def process_all_globs(glob_patterns,
show=False,
individual=False,
subArray=None,
normalization="peak",
normalizationArray=None,
labelList=None,
filterList=None,
recompute=False,
scale='angle',
ncores=None):
"""
Return scaled, background-subtracted, and normalized powder patterns for data
corresponding to each pattern in glob_patterns
"""
if subArray is None:
subArray = [None] * len(glob_patterns)
if labelList is None:
labelList = [None] * len(glob_patterns)
if filterList is None:
filterList = [None] * len(glob_patterns)
if normalizationArray is None:
normalizationArray = [None] * len(glob_patterns)
def run_and_plot(glob,
bgsub=None,
label=None,
data_filter=None,
normalization=None):
return process_radial_distribution(sum_radial_densities(glob,
False,
True,
ncores=ncores),
normalization=normalization,
bgsub=bgsub,
label=glob,
scale=scale,
plot=show)
outputs = []
for patt, subtraction, label, one_filter, norm in zip(
glob_patterns, subArray, labelList, filterList,
normalizationArray):
outputs = outputs + [
run_and_plot(patt,
bgsub=subtraction,
label=label,
data_filter=one_filter,
normalization=norm)
]
if show:
plt.legend()
plt.show(block=False)
#output format: angles, intensities, angles, intensities, etc.
return np.vstack((_ for _ in outputs))
def process_all_globs(
glob_patterns,
show=False,
individual=False,
subArray=None,
normalization="peak",
normalizationArray=None,
labelList=None,
filterList=None,
recompute=False,
scale="angle",
ncores=None,
):
"""
Return scaled, background-subtracted, and normalized powder patterns for data
corresponding to each pattern in glob_patterns
"""
if subArray is None:
subArray = [None] * len(glob_patterns)
if labelList is None:
labelList = [None] * len(glob_patterns)
if filterList is None:
filterList = [None] * len(glob_patterns)
if normalizationArray is None:
normalizationArray = [None] * len(glob_patterns)
def run_and_plot(glob, bgsub=None, label=None, data_filter=None, normalization=None):
return process_radial_distribution(
sum_radial_densities(glob, False, True, ncores=ncores),
normalization=normalization,
bgsub=bgsub,
label=glob,
scale=scale,
plot=show,
)
outputs = []
for patt, subtraction, label, one_filter, norm in zip(
glob_patterns, subArray, labelList, filterList, normalizationArray
):
outputs = outputs + [
run_and_plot(patt, bgsub=subtraction, label=label, data_filter=one_filter, normalization=norm)
]
if show:
plt.legend()
plt.show(block=False)
# output format: angles, intensities, angles, intensities, etc.
return np.vstack((_ for _ in outputs))
def process_and_plot(pattern_list,
deconvolution_iterations=100,
plot_powder=True,
show=True,
dtheta=5e-4,
filtsize=2,
center=CENTER,
airfactor=1.0,
kaptonfactor=0.0,
smooth_size=0.0,
normalize='',
peak_ranges=None):
# TODO update docstring
"""
Process data specified by glob patterns in pattern_list, using the
parameters extracted from the filepath prefix
Inputs:
normalize: if == height, normalizes spectra by peak value; if == a tuple
(assumed to contain two ints), normalizes by the integral of the
specified range; if bool(height) == False, no normalization is done.
peak_ranges: a list of tuples storing min and max angles of powder peaks
(necessary for generating a plot of integrated Bragg peak intensities)
"""
def one_spectrum(pattern):
npulses = glob_npulses(pattern)
x, y = full_process(pattern,
glob_attenuator(pattern),
dtheta=dtheta,
filtsize=filtsize,
npulses=npulses,
center=center,
airfactor=airfactor,
kaptonfactor=kaptonfactor,
smooth_size=smooth_size,
deconvolution_iterations=deconvolution_iterations)
#x, y = estimator(deconvolution_iterations)
# normalize by peak height
if normalize == 'height':
return [x, y / np.max(y)]
# normalize by integral of the specified range
elif isinstance(normalize, tuple):
integral = peak_sizes(x, y, [normalize], bg_subtract=False)
return [x, y / integral]
elif normalize:
raise ValueError("invalid value for normalize: " + str(normalize))
else:
return x, y
spectra = [one_spectrum(pattern) for pattern in pattern_list]
def plot_curves(cmap_start=0., cmap_max=1.0):
# TODO: implement this
#cmap = plt.get_cmap('coolwarm')
ncurves = len(spectra)
for i, pattern, curve in zip(range(len(spectra)), pattern_list,
spectra):
#plt.plot(*curve, label = pattern, color = cmap(cmap_start + i * (cmap_max - cmap_start)/ncurves))
plt.plot(*curve, label=pattern)
plt.legend()
# ax.set_xlabel('Angle (rad)')
# ax.set_ylabel('Intensity (arb)')
plt.xlabel('Angle (rad)')
plt.ylabel('Intensity (arb)')
if plot_powder:
plot_curves()
if show:
plt.show()
return spectra
def show():
plt.xlabel("angle (degrees)")
plt.ylabel("inensity (arb)")
plt.legend()
plt.show()
def process_and_plot(
pattern_list,
deconvolution_iterations=100,
plot_powder=True,
show=True,
dtheta=5e-4,
filtsize=2,
center=CENTER,
airfactor=1.0,
kaptonfactor=0.0,
smooth_size=0.0,
normalize="",
peak_ranges=None,
):
# TODO update docstring
"""
Process data specified by glob patterns in pattern_list, using the
parameters extracted from the filepath prefix
Inputs:
normalize: if == height, normalizes spectra by peak value; if == a tuple
(assumed to contain two ints), normalizes by the integral of the
specified range; if bool(height) == False, no normalization is done.
peak_ranges: a list of tuples storing min and max angles of powder peaks
(necessary for generating a plot of integrated Bragg peak intensities)
"""
def one_spectrum(pattern):
npulses = glob_npulses(pattern)
x, y = full_process(
pattern,
glob_attenuator(pattern),
dtheta=dtheta,
filtsize=filtsize,
npulses=npulses,
center=center,
airfactor=airfactor,
kaptonfactor=kaptonfactor,
smooth_size=smooth_size,
deconvolution_iterations=deconvolution_iterations,
)
# x, y = estimator(deconvolution_iterations)
# normalize by peak height
if normalize == "height":
return [x, y / np.max(y)]
# normalize by integral of the specified range
elif isinstance(normalize, tuple):
integral = peak_sizes(x, y, [normalize], bg_subtract=False)
return [x, y / integral]
elif normalize:
raise ValueError("invalid value for normalize: " + str(normalize))
else:
return x, y
spectra = [one_spectrum(pattern) for pattern in pattern_list]
def plot_curves(cmap_start=0.0, cmap_max=1.0):
# TODO: implement this
# cmap = plt.get_cmap('coolwarm')
ncurves = len(spectra)
for i, pattern, curve in zip(range(len(spectra)), pattern_list, spectra):
# plt.plot(*curve, label = pattern, color = cmap(cmap_start + i * (cmap_max - cmap_start)/ncurves))
plt.plot(*curve, label=pattern)
plt.legend()
# ax.set_xlabel('Angle (rad)')
# ax.set_ylabel('Intensity (arb)')
plt.xlabel("Angle (rad)")
plt.ylabel("Intensity (arb)")
if plot_powder:
plot_curves()
if show:
plt.show()
return spectra
def show():
plt.xlabel("angle (degrees)")
plt.ylabel("inensity (arb)")
plt.legend()
plt.show()