def fitted_flat_field(q, FoV, ff_samples, it_no, data_dir, verbose=False):
''' Saves out data for the fitted flat-field at sample points across the
focal plane.
Parameters
----------
q : numpy array
The fitted flat-field parameters
FoV : float array
The imagers field of view in degrees (dalpha, dbeta)
it_no : int
The self-calibration iteration number
data_dir: string
The directory name in which the output data should be stored
verbose : boolean
True to run function in verbose mode
'''
filename = '{0}/FF/{1}_ff.p'.format(data_dir, it_no)
if verbose:
print("Saving out fitted flat-field data to {0}...".format(filename))
x = np.linspace(-FoV[0] / 2, FoV[0] / 2, ff_samples[0])
y = np.linspace(-FoV[1] / 2, FoV[1] / 2, ff_samples[1])
X, Y = np.meshgrid(x, y)
z = self_calibration.evaluate_flat_field(X.flatten(), Y.flatten(), q)
fitted_ff = np.reshape(z, (len(X[:, 0]), len(X[0, :])))
dic = {'x': X, 'y': Y, 'fitted_ff': fitted_ff, 'iteration_number': it_no}
pickle.dump(dic, open(filename, "wb"))
if verbose:
print("...done!")
def best_in_basis(q, FoV, ff_samples, best_fit_params, verbose=False):
''' Calculates the 'best-in-basis badness' of the instrument response fit.
This is defined as the root-mean-square difference between the fitted
instrument response and the *best fit possible* with the model basis used
for the fit (on a regular grid of sample points across the focal plane).
Parameters
----------
q : numpy array
the fitted instrument response parameters
FoV : Float Array
The simulate imager's field-of-view in degrees [alpha, beta]
ff_samples : integer array
The number of sample points on the focal plane for the best-in-basis
fitting and all of the badness measures
verbose : Boolean
Set to true to run simulations in verbose mode
Returns
-------
out : float
the calculated best-in-basis badness
'''
if verbose:
print("Calculating best-in-basis badness of fitted flat-field...")
x = np.linspace(- FoV[0] / 2, FoV[0] / 2, ff_samples[0])
y = np.linspace(- FoV[1] / 2, FoV[1] / 2, ff_samples[1])
X, Y = np.meshgrid(x, y)
our_ff = self_cal.evaluate_flat_field(x.flatten(), y.flatten(), q)
bf_ff = self_cal.evaluate_flat_field(x.flatten(), y.flatten(),
best_fit_params)
best_in_basis_badness = \
100. * np.sqrt(np.mean(((our_ff - bf_ff) / bf_ff) ** 2))
if verbose:
print("...done!")
return best_in_basis_badness
def badness(q, FoV, ff_samples, verbose=False):
''' Calculates the 'badness' of the instrument response fit. The
'badness' is defined as the root-mean-square difference between
the true instrument response and the fitted instrument response
measured on a regular grid of sample points across the focal plane.
Parameters
----------
q : numpy array
The fitted instrument response parameters
FoV : float array
The imagers field-of-view in degrees (dalpha, dbeta)
ff_samples : float array
The number of focal plane sample points (alpha-axis, beta-axis)
Returns
-------
out : float
the calculated badness
'''
if verbose:
print("Calculating best-in-basis badness of fitted flat-field...")
x = np.linspace(-FoV[0] / 2, FoV[0] / 2, ff_samples[0])
y = np.linspace(-FoV[1] / 2, FoV[1] / 2, ff_samples[1])
X, Y = np.meshgrid(x, y)
our_ff = self_cal.evaluate_flat_field(x.flatten(), y.flatten(), q)
true_ff = true_functions.flat_field(x.flatten(), y.flatten(), FoV)
badness = 100. * np.sqrt(np.mean(((our_ff - true_ff) / true_ff) ** 2))
if verbose:
print("...done!")
return badness
def flat_fields(filename,
FoV,
ff_samples,
best_fit_params,
output_path=False,
fig_width=8.3,
suffix='.png',
verbose=False):
''' This function plots the fitted flat-field against the *true* (top) and
the best-in-basis (bottom) flat-fields. Left: contour plots to compare
flat-fields, Right: residuals between two flat-fields.
