Develop Reference

Classify 3D points of Dataframe as inside or outside cylinder surface in 3D plot

I have a question considering a 3D dataset.
I have two different datasets consisting of 3D coordinates, one of these datasets I use to create surfaces in the form of cylinders (lets call it blue for now), from the other dataset I should be able to count the amount of 'points' (x,y,z) that are in this cylinder surface (lets call this dataset orange for now).
I found some code on stackoverflow which I use to create a cylinder in 3D for 2 points out the blue dataset, this all works.
However, now I should classify for every coordinate of the orange dataset if it falls inside this cylinder surface.
This is the code I use to plot the cylinder surface (found here: Plotting a solid cylinder centered on a plane in Matplotlib):
p0 = np.array([-0.0347944, 0.0058072, -0.022887199999999996]) #point at one end
p1 = np.array([-0.0366488, 0.0061488, -0.023424) #point at other end
R = 0.00005
#vector in direction of axis
v = p1 - p0
#find magnitude of vector
mag = norm(v)
#unit vector in direction of axis
v = v / mag
#make some vector not in the same direction as v
not_v = np.array([1, 0, 0])
if (v == not_v).all():
not_v = np.array([0, 1, 0])
#make vector perpendicular to v
n1 = np.cross(v, not_v)
#normalize n1
n1 /= norm(n1)
#make unit vector perpendicular to v and n1
n2 = np.cross(v, n1)
#surface ranges over t from 0 to length of axis and 0 to 2*pi
t = np.linspace(0, mag, 2)
theta = np.linspace(0, 2 * np.pi, 100)
rsample = np.linspace(0, R, 2)
#use meshgrid to make 2d arrays
t, theta2 = np.meshgrid(t, theta)
rsample,theta = np.meshgrid(rsample, theta)
#generate coordinates for surface
# "Tube"
X, Y, Z = [p0[i] + v[i] * t + R * np.sin(theta2) * n1[i] + R * np.cos(theta2) * n2[i] for i in [0, 1, 2]]
# "Bottom"
X2, Y2, Z2 = [p0[i] + rsample[i] * np.sin(theta) * n1[i] + rsample[i] * np.cos(theta) * n2[i] for i in [0, 1, 2]]
# "Top"
X3, Y3, Z3 = [p0[i] + v[i]*mag + rsample[i] * np.sin(theta) * n1[i] + rsample[i] * np.cos(theta) * n2[i] for i in [0, 1, 2]]
ax=plt.subplot(111, projection='3d')
ax.plot_surface(X, Y, Z, color='blue', alpha=0.5)
ax.plot_surface(X2, Y2, Z2, color='blue', alpha=0.5)
ax.plot_surface(X3, Y3, Z3, color='green', alpha=0.7)
plt.show()
Lets now say I need to classify the following points as 'inside' or 'outside' the cylinder surface:
point1 = (-0.0321, 0.003, -0.01) point2 = (-0.5, 0.004, 0.03) point3 = (0.0002, -0.02, 0.00045)
It is important to mention that these points in the orange dataset of which I need to find out whether or not these are inside or outside the cylinder surface, do not have to be points on the top and bottom part enclosing the cylinder surface, these points can be anywhere in a 3D space in and outside the cylinder surface.
The code mentioned here outputs the following result.
Cylinder surface enclosing two point of the blue dataset
UPDATE: Thanks for the help! However, I found the solution here: How to check if a 3D point is inside a cylinder

Your question sounds more about geometry than about coding. What exactly do you need?
Geometrically, if you want to check whether the point (a, b, c) lies inside of a (canonical) cylindrical surface
(x, y, z) such that {x^2 + y^2 = r^2, z in [zi, zf] },
simply check whether a^2 + b^2 < r^2 and c in [zi, zf] is True.
If the axis of your cylinder is not aligned to the z-axis, just bring the point (a, b, c) to a new system where the cylinder is in canonical form. In general, if the cylinder axis lies along the (unit) vector v. the angle with respect to the z-axis is simply beta = arccos(v[2]) (v is already normalized!). Then you want to use Rodrigues' rotation formula with k = v and theta = -beta (note the sign) to transform (a, b, c). You should also check whether the rotated p0 and p1 already lie on the (rotated) z-axis, otherwise you should apply an additional translation to (a, b, c) before making the comparison.
If what you need is help coding this up, well, post your attempt and ask away :)

How to fit a curve (3d), where points have specified normal in python?

