I'm trying to solve the 1-d wave equation, and I coding the program for numerical computing solutions and animating, saving data in the file. I don't know how to fix the error and finally get the working code.
u_tt = a**2 * u_xx + f(x,t)
It is necessary for the program to solve equations when entering both an additional function and with non-zero initial and boundary conditions, with graphic visualization and saving data to a file.
So I attach my code (Python 3.9), and error message:
import numpy as np
import math
import matplotlib.pyplot as plt
import os
import time
import glob
def sol(I, V, f, a, L, C, T, U_0, U_L, dt, user_func = None):
"""
solver for wave equation
u_tt = a**2*u_xx + f(x,t) (0,L) where u=0 for
x=0,L, for t in (0,T].
:param I:
:param V:
:param f:
:param a:
:param L:
:param C:
:param T:
:param U_0:
:param U_L:
:param dt:
:param user_func:
:return:
"""
nt = int(round(T / dt))
t = np.linspace(0, nt * dt, nt + 1) # array for time points
dx = dt * a / float(C)
nx = int(round(L / dx))
x = np.linspace(0, L, nx + 1) # array for coord points
q = a ** 2
C2 = (dt / dx) ** 2
dt2 = dt * dt
# --- checking f(x,t) ---
if f is None or f == 0:
f = lambda x, t: 0
# --- check the initial conds dU(x,0)/dt ---
if V is None or V == 0:
V = lambda x: 0
# boundary conds
if U_0 is not None:
if isinstance(U_0, (float, int)) and U_0 == 0:
U_0 = lambda t: 0
if U_L is not None:
if isinstance(U_L, (float, int)) and U_L == 0:
U_L = lambda t: 0
# --- allocate memory ---
u = np.zeros(nx + 1)
u_n = np.zeros(nx + 1)
u_nm = np.zeros(nx + 1)
# --- valid indexing check ---
Ix = range(0, nx + 1)
It = range(0, nt + 1)
# --- set the boundary conds ---
for i in range(0, nx + 1):
u_n[i] = I(x[i])
if user_func is not None:
user_func(u_n, x, t, 0)
# --- finite difference step ---
for i in Ix[1:-1]:
u[i] = u_n[i] + dt * V(x[i]) + 0.5 * C2 * (0.5 * (q[i] + q[i + 1]) * (u_n[i + 1] - u_n[i]) -
0.5 * (q[i] + q[i - 1]) * (u_n[i] - u_n[i - 1])) + 0.5 * dt2 * f(x[i], t[0])
i = Ix[0]
if U_0 is None:
# set the boundary conds (x=0: i-1 -> i+1 u[i-1]=u[i+1]
# where du/dn = 0, on x=L: i+1 -> i-1 u[i+1]=u[i-1])
ip1 = i + 1
im1 = ip1 # i-1 -> i+1
u[i] = u_n[i] + dt * V(x[i]) + \
0.5 * C2 * (0.5 * (q[i] + q[ip1]) * (u_n[ip1] - u_n[i]) - 0.5 * (q[i] + q[im1])
* (u_n[i] - u_n[im1])) + 0.5 * dt2 * f(x[i], t[0])
else:
u[i] = U_0(dt)
i = Ix[-1]
if U_L is None:
im1 = i - 1
ip1 = im1 # i+1 -> i-1
u[i] = u_n[i] + dt * V(x[i]) + \
0.5 * C2 * (0.5 * (q[i] + q[ip1]) * (u_n[ip1] - u_n[i]) - 0.5 * (q[i] + q[im1]) * (u_n[i] - u_n[im1])) + \
0.5 * dt2 * f(x[i], t[0])
else:
u[i] = U_L(dt)
if user_func is not None:
user_func(u, x, t, 1)
# update data
u_nm, u_n, u = u_n, u, u_nm
# --- time looping ---
for n in It[1:-1]:
# update all inner points
for i in Ix[1:-1]:
u[i] = - u_nm[i] + 2 * u_n[i] + \
C2 * (0.5 * (q[i] + q[i + 1]) * (u_n[i + 1] - u_n[i]) -
0.5 * (q[i] + q[i - 1]) * (u_n[i] - u_n[i - 1])) + dt2 * f(x[i], t[n])
# --- set boundary conds ---
i = Ix[0]
if U_0 is None:
