from pyobjcryst import loadCrystal
from pyobjcryst.molecule import Molecule
from pdfstream.io import load_img, load_data
NI_PONI = resource_filename('tests', 'test_data/Ni_poni_file.poni')
NI_GR = resource_filename('tests', 'test_data/Ni_gr_file.gr')
NI_CHI = resource_filename('tests', 'test_data/Ni_chi_file.chi')
NI_FGR = resource_filename('tests', 'test_data/Ni_fgr_file.fgr')
NI_IMG = resource_filename('tests', 'test_data/Ni_img_file.tiff')
NI_CIF = resource_filename('tests', 'test_data/Ni_cif_file.cif')
KAPTON_IMG = resource_filename('tests', 'test_data/Kapton_img_file.tiff')
BLACK_IMG = resource_filename('tests', 'test_data/black_img.tiff')
WHITE_IMG = resource_filename('tests', 'test_data/white_img.tiff')
NI_CONFIG = PDFConfig()
NI_CONFIG.readConfig(NI_GR)
NI_PDFGETTER = PDFGetter(NI_CONFIG)
ZRP_CIF = resource_filename('tests', 'test_data/ZrP.cif')
NI_CRYSTAL = loadCrystal(NI_CIF)
ZRP_CRYSTAL = loadCrystal(ZRP_CIF)
NI_DIFFPY = loadStructure(NI_CIF)
DB = {
'Ni_img_file': NI_IMG,
'Ni_img': load_img(NI_IMG),
'Kapton_img_file': KAPTON_IMG,
'Kapton_img': load_img(KAPTON_IMG),
'Ni_poni_file': NI_PONI,
'Ni_gr_file': NI_GR,
'Ni_chi_file': NI_CHI,
'Ni_fgr_file': NI_FGR,
def load_pdfconfig(cfg_file: str) -> PDFConfig:
"""Load the PDFConfig from the processed data file or configuration file."""
pdfconfig = PDFConfig()
pdfconfig.readConfig(cfg_file)
return pdfconfig
NI_GR_FILE = resource_filename('tests', 'test_data/Ni_gr_file.gr')
NI_CHI_FILE = resource_filename('tests', 'test_data/Ni_chi_file.chi')
NI_FGR_FILE = resource_filename('tests', 'test_data/Ni_fgr_file.fgr')
NI_IMG_FILE = resource_filename('tests', 'test_data/Ni_img_file.tiff')
MASK_FILE = resource_filename("tests", "test_data/mask_file.npy")
KAPTON_IMG_FILE = resource_filename('tests', 'test_data/Kapton_img_file.tiff')
BLACK_IMG_FILE = resource_filename('tests', 'test_data/black_img.tiff')
WHITE_IMG_FILE = resource_filename('tests', 'test_data/white_img.tiff')
NI_IMG = load_img(NI_IMG_FILE)
NI_FRAMES = numpy.expand_dims(NI_IMG, 0)
KAPTON_IMG = load_img(KAPTON_IMG_FILE)
NI_GR = load_array(NI_GR_FILE)
NI_CHI = load_array(NI_CHI_FILE)
NI_FGR = load_array(NI_FGR_FILE)
NI_CONFIG = PDFConfig()
NI_CONFIG.readConfig(NI_GR_FILE)
NI_PDFGETTER = PDFGetter(NI_CONFIG)
AI = pyFAI.load(NI_PONI_FILE)
MASK = numpy.load(MASK_FILE)
BLACK_IMG = load_img(BLACK_IMG_FILE)
WHITE_IMG = load_img(WHITE_IMG_FILE)
START_DOC_FILE = resource_filename('tests', 'test_data/start.json')
with Path(START_DOC_FILE).open("r") as f:
START_DOC = json.load(f)
DB = {
'Ni_img_file': NI_IMG_FILE,
'Ni_img': NI_IMG,
'Kapton_img_file': KAPTON_IMG_FILE,
'Kapton_img': KAPTON_IMG,
'Ni_poni_file': NI_PONI_FILE,
def run(args):
FILE = args.INPUT
FILE_NO_EXTENSION = FILE[:-6]
SAVE_PATH = os.getcwd()
print(SAVE_PATH)
if args.OUTPUT:
SAVE_PATH = args.OUTPUT
else:
print(
'!!! Warning files will be saved in the current folder because no output was defined.'
