def plot_90deg_summary():
# load data
data29 = load_data(join(datapath, f66029)) # 90 degrees
data57 = load_data(join(datapath, f66057)) # 90 degrees
data58 = load_data(join(datapath, f66058)) # 90 degrees
data_1hour = data57 + data58
# get monitor counts
mon57 = get_monitor_counts("{}.yaml".format(os.path.splitext(f66057)[0]))
mon58 = get_monitor_counts("{}.yaml".format(os.path.splitext(f66058)[0]))
mon29 = get_monitor_counts("{}.yaml".format(os.path.splitext(f66029)[0]))
simulation = get_simulation()
axes_limits = ba.get_axes_limits(simulation.result(), ba.AxesUnits.QSPACE)
qy = np.linspace(axes_limits[0], axes_limits[1], data29.shape[0])
simulation_1 = get_simulation(u0=46.0)
axes_limits_1 = ba.get_axes_limits(simulation_1.result(),
ba.AxesUnits.QSPACE)
qy_1hour = np.linspace(axes_limits_1[0], axes_limits_1[1], data57.shape[0])
sum_29 = np.sum(data29, axis=0) / mon29
sum_1hour = np.sum(data_1hour, axis=0) / (mon57 + mon58)
plt.style.use('seaborn-talk')
plt.subplot(1, 2, 1)
plt.semilogy(qy,
sum_29,
color='k',
marker='o',
markersize=5,
linestyle='None',
label=r"Over the night")
plt.semilogy(qy_1hour,
sum_1hour,
color='b',
marker='s',
markersize=5,
linestyle='None',
label=r"1 hour")
plt.xlabel(r'$Q_y$ (nm$^{-1}$)')
plt.ylabel(r'$I$ (a. u.)')
plt.ylim([2.0e-07, 5.0e-06])
plt.title(r"Sample rotation $90^\circ$ summary", fontsize=16)
plt.legend(loc='upper right', fontsize=14)
plt.subplot(1, 2, 2)
img = plt.imread(join(datapath, 'rot90deg.jpg'))
plt.imshow(img)
plt.axis('off')
plt.show()
def plot_45deg_summary():
# load data
data28 = load_data(join(datapath, f66028)) # 45 degrees
# get monitor counts
mon28 = get_monitor_counts("{}.yaml".format(os.path.splitext(f66028)[0]))
simulation = get_simulation()
axes_limits = ba.get_axes_limits(simulation.result(), ba.AxesUnits.QSPACE)
qy = np.linspace(axes_limits[0], axes_limits[1], data28.shape[0])
sum_28 = np.sum(data28, axis=0) / mon28
plt.style.use('seaborn-talk')
plt.subplot(1, 2, 1)
plt.semilogy(qy,
sum_28,
color='k',
marker='o',
markersize=5,
linestyle='None',
label=r"Over the night")
plt.xlabel(r'$Q_y$ (nm$^{-1}$)')
plt.ylabel(r'$I$ (a. u.)')
