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Finding the area of an overlap between curves (python)

Is it possible to calculate the area of the overlap of two curves?
I found two answers here but they are written in R which I am not familiar with. Or struggling to convert them to python.
Area between the two curves and Find area of overlap between two curves
For example, for a given dataset with defined x, y points. (x1,y1,x2,y2)
I am able to get the area of each curve using :
np.trapz
However, to get the overlap only is challenging and I haven't found a solution to show. Any guidance or maths formulas will be appreciated.

So this can be done using the shapely module within Python.
Firstly, Join the two curves together to create one self-intersecting polygon (shown in code below).
Then using the unary_union() function from shapely, you will:
Split the complex polygon into seperate simple polygons.
Find the area of each simple polygon.
Sum it to find the overall area of the two curves.
Full code shown below:
import numpy as np
from shapely.geometry import LineString
from shapely.ops import unary_union, polygonize
avg_coords = [(0.0, 0.0), (4.872117, 2.29658), (5.268545, 2.4639225), (5.664686, 2.6485724), (6.059776, 2.8966842), (6.695151, 3.0986626), (7.728006, 3.4045217), (8.522297, 3.652668), (9.157002, 3.895031), (10.191483, 4.1028132), (10.827622, 4.258638), (11.38593, 4.2933016), (11.86478, 4.3048816), (12.344586, 4.258769), (12.984073, 4.2126703), (13.942729, 4.1781383), (14.58212, 4.137809), (15.542498, 3.99943), (16.502588, 3.878359), (17.182951, 3.7745714), (18.262657, 3.6621647), (19.102558, 3.567045), (20.061789, 3.497897), (21.139917, 3.4806826), (22.097425, 3.5153809), (23.65388, 3.5414772), (24.851482, 3.541581), (26.04966, 3.507069), (27.72702, 3.463945), (28.925198, 3.429433), (29.883854, 3.3949006), (31.08246, 3.3344274), (31.92107, 3.317192), (33.716183, 3.3952322), (35.63192, 3.4213595), (37.427895, 3.4474766), (39.343628, 3.473604), (41.49874, 3.508406), (43.773468, 3.5518723), (46.287716, 3.595359), (49.28115, 3.6302335), (52.633293, 3.6997545), (54.30922, 3.7431688), (55.8651, 3.8038807), (58.738773, 3.8387446), (60.893887, 3.8735466), (63.647655, 3.9170544), (66.760704, 3.960593), (68.79663, 3.9607692), (70.23332, 3.986855), (72.867905, 3.995737), (75.38245, 4.0219164), (77.778656, 3.9615464), (79.337975, 3.8145657), (80.41826, 3.6675436), (80.899734, 3.5204697), (81.62059, 3.38207), (82.34045, 3.3042476), (83.30039, 3.1918304), (84.38039, 3.062116), (84.50359, 2.854434), (83.906364, 2.7591898), (83.669716, 2.586092), (83.43435, 2.3351095), (83.19727, 2.1879735), (82.84229, 1.9283267), (82.48516, 1.7984879), (81.65014, 1.5993768), (80.454544, 1.4781193), (79.13962, 1.3308897), (77.944595, 1.1750168), (76.39001, 1.0364205), (74.59633, 0.87184185), (71.60447, 0.741775), (70.04903, 0.6551017), (58.3, 0.0)]
model_coords = [(0.0, 0.0), (0.6699889, 0.18807), (1.339894, 0.37499), (2.009583, 0.55966), (2.67915, 0.74106), (3.348189, 0.91826), (4.016881, 1.0904), (4.685107, 1.2567), (5.359344, 1.418), (6.026172, 1.5706), (6.685472, 1.714), (7.350604, 1.8508), (8.021434, 1.9803), (8.684451, 2.0996), (9.346408, 2.2099), (10.0066, 2.311), (10.66665, 2.4028), (11.32436, 2.4853), (11.98068, 2.5585), (12.6356, 2.6225), (13.29005, 2.6775), (13.93507, 2.7232), (14.58554, 2.7609), (15.23346, 2.7903), (15.87982, 2.8116), (16.52556, 2.8254), (17.16867, 2.832), (17.80914, 2.8317), (18.44891, 2.825), (19.08598, 2.8124), (19.72132, 2.7944), (20.35491, 2.7713), (20.98673, 2.7438), (21.61675, 2.7121), (22.24398, 