Input
-----
filename : string
The path to the survey files
FoV : float array
The size of the imagers field-of-view in degrees (dalpha, dbeta)
ff_samples : float array
The number of points at which the instrument responses are sampled in
the (x-direction, y-direction)
The imager's field-of-view in degrees (dalpha, dbeta)
best_fit_params : numpy array
Array of the best-in-basis parameters
output_path : string
The path to save the figure to. If no value is given, figure is saved
in same directory as source pickle
figure_width : float
The width of the figure in inches, default it 8.3
suffix : string
The format of the saved figure, either '.pdf', '.eps' or '.png'.
Default is '.png'
verbose : Boolean
Set to true to run function in verbose mode
'''
if verbose:
print("Plotting flat-field from {0}...".format(filename))
scale = 0.88
dic = pickle.load(open(filename))
fig = plt.figure(figsize=(fig_width, scale * fig_width))
fig.clf()
ax_cb = fig.add_axes([0.85, 0.075 / scale, 0.05, 0.75 / scale])
ax1 = fig.add_axes([0.075, 0.075 / scale, 0.375, 0.375 / scale])
ax2 = fig.add_axes([0.075, 0.45 / scale, 0.375, 0.375 / scale])
ax3 = fig.add_axes([0.45, 0.075 / scale, 0.375, 0.375 / scale])
ax4 = fig.add_axes([0.45, 0.45 / scale, 0.375, 0.375 / scale])
ax1.text(0.95,
0.96,
'(c)',
va='center',
ha='center',
bbox=dict(facecolor='w', edgecolor='w', alpha=0.7),
zorder=2000,
transform=ax1.transAxes)
ax2.text(0.95,
0.04,
'(a)',
va='center',
ha='center',
bbox=dict(facecolor='w', edgecolor='w', alpha=0.7),
zorder=2000,
transform=ax2.transAxes)
ax3.text(0.05,
0.96,
'(d)',
va='center',
ha='center',
zorder=2000,
transform=ax3.transAxes)
ax4.text(0.05,
0.04,
'(b)',
va='center',
ha='center',
zorder=2000,
transform=ax4.transAxes)
ax2.set_xticklabels([])
ax4.set_xticklabels([])
ax4.set_yticklabels([])
ax3.set_yticklabels([])
ax_cb.set_xticks([])
fig.text(0.45,
0.025 / scale,
r'Focal Plane Position $x$ (deg)',
va='center',
ha='center')
fig.text(0.015,
0.45 / scale,
r'Focal Plane Position $y$ (deg)',
va='center',
ha='center',
rotation='vertical')
true_ff = true_functions.flat_field(dic['x'], dic['y'], FoV)
best_ff = self_calibration.evaluate_flat_field(
dic['x'].flatten(), dic['y'].flatten(),
best_fit_params).reshape(ff_samples)
levels = np.linspace(np.min(true_ff), np.max(true_ff), 25)
labels = levels[::2]
cs = ax2.contour(dic['x'], dic['y'], true_ff, colors='0.75', levels=levels)
cs = ax1.contour(dic['x'], dic['y'], best_ff, colors='0.75', levels=levels)
cs = ax1.contour(dic['x'],
dic['y'],
dic['fitted_ff'],
colors='k',
levels=levels)
cs.clabel(labels, fontsize=8)
cs = ax2.contour(dic['x'],
dic['y'],
dic['fitted_ff'],
colors='k',
levels=levels)
cs.clabel(labels, fontsize=8)
true_residual = 100 * (dic['fitted_ff'] - true_ff) / true_ff
best_residual = 100 * (dic['fitted_ff'] - best_ff) / best_ff
fp = (-FoV[0] / 2, FoV[0] / 2, -FoV[1] / 2, FoV[1] / 2)
a = ax4.imshow(true_residual,
extent=fp,
vmin=-2.,
vmax=2.,
cmap='gray',
aspect='auto')
ax3.imshow(best_residual,
extent=fp,
vmin=-2.,
vmax=2.,
cmap='gray',
aspect='auto')
cbar = fig.colorbar(a, ax_cb, orientation='vertical')
cbar.set_label(r'Residuals (percent)')
if output_path:
filename = output_path + suffix
else:
filename = string.replace(filename, '.p', suffix)
fig.savefig(filename)
fig.clf()
if verbose:
print('...done!')
def flat_fields(filename, FoV, ff_samples, best_fit_params, output_path=False,
fig_width=8.3, suffix='.png', verbose=False):
''' This function plots the fitted flat-field against the *true* (top) and
the best-in-basis (bottom) flat-fields. Left: contour plots to compare
flat-fields, Right: residuals between two flat-fields.
