def run_gsasii_test_2():
import os, sys
sys.path.insert(0, '/Users/lrebuffi/Documents/Workspace/Wonder/GSAS-II-WONDER-OSX/GSAS-II-WONDER') # needed to "find" GSAS-II modules
datadir = "/Users/lrebuffi/Documents/Workspace/Wonder/GSAS-TEST"
import GSASIIscriptable as G2sc
PathWrap = lambda fil: os.path.join(datadir, fil)
gpx = G2sc.G2Project(newgpx=PathWrap('pkfit.gpx'))
hist = gpx.add_powder_histogram(PathWrap('FAP.XRA'), PathWrap('INST_XRY.PRM'),
fmthint='GSAS powder')
hist.set_refinements({'Limits': [16., 24.],
'Background': {"no. coeffs": 2, 'type': 'chebyschev-1', 'refine': True}
})
peak1 = hist.add_peak(1, ttheta=16.8)
peak2 = hist.add_peak(1, ttheta=18.9)
peak3 = hist.add_peak(1, ttheta=21.8)
peak4 = hist.add_peak(1, ttheta=22.9)
hist.set_peakFlags(area=True)
hist.refine_peaks()
hist.set_peakFlags(area=True, pos=True)
hist.refine_peaks()
hist.set_peakFlags(area=True, pos=True, sig=True, gam=True)
hist.refine_peaks()
print('peak positions: ', [i[0] for i in hist.PeakList])
for i in range(len(hist.Peaks['peaks'])):
print('peak', i, 'pos=', hist.Peaks['peaks'][i][0], 'sig=', hist.Peaks['sigDict']['pos' + str(i)])
hist.Export_peaks('pkfit.txt')
return hist
def _initialise_GSAS(self):
"""
Initialise a GSAS project object with a spectrum and an instrument parameter file
:return: GSAS project object
"""
gsas_path = self.getPropertyValue(self.PROP_PATH_TO_GSASII)
sys.path.append(gsas_path)
try:
global GSASII
global GSASIIlattice
global GSASIIspc
global GSASIImath
import GSASIIscriptable as GSASII
import GSASIIlattice
import GSASIIspc
import GSASIImath
except ImportError:
error_msg = "Could not import GSAS-II. Are you sure it's installed at {}?".format(gsas_path)
logger.error(error_msg)
raise ImportError(error_msg)
gsas_proj_path = self.getPropertyValue(self.PROP_GSAS_PROJ_PATH)
gsas_proj = GSASII.G2Project(filename=gsas_proj_path)
spectrum = self._extract_spectrum_from_workspace()
spectrum_path = self._save_temporary_fxye(spectrum=spectrum)
inst_param_path = self.getPropertyValue(self.PROP_PATH_TO_INST_PARAMS)
gsas_proj.add_powder_histogram(datafile=spectrum_path, iparams=inst_param_path, fmthint="xye")
self._remove_temporary_fxye(spectrum_path=spectrum_path)
return gsas_proj
def initialize(self):
# create a GSAS-II project
self.gpx = gsasii.G2Project(newgpx="{}.gpx".format(self.name))
# add histograms
for det in self.detectors:
self.gpx.add_powder_histogram(det.data_file, det.detector_file)
# add phases
for phase in self.phases:
self.gpx.add_phase(phase.phase_file,
phase.phase_label,
histograms=self.gpx.histograms())
# turn on background refinement
args = {
"Background": {
"no. coeffs": 3,
"refine": True,
}
}
for hist in self.gpx.histograms():
hist.set_refinements(args)
# refine
self.gpx.do_refinements([{}])
self.gpx.save("step_1.gpx")
def __init__(self,
cif_file,
wavelength,
twotheta_min=0.0,
twotheta_max=180.0):
self.__data = {}
GSASII_MODE = QSettings().value("output/wonder-default-gsasii-mode",
GSASII_MODE_ONLINE, int)