2.677), (22.86939, 2.6387), (23.49297, 2.5978), (24.1147, 2.5548), (24.73458, 2.51), (25.3526, 2.464), (25.96874, 2.4171), (26.58301, 2.3697), (27.1954, 2.3223), (27.80491, 2.2751), (28.41354, 2.2285), (29.02028, 2.1829), (29.62512, 2.1384), (30.22809, 2.0954), (30.82917, 2.0541), (31.42837, 2.0147), (32.02669, 1.9775), (32.62215, 1.9425), (33.21674, 1.9099), (33.80945, 1.8799), (34.40032, 1.8525), (34.98933, 1.8277), (35.5765, 1.8058), (36.16283, 1.7865), (36.74733, 1.7701), (37.33002, 1.7564), (37.91187, 1.7455), (38.49092, 1.7372), (39.06917, 1.7316), (39.64661, 1.7285), (40.22127, 1.7279), (40.79514, 1.7297), (41.36723, 1.7337), (41.93759, 1.7399), (42.50707, 1.748), (43.07386, 1.7581), (43.63995, 1.7699), (44.20512, 1.7832), (44.76772, 1.7981), (45.3295, 1.8143), (45.88948, 1.8318), (46.44767, 1.8504), (47.00525, 1.8703), (47.55994, 1.8911), (48.11392, 1.9129), (48.6661, 1.9356), (49.21658, 1.959), (49.76518, 1.9832), (50.31305, 2.0079), (50.85824, 2.033), (51.40252, 2.0586), (51.94501, 2.0845), (52.48579, 2.1107), (53.02467, 2.1369), (53.56185, 2.1632), (54.09715, 2.1895), (54.63171, 2.2156), (55.1634, 2.2416), (55.69329, 2.2674), (56.22236, 2.2928), (56.74855, 2.3179), (57.27392, 2.3426), (57.7964, 2.3668), (58.31709, 2.3905), (58.83687, 2.4136), (59.35905, 2.4365), (59.87414, 2.4585), (60.38831, 2.4798), (60.8996, 2.5006), (61.40888, 2.5207), (61.91636, 2.5401), (62.42194, 2.5589), (62.92551, 2.577), (63.42729, 2.5945), (63.92607, 2.6113), (64.42384, 2.6275), (64.91873, 2.643), (65.4127, 2.658), (65.90369, 2.6724), (66.39266, 2.6862), (66.87964, 2.6995), (67.36373, 2.7123), (67.84679, 2.7246), (68.32689, 2.7364), (68.80595, 2.7478), (69.28194, 2.7588), (69.756, 2.7695), (70.22709, 2.7798), (70.69707, 2.7898), (71.16405, 2.7995), (71.62902, 2.809), (72.0919, 2.8183), (72.55277, 2.8273), (73.01067, 2.8362), (73.46734, 2.845), (73.92112, 2.8536), (74.37269, 2.8622), (74.82127, 2.8706), (75.26884, 2.8791), (75.71322, 2.8875), (76.15559, 2.8958), (76.59488, 2.9042), (77.03304, 2.9126), (77.46812, 2.921), (77.90111, 2.9294), (78.33199, 2.9379), (78.75986, 2.9464), (79.18652, 2.955), (79.60912, 2.9637), (80.03049, 2.9724), (80.44985, 2.9811), (80.86613, 2.99), (81.2802, 2.9989), (81.69118, 3.0078), (82.10006, 3.0168), (82.50674, 3.0259), (82.91132, 3.035), (83.31379, 3.0441), (83.71307, 3.0533), (84.10925, 3.0625), (84.50421, 3.0717), (84.8961, 3.0809), (85.28577, 3.0901), (85.67334, 3.0993), (86.05771, 3.1085), (86.43989, 3.1176), (86.81896, 3.1267), (87.19585, 3.1358), (87.57063, 3.1448), (87.94319, 3.1537), (88.31257, 3.1626), (88.67973, 3.1713), (89.04372, 3.18), (89.40659, 3.1886), (89.7652, 3.197), (90.12457, 3.2053), (90.47256, 3.2135), (90.82946, 3.2216), (91.17545, 3.2295), (91.52045, 3.2373), (91.86441, 3.2449), (92.20641, 3.2524), (92.54739, 3.2597), (92.88728, 3.2669), (93.21538, 3.2739), (93.55325, 3.2807), (93.87924, 3.2874), (94.20424, 3.2939), (94.52822, 3.3002), (94.85012, 3.3064), (95.16219, 3.3123), (95.48208, 3.3182), (95.79107, 3.3238), (96.09807, 3.3293), (96.40505, 3.3346), (96.71003, 3.3397), (97.01401, 3.3447), (97.31592, 3.3496), (97.60799, 3.3542), (97.90789, 3.3587), (98.19686, 3.3631), (98.48386, 3.3673), (98.77085, 3.3714), (99.05574, 3.3753), (99.32983, 3.3791), (99.6127, 3.3828), (99.8837, 3.3863), (100.1538, 3.3897), (100.4326, 3.393), (100.6897, 3.3961), (100.9566, 3.3991), (101.2215, 3.402), (101.4756, 3.4048), (101.7375, 3.4075), (101.9885, 3.4101), (102.2385, 3.4126), (102.4875, 3.4149), (102.7354, 3.4172), (102.9714, 3.4194), (103.2163, 3.4214), (103.4493, 3.4234), (103.6823, 