plt.ylim([2.0e-07, 2.0e-06])
plt.title(r"Sample rotation $45^\circ$ summary", fontsize=16)
plt.legend(loc='upper right', fontsize=14)
plt.subplot(1, 2, 2)
img = plt.imread(join(datapath, 'rot45deg.jpg'))
plt.imshow(img)
plt.axis('off')
plt.show()
def get_i_of_q(data, nbins=256):
"""
calculates I(Q) where Q = sqrt(Qx^2 + Qy^2)
:param data:
:return:
"""
simulation = get_simulation()
result = simulation.result()
axes_limits = ba.get_axes_limits(result, ba.AxesUnits.QSPACE)
shape = data.shape
x = np.linspace(axes_limits[0], axes_limits[1], shape[0])
y = np.linspace(axes_limits[2], axes_limits[3], shape[1])
# xx, yy = np.meshgrid (x, y)
# q = np.sqrt(xx**2 + yy**2)
# result = np.array([q.flatten(), data.flatten()]).transpose()
result = []
for i in range(shape[0]):
for j in range(shape[1]):
q = np.sqrt(x[i]**2 + y[j]**2)
result.append([q, data[i, j]])
result = np.array(result)
# sort data
result = result[result[:, 0].argsort()]
# bin data
bins = np.linspace(0.0, np.max(q), nbins)
indices = np.digitize(result[:, 0], bins)
a = []
for i in range(bins.size):
idx = np.where(indices == i)
if idx[0].size > 0:
a.append([bins[i], np.mean(result[:, 1][idx])])
return np.array(a)
def plot_s3278_2d():
fname = "0006543{r1}_000{r2}_p15749_S3278-GiSANS-0.6dgr-rot-scan-m15-15dgr_HRD_standard.{ext}"
runs = [(0, 1), (1, 1), (2, 2), (3, 3), (4, 4), (5, 1), (6, 2), (7, 3),
(8, 0)]
simulation = get_simulation()
result = simulation.result()
axes_limits = ba.get_axes_limits(result, ba.AxesUnits.QSPACE)
plt.style.use('seaborn-talk')
plt.suptitle("Sample S3278 SDD=1300 mm", fontsize=16)
for i in runs:
data = load_data(
join(datapath, fname.format(r1=i[0], r2=i[1], ext='det')))
omega = get_rotation_angle(
join(datapath, fname.format(r1=i[0], r2=i[1], ext='yaml')))
ax = plt.subplot(3, 3, i[0] + 1)
plt.subplots_adjust(wspace=0.2, hspace=0.3)
im = plt.imshow(data,
norm=matplotlib.colors.LogNorm(0.1, 1000),
extent=axes_limits,
aspect='auto',
cmap='jet')
cb = plt.colorbar(im)
# plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
plt.xlabel(r'$Q_y$ (nm$^{-1}$)')
plt.ylabel(r'$Q_z$ (nm$^{-1}$)')
plt.title(r"Run 0006543{n}, $\omega={om}^\circ$".format(n=i[0],
om=omega))
plt.show()
def plot_all_2d():
data65952 = load_data(join(datapath, f65952))
data65953 = load_data(join(datapath, f65953))
data66023 = load_data(join(datapath, f66023))
data66024 = load_data(join(datapath, f66024))
data66028 = load_data(join(datapath, f66028))
data66029 = load_data(join(datapath, f66029))
data66055 = load_data(join(datapath, f66055))
data66057 = load_data(join(datapath, f66057))
data66058 = load_data(join(datapath, f66058))
datalist = [(65952, data65952), (65953, data65953), (66023, data66023),
(66024, data66024), (66028, data66028), (66029, data66029),
(66055, data66055), (66057, data66057), (66058, data66058)]
simulation = get_simulation()
result = simulation.result()
axes_limits = ba.get_axes_limits(result, ba.AxesUnits.QSPACE)
plt.style.use('seaborn-talk')
plt.suptitle("Sample S3098 SDD=9200 mm", fontsize=16)
i = 1
for run, data in datalist:
ax = plt.subplot(3, 3, i)
plt.subplots_adjust(wspace=0.2, hspace=0.3)
im = plt.imshow(data,
norm=matplotlib.colors.LogNorm(0.1, 50),
extent=axes_limits,
aspect='auto',
cmap='jet')
cb = plt.colorbar(im)
# plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
plt.xlabel(r'$Q_y$ (nm$^{-1}$)')
plt.ylabel(r'$Q_z$ (a. u.)')
