def generate_q_bins(rmax, qmax, pixel_size, distance, wavelength):
"""
Generate the Q bins at the resolution of the detector
Parameters
-----------
rmax: float
The maximum radial distance on the detector
qmax: float
The maximum Q on the detector
pixel_size: float
The size of the pixels, in the same units as rmax
distance: float
The sample to detector distance, in the same units as rmax
wavelength: float
The wavelength of the x-rays
Returns
-------
ndarray:
The bin edges, suitable for np.histogram or
scipy.stats.binned_statistic
"""
base_pixels = np.arange(0, rmax, pixel_size)
pixel_bottom = base_pixels
pixel_top = base_pixels + pixel_size
tthb = np.arctan(pixel_bottom / distance)
ttht = np.arctan(pixel_top / distance)
dq = twotheta_to_q(ttht, wavelength) - twotheta_to_q(tthb, wavelength)
fq = np.linspace(0, qmax, len(dq))
b = np.zeros(len(fq) + 1)
b[1:] = dq + fq
return b
def test_q_twotheta_conversion():
wavelength = 1
q = np.linspace(0, 4 * np.pi, 100)
assert_array_almost_equal(q,
core.twotheta_to_q(
core.q_to_twotheta(q, wavelength),
wavelength),
decimal=12)
two_theta = np.linspace(0, np.pi, 100)
assert_array_almost_equal(two_theta,
core.q_to_twotheta(
core.twotheta_to_q(two_theta, wavelength),
wavelength),
decimal=12)
def test_q_twotheta_conversion():
wavelength = 1
q = np.linspace(0, 4 * np.pi, 100)
assert_array_almost_equal(q,
core.twotheta_to_q(
core.q_to_twotheta(q, wavelength),
wavelength),
decimal=12)
two_theta = np.linspace(0, np.pi, 100)
assert_array_almost_equal(two_theta,
core.q_to_twotheta(
core.twotheta_to_q(two_theta,
wavelength),
wavelength),
decimal=12)
def angularIntegrationBackDetector(n_bins=100,center=(265,655),min_bin=40,max_bin=800,threshold=(0.001,10000)): ''' Does the circular integration and q-calibration on the back detector, with an additional threshold masking ''' # -- parameters Ldet = 5080. # detector to sample distance dpix = 0.055 # um (pixel size) energy = 8.4 #keV # -- constants h = 4.135667516*1e-18 #kev*sec c = 3*1e8 #m/s lambda_ = h*c/energy*1e10 # wavelength of the X-rays # -- calulate q-range width,spacing = float(max_bin-min_bin)/float(n_bins),0 edges = roi.ring_edges(min_bin, width, spacing, n_bins) two_theta = utils.radius_to_twotheta(Ldet, edges*dpix) q_val = utils.twotheta_to_q(two_theta, lambda_) q = np.mean(q_val, axis=1) # -- apply threshold mask data self.data[data<threshold[0]]=0 self.data[data>threshold[1]]=0 # -- angular average Iq= circularAverage(self.data,calibrated_center=center,nx=n_bins,min_x=min_bin,max_x=max_bin) print q.shape,Iq[1].shape return Iq[0],Iq[1]
def generate_q_bins(rmax, pixel_size, distance, wavelength, rmin=0):
"""
Generate the Q bins at the resolution of the detector
Parameters
-----------
rmax: float
The maximum radial distance on the detector in distance units.
Note that this should go to the bottom edge of the pixel.