# set the boundary conds
# x=0: i-1 -> i+1 u[i-1]=u[i+1] where du/dn=0
# x=L: i+1 -> i-1 u[i+1]=u[i-1] where du/dn=0
ip1 = i + 1
im1 = ip1
u[i] = - u_nm[i] + 2 * u_n[i] + \
C2 * (0.5 * (q[i] + q[ip1]) * (u_n[ip1] - u_n[i]) - 0.5 * (q[i] + q[im1]) * (u_n[i] - u_n[im1])) + \
dt2 * f(x[i], t[n])
else:
u[i] = U_0(t[n + 1])
i = Ix[-1]
if U_L is None:
im1 = i - 1
ip1 = im1
u[i] = - u_nm[i] + 2 * u_n[i] + \
C2 * (0.5 * (q[i] + q[ip1]) * (u_n[ip1] - u_n[i]) - 0.5 * (q[i] + q[im1]) * (u_n[i] - u_n[im1])) + \
dt2 * f(x[i], t[n])
else:
u[i] = U_L(t[n + 1])
if user_func is not None:
if user_func(u, x, t, n + 1):
break
u_nm, u_n, u = u_n, u, u_nm
return u, x, t
# --- here function for return functions ---
# return func(x)
def func(x):
"""
:param x:
:return:
"""
return # expression
# start simulate and animate or visualisation and savin the data from file
def simulate(
I, V, f, a, L, C, T, U_0, U_L, dt, # params
umin, umax, # amplitude
animate = True, # animate or not?
solver_func = sol, # call the solver
mode = 'plotter', # mode: plotting the graphic or saving to file
):
# code for visualization and simulate
...........
# start simulate
solver_func(I, V, f, a, L, C, T, U_0, U_L, dt, user_func)
return 0
def task( ):
'''
test tasking for solver and my problem
:return:
'''
I
L = 1
a = 1
C = 0.85
T = 1
dt = 0.05
U_0, U_L, V, f
umax = 2
umin = -umax
simulate(I, V, f, a, L, C, T, U_0, U_L, dt, umax, umin, animate = True, solver_func = sol, mode = 'plotter',)
if __name__ == '__main__':
task()
And I get the same error:
File "C:\\LR2-rep\wave_eq_1d.py", line 102, in sol
u[i] = u_n[i] + dt * V(x[i]) + 0.5 * C2 * (0.5 * (q[i] + q[i + 1]) * (u_n[i + 1] - u_n[i]) -
TypeError: 'int' object is not subscriptable
I understand the meaning of the error, but I do not understand how it can be fixed, and for almost two weeks I have not been able to write a program ... I ask for help with solving this problem! Thank you very much in advance!
I have 3 geographic coordinates
first_coordinate = [55.266346, 25.053268]
second_coordinate = [55.266472, 25.053311]
third_coordinate = [55.266370, 25.0532]
Then I convert them into EPSG:3997:
def convert(point):
return Point(transformer.transform(point[0], point[1]))
first_point_in_meters = convert(first_coordinate)
second_point_in_meters = convert(second_coordinate)
third_point_in_meter = convert(third_coordinate)
Then tries to determine origin third coordinate using:
def calculate_third_point(Ax, Ay, Cx, Cy, b, c, A, alt):
uACx = (Cx - Ax) / b
uACy = (Cy - Ay) / b
if alt:
uABx = uACx * math.cos(A) - uACy * math.sin(A)
uABy = uACx * math.sin(A) + uACy * math.cos(A)
Bx = Ax + c * uABx
By = Ay + c * uABy
else:
uABx = uACx * math.cos(A) + uACy * math.sin(A)
uABy = - uACx * math.sin(A) + uACy * math.cos(A)
Bx = Ax + c * uABx
By = Ay + c * uABy
return Point([Bx, By])
And converted this 2 coordinates back to EPSG:4326. But closest coordinate is 2 meters far from origin
one distance: 2.6033912103159342 m.
anther distance: 19.215538365617018 m.