3.4253), (103.9133, 3.4271), (104.1433, 3.4288), (104.3712, 3.4304), (104.5882, 3.4319), (104.8141, 3.4333), (105.0291, 3.4346), (105.2421, 3.4358), (105.4541, 3.437), (105.6651, 3.438), (105.8751, 3.439), (106.083, 3.4399), (106.28, 3.4407), (106.4759, 3.4414), (106.6699, 3.442), (106.8629, 3.4425), (107.0549, 3.443), (107.2458, 3.4433), (107.4249, 3.4435), (107.6128, 3.4437), (107.7897, 3.4438), (107.9647, 3.4437), (108.1387, 3.4436), (108.3116, 3.4433), (108.4737, 3.443), (108.6436, 3.4426), (108.8027, 3.4421), (108.9706, 3.4414), (109.1265, 3.4407), (109.2814, 3.4399), (109.4255, 3.439), (109.5784, 3.4379), (109.7195, 3.4368), (109.8694, 3.4356), (110.0084, 3.4342), (110.1454, 3.4328), (110.2813, 3.4313), (110.4162, 3.4296), (110.5403, 3.4279), (110.6722, 3.426), (110.7932, 3.424), (110.9132, 3.422), (111.0322, 3.4198), (111.1492, 3.4175), (111.2651, 3.4151), (111.3701, 3.4127), (111.483, 3.4101), (111.585, 3.4074), (111.686, 3.4046), (111.786, 3.4017), (111.884, 3.3987), (111.9809, 3.3956), (112.0669, 3.3924), (112.1608, 3.3891), (112.2448, 3.3857), (112.3268, 3.3822), (112.4078, 3.3786), (112.4867, 3.3749), (112.5548, 3.3711), (112.6317, 3.3672), (112.6978, 3.3632), (112.7726, 3.3591), (112.8356, 3.3549), (112.8975, 3.3506), (112.9476, 3.3462), (113.0076, 3.3417), (113.0655, 3.3372), (113.1125, 3.3325), (113.1584, 3.3278), (113.2024, 3.3229), (113.2464, 3.318), (113.2884, 3.313), (113.3283, 3.3079), (113.3584, 3.3027), (113.3963, 3.2974), (113.4233, 3.292), (113.4492, 3.2865), (113.4742, 3.281), (113.4972, 3.2753), (113.5201, 3.2696), (113.5312, 3.2638), (113.5501, 3.2579), (113.5591, 3.2519), (113.5661, 3.2459), (113.5721, 3.2397), (113.577, 3.2335), (113.5809, 3.2272), (113.573, 3.2208), (113.5749, 3.2143), (113.5649, 3.2077), (113.5539, 3.2011), (113.5409, 3.1944), (113.5278, 3.1876), (113.5128, 3.1807), (113.4967, 3.1737), (113.4697, 3.1667), (113.4418, 3.1596), (113.4227, 3.1524), (113.3917, 3.145), (113.3597, 3.1375), (113.3266, 3.1298), (113.2827, 3.1218), (113.2475, 3.1136), (113.2016, 3.1051), (113.1635, 3.0964), (113.1155, 3.0873), (113.0655, 3.0779), (113.0144, 3.0683), (112.9525, 3.0583), (112.8994, 3.048), (112.8345, 3.0373), (112.7793, 3.0264), (112.7123, 3.0152), (112.6453, 3.0037), (112.5763, 2.9919), (112.5063, 2.9798), (112.4352, 2.9674), (112.3533, 2.9548), (112.2801, 2.9419), (112.1952, 2.9287), (112.1102, 2.9153), (112.034, 2.9017), (111.9361, 2.8879), (111.8481, 2.8739), (111.7581, 2.8597), (111.667, 2.8453), (111.5661, 2.8307), (111.473, 2.816), (111.3689, 2.801), (111.2639, 2.786), (111.1579, 2.7708), (111.0509, 2.7555), (110.9428, 2.74), (110.8239, 2.7245), (110.7138, 2.7088), (110.5928, 2.6931), (110.4709, 2.6772), (110.3578, 2.6613), (110.2338, 2.6453), (110.1087, 2.6292), (109.9826, 2.613), (109.8457, 2.5968), (109.7176, 2.5805), (109.5787, 2.5642), (109.4496, 2.5478), (109.3086, 2.5314), (109.1666, 2.5149), (109.0236, 2.4984), (108.8806, 2.4819), (108.7355, 2.4653), (108.5905, 2.4488), (108.4434, 2.4322), (108.2865, 2.4155), (108.1384, 2.3989), (107.9794, 2.3822), (107.8195, 2.3655), (107.6684, 2.3488), (107.5063, 2.3321), (107.3374, 2.3156), (107.1744, 2.2989), (107.0104, 2.2822), (106.8442, 2.2654), (106.6683, 2.2487), (106.5012, 2.232), (106.3242, 2.2152), (106.1452, 2.1985), (105.9662, 2.1818), (105.7862, 2.165), (105.6052, 2.1483), (105.4232, 2.1316), (105.2402, 2.1149), (105.0572, 2.0981), (104.8721, 2.0814), (104.6772, 2.0647), (104.492, 2.048), (104.295, 2.0313), (104.098, 2.0147), (103.9, 1.998), (103.701, 1.9813), (103.502, 1.9647), (103.301, 1.948), (103.1, 1.9314), (102.899, 