def fit_nominal_lens_2d(thickness,
pixelsize,
radius4fit,
p0=[20e-6, 1.005e-6, -.005e-6, -.005e-6],
bounds=([10e-6, -2.05e-6, -2.05e-6,
-2.05e-6], [50e-6, 2.05e-6, 2.05e-6,
2.05e-6]),
saveFigFlag=False,
str4title='',
kwargs4fit={}):
# thickness = center_CM_2darray(thickness)
xmatrix, ymatrix = wpu.grid_coord(thickness, pixelSize)
r2 = np.sqrt(xmatrix**2 + ymatrix**2)
args4fit = np.where(r2.flatten() < radius4fit)
mask = xmatrix * np.nan
mask[np.where(r2 < radius4fit)] = 1.0
data2fit = thickness.flatten()[args4fit]
xxfit = xmatrix.flatten()[args4fit]
yyfit = ymatrix.flatten()[args4fit]
xyfit = [xxfit, yyfit]
# FIT
popt, pcov = curve_fit(_2Dparabol_4_fit,
xyfit,
data2fit,
p0=p0,
bounds=bounds,
method='trf',
**kwargs4fit)
wpu.print_blue("Nominal Parabolic 2D Fit")
wpu.print_blue("Curv Radius, xo, yo, offset")
wpu.print_blue(popt)
wpu.print_blue("Nominal Parabolic 2D Fit: Radius of 1 face / nfaces, " +
"x direction: {:.4g} um".format(popt[0] * 1e6))
lim_x = np.argwhere(xmatrix[0, :] <= -radius4fit * 1.01)[-1, 0]
lim_y = np.argwhere(ymatrix[:, 0] <= -radius4fit * 1.01)[-1, 0]
fitted = _2Dparabol_4_fit([xmatrix, ymatrix], popt[0], popt[1], popt[2],
popt[3])
if (lim_x <= 1 or lim_y <= 1):
thickness_cropped = thickness * mask
fitted_cropped = fitted * mask
else:
thickness_cropped = (thickness[lim_y:-lim_y + 1, lim_x:-lim_x + 1] *
mask[lim_y:-lim_y + 1, lim_x:-lim_x + 1])
fitted_cropped = (fitted[lim_y:-lim_y + 1, lim_x:-lim_x + 1] *
mask[lim_y:-lim_y + 1, lim_x:-lim_x + 1])
return (thickness_cropped, fitted_cropped, popt)
def center_lens_array_max(array, pixelSize, radius):
'''
crop the array in order to have the max at the center of the array
'''
array = np.copy(array)
xx, yy = wpu.grid_coord(array, pixelSize)
r2 = np.sqrt(xx**2 + yy**2)
mask = 0 * xx
mask[np.where((r2 < radius * 1.1) & (r2 > radius * .9))] = 1.0
mean_at_r2 = np.mean(array * mask)
mask2 = 0 * xx
mask2[np.where(array > mean_at_r2)] = 1.0
i, j = np.mgrid[0:array.shape[0], 0:array.shape[1]]
center_i = int(np.average(i, weights=array * mask2))
center_j = int(np.average(j, weights=array * mask2))
if 2 * center_i > array.shape[0]:
array = array[2 * center_i - array.shape[0]:, :]
else:
array = array[0:2 * center_i, :]
if 2 * center_j > array.shape[1]:
array = array[:, 2 * center_j - array.shape[1]:]
else:
array = array[:, 0:2 * center_j]
return array
def center_lens_array_max_fit(array, pixelSize, radius4fit=100e-6):
'''
crop the array in order to have the max at the center of the array. It uses
a fitting procedure of a 2D parabolic function to determine the center
'''
radius4fit = _biggest_radius(array, pixelSize, radius4fit * .8)
array = np.copy(array)
xx, yy = wpu.grid_coord(array, pixelSize)
(_, _, fitParameters) = fit_parabolic_lens_2d(array,
pixelSize,
radius4fit=radius4fit)
center_i = np.argmin(np.abs(yy[:, 0] - fitParameters[2]))
center_j = np.argmin(np.abs(xx[0, :] - fitParameters[1]))
if 2 * center_i > array.shape[0]:
array = array[2 * center_i - array.shape[0]:, :]
else:
array = array[0:2 * center_i, :]
if 2 * center_j > array.shape[1]:
array = array[:, 2 * center_j - array.shape[1]:]
else:
array = array[:, 0:2 * center_j]
return array
def _plot_profile(data, pixelsize, title, arg4main={'cmap': 'viridis'}):
xxGrid, yyGrid = wpu.grid_coord(data, pixelsize)
wpu.plot_profile(xxGrid*1e6, yyGrid*1e6, data[::-1, :],
xlabel=r'$x [\mu m]$', ylabel=r'$y [\mu m]$',
title=title,