plt.title(r"Run {}".format(run))
i += 1
plt.show()
def plot_all_1d():
data65952 = load_data(join(datapath, f65952))
data65953 = load_data(join(datapath, f65953))
data66023 = load_data(join(datapath, f66023))
data66024 = load_data(join(datapath, f66024))
data66028 = load_data(join(datapath, f66028))
data66029 = load_data(join(datapath, f66029))
data66055 = load_data(join(datapath, f66055))
data66057 = load_data(join(datapath, f66057))
data66058 = load_data(join(datapath, f66058))
datalist = [(65952, data65952), (65953, data65953), (66023, data66023),
(66024, data66024), (66028, data66028), (66029, data66029),
(66055, data66055), (66057, data66057), (66058, data66058)]
simulation = get_simulation()
result = simulation.result()
axes_limits = ba.get_axes_limits(result, ba.AxesUnits.QSPACE)
plt.style.use('seaborn-talk')
plt.suptitle("Sample S3098 SDD=9200 mm", fontsize=16)
i = 1
for run, data in datalist:
plt.subplot(3, 3, i)
plt.subplots_adjust(wspace=0.2, hspace=0.3)
qy = np.linspace(axes_limits[0], axes_limits[1], data.shape[0])
counts = np.sum(data, axis=0)
plt.semilogy(qy,
counts,
color='k',
marker='o',
markersize=5,
linestyle='None')
plt.xlabel(r'$Q_y$ (nm$^{-1}$)')
plt.ylabel(r'$I$ (a. u.)')
plt.title(r"Run {}".format(run))
i += 1
plt.show()
def plot_0deg_summary():
# load data
data23 = load_data(join(datapath, f66023)) # 0 degrees
data24 = load_data(join(datapath, f66024)) # 0 degrees
data55 = load_data(join(datapath, f66055)) # 0 degrees
data52 = load_data(join(datapath, f65952)) # 0 degrees
data53 = load_data(join(datapath, f65953)) # 0 degrees
data_large_slit = data52 + data53
data_small_slit = data23 + data24
# get monitor counts
mon55 = get_monitor_counts("{}.yaml".format(os.path.splitext(f66055)[0]))
mon23 = get_monitor_counts("{}.yaml".format(os.path.splitext(f66023)[0]))
mon24 = get_monitor_counts("{}.yaml".format(os.path.splitext(f66024)[0]))
mon52 = get_monitor_counts("{}.yaml".format(os.path.splitext(f65952)[0]))
mon53 = get_monitor_counts("{}.yaml".format(os.path.splitext(f65953)[0]))
simulation = get_simulation()
axes_limits = ba.get_axes_limits(simulation.result(), ba.AxesUnits.QSPACE)
qy = np.linspace(axes_limits[0], axes_limits[1], data23.shape[0])
simulation_1 = get_simulation(u0=45.0)
axes_limits_1 = ba.get_axes_limits(simulation_1.result(),
ba.AxesUnits.QSPACE)
qy_1hour = np.linspace(axes_limits_1[0], axes_limits_1[1], data55.shape[0])
sum_large_slit = np.sum(data_large_slit, axis=0) / (mon52 + mon53)
sum_small_slit = np.sum(data_small_slit, axis=0) / (mon23 + mon24)
sum_1hour = np.sum(data55, axis=0) / mon55
plt.style.use('seaborn-talk')
plt.subplot(1, 2, 1)
plt.semilogy(qy,
sum_small_slit,
color='k',
marker='o',
markersize=5,
linestyle='None',
label=r"Small slit")
plt.semilogy(qy,
sum_large_slit,
color='b',
marker='s',
markersize=5,
linestyle='None',
label=r"Large slit")
plt.semilogy(qy_1hour,
sum_1hour,
color='g',
marker='*',
markersize=5,
linestyle='None',
label=r"1 hour")
plt.xlabel(r'$Q_y$ (nm$^{-1}$)')
plt.ylabel(r'$I$ (a. u.)')