1.9148), (102.6959, 1.8982), (102.483, 1.8816), (102.2789, 1.865), (102.0649, 1.8484), (101.8588, 1.8318), (101.6428, 1.8153), (101.4268, 1.7988), (101.2098, 1.7822), (100.9918, 1.7657), (100.7728, 1.7492), (100.5538, 1.7328), (100.3338, 1.7163), (100.1128, 1.6999), (99.89169, 1.6834), (99.65978, 1.667), (99.43769, 1.6506), (99.20477, 1.6343), (98.98066, 1.6179), (98.74665, 1.6016), (98.51164, 1.5852), (98.27574, 1.5689), (98.04964, 1.5527), (97.81264, 1.5364), (97.57562, 1.5202), (97.33752, 1.5039), (97.08962, 1.4877), (96.8506, 1.4716), (96.61061, 1.4554), (96.37051, 1.4393), (96.12058, 1.4232), (95.87949, 1.4071), (95.62759, 1.391), (95.38547, 1.375), (95.13258, 1.359), (94.88946, 1.343), (94.63548, 1.3271), (94.38145, 1.3111), (94.12645, 1.2952), (93.87144, 1.2793), (93.61545, 1.2635), (93.35946, 1.2477), (93.10343, 1.2319), (92.84642, 1.2161), (92.58843, 1.2004), (92.33042, 1.1846), (92.07232, 1.169), (91.8034, 1.1533), (91.54331, 1.1377), (91.2744, 1.1221), (91.0133, 1.1065), (90.7434, 1.091), (90.48229, 1.0755), (90.21139, 1.0601), (89.9493, 1.0446), (89.67728, 1.0292), (89.40428, 1.0139), (89.13137, 0.99855), (88.86826, 0.98325), (88.59427, 0.96799), (88.32026, 0.95277), (88.04527, 0.93758), (87.77126, 0.92242), (87.4972, 0.90731), (87.21732, 0.89222), (86.94719, 0.87718), (86.66711, 0.86217), (86.3773, 0.8472), (86.10719, 0.83227), (85.82721, 0.81738), (85.5472, 0.80252), (85.26721, 0.7877), (84.9872, 0.77292), (84.7071, 0.75819), (84.41721, 0.74349), (84.1371, 0.72883), (83.84721, 0.71421), (83.5671, 0.69963), (83.27721, 0.68509), (82.99711, 0.6706), (82.70711, 0.65615), (82.41721, 0.64173), (82.1371, 0.62736), (81.8471, 0.61304), (81.55722, 0.59875), (81.27709, 0.58451), (80.98712, 0.57031), (80.697, 0.55616), (80.39711, 0.54205), (80.10722, 0.52798), (79.8271, 0.51396), (79.53701, 0.49999), (79.23711, 0.48605), (78.9471, 0.47217), (78.65701, 0.45833), (78.3571, 0.44453), (78.06712, 0.43078), (77.77701, 0.41708), (77.4771, 0.40343), (77.18701, 0.38982), (76.8871, 0.37626), (76.59711, 0.36274), (76.30701, 0.34928), (76.0071, 0.33586), (75.7169, 0.32249), (75.4071, 0.30917), (75.11701, 0.29589), (74.8171, 0.28267), (74.52701, 0.26949), (74.22711, 0.25636), (73.937, 0.24329), (73.63691, 0.23026), (73.3271, 0.21728), (73.03699, 0.20436), (72.73712, 0.19148), (72.4469, 0.17865), (72.13712, 0.16588), (71.84701, 0.15315), (71.547, 0.14048), (71.24701, 0.12786), (70.947, 0.11528), (70.64701, 0.10277), (70.3471, 0.090298), (70.05691, 0.077883), (69.74712, 0.06552), (69.457, 0.05321), (69.1569, 0.040952), (68.84709, 0.028747), (68.557, 0.016595), (68.25701, 0.0)]
polygon_points = [] #creates a empty list where we will append the points to create the polygon
for xyvalue in avg_coords:
polygon_points.append([xyvalue[0],xyvalue[1]]) #append all xy points for curve 1
for xyvalue in model_coords[::-1]:
polygon_points.append([xyvalue[0],xyvalue[1]]) #append all xy points for curve 2 in the reverse order (from last point to first point)
for xyvalue in avg_coords[0:1]:
polygon_points.append([xyvalue[0],xyvalue[1]]) #append the first point in curve 1 again, to it "closes" the polygon
avg_poly = []
model_poly = []
for xyvalue in avg_coords:
avg_poly.append([xyvalue[0],xyvalue[1]])
for xyvalue in model_coords:
model_poly.append([xyvalue[0],xyvalue[1]])
line_non_simple = LineString(polygon_points)
mls = unary_union(line_non_simple)
Area_cal =[]
for polygon in polygonize(mls):
Area_cal.append(polygon.area)
print(polygon.area)# print area of each section
Area_poly = (np.asarray(Area_cal).sum())
print(Area_poly)#print combined area

