def test_transform(num_free_param=10,co_ran=.1):
#x1 = np.linspace(0,2000,num=1000) - 1000
#x1 = np.concatenate([x1, x1, x1, x1, x1, x1, x1, x1, x1, x1])
#y1 = np.zeros(x1.shape)
#for i in range(10):
# for j in range(1000):
# y1[i*1000 + j] = i*200.0-1000
#print y1
x1 = np.random.rand(10000)*4096 - 2048
y1 = np.random.rand(10000)*4096 - 2048
coeff_trans_x = np.random.randn(num_free_param)*co_ran
coeff_trans_y = np.random.randn(num_free_param)*co_ran
del_x = high_order.poly(np.array([x1,y1]), coeff_trans_x)
del_y = high_order.poly(np.array([x1,y1]), coeff_trans_y)
xref = x1 + del_x
yref = y1 + del_y
c_x, c_y = high_order.fit_poly(x1, y1, del_x, del_y , len(coeff_trans_x))
print 'Transform Coefficents (given)'
print coeff_trans_x
print coeff_trans_y
print 'Calculated Coefficients'
print c_x
print c_y
return x1, y1, xref, yref
def test_polynomial_obj(num_co=10, order_poly=5, num_knots=4):
'''
test 5th order polynomial on spline and polnyomial fits
'''
'''
keep rotation zmall, maybe translation a few pixels
'''
xref = (np.random.random(1000) -.5 ) * 4096
yref = (np.random.random(1000) -.5 ) * 4096
#coeff_x = (np.random.random(num_co) - .5) * .01
#coeff_y = (np.random.random(num_co) - .5) * .01
#pick static set instead, then tweaking once it works better
coeff_x = np.array([20.0, .5, .2, .02 , .001 , .07, .009, .003, .004, .006, .0005,.0003,.0007,.0001,.0004])
coeff_y = np.array([20.0, .7, .3, .06 , .004 , .09, .004, .007, .002, .005, .0007,.0005,.0003,.0002,.0005])
x_t = high_order.poly(np.array([xref,yref]), coeff_x)
y_t = high_order.poly(np.array([xref,yref]), coeff_y)
t = high_order.transform(xref,yref,x_t,y_t,order_poly=order_poly, num_knots=num_knots)
x_t_ev, y_t_ev = t.evaluate(xref, yref)
x_u = (np.random.random(2000) -.5 ) * 4096
y_u = (np.random.random(2000) -.5 ) * 4096
xt_u = high_order.poly(np.array([x_u,y_u]), coeff_x)
yt_u = high_order.poly(np.array([x_u,y_u]), coeff_y)
xt_u_m, yt_u_m = t.evaluate(x_u, y_u)
print 'REFERENCE LISTS:average difference between transform and input in x', np.sum(np.abs(xt_u_m - xt_u))/len(xt_u)
print 'REFERENCE LISTS: average difference between transform and input in y', np.sum(np.abs(yt_u_m - yt_u))/len(yt_u)
print 'RANDOM LIST:average difference between transform and input in x', np.sum(np.abs(x_t_ev - x_t))/len(x_t)
print 'RANDOM LIST:average difference between transform and input in y', np.sum(np.abs(y_t_ev - y_t))/len(y_t)
print np.max(x_u), np.min(x_u)
print np.max(xt_u), np.min(xt_u)
return t, coeff_x, coeff_y
def coeff_err(num_poly_param=10, size_rand_co=.1, trials=1000, num_points=100):
'''
calulates then plots the fracinoal error in polynomial coefficients copmared to known transform
'''
first_time = True
co_x_frac = []
co_y_frac = []
for i in range(trials):
x = (np.random.random(num_points) -.5 ) * 4096
y = (np.random.random(num_points) -.5 ) * 4096
co_x = np.random.random(num_poly_param) * size_rand_co
co_y = np.random.random(num_poly_param) * size_rand_co
xref = high_order.poly(np.array([x,y]) , co_x)
yref = high_order.poly(np.array([x,y]) , co_y)
c_x, c_y = high_order.fit_poly(x,y,xref,yref,num_poly_param)
for ii in range(num_poly_param):
if first_time:
co_x_frac.append([])
co_y_frac.append([])
co_x_frac[ii].append(np.abs((co_x[ii]-c_x[ii])/co_x[ii]))
co_y_frac[ii].append(np.abs((co_y[ii]-c_y[ii])/co_y[ii]))
first_time = False
co_x_frac = np.median(np.array(co_x_frac), axis=1)
co_y_frac = np.median(np.array(co_y_frac), axis=1)
plt.plot(co_x_frac[1:])
plt.plot(co_y_frac[1:])
plt.show()
def mk_fake_data(points = 1000, param=15):
'''
go ahead and make some fake data
'''
x1 = np.random.rand(1000)*4096 - 2048
y1 = np.random.rand(1000)*4096 - 2048
xs = np.random.rand(1000)*4096 - 2048
ys = np.random.rand(1000)*4096 - 2048
co_ran = [1,10**-6,10**-6 ,10**-8,10**-8,10**-8, 10**-9,10**-9,10**-9,10**-9,10**-10,10**-10,10**-10,10**-10,10**-10]
coeff_trans_x = np.random.randn(param)
coeff_trans_y = np.random.randn(param)
for i in range(len(coeff_trans_x)):
if i ==1:
coeff_trans_x[i] = 1.0
elif i < 15:
coeff_trans_x[i] = coeff_trans_x[i] * co_ran[i]
else:
coeff_trans_x[i] = coeff_trans_x[i] * co_ran[-1]
for i in range(len(coeff_trans_y)):
if i ==2:
coeff_trans_y[i] = 1.0
elif i < 15:
coeff_trans_y[i] = coeff_trans_y[i] * co_ran[i]
else:
coeff_trans_y[i] = coeff_trans_y[i] * co_ran[-1]
coeff_trans_x =[ -3.71658981e-01 , 1.00000000e+00 , 0 , -1.20639707e-09, -1.37110890e-08 , 8.39249153e-09 , 1.69986169e-10 , -1.39590886e-10,-2.00797325e-09, -3.08447522e-10, 1.50453620e-11, -3.51821590e-11,-9.86845535e-11, 8.69664940e-11, -3.52721864e-11]
coeff_trans_y = [ 2.58551652e-01, 0, 1.00000000e+00, 1.18005089e-09,-4.63985850e-09 , 1.42579849e-09 , 1.58246425e-09 , -1.40892711e-09,5.57847993e-11 , 1.16534465e-10 , -3.50121458e-11, 1.49194611e-11,5.37039418e-11, -6.21582929e-11, 6.93338443e-12]
# old coeff -4.40296981e-07
#old coeff -1.26099851e-06
xref = high_order.poly(np.array([x1,y1]), coeff_trans_x)
yref = high_order.poly(np.array([x1,y1]), coeff_trans_y)
xrefs = high_order.poly(np.array([xs,ys]), coeff_trans_x)
yrefs = high_order.poly(np.array([xs,ys]), coeff_trans_y)
plt.quiver(x1,y1,xref-x1,yref-y1)
plt.show()
print 'difference after trnasform', np.sum(np.abs(xref-x1)), np.sum(np.abs(yref-y1))
print np.max(xref), np.min(xref)
print np.max(yref), np.min(yref)
#xref = x1 + del_x
#yref = y1 + del_y
tpoly = high_order.transform(x1,y1, xref, yref, order_poly=3, fit_spline_b=False, leg=False)
xpoly, ypoly = tpoly.evaluate(x1,y1)
xpolys,ypolys = tpoly.evaluate(xs,ys)
print np.sum(np.abs(xpolys-xrefs))
print np.sum(np.abs(ypoly-yref))
#print np.sum(np.abs(xs-xrefs))
print coeff_trans_x
print coeff_trans_y
print (coeff_trans_x[0:len(tpoly.coeff_x)]- tpoly.coeff_x) #/ coeff_trans_x[0:len(tpoly.coeff_x)]
print (coeff_trans_y[0:len(tpoly.coeff_x)]- tpoly.coeff_y) #/ coeff_trans_y[0:len(tpoly.coeff_x)]
tpoly = high_order.transform(x1,y1,xref,yref,leg=True)
tpoly.c_x = np.reshape(np.random.random(25), (5,5))
tpoly.c_y = np.reshape(np.random.random(25), (5,5))
tpoly.leg = True
xref, yref = tpoly.evaluate(x1,y1)
xrefs, yrefs = tpoly.evaluate(xs,ys)
print np.max(xref), np.min(xref)
tleg = high_order.transform(x1,y1,xref,yref, order_poly=5, fit_spline_b=False, leg=True)
xleg, yleg = tleg.evaluate(x1,y1)
xlegs, ylegs = tleg.evaluate(xs,ys)
print np.sum(np.abs(xlegs-xrefs)) / len(xlegs)
