Estoy trabajando en la adaptación de una función de distribución 3D en Scipy. Tengo una matriz numpy con conteos en los contenedores xy y, y estoy tratando de adaptar eso a una función de distribución tridimensional bastante complicada. Los datos se ajustan a 26 (!) Parámetros, que describen la forma de sus dos poblaciones constituyentes.

Aprendí aquí que tengo que pasar mis coordenadas X e Y como 'args' cuando llamo Lesssq. El código presentado por unutbu funciona como está escrito para mí, pero cuando trato de aplicarlo a mi caso específico, aparece el error "TypeError: leastsq () obtuvo varios valores para el argumento de la palabra clave 'args'"

Aquí está mi código (perdón por la longitud):

import numpy as np
import matplotlib.pyplot as plt
import scipy.optimize as spopt
from textwrap import wrap
import collections

cl = 0.5
ch = 3.5
rl = -23.5
rh = -18.5
mbins = 10
cbins = 10

def hist_data(mixed_data, mbins, cbins):
    import numpy as np
    H, xedges, yedges = np.histogram2d(mixed_data[:,1], mixed_data[:,2], bins = (mbins, cbins), weights = mixed_data[:,3])
    x, y = 0.5 * (xedges[:-1] + xedges[1:]), 0.5 * (yedges[:-1] + yedges[1:])
    return H.T, x, y

def gauss(x, s, mu, a):
    import numpy as np
    return a * np.exp(-((x - mu)**2. / (2. * s**2.)))

def tanhlin(x, p0, p1, q0, q1, q2):
    import numpy as np
    return p0 + p1 * (x + 20.) + q0 * np.tanh((x - q1)/q2)

def func3d(p, x, y):
    import numpy as np
    from sys import exit
    rsp0, rsp1, rsq0, rsq1, rsq2, rmp0, rmp1, rmq0, rmq1, rmq2, rs, rm, ra, bsp0, bsp1, bsq0, bsq1, bsq2, bmp0, bmp1, bmq0, bmq1, bmq2, bs, bm, ba = p
x, y = np.meshgrid(coords[0], coords[1])
    rs = tanhlin(x, rsp0, rsp1, rsq0, rsq1, rsq2)
    rm = tanhlin(x, rmp0, rmp1, rmq0, rmq1, rmq2)
    ra = schechter(x, rap, raa, ram) # unused
    bs = tanhlin(x, bsp0, bsp1, bsq0, bsq1, bsq2)
    bm = tanhlin(x, bmp0, bmp1, bmq0, bmq1, bmq2)
    ba = schechter(x, bap, baa, bam) # unused
    red_dist = ra / (rs * np.sqrt(2 * np.pi)) * gauss(y, rs, rm, ra)
    blue_dist = ba / (bs * np.sqrt(2 * np.pi)) * gauss(y, bs, bm, ba)
    result = red_dist + blue_dist
return result

def residual(p, coords, data):
    import numpy as np
    model = func3d(p, coords)
    res = (model.flatten() - data.flatten())
    # can put parameter restrictions in here
    return res

def poiss_err(data):
    import numpy as np
    return np.where(np.sqrt(H) > 0., np.sqrt(H), 2.)

# =====

H, x, y = hist_data(mixed_data, mbins, cbins)

data = H

coords = x, y
# x and y will be the projected coordinates of the data H onto the plane z = 0

# x has bins of width 0.5, with centers at -23.25, -22.75, ... , -19.25, -18.75
# y has bins of width 0.3, with centers at 0.65, 0.95, ... , 3.05, 3.35    

Param = collections.namedtuple('Param', 'rsp0 rsp1 rsq0 rsq1 rsq2 rmp0 rmp1 rmq0 rmq1 rmq2 rs rm ra bsp0 bsp1 bsq0 bsq1 bsq2 bmp0 bmp1 bmq0 bmq1 bmq2 bs bm ba')
p_guess = Param(rsp0 = 0.152, rsp1 = 0.008, rsq0 = 0.044, rsq1 = -19.91, rsq2 = 0.94, rmp0 = 2.279, rmp1 = -0.037, rmq0 = -0.108, rmq1 = -19.81, rmq2 = 0.96, rs = 1., rm = -20.5, ra = 10000., bsp0 = 0.298, bsp1 = 0.014, bsq0 = -0.067, bsq1 = -19.90, bsq2 = 0.58, bmp0 = 1.790, bmp1 = -0.053, bmq0 = -0.363, bmq1 = -20.75, bmq2 = 1.12, bs = 1., bm = -20., ba = 2000.)

opt, cov, infodict, mesg, ier = spopt.leastsq(residual, p_guess, poiss_err(H), args = coords, maxfev = 100000, full_output = True)

Aquí están mis datos, solo con menos contenedores:

[[  1.00000000e+01   1.10000000e+01   2.10000000e+01   1.90000000e+01
1.70000000e+01   2.10000000e+01   2.40000000e+01   1.90000000e+01
2.80000000e+01   1.90000000e+01]
[  1.40000000e+01   4.50000000e+01   6.00000000e+01   6.80000000e+01
1.34000000e+02   1.97000000e+02   2.23000000e+02   2.90000000e+02
3.23000000e+02   3.03000000e+02]
[  3.00000000e+01   1.17000000e+02   3.78000000e+02   9.74000000e+02
1.71900000e+03   2.27700000e+03   2.39000000e+03   2.25500000e+03
1.85600000e+03   1.31000000e+03]
[  1.52000000e+02   9.32000000e+02   2.89000000e+03   5.23800000e+03
6.66200000e+03   6.19100000e+03   4.54900000e+03   3.14600000e+03
2.09000000e+03   1.33800000e+03]
[  5.39000000e+02   2.58100000e+03   6.51300000e+03   8.89900000e+03
8.52900000e+03   6.22900000e+03   3.55000000e+03   2.14300000e+03
1.19000000e+03   6.92000000e+02]
[  1.49600000e+03   4.49200000e+03   8.77200000e+03   1.07610000e+04
9.76700000e+03   7.04900000e+03   4.23200000e+03   2.47200000e+03
1.41500000e+03   7.02000000e+02]
[  2.31800000e+03   7.01500000e+03   1.28870000e+04   1.50840000e+04
1.35590000e+04   8.55600000e+03   4.15600000e+03   1.77100000e+03
6.57000000e+02   2.55000000e+02]
[  1.57500000e+03   3.79300000e+03   5.20900000e+03   4.77800000e+03
3.26600000e+03   1.44700000e+03   5.31000000e+02   1.85000000e+02
9.30000000e+01   4.90000000e+01]
[  7.01000000e+02   1.21600000e+03   1.17600000e+03   7.93000000e+02
4.79000000e+02   2.02000000e+02   8.80000000e+01   3.90000000e+01
2.30000000e+01   1.90000000e+01]
[  2.93000000e+02   3.93000000e+02   2.90000000e+02   1.97000000e+02
1.18000000e+02   6.40000000e+01   4.10000000e+01   1.20000000e+01
1.10000000e+01   4.00000000e+00]]

¡Muchas gracias!