xunit='\mu m', yunit='\mu m',
arg4main=arg4main)
def _lsq_fit_parabola(zz, pixelsize, mode='2D'):
xx, yy = wpu.grid_coord(zz, pixelsize)
if np.all(np.isfinite(zz)): # if there is no nan
f = zz.flatten()
x = xx.flatten()
y = yy.flatten()
else:
argNotNAN = np.isfinite(zz)
f = zz[argNotNAN].flatten()
x = xx[argNotNAN].flatten()
y = yy[argNotNAN].flatten()
if '2D' in mode:
X_matrix = np.vstack([x**2 + y**2, x, y, x * 0.0 + 1]).T
beta_matrix = np.linalg.lstsq(X_matrix, f)[0]
fit = (beta_matrix[0] * (xx**2 + yy**2) + beta_matrix[1] * xx +
beta_matrix[2] * yy + beta_matrix[3])
elif '1Dx' in mode:
X_matrix = np.vstack([x**2, x, y, x * 0.0 + 1]).T
beta_matrix = np.linalg.lstsq(X_matrix, f)[0]
fit = (beta_matrix[0] * (xx**2) + beta_matrix[1] * xx +
beta_matrix[2] * yy + beta_matrix[3])
elif '1Dy' in mode:
X_matrix = np.vstack([y**2, x, y, x * 0.0 + 1]).T
beta_matrix = np.linalg.lstsq(X_matrix, f)[0]
fit = (beta_matrix[0] * (yy**2) + beta_matrix[1] * xx +
beta_matrix[2] * yy + beta_matrix[3])
if np.all(np.isfinite(zz)):
mask = zz * 0.0 + 1.0
else:
mask = zz * 0.0 + 1.0
mask[~argNotNAN] = np.nan
R_o = 1 / 2 / beta_matrix[0]
x_o = -beta_matrix[1] / beta_matrix[0] / 2
y_o = -beta_matrix[2] / beta_matrix[0] / 2
offset = beta_matrix[3]
popt = [R_o, x_o, y_o, offset]
return fit * mask, popt
def _fit_lin_surface(zz, pixelsize):
from numpy.polynomial import polynomial
xx, yy = wpu.grid_coord(zz, pixelsize)
f = zz.flatten()
deg = np.array([1, 1])
vander = polynomial.polyvander2d(xx.flatten(), yy.flatten(), deg)
vander = vander.reshape((-1, vander.shape[-1]))
f = f.reshape((vander.shape[0],))
c = np.linalg.lstsq(vander, f)[0]
print(c)
return polynomial.polyval2d(xx, yy, c.reshape(deg+1))
def fit_parabolic_lens_2d(thickness, pixelsize, radius4fit, mode='2D'):
# FIT
xx, yy = wpu.grid_coord(thickness, pixelSize)
mask = xx * np.nan
lim_x = np.argwhere(xx[0, :] <= -radius4fit * 1.01)[-1, 0]
lim_y = np.argwhere(yy[:, 0] <= -radius4fit * 1.01)[-1, 0]
if '2D' in mode:
r2 = np.sqrt(xx**2 + yy**2)
mask[np.where(r2 < radius4fit)] = 1.0
elif '1Dx' in mode:
mask[np.where(xx**2 < radius4fit)] = 1.0
lim_y = 2
elif '1Dy' in mode:
mask[np.where(yy**2 < radius4fit)] = 1.0
lim_x = 2
fitted, popt = _lsq_fit_parabola(thickness * mask, pixelSize, mode=mode)
wpu.print_blue("Parabolic 2D Fit")
wpu.print_blue("Curv Radius, xo, yo, offset")
wpu.print_blue(popt)
wpu.print_blue("Parabolic 2D Fit: Radius of 1 face / nfaces, " +
"x direction: {:.4g} um".format(popt[0] * 1e6))
if (lim_x <= 1 or lim_y <= 1):
thickness_cropped = thickness * mask
fitted_cropped = fitted * mask
else:
thickness_cropped = (thickness[lim_y:-lim_y + 1, lim_x:-lim_x + 1] *
mask[lim_y:-lim_y + 1, lim_x:-lim_x + 1])
fitted_cropped = (fitted[lim_y:-lim_y + 1, lim_x:-lim_x + 1] *
mask[lim_y:-lim_y + 1, lim_x:-lim_x + 1])
return (thickness_cropped, fitted_cropped, popt)
def plot_integration(integrated,
pixelsize,
titleStr='Title',
ctitle=' ',
max3d_grid_points=101,
plotProfile=True,
plot3dFlag=True,
saveFigFlag=False,
saveFileSuf='graph',
**kwarg4surf):
'''
TODO: Write Docstring
'''
xxGrid, yyGrid = wpu.grid_coord(integrated, pixelsize)
factor_x, unit_x = wpu.choose_unit(xxGrid)
factor_y, unit_y = wpu.choose_unit(yyGrid)
# Plot Integration 2
if plot3dFlag:
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
rstride = integrated.shape[0] // max3d_grid_points + 1
cstride = integrated.shape[1] // max3d_grid_points + 1