pixel_size: float
The size of the pixels, in the same units as rmax
distance: float
The sample to detector distance, in the same units as rmax
wavelength: float
The wavelength of the x-rays
rmin: float, optional
The minimum radial distance on the detector in distance units. Defaults
to zero. Note that this should be the bottom of the pixel
Returns
-------
ndarray:
The bin edges, suitable for np.histogram or
scipy.stats.binned_statistic
"""
pixel_bottom = np.arange(rmin, rmax, pixel_size)
pixel_top = pixel_bottom + pixel_size
bottom_tth = np.arctan(pixel_bottom[0] / distance)
top_tth = np.arctan(pixel_top / distance)
top_q = twotheta_to_q(top_tth, wavelength)
bins = np.zeros(len(top_q) + 1)
bins[0] = twotheta_to_q(bottom_tth, wavelength)
bins[1:] = top_q
return bins
def calculate_g2(data,mask=None,g2q=180,center = [265,655]):
'''
Calculates the intensity-intensity temporal autocorrelation using
scikit-beam packages on the back detector
for a q-range (g2q) and width (width, num_rings,spacing)
'''
# -- parameters
inner_radius = g2q#180 # radius of the first ring
width = 1
spacing = 0
num_rings = 10
#center = (273, 723) # center of the speckle pattern
dpix = 0.055 # The physical size of the pixels
energy = 8.4 #keV
h = 4.135667516*1e-18#kev*sec
c = 3*1e8 #m/s
lambda_ = h*c/energy*1e10 # wavelength of the X-rays
Ldet = 5080. # # detector to sample distance
# -- average and mask data
avg_data = np.average(data,axis=0)#*mask
# -- ring array
edges = roi.ring_edges(inner_radius, width, spacing, num_rings)
# -- convert ring to q
two_theta = utils.radius_to_twotheta(Ldet, edges*dpix)
q_val = utils.twotheta_to_q(two_theta, lambda_)
q_ring = np.mean(q_val, axis=1)
# -- ring mask
rings = roi.rings(edges, center, data[0].shape)
ring_mask = rings*mask
# -- calulate g2
num_levels = 1#7
num_bufs = 100#2
g2, lag_steps = corr.multi_tau_auto_corr(num_levels,num_bufs,ring_mask,mask*data)
# -- average
g2_avg = np.average(g2,axis=1)
qt = np.average(q_ring)
# -- standard error
g2_err = np.std(g2,axis=1)/np.sqrt(len(g2[0,:]))
return qt,lag_steps[1:],g2_avg[1:],g2_err[1:]
def angular_integration_back_detector(data,
n_bins=100,
mask=None,
center=(265, 655),
min_bin=40,
max_bin=800,
threshold=(0.001, 10000)):
'''
Does the circular integration and q-calibration on the back
detector, with an additional threshold masking
'''
# -- parameters
Ldet = 5080. # detector to sample distance
dpix = 0.055 # um (pixel size)
energy = 8.4 #keV
# -- constants
h = 4.135667516 * 1e-18 #kev*sec
c = 3 * 1e8 #m/s
lambda_ = h * c / energy * 1e10 # wavelength of the X-rays
# -- calulate q-range
width, spacing = float(max_bin - min_bin) / float(n_bins), 0
edges = roi.ring_edges(min_bin, width, spacing, n_bins)
two_theta = utils.radius_to_twotheta(Ldet, edges * dpix)
q_val = utils.twotheta_to_q(two_theta, lambda_)
q = np.mean(q_val, axis=1)
# -- apply threshold mask data
data[data < threshold[0]] = 0
data[data > threshold[1]] = 0
# -- angular average
Iq = circular_average(data,
calibrated_center=center,
mask=mask,
nx=n_bins,
min_x=min_bin,
max_x=max_bin)
print q.shape, Iq[1].shape
return Iq[0], Iq[1]
def calculateG2(self, g2q=180, center=[265,655]):
"""
--------------------------------------------
Method: SpecklePattern.calculateG2
--------------------------------------------
Calculates the intensity-intensity temporal autocorrelation using
scikit-beam packages on the back detector
for a q-range (g2q) and width (width, num_rings,spacing)
--------------------------------------------
Usage: mySpeckles.calculateG2()
--------------------------------------------
Prerequisites:
data - data stored as numpy 3D array
--------------------------------------------
Arguments: (optional)
g2q - at which q (pixels) you wanna calculate g2
center - beam position in the data array (y, x)
--------------------------------------------
Returns tuple with:
qt - q (in Ang-1) at which you calculated g2
lag_steps[1:] - time array
g2_avg[1:] - g2 average array
g2_err[1:] - g2 uncertainty array