print np.sum(np.abs(xleg-xref)) / len(xref)
#print np.sum(np.abs(xs-xrefs))
print tleg.c_y
print tleg.c_x
#print coeff_trans_y
#print (coeff_trans_x[0:len(tpoly.coeff_x)]- tpoly.coeff_x) #/ coeff_trans_x[0:len(tpoly.coeff_x)]
#print (coeff_trans_y[0:len(tpoly.coeff_x)]- tpoly.coeff_y) #/ coeff_trans_y[0:len(tpoly.coeff_x)]
#plt.scatter(xpoly,ypoly)
#plt.show()
return xref, yref
def test_total(num_poly=10, num_knots=4, noise=0.1, smooth=False):
'''
Function that performs transformation between x, y and xref yref then fits that difference using polynomial fit followed by spline
num_poly is the number of polynomial terms used, 10 gets all quadratic terms
num_knots is th enumber of knots in the slpine along each axis, that is 4 means there is a total of 16 knots
noise is the sigma (in pixels) used to
'''
xref = np.random.rand(10000) * 4096 - 2048
yref = np.random.rand(10000) * 4096 - 2048
'''
these star lists will be put through both the known tranformation (rotation and translation) and also put through the derived polynomial and spline fits as a check
'''
x_dim_ref = np.random.rand(1000) * 4096 - 2048
y_dim_ref = np.random.rand(1000) * 4096 - 2048
trans = transforms.Affine2D()
trans.rotate_deg(75.0)
trans.translate(184, -45)
cooref = np.array([xref, yref]).T
coo1 = trans.transform(cooref)
coo_dim = trans.transform(np.array([x_dim_ref,y_dim_ref]).T)
x_dim1 = coo_dim[:,0] + noise*np.random.randn(len(x_dim_ref))
y_dim1 = coo_dim[:,1] + noise*np.random.randn(len(y_dim_ref))
x1 = coo1[:,0] + noise*np.random.randn(len(xref))
y1 = coo1[:,1] + noise*np.random.randn(len(yref))
c_x, c_y = high_order.fit_poly(x1, y1, xref, yref, num_poly)
x_poly = high_order.poly(np.array([x1,y1]), c_x)
y_poly = high_order.poly(np.array([x1,y1]), c_y)
#if np.sum(np.abs(x_poly-xref)) < 1 and np.sum(np.abs(y_poly-yref)) < len(xref):
# print 'Polynomial Fit was sufficient'
# return c_x, c_y
'''
Now do spline fit between the polynomial fit and reference, to get rid of residual
'''
dx_sp, spline_dx = high_order.fit_spline(x_poly, y_poly, xref-x_poly, num_knots=num_knots, smooth=smooth)
dy_sp, spline_dy = high_order.fit_spline(x_poly, y_poly, yref-y_poly, num_knots=num_knots, smooth=smooth)
x_sp, spline_x = high_order.fit_spline(x_poly, y_poly, xref, num_knots=num_knots, smooth=smooth)
y_sp, spline_y = high_order.fit_spline(x_poly, y_poly, yref, num_knots=num_knots, smooth=smooth)
assert np.sum(np.abs(x_sp-(x_poly+dx_sp)))/len(x_poly) < noise
assert np.sum(np.abs(y_sp-(y_poly+dy_sp)))/len(y_poly) < noise
assert np.sum(np.abs(x_sp - xref))/len(x_sp) < noise
assert np.sum(np.abs(y_sp - yref))/len(y_sp) < noise
x_dim_poly = high_order.poly(np.array([x_dim1,y_dim1]), c_x)
y_dim_poly = high_order.poly(np.array([x_dim1,y_dim1]), c_y)
assert np.sum(np.abs(x_dim_poly - x_dim_ref)) / len(x_dim_ref) < noise
assert np.sum(np.abs(y_dim_poly - y_dim_ref)) / len(y_dim_ref) < noise
x_dim_sp = spline_x.ev(x_dim_poly, y_dim_poly)
y_dim_sp = spline_y.ev(x_dim_poly,y_dim_poly)
assert np.sum(np.abs(x_dim_sp - x_dim_ref)) / len(x_dim_ref) < noise
assert np.sum(np.abs(y_dim_sp - y_dim_ref)) / len(y_dim_ref) < noise
return x_sp, y_sp, xref, yref, spline_x, spline_y , c_x, c_y, x1, y1