surf = ax.plot_surface(xxGrid * factor_x,
yyGrid * factor_y,
integrated[::-1, :],
rstride=rstride,
cstride=cstride,
cmap='viridis',
linewidth=0.1,
**kwarg4surf)
ax_lim = np.max([np.abs(xxGrid * factor_x), np.abs(yyGrid * factor_y)])
ax.set_xlim3d(-ax_lim, ax_lim)
ax.set_ylim3d(-ax_lim, ax_lim)
if 'vmin' in kwarg4surf:
ax.set_zlim3d(bottom=kwarg4surf['vmin'])
if 'vmax' in kwarg4surf:
ax.set_zlim3d(top=kwarg4surf['vmax'])
plt.xlabel(r'$x [' + unit_x + ' m]$', fontsize=24)
plt.ylabel(r'$y [' + unit_y + ' m]$', fontsize=24)
plt.title(titleStr, fontsize=24, weight='bold')
cbar = plt.colorbar(surf, shrink=.8, aspect=20)
cbar.ax.set_title(ctitle, y=1.01)
plt.tight_layout(rect=[0, 0, 1, 1])
ax.text2D(0.05,
0.9,
'strides = {}, {}'.format(rstride, cstride),
transform=ax.transAxes)
if saveFigFlag:
ax.view_init(elev=30, azim=60)
wpu.save_figs_with_idx(saveFileSuf)
ax.view_init(elev=30, azim=-120)
wpu.save_figs_with_idx(saveFileSuf)
plt.pause(.5)
plt.show(block=False)
if plotProfile:
wpu.plot_profile(xxGrid * factor_x,
yyGrid * factor_y,
integrated[::-1, :],
xlabel=r'$x [' + unit_x + ' m]$',
ylabel=r'$y [' + unit_y + ' m]$',
title=titleStr,
xunit='\mu m',
yunit='\mu m',
arg4main={
'cmap': 'viridis',
'lw': 3
})
if saveFigFlag:
plt.ioff()
plt.figure(figsize=(10, 8))
plt.imshow(integrated[::-1, :],
cmap='viridis',
extent=wpu.extent_func(integrated, pixelsize) * factor_x,
**kwarg4surf)
plt.xlabel(r'$x [' + unit_x + ' m]$', fontsize=24)
plt.ylabel(r'$y [' + unit_x + ' m]$', fontsize=24)
plt.title(titleStr, fontsize=18, weight='bold')
cbar = plt.colorbar()
cbar.ax.set_title(ctitle, y=1.01)
wpu.save_figs_with_idx(saveFileSuf)
plt.close()
plt.ion()
def plot_residual_parabolic_lens_2d(thickness,
pixelsize,
fitted,
fitParameters,
saveFigFlag=False,
savePickle=False,
str4title='',
saveSdfData=False,
vlimErrSigma=1,
plotProfileFlag=True,
plot3dFlag=True,
makeAnimation=False):
xmatrix, ymatrix = wpu.grid_coord(thickness, pixelsize)
errorThickness = thickness - fitted
argNotNAN = np.isfinite(errorThickness)
factorx, unitx = wpu.choose_unit(xmatrix)
factory, unity = wpu.choose_unit(ymatrix)
factorz, unitz = wpu.choose_unit(errorThickness[argNotNAN])
ptp = np.ptp(errorThickness[argNotNAN].flatten() * factorz)
wpu.print_red('PV: {0:4.3g} '.format(ptp) + unitz[-1] + 'm')
sigmaError = np.std(errorThickness[argNotNAN].flatten() * factorz)
wpu.print_red('SDV: {0:4.3g} '.format(sigmaError) + unitz[-1] + 'm')
str4title += r'Residual, ' + \
r'R $= {:.4g} \mu m$,'.format(fitParameters[0]*1e6) + '\n' + \
r'PV $= {0:.2f}$ '.format(ptp) + '$' + unitz + ' m$, '\
'SDV $= {0:.2f}$ '.format(sigmaError) + '$' + unitz + ' m$'
# Plot Histogram
plt.figure(figsize=(7, 8))
plt.hist(errorThickness[argNotNAN] * factorz,
100,
color='r',
histtype='step')
plt.xlabel(r'Residual [$' + unitz + ' m$ ]')
plt.title(str4title)
if saveFigFlag:
wpu.save_figs_with_idx(fname2save, extension='png')
plt.show(block=False)
# Plot Profiles
vlimErr = wpu.mean_plus_n_sigma(errorThickness[argNotNAN] * factorz,
vlimErrSigma / 2)
cmap4graph = plt.cm.Spectral_r
cmap4graph.set_over('m')
cmap4graph.set_under('c')
if plotProfileFlag:
wpu.plot_profile(xmatrix * factorx,
ymatrix * factory,
errorThickness * factorz,
title=str4title,
xlabel=r'[$' + unitx + ' m$ ]',
ylabel=r'[$' + unity + ' m$ ]',
zlabel=r'[$' + unitz + ' m$ ]',
arg4main={
'cmap': 'Spectral_r',
'vmin': -vlimErr,
'vmax': vlimErr,
'extend': 'both'