if GSASII_MODE == GSASII_MODE_ONLINE:
gpx = G2sc.G2Project(newgpx=project_file)
gpx.add_phase(cif_file, phasename="wonder_phase", fmthint='CIF')
hist1 = gpx.add_simulated_powder_histogram(
"wonder_histo",
self.create_temp_prm_file(wavelength),
twotheta_min,
twotheta_max,
0.01,
phases=gpx.phases())
gpx.data['Controls']['data'][
'max cyc'] = 0 # refinement not needed
gpx.do_refinements([{}])
gsasii_data = hist1.reflections()["wonder_phase"]["RefList"]
for item in gsasii_data:
entry = GSASIIReflectionData(item[0], item[1], item[2],
item[5], item[3], item[9])
self.__data[entry.get_key()] = entry
elif GSASII_MODE == GSASII_MODE_EXTERNAL:
try:
pipe = subprocess.Popen([
sys.executable,
self.create_python_script(
gsasii_dirname, gsasii_temp_dir,
os.path.join(gsasii_temp_dir, "gsasii_data.dat"),
project_file, cif_file,
self.create_temp_prm_file(wavelength), twotheta_min,
twotheta_max)
],
stdout=subprocess.PIPE)
gsasii_data = pickle.loads(pipe.stdout.read())
except CalledProcessError as error:
raise Exception(
"Failed to call GSAS-II: " +
''.join(traceback.format_tb(error.__traceback__)))
for item in gsasii_data:
entry = GSASIIReflectionData(item[0], item[1], item[2],
item[5], item[3], item[9])
self.__data[entry.get_key()] = entry
def checkPDFselection():
'Read PDF entry from input GPX file & show in GUI'
pdfEntry = self.pdfSel.GetStringSelection()
if not self.pdfList:
self.params['ComputePDF'] = False
lbl4b.Enable(False)
return
else:
lbl4b.Enable(True)
if pdfEntry not in [i.name for i in self.pdfList]:
# strange something -- not selected
self.pdfSel.SetSelection(0)
pdfEntry = self.pdfSel.GetStringSelection()
if self.gpxInp is None or self.gpxInp.filename != self.gpxin[3]:
self.gpxInp = G2sc.G2Project(self.gpxin[3])
try:
PDFobj = self.gpxInp.pdf(pdfEntry)
except KeyError:
print("PDF entry not found: {}".format(pdfEntry))
return
histNames = [i.name for i in self.histList]
for i, lbl in enumerate(
('Sample Bkg.', 'Container', 'Container Bkg.')):
self.pbkg[i][4].SetValue(
str(PDFobj.data['PDF Controls'][lbl]['Mult']))
self.pbkg[i][6] = PDFobj.data['PDF Controls'][lbl]['Mult']
try:
i = 1 + histNames.index(
PDFobj.data['PDF Controls'][lbl]['Name'])
self.pbkg[i][1].SetSelection(i)
except ValueError:
i = 0
self.pbkg[i][1].SetSelection(0)
if PDFobj.data['PDF Controls'][lbl]['Name']:
print('PDF {} hist entry {} not found'.format(
lbl, PDFobj.data['PDF Controls'][lbl]['Name']))
PDFobj.data['PDF Controls'][lbl]['Name'] = ''
self.formula = ''
for el in PDFobj.data['PDF Controls']['ElList']:
i = PDFobj.data['PDF Controls']['ElList'][el]['FormulaNo']
if i <= 0:
continue
elif i == 1:
if self.formula: self.formula += ' '
self.formula += '{}'.format(el)
else:
if self.formula: self.formula += ' '
self.formula += '{}({:.1f})'.format(el, i)
if not self.formula:
self.formula = 'Error: no chemical formula'
lbl5b.SetLabel(self.formula)
TestInput()
def gpx(self):
"""Create a GSAS-II GPX project file.
This file is necessary for further GSAS refinement operations.