I have following three points in 3d space with their respective normal vectors.
A, B, C are positions and N_A, N_B,and N_C are their respective normal vectors.
A = np.array([ 348.92065834, -1402.3305998, 32.69313966])
N_A = np.array([-0.86925426, 0.02836434, -0.49355091])
B = np.array([282.19332067, 82.52027998, -5.92595371])
N_B = np.array([-0.82339849, 0.43041935, 0.3698028])
C = np.array([247.37475615, -3.70129865, -22.10494737])
N_C = np.array([-0.83989222, 0.23796899, 0.48780305])
Three points are almost in one plane, but there is a slight directional change between the two closest point B and C. From there to the point A, I assumed there could be a curvature in X-Y coordinates as shown , but parabole in X-Z coordinates as shown in
Supposed condition is cylinder can fit the three points if the curvature was much obvious. And considering their respective normal vectors from X and Z coordinates, B and C normal vectors face lower direction while the that of A faces upper direction. So, all in all it it could be a paraboloid. Question is how to fit them taking their normal vectors into account. If not possible, then how to fit them with some curvature from X and Y direction.
Here is a code for the plot
import numpy as np
import matplotlib.pyplot as plt
def set_axes_radius(ax, origin, radius):
ax.set_xlim3d([origin[0] - radius, origin[0] + radius])
ax.set_ylim3d([origin[1] - radius, origin[1] + radius])
ax.set_zlim3d([origin[2] - radius, origin[2] + radius])
def set_axes_equal(ax, zoom=1.):
'''
Make axes of 3D plot have equal scale so that spheres appear as spheres,
cubes as cubes, etc.. This is one possible solution to Matplotlib's
ax.set_aspect("equal") and ax.axis("equal") not working for 3D.
input:
ax: a matplotlib axis, e.g., as output from plt.gca().
'''
limits = np.array([
ax.get_xlim3d(),
ax.get_ylim3d(),
ax.get_zlim3d(),
])
origin = np.mean(limits, axis=1)
radius = 0.5 * np.max(np.abs(limits[:, 1] - limits[:, 0])) / zoom
set_axes_radius(ax, origin, radius)
%matplotlib qt
# positions and their respective normal vectors
A = np.array([ 348.92065834, -1402.3305998, 32.69313966])
N_A = np.array([-0.86925426, 0.02836434, -0.49355091])
B = np.array([282.19332067, 82.52027998, -5.92595371])
N_B = np.array([-0.82339849, 0.43041935, 0.3698028])
C = np.array([247.37475615, -3.70129865, -22.10494737])
N_C = np.array([-0.83989222, 0.23796899, 0.48780305])
# A plane is given by
# a*x + b*y + c*z + d = 0
# where (a, b, c) is the normal.
# If the point (x, y, z) lies on the plane, then solving for d yield:
# d = -(a*x + b*y + c*z)
d_A = -np.sum(N_A * A)
d_B = -np.sum(N_B * B)
d_C = -np.sum(N_C * C)
# Create a meshgrid:
delta = 200
xlim_A = A[0] - delta, A[0] + delta
ylim_A = A[1] - delta, A[1] + delta
xx_A, yy_A = np.meshgrid(np.arange(*xlim_A), np.arange(*ylim_A))
xlim_B = B[0] - delta, B[0] + delta
ylim_B = B[1] - delta, B[1] + delta
xx_B, yy_B = np.meshgrid(np.arange(*xlim_B), np.arange(*ylim_B))
xlim_C = C[0] - delta, C[0] + delta
ylim_C = C[1] - delta, C[1] + delta
xx_C, yy_C = np.meshgrid(np.arange(*xlim_C), np.arange(*ylim_C))
# Solving the equation above for z:
# z = -(a*x + b*y +d) / c
zz_A = -(N_A[0] * xx_A + N_A[1] * yy_A + d_A) / N_A[2]
zz_B = -(N_B[0] * xx_B + N_B[1] * yy_B + d_B) / N_B[2]
zz_C = -(N_C[0] * xx_C + N_C[1] * yy_C + d_C) / N_C[2]
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
ax.plot_surface(xx_A, yy_A, zz_A, alpha=0.5, color='g')
ax.plot_surface(xx_B, yy_B, zz_B, alpha=0.5, color='cyan')
ax.plot_surface(xx_C, yy_C, zz_C, alpha=0.5, color='crimson')
# Plot point.
x_A, y_A, z_A = A
x_B, y_B, z_B = B
x_C, y_C, z_C = C
Plane_A, = ax.plot(x_A, y_A, z_A, marker='o', markersize=5, color='g')
Plane_A.set_label('A position')
ax.legend()
Plane_B, = ax.plot(x_B, y_B, z_B, marker='o', markersize=5, color='cyan')
Plane_B.set_label('B position')
ax.legend()
Plane_C, = ax.plot(x_C, y_C, z_C, marker='o', markersize=5, color='crimson')
Plane_C.set_label('C position')
ax.legend()
# Plot normal.
dx_A, dy_A, dz_A = delta * N_A
ax.quiver(x_A, y_A, z_A, dx_A, dy_A, dz_A, arrow_length_ratio=0.15, linewidth=3, color='g')
dx_B, dy_B, dz_B = delta * N_B
ax.quiver(x_B, y_B, z_B, dx_B, dy_B, dz_B, arrow_length_ratio=0.15, linewidth=3, color='cyan')
dx_C, dy_C, dz_C = delta * N_C
ax.quiver(x_B, y_C, z_C, dx_C, dy_C, dz_C, arrow_length_ratio=0.15, linewidth=3, color='crimson')
# Enforce equal axis aspects so that the normal also appears to be normal.
ax.set_xlim(xmax=1500,xmin=-1500)
ax.set_ylim(ymax=400, ymin=-400)
zlim = max(A[2], B[2], C[2]) - delta, max(A[2], B[2], C[2]) + delta
ax.set_zlim(*zlim)
ax = plt.gca()
#ax.set_box_aspect([1,1,1])
set_axes_equal(ax)
ax.set_xlabel('X', fontsize=20)
ax.set_ylabel('Y', fontsize=20)
ax.set_zlabel('Z', fontsize=20)
plt.show()

Meta-problems: next time,
explain what you're actually doing (attempting regression on interplanetary electromagnetic bow shock from satellite readings) or else this is clearly an x/y problem
invest some time in learning fundamental vector calculus, which is inescapable for this class of problem
Do not discard the magnitude from your field readings.
It is physically invalid to attempt a surface fit containing the three satellite points. A surface is only applicable if the magnitude read at each satellite is exactly the same, and you've described that it isn't. The magnetic field is literally a field: a vector field with orientations defined at all points in spacetime.
When you perform regression to establish an estimate for the vector field orientation as a function of position, you will not have one surface. You will have three different isosurfaces, one for each of your satellites, each with a different path through space where the field magnitude is constant.
Don't perform your calculations in rectilinear space. Perform them in polar space. Your unit normals have too many degrees of freedom for regression (they have three when functionally they need to only have two).
You need many, many, many more data points to do meaningful regression. As it stands, "you could fit anything". So the following results are numerically accurate but physically meaningless.
Interpret the system as three independent variables (x, y, z), and two dependent variables (θ and φ, the unit normal rotation angles). This is vulnerable to gimbal lock but we disregard that here.
After all of the above bad news, the good news is that - as interpreted - the system is highly linear. (It's no surprise given the poverty of data.) You can express the fit as a linear matrix transformation:
import numpy as np
points = np.array((
(348.92065834, -1402.33059980, 32.69313966),
(282.19332067, 82.52027998, -5.92595371),
(247.37475615, -3.70129865, -22.10494737),
))
rect_normals = np.array((
(-0.86925426, 0.02836434, -0.49355091),
(-0.82339849, 0.43041935, 0.36980280),
(-0.83989222, 0.23796899, 0.48780305),
))
x, y, z = rect_normals.T
angles = np.stack((
np.arccos(z), # θ
np.arctan2(y, x), # φ
)).T
transform, *_ = np.linalg.lstsq(points, angles, rcond=None)
print('Transformation matrix:')
print(transform)
print()
est_angles = points # transform
print('Measured vs. estimated angles:')
print(np.stack((angles, est_angles)))
print()
θ, φ = est_angles.T
est_normals = np.stack((
np.sin(θ) * np.cos(φ),
np.sin(θ) * np.sin(φ),
np.cos(θ),
)).T
print('Measured vs. estimated unit normals:')
print(np.stack((rect_normals, est_normals)))
print()
Transformation matrix:
[[ 0.00435349 0.00901185]
[-0.00038691 -0.00064318]
[ 0.00077585 -0.02867279]]
Measured vs. estimated angles:
[[[2.08696421 3.10897357]
[1.19199956 2.65992277]
[1.06122503 2.86549615]]
[[2.08696421 3.10897357]
[1.19199956 2.65992277]
[1.06122503 2.86549615]]]
Measured vs. estimated unit normals:
[[[-0.86925426 0.02836434 -0.49355091]
[-0.82339849 0.43041935 0.3698028 ]
[-0.83989222 0.23796899 0.48780305]]
[[-0.86925426 0.02836434 -0.49355091]
[-0.82339849 0.43041935 0.3698028 ]
[-0.83989222 0.23796899 0.48780305]]]
So the fit is essentially perfect. The above result is related to (though not equal to) the gradient. To find your isosurfaces - one for each satellite - you need to perform a vector integral. If you do not know how to do this, it's time for research in a different community such as physics or mathematics StackExchange sites.