})
if savePickle or saveFigFlag:
fig = plt.figure(figsize=(10, 7))
cf = plt.contourf(xmatrix * factorx,
ymatrix * factory,
errorThickness * factorz,
256,
cmap=cmap4graph,
extend='both')
plt.clim(-vlimErr, vlimErr)
plt.contour(cf, levels=cf.levels[::32], colors='gray')
plt.xlabel(r'[$' + unitx + ' m$ ]', fontsize=22)
plt.ylabel(r'[$' + unity + ' m$ ]', fontsize=22)
plt.title(str4title, fontsize=22)
cbar = plt.colorbar(cf, shrink=.8, aspect=20)
# cbar.set_clim(-vlimErr, vlimErr)
cbar.ax.set_title(r'[$' + unitz + ' m$ ]', y=1.01)
plt.gca().set_aspect('equal', adjustable='box')
plt.grid(color='grey')
if saveFigFlag:
wpu.save_figs_with_idx(fname2save, extension='png')
if savePickle:
wpu.save_figs_with_idx_pickle(fig, fname2save)
plt.show(block=True)
# Plot 3D
if plot3dFlag:
wpu.print_red('MESSAGE: Ploting 3d in the background')
fig = plt.figure(figsize=(10, 7), facecolor="white")
ax = fig.gca(projection='3d')
plt.tight_layout(pad=2.5)
surf = ax.plot_trisurf(xmatrix[argNotNAN].flatten() * factorx,
ymatrix[argNotNAN].flatten() * factory,
errorThickness[argNotNAN].flatten() * factorz,
vmin=-vlimErr,
vmax=vlimErr,
cmap=cmap4graph,
linewidth=0.1,
shade=False)
ax.view_init(azim=-120, elev=40)
plt.xlabel(r'$x$ [$' + unitx + ' m$ ]')
plt.ylabel(r'$y$ [$' + unity + ' m$ ]')
plt.title(str4title)
cbar = plt.colorbar(surf, shrink=.8, aspect=20, extend='both')
cbar.ax.set_title(r'[$' + unitz + ' m$ ]', y=1.01)
plt.tight_layout()
if saveFigFlag:
wpu.save_figs_with_idx(fname2save, extension='png')
ax.view_init(azim=690, elev=40)
wpu.save_figs_with_idx(fname2save, extension='png')
if makeAnimation:
# plt.show(block=False)
plt.pause(1.0)
wpu.rocking_3d_figure(ax,
wpu.get_unique_filename(fname2save, 'gif'),
elevOffset=45,
azimOffset=60,
elevAmp=0,
azimAmpl=-1,
dpi=80,
npoints=5)
plt.pause(1.0)
plt.close('all')
# plt.show(block=True)
if saveSdfData:
mask_for_sdf = errorThickness * 0.0
mask_for_sdf[~argNotNAN] = 1.0
errorThickness[~argNotNAN] = 00000000
wpu.save_sdf_file(
errorThickness, pixelsize,
wpu.get_unique_filename(fname2save + '_residual', 'sdf'))
wpu.save_sdf_file(
mask_for_sdf, pixelsize,
wpu.get_unique_filename(fname2save + '_residual_mask', 'sdf'))
return sigmaError / factorz, ptp / factorz
(thickness_cropped, fitted,
fitParameters) = fit_nominal_lens_2d(thickness,
pixelSize,
radius4fit=radius4fit,
p0=p0,
bounds=bounds,
saveFigFlag=saveFigFlag,
str4title=str4graphs,
kwargs4fit={
'verbose': 2,
'ftol': 1e-12,
'gtol': 1e-12
})
xmatrix, ymatrix = wpu.grid_coord(thickness_cropped, pixelSize)
isNotNAN = np.isfinite(thickness_cropped[thickness_cropped.shape[0] //
2, :])
plot_residual_1d(
xmatrix[0, isNotNAN],
thickness_cropped[thickness_cropped.shape[0] // 2, isNotNAN],
fitted[thickness_cropped.shape[0] // 2, isNotNAN],
str4title=str4graphs + '\nFit center profile Horizontal, ' +
' R = {:.4g} um'.format(fitParameters[0] * 1e6),
saveFigFlag=True,
saveAsciiFlag=True)
isNotNAN = np.isfinite(
thickness_cropped[:, thickness_cropped.shape[1] // 2])
plot_residual_1d(
def _n_profiles_H_V(arrayH,
arrayV,
virtual_pixelsize,
zlabel=r'z',
titleH='Horiz',
titleV='Vert',
nprofiles=5,
filter_width=0,
remove2ndOrder=False,
saveFileSuf='',
saveFigFlag=True):
xxGrid, yyGrid = wpu.grid_coord(arrayH, virtual_pixelsize)
fit_coefs = [[], []]
data2saveH = None
data2saveV = None
labels_H = None
labels_V = None
plt.rcParams['lines.markersize'] = 4
plt.rcParams['lines.linewidth'] = 2