"""
if self._gpx is None:
# Check if an existing file should be used
gpx_fname = self.file_root + '.gpx'
if os.path.exists(gpx_fname):
gpx_kw = dict(gpxfile=gpx_fname)
else:
gpx_kw = dict(newgpx=gpx_fname)
# Create the GPX project file
self._gpx = gsas.G2Project(**gpx_kw)
# Make any necessary parent directories
dir_, fname = os.path.split(self._gpx.filename)
if not os.path.exists(dir_):
os.makedirs(dir_)
# Save the newly created GPX file
self._gpx.save()
return self._gpx
def SetGPXInputFile():
gpx = G2sc.G2Project(self.gpxin[3])
self.imgList = gpx.images()
self.histList = gpx.histograms()
self.pdfList = gpx.pdfs()
if not self.imgList:
G2G.G2MessageBox(
self, 'Error: no images in {}.'.format(self.gpxin[3]))
return
self.gpxin[1].SetValue(self.gpxin[3])
self.imprm[1].Clear()
self.imprm[1].AppendItems([i.name for i in self.imgList])
if len(self.imgList) == 1:
self.imprm[1].SetSelection(0)
self.maskfl[1].Clear()
self.maskfl[1].AppendItems([''] + [i.name for i in self.imgList])
for i in range(3):
self.pbkg[i][1].Clear()
self.pbkg[i][1].AppendItems([''] +
[i.name for i in self.histList])
self.pdfSel.Clear()
self.pdfSel.AppendItems([i.name for i in self.pdfList])
showPDFctrls(None)
def ProcessImage(newImage, imgprms, mskprms, xydata, PDFdict, InterpVals,
calcModes, outputModes):
'''Process one image that is read from file newImage and is integrated into
one or more diffraction patterns and optionally each diffraction pattern can
be transformed into a pair distribution function.
:param str newImage: file name (full path) for input image
:param dict imgprms: dict with some nested lists & dicts describing the image
settings and integration parameters
:param dict mskprms: dict with areas of image to be masked
:param dict xydata: contains histogram information with about background
contributions, used for PDF computation (used if ComputePDF is True)
:param PDFdict: contains PDF parameters (used if ComputePDF is True)
:param InterpVals: contains interpolation table (used if TableMode is True)
:param tuple calcModes: set of values for which computations are
performed and how
:param tuple outputModes: determines which files are written and where
'''
(TableMode, ComputePDF, SeparateDir, optPDF) = calcModes
(outputSelect, PDFformats, fmtlist, outdir,
multipleFmtChoices) = outputModes
if SeparateDir:
savedir = os.path.join(outdir, 'gpx')
if not os.path.exists(savedir): os.makedirs(savedir)
else:
savedir = outdir
outgpx = os.path.join(
savedir,
os.path.split(os.path.splitext(newImage)[0] + '.gpx')[1])
gpxout = G2sc.G2Project(filename=outgpx)
print('creating', gpxout.filename)
# looped because a file can contain multiple images
if TableMode: # look up parameter values from table
imgprms, mskprms = LookupFromTable(
im.data['Image Controls'].get('setdist'), InterpVals)
for im in gpxout.add_image(newImage):
# apply image parameters
im.setControls(imgprms)
setdist = '{:.2f}'.format(
im.getControls()
['setdist']) # ignore differences in position less than 0.01 mm
if setdist not in MapCache['distanceList']:
if mskprms:
im.setMasks(mskprms)
else:
im.initMasks()
MapCache['distanceList'].append(setdist)
MapCache['maskMap'][setdist] = G2sc.calcMaskMap(
im.getControls(), im.getMasks())
MapCache['ThetaAzimMap'][setdist] = G2sc.calcThetaAzimMap(
im.getControls())
# else: # debug
# print('*** reusing',setdist)
#if mskprms:
# im.setMasks(mskprms)
#else:
# im.initMasks()
hists = im.Integrate(MaskMap=MapCache['maskMap'][setdist],
ThetaAzimMap=MapCache['ThetaAzimMap'][setdist])
# write requested files
for dfmt in fmtlist:
fmt = dfmt[1:]
if not outputSelect[fmt]: continue
if fmtlist[dfmt] is None: continue
hint = ''
if fmt in multipleFmtChoices:
hint = multipleFmtChoices[fmt]
if SeparateDir:
savedir = os.path.join(outdir, fmt)
else:
savedir = outdir
if not os.path.exists(savedir): os.makedirs(savedir)
# loop over created histgrams (multiple if caked), writing them as requested
for i, h in enumerate(hists):
fname = h.name[5:].replace(' ', '_')
#if len(hists) > 1:
#GSASIIpath.IPyBreak_base()
try:
fil = os.path.join(savedir, fname)
print('Wrote', h.Export(fil, dfmt, hint))
except Exception as msg:
print('Failed to write {} as {}. Error msg\n{}'.format(
fname, dfmt, msg))
if ComputePDF: # compute PDF
for h in hists:
pdf = gpxout.copy_PDF(PDFdict, h)
pdf.data['PDF Controls']['Sample']['Name'] = h.name
xydata['Sample'] = h.data['data']
fname = h.name[5:].replace(' ', '_')
limits = h.data['Limits'][1]
inst = h.data['Instrument Parameters'][0]
pdf.calculate(copy.deepcopy(xydata), limits, inst)
if optPDF:
for i in range(5):
if pdf.optimize(True, 5, copy.deepcopy(xydata), limits,
inst):
break
pdf.calculate(copy.deepcopy(xydata), limits, inst)
for fmt in PDFformats:
if not outputSelect[fmt]: continue
if SeparateDir:
savedir = os.path.join(
outdir,
fmt.replace("(", "_").replace(")", ""))
else:
savedir = outdir
pdf.export(os.path.join(savedir, fname), fmt)
if outputSelect.get('gpx'):
gpxout.save()
else:
del gpxout
def OnTimerLoop(self, event):
'''A method that is called every :meth:`PollTime` seconds that is
used to check for new files and process them. Integrates new images.