What I'm doing wrong?
Full code:
import math
import numpy as np
from pyproj import Transformer
from shapely.geometry import Point
transformer_4326 = Transformer.from_crs(3997, 4326, always_xy=True)
transformer = Transformer.from_crs(4326, 3997, always_xy=True)
def calculate_third_point(Ax, Ay, Cx, Cy, b, c, A, alt):
uACx = (Cx - Ax) / b
uACy = (Cy - Ay) / b
if alt:
uABx = uACx * math.cos(A) - uACy * math.sin(A)
uABy = uACx * math.sin(A) + uACy * math.cos(A)
Bx = Ax + c * uABx
By = Ay + c * uABy
else:
uABx = uACx * math.cos(A) + uACy * math.sin(A)
uABy = - uACx * math.sin(A) + uACy * math.cos(A)
Bx = Ax + c * uABx
By = Ay + c * uABy
return Point([Bx, By])
def find_angle(rone, rtwo, rthree):
one = np.array(rone)
two = np.array(rtwo)
three = np.array(rthree)
ba = one - two
bc = three - two
cosine_angle = np.dot(ba, bc) / (np.linalg.norm(ba) * np.linalg.norm(bc))
angle = np.arccos(cosine_angle)
return np.degrees(angle)
def convert(point):
return Point(transformer.transform(point[0], point[1]))
first_coordinate = [55.266346, 25.053268]
second_coordinate = [55.266472, 25.053311]
third_coordinate = [55.266370, 25.0532]
first_point_in_meters = convert(first_coordinate)
second_point_in_meters = convert(second_coordinate)
third_point_in_meter = convert(third_coordinate)
angle = find_angle(third_point_in_meter, second_point_in_meters, first_point_in_meters)
distance = first_point_in_meters.distance(second_point_in_meters)
distance2 = first_point_in_meters.distance(third_point_in_meter)
one = calculate_third_point(
first_point_in_meters.x,
first_point_in_meters.y,
second_point_in_meters.x,
second_point_in_meters.y,
distance,
distance2,
angle,
True)
another = calculate_third_point(
first_point_in_meters.x,
first_point_in_meters.y,
second_point_in_meters.x,
second_point_in_meters.y,
distance,
distance2,
angle,
False)
print("one distance: {0} m.".format(one.distance(third_point_in_meter)))
print("anther distance: {0} m.".format(another.distance(third_point_in_meter)))
I am converting an IDL code (written by Oleg Kochukhov) to Python. The code generates star surface map over spectral line profiles using Tikhonov or Maximum Entropy methods.
I use scipy.optimize.minimize to generate map over line profiles. But process is too slow and results is not compatible. I search solution on internet but i dont find any usefull solution.