# Horizontal
if np.all(np.isfinite(arrayH)):
plt.figure(figsize=(12, 12 * 9 / 16))
xvec = xxGrid[0, :]
data2saveH = np.c_[xvec]
header = ['x [m]']
ls_cycle, lc_jet = wpu.line_style_cycle(['-'], ['o', 's', 'd', '^'],
ncurves=nprofiles,
cmap_str='gist_rainbow_r')
lc = []
labels_H = []
for i, row in enumerate(
np.linspace(filter_width // 2,
np.shape(arrayV)[0] - filter_width // 2 - 1,
nprofiles + 2,
dtype=int)):
if i == 0 or i == nprofiles + 1:
continue
if filter_width != 0:
yvec = arrayH[row - filter_width:row + filter_width, :]
yvec = np.sum(yvec, 0) / filter_width / 2
else:
yvec = arrayH[row, :]
lc.append(next(lc_jet))
p01 = np.polyfit(xvec, yvec, 1)
fit_coefs[0].append(p01)
if remove2ndOrder:
yvec -= p01[0] * xvec + p01[1]
plt.plot(xvec * 1e6,
yvec,
next(ls_cycle),
color=lc[i - 1],
label=str(row))
if not remove2ndOrder:
plt.plot(xvec * 1e6,
p01[0] * xvec + p01[1],
'--',
color=lc[i - 1],
lw=3)
data2saveH = np.c_[data2saveH, yvec]
header.append(str(row))
labels_H.append(str(row))
if remove2ndOrder:
titleH = titleH + ', 2nd order removed'
plt.legend(title='Pixel Y', loc=0, fontsize=12)
plt.xlabel(r'x [$\mu m$]', fontsize=18)
plt.ylabel(zlabel, fontsize=18)
plt.title(titleH + ', Filter Width = {:d} pixels'.format(filter_width),
fontsize=20)
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf + '_H')
plt.show(block=False)
header.append(zlabel +
', Filter Width = {:d} pixels'.format(filter_width))
wpu.save_csv_file(data2saveH,
wpu.get_unique_filename(
saveFileSuf + '_WF_profiles_H', 'csv'),
headerList=header)
plt.figure(figsize=(12, 12 * 9 / 16))
plt.imshow(arrayH,
cmap='RdGy',
vmin=wpu.mean_plus_n_sigma(arrayH, -3),
vmax=wpu.mean_plus_n_sigma(arrayH, 3))
plt.xlabel('Pixel')
plt.ylabel('Pixel')
plt.title(titleH + ', Profiles Position')
currentAxis = plt.gca()
_, lc_jet = wpu.line_style_cycle(['-'], ['o', 's', 'd', '^'],
ncurves=nprofiles,
cmap_str='gist_rainbow_r')
for i, row in enumerate(
np.linspace(filter_width // 2,
np.shape(arrayV)[0] - filter_width // 2 - 1,
nprofiles + 2,
dtype=int)):
if i == 0 or i == nprofiles + 1:
continue
currentAxis.add_patch(
Rectangle((-.5, row - filter_width // 2 - .5),
np.shape(arrayH)[1],
filter_width,
facecolor=lc[i - 1],
alpha=.5))
plt.axhline(row, color=lc[i - 1])
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf + '_H')
plt.show(block=True)
# Vertical
if np.all(np.isfinite(arrayV)):
plt.figure(figsize=(12, 12 * 9 / 16))
xvec = yyGrid[:, 0]
data2saveV = np.c_[xvec]
header = ['y [m]']
ls_cycle, lc_jet = wpu.line_style_cycle(['-'], ['o', 's', 'd', '^'],
ncurves=nprofiles,
cmap_str='gist_rainbow_r')
lc = []
labels_V = []
for i, col in enumerate(
np.linspace(filter_width // 2,
np.shape(arrayH)[1] - filter_width // 2 - 1,
nprofiles + 2,
dtype=int)):
if i == 0 or i == nprofiles + 1:
continue
if filter_width != 0:
yvec = arrayV[:, col - filter_width:col + filter_width]
yvec = np.sum(yvec, 1) / filter_width / 2
else:
yvec = arrayV[:, col]
lc.append(next(lc_jet))
p10 = np.polyfit(xvec, yvec, 1)
fit_coefs[1].append(p10)
if remove2ndOrder:
yvec -= p10[0] * xvec + p10[1]
plt.plot(xvec * 1e6,
yvec,
next(ls_cycle),
color=lc[i - 1],
label=str(col))
if not remove2ndOrder:
plt.plot(xvec * 1e6,
p10[0] * xvec + p10[1],
'--',
color=lc[i - 1],
lw=3)
data2saveV = np.c_[data2saveV, yvec]
header.append(str(col))
labels_V.append(str(col))
if remove2ndOrder:
titleV = titleV + ', 2nd order removed'