Interpolating non-uniformly distributed points on a 3D sphere

I have several points on the unit sphere that are distributed according to the algorithm described in https://www.cmu.edu/biolphys/deserno/pdf/sphere_equi.pdf (and implemented in the code below). On each of these points, I have a value that in my particular case represents 1 minus a small error. The errors are in [0, 0.1] if this is important, so my values are in [0.9, 1].
Sadly, computing the errors is a costly process and I cannot do this for as many points as I want. Still, I want my plots to look like I am plotting something "continuous".
So I want to fit an interpolation function to my data, to be able to sample as many points as I want.
After a little bit of research I found scipy.interpolate.SmoothSphereBivariateSpline which seems to do exactly what I want. But I cannot make it work properly.
Question: what can I use to interpolate (spline, linear interpolation, anything would be fine for the moment) my data on the unit sphere? An answer can be either "you misused scipy.interpolation, here is the correct way to do this" or "this other function is better suited to your problem".
Sample code that should be executable with numpy and scipy installed:
import typing as ty
import numpy
import scipy.interpolate
def get_equidistant_points(N: int) -> ty.List[numpy.ndarray]:
"""Generate approximately n points evenly distributed accros the 3-d sphere.
This function tries to find approximately n points (might be a little less
or more) that are evenly distributed accros the 3-dimensional unit sphere.
The algorithm used is described in
https://www.cmu.edu/biolphys/deserno/pdf/sphere_equi.pdf.
"""
# Unit sphere
r = 1
points: ty.List[numpy.ndarray] = list()
a = 4 * numpy.pi * r ** 2 / N
d = numpy.sqrt(a)
m_v = int(numpy.round(numpy.pi / d))
d_v = numpy.pi / m_v
d_phi = a / d_v
for m in range(m_v):
v = numpy.pi * (m + 0.5) / m_v
m_phi = int(numpy.round(2 * numpy.pi * numpy.sin(v) / d_phi))
for n in range(m_phi):
phi = 2 * numpy.pi * n / m_phi
points.append(
numpy.array(
[
numpy.sin(v) * numpy.cos(phi),
numpy.sin(v) * numpy.sin(phi),
numpy.cos(v),
]
)
)
return points
def cartesian2spherical(x: float, y: float, z: float) -> numpy.ndarray:
r = numpy.linalg.norm([x, y, z])
theta = numpy.arccos(z / r)
phi = numpy.arctan2(y, x)
return numpy.array([r, theta, phi])
n = 100
points = get_equidistant_points(n)
# Random here, but costly in real life.
errors = numpy.random.rand(len(points)) / 10
# Change everything to spherical to use the interpolator from scipy.
ideal_spherical_points = numpy.array([cartesian2spherical(*point) for point in points])
r_interp = 1 - errors
theta_interp = ideal_spherical_points[:, 1]
phi_interp = ideal_spherical_points[:, 2]
# Change phi coordinate from [-pi, pi] to [0, 2pi] to please scipy.
phi_interp[phi_interp < 0] += 2 * numpy.pi
# Create the interpolator.
interpolator = scipy.interpolate.SmoothSphereBivariateSpline(
theta_interp, phi_interp, r_interp
)
# Creating the finer theta and phi values for the final plot
theta = numpy.linspace(0, numpy.pi, 100, endpoint=True)
phi = numpy.linspace(0, numpy.pi * 2, 100, endpoint=True)
# Creating the coordinate grid for the unit sphere.
X = numpy.outer(numpy.sin(theta), numpy.cos(phi))
Y = numpy.outer(numpy.sin(theta), numpy.sin(phi))
Z = numpy.outer(numpy.cos(theta), numpy.ones(100))
thetas, phis = numpy.meshgrid(theta, phi)
heatmap = interpolator(thetas, phis)
Issue with the code above:
With the code as-is, I have a
ValueError: The required storage space exceeds the available storage space: nxest or nyest too small, or s too small. The weighted least-squares spline corresponds to the current set of knots.
that is raised when initialising the interpolator instance.
The issue above seems to say that I should change the value of s that is one on the parameters of scipy.interpolate.SmoothSphereBivariateSpline. I tested different values of s ranging from 0.0001 to 100000, the code above always raise, either the exception described above or:
ValueError: Error code returned by bispev: 10
Edit: I am including my findings here. They can't really be considered as a solution, that is why I am editing and not posting as an answer.
With more research I found this question Using Radial Basis Functions to Interpolate a Function on a Sphere. The author has exactly the same problem as me and use a different interpolator: scipy.interpolate.Rbf. I changed the above code by replacing the interpolator and plotting:
# Create the interpolator.
interpolator = scipy.interpolate.Rbf(theta_interp, phi_interp, r_interp)
# Creating the finer theta and phi values for the final plot
plot_points = 100
theta = numpy.linspace(0, numpy.pi, plot_points, endpoint=True)
phi = numpy.linspace(0, numpy.pi * 2, plot_points, endpoint=True)
# Creating the coordinate grid for the unit sphere.
X = numpy.outer(numpy.sin(theta), numpy.cos(phi))
Y = numpy.outer(numpy.sin(theta), numpy.sin(phi))
Z = numpy.outer(numpy.cos(theta), numpy.ones(plot_points))
thetas, phis = numpy.meshgrid(theta, phi)
heatmap = interpolator(thetas, phis)
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import cm
colormap = cm.inferno
normaliser = mpl.colors.Normalize(vmin=numpy.min(heatmap), vmax=1)
scalar_mappable = cm.ScalarMappable(cmap=colormap, norm=normaliser)
scalar_mappable.set_array([])
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
ax.plot_surface(
X,
Y,
Z,
facecolors=colormap(normaliser(heatmap)),
alpha=0.7,
cmap=colormap,
)
plt.colorbar(scalar_mappable)
plt.show()
This code runs smoothly and gives the following result:
The interpolation seems OK except on one line that is discontinuous, just like in the question that led me to this class. One of the answer give the idea of using a different distance, more adapted the the spherical coordinates: the Haversine distance.
def haversine(x1, x2):
theta1, phi1 = x1
theta2, phi2 = x2
return 2 * numpy.arcsin(
numpy.sqrt(
numpy.sin((theta2 - theta1) / 2) ** 2
+ numpy.cos(theta1) * numpy.cos(theta2) * numpy.sin((phi2 - phi1) / 2) ** 2
)
)
# Create the interpolator.
interpolator = scipy.interpolate.Rbf(theta_interp, phi_interp, r_interp, norm=haversine)
which, when executed, gives a warning:
LinAlgWarning: Ill-conditioned matrix (rcond=1.33262e-19): result may not be accurate.
self.nodes = linalg.solve(self.A, self.di)
and a result that is not at all the one expected: the interpolated function have values that may go up to -1 which is clearly wrong.