I added a runnable code below:
import numpy as np
from scipy.optimize import minimize
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import matplotlib.gridspec as gridspec
#syc = 0
def DI_GridInit(ntot):
# generate stellar surface grid
nlat = int(round(0.5 * (1.0 + np.sqrt(1.0 + np.pi * ntot))) - 1)
nlon = np.zeros(nlat, dtype=int)
xlat = np.pi * (np.arange(nlat, dtype=float) + 0.5) / nlat - np.pi / 2.0
xcirc = 2.0 * np.cos(xlat[1:])
nlon[1:] = np.around(xcirc * nlat) + 1
nlon[0] = ntot - sum(nlon[1:])
if abs(nlon[0] - nlon[nlat - 1]) > nlat:
nlon[1:] = nlon[1:] + (nlon[0] - nlon[nlat - 1]) / nlat
nlon[0] = ntot - sum(nlon[1:])
if nlon[0] < nlon[nlat - 1]:
nlon[1:] = nlon[1:] - 1
nlon[0] = ntot - sum(nlon[1:])
# generate Descartes coordinates for the surface grid in
# stellar coordinates, areas of surface elements and
# regularization indices: (lower, upper, right, left)
x0, j = np.zeros((ntot, 3), dtype=float), 0
latitude, longitude = np.zeros(ntot, dtype=float), np.zeros(ntot, dtype=float)
sa, ireg = np.zeros(ntot, dtype=float), np.zeros((ntot, 4), dtype=int)
slt = np.hstack((0., (xlat[1:nlat] + xlat[0:nlat - 1]) / 2. + np.pi / 2., np.pi))
for i in range(nlat):
coslat = np.cos(xlat[i])
sinlat = np.sin(xlat[i])
xlon = 2 * np.pi * (np.arange(nlon[i]) + 0.5) / nlon[i]
sinlon = np.sin(xlon)
coslon = np.cos(xlon)
x0[:, 0][j:j + nlon[i]] = coslat * sinlon
x0[:, 1][j:j + nlon[i]] = -coslat * coslon
x0[:, 2][j:j + nlon[i]] = sinlat
latitude[j:j + nlon[i]] = xlat[i]
longitude[j:j + nlon[i]] = xlon
sa[j:j + nlon[i]] = 2. * np.pi * (np.cos(slt[i]) - np.cos(slt[i + 1])) / nlon[i]
ireg[:, 2][j:j + nlon[i]] = np.roll(j + np.arange(nlon[i], dtype=int), -1)
ireg[:, 3][j:j + nlon[i]] = np.roll(j + np.arange(nlon[i], dtype=int), 1)
if (i > 0):
il_lo = j - nlon[i - 1] + np.arange(nlon[i - 1], dtype=int)
else:
il_lo = j + nlon[i] + np.arange(nlon[i + 1], dtype=int)
if (i < nlat - 1):
il_up = j + nlon[i] + np.arange(nlon[i + 1], dtype=int)
else:
il_up = il_lo
for k in range(j, j + nlon[i]):
dlat_lo = longitude[k] - longitude[il_lo]
ll = np.argmin(abs(dlat_lo))
ireg[k][0] = il_lo[ll]
dlat_up = longitude[k] - longitude[il_up]
ll = np.argmin(abs(dlat_up))
ireg[k][1] = il_up[ll]
j += nlon[i]
theta = np.arccos(x0[:, 2])
phi = np.arctan2(x0[:, 0], -x0[:, 1])
ii = np.argwhere(phi < 0).T[0]
nii = len(ii)
phi[ii] = 2.0 * np.pi - abs(phi[ii]) if nii else None
grid = {'ntot': ntot, 'nlat': nlat, 'nlon': nlon, 'xyz': x0, 'lat': latitude,
'lon': longitude, 'area': sa, 'ireg': ireg, 'phi': phi, 'theta': theta}
return grid
def DI_Map(grid, spots):
map = np.ones(grid['ntot'], dtype=float)
for i in range(spots['n']):
dlon = grid['lon'] - np.deg2rad(spots['tbl'][i, 0])
dlat = grid['lat'] - np.deg2rad(spots['tbl'][i, 1])
da = (2.0 * np.arcsin(np.sqrt(np.sin(0.5 * dlat) ** 2 +
np.cos(np.deg2rad(spots['tbl'][i, 1])) *
np.cos(grid['lat']) * np.sin(0.5 * dlon) ** 2)))
ii = np.argwhere(da <= np.deg2rad(spots['tbl'][i, 2])).T[0]
ni = len(ii)
map[ii] = spots['tbl'][i, 3] if ni > 0 else None