plt.legend(title='Pixel X', loc=0, fontsize=12)
plt.xlabel(r'y [$\mu m$]', fontsize=18)
plt.ylabel(zlabel, fontsize=18)
plt.title(titleV + ', Filter Width = {:d} pixels'.format(filter_width),
fontsize=20)
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf + '_Y')
plt.show(block=False)
header.append(zlabel +
', Filter Width = {:d} pixels'.format(filter_width))
wpu.save_csv_file(data2saveV,
wpu.get_unique_filename(
saveFileSuf + '_WF_profiles_V', 'csv'),
headerList=header)
plt.figure(figsize=(12, 12 * 9 / 16))
plt.imshow(arrayV,
cmap='RdGy',
vmin=wpu.mean_plus_n_sigma(arrayV, -3),
vmax=wpu.mean_plus_n_sigma(arrayV, 3))
plt.xlabel('Pixel')
plt.ylabel('Pixel')
plt.title(titleV + ', Profiles Position')
currentAxis = plt.gca()
for i, col in enumerate(
np.linspace(filter_width // 2,
np.shape(arrayH)[1] - filter_width // 2 - 1,
nprofiles + 2,
dtype=int)):
if i == 0 or i == nprofiles + 1:
continue
currentAxis.add_patch(
Rectangle((col - filter_width // 2 - .5, -.5),
filter_width,
np.shape(arrayV)[0],
facecolor=lc[i - 1],
alpha=.5))
plt.axvline(col, color=lc[i - 1])
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf + '_Y')
plt.show(block=True)
return data2saveH, data2saveV, labels_H, labels_V, fit_coefs
def _n_profiles_H_V(arrayH, arrayV, virtual_pixelsize,
zlabel=r'z',
titleH='Horiz', titleV='Vert',
nprofiles=5, filter_width=0,
remove1stOrderDPC=False,
saveFileSuf='',
saveFigFlag=True):
xxGrid, yyGrid = wpu.grid_coord(arrayH, virtual_pixelsize)
fit_coefs = [[], []]
data2saveH = None
data2saveV = None
labels_H = None
labels_V = None
plt.rcParams['lines.markersize'] = 4
plt.rcParams['lines.linewidth'] = 2
# Horizontal
if np.all(np.isfinite(arrayH)):
plt.figure(figsize=(12, 12*9/16))
xvec = xxGrid[0, :]
data2saveH = np.c_[xvec]
header = ['x [m]']
if filter_width != 0:
arrayH_filtered = uniform_filter1d(arrayH, filter_width, 0)
else:
arrayH_filtered = arrayH
ls_cycle, lc_jet = wpu.line_style_cycle(['-'], ['o', 's', 'd', '^'],
ncurves=nprofiles,
cmap_str='gist_rainbow_r')
lc = []
labels_H = []
for i, row in enumerate(np.linspace(filter_width//2,
np.shape(arrayV)[0]-filter_width//2-1,
nprofiles + 2, dtype=int)):
if i == 0 or i == nprofiles + 1:
continue
yvec = arrayH_filtered[row, :]
lc.append(next(lc_jet))
p01 = np.polyfit(xvec, yvec, 1)
fit_coefs[0].append(p01)
if remove1stOrderDPC:
yvec -= p01[0]*xvec + p01[1]
plt.plot(xvec*1e6, yvec, next(ls_cycle), color=lc[i-1],
label=str(row))
if not remove1stOrderDPC:
plt.plot(xvec*1e6, p01[0]*xvec + p01[1], '--',
color=lc[i-1], lw=3)
data2saveH = np.c_[data2saveH, yvec]
header.append(str(row))
labels_H.append(str(row))
if remove1stOrderDPC:
titleH = titleH + ', 2nd order removed'
plt.legend(title='Pixel Y', loc=0, fontsize=12)
plt.xlabel(r'x [$\mu m$]', fontsize=18)
plt.ylabel(zlabel, fontsize=18)
plt.title(titleH + ', Filter Width = {:d} pixels'.format(filter_width),
fontsize=20)
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf + '_H')
plt.show(block=False)
header.append(zlabel + ', Filter Width = {:d} pixels'.format(filter_width))
wpu.save_csv_file(data2saveH,
wpu.get_unique_filename(saveFileSuf +
'_WF_profiles_H', 'csv'),
headerList=header)
plt.figure(figsize=(12, 12*9/16))
plt.imshow(arrayH, cmap='RdGy',
vmin=wpu.mean_plus_n_sigma(arrayH, -3),
vmax=wpu.mean_plus_n_sigma(arrayH, 3))
plt.xlabel('Pixel')
plt.ylabel('Pixel')
plt.title(titleH + ', Profiles Position')
currentAxis = plt.gca()