)
REFERENCE_SLICE_NUMBER = 100
### Collecting data informations ###
try:
inputFile = h5py.File(FILE, 'r')
print("File " + FILE + " loaded")
except ValueError:
raise
### Creating matrix from data ###
rawData = np.array(inputFile['data/data'])
rawTheta = np.array(inputFile['data/theta'])
dataX = np.ndarray.flatten(np.array(inputFile['data/dataX']))
theta = np.sort(rawTheta)
sinogramData = np.zeros(np.shape(rawData))
### Corrections ###
#Correcting unsorted 2theta acquisition
argsortVal = np.argsort(rawTheta)
if not np.array_equal(theta, rawTheta):
sorting = 0
while sorting < np.max(argsortVal) - 1:
sinogramData[sorting, :, :] = rawData[argsortVal[sorting], :, :]
progression("Sorting data................ ",
np.size(argsortVal) - 2, sorting)
sorting = sorting + 1
print()
##Deleting lines
if args.DELETE:
deleted_line = np.fromstring(args.DELETE, dtype=int, sep=',')
for i in range(0, len(deleted_line)):
sinogramData = np.delete(sinogramData, deleted_line[i], axis=0)
theta = np.delete(theta, deleted_line[i], axis=0)
progression("Deleting lines.............. ", len(deleted_line), i)
print()
### Removing outlier pixels from data ###
if args.OUTLIERS:
for i in range(0, np.size(sinogramData, 2)):
sinogramData[:, :, i] = findOutlierPixels(sinogramData[:, :, i],
tolerance=10,
worry_about_edges=False)
progression("Correcting wrong pixels..... ",
np.size(sinogramData, 2), i)
print()
### Subtract air from raw data ###
if args.AIR:
dataAir = np.genfromtxt(args.AIR, dtype=float)
FILE_NO_EXTENSION = FILE_NO_EXTENSION + '_SUBAIR'
for i in range(0, np.size(sinogramData, 0)):
currentAir = dataAir[:, 1] * (
0.85 * np.average(sinogramData[i, :, 50]) / dataAir[50, 1])
for j in range(0, np.size(sinogramData, 1)):
sinogramData[i, j, :] = sinogramData[i, j, :] - currentAir
progression("Substacting air............. ",
np.size(sinogramData, 0), i)
print()
### Correcting thermal/beam drifts ###
if args.CORRECT:
thresholdedSinogramData = np.copy(sinogramData[:, :,
REFERENCE_SLICE_NUMBER])
thresholdedSinogramData[sinogramData[:, :,
REFERENCE_SLICE_NUMBER] < 40] = 0
thresholdedSinogramData[sinogramData[:, :,
REFERENCE_SLICE_NUMBER] >= 40] = 1
thresholdedSinogramData = binary_fill_holes(thresholdedSinogramData)
thresholdedSinogramData = imageFilterBigPart(thresholdedSinogramData)
CoMThresh = centerOfMass(thresholdedSinogramData *
sinogramData[:, :, REFERENCE_SLICE_NUMBER],
axis=1)
for i in range(0, np.size(sinogramData, 2)):
sinogramData[:, :, i] = fixDrift(sinogramData[:, :, i], CoMThresh)
progression("Correcting drifts........... ",
np.size(sinogramData, 2), i)
print()
### Subtract extra pattern from raw data ###
if args.EXTRA:
dataExtra = np.genfromtxt(args.EXTRA, dtype=float)
FILE_NO_EXTENSION = FILE_NO_EXTENSION + '_SUBPAP'
for i in range(0, np.size(sinogramData, 0)):
for j in range(0, np.size(sinogramData, 1)):
currentExtra = dataExtra[:, 1] * (sinogramData[i, j, 100] /
dataExtra[100, 1])
sinogramData[i, j, :] = sinogramData[i, j, :] - currentExtra
progression("Substacting extra........... ",
np.size(sinogramData, 0), i)
print()
### Extract PDF signal ###
sinogramDataPdf = np.copy(sinogramData)
if args.PDF:
cfg = PDFConfig()
cfg.readConfig(args.PDF)
pdfget = PDFGetter()
pdfget.configure(cfg)
sinogramDataPdf = np.zeros(
(np.size(sinogramData, 0), np.size(sinogramData, 1),
round((cfg.rmax - cfg.rmin) / cfg.rstep) + 1))
for i in range(0, np.size(sinogramDataPdf, 0)):
for j in range(0, np.size(sinogramDataPdf, 1)):
currentPdfDataY = sinogramData[i, j, :]
pdfget.getTransformation('gr')
pdfget(dataX, currentPdfDataY)
pdfResults = pdfget.results
pdfResults = pdfResults[8]
sinogramDataPdf[i, j, :] = pdfResults[1]
progression("Extracting PDF.............. ",
np.size(sinogramDataPdf, 0), i)
for i in range(0, np.size(sinogramDataPdf, 2)):
sinogramDataPdf[:, :, i] = np.average(sinogramData[:, :, :],
axis=2) * np.copy(
sinogramDataPdf[:, :, i])
sinogramData = np.copy(sinogramDataPdf)
FILE_NO_EXTENSION = FILE_NO_EXTENSION + '_PDF'
print()
### Saving ###
if (args.OVERWRITE == True
or os.path.isfile(FILE_NO_EXTENSION + '_corrected.h5') == False):
saveHdf5File(sinogramData,
SAVE_PATH,
FILE_NO_EXTENSION + '_corrected.h5',
mode='stack')
else:
print(
'!!! Warning sinogram file exists, use command -R to overwrite it')
### Reconstruction ###
if args.RECONSTRUCT:
reconstructedData = np.zeros(
(np.size(sinogramData, 1), np.size(sinogramData,
1), np.size(sinogramData, 2)))
for i in range(0, np.size(sinogramData, 2)):
reconstructedData[:, :, i] = reconstruction(sinogramData[:, :, i],
theta,
output_size=np.size(
sinogramData, 1))
progression("Reconstructing data......... ",
np.size(sinogramData, 2), i)
print()
if args.OVERWRITE and args.RECONSTRUCT:
saveHdf5File(reconstructedData,
SAVE_PATH,
FILE_NO_EXTENSION + '_reconstructed_stack.h5',
mode='stack')
else:
print(
'!!! Warning reconstruction file exists, use command -R to overwrite it'
)