You can use Cartesian coordinate instead of Spherical coordinate.
The default norm parameter ('euclidean') used by Rbf is sufficient
# interpolation
x, y, z = numpy.array(points).T
interpolator = scipy.interpolate.Rbf(x, y, z, r_interp)
# predict
heatmap = interpolator(X, Y, Z)
Here the result:
ax.plot_surface(
X, Y, Z,
rstride=1, cstride=1,
# or rcount=50, ccount=50,
facecolors=colormap(normaliser(heatmap)),
cmap=colormap,
alpha=0.7, shade=False
)
ax.set_xlabel('x axis')
ax.set_ylabel('y axis')
ax.set_zlabel('z axis')
You can also use a cosine distance if you want (norm parameter):
def cosine(XA, XB):
if XA.ndim == 1:
XA = numpy.expand_dims(XA, axis=0)
if XB.ndim == 1:
XB = numpy.expand_dims(XB, axis=0)
return scipy.spatial.distance.cosine(XA, XB)
In order to better see the differences,
I stacked the two images, substracted them and inverted the layer.

How can I create a Circle with a small notch at bottom in Matplotlib?

I am trying to create a circle on co-ordinate plane and fill the pixels in it with conditional colors. However, the circle also need to show a small triangular notch at the bottom from the center. Something like the attached picture.
I have used the matplotlib's patches class to create acircle and tried different values in attributes but of no help. I googled enough but I couldn't find it.
circle = matplotlib.patches.Circle((0,0),150,facecolor='lightgrey')
ax.add_patch(circle)
Sample
Can someone please help me or provide me a hint or direct me to right library which can do this.

Your problem looks like 2D vector graphics which in this case you should look for SVG
if it is a CAD problem then you can check some CAD libraries
- Python module for parametric CAD
I think CAD approach is much better for engineering operations which you can find https://www.freecadweb.org/wiki/Part_Slice and https://www.freecadweb.org/wiki/Part_SliceApart#Scripting
or for SVG approach found something: https://inkscape.org/~Moini/%E2%98%85multi-bool-extension-cut-difference-division
If you have to do that in mathplotlib then you can export/import vector graphics into plots

I tried to build the proposed shape using parametric equations.
There are some approximations, but if you get the exact geometry equations you could refine this to a better version.
In fact, the key is to define the correct equations ... the ideal case would be to ensure continuity.
This example has some caveats: approximations when joining the 2 circles due to geometrical simplification when drawing the small circle from pi to 0. Small circle start/end angles should be chosen as the interception of the 2 full circles for a more accurate shape continuity. But again, in the end it depends entirely on your shape specification
Heavly inspired from Plot equation showing a circle
import math
import numpy as np
import matplotlib.pyplot as plt
def compute_x_values(r, theta):
return r * np.cos(theta)
def compute_y_values(r, theta):
return r * np.sin(theta)
def compute_circle(r, theta):
return compute_x_values(r, theta), compute_y_values(r, theta)
def build_big_circle(crop_angle, offset, radius):
start_angle = offset + crop_angle
end_angle = offset + (2 * np.pi) - crop_angle
theta = np.linspace(start_angle, end_angle, 250)
# compute main circle vals
x, y = compute_circle(radius, theta)
return x, y
r = 1
offset = - np.pi / 2
crop_angle = np.pi / 20
x, y = build_big_circle(crop_angle, offset, r)
# now the other form:
# its a half circle from pi to 0
theta2 = np.linspace(np.pi, 0, 100)
# according our code above, angular space left on the circle for the notch is
missing_angle = crop_angle * 2
# the length between to points on a circle is given by the formula
# length = 2 * r * sin(angle/2)
l = math.sin(missing_angle / 2) * r * 2
# we want half the length for a future radius
r2 = l / 2
# the above lines could be optimized to this
# r2 = math.sin(crop_angle) * r
# but I kept intermediate steps for sake of geometric clarity
# equation is same of a circle
x1, y1 = compute_circle(r2, theta2)
# change center on y axis to - big circle radius
y1 = y1 - r
# merge the 2
x_total = np.append(x, x1)
y_total = np.append(y, y1)
# create the global figure
fig, ax = plt.subplots(1)
ax.plot(x_total, y_total)
ax.fill(x_total, y_total, facecolor='lightgrey', linewidth=1)
ax.set_aspect(1)
plt.show()
fig, ax = plt.subplots(1)
ax.plot(x, y)
ax.plot(x1, y1)
ax.set_aspect(1)
plt.show()