_, lc_jet = wpu.line_style_cycle(['-'], ['o', 's', 'd', '^'],
ncurves=nprofiles,
cmap_str='gist_rainbow_r')
for i, row in enumerate(np.linspace(filter_width//2,
np.shape(arrayV)[0]-filter_width//2-1,
nprofiles + 2, dtype=int)):
if i == 0 or i == nprofiles + 1:
continue
currentAxis.add_patch(Rectangle((-.5, row - filter_width//2 - .5),
np.shape(arrayH)[1], filter_width,
facecolor=lc[i-1], alpha=.5))
plt.axhline(row, color=lc[i-1])
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf + '_H')
plt.show(block=True)
# Vertical
if np.all(np.isfinite(arrayV)):
plt.figure(figsize=(12, 12*9/16))
xvec = yyGrid[:, 0]
data2saveV = np.c_[xvec]
header = ['y [m]']
if filter_width != 0:
arrayV_filtered = uniform_filter1d(arrayV, filter_width, 1)
else:
arrayV_filtered = arrayV
ls_cycle, lc_jet = wpu.line_style_cycle(['-'], ['o', 's', 'd', '^'],
ncurves=nprofiles,
cmap_str='gist_rainbow_r')
lc = []
labels_V = []
for i, col in enumerate(np.linspace(filter_width//2,
np.shape(arrayH)[1]-filter_width//2-1,
nprofiles + 2, dtype=int)):
if i == 0 or i == nprofiles + 1:
continue
yvec = arrayV_filtered[:, col]
lc.append(next(lc_jet))
p10 = np.polyfit(xvec, yvec, 1)
fit_coefs[1].append(p10)
if remove1stOrderDPC:
yvec -= p10[0]*xvec + p10[1]
plt.plot(xvec*1e6, yvec, next(ls_cycle), color=lc[i-1],
label=str(col))
if not remove1stOrderDPC:
plt.plot(xvec*1e6, p10[0]*xvec + p10[1], '--',
color=lc[i-1], lw=3)
data2saveV = np.c_[data2saveV, yvec]
header.append(str(col))
labels_V.append(str(col))
if remove1stOrderDPC:
titleV = titleV + ', 2nd order removed'
plt.legend(title='Pixel X', loc=0, fontsize=12)
plt.xlabel(r'y [$\mu m$]', fontsize=18)
plt.ylabel(zlabel, fontsize=18)
plt.title(titleV + ', Filter Width = {:d} pixels'.format(filter_width),
fontsize=20)
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf + '_Y')
plt.show(block=False)
header.append(zlabel + ', Filter Width = {:d} pixels'.format(filter_width))
wpu.save_csv_file(data2saveV,
wpu.get_unique_filename(saveFileSuf +
'_WF_profiles_V', 'csv'),
headerList=header)
plt.figure(figsize=(12, 12*9/16))
plt.imshow(arrayV, cmap='RdGy',
vmin=wpu.mean_plus_n_sigma(arrayV, -3),
vmax=wpu.mean_plus_n_sigma(arrayV, 3))
plt.xlabel('Pixel')
plt.ylabel('Pixel')
plt.title(titleV + ', Profiles Position')
currentAxis = plt.gca()
for i, col in enumerate(np.linspace(filter_width//2,
np.shape(arrayH)[1]-filter_width//2-1,
nprofiles + 2, dtype=int)):
if i == 0 or i == nprofiles + 1:
continue
currentAxis.add_patch(Rectangle((col - filter_width//2 - .5, -.5),
filter_width, np.shape(arrayV)[0],
facecolor=lc[i-1], alpha=.5))
plt.axvline(col, color=lc[i-1])
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf + '_Y')
plt.show(block=True)
return data2saveH, data2saveV, labels_H, labels_V, fit_coefs
def plot_integration(integrated, pixelsize,
titleStr='Title', ctitle=' ',
max3d_grid_points=101,
plotProfile=True,
plot3dFlag=True,
saveFigFlag=False,
saveFileSuf='graph',
**kwarg4surf):
'''
TODO: Write Docstring
'''
xxGrid, yyGrid = wpu.grid_coord(integrated, pixelsize)
factor_x, unit_x = wpu.choose_unit(xxGrid)
factor_y, unit_y = wpu.choose_unit(yyGrid)
# Plot Integration 2
if plot3dFlag:
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
rstride = integrated.shape[0] // max3d_grid_points + 1
cstride = integrated.shape[1] // max3d_grid_points + 1
surf = ax.plot_surface(xxGrid*factor_x, yyGrid*factor_y,
integrated[::-1, :],
rstride=rstride,
