def curv_from_height(height,
virtual_pixelsize,
grazing_angle=0.0,
projectionFromDiv=1.0,
labels=[],
xlabel='x',
ylabel='Curvature',
titleStr='',
saveFileSuf=''):
ls_cycle, lc_cycle = wpu.line_style_cycle(['-'], ['o', 's', 'd', '^'],
ncurves=height.shape[1] - 1,
cmap_str='gist_rainbow_r')
if grazing_angle // .00001 > 0:
projection = 1 / np.sin(grazing_angle) * projectionFromDiv
else:
projection = projectionFromDiv
projected_pixel = virtual_pixelsize * projection
xvec = wpu.realcoordvec(height.shape[0] - 2, projected_pixel)
print('projected_pixel')
print(projected_pixel)
plt.figure(figsize=(12, 12 * 9 / 16))
list_curv = [xvec]
header = [xlabel + ' [m]']
for j_line in range(1, height.shape[1]):
curv = np.diff(np.diff(height[:, j_line])) / projected_pixel**2
if j_line == 1:
factor_x, unit_x = wpu.choose_unit(xvec)
#factor_y, unit_y = wpu.choose_unit(curv)
list_curv.append(curv)
header.append(labels[j_line - 1])
plt.plot(xvec * factor_x,
curv,
next(ls_cycle),
c=next(lc_cycle),
label=labels[j_line - 1])
marginx = 0.1 * np.ptp(xvec * factor_x)
plt.xlim(
[np.min(xvec * factor_x) - marginx,
np.max(xvec * factor_x) + marginx])
plt.xlabel(xlabel + r' [$' + unit_x + ' m$]')
plt.ylabel(ylabel + r'[$m^{-1}$]')
plt.legend(loc=0, fontsize=12)
if grazing_angle // .00001 > 0:
plt.title(titleStr + 'Mirror Curvature,\n' +
'grazing angle {:.2f} mrad,\n'.format(grazing_angle * 1e3) +
'projection due divergence = ' +
r'$ \times $ {:.2f}'.format(projectionFromDiv))
else:
plt.title(titleStr + 'Curvature')
plt.tight_layout()
wpu.save_figs_with_idx(saveFileSuf)
plt.show()
data2saveV = np.asarray(list_curv).T
header.append(ylabel + ' [1/m]')
if grazing_angle // .00001 > 0:
header.append(', grazing_angle = {:.4g}'.format(grazing_angle))
if projectionFromDiv // 1 != 1:
header.append('projection due divergence = ' +
'{:.2f}x'.format(projectionFromDiv))
wpu.save_csv_file(data2saveV,
wpu.get_unique_filename(saveFileSuf + '_curv_' + xlabel,
'csv'),
headerList=header)
return np.asarray(list_curv).T
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 integrate_DPC_cumsum(data_DPC,
wavelength,
grazing_angle=0.0,
projectionFromDiv=1.0,
labels=[],
xlabel='x',
ylabel='Height',
titleStr='',
saveFileSuf=''):
ls_cycle, lc_cycle = wpu.line_style_cycle(['-'], ['o', 's', 'd', '^'],
ncurves=data_DPC.shape[1] - 1,
cmap_str='gist_rainbow_r')
if grazing_angle // .00001 > 0:
projection = 1 / np.sin(grazing_angle) * projectionFromDiv
else:
projection = projectionFromDiv
xvec = data_DPC[:, 0] * projection
plt.figure(figsize=(12, 12 * 9 / 16))
list_integrated = [xvec]
header = [xlabel + ' [m]']
for j_line in range(1, data_DPC.shape[1]):
integrated = (
np.cumsum(data_DPC[:, j_line] - np.mean(data_DPC[:, j_line])) *
(xvec[1] - xvec[0]))
integrated *= -1 / 2 / np.pi * wavelength * np.abs(projection)