If possible, represent your overlap regions as polygons. From there the polygon area is computable by a remarkably concise formula as explained on Paul Bourke's site.
Suppose (x[i], y[i]), i = 0, ..., N, are the polygon vertices, with (x[0], y[0]) = (x[N], y[N]) so that the polygon is closed, and consistently all in clockwise order or all in counter-clockwise order. Then the area is
area = |0.5 * sum_i (x[i] * y[i+1] - x[i+1] * y[i])|
where the sum goes over i = 0, ..., N-1. This is valid even for nonconvex polygons. This formula is essentially the same principle behind how a planimeter works to measure area of an arbitrary two-dimensional shape, a special case of Green's theorem.

If your functions are actually "function" meaning that no vertical lines intersect the functions more than once, then finding the overlaps is the matter of finding zeros.
import numpy as np
import matplotlib.pyplot as plt
dx = 0.01
x = np.arange(-2, 2, dx)
f1 = np.sin(4*x)
f2 = np.cos(4*x)
plt.plot(x, f1)
plt.plot(x, f2)
eps = 1e-1; # threshold of intersection points.
df = f1 - f2
idx_zeros = np.where(abs(df) <= eps)[0]
area = 0
for i in range(len(idx_zeros) - 1):
idx_left = idx_zeros[i]
idx_rite = idx_zeros[i+1]
area += abs(np.trapz(df[idx_left:idx_rite])) * dx
I have assumed areas to be considered positive.
The analytical value for the example I used is
sufficiently close to the computed value (area=2.819). Of course, you can improve this if your grids are finer, and threshold eps smaller.