Also optionally sets up and computes PDF.
This is called only after the "Start" button is pressed (then its label reads "Pause").
'''
if GSASIIpath.GetConfigValue('debug'):
import datetime
print("DBG_Timer tick at {:%d %b %Y %H:%M:%S}\n".format(
datetime.datetime.now()))
if self.PreventTimerReEntry: return
self.PreventTimerReEntry = True
self.ShowMatchingFiles(None)
if not self.currImageList:
self.PreventTimerReEntry = False
return
updateList = False
# get input for integration
imgprms = mskprms = None
if not self.params['TableMode']:
# read in image controls/masks, used below in loop. In Table mode
# we will get this image-by image.
gpxinp = G2sc.G2Project(self.gpxin[3])
print('reading template project', gpxinp.filename)
img = gpxinp.image(self.imprm[1].GetStringSelection())
imgprms = img.getControls(True)
if self.maskfl[1].GetStringSelection().strip():
img = gpxinp.image(self.maskfl[1].GetStringSelection())
mskprms = img.getMasks()
# setup shared input for PDF computation (for now will not be table mode)
xydata = {}
if self.params['ComputePDF']:
pdfEntry = self.pdfSel.GetStringSelection()
try:
PDFobj = gpxinp.pdf(pdfEntry)
except KeyError:
print("PDF entry not found: {}".format(pdfEntry))
# update with GUI input
for i, lbl in enumerate(
('Sample Bkg.', 'Container', 'Container Bkg.')):
name = self.pbkg[i][1].GetStringSelection()
try:
xydata[lbl] = gpxinp.histogram(name).data['data']
except AttributeError:
pass
PDFobj.data['PDF Controls'][lbl]['Mult'] = self.pbkg[i][6]
PDFobj.data['PDF Controls'][lbl]['Name'] = name
else:
PDFobj = None
if self.MPpool:
self.MPpool.imap_unordered(
ProcessImageMP, self.ArgGen(PDFobj, imgprms, mskprms, xydata))
else:
for intArgs in self.ArgGen(PDFobj, imgprms, mskprms, xydata):
newImage = intArgs[0]
print('processing ', newImage)
ProcessImage(*intArgs)
updateList = True
for newImage in self.currImageList:
self.ProcessedList.append(newImage)
if updateList: self.ShowMatchingFiles(None)
self.PreventTimerReEntry = False
self.Raise()
def generate_sulfur_pdf(inputs=[]):
import sys
sys.path.append(inputs[1])
def generate_charts(data, prefix, dest, xlabel, ylabel, volume):
import plotly.graph_objects as go
import plotly
import numpy as np
x, y = np.loadtxt(data, unpack=True)
layout = go.Layout(
title=prefix,
xaxis=dict(
title=xlabel
),
yaxis=dict(
title=ylabel
))
fig = go.Figure(layout=layout, layout_xaxis_range=[0, 20], layout_yaxis_range=[-20, 20])
rho = 1.0
if ylabel == 'S(Q)':
fig.add_shape(type='line',
x0=min(x),
y0=1,
x1=max(x),
y1=1,
line_dash="dash",
line=dict(color='Red'))
if ylabel == 'G(R)':
fig.add_shape(type='line',
x0=min(x),
y0=0,
x1=max(x),
y1=0,
line_dash="dash",
line=dict(color='Blue'))
noa = 1 # Get from gpx PDF controls sample information->element
#volume = 13.306 # Get from Sample information->formula volume
fig.add_trace(go.Scatter(x=x[:250], y=-4*3.142*x*noa/volume,
name="Atomic Density Line",line=dict(color='Green')))
line = dict(color="#ffe476")
scatter = go.Scatter(x=x, y=y,
mode='lines',
line=dict(color="#003865"),
name='Sulfur')
fig.add_trace(scatter)
data = [scatter]
div = plotly.offline.plot(data, include_plotlyjs=False, output_type='div')