--------------------------------------------
"""
# -- parameters
inner_radius = g2q#180 # radius of the first ring
width = 1
spacing = 0
num_rings = 10
#center = (273, 723) # center of the speckle pattern
dpix = 0.055 # The physical size of the pixels
energy = 8.4 #keV
h = 4.135667516*1e-18#kev*sec
c = 3*1e8 #m/s
lambda_ = h*c/energy*1e10 # wavelength of the X-rays
Ldet = 5080. # # detector to sample distance
# -- average and mask data
avg_data = np.average(self.data,axis=0)#*mask
# -- ring array
edges = roi.ring_edges(inner_radius, width, spacing, num_rings)
# -- convert ring to q
two_theta = utils.radius_to_twotheta(Ldet, edges*dpix)
q_val = utils.twotheta_to_q(two_theta, lambda_)
q_ring = np.mean(q_val, axis=1)
# -- ring mask
rings = roi.rings(edges, center, self.data[0].shape)
ring_mask = rings*mask
# -- calulate g2
num_levels = 1 #7
num_bufs = 100 #2
g2, lag_steps = corr.multi_tau_auto_corr(num_levels,num_bufs,ring_mask,self.mask*self.data)
# -- average
g2_avg = np.average(g2,axis=1)
qt = np.average(q_ring)
# -- standard error
g2_err = np.std(g2,axis=1)/np.sqrt(len(g2[0,:]))
return qt, lag_steps[1:], g2_avg[1:], g2_err[1:]
geo = Geometry(
detector='Perkin', pixel1=.0002, pixel2=.0002,
dist=.23,
poni1=.209, poni2=.207,
# rot1=.0128, rot2=-.015, rot3=-5.2e-8,
wavelength=1.43e-11
)
r = geo.rArray((2048, 2048))
q = geo.qArray((2048, 2048))
pixels = np.arange(0, np.max(r), geo.pixel1)
pixel_bottom = pixels
pixel_top = pixels + geo.pixel1
tthb = np.arctan(pixel_bottom / geo.dist)
ttht = np.arctan(pixel_top / geo.dist)
dq = twotheta_to_q(ttht, .143) - twotheta_to_q(tthb, .143)
fq = np.linspace(0, np.max(q), len(dq))
b = np.zeros(len(fq)+1)
b[1:] = dq + fq
int_q = np.zeros(q.shape, dtype=np.int)
for i in range(len(b)-1):
t_array = (b[i] <= q) & (q < b[i+1])
int_q[t_array] = i - 1
save_stem = '/mnt/bulk-data/Masters_Thesis/dp/figures/'
for j in [100, 300, 500, 1000]:
#make some sample data
Z = 100*np.cos(50*r)**2 + 150
def calculateG2(self, g2q=180, center=[265, 655]):
"""
--------------------------------------------
Method: SpecklePattern.calculateG2
--------------------------------------------
Calculates the intensity-intensity temporal autocorrelation using
scikit-beam packages on the back detector
for a q-range (g2q) and width (width, num_rings,spacing)
--------------------------------------------
Usage: mySpeckles.calculateG2()
--------------------------------------------
Prerequisites:
data - data stored as numpy 3D array
--------------------------------------------
Arguments: (optional)
g2q - at which q (pixels) you wanna calculate g2
center - beam position in the data array (y, x)
--------------------------------------------
Returns tuple with:
qt - q (in Ang-1) at which you calculated g2
lag_steps[1:] - time array
g2_avg[1:] - g2 average array
g2_err[1:] - g2 uncertainty array
--------------------------------------------
"""
# -- parameters
inner_radius = g2q #180 # radius of the first ring
width = 1
spacing = 0
num_rings = 10
#center = (273, 723) # center of the speckle pattern
dpix = 0.055 # The physical size of the pixels
energy = 8.4 #keV
h = 4.135667516 * 1e-18 #kev*sec
c = 3 * 1e8 #m/s
lambda_ = h * c / energy * 1e10 # wavelength of the X-rays
Ldet = 5080. # # detector to sample distance
# -- average and mask data
avg_data = np.average(self.data, axis=0) #*mask
# -- ring array
edges = roi.ring_edges(inner_radius, width, spacing, num_rings)
# -- convert ring to q
two_theta = utils.radius_to_twotheta(Ldet, edges * dpix)
q_val = utils.twotheta_to_q(two_theta, lambda_)
q_ring = np.mean(q_val, axis=1)
# -- ring mask
rings = roi.rings(edges, center, self.data[0].shape)
ring_mask = rings * mask
# -- calulate g2
num_levels = 1 #7
num_bufs = 100 #2
g2, lag_steps = corr.multi_tau_auto_corr(num_levels, num_bufs,
ring_mask,
self.mask * self.data)
# -- average
g2_avg = np.average(g2, axis=1)
qt = np.average(q_ring)
# -- standard error
g2_err = np.std(g2, axis=1) / np.sqrt(len(g2[0, :]))
return qt, lag_steps[1:], g2_avg[1:], g2_err[1:]