How to crop and interpolate part of an image with python [duplicate]

I have used interp2 in Matlab, such as the following code, that is part of #rayryeng's answer in: Three dimensional (3D) matrix interpolation in Matlab:
d = size(volume_image)
[X,Y] = meshgrid(1:1/scaleCoeff(2):d(2), 1:1/scaleCoeff(1):d(1));
for ind = z
%Interpolate each slice via interp2
M2D(:,:,ind) = interp2(volume_image(:,:,ind), X, Y);
end
Example of Dimensions:
The image size is 512x512 and the number of slices is 133. So:
volume_image(rows, columns, slices in 3D dimenson) : 512x512x133 in 3D dimenson
X: 288x288
Y: 288x288
scaleCoeff(2): 0.5625
scaleCoeff(1): 0.5625
z = 1 up to 133 ,hence z: 1x133
ind: 1 up to 133
M2D(:,:,ind) finally is 288x288x133 in 3D dimenson
Aslo, Matlabs syntax for size: (rows, columns, slices in 3rd dimenson) and Python syntax for size: (slices in 3rd dim, rows, columns).
However, after convert the Matlab code to Python code occurred an error, ValueError: Invalid length for input z for non rectangular grid:
for ind in range(0, len(z)+1):
M2D[ind, :, :] = interpolate.interp2d(X, Y, volume_image[ind, :, :]) # ValueError: Invalid length for input z for non rectangular grid
What is wrong? Thank you so much.