fig.write_html(dest + "/" + prefix + ".html")
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pyplot import axhline
from matplotlib.axis import Axis
import GSASIIscriptable as G2sc
data = inputs[0]
config = inputs[2]
# Create workflow GSAS2 project
gpx = G2sc.G2Project(data['filename'])
sulfurImageFile = os.path.abspath(data['images']['Sulfur'])
print(sulfurImageFile)
# Get project images
ceo2Image = gpx.images()[0]
capillaryImage = gpx.images()[1]
sulfurImage = gpx.images()[2]
# Export sulfur image control settings so we can apply it to newly added images
sulfurControlFile = os.path.abspath(data['export']['prefix']+'.imctrl')
sulfurImage.saveControls(sulfurControlFile)
# Apply sulfur image control settings to newly loaded sulfur image
imageList = gpx.add_image(sulfurImageFile)
newSulfurImage = imageList[0]
newSulfurImage.loadControls(sulfurControlFile)
sulfurPWDRList = newSulfurImage.Integrate()
capillaryPWDRList = capillaryImage.Integrate()
print("PDFs",gpx.pdfs())
pdf = gpx.pdfs()[0]
if config is not None:
pdf.data['PDF Controls'].update(config)
pdf.data['PDF Controls']['Container']['Name'] = capillaryPWDRList[0].name
for i in range(5):
if pdf.optimize():
break
pdf.calculate()
pdf.export(data['export']['prefix'], 'I(Q), S(Q), F(Q), G(r)')
gpx.save()
x, y = np.loadtxt(data['export']['prefix'] + '.gr', unpack=True)
plt.plot(x, y, label='Sulfur')
fig = plt.figure(1)
plt.title('Sulfur G(R)')
plt.xlabel('x')
plt.ylabel('y')
axes = plt.gca()
plt.xlim(data['limits']['xlim'])
plt.ylim(data['limits']['ylim'])
plt.savefig(data['outputfig'])
os.makedirs(data['datadir'] + "/charts", exist_ok=True)
volume = pdf.data['PDF Controls']['Form Vol']
generate_charts(data['export']['prefix'] + '.gr', os.path.basename(data['export']['prefix']) + "-gr", data['datadir'] + "/charts", "Angstroms", "G(R)", volume)
x, y = np.loadtxt(data['export']['prefix'] + '.sq', unpack=True)
plt.plot(x, y, label='Sulfur')
fig = plt.figure(1)
plt.title('Sulfur S(Q)')
plt.xlabel('x')
plt.ylabel('y')
axes = plt.gca()
plt.xlim(data['limits']['xlim'])
plt.ylim(data['limits']['ylim'])
plt.savefig(data['outputfig'])
os.makedirs(data['datadir'] + "/charts", exist_ok=True)
generate_charts(data['export']['prefix'] + '.sq', os.path.basename(data['export']['prefix']) + "-sq", data['datadir'] + "/charts", "Inverse Angstroms", "S(Q)", volume)
return True
def import_refinements_gsas2(refinement_gpx,
hdf_groupname,
hdf_filename=DEFAULT_HDF_FILENAME,
gsas_path=DEFAULT_GSAS_PATH):
# Try and import
try:
gsas
except NameError:
sys.path.append(str(Path(gsas_path).expanduser()))
import GSASIIscriptable as gsas
# Open the GPX project file
project = gsas.G2Project(refinement_gpx)
seq = project.seqref()
enums = ['x', 'yobs', 'yweight', 'ycalc', 'background', 'residual']
results = {k: [] for k in enums}
param_rows = []
filename_re = re.compile('(?:PWDR )?(.*)')
phases = project.phases()
histograms = project.histograms()
for pattern in tqdm(histograms, desc="Importing"):
# Extract the data
data_list = pattern.data['data'][1]
for key in enums:
results[key].append(data_list[enums.index(key)])