plt.ylim([2.0e-07, 4.0e-05])
plt.title(r"Sample rotation $0^\circ$ summary", fontsize=16)
plt.legend(loc='upper right', fontsize=14)
plt.subplot(1, 2, 2)
img = plt.imread(join(datapath, 'rot0deg.jpg'))
plt.imshow(img)
plt.axis('off')
plt.show()
def plot_summary():
data00deg = load_data(join(datapath, f66023)) # 0 degrees
data45deg = load_data(join(datapath, f66028)) # 45 degrees
data90deg = load_data(join(datapath, f66029)) # 90 degrees
simulation = get_simulation()
result = simulation.result()
axes_limits = ba.get_axes_limits(result, ba.AxesUnits.QSPACE)
qy = np.linspace(axes_limits[0], axes_limits[1], data00deg.shape[0])
sum00deg = np.sum(data00deg, axis=0)
sum45deg = np.sum(data45deg, axis=0)
sum90deg = np.sum(data90deg, axis=0)
mon00deg = get_monitor_counts("{}.yaml".format(
os.path.splitext(f66023)[0]))
mon45deg = get_monitor_counts("{}.yaml".format(
os.path.splitext(f66028)[0]))
mon90deg = get_monitor_counts("{}.yaml".format(
os.path.splitext(f66029)[0]))
plt.semilogy(qy,
sum00deg / mon00deg,
color='k',
marker='o',
markersize=5,
linestyle='None',
label=r"$\omega=0^\circ$")
plt.semilogy(qy,
sum45deg / mon45deg,
color='b',
marker='s',
markersize=5,
linestyle='None',
label=r"$\omega=45^\circ$")
plt.semilogy(qy,
sum90deg / mon90deg,
color='g',
marker='*',
markersize=5,
linestyle='None',
label=r"$\omega=90^\circ$")
ymin, ymax = 2e-07, 2.5e-06
plt.axvline(x=0.0125, color='yellow', linestyle='--')
plt.axvline(x=0.02, color='yellow', linestyle='--')
plt.fill_between(qy,
ymin,
ymax,
where=np.logical_and(qy >= 0.0125, qy <= 0.02),
facecolor='yellow',
alpha=0.3)
plt.text(0.0135, 1.2e-06, "300-500 nm", fontsize=14)
plt.axvline(x=0.003, color='0.7', linestyle=':')
plt.axvline(x=0.006, color='0.7', linestyle=':')
plt.fill_between(qy,
ymin,
ymax,
where=np.logical_and(qy >= 0.003, qy <= 0.006),
facecolor='0.7',
alpha=0.3)
plt.text(0.003, 6.0e-07, r"1-2 $\mu$m", fontsize=14)
plt.axvline(x=-0.0125, color='yellow', linestyle='--')
plt.axvline(x=-0.02, color='yellow', linestyle='--')
plt.text(-0.019, 1.2e-06, "300-500 nm", fontsize=14)
plt.fill_between(qy,
ymin,
ymax,
where=np.logical_and(qy >= -0.02, qy <= -0.0125),
facecolor='yellow',
alpha=0.3)
plt.axvline(x=-0.003, color='0.7', linestyle=':')
plt.axvline(x=-0.006, color='0.7', linestyle=':')
plt.fill_between(qy,
ymin,
ymax,
where=np.logical_and(qy >= -0.006, qy <= -0.003),
facecolor='0.7',
alpha=0.3)
plt.text(-0.006, 6.0e-07, r"1-2 $\mu$m", fontsize=14)
plt.xlabel(r'$Q_y$ (nm$^{-1}$)')
plt.ylabel(r'$I$ (a. u.)')
plt.ylim([ymin, ymax])
plt.title(r"Sample rotation summary")
plt.legend(loc='upper right', fontsize=14)
plt.show()
def plot_sim_qspace(expdata, title, radius=150.0, d=1000.0, zmin=1.0e-09):
simulation = get_simulation()