In MATLAB, interp2 has as arguments:
result = interp2(input_x, input_y, input_z, output_x, output_y)
You are using only the latter 3 arguments, the first two are assumed to be input_x = 1:size(input_z,2) and input_y = 1:size(input_z,1).
In Python, scipy.interpolate.interp2 is quite different: it takes the first 3 input arguments of the MATLAB function, and returns an object that you can call to get interpolated values:
f = scipy.interpolate.interp2(input_x, input_y, input_z)
result = f(output_x, output_y)
Following the example from the documentation, I get to something like this:
from scipy import interpolate
x = np.arange(0, volume_image.shape[2])
y = np.arange(0, volume_image.shape[1])
f = interpolate.interp2d(x, y, volume_image[ind, :, :])
xnew = np.arange(0, volume_image.shape[2], 1/scaleCoeff[0])
ynew = np.arange(0, volume_image.shape[1], 1/scaleCoeff[1])
M2D[ind, :, :] = f(xnew, ynew)
[Code not tested, please let me know if there are errors.]

You might be interested in scipy.ndimage.zoom. If you are interpolating from one regular grid to another, it is much faster and easier to use than scipy.interpolate.interp2d.
See this answer for an example:
https://stackoverflow.com/a/16984081/1295595
You'd probably want something like:
import scipy.ndimage as ndimage
M2D = ndimage.zoom(volume_image, (1, scaleCoeff[0], scaleCoeff[1])

Numpy einsum() for rotation of meshgrid

I have a set of 3d coordinates that was generated using meshgrid(). I want to be able to rotate these about the 3 axes.
I tried unraveling the meshgrid and doing a rotation on each point but the meshgrid is large and I run out of memory.
This question addresses this in 2d with einsum(), but I can't figure out the string format when extending it to 3d.
I have read several other pages about einsum() and its format string but haven't been able to figure it out.
EDIT:
I call my meshgrid axes X, Y, and Z, each is of shape (213, 48, 37). Also, the actual memory error came when I tried to put the results back into a meshgrid.
When I attempted to 'unravel' it to do point by point rotation I used the following function:
def mg2coords(X, Y, Z):
return np.vstack([X.ravel(), Y.ravel(), Z.ravel()]).T
I looped over the result with the following:
def rotz(angle, point):
rad = np.radians(angle)
sin = np.sin(rad)
cos = np.cos(rad)
rot = [[cos, -sin, 0],
[sin, cos, 0],
[0, 0, 1]]
return np.dot(rot, point)
After the rotation I will be using the points to interpolate onto.

Working with your definitions:
In [840]: def mg2coords(X, Y, Z):
return np.vstack([X.ravel(), Y.ravel(), Z.ravel()]).T
In [841]: def rotz(angle):
rad = np.radians(angle)
sin = np.sin(rad)
cos = np.cos(rad)
rot = [[cos, -sin, 0],
[sin, cos, 0],
[0, 0, 1]]
return np.array(rot)
# just to the rotation matrix
define a sample grid:
In [842]: X,Y,Z=np.meshgrid([0,1,2],[0,1,2,3],[0,1,2],indexing='ij')
In [843]: xyz=mg2coords(X,Y,Z)
rotate it row by row:
In [844]: xyz1=np.array([np.dot(rot,i) for i in xyz])
equivalent einsum row by row calculation:
In [845]: xyz2=np.einsum('ij,kj->ki',rot,xyz)
They match:
In [846]: np.allclose(xyz2,xyz1)
Out[846]: True
Alternatively I could collect the 3 arrays into one 4d array, and rotate that with einsum. Here np.array adds a dimension at the start. So the dot sum j dimension is 1st, and the 3d of the arrays follow:
In [871]: XYZ=np.array((X,Y,Z))
In [872]: XYZ2=np.einsum('ij,jabc->iabc',rot,XYZ)
In [873]: np.allclose(xyz2[:,0], XYZ2[0,...].ravel())
Out[873]: True
Similary for the 1 and 2.
Alternatively I could split XYZ2 into 3 component arrays:
In [882]: X2,Y2,Z2 = XYZ2
In [883]: np.allclose(X2,xyz2[:,0].reshape(X.shape))
Out[883]: True
Use ji instead of ij if you want to rotate in the other direction, i.e. use rot.T.

How can I create a tridimensional map in python?