# Extract the refined parameters
histname = pattern.data['data'][-1]
sample_params = pattern.data['Sample Parameters']
residuals = pattern.data['data'][0].get('Residuals', {})
params = {
'name': filename_re.match(pattern.data['data'][-1]).group(1),
'displacement': sample_params['DisplaceX'][0],
'pattern_scale': sample_params['Scale'][0],
'absorption': sample_params['Absorption'][0],
'wR': residuals.get('wR', np.nan),
}
# Get phase-histogram parameters
for phase in phases:
pid = phase['pId']
params.update(_extract_cell_params(seq, pid, pattern.name))
params.update(_extract_atom_params(seq, pid, pattern.name))
try:
hap_values = phase.getHAPvalues(pattern)
except KeyError:
continue
params.update(_extract_hap_sizes(hap_values, pid))
params.update(_extract_hap_strains(hap_values, pid))
params.update(_extract_phase_weight(hap_values, phase, pid))
params[f"{pid}:*:Scale"] = hap_values['Scale'][0]
# Add overall weight fractions for the phases
total_weight = np.nansum(
[v for k, v in params.items() if k[-3:] == 'Wgt'])
fracs = {
f"{k}Frac": v / total_weight
for k, v in params.items() if k[-3:] == 'Wgt'
}
params.update(fracs)
# Add to the list of imported histograms
param_rows.append(params)
# Convert the data to numpy arrays
for key in enums:
results[key] = np.asarray(results[key])
# Convert the refined parameters to a pandas dataframe
metadata = pd.DataFrame.from_dict(param_rows, orient='columns')
# Save to disk
metadata.to_hdf(hdf_filename,
key=os.path.join(hdf_groupname, 'refinements',
'parameters'))
with h5py.File(hdf_filename, mode='a') as h5fp:
base_grp = h5fp[hdf_groupname]
for key in enums:
ds_name = os.path.join('refinements', key)
if ds_name in base_grp:
del base_grp[ds_name]
base_grp.create_dataset(ds_name, data=results[key])
def generate_sulfur_pdf(inputs=[]):
def generate_charts(data, prefix, dest, xlabel, ylabel):
import plotly.graph_objects as go
import plotly
import numpy as np
x, y = np.loadtxt(data, unpack=True)
layout = go.Layout(
title=prefix,
xaxis=dict(
title=xlabel
),
yaxis=dict(
title=ylabel
))
# Create traces
fig = go.Figure(layout=layout, layout_xaxis_range=[0, 20])
line = dict(color="#ffe476")
scatter = go.Scatter(x=x, y=y,
mode='lines',
line=dict(color="#003865"),
name='lines')
fig.add_trace(scatter)
data = [scatter]
div = plotly.offline.plot(data, include_plotlyjs=False, output_type='div')
fig.write_html(dest + "/" + prefix + ".html")
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.axis import Axis
import GSASIIscriptable as G2sc
data = inputs[0]
filename = data['filename']
ceo2ImageFile = data['images']['CeO2']
ceo2ControlFile = data['controls']['CeO2']
capillaryImageFile = data['images']['Capillary']
capillaryControlFile = data['controls']['Capillary']
sulfurImageFile = data['images']['Sulfur']
sulfurControlFile = data['controls']['Sulfur']
# Create workflow GSAS2 project
gpx = G2sc.G2Project(filename=filename)
# Load tif images
gpx.add_image(ceo2ImageFile)
gpx.add_image(capillaryImageFile)
gpx.add_image(sulfurImageFile)
ceo2Image = gpx.images()[0]
capillaryImage = gpx.images()[1]
sulfurImage = gpx.images()[2]