Locating the centroid (center of mass) of spherical polygons

I'm trying to work out how best to locate the centroid of an arbitrary shape draped over a unit sphere, with the input being ordered (clockwise or anti-cw) vertices for the shape boundary. The density of vertices is irregular along the boundary, so the arc-lengths between them are not generally equal. Because the shapes may be very large (half a hemisphere) it is generally not possible to simply project the vertices to a plane and use planar methods, as detailed on Wikipedia (sorry I'm not allowed more than 2 hyperlinks as a newcomer). A slightly better approach involves the use of planar geometry manipulated in spherical coordinates, but again, with large polygons this method fails, as nicely illustrated here. On that same page, 'Cffk' highlighted this paper which describes a method for calculating the centroid of spherical triangles. I've tried to implement this method, but without success, and I'm hoping someone can spot the problem?
I have kept the variable definitions similar to those in the paper to make it easier to compare. The input (data) is a list of longitude/latitude coordinates, converted to [x,y,z] coordinates by the code. For each of the triangles I have arbitrarily fixed one point to be the +z-pole, the other two vertices being composed of a pair of neighboring points along the polygon boundary. The code steps along the boundary (starting at an arbitrary point), using each boundary segment of the polygon as a triangle side in turn. A sub-centroid is determined for each of these individual spherical triangles and they are weighted according to triangle area and added to calculate the total polygon centroid. I don't get any errors when running the code, but the total centroids returned are clearly wrong (I have run some very basic shapes where the centroid location is unambiguous). I haven't found any sensible pattern in the location of the centroids returned...so at the moment I'm not sure what is going wrong, either in the math or code (although, the suspicion is the math).
The code below should work copy-paste as is if you would like to try it. If you have matplotlib and numpy installed, it will plot the results (it will ignore plotting if you don't). You just have to put the longitude/latitude data below the code into a text file called example.txt.
from math import *
try:
import matplotlib as mpl
import matplotlib.pyplot
from mpl_toolkits.mplot3d import Axes3D
import numpy
plotting_enabled = True
except ImportError:
plotting_enabled = False
def sph_car(point):
if len(point) == 2:
point.append(1.0)
rlon = radians(float(point[0]))
rlat = radians(float(point[1]))
x = cos(rlat) * cos(rlon) * point[2]
y = cos(rlat) * sin(rlon) * point[2]
z = sin(rlat) * point[2]
return [x, y, z]
def xprod(v1, v2):
x = v1[1] * v2[2] - v1[2] * v2[1]
y = v1[2] * v2[0] - v1[0] * v2[2]
z = v1[0] * v2[1] - v1[1] * v2[0]
return [x, y, z]
def dprod(v1, v2):
dot = 0
for i in range(3):
dot += v1[i] * v2[i]
return dot
def plot(poly_xyz, g_xyz):
fig = mpl.pyplot.figure()
ax = fig.add_subplot(111, projection='3d')
# plot the unit sphere
u = numpy.linspace(0, 2 * numpy.pi, 100)
v = numpy.linspace(-1 * numpy.pi / 2, numpy.pi / 2, 100)
x = numpy.outer(numpy.cos(u), numpy.sin(v))
y = numpy.outer(numpy.sin(u), numpy.sin(v))
z = numpy.outer(numpy.ones(numpy.size(u)), numpy.cos(v))
ax.plot_surface(x, y, z, rstride=4, cstride=4, color='w', linewidth=0,
alpha=0.3)
# plot 3d and flattened polygon
x, y, z = zip(*poly_xyz)
ax.plot(x, y, z)
ax.plot(x, y, zs=0)
# plot the alleged 3d and flattened centroid
x, y, z = g_xyz
ax.scatter(x, y, z, c='r')
ax.scatter(x, y, 0, c='r')
# display
ax.set_xlim3d(-1, 1)
ax.set_ylim3d(-1, 1)
ax.set_zlim3d(0, 1)
mpl.pyplot.show()
lons, lats, v = list(), list(), list()
# put the two-column data at the bottom of the question into a file called
# example.txt in the same directory as this script
with open('example.txt') as f:
for line in f.readlines():
sep = line.split()
lons.append(float(sep[0]))
lats.append(float(sep[1]))
# convert spherical coordinates to cartesian
for lon, lat in zip(lons, lats):
v.append(sph_car([lon, lat, 1.0]))
# z unit vector/pole ('north pole'). This is an arbitrary point selected to act as one
#(fixed) vertex of the summed spherical triangles. The other two vertices of any
#triangle are composed of neighboring vertices from the polygon boundary.
np = [0.0, 0.0, 1.0]
# Gx,Gy,Gz are the cartesian coordinates of the calculated centroid
Gx, Gy, Gz = 0.0, 0.0, 0.0
for i in range(-1, len(v) - 1):
# cycle through the boundary vertices of the polygon, from 0 to n
if all((v[i][0] != v[i+1][0],
v[i][1] != v[i+1][1],
v[i][2] != v[i+1][2])):
# this just ignores redundant points which are common in my larger input files
# A,B,C are the internal angles in the triangle: 'np-v[i]-v[i+1]-np'
A = asin(sqrt((dprod(np, xprod(v[i], v[i+1])))**2
/ ((1 - (dprod(v[i+1], np))**2) * (1 - (dprod(np, v[i]))**2))))
B = asin(sqrt((dprod(v[i], xprod(v[i+1], np)))**2
/ ((1 - (dprod(np , v[i]))**2) * (1 - (dprod(v[i], v[i+1]))**2))))
C = asin(sqrt((dprod(v[i + 1], xprod(np, v[i])))**2
/ ((1 - (dprod(v[i], v[i+1]))**2) * (1 - (dprod(v[i+1], np))**2))))
# A/B/Cbar are the vertex angles, such that if 'O' is the sphere center, Abar
# is the angle (v[i]-O-v[i+1])
Abar = acos(dprod(v[i], v[i+1]))
Bbar = acos(dprod(v[i+1], np))
Cbar = acos(dprod(np, v[i]))
# e is the 'spherical excess', as defined on wikipedia
e = A + B + C - pi
# mag1/2/3 are the magnitudes of vectors np,v[i] and v[i+1].
mag1 = 1.0
mag2 = float(sqrt(v[i][0]**2 + v[i][1]**2 + v[i][2]**2))
mag3 = float(sqrt(v[i+1][0]**2 + v[i+1][1]**2 + v[i+1][2]**2))
# vec1/2/3 are cross products, defined here to simplify the equation below.
vec1 = xprod(np, v[i])
vec2 = xprod(v[i], v[i+1])
vec3 = xprod(v[i+1], np)
# multiplying vec1/2/3 by e and respective internal angles, according to the
#posted paper
for x in range(3):
vec1[x] *= Cbar / (2 * e * mag1 * mag2
* sqrt(1 - (dprod(np, v[i])**2)))
vec2[x] *= Abar / (2 * e * mag2 * mag3
* sqrt(1 - (dprod(v[i], v[i+1])**2)))
vec3[x] *= Bbar / (2 * e * mag3 * mag1
* sqrt(1 - (dprod(v[i+1], np)**2)))
Gx += vec1[0] + vec2[0] + vec3[0]
Gy += vec1[1] + vec2[1] + vec3[1]
Gz += vec1[2] + vec2[2] + vec3[2]
approx_expected_Gxyz = (0.78, -0.56, 0.27)
print('Approximate Expected Gxyz: {0}\n'
' Actual Gxyz: {1}'
''.format(approx_expected_Gxyz, (Gx, Gy, Gz)))
if plotting_enabled:
plot(v, (Gx, Gy, Gz))
Thanks in advance for any suggestions or insight.
EDIT: Here is a figure that shows a projection of the unit sphere with a polygon and the resulting centroid I calculate from the code. Clearly, the centroid is wrong as the polygon is rather small and convex but yet the centroid falls outside its perimeter.
EDIT: Here is a highly-similar set of coordinates to those above, but in the original [lon,lat] format I normally use (which is now converted to [x,y,z] by the updated code).
-39.366295 -1.633460
-47.282630 -0.740433
-53.912136 0.741380
-59.004217 2.759183
-63.489005 5.426812
-68.566001 8.712068
-71.394853 11.659135
-66.629580 15.362600
-67.632276 16.827507
-66.459524 19.069327
-63.819523 21.446736
-61.672712 23.532143
-57.538431 25.947815
-52.519889 28.691766
-48.606227 30.646295
-45.000447 31.089437
-41.549866 32.139873
-36.605156 32.956277
-32.010080 34.156692
-29.730629 33.756566
-26.158767 33.714080
-25.821513 34.179648
-23.614658 36.173719
-20.896869 36.977645
-17.991994 35.600074
-13.375742 32.581447
-9.554027 28.675497
-7.825604 26.535234
-7.825604 26.535234
-9.094304 23.363132
-9.564002 22.527385
-9.713885 22.217165
-9.948596 20.367878
-10.496531 16.486580
-11.151919 12.666850
-12.350144 8.800367
-15.446347 4.993373
-20.366139 1.132118
-24.784805 -0.927448
-31.532135 -1.910227
-39.366295 -1.633460
EDIT: A couple more examples...with 4 vertices defining a perfect square centered at [1,0,0] I get the expected result:
However, from a non-symmetric triangle I get a centroid that is nowhere close...the centroid actually falls on the far side of the sphere (here projected onto the front side as the antipode):
Interestingly, the centroid estimation appears 'stable' in the sense that if I invert the list (go from clockwise to counterclockwise order or vice-versa) the centroid correspondingly inverts exactly.