I need to create a tridimensional map. I would create an x axis with values between 0 and 255 with step 0.5. The same thing with the y axis.
And then I would assign a value for each coordinate (for example at the point (10.5,10)).
Matrix is not the solution because I can't decide values in the x and y axes.
Can you help me?
EDIT: I try to explain better the question. This is a piece of my code:
img = cv2.imread('Lena256.bmp',0)
M = cv2.getRotationMatrix2D((cols/2,rows/2),angle,1)
img_rotate = cv2.warpAffine(img,M,(cols,rows))
Then I locate some point in the img_rotate: for example p=(10,10). I want to map "p" to the corresponding point in the original image. To do that I have written this code:
T = np.zeros((rows,cols))
T[10][10] = 1
M_INV = cv2.getRotationMatrix2D((cols/2,rows/2),-angle,1)
T = cv2.warpAffine(T,M_INV,(cols,rows))
In this way it works. But if I locate a point with no integer coordinates (in the img_rotate), for example (10.5,10), I should to create a matrix T with double dimensions where I could assign values 0, 0.5, 1, ecc in order to identify point (10.5,10). And then I could apply the inverse rotation.
I hope to be enough clear

You could use a dictionary:
d = {
(10.5, 10): 23,
(0,0): 42,
(-100,999): 15,
#etc
}
Then you can access the value at some coordinates (x,y) by doing d[x,y]. (or d.get((x,y), default_value_goes_here) if you're not sure whether that coordinate exists in the collection yet)

you can use np.meshgrid for that
import numpy as np
x_ = np.linspace(0., .5, 255)
y_ = np.linspace(1., .5, 255)
# You can make what you want in z ex:
z_ = np.linspace(3., 4., 30)
x, y, z = np.meshgrid(x_, y_, z_, indexing='ij')

Translating Matlab (Octave) group coloring code into python (numpy, pyplot)

I want to translate the following group coloring octave function to python and use it with pyplot.
Function input:
x - Data matrix (m x n)
a - A parameter.
index - A vector of size "m" with values in range [: a]
(For example if a = 4, index can be [random.choice(range(4)) for i in range(m)]
The values in "index" indicate the number of the group the "m"th data point belongs to.
The function should plot all the data points from x and color them in different colors (Number of different colors is "a").
The function in octave:
p = hsv(a); % This is a x 3 metrix
colors = p(index, :); % ****This is m x 3 metrix****
scatter(X(:,1), X(:,2), 10, colors);
I couldn't find a function like hsv in python, so I wrote it myself (I think I did..):
p = colors.hsv_to_rgb(numpy.column_stack((
numpy.linspace(0, 1, a), numpy.ones((a ,2)) )) )
But I can't figure out how to do the matrix selection p(index, :) in python (numpy).
Specially because the size of "index" is bigger then "a".
Thanks in advance for your help.

So, you want to take an m x 3 of HSV values, and convert each row to RGB?
import numpy as np
import colorsys
mymatrix = np.matrix([[11,12,13],
[21,22,23],
[31,32,33]])
def to_hsv(x):
return colorsys.rgb_to_hsv(*x)
#Apply the to_hsv function to each matrix row.
print np.apply_along_axis(to_hsv, axis=1, arr=mymatrix)
This produces:
[[ 0.5 0. 13. ]
[ 0.5 0. 23. ]
[ 0.5 0. 33. ]]
Follow through on your comment:
If I understand you have a matrix p that is an a x 3 matrix, and you want to randomly select rows from the matrix over and over again, until you have a new matrix that is m x 3?
Ok. Let's say you have a matrix p defined as follows:
a = 5
p = np.random.randint(5, size=(a, 3))
Now, make a list of random integers between the range 0 -> 3 (index starts at 0 and ends to a-1), That is m in length:
m = 20
index = np.random.randint(a, size=m)
Now access the right indexes and plug them into a new matrix:
p_prime = np.matrix([p[i] for i in index])
Produces a 20 x 3 matrix.