cstride=cstride,
cmap='viridis', linewidth=0.1, **kwarg4surf)
ax_lim = np.max([np.abs(xxGrid*factor_x), np.abs(yyGrid*factor_y)])
ax.set_xlim3d(-ax_lim, ax_lim)
ax.set_ylim3d(-ax_lim, ax_lim)
if 'vmin' in kwarg4surf:
ax.set_zlim3d(bottom=kwarg4surf['vmin'])
if 'vmax' in kwarg4surf:
ax.set_zlim3d(top=kwarg4surf['vmax'])
plt.xlabel(r'$x [' + unit_x + ' m]$', fontsize=24)
plt.ylabel(r'$y [' + unit_y + ' m]$', fontsize=24)
plt.title(titleStr, fontsize=24, weight='bold')
cbar = plt.colorbar(surf, shrink=.8, aspect=20)
cbar.ax.set_title(ctitle, y=1.01)
plt.tight_layout(rect=[0, 0, 1, 1])
ax.text2D(0.05, 0.9, 'strides = {}, {}'.format(rstride, cstride),
transform=ax.transAxes)
if saveFigFlag:
ax.view_init(elev=30, azim=60)
wpu.save_figs_with_idx(saveFileSuf)
ax.view_init(elev=30, azim=-120)
wpu.save_figs_with_idx(saveFileSuf)
plt.pause(.5)
plt.show(block=False)
if plotProfile:
wpu.plot_profile(xxGrid*factor_x, yyGrid*factor_y,
integrated[::-1, :],
xlabel=r'$x [' + unit_x + ' m]$',
ylabel=r'$y [' + unit_y + ' m]$',
title=titleStr,
xunit='\mu m', yunit='\mu m',
arg4main={'cmap': 'viridis', 'lw': 3})
if saveFigFlag:
plt.ioff()
plt.figure(figsize=(10, 8))
plt.imshow(integrated[::-1, :], cmap='viridis',
extent=wpu.extent_func(integrated, pixelsize)*factor_x,
**kwarg4surf)
plt.xlabel(r'$x [' + unit_x + ' m]$', fontsize=24)
plt.ylabel(r'$y [' + unit_x + ' m]$', fontsize=24)
plt.title(titleStr, fontsize=18, weight='bold')
cbar = plt.colorbar()
cbar.ax.set_title(ctitle, y=1.01)
wpu.save_figs_with_idx(saveFileSuf)
plt.close()
plt.ion()
def plot_integration(integrated, pixelsize,
titleStr='Title', ctitle=' ', saveFigFlag=False,
saveFileSuf='graph'):
'''
TODO: Write Docstring
'''
xxGrid, yyGrid = wpu.grid_coord(integrated, pixelsize)
factor_x, unit_x = wpu.choose_unit(xxGrid)
factor_y, unit_y = wpu.choose_unit(yyGrid)
wpu.plot_profile(xxGrid*factor_x, yyGrid*factor_y, integrated[::-1, :],
xlabel=r'$x [' + unit_x + ' m]$',
ylabel=r'$y [' + unit_y + ' m]$',
title=titleStr,
xunit='\mu m', yunit='\mu m',
arg4main={'cmap': 'viridis', 'lw': 3})
if saveFigFlag:
plt.ioff()
plt.figure(figsize=(10, 8))
plt.imshow(integrated[::-1, :], cmap='viridis',
extent=wpu.extent_func(integrated, pixelsize)*factor_x)
plt.xlabel(r'$x [' + unit_x + ' m]$', fontsize=24)
plt.ylabel(r'$y [' + unit_x + ' m]$', fontsize=24)
plt.title(titleStr, fontsize=18, weight='bold')
cbar = plt.colorbar()
cbar.ax.set_title(ctitle, y=1.01)
wpu.save_figs_with_idx(saveFileSuf)
# plt.show(block=False)
plt.close(plt.gcf())
plt.ion()
# Plot Integration 2
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
rstride = integrated.shape[0] // 101 + 1
cstride = integrated.shape[1] // 101 + 1
surf = ax.plot_surface(xxGrid*factor_x, yyGrid*factor_y,
integrated[::-1, :],
rstride=rstride,
cstride=cstride,
cmap='viridis', linewidth=0.1)
ax_lim = np.max([np.abs(xxGrid*factor_x), np.abs(yyGrid*factor_y)])
ax.set_xlim3d(-ax_lim, ax_lim)
ax.set_ylim3d(-ax_lim, ax_lim)
plt.xlabel(r'$x [' + unit_x + ' m]$', fontsize=24)
plt.ylabel(r'$y [' + unit_y + ' m]$', fontsize=24)
plt.title(titleStr, fontsize=24, weight='bold')
cbar = plt.colorbar(surf, shrink=.8, aspect=20)
cbar.ax.set_title(ctitle, y=1.01)
fig.tight_layout()
plt.tight_layout()
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf)
ax.text2D(0.05, 0.9, 'strides = {}, {}'.format(rstride, cstride),
transform=ax.transAxes)
plt.show(block=False)
return ax