Input
-----
filename : string
The path to the survey files
FoV : float array
The size of the imagers field-of-view in degrees (dalpha, dbeta)
ff_samples : float array
The number of points at which the instrument responses are sampled in
the (x-direction, y-direction)
The imager's field-of-view in degrees (dalpha, dbeta)
best_fit_params : numpy array
Array of the best-in-basis parameters
output_path : string
The path to save the figure to. If no value is given, figure is saved
in same directory as source pickle
figure_width : float
The width of the figure in inches, default it 8.3
suffix : string
The format of the saved figure, either '.pdf', '.eps' or '.png'.
Default is '.png'
verbose : Boolean
Set to true to run function in verbose mode
'''
if verbose:
print("Plotting flat-field from {0}...".format(filename))
scale = 0.88
dic = pickle.load(open(filename))
fig = plt.figure(figsize=(fig_width, scale * fig_width))
fig.clf()
ax_cb = fig.add_axes([0.85, 0.075 / scale, 0.05, 0.75 / scale])
ax1 = fig.add_axes([0.075, 0.075 / scale, 0.375, 0.375 / scale])
ax2 = fig.add_axes([0.075, 0.45 / scale, 0.375, 0.375 / scale])
ax3 = fig.add_axes([0.45, 0.075 / scale, 0.375, 0.375 / scale])
ax4 = fig.add_axes([0.45, 0.45 / scale, 0.375, 0.375 / scale])
ax1.text(0.95, 0.96, '(c)', va='center', ha='center',
bbox=dict(facecolor='w', edgecolor='w', alpha=0.7),
zorder=2000, transform=ax1.transAxes)
ax2.text(0.95, 0.04, '(a)', va='center', ha='center',
bbox=dict(facecolor='w', edgecolor='w', alpha=0.7),
zorder=2000, transform=ax2.transAxes)
ax3.text(0.05, 0.96, '(d)', va='center', ha='center',
zorder=2000, transform=ax3.transAxes)
ax4.text(0.05, 0.04, '(b)', va='center', ha='center',
zorder=2000, transform=ax4.transAxes)
ax2.set_xticklabels([])
ax4.set_xticklabels([])
ax4.set_yticklabels([])
ax3.set_yticklabels([])
ax_cb.set_xticks([])
fig.text(0.45, 0.025 / scale, r'Focal Plane Position $x$ (deg)',
va='center', ha='center')
fig.text(0.015, 0.45 / scale, r'Focal Plane Position $y$ (deg)',
va='center', ha='center', rotation='vertical')
true_ff = true_functions.flat_field(dic['x'], dic['y'], FoV)
best_ff = self_calibration.evaluate_flat_field(dic['x'].flatten(),
dic['y'].flatten(),
best_fit_params).reshape(ff_samples)
levels = np.linspace(np.min(true_ff), np.max(true_ff), 25)
labels = levels[::2]
cs = ax2.contour(dic['x'], dic['y'], true_ff, colors='0.75', levels=levels)
cs = ax1.contour(dic['x'], dic['y'], best_ff, colors='0.75', levels=levels)
cs = ax1.contour(dic['x'], dic['y'], dic['fitted_ff'], colors='k',
levels=levels)
cs.clabel(labels, fontsize=8)
cs = ax2.contour(dic['x'], dic['y'], dic['fitted_ff'], colors='k',
levels=levels)
cs.clabel(labels, fontsize=8)
true_residual = 100 * (dic['fitted_ff'] - true_ff) / true_ff
best_residual = 100 * (dic['fitted_ff'] - best_ff) / best_ff
fp = (-FoV[0] / 2, FoV[0] / 2, -FoV[1] / 2, FoV[1] / 2)
a = ax4.imshow(true_residual, extent=fp, vmin=-2., vmax=2.,
cmap='gray', aspect='auto')
ax3.imshow(best_residual, extent=fp, vmin=-2., vmax=2.,
cmap='gray', aspect='auto')
cbar = fig.colorbar(a, ax_cb, orientation='vertical')
cbar.set_label(r'Residuals (percent)')
if output_path:
filename = output_path + suffix
else:
filename = string.replace(filename, '.p', suffix)
fig.savefig(filename)
fig.clf()
if verbose:
print('...done!')