Anybody finding this, make sure to check Don Hatch's answer which is probably better.
I think this will do it. You should be able to reproduce this result by just copy-pasting the code below.
You will need to have the latitude and longitude data in a file called longitude and latitude.txt. You can copy-paste the original sample data which is included below the code.
If you have mplotlib it will additionally produce the plot below
For non-obvious calculations, I included a link that explains what is going on
In the graph below, the reference vector is very short (r = 1/10) so that the 3d-centroids are easier to see. You can easily remove the scaling to maximize accuracy.
Note to op: I rewrote almost everything so I'm not sure exactly where the original code was not working. However, at least I think it was not taking into consideration the need to handle clockwise / counterclockwise triangle vertices.
Legend:
(black line) reference vector
(small red dots) spherical triangle 3d-centroids
(large red / blue / green dot) 3d-centroid / projected to the surface / projected to the xy plane
(blue / green lines) the spherical polygon and the projection onto the xy plane
from math import *
try:
import matplotlib as mpl
import matplotlib.pyplot
from mpl_toolkits.mplot3d import Axes3D
import numpy
plotting_enabled = True
except ImportError:
plotting_enabled = False
def main():
# get base polygon data based on unit sphere
r = 1.0
polygon = get_cartesian_polygon_data(r)
point_count = len(polygon)
reference = ok_reference_for_polygon(polygon)
# decompose the polygon into triangles and record each area and 3d centroid
areas, subcentroids = list(), list()
for ia, a in enumerate(polygon):
# build an a-b-c point set
ib = (ia + 1) % point_count
b, c = polygon[ib], reference
if points_are_equivalent(a, b, 0.001):
continue # skip nearly identical points
# store the area and 3d centroid
areas.append(area_of_spherical_triangle(r, a, b, c))
tx, ty, tz = zip(a, b, c)
subcentroids.append((sum(tx)/3.0,
sum(ty)/3.0,
sum(tz)/3.0))
# combine all the centroids, weighted by their areas
total_area = sum(areas)
subxs, subys, subzs = zip(*subcentroids)
_3d_centroid = (sum(a*subx for a, subx in zip(areas, subxs))/total_area,
sum(a*suby for a, suby in zip(areas, subys))/total_area,
sum(a*subz for a, subz in zip(areas, subzs))/total_area)
# shift the final centroid to the surface
surface_centroid = scale_v(1.0 / mag(_3d_centroid), _3d_centroid)
plot(polygon, reference, _3d_centroid, surface_centroid, subcentroids)
def get_cartesian_polygon_data(fixed_radius):
cartesians = list()
with open('longitude and latitude.txt') as f:
for line in f.readlines():
spherical_point = [float(v) for v in line.split()]
if len(spherical_point) == 2:
spherical_point.append(fixed_radius)
cartesians.append(degree_spherical_to_cartesian(spherical_point))
return cartesians
def ok_reference_for_polygon(polygon):
point_count = len(polygon)
# fix the average of all vectors to minimize float skew
polyx, polyy, polyz = zip(*polygon)
# /10 is for visualization. Remove it to maximize accuracy
return (sum(polyx)/(point_count*10.0),
sum(polyy)/(point_count*10.0),
sum(polyz)/(point_count*10.0))
def points_are_equivalent(a, b, vague_tolerance):
# vague tolerance is something like a percentage tolerance (1% = 0.01)
(ax, ay, az), (bx, by, bz) = a, b
return all(((ax-bx)/ax < vague_tolerance,
(ay-by)/ay < vague_tolerance,
(az-bz)/az < vague_tolerance))
def degree_spherical_to_cartesian(point):
rad_lon, rad_lat, r = radians(point[0]), radians(point[1]), point[2]
x = r * cos(rad_lat) * cos(rad_lon)
y = r * cos(rad_lat) * sin(rad_lon)
z = r * sin(rad_lat)
return x, y, z
def area_of_spherical_triangle(r, a, b, c):
# points abc
# build an angle set: A(CAB), B(ABC), C(BCA)
# http://math.stackexchange.com/a/66731/25581
A, B, C = surface_points_to_surface_radians(a, b, c)
E = A + B + C - pi # E is called the spherical excess
area = r**2 * E
# add or subtract area based on clockwise-ness of a-b-c
# http://stackoverflow.com/a/10032657/377366
if clockwise_or_counter(a, b, c) == 'counter':
area *= -1.0
return area
def surface_points_to_surface_radians(a, b, c):
"""build an angle set: A(cab), B(abc), C(bca)"""
points = a, b, c
angles = list()
for i, mid in enumerate(points):
start, end = points[(i - 1) % 3], points[(i + 1) % 3]
x_startmid, x_endmid = xprod(start, mid), xprod(end, mid)
ratio = (dprod(x_startmid, x_endmid)
/ ((mag(x_startmid) * mag(x_endmid))))
angles.append(acos(ratio))
return angles
def clockwise_or_counter(a, b, c):
ab = diff_cartesians(b, a)
bc = diff_cartesians(c, b)
x = xprod(ab, bc)
if x < 0:
return 'clockwise'
elif x > 0:
return 'counter'
else:
raise RuntimeError('The reference point is in the polygon.')
def diff_cartesians(positive, negative):
return tuple(p - n for p, n in zip(positive, negative))
def xprod(v1, v2):
x = v1[1] * v2[2] - v1[2] * v2[1]
y = v1[2] * v2[0] - v1[0] * v2[2]
z = v1[0] * v2[1] - v1[1] * v2[0]
return [x, y, z]
def dprod(v1, v2):
dot = 0
for i in range(3):
dot += v1[i] * v2[i]
return dot
def mag(v1):
return sqrt(v1[0]**2 + v1[1]**2 + v1[2]**2)
def scale_v(scalar, v):
return tuple(scalar * vi for vi in v)
def plot(polygon, reference, _3d_centroid, surface_centroid, subcentroids):
fig = mpl.pyplot.figure()
ax = fig.add_subplot(111, projection='3d')
# plot the unit sphere
u = numpy.linspace(0, 2 * numpy.pi, 100)
v = numpy.linspace(-1 * numpy.pi / 2, numpy.pi / 2, 100)
x = numpy.outer(numpy.cos(u), numpy.sin(v))
y = numpy.outer(numpy.sin(u), numpy.sin(v))
z = numpy.outer(numpy.ones(numpy.size(u)), numpy.cos(v))
ax.plot_surface(x, y, z, rstride=4, cstride=4, color='w', linewidth=0,
alpha=0.3)
# plot 3d and flattened polygon
x, y, z = zip(*polygon)
ax.plot(x, y, z, c='b')
ax.plot(x, y, zs=0, c='g')
# plot the 3d centroid
x, y, z = _3d_centroid
ax.scatter(x, y, z, c='r', s=20)
# plot the spherical surface centroid and flattened centroid
x, y, z = surface_centroid
ax.scatter(x, y, z, c='b', s=20)
ax.scatter(x, y, 0, c='g', s=20)
# plot the full set of triangular centroids
x, y, z = zip(*subcentroids)
ax.scatter(x, y, z, c='r', s=4)
# plot the reference vector used to findsub centroids
x, y, z = reference
ax.plot((0, x), (0, y), (0, z), c='k')
ax.scatter(x, y, z, c='k', marker='^')
# display
ax.set_xlim3d(-1, 1)
ax.set_ylim3d(-1, 1)
ax.set_zlim3d(0, 1)
mpl.pyplot.show()
# run it in a function so the main code can appear at the top
main()
Here is the longitude and latitude data you can paste into longitude and latitude.txt
-39.366295 -1.633460
-47.282630 -0.740433
-53.912136 0.741380
-59.004217 2.759183
-63.489005 5.426812
-68.566001 8.712068
-71.394853 11.659135
-66.629580 15.362600
-67.632276 16.827507
-66.459524 19.069327
-63.819523 21.446736
-61.672712 23.532143
-57.538431 25.947815
-52.519889 28.691766
-48.606227 30.646295
-45.000447 31.089437
-41.549866 32.139873
-36.605156 32.956277
-32.010080 34.156692
-29.730629 33.756566
-26.158767 33.714080
-25.821513 34.179648
-23.614658 36.173719
-20.896869 36.977645
-17.991994 35.600074
-13.375742 32.581447
-9.554027 28.675497
-7.825604 26.535234
-7.825604 26.535234
-9.094304 23.363132
-9.564002 22.527385
-9.713885 22.217165
-9.948596 20.367878
-10.496531 16.486580
-11.151919 12.666850
-12.350144 8.800367
-15.446347 4.993373
-20.366139 1.132118
-24.784805 -0.927448
-31.532135 -1.910227
-39.366295 -1.633460

