def _calibration(img, calibration, calib_ref_fp=None, **kwargs):
"""engine for performing calibration on a image with geometry
correction software. current backend is ``pyFAI``.
Parameters
----------
img : ndarray
image will be used for calibration process.
calibration : pyFAI.calibration.Calibration instance
Calibration instance with wavelength, calibrant and
detector configured.
calib_ref_fp : str
full file path to where the native pyFAI calibration information
will be saved.
kwargs:
additional keyword argument for calibration. please refer to
pyFAI documentation for all options.
"""
print('{:=^20}'.format("INFO: you are able to perform calibration, "
"please refer to pictorial guide here:\n"))
print('{:^20}'.format(
"http://xpdacq.github.io/usb_Running.html#calib-manual\n"))
# default params
interactive = True
# calibration
c = calibration # shorthand notation
timestr = _timestampstr(time.time())
c.gui = interactive
# annoying pyFAI logic, you need valid fn to start calibration
_is_tmp_dir = False
if calib_ref_fp is None:
_is_tmp_dir = True
td = TemporaryDirectory()
calib_ref_fp = os.path.join(td.name, 'from_calib_func')
basename, ext = os.path.splitext(calib_ref_fp)
poni_fn = basename + ".npt"
c.basename = basename
c.pointfile = poni_fn
c.peakPicker = PeakPicker(img,
reconst=True,
pointfile=c.pointfile,
calibrant=c.calibrant,
wavelength=c.wavelength,
**kwargs)
c.peakPicker.gui(log=True, maximize=True, pick=True)
update_fig(c.peakPicker.fig)
c.gui_peakPicker()
# TODO: open issue at pyFAI on this crazyness
c.ai.setPyFAI(**c.geoRef.getPyFAI())
c.ai.wavelength = c.geoRef.wavelength
if _is_tmp_dir:
td.cleanup()
return c, timestr
def templater2_func(doc, template, aux=None, short_aux=None):
"""format with auxiliary and time"""
if aux is None:
aux = ['temperature', 'diff_x', 'diff_y', 'eurotherm']
if short_aux is None:
short_aux = ['temp', 'x', 'y', 'euro']
aux_res = ['{}={}'.format(b, doc['data'].get(a, ''))
for a, b in zip(aux, short_aux)]
aux_res_str = '_'.join(aux_res)
# Add a separator between timestamp and extras
if aux_res_str:
aux_res_str = '_' + aux_res_str
return template.format(
# Change to include name as well
auxiliary=aux_res_str,
human_timestamp=_timestampstr(doc['time']))
def _calibration(img, calibration, calib_ref_fp, **kwargs):
"""engine for performing calibration on a image with geometry
correction software. current backend is ``pyFAI``.
Parameters
----------
img : ndarray
image will be used for calibration process.
calibration : pyFAI.calibration.Calibration instance
Calibration instance with wavelength, calibrant and
detector configured.
calib_ref_fp : str
full file path to where the native pyFAI calibration information
will be saved.
kwargs:
additional keyword argument for calibration. please refer to
pyFAI documentation for all options.
"""
print('{:=^20}'.format("INFO: you are able to perform calibration, "
"please refer to pictorial guide here:\n"))
print('{:^20}'
.format("http://xpdacq.github.io/usb_Running.html#calib-manual\n"))
# default params
interactive = True
# calibration
c = calibration # shorthand notation
timestr = _timestampstr(time.time())
c.gui = interactive
# annoying pyFAI logic, you need valid fn to start calibration
# TODO: send to tmp folder then delete tmp folder
if calib_ref_fp is None:
calib_ref_fp = os.path.join(os.getcwd(), 'from_calib_func')
basename, ext = os.path.splitext(calib_ref_fp)
poni_fn = basename + ".npt"
c.basename = basename
c.pointfile = poni_fn
c.peakPicker = PeakPicker(img, reconst=True,
pointfile=c.pointfile,
calibrant=c.calibrant,
wavelength=c.wavelength,
**kwargs)
c.peakPicker.gui(log=True, maximize=True, pick=True)
update_fig(c.peakPicker.fig)
c.gui_peakPicker()
return c, timestr
def templater2_func(doc, template, aux=None, short_aux=None):
"""format with auxiliary and time"""
if aux is None:
aux = ['temperature', 'diff_x', 'diff_y', 'eurotherm']
if short_aux is None:
short_aux = ['temp', 'x', 'y', 'euro']
aux_res = [
'{}={}'.format(b, doc['data'].get(a, ''))
for a, b in zip(aux, short_aux)
]
aux_res_str = '_'.join(aux_res)
# Add a separator between timestamp and extras
if aux_res_str:
aux_res_str = '_' + aux_res_str
return template.format(
# Change to include name as well
auxiliary=aux_res_str,
human_timestamp=_timestampstr(doc['time']))
def dark_template_func(timestamp, template):
"""Format template for dark images
Parameters
----------
timestamp: float
The time in unix epoch
template: str
The string to be formatted
Returns
-------
str:
"""
d = {'human_timestamp': _timestampstr(timestamp), 'ext': '.tiff'}
t = template.format(**d)
os.makedirs(os.path.split(t)[0])
return t
def dark_template_func(timestamp, template):
"""Format template for dark images
Parameters
----------
timestamp: float
The time in unix epoch
template: str
The string to be formatted
Returns
-------
str:
"""
d = {'human_timestamp': _timestampstr(timestamp), 'ext': '.tiff'}
t = template.format(**d)
os.makedirs(os.path.split(t)[0])
return t
def event(self, doc):
if self.dark_img is not None:
doc = next(self.db.fill_events([doc], self.descs))
if "cryostat_T" in doc["data"]:
doc["data"]["temperature"] = doc["data"].pop("cryostat_T")
# human readable timestamp
h_timestamp = _timestampstr(doc["time"])
# dark subtraction
img = doc["data"][self.image_data_key]
if str(img.dtype) == "uint16":
img = img.astype("float32")
if self.dark_img is not None:
img -= self.dark_img
if self.vis:
self.vis_callbacks["dark_sub_iq"]("event",
format_event(img=img))
if self.write_to_disk:
tiff_name = render_clean_makedir(
self.light_template,
human_timestamp=h_timestamp,
raw_event=doc,
raw_start=self.start_doc,
raw_descriptor=self.descriptor_doc,
analysis_stage="dark_sub",
ext=".tiff",
)
tifffile.imsave(tiff_name, img)
if self.analysis_setting == "full":
# background correction
if self.background_img is not None:
img -= self.background_img
# get calibration
if self.is_calibration:
calibration, geo = img_calibration(img, self.wavelength,
self.calibrant,
self.detector)
_save_calib_param(
calibration,
h_timestamp,
os.path.join(self.calibration_md_folder,
"xpdAcq_calib_info.yml"),
)
if self.write_to_disk:
poni_name = render_clean_makedir(
self.light_template,
human_timestamp=h_timestamp,
raw_event=doc,
raw_start=self.start_doc,
raw_descriptor=self.descriptor_doc,
analysis_stage="calib",
ext=".poni",
)
poni_saver(poni_name, calibration)
elif self.calibrant:
geo = load_geo(self.calibrant)
else:
geo = None
if geo:
img = polarization_correction(img, geo,
self.polarization_factor)
# Masking
if doc["seq_num"] == 1:
if (self.start_doc["sample_name"] == "Setup"
or self.mask_setting is None):
self.mask = np.ones(img.shape, dtype=bool)
else:
binner = generate_binner(geo, img.shape, self.mask)
self.mask = mask_img(img, binner,
**self.mask_kwargs)
if self.write_to_disk:
mask_name = render_clean_makedir(
self.light_template,
human_timestamp=h_timestamp,
raw_event=doc,
raw_start=self.start_doc,
raw_descriptor=self.descriptor_doc,
analysis_stage="mask",
ext="",
)
fit2d_save(self.mask, mask_name)
if self.vis:
overlay = overlay_mask(img, self.mask)
self.vis_callbacks["masked_img"](
"event", format_event(overlay_mask=overlay))
# binner
binner = generate_binner(geo, img.shape, self.mask)
q, iq = (
binner.bin_centers,
np.nan_to_num(binner(img.flatten())),
)
if self.vis:
self.vis_callbacks["iq"]("event",
format_event(q=q, iq=iq))
self.vis_callbacks["zscore"](
"event",
format_event(img=z_score_image(img, binner)),
)
if self.write_to_disk:
iq_name = render_clean_makedir(
self.light_template,
human_timestamp=h_timestamp,
raw_event=doc,
raw_start=self.start_doc,
raw_descriptor=self.descriptor_doc,
analysis_stage="iq_q",
ext="_Q.chi",
)
save_output(q, iq, iq_name, "Q")
tth = np.rad2deg(q_to_twotheta(q, self.wavelength))
if self.vis:
self.vis_callbacks["itth"]("event",
format_event(tth=tth,
iq=iq))
if self.write_to_disk:
itth_name = render_clean_makedir(
self.light_template,
human_timestamp=h_timestamp,
raw_event=doc,
raw_start=self.start_doc,
raw_descriptor=self.descriptor_doc,
analysis_stage="iq_tth",
ext="_tth.chi",
)
save_output(tth, iq, itth_name, "2theta")
if self.composition:
fq_q, fq, fq_config = fq_getter(
q,
iq,
composition=self.composition,
**self.fq_kwargs)
if self.vis:
self.vis_callbacks["fq"]("event",
format_event(q=fq_q,
