def contour_2dfit(x, y, data, fit=None, par=None, nfig=None, interp=True):
""" Create a figure with the fit shown as contours
:param x: input xaxis coordinates - array
:param y: input yaxis coordinates - array (should have the same dim as x)
:param data: input data points (should have the same dim as x)
:param interp: interpolation used or not (Default is True)
:param par: parameters of the fit - Default is None. Only used if interpolation (interp=True) is used
:param fit: fitted points (should have the same dim as x)
:returns: Nothing
"""
from pygme.binning.voronoibinning import derive_unbinned_field, guess_regular_grid
from matplotlib.mlab import griddata
from pygme.mgefunctions import convert_xy_to_polar
from pygme.fitting.fitn2dgauss_mpfit import n_centred_twodgaussian_I
if nfig is None:
fig = plt.gcf()
else:
fig = plt.figure(nfig)
fig.clf()
## If interpolation is requested
if interp:
if fit is None:
print "ERROR: you did not provide 'fit' data"
return
xu, yu = guess_regular_grid(x, y)
du = griddata(x, y, data, xu, yu, interp="nn")
## if par is provided, then we compute the real MGE function
if par is None:
fu = griddata(x, y, fit, xu, yu, interp="nn")
else:
r, t = convert_xy_to_polar(xu, yu)
fu = n_centred_twodgaussian_I(pars=par)(r, t)
fu = fu.reshape(xu.shape)
## Otherwise we just use the nearest neighbour
else:
xu, yu, du = derive_unbinned_field(x, y, data)
xu, yu, fu = derive_unbinned_field(x, y, fit)
CS = plt.contour(xu, yu, np.log10(du), colors="k", label="Data")
CSfit = plt.contour(xu, yu, np.log10(fu), levels=CS.levels, colors="r", label="MGE Fit")
plt.axes().set_aspect("equal")
plt.legend()
def show_2dfit_residuals(x, y, data, fit, xfield=None, yfield=None, nfig=None, cmap=None):
""" Create a figure with a map of the residuals in %
:param x: input xaxis coordinates - array
:param y: input yaxis coordinates - array (should have the same dim as x)
:param data: input data points (should have the same dim as x)
:param fit: fitted points (should have the same dim as x)
:param xfield: 2d rectangular array for the field to show (xaxis)
:param yfield: 2d rectangular array (yaxis)
:param nfig: if None, will use the existing figure, otherwise will use that number
:returns: Nothing
"""
if nfig is None:
fig = plt.gcf()
else:
fig = plt.figure(nfig)
fig.clf()
## Making the arrays as 1D
x_rav = x.ravel()
y_rav = y.ravel()
d_rav = data.ravel()
f_rav = fit.ravel()
lx = len(x_rav)
ly = len(y_rav)
ld = len(d_rav)
lf = len(f_rav)
## checking that the dimensions are correct
if (lx != ld) or (lx != lf) or (lx != ly):
print "ERROR: dimensions for x, y, data, and fit are not the same"
print " (respectively: %d %d %d and %d)" % (lx, ly, ld, lf)
return
unbinned_residuals = derive_unbinned_field(x, y, 100.0 * (data - fit) / fit, xfield, yfield)
Sauron_Cmap = get_SauronCmap()
plt.imshow(unbinned_residuals, vmin=-20.0, vmax=20.0, cmap=Sauron_Cmap)
plt.colorbar()