To clarify: the quantity of interest is the projection of the true 3d centroid
(i.e. 3d center-of-mass, i.e. 3d center-of-area) onto the unit sphere.
Since all you care about is the direction from the origin to the 3d centroid,
you don't need to bother with areas at all;
it's easier to just compute the moment (i.e. 3d centroid times area).
The moment of the region to the left of a closed path on the unit sphere
is half the integral of the leftward unit vector as you walk around the path.
This follows from a non-obvious application of Stokes' theorem; see Frank Jones's vector calculus book, chapter 13 Problem 13-12.
In particular, for a spherical polygon, the moment is half the sum of
(a x b) / ||a x b|| * (angle between a and b) for each pair of consecutive vertices a,b.
(That's for the region to the left of the path;
negate it for the region to the right of the path.)
(And if you really did want the 3d centroid, just compute the area and divide the moment by it. Comparing areas might also be useful in choosing which of the two regions to call "the polygon".)
Here's some code; it's really simple:
#!/usr/bin/python
import math
def plus(a,b): return [x+y for x,y in zip(a,b)]
def minus(a,b): return [x-y for x,y in zip(a,b)]
def cross(a,b): return [a[1]*b[2]-a[2]*b[1], a[2]*b[0]-a[0]*b[2], a[0]*b[1]-a[1]*b[0]]
def dot(a,b): return sum([x*y for x,y in zip(a,b)])
def length(v): return math.sqrt(dot(v,v))
def normalized(v): l = length(v); return [1,0,0] if l==0 else [x/l for x in v]
def addVectorTimesScalar(accumulator, vector, scalar):
for i in xrange(len(accumulator)): accumulator[i] += vector[i] * scalar
def angleBetweenUnitVectors(a,b):
# https://www.plunk.org/~hatch/rightway.html
if dot(a,b) < 0:
return math.pi - 2*math.asin(length(plus(a,b))/2.)
else:
return 2*math.asin(length(minus(a,b))/2.)
def sphericalPolygonMoment(verts):
moment = [0.,0.,0.]
for i in xrange(len(verts)):
a = verts[i]
b = verts[(i+1)%len(verts)]
addVectorTimesScalar(moment, normalized(cross(a,b)),
angleBetweenUnitVectors(a,b) / 2.)
return moment
if __name__ == '__main__':
import sys
def lonlat_degrees_to_xyz(lon_degrees,lat_degrees):
lon = lon_degrees*(math.pi/180)
lat = lat_degrees*(math.pi/180)
coslat = math.cos(lat)
return [coslat*math.cos(lon), coslat*math.sin(lon), math.sin(lat)]
verts = [lonlat_degrees_to_xyz(*[float(v) for v in line.split()])
for line in sys.stdin.readlines()]
#print "verts = "+`verts`
moment = sphericalPolygonMoment(verts)
print "moment = "+`moment`
print "centroid unit direction = "+`normalized(moment)`
For the example polygon, this gives the answer (unit vector):
[-0.7644875430808217, 0.579935445918147, -0.2814847687566214]
This is roughly the same as, but more accurate than, the answer computed by #KobeJohn's code, which uses rough tolerances and planar approximations to the sub-centroids:
[0.7628095787179151, -0.5977153368303585, 0.24669398601094406]
The directions of the two answers are roughly opposite (so I guess KobeJohn's code
decided to take the region to the right of the path in this case).

