def ripple_minima(chi_file, background_file, background_level, composition,
qmin, qmaxinst):
pwd = os.getcwd()
if os.path.exists(os.path.join(pwd, 'ripple_minima_test')) is False:
os.mkdir(os.path.join(pwd, 'ripple_minima_test'))
ripple_list = []
gr_list = []
qmax_min = 18.5
qmax_max = qmaxinst
qmax_step = .01
print 'Start qmax refinement'
for qmax in np.arange(qmax_min, qmax_max, qmax_step):
print qmax
gr_file, bg, q=Calculate.pdfgetx3(
chi_file,
background_file,
background_level,
composition,
qmax,
qmin,
qmax,
'QA',
os.path.join(pwd, 'ripple_minima_test', '@b_ripple_test_'+str(
qmax).ljust(5,'0')+'.@o'))
gr_file = os.path.join(pwd, 'ripple_minima_test', os.path.split(
os.path.splitext(chi_file)[0])[1]+'_ripple_test_'+str(qmax).ljust(5,'0')+'.gr')
x, y = IO.load_gr_file(gr_file)
w = y - np.convolve(y, np.ones(3)/3, 'same')
ripple_sum = np.sum(abs(w))
ripple_list.append(ripple_sum)
gr_list.append(gr_file)
t = np.arange(qmax_min, qmax_max, qmax_step)
ripple_array=np.array(ripple_list)
minima_index = signal.argrelextrema(ripple_array, np.greater_equal,
order=2*len(ripple_array)/100)[0]
minima = ripple_array[minima_index]
minima_q = t[minima_index]
maxima_index = signal.argrelextrema(ripple_array, np.less_equal,
order=2*len(ripple_array)/100)[0]
maxima = ripple_array[maxima_index]
maxima_q = t[maxima_index]
plt.plot(t, ripple_array, 'g')
plt.plot(minima_q, minima, 'o', markerfacecolor='none', mec='r')
plt.plot(maxima_q, maxima, 'o', markerfacecolor='none', mec='b')
plt.title('Ripples in PDF')
plt.xlabel('Qmax (1/A)')
plt.ylabel('Ripple Cumulative Sum')
plt.show()
plt.cla()
# data_list = []
# key_list = []
# for minima_q_point in minima_q:
# data = IO.load_gr_file([x for x in gr_list if str(qmax) in x] [0])
# data_list_element = data
# key_list_element = minima_q_point
# data_list.append(data_list_element)
# key_list.append(key_list_element)
# plot_stack(data_list, key_list)
ziped_minima = zip(minima_q, minima)
while True:
for q, value in ziped_minima:
print 'q= ', q,'rippleness= ', value
qmax= float(raw_input('Please pick a qmax. qmax= '))
gr_file = [x for x in gr_list if str(qmax) in x] [0]
print gr_file
x, y = IO.load_gr_file(gr_file)
plt.plot(x, y)
plt.show()
if raw_input('Is this the file you wanted? y/[n] ') == 'y':
break
return qmax
def write_pdf(chi_file=None, background_file=None, background_level=None,
pdf_format='QA', output='@r.@o', qmax='statistics',
composition=None, qmaxinst=0.0,
qmin=0.0, relative_max_uncertainty=.90,
plot_verbose=False, bg_refine_qmin_index=None,
bg_refine_qmax_index=None):
"""
Generates the G(r) and F(q) files, potentially using optimized values
for the background subtraction and qmax determination
Parameters
----------
chi_file: str
Location of the chi file to use as the foreground to generate the PDF
background_file: str
Location of the chi file to use as the background
background_level: float
The background level, if not set automatically generate the background
scale
pdf_format: str
The format of the chi file
out_put: str
The naming convention for the output PDF
qmax: float or {'statistics', 'ripple'}
If float, the qmax value to be used in the FFT. If statistics,
generate qmax by examining the statistical uncertainty of I(Q). If
ripple generate the PDFs at various qmax and compare the rippleness
between the various qmax values
composition: str
The compostion of the material to be studied
qmaxinst: float
The maximum reliable qmax generated by the detector, if 0.0 the max
of the chi file
qmin: float
The minimum q to be used in the FFT
relative_max_uncertainty: float [0,1]
If qmax is statistics the percentile of the uncertainty to except
plot_verbose: bool
If true, generate plots
bg_refine_qmin_index: int
The lower bound on the range of values to fit between the background
and foreground for determining the background level.
bg_refine_qmax_index: int
The upper bound on the range of values to fit between the background
and foreground for determining the background level.
Returns
-------
str:
The file name of the output
float:
The background level
float:
The qmax
int:
The background refine lower bound
int:
The background refine upper bound
"""
#Get composition
if composition is None:
composition = pdf_parameters.generate_composition()
# load up sample I[Q]
chi, chi_file = IO.load_chi_file(chi_file)
if os.path.split(chi_file)[0] != '':
os.chdir(os.path.split(chi_file)[0])
sample_q, sample, uncertainty = chi[:, 0], chi[:, 1], chi[:, 2]
if qmaxinst > np.amax(sample_q):
qmaxinst = np.amax(sample_q)
elif qmaxinst == 0:
qmaxinst = np.amax(sample_q)
# perform background subtraction
if background_file is not '' and background_level is None:
# implies that you want to load a background file
background_level, background_q, background, background_file, \
bg_refine_qmin_index, bg_refine_qmax_index = \
pdf_parameters.background_subtract(sample, background_file,
background_level,
bg_refine_qmin_index=bg_refine_qmin_index,
bg_refine_qmax_index=bg_refine_qmax_index)
if plot_verbose is True:
fig, ax = plt.subplots()
ax.plot(sample_q, sample, 'g', label='Sample')
ax.legend(loc='upper right')
plt.title('Sample I(Q)')
plt.xlabel('Q (1/A)')
plt.ylabel('Raw Counts')
plt.savefig('/home/christopher/sample.png',
bbox_inches='tight',
transparent=True)
plt.show()
fig, ax = plt.subplots()
ax.plot(sample_q, background, 'b',
label='Background')
ax.legend(loc='upper right')
plt.title('Background (IQ)')
plt.xlabel('Q (1/A)')
plt.ylabel('Raw Counts')
plt.savefig('/home/christopher/background.png',
bbox_inches='tight',
transparent=True)
plt.show()
fig, ax = plt.subplots()
ax.plot(sample_q, sample, 'g', label='Sample')
ax.plot(sample_q, background * background_level, 'b',
label='Scaled Background')
ax.plot(sample_q, sample - background * background_level, 'k',
label='Sample-Background')
ax.legend(loc='upper right')
plt.title('Background Subtraction')
plt.xlabel('Q (1/A)')
plt.ylabel('Raw Counts')
plt.savefig('/home/christopher/integrated_minus_background.png',
bbox_inches='tight',
transparent=True)
plt.show()
# Determine Qmax
if qmax is 'statistics':
qmax = pdf_parameters.qmax_statistics(sample_q, sample, uncertainty,
relative_max_uncertainty)
elif qmax is 'ripple':
qmax = pdf_parameters.ripple_minima(chi_file, background_file,
background_level, composition,
qmin, qmaxinst)
if plot_verbose is True:
plt.plot(sample_q, uncertainty/sample * 100)
plt.show()
# PDFGETX3 call/ Generate PDF
gr_file, background_level, qmax = Calculate.pdfgetx3(chi_file,
background_file, background_level,
composition, qmax, qmin, qmaxinst, pdf_format, output)
return gr_file, background_level, qmax, bg_refine_qmin_index, bg_refine_qmax_index