision
for i in range(len(pair_d)):
for j in range(i):
# Only looking at upper triangular matrix (not inc. diagonal)
if pair_d[i,j] ==True:
# If two particles are too close then swap velocities
# It's a bad hack but it'll work for now.
vel_1 = velocity[j][:]
velocity[j] = velocity[i][:]*0.9
velocity[i] = vel_1*0.9
# Masks for particles beyond the boundary
xmax = position[:, 0] > limit_x
xmin = position[:, 0] < 0
ymax = position[:, 1] > limit_y
ymin = position[:, 1] < 0
# flip velocity and assume that it looses 10% of energy
velocity[xmax | xmin, 0] *= -0.9
velocity[ymax | ymin, 1] *= -0.9
# Force maximum positions of being +/- 2*radius from edge
position[xmax, 0] = limit_x-2*radius
position[xmin, 0] = 2*radius
position[ymax, 0] = limit_y-2*radius
position[ymin, 0] = 2*radius
After updating it and letting it run to completion I get this result:
This is infinitely better than before but there are still patches that are too close together - such as:
Too close together. I think the updating works... and thanks to #kalhartt my code is wayyyy better and faster (and I learnt some things about numpy... props #kalhartt) but I still don't know where it's screwing up. I've tried changing the order of the actual updates with the pair-wise distance going last or the position +=velocity going last but to no avail. I added the *0.9 to make the entire thing die down faster and I tried it with 4 to make sure that 2*radius (=2) wasn't too tight a criteria... but nothing seems to work.
Any and all help would be appreciated.
There are just two typos standing in your way. First for i in range(len(positions)/2): only iterates over half of your particles. This is why half the particles stay in the x bounds (if you watch for large iterations its more clear). Second, the second y condition should be a minimum (I assume) position[i][1] < 0. The following block works to bound the particles for me (I didn't test with the collision code so there could be problems there).
for i in range(len(position)):
if position[i][0] > limit_x or position[i][0] < 0:
velocity[i][0] = -velocity[i][0]
if position[i][1] > limit_y or position[i][1] < 0:
velocity[i][1] = -velocity[i][1]
As an aside, try to leverage numpy to eliminate loops when possible. It is faster, more efficient, and in my opinion more readable. For example force_init would look like this:
def force_init(n):
# equivalent to np.array(list(range(number_of_particles)))
count = np.linspace(0, number_of_particles-1, number_of_particles)
x = (count * 2) % limit_x + radius
y = (count * 2) / limit_x + radius
return np.column_stack((x, y))
And your boundary conditions would look like this:
while np.amax(abs(velocity)) > 0.01:
position += velocity
velocity *= 0.995
# Masks for particles beyond the boundary
xmax = position[:, 0] > limit_x
xmin = position[:, 0] < 0
ymax = position[:, 1] > limit_y
ymin = position[:, 1] < 0
# flip velocity
velocity[xmax | xmin, 0] *= -1
velocity[ymax | ymin, 1] *= -1
Final note, it is probably a good idea to hard clip position to the bounding box with something like position[xmax, 0] = limit_x; position[xmin, 0] = 0. There may be cases where velocity is small and a particle outside the box will be reflected but not make it inside in the next iteration. So it will just sit outside the box being reflected forever.
EDIT: Collision
The collision detection is a much harder problem, but lets see what we can do. Lets take a look at your current implementation.
pair_dist = d.cdist(position, position)
pair_d = pair_dist<=4
for i in range(len(pair_d)):
for j in range(i):
# Only looking at upper triangular matrix (not inc. diagonal)
if pair_d[i,j] ==True:
# If two particles are too close then swap velocities
# It's a bad hack but it'll work for now.
vel_1 = velocity[j][:]
velocity[j] = velocity[i][:]*0.9
velocity[i] = vel_1*0.9
Overall a very good approach, cdist will efficiently calculate the distance
between sets of points and you find which points collide with pair_d = pair_dist<=4.
The nested for loops are the first problem. We need to iterate over True values of pair_d where j > i. First your code actually iterate over the lower triangular region by using for j in range(i) so that j < i, not particularly important in this instance as long since i,j pairs are not repeated. However Numpy has two builtins we can use instead, np.triu lets us set all values below a diagonal to 0 and np.nonzero will give us the indices of non-zero elements in a matrix. So this:
pair_dist = d.cdist(position, position)
pair_d = pair_dist<=4
for i in range(len(pair_d)):
for j in range(i+1, len(pair_d)):
if pair_d[i, j]:
...
is equivalent to
pair_dist = d.cdist(position, position)
pair_d = np.triu(pair_dist<=4, k=1) # k=1 to exclude the diagonal
for i, j in zip(*np.nonzero(pair_d)):
...