# experimental data
s_hist = simulation.result().histogram2d(ba.AxesUnits.QSPACE)
s_hist.setContent(expdata)
# run simulation
sample = get_sample(radius=radius, d=d)
simulation.setSample(sample)
simulation.setTerminalProgressMonitor()
simulation.runSimulation()
result = simulation.result()
axes_limits = ba.get_axes_limits(result, ba.AxesUnits.QSPACE)
# plot the result
plt.style.use('seaborn-talk')
grid = plt.GridSpec(2, 2, wspace=0.4, hspace=0.3)
# ==============
# experimental data
# ===============
ax = plt.subplot(grid[0, 0])
plt.subplots_adjust(wspace=0.2, hspace=0.3)
im = plt.imshow(s_hist.array(),
norm=matplotlib.colors.LogNorm(zmin, 01.0e-06),
extent=axes_limits,
aspect='auto',
cmap='jet')
cb = plt.colorbar(im)
plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
plt.xlabel(r'$Q_y$ (nm$^{-1}$)')
plt.ylabel(r'$Q_x$ (nm$^{-1}$)')
plt.title("{} experiment".format(title))
# ==============
# Simulation
# ===============
ax = plt.subplot(grid[0, 1])
plt.subplots_adjust(wspace=0.2, hspace=0.3)
im = plt.imshow(result.array(),
norm=matplotlib.colors.LogNorm(zmin, 01.0e-06),
extent=axes_limits,
aspect='auto',
cmap='jet')
cb = plt.colorbar(im)
plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
plt.xlabel(r'$Q_y$ (nm$^{-1}$)')
plt.ylabel(r'$Q_x$ (nm$^{-1}$)')
plt.title("Simulation")
# =======
# I(Q)
# =======
plt.subplot(grid[1, 0:])
qy = np.array(result.axis(0, ba.AxesUnits.QSPACE))
exp_slice = np.mean(s_hist.array(), axis=0)
sim_slice = np.mean(result.array(), axis=0)
plt.semilogy(qy,
exp_slice,
color='k',
marker='.',
markersize=7,
linestyle='None',
label='Experiment')
plt.semilogy(qy, sim_slice, color='b', linestyle='-', label='Simulation')
plt.axvline(x=0.003, color='0.7', linestyle='--', linewidth=1)
plt.axvline(x=0.0055, color='r', linestyle='--', linewidth=1)
plt.axvline(x=0.006, color='0.7', linestyle='--', linewidth=1)
plt.annotate(r'{} $\mu$m'.format(1.14),
xy=(0.0055, 1.3e-07),
xytext=(0.007, 4.0e-07),
fontsize=14,
arrowprops=dict(facecolor='red',
shrink=0.0,
width=0.5,
headwidth=4))
plt.axvline(x=0.020, color='g', linestyle='--', linewidth=1)
plt.annotate(r'{} nm'.format(314),
xy=(0.02, 0.17e-07),
xytext=(0.017, 0.4e-07),
fontsize=14,
arrowprops=dict(facecolor='green',
shrink=0.0,
width=0.5,
headwidth=4))
plt.axvline(x=0.0115, color='g', linestyle='--', linewidth=1)
plt.annotate(r'{} nm'.format(546),
xy=(0.0115, 0.17e-07),
xytext=(0.013, 0.4e-07),
fontsize=14,
arrowprops=dict(facecolor='green',
shrink=0.0,
width=0.5,
headwidth=4))
plt.fill_between(qy,
0,
1,
where=np.logical_and(qy >= 0.0029, qy <= 0.0061),
facecolor='0.7',
alpha=0.5)
plt.xlim(0.0, 0.024)
plt.ylim(1.0e-10, 1.0e-06)
# plt.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))
plt.xlabel(r'$Q_y$ (nm$^{-1}$)')
plt.ylabel(r'$I(Q_y)$, a.u.')