return map
def DI_Prf(grid, star, map, phase=None, vv=None, vr=None, nonoise=None):
# velocity array
if vv is not None:
nv = len(vv)
else:
nv = int(np.ceil(2.0 * star['vrange'] / star['vstep']))
vv = -star['vrange'] + np.arange(nv, dtype=float) * star['vstep']
# phase array
if phase is None:
phase = np.arange(star['nphases'], dtype=float) / star['nphases']
# velocity correction for each phase
vr = np.zeros(star['nphases'], dtype=float) if vr == None else None
# fixed trigonometric quantities
cosi = np.cos(np.deg2rad(star['incl'])); sini = np.sin(np.deg2rad(star['incl']))
coslat = np.cos(grid['lat']); sinlat = np.sin(grid['lat'])
# FWHM to Gaussian sigma
sigm = star['fwhm'] / np.sqrt(8.0 * np.log(2.0))
isig = (-0.5 / sigm ** 2)
# initialize line profile and integrated field arrays
prf = np.zeros((nv, len(phase)), dtype=float)
# gradient if called with 5 - variable input
grad = np.zeros((nv, len(phase), grid['ntot']), dtype=float)
# phase loop
for i in range(len(phase)):
coslon = np.cos(grid['lon'] + 2.0 * np.pi * phase[i])
sinlon = np.sin(grid['lon'] + 2.0 * np.pi * phase[i])
mu = sinlat * cosi + coslat * sini * coslon
ivis = np.argwhere(mu > 0.).T[0]
dv = -sinlon[ivis] * coslat[ivis] * star['vsini']
avis = grid['area'][ivis] * mu[ivis] * (1.0 - star['limbd'] + star['limbd'] * mu[ivis])
if star['type'] == 0:
wgt = avis * map[ivis]
wgtn = sum(wgt)
for j in range(nv):
plc = 1.0 - star['d'] * np.exp(isig * (vv[j] + dv - vr[i]) ** 2)
prf[j][i] = sum(wgt * plc) / wgtn
grad[j][i][ivis] = avis * plc / wgtn - avis * prf[j][i] / wgtn
elif star['type'] == 1:
wgt = avis
wgtn = sum(wgt)
for j in range(nv):
plc = 1.0 - map[ivis] * star['d'] * np.exp(isig * (vv[j] + dv - vr[i]) ** 2)
prf[j][i] = sum(wgt * plc) / wgtn
grad[j][i][ivis] = -wgt / wgtn * star['d'] * np.exp(isig * (vv[j] + dv - vr[i]) ** 2)
# output structure
syn = {'v': vv, 'phase': phase, 'prf': prf}
# add noise
if star['snr'] != -1 and nonoise != None:
obs = syn['prf'] * 0.0
for i in range(star['nphases']):
obs[:, i] = syn['prf'][:, i] + np.random.standard_normal((len(syn['v']),)) / star['snr']
syn['obs'] = obs
return syn, grad
def DI_func(cmap, functargs):
# global syc
star = functargs['star']
grid = functargs['grid']
obs = functargs['obs']
invp = functargs['invp']
nv = len(obs['v'])
er = 1.0 / abs(star['snr'])
if 'vr' in obs.keys():
syn, grad = DI_Prf(grid, star, cmap, phase=obs['phase'], vv=obs['v'], vr=obs['vr'])
else:
syn, grad = DI_Prf(grid, star, cmap, phase=obs['phase'], vv=obs['v'])
# shf = 0
# for i in range(len(obs['phase'])):
# plt.plot(obs['v'], obs['obs'][:, i] + shf, 'bo')
# plt.plot(obs['v'], syn['prf'][:, i] + shf, 'r')
# plt.plot(obs['v'], obs['obs'][:, i] - syn['prf'][:, i] + shf, 'k')
# shf += 0.1
# plt.show()
fchi = 0.0
sign = (-1) ** invp['regtype']
for i in range(star['nphases']):
fchi = fchi + sign * sum((syn['prf'][:, i] - obs['obs'][:, i]) ** 2 / er ** 2) / nv
freg = 0
if invp['lambda'] > 0:
if invp['regtype'] == 0:
ir = grid['ireg']
for k in range(len(ir[0, :])):