# TODO: check here!!
if j_line == 1:
factor_x, unit_x = wpu.choose_unit(xvec)
factor_y, unit_y = wpu.choose_unit(integrated)
list_integrated.append(integrated)
header.append(labels[j_line - 1])
plt.plot(xvec * factor_x,
integrated * factor_y,
next(ls_cycle),
c=next(lc_cycle),
label=labels[j_line - 1])
marginx = 0.1 * np.ptp(xvec * factor_x)
plt.xlim(
[np.min(xvec * factor_x) - marginx,
np.max(xvec * factor_x) + marginx])
plt.xlabel(xlabel + r' [$' + unit_x + ' m$]')
plt.ylabel(ylabel + r' [$' + unit_y + ' m$]')
plt.legend(loc=0, fontsize=12)
if grazing_angle // .00001 > 0:
plt.title(titleStr + 'Mirror Height,\n' +
'grazing angle {:.2f} mrad,\n'.format(grazing_angle * 1e3) +
'projection due divergence = ' +
r'$ \times $ {:.2f}'.format(projectionFromDiv))
else:
plt.title(titleStr + 'Integration Cumulative Sum')
plt.tight_layout()
wpu.save_figs_with_idx(saveFileSuf)
plt.show()
data2saveV = np.asarray(list_integrated).T
header.append(ylabel + ' [m]')
if grazing_angle // .00001 > 0:
header.append('grazing_angle = {:.4g}'.format(grazing_angle))
if projectionFromDiv // 1 != 1:
header.append('projection due divergence = ' +
'{:.2f}x'.format(projectionFromDiv))
wpu.save_csv_file(data2saveV,
wpu.get_unique_filename(
saveFileSuf + '_integrated_' + xlabel, 'csv'),
headerList=header)
return np.asarray(list_integrated).T
# data[:,1:] *= np.pi
list_all_data.append(data)
xmin = np.min([xmin, np.min(data[:, 0])])
xmax = np.max([xmax, np.max(data[:, 0])])
# cant convert list_all_data to array because each array in the list can
# have different sizes. It is necessary to do the spline to have same
# vector sizes
# %%
lstyle_cycle, lc_cycle = wpu.line_style_cycle(ls=['-', '--'],
ms=['s', 'o', 'd', '^', 'v'],
cmap_str='default',
ncurves=nfiles)
# %% Plot Raw data
curve_index = 3
plt.figure(figsize=(12, 12 * 9 / 16))
for i in range(0, nfiles, step_files):
data = list_all_data[i]
plt.plot(data[:, 0] * 1e3,
data[:, curve_index] * 1e9,
next(lstyle_cycle),
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 curv_from_height(height, virtual_pixelsize,
grazing_angle=0.0, projectionFromDiv=1.0,
labels=[],
xlabel='x', ylabel='Curvature',
titleStr='', saveFileSuf=''):
ls_cycle, lc_cycle = wpu.line_style_cycle(['-'], ['o', 's', 'd', '^'],
ncurves=height.shape[1] - 1,
cmap_str='gist_rainbow_r')
if grazing_angle//.00001 > 0:
projection = 1/np.sin(grazing_angle)*projectionFromDiv
else:
projection = projectionFromDiv
projected_pixel = virtual_pixelsize*projection
xvec = wpu.realcoordvec(height.shape[0]-2, projected_pixel)
print('projected_pixel')
print(projected_pixel)
plt.figure(figsize=(12, 12*9/16))
list_curv = [xvec]
header = [xlabel + ' [m]']
for j_line in range(1, height.shape[1]):
curv = np.diff(np.diff(height[:, j_line]))/projected_pixel**2
if j_line == 1:
factor_x, unit_x = wpu.choose_unit(xvec)
#factor_y, unit_y = wpu.choose_unit(curv)
list_curv.append(curv)
header.append(labels[j_line - 1])
plt.plot(xvec*factor_x, curv,
next(ls_cycle), c=next(lc_cycle),
label=labels[j_line - 1])
marginx = 0.1*np.ptp(xvec*factor_x)
plt.xlim([np.min(xvec*factor_x)-marginx,
np.max(xvec*factor_x)+marginx])
plt.xlabel(xlabel + r' [$' + unit_x + ' m$]')
plt.ylabel(ylabel + r'[$m^{-1}$]')