plt.legend(loc='upper right', fontsize=18)
plt.title(r"Integrated over $Q_x$")
plt.show()
def plot_s3098_1deg(ai=1.2):
fname = '00065{r}_00{s:02d}_p15749_SiO2,_Si,_Au,_Cu_SiO2,_Si,_Au,_Cu_HRD_standard.{ext}'
runs = range(1) # put 22 to view the all 22 files
simulation = get_simulation(ai=ai, v0=30.2)
result = simulation.result()
axes_limits = ba.get_axes_limits(result, ba.AxesUnits.QSPACE)
# ROI, nm^-1
xmin, ymin, xmax, ymax = -0.05, 0.02, 0.05, 0.17
qSi = calc_q(ai, a_Si)
qAu = calc_q(ai, a_Au)
qCu = calc_q(ai, a_Cu)
plt.style.use('seaborn-talk')
grid = plt.GridSpec(2, 2, wspace=0.4, hspace=0.3)
for i in runs:
data = load_data(
join(datapath, fname.format(r=483 + i, s=i, ext='det')))
omega = get_rotation_angle(
join(datapath, fname.format(r=483 + i, s=i, ext='yaml')))
plt.suptitle(r"Run 00065{n}, $\omega={om}^\circ$".format(n=483 + i,
om=omega),
fontsize=16)
# ===================
# raw data
# ===================
plt.subplot(grid[0, 0])
plt.subplots_adjust(wspace=0.2, hspace=0.3)
im = plt.imshow(data,
norm=matplotlib.colors.LogNorm(0.1, 500),
extent=axes_limits,
aspect='auto',
cmap='jet')
cb = plt.colorbar(im)
# plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
plt.xlabel(r'$Q_y$ (nm$^{-1}$)')
plt.ylabel(r'$Q_z$ (nm$^{-1}$)')
plt.title(r"Raw data")
# ===================
# raw data ROI
# ===================
plt.subplot(grid[0, 1])
plt.subplots_adjust(wspace=0.2, hspace=0.3)
hist = result.histogram2d(ba.AxesUnits.QSPACE)
hist.setContent(data)
h = hist.crop(xmin, ymin, xmax, ymax)
im = plt.imshow(h.array(),
norm=matplotlib.colors.LogNorm(0.1, 500),
extent=[xmin, xmax, ymin, ymax],
aspect='auto',
cmap='jet')
cb = plt.colorbar(im)
plt.axhline(y=qSi, color='0.3', linestyle='--', linewidth=1)
plt.axhline(y=qAu, color='b', linestyle='--', linewidth=1)
plt.axhline(y=qCu, color='g', linestyle='--', linewidth=1)
# plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
plt.xlabel(r'$Q_y$ (nm$^{-1}$)')
plt.ylabel(r'$Q_z$ (nm$^{-1}$)')
plt.title(r"Raw data (ROI)")
# ===================
# qz slice (degrees)
# ===================
plt.subplot(grid[1, 0])
plt.subplots_adjust(wspace=0.2, hspace=0.3)
hist = result.histogram2d(ba.AxesUnits.DEGREES)
hist.setContent(data)
zslice = hist.projectionY(0.0)
plt.semilogy(zslice.getBinCenters(),
zslice.getBinValues(),
color='k',
marker='o',
markersize=5,
linestyle='None')
plt.axvline(x=ai, color='r', linestyle='--')
plt.annotate(r'$\alpha_i={}^\circ$'.format(ai),
xy=(ai, 300),
xytext=(0.9, 500),
fontsize=14,
arrowprops=dict(facecolor='red',
shrink=0.0,
width=0.5,
headwidth=4))
plt.xlabel(r'$\alpha_f$ ($^{\circ}$)')
plt.title(r"Slice along $\alpha_f$")
# ===================
# qy slice (nm^-1)
# ===================
plt.subplot(grid[1, 1])
plt.subplots_adjust(wspace=0.2, hspace=0.3)
hist = result.histogram2d(ba.AxesUnits.QSPACE)
hist.setContent(data)
ysliceSi = hist.projectionX(qSi)
ysliceCu = hist.projectionX(qCu)
ysliceAu = hist.projectionX(qAu)
plt.semilogy(ysliceSi.getBinCenters(),
ysliceSi.getBinValues(),
color='0.3',
marker='o',
markersize=7,
linestyle='None',
label=r'$Q_c(Si)={:.3f} $'.format(qSi) + r'nm$^{-1}$')
plt.semilogy(ysliceAu.getBinCenters(),
ysliceAu.getBinValues(),
color='b',
marker='s',
markersize=7,
linestyle='None',
label=r'$Q_c$(Au)={:.3f} '.format(qAu) + r'nm$^{-1}$')
plt.semilogy(ysliceCu.getBinCenters(),
ysliceCu.getBinValues(),
color='g',
marker='*',
markersize=9,
linestyle='None',
label=r'$Q_c$(Cu)={:.3f} '.format(qCu) + r'nm$^{-1}$')
plt.xlim([0.0, 0.2])
plt.ylim([0.7, 10])
plt.xlabel(r'$Q_y$ (nm$^{-1}$)')
plt.title(r"Slices along $Q_y$")
plt.legend(loc='upper right', fontsize=12)
plt.show()