freg = freg + invp['lambda'] / grid['ntot'] * sum((cmap - cmap[ir[:, k]]) ** 2)
elif invp['regtype'] == 1:
mmap = sum(cmap) / grid['ntot']
nmap = cmap / mmap
freg = freg - invp['lambda'] / grid['ntot'] * sum(nmap * np.log(nmap))
ftot = fchi + freg
syn['obs'] = obs['obs']
# syc += 1
# if syc % 1000 == 0:
# plotting(grid, cmap, syn, star['incl'], typ=star['type'])
#
# print(syc, ftot, sum(cmap))
return ftot
def plotting(grid, map, syn, incl, typ):
nlon = grid['nlon']
nln = max(nlon)
nlt = len(nlon)
ll = np.zeros(nlt + 1, dtype=int)
ll[0] = 0
for i in range(nlt):
ll[i + 1] = ll[i] + nlon[i]
map1 = np.zeros((nlt, nln), dtype=float)
x = np.arange(nln, dtype=float) + 0.5
for i in range(nlt):
lll = ((np.arange(nlon[i] + 2, dtype=float) - 0.5) * nln) / nlon[i]
y = np.hstack((map[ll[i + 1] - 1], map[ll[i]:ll[i+1]-1], map[ll[i]]))
for j in range(nln):
imin = np.argmin(abs(x[j] - lll))
map1[i, j] = y[imin]
light = (190 * (map1 - np.min(map1)) / (np.max(map1) - np.min(map1))) + 50
light_rect = np.flipud(light)
if typ == 0:
cmap = 'gray'
else:
cmap = 'gray_r'
fig = plt.figure()
fig.clear()
spec = gridspec.GridSpec(ncols=3, nrows=3, left=0.10, right=0.98,
top=0.97, bottom=0.07, hspace=0.2, wspace=0.36)
# naive IDW-like interpolation on regular grid
shape = light.shape
nrows, ncols = (shape[0], shape[1])
lon, lat = np.meshgrid(np.linspace(0, 360, ncols), np.linspace(-90, 90, nrows))
for i, item in enumerate([[(0, 0), -0], [(0, 1), -90], [(1, 0,), -180], [(1, 1), -270]]):
ax = fig.add_subplot(spec[item[0]])
# set up map projection
m = Basemap(projection='ortho', lat_0=90 - incl, lon_0=item[1], ax=ax)
# draw lat/lon grid lines every 30 degrees.
m.drawmeridians(np.arange(0, 360, 30))
m.drawparallels(np.arange(-90, 90, 30))
# compute native map projection coordinates of lat/lon grid.
x, y = m(lon, lat)
# contour data over the map.
m.contourf(x, y, light, 15, vmin=0., vmax=255., cmap=cmap)
if i in [0, 2]:
x2, y2 = m(180 - item[1], incl)
else:
x2, y2 = m(180 + item[1], incl)
x1, y1 = (-10, 5)
ax.annotate(str('%0.2f' % (abs(item[1]) / 360.)), xy=(x2, y2), xycoords='data',
xytext=(x1, y1), textcoords='offset points',
color='r')
ax5 = fig.add_subplot(spec[-1, :2])
ax5.imshow(light_rect, vmin=0., vmax=255., cmap=cmap, interpolation='none', extent=[0, 360, -90, 90])
ax5.set_xticks(np.arange(0, 420, 60))
ax5.set_yticks(np.arange(-90, 120, 30))
ax5.set_xlabel('Longitude ($^\circ$)', fontsize=7)
ax5.set_ylabel('Latitude ($^\circ$)', fontsize=7)
ax5.tick_params(labelsize=7)
ax6 = fig.add_subplot(spec[0:, 2])
shf = 0.0
for i in range(len(syn['phase'])):
ax6.plot(syn['v'], syn['obs'][:, -i - 1] + shf, 'bo', ms=2)
ax6.plot(syn['v'], syn['prf'][:, -i - 1] + shf, 'r', linewidth=1)
ax6.text(min(syn['v']), max(syn['obs'][:, -i - 1] + shf), str('%0.2f' % syn['phase'][-i - 1]),
fontsize=7)
shf += 0.1
p1 = ax6.lines[0]
p2 = ax6.lines[-1]
p1datay = p1.get_ydata()
p1datax = p1.get_xdata()
p2datay = p2.get_ydata()
y1, y2 = min(p1datay) - min(p1datay) / 20.,max(p2datay) + min(p1datay) / 10.