I think a good approximation would be to compute the center of mass using weighted cartesian coordinates and projecting the result onto the sphere (supposing the origin of coordinates is (0, 0, 0)^T).
Let be (p[0], p[1], ... p[n-1]) the n points of the polygon. The approximative (cartesian) centroid can be computed by:
c = 1 / w * (sum of w[i] * p[i])
whereas w is the sum of all weights and whereas p[i] is a polygon point and w[i] is a weight for that point, e.g.
w[i] = |p[i] - p[(i - 1 + n) % n]| / 2 + |p[i] - p[(i + 1) % n]| / 2
whereas |x| is the length of a vector x.
I.e. a point is weighted with half the length to the previous and half the length to the next polygon point.
This centroid c can now projected onto the sphere by:
c' = r * c / |c|
whereas r is the radius of the sphere.
To consider orientation of polygon (ccw, cw) the result may be
c' = - r * c / |c|.

Sorry I (as a newly registered user) had to write a new post instead of just voting/commenting on the above answer by Don Hatch. Don's answer, I think, is the best and most elegant. It is mathematically rigorous in computing the center of mass (first moment of mass) in a simple way when applying to the spherical polygon.
Kobe John's answer is a good approximation but only satisfactory for smaller areas. I also noticed a few glitches in the code. Firstly, the reference point should be projected to the spherical surface to compute the actual spherical area. Secondly, function points_are_equivalent() might need to be refined to avoid divided-by-zero.
The approximation error in Kobe's method lies in the calculation of the centroid of spherical triangles. The sub-centroid is NOT the center of mass of the spherical triangle but the planar one. This is not an issue if one is to determine that single triangle (sign may flip, see below). It is also not an issue if triangles are small (e.g. a dense triangulation of the polygon).
A few simple tests could illustrate the approximation error. For example if we use just four points:
10 -20
10 20
-10 20
-10 -20
The exact answer is (1,0,0) and both methods are good. But if you throw in a few more points along one edge (e.g. add {10,-15},{10,-10}... to the first edge), you'll see the results from Kobe's method start to shift. Further more, if you increase the longitude from [10,-10] to [100,-100], you'll see Kobe's result flips the direction. A possible improvement might be to add another level(s) for sub-centroid calculation (basically refine/reduce sizes of triangles).
For our application, the spherical area boundary is composed of multiple arcs and thus not polygon (i.e. the arc is not part of great circle). But this will just be a little more work to find the n-vector in the curve integration.
EDIT: Replacing the subcentroid calculation with the one given in Brock's paper should fix Kobe's method. But I did not try though.