ceo2Image.loadControls(ceo2ControlFile)
capillaryImage.loadControls(capillaryControlFile)
sulfurImage.loadControls(sulfurControlFile)
sulfurPWDRList = sulfurImage.Integrate()
capillaryPWDRList = capillaryImage.Integrate()
sulfurPWDRList[0].SaveProfile('pwdr_Sulfur')
capillaryPWDRList[0].SaveProfile('pwdr_Capillary')
sulfurPowerFile = data['powders']['Sulfur']
print(sulfurPWDRList)
pdf = gpx.add_PDF(sulfurPowerFile, 0)
pdf.set_formula(['S', 1])
pdf.data['PDF Controls']['Container']['Name'] = capillaryPWDRList[0].name
pdf.data['PDF Controls']['Container']['Mult'] = -0.988
pdf.data['PDF Controls']['Form Vol'] = 13.306
pdf.data['PDF Controls']['Geometry'] = 'Cylinder'
pdf.data['PDF Controls']['DetType'] = 'Area Detector'
pdf.data['PDF Controls']['ObliqCoeff'] = 0.2
pdf.data['PDF Controls']['Flat Bkg'] = 5081
pdf.data['PDF Controls']['BackRatio'] = 1.0
pdf.data['PDF Controls']['Ruland'] = 0.1
pdf.data['PDF Controls']['Lorch'] = True
pdf.data['PDF Controls']['QScaleLim'] = [23.4, 26.0]
pdf.data['PDF Controls']['Pack'] = 1.0
pdf.data['PDF Controls']['Diam'] = 1.5
pdf.data['PDF Controls']['Trans'] = 0.2
pdf.data['PDF Controls']['Ruland'] = 0.1
pdf.data['PDF Controls']['BackRatio'] = 0.184
pdf.data['PDF Controls']['Rmax'] = 20.0
for i in range(5):
if pdf.optimize():
break
pdf.calculate()
pdf.export(data['export']['prefix'], 'I(Q), S(Q), F(Q), G(r)')
gpx.save()
x, y = np.loadtxt(data['export']['prefix'] + '.gr', unpack=True)
plt.plot(x, y, label='Sulfur')
fig = plt.figure(1)
plt.title('Sulfur G(R)')
plt.xlabel('x')
plt.ylabel('y')
axes = plt.gca()
plt.xlim(data['limits']['xlim'])
plt.ylim(data['limits']['ylim'])
plt.savefig(data['outputfig'])
os.makedirs(data['datadir'] + "/charts", exist_ok=True)
generate_charts(data['export']['prefix'] + '.gr', os.path.basename(data['export']['prefix']) + "-gr", data['datadir'] + "/charts", "heat", "G(R)")
x, y = np.loadtxt(data['export']['prefix'] + '.sq', unpack=True)
plt.plot(x, y, label='Sulfur')
fig = plt.figure(1)
plt.title('Sulfur S(Q)')
plt.xlabel('x')
plt.ylabel('y')
axes = plt.gca()
plt.xlim(data['limits']['xlim'])
plt.ylim(data['limits']['ylim'])
plt.savefig(data['outputfig'])
os.makedirs(data['datadir'] + "/charts", exist_ok=True)
generate_charts(data['export']['prefix'] + '.sq', os.path.basename(data['export']['prefix']) + "-sq", data['datadir'] + "/charts", "heat", "S(Q)")
return True
# sort list of phases by name
phases.sort()
# creating and setting projects
projs = []
proj_dirs = []
for powder in powders:
# create dirs
proj_dir = os.path.join(OUTDIR, powder[:powder.find('-')])
proj_dirs.append(proj_dir)
if os.path.exists(proj_dir):
#os.rmdir(proj_dir)
shutil.rmtree(proj_dir)
os.makedirs(proj_dir)
# create project
gpx = G2sc.G2Project(newgpx=os.path.join(proj_dir, PNAME + PRJEXT))
# add phases
for phase in phases:
gpx.add_phase(os.path.join(INDIR, phase))
# add powder data
gpx.add_powder_histogram(datafile=os.path.join(INDIR, powder),
iparams=os.path.join(INDIR, PRM),
phases='all')
# # set controls
gpx.set_Controls('cycles', 10)
# save project
gpx.save()
# print(gpx)
projs.append(gpx)
# ----------------------------------------------
"""