ax6.set_ylim([y1, y2])
ax6.set_xlabel('V ($km s^{-1}$)', fontsize=7)
ax6.set_ylabel('I / Ic', fontsize=7)
ax6.tick_params(labelsize=7)
max_ = int(max(p1datax))
ax6.set_xticks([-max_, np.floor(-max_ / 2.), 0.0, np.ceil(max_ / 2.), max_])
plt.show()
if __name__ == "__main__":
# Star parameters
star = {'ntot': 1876, 'type': 0, 'incl': 70, 'vsini': 50, 'fwhm': 7.0, 'd': 0.6,
'limbd': 0.5, 'nphases': 5, 'vrange': np.sqrt(50 ** 2 + 7.0 ** 2) * 1.4,
'vstep': 1.0, 'snr': 500}
# Spot parameters
lon_spot = [40, 130, 220, 310]
lat_spot = [-30, 0, 60, 30]
r_spot = [20, 20, 20, 20]
c_spot = [0.1, 0.2, 0.25, 0.3]
tbl = np.array([lon_spot, lat_spot, r_spot, c_spot]).T
spots = {'n': len(lon_spot), 'type': star['type'], 'tbl': tbl}
# Generate grid
grid = DI_GridInit(star['ntot'])
# Generate map
cmap = DI_Map(grid, spots)
# Generate spectral line profiles
csyn, grad = DI_Prf(grid, star, cmap, nonoise=True)
# Plotting map and line profiles
plotting(grid, cmap, csyn, star['incl'], star['type'])
# Generate map over the line profiles using scipy.optimize.minimize
invp = {'lambda': 20, 'regtype': 0, 'maxiter': 10}
grid_inv = DI_GridInit(star['ntot'])
functargs = {'star': star, 'grid': grid_inv, 'obs': csyn, 'invp': invp}
cmap = np.ones(star['ntot'])
cmap[0] = 0.99
bnd = list(zip(np.zeros(len(cmap), dtype=float), np.ones(len(cmap), dtype=float)))
minimize(DI_func, cmap, args=functargs, method='TNC', bounds=bnd,
callback=None, options={'eps': 0.1, 'maxiter': 5, 'disp': True})
The code includes followed parts.
'DI_GridInit' : Generates grids for the map
'DI_Map' : Generates star surface map according to starspot parameters (such as longitude, latitude, radius and contrast)
'DI_Prf' : Generates spectral line profiles according to map
Now I want to obtain the surface map over the generated and noised line profiles. I use scipy.optimize.minimize (TNC method) for obtain the surface map. I use 'DI_func' as function in minimize. But 'minimize' is so slow. What is the problem. How can I speed this up.