plt.legend(loc=0, fontsize=12)
if grazing_angle//.00001 > 0:
plt.title(titleStr + 'Mirror Curvature,\n' +
'grazing angle {:.2f} mrad,\n'.format(grazing_angle*1e3) +
'projection due divergence = ' +
r'$ \times $ {:.2f}'.format(projectionFromDiv))
else:
plt.title(titleStr + 'Curvature')
plt.tight_layout()
wpu.save_figs_with_idx(saveFileSuf)
plt.show()
data2saveV = np.asarray(list_curv).T
header.append(ylabel + ' [1/m]')
if grazing_angle//.00001 > 0:
header.append(', grazing_angle = {:.4g}'.format(grazing_angle))
if projectionFromDiv//1 != 1:
header.append('projection due divergence = ' +
'{:.2f}x'.format(projectionFromDiv))
wpu.save_csv_file(data2saveV,
wpu.get_unique_filename(saveFileSuf +
'_curv_' + xlabel, 'csv'),
headerList=header)
return np.asarray(list_curv).T
def integrate_DPC_cumsum(data_DPC, wavelength,
grazing_angle=0.0, projectionFromDiv=1.0,
remove2ndOrder=False,
labels=[],
xlabel='x', ylabel='Height',
titleStr='', saveFileSuf=''):
ls_cycle, lc_cycle = wpu.line_style_cycle(['-'], ['o', 's', 'd', '^'],
ncurves=data_DPC.shape[1] - 1,
cmap_str='gist_rainbow_r')
if grazing_angle//.00001 > 0:
projection = 1/np.sin(grazing_angle)*projectionFromDiv
else:
projection = projectionFromDiv
xvec = data_DPC[:, 0]*projection
plt.figure(figsize=(12, 12*9/16))
list_integrated = [xvec]
header = [xlabel + ' [m]']
for j_line in range(1, data_DPC.shape[1]):
integrated = (np.cumsum(data_DPC[:, j_line] - np.mean(data_DPC[:, j_line]))
* (xvec[1]-xvec[0])) # TODO: removed mean 20181020
# integrated = (np.cumsum(data_DPC[:, j_line])) * (xvec[1]-xvec[0])
integrated *= -1/2/np.pi*wavelength*np.abs(projection)
p02 = np.polyfit(xvec, integrated, 2)
fitted_pol2 = p02[0]*xvec**2 + p02[1]*xvec + p02[2]
if remove2ndOrder:
integrated -= fitted_pol2
titleStr += 'Removed 2nd order, '
# TODO: check here!!
if j_line == 1:
factor_x, unit_x = wpu.choose_unit(xvec)
factor_y, unit_y = wpu.choose_unit(integrated)
list_integrated.append(integrated)
header.append(labels[j_line - 1])
lc = next(lc_cycle)
plt.plot(xvec*factor_x,
integrated*factor_y,
next(ls_cycle), c=lc,
label=labels[j_line - 1])
if not remove2ndOrder:
plt.plot(xvec*1e6, (fitted_pol2)*factor_y,
'--', color=lc, lw=3)
marginx = 0.1*np.ptp(xvec*factor_x)
plt.xlim([np.min(xvec*factor_x)-marginx,
np.max(xvec*factor_x)+marginx])
plt.xlabel(xlabel + r' [$' + unit_x + ' m$]')
plt.ylabel(ylabel + r' [$' + unit_y + ' m$]')
plt.legend(loc=0, fontsize=12)
if grazing_angle//.00001 > 0:
plt.title(titleStr + 'Mirror Height,\n' +
'grazing angle {:.2f} mrad,\n'.format(grazing_angle*1e3) +
'projection due divergence = ' +
r'$ \times $ {:.2f}'.format(projectionFromDiv))
else:
plt.title(titleStr + 'Integration Cumulative Sum')
plt.tight_layout()
wpu.save_figs_with_idx(saveFileSuf)
plt.show()
data2saveV = np.asarray(list_integrated).T
header.append(ylabel + ' [m]')
if grazing_angle//.00001 > 0:
header.append('grazing_angle = {:.4g}'.format(grazing_angle))
if projectionFromDiv//1 != 1:
header.append('projection due divergence = ' +
'{:.2f}x'.format(projectionFromDiv))
wpu.save_csv_file(data2saveV,
wpu.get_unique_filename(saveFileSuf +
'_integrated_' + xlabel, 'csv'),
headerList=header)
return np.asarray(list_integrated).T