print(u"*** profile Rwp, " + os.path.split(gpx.filename)[1])
for hist in gpx.histograms():
print("\t{:20s}: {:.2f}".format(hist.name,hist.get_wR()))
print("")
# where files will be written
work_dir = "tmp_gsasii"
if not os.path.exists(work_dir):
os.mkdir(work_dir)
# where data files are stored
data_dir = os.getcwd()
# create a GSAS-II project
gpx = gsasii.G2Project(newgpx=os.path.join(work_dir, "PbSO4.gpx"))
# add histograms
hist1 = gpx.add_powder_histogram(
os.path.join(data_dir,"PBSO4.xra"),
os.path.join(data_dir,"INST_XRY.prm"))
hist2 = gpx.add_powder_histogram(
os.path.join(data_dir,"PBSO4.cwn"),
os.path.join(data_dir,"inst_d1a.prm"))
# add phase
phase1 = gpx.add_phase(os.path.join(data_dir, "PbSO4-Wyckoff.cif"),
phasename="PbSO4",
histograms=[hist1, hist2])
# increase number of cycles to improve convergence
def compute(self):
# create a GSAS-II project
self.gpx = gsasii.G2Project("step_1.gpx")
self.gpx.save("step_2.gpx")
# change lattice parameters
for phase in self.gpx["Phases"].keys():
# ignore data key
if phase == "data":
continue
# handle PBSO4 phase
elif phase == "PBSO4":
cell = self.gpx["Phases"][phase]["General"]["Cell"]
a, b, c = self.get("PBSO4_A"), self.get("PBSO4_B"), self.get(
"PBSO4_C")
t11, t22, t33 = cell[1] / a, cell[2] / b, cell[3] / c
self.gpx["Phases"][phase]["General"][
"Cell"][1:] = lattice.TransformCell(
cell[1:7],
[[t11, 0.0, 0.0], [0.0, t22, 0.0], [0.0, 0.0, t33]])
# otherwise raise error because refinement plan does not support this phase
else:
raise NotImplementedError(
"Refinement plan cannot handle phase {}".format(phase))
# turn on unit cell refinement
args = {
"set": {
"Cell": True,
}
}
# refine
self.gpx.set_refinement(args)
self.gpx.do_refinements([{}])
# get histograms and phases
hist1, hist2 = self.gpx.histograms()
phase1 = self.gpx.phases()[0]
# turn on Dij terms (HAP) for phase 1 only
args6 = {
"set": {
"HStrain": True
},
"histograms": [hist1],
"phases": [phase1],
}
# turn on size and strain broadening (HAP) for histogram 1 only
args7 = {
"set": {
"Mustrain": {
"type": "isotropic",
"refine": True,
},
"Size": {
"type": "isotropic",
"refine": True
},
},
"histograms": [hist1],
}
# turn on sample parameters and radius (Hist)
# turn on atom parameters (phase)
args8a = {
"set": {
"Sample Parameters": ["Shift"]
},
"histograms": [hist1],
"skip": True,
}
args8b = {
"set": {
"Atoms": {
"all": "XU",
},
"Sample Parameters": ["DisplaceX", "DisplaceY"],
},
"histograms": [hist2],
}
# change data limits and instrument parmeter refinements (Hist)
args9a = {
"set": {
"Limits": [16.0, 158.4],
},
"histograms": [hist1],
"skip": True,
}
args9b = {
"set": {
"Limits": [19.0, 153.0],
},
"histograms": [hist2],
"skip": True,
}
args9c = {
"set": {
"Instrument Parameters": ["U", "V", "W"],
},
}
# change number of cycles and radius
self.gpx.data["Controls"]["data"]["max cyc"] = 8
hist2.data["Sample Parameters"]["Gonio. radius"] = 650.0
# refine
args_list = [args6, args7, args8a, args8b, args9a, args9b, args9c]
self.gpx.do_refinements(args_list)
# get minimization statistic
stat = self.gpx["Covariance"]["data"]["Rvals"]["Rwp"]
return stat