Here is a modified version of DI_Prf, where is the major computation time during the execution of DI_func:
def DI_Prf(grid, star, map, phase=None, vv=None, vr=None, nonoise=None):
# velocity array
if vv is not None:
nv = len(vv)
else:
nv = int(np.ceil(2.0 * star['vrange'] / star['vstep']))
vv = -star['vrange'] + np.arange(nv, dtype=float) * star['vstep']
# phase array
if phase is None:
phase = np.arange(star['nphases'], dtype=float) / star['nphases']
# velocity correction for each phase
vr = np.zeros(star['nphases'], dtype=float) if vr == None else None
# fixed trigonometric quantities
cosi = np.cos(np.deg2rad(star['incl'])); sini = np.sin(np.deg2rad(star['incl']))
coslat = np.cos(grid['lat']); sinlat = np.sin(grid['lat'])
# FWHM to Gaussian sigma
sigm = star['fwhm'] / np.sqrt(8.0 * np.log(2.0))
isig = (-0.5 / sigm ** 2)
# initialize line profile and integrated field arrays
prf = np.zeros((nv, len(phase)), dtype=float)
# gradient if called with 5 - variable input
grad = np.zeros((nv, len(phase), grid['ntot']), dtype=float)
# phase loop
for i in range(len(phase)):
coslon = np.cos(grid['lon'] + 2.0 * np.pi * phase[i])
sinlon = np.sin(grid['lon'] + 2.0 * np.pi * phase[i])
mu = sinlat * cosi + coslat * sini * coslon
ivis = np.argwhere(mu > 0.).T[0]
dv = -sinlon[ivis] * coslat[ivis] * star['vsini']
avis = grid['area'][ivis] * mu[ivis] * (1.0 - star['limbd'] + star['limbd'] * mu[ivis])
if star['type'] == 0:
wgt = avis * map[ivis]
wgtn = sum(wgt)
#for j in range(nv):
# plc = 1.0 - star['d'] * np.exp(isig * (vv[j] + dv - vr[i]) ** 2)
# prf[j][i] = sum(wgt * plc) / wgtn
# grad[j][i][ivis] = avis * plc / wgtn - avis * prf[j][i] / wgtn
plc = 1.0 - star['d'] * np.exp(isig * (vv[:, np.newaxis] + dv[np.newaxis, :] - vr[i]) ** 2)
prf[:, i] = np.sum(wgt * plc, axis=1) / wgtn
grad[:, i, ivis] = avis * plc / wgtn - (avis[:, np.newaxis]*prf[:, i]).T / wgtn
elif star['type'] == 1:
wgt = avis
wgtn = sum(wgt)
for j in range(nv): # to be modified too
plc = 1.0 - map[ivis] * star['d'] * np.exp(isig * (vv[j] + dv - vr[i]) ** 2)
prf[j][i] = sum(wgt * plc) / wgtn
grad[j][i][ivis] = -wgt / wgtn * star['d'] * np.exp(isig * (vv[j] + dv - vr[i]) ** 2)
# output structure
syn = {'v': vv, 'phase': phase, 'prf': prf}
# add noise
if star['snr'] != -1 and nonoise != None:
#for i in range(star['nphases']):
obs = syn['prf'] + np.random.standard_normal(size=syn['prf'].shape) / star['snr']
syn['obs'] = obs
return syn, grad
It reduces the time by 3:
%%timeit
syn, grad = DI_Prf(grid, star, cmap, phase=obs['phase'], vv=obs['v'])
# 127 ms ± 2.61 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
# 40.7 ms ± 683 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
The main idea with Numpy is to not use loops, but work with multidimensional array, and use the broadcasting capabilities.
For instance:
fchi = 0.0
for i in range(star['nphases']):
fchi = fchi + sign * sum((syn['prf'][:, i] - obs['obs'][:, i]) ** 2 / er ** 2) / nv
could be replaced with:
fchi = sign / nv / er ** 2 * np.sum( np.sum((syn['prf'] - obs['obs']) ** 2, axis=1 ) )
same for np.random.standard_normal(size=syn['prf'].shape)
It's not a big improvement here because star['nphases'] is small, but it is relatively important for the other axis. You could go further and remove the for loop over the phases in DI_Prf but it requires some thinking