The second problem (as you noted) is that the velocities are just switched and scaled instead of reflected. What we really want to do is negate and scale the component of each particles velocity along the axis that connects them. Note that to do this we will need the vector connecting them position[j] - position[i] and the length of the vector connecting them (which we already calculated). So unfortunately part of the cdist calculation gets repeated. Lets quit using cdist and do it ourselves instead. The goal here is to make two arrays diff and norm where diff[i][j] is a vector pointing from particle i to j (so diff is a 3D array) and norm[i][j] is the distance between particles i and j. We can do this with numpy like so:
nop = number_of_particles
# Give pos a 3rd index so we can use np.repeat below
# equivalent to `pos3d = np.array([ position ])
pos3d = position.reshape(1, nop, 2)
# 3D arras with a repeated index so we can form combinations
# diff_i[i][j] = position[i] (for all j)
# diff_j[i][j] = position[j] (for all i)
diff_i = np.repeat(pos3d, nop, axis=1).reshape(nop, nop, 2)
diff_j = np.repeat(pos3d, nop, axis=0)
# diff[i][j] = vector pointing from position[i] to position[j]
diff = diff_j - diff_i
# norm[i][j] = sqrt( diff[i][j]**2 )
norm = np.linalg.norm(diff, axis=2)
# check for collisions and take the region above the diagonal
collided = np.triu(norm < radius, k=1)
for i, j in zip(*np.nonzero(collided)):
# unit vector from i to j
unit = diff[i][j] / norm[i][j]
# flip velocity
velocity[i] -= 1.9 * np.dot(unit, velocity[i]) * unit
velocity[j] -= 1.9 * np.dot(unit, velocity[j]) * unit
# push particle j to be radius units from i
# This isn't particularly effective when 3+ points are close together
position[j] += (radius - norm[i][j]) * unit
...
Since this post is long enough already, here is a gist of the code with my modifications.
I have a 570 x 800 matrix with id values. What I would like to do if find the adjacent neighbors for each item. The max number of neighbors would be 8 unless the cell is along a boundary. In that case, there would be three neighbors. I want to append the neighbors to a list. I saw the posting for finding neighbors when each cell has x and y coordinates which was very helpful, but how would modify the code with no coordinates. The ids come in as a string which is fine because I use it as a key in a dictionary. Any help would be appreciated.
Assuming that what you're trying to do is construct an eight-connected grid on the matrix, and that the position of item in the the matrix defines an x- and y- co-ordinate, you can use something like this:
def eight_connected_neighbours( xmax, ymax, x, y ):
"""The x- and y- components for a single cell in an eight connected grid
Parameters
----------
xmax : int
The width of the grid
ymax: int
The height of the grid
x : int
The x- position of cell to find neighbours of
y : int
The y- position of cell to find neighbours of
Returns
-------
results : list of tuple
A list of (x, y) indices for the neighbours
"""
results = []
for dx in [-1,0,1]:
for dy in [-1,0,1]:
newx = x+dx
newy = y+dy
if (dx == 0 and dy == 0):
continue
if (newx>=0 and newx<xmax and newy >=0 and newy<ymax):
results.append( (newx, newy) )
return results
Let me give an alternate answer with numpy, which is a library you might want to consider if you're doing anything a bit more heavy duty with your data. The advantage with this method is the extensibility to the number of nearest neighbors with the parameter k. The setup:
from numpy import *
k = 1
# Create the nearest neighbors
Xidx, Yidx = mgrid[-k:k+1,-k:k+1]
# Remove the center (0,0) index
center = (Xidx==0) & (Yidx==0)
Xidx = Xidx[~center]
Yidx = Yidx[~center]
Now you can access the nearest neighbors with A[Xidx+dx, Yidx+dy] where dx and dy are the offsets for the x and y coordinates.
Example
Let's take a random matrix:
A = random.random((5,5))
print A
which for me looks like:
[[ 0.90779297 0.91195651 0.32751438 0.44830373 0.2528675 ]
[ 0.02542108 0.52542962 0.28203009 0.35606998 0.88076027]
[ 0.08955781 0.98903843 0.86881875 0.21246095 0.92005691]
[ 0.57253561 0.08830487 0.06418296 0.59632344 0.53604546]
[ 0.7646322 0.50869651 0.00229266 0.26363367 0.64899637]]
Now we can view the nearest neighbors with
dx, dy = 2,1
print "Cell value A[%i,%i] = %f " % (dx, dy, A[dx,dy])
print "k=%i nearest neighbors: "%k, A[Xidx+dx, Yidx+dy]
Giving:
Cell value A[2,1] = 0.989038
k=1 nearest neighbors: [ 0.02542108 0.52542962 0.28203009 0.08955781 0.86881875 0.57253561 0.08830487 0.06418296]
Bonus
As mentioned, by changing k you can easily get the next nearest neighbors, and next-next neighbors, etc... In addition, the ability to index a higher order array (say a tensor of rank 3) is now possible by adding an additional variable Zidx in a similar way.
Caveats
This works nicely when you go to the rightmost and bottom of your matrix - you'll get smaller lists (as you specified you wanted). However, numpy indexing (and Python) as well, wraps around, so an index of -1 will give you the last element. Thus asking for the offset at 0,0 will still give you eight entries by wrapping around. The other answers here show a good way to check for this.
If you want to grab something on the left side edge (and you really don't want to use an if statement), you might change the index as such (making sure to remove the center element as above):
# Create the nearest neighbors (ON THE LEFT EDGE)
Xidx_left, Yidx_left = mgrid[0:k+1,-k:k+1]
code with no coordinates? Do you mean like this:
XMAX = 800
YMAX = 570
NEIGHBOURS = [(-1, -1), (0, -1), (1, -1), (-1, 0), (1, 0), (-1, 1), (0, 1), (1, 1)]
matrix = range(XMAX * YMAX)
def all_neighbours(m):
for i in xrange(len(m)):
ns = []
y, x = divmod(i, XMAX)
for u, v in NEIGHBOURS:
ux = u + x
vy = v + y
if 0 <= ux < XMAX and 0 <= vy < YMAX:
ns.append(ux + vy * YMAX)
yield i, ns
if __name__ == '__main__':
for field, neighbours in all_neighbours(matrix):
print field, neighbours