fq=fq))
r, gr, pdf_config = pdf_getter(
q,
iq,
composition=self.composition,
**self.pdf_kwargs)
if self.vis:
self.vis_callbacks["pdf"]("event",
format_event(r=r,
pdf=gr))
if self.write_to_disk:
pdf_name = render_clean_makedir(
self.light_template,
human_timestamp=h_timestamp,
raw_event=doc,
raw_start=self.start_doc,
raw_descriptor=self.descriptor_doc,
analysis_stage="pdf",
ext=".gr",
)
pdf_saver(r, gr, pdf_config, pdf_name)
def event(self, doc):
if self.dark_img is not None:
doc = next(self.db.fill_events([doc], self.descs))
if 'cryostat_T' in doc['data']:
doc['data']['temperature'] = doc['data'].pop('cryostat_T')
# human readable timestamp
h_timestamp = _timestampstr(doc['time'])
# dark subtraction
img = doc['data'][self.image_data_key]
if str(img.dtype) == 'uint16':
img = img.astype('float32')
if self.dark_img is not None:
img -= self.dark_img
if self.vis:
self.vis_callbacks['dark_sub_iq']('event',
format_event(img=img))
if self.write_to_disk:
tiff_name = render_clean_makedir(
self.light_template,
human_timestamp=h_timestamp,
raw_event=doc,
raw_start=self.start_doc,
raw_descriptor=self.descriptor_doc,
analysis_stage='dark_sub',
ext='.tiff')
tifffile.imsave(tiff_name, img)
if self.analysis_setting == 'full':
# background correction
if self.background_img is not None:
img -= self.background_img
# get calibration
if self.is_calibration:
calibration, geo = img_calibration(img, self.wavelength,
self.calibrant,
self.detector)
_save_calib_param(
calibration, h_timestamp,
os.path.join(self.calibration_md_folder,
'xpdAcq_calib_info.yml'))
if self.write_to_disk:
poni_name = render_clean_makedir(
self.light_template,
human_timestamp=h_timestamp,
raw_event=doc,
raw_start=self.start_doc,
raw_descriptor=self.descriptor_doc,
analysis_stage='calib',
ext='.poni')
poni_saver(poni_name, calibration)
elif self.calibrant:
geo = load_geo(self.calibrant)
else:
geo = None
if geo:
img = polarization_correction(img, geo,
self.polarization_factor)
# Masking
if doc['seq_num'] == 1:
if (self.start_doc['sample_name'] == 'Setup'
or self.mask_setting is None):
self.mask = np.ones(img.shape, dtype=bool)
else:
binner = generate_binner(geo, img.shape, self.mask)
self.mask = mask_img(img, binner,
**self.mask_kwargs)
if self.write_to_disk:
mask_name = render_clean_makedir(
self.light_template,
human_timestamp=h_timestamp,
raw_event=doc,
raw_start=self.start_doc,
raw_descriptor=self.descriptor_doc,
analysis_stage='mask',
ext='')
fit2d_save(self.mask, mask_name)
if self.vis:
overlay = overlay_mask(img, self.mask)
self.vis_callbacks['masked_img'](
'event', format_event(overlay_mask=overlay))
# binner
binner = generate_binner(geo, img.shape, self.mask)
q, iq = binner.bin_centers, np.nan_to_num(
binner(img.flatten()))
if self.vis:
self.vis_callbacks['iq']('event',
format_event(q=q, iq=iq))
self.vis_callbacks['zscore'](
'event',
format_event(img=z_score_image(img, binner)))
if self.write_to_disk:
iq_name = render_clean_makedir(
self.light_template,
human_timestamp=h_timestamp,
raw_event=doc,
raw_start=self.start_doc,
raw_descriptor=self.descriptor_doc,
analysis_stage='iq_q',
ext='_Q.chi')
save_output(q, iq, iq_name, 'Q')
tth = np.rad2deg(q_to_twotheta(q, self.wavelength))
if self.vis:
self.vis_callbacks['itth']('event',
format_event(tth=tth,
iq=iq))
if self.write_to_disk:
itth_name = render_clean_makedir(
self.light_template,
human_timestamp=h_timestamp,
raw_event=doc,
raw_start=self.start_doc,
raw_descriptor=self.descriptor_doc,
analysis_stage='iq_tth',
ext='_tth.chi')
save_output(tth, iq, itth_name, '2theta')
if self.composition:
fq_q, fq, fq_config = fq_getter(
q,
iq,
composition=self.composition,
**self.fq_kwargs)
if self.vis:
self.vis_callbacks['fq']('event',
format_event(q=fq_q,
fq=fq))
r, gr, pdf_config = pdf_getter(
q,
iq,
composition=self.composition,
**self.pdf_kwargs)
if self.vis:
self.vis_callbacks['pdf']('event',
format_event(r=r,
pdf=gr))
if self.write_to_disk:
pdf_name = render_clean_makedir(
self.light_template,
human_timestamp=h_timestamp,
raw_event=doc,
raw_start=self.start_doc,
raw_descriptor=self.descriptor_doc,
analysis_stage='pdf',
ext='.gr')
pdf_saver(r, gr, pdf_config, pdf_name)