def update_mp(self):
"""
Update memory profile curve
"""
if not self.do_memprofile:
return
self.memory_profile[0].append(time.time() - self.starttime)
self.memory_profile[1].append(self.get_mem())
if not self.fig_mp:
self.fig_mp = pylab.figure()
self.fig_mp.show()
self.ax_mp = self.fig_mp.add_subplot(1, 1, 1)
self.ax_mp.set_autoscale_on(False)
self.ax_mp.set_xlabel("Run time (s)")
self.ax_mp.set_xlim(0, 100)
self.ax_mp.set_ylim(0, 2**10)
self.ax_mp.set_ylabel("Memory occupancy (MB)")
self.ax_mp.set_title("Memory leak hunter")
self.plot_mp = self.ax_mp.plot(*self.memory_profile)[0]
else:
self.plot_mp.set_data(*self.memory_profile)
tmax = self.memory_profile[0][-1]
mmax = max(self.memory_profile[1])
if tmax > self.ax_mp.get_xlim()[-1]:
self.ax_mp.set_xlim(0, tmax)
if mmax > self.ax_mp.get_ylim()[-1]:
self.ax_mp.set_ylim(0, mmax)
if self.fig_mp.canvas:
update_fig(self.fig_mp)
def update_mp(self):
"""
Update memory profile curve
"""
if not self.do_memprofile:
return
self.memory_profile[0].append(time.time() - self.starttime)
self.memory_profile[1].append(self.get_mem())
if not self.fig_mp:
self.fig_mp = pylab.figure()
self.fig_mp.show()
self.ax_mp = self.fig_mp.add_subplot(1, 1, 1)
self.ax_mp.set_autoscale_on(False)
self.ax_mp.set_xlabel("Run time (s)")
self.ax_mp.set_xlim(0, 100)
self.ax_mp.set_ylim(0, 2 ** 10)
self.ax_mp.set_ylabel("Memory occupancy (MB)")
self.ax_mp.set_title("Memory leak hunter")
self.plot_mp = self.ax_mp.plot(*self.memory_profile)[0]
else:
self.plot_mp.set_data(*self.memory_profile)
tmax = self.memory_profile[0][-1]
mmax = max(self.memory_profile[1])
if tmax > self.ax_mp.get_xlim()[-1]:
self.ax_mp.set_xlim(0, tmax)
if mmax > self.ax_mp.get_ylim()[-1]:
self.ax_mp.set_ylim(0, mmax)
if self.fig_mp.canvas:
update_fig(self.fig_mp)
def new_curve(self, results, label):
"""
Create a new curve within the current graph
"""
self.update_mp()
if not self.fig:
return
self.plot_x = list(results.keys())
self.plot_x.sort()
self.plot_y = [1000.0 / results[i] for i in self.plot_x]
self.plot = self.ax.plot(self.plot_x, self.plot_y, "o-", label=label)[0]
self.ax.legend()
update_fig(self.fig)
def new_point(self, size, exec_time):
"""
Add new point to current curve
@param size: of the system
@parm exec_time: execution time in ms
"""
self.update_mp()
if not self.plot:
return
self.plot_x.append(size)
self.plot_y.append(1000.0 / exec_time)
self.plot.set_data(self.plot_x, self.plot_y)
update_fig(self.fig)
def new_point(self, size, exec_time):
"""
Add new point to current curve
@param size: of the system
@parm exec_time: execution time in ms
"""
self.update_mp()
if not self.plot:
return
self.plot_x.append(size)
self.plot_y.append(1000.0 / exec_time)
self.plot.set_data(self.plot_x, self.plot_y)
update_fig(self.fig)
def new_curve(self, results, label, style="-"):
"""
Create a new curve within the current graph
@param results: dict with execution time in function of size
@param label: string with the title of the curve
@param style: the style of the line: "-" for plain line, "--" for dashed
"""
self.update_mp()
if not self.fig:
return
self.plot_x = list(results.keys())
self.plot_x.sort()
self.plot_y = [1000.0 / results[i] for i in self.plot_x]
self.plot = self.ax.plot(self.plot_x, self.plot_y, "o" + style, label=label)[0]
self.ax.legend()
update_fig(self.fig)
def init_curve(self):
self.update_mp()
if self.fig:
print("Already initialized")
return
if "DISPLAY" in os.environ:
self.fig = pylab.figure()
self.fig.show()
self.ax = self.fig.add_subplot(1, 1, 1)
self.ax.set_autoscale_on(False)
self.ax.set_xlabel("Image size in Mega-Pixels")
self.ax.set_ylabel("Frames processed per second")
self.ax.set_yscale("log", basey=2)
t = [1, 2, 5, 10, 20, 50, 100, 200, 400, 500]
self.ax.set_yticks([float(i) for i in t])
self.ax.set_yticklabels([str(i)for i in t])
self.ax.set_xlim(0.5, 17)
self.ax.set_ylim(0.5, 500)
self.ax.set_title(self.get_cpu() + " / " + self.get_gpu())
update_fig(self.fig)
def new_curve(self, results, label, style="-"):
"""
Create a new curve within the current graph
@param results: dict with execution time in function of size
@param label: string with the title of the curve
@param style: the style of the line: "-" for plain line, "--" for dashed
"""
self.update_mp()
if not self.fig:
return
self.plot_x = list(results.keys())
self.plot_x.sort()
self.plot_y = [1000.0 / results[i] for i in self.plot_x]
self.plot = self.ax.plot(self.plot_x,
self.plot_y,
"o" + style,
label=label)[0]
self.ax.legend()
update_fig(self.fig)
def init_curve(self):
self.update_mp()
if self.fig:
print("Already initialized")
return
if (sys.platform in ["win32", "darwin"]) or ("DISPLAY" in os.environ):
self.fig = pylab.figure()
self.fig.show()
self.ax = self.fig.add_subplot(1, 1, 1)
self.ax.set_autoscale_on(False)
self.ax.set_xlabel("Image size in Mega-Pixels")
self.ax.set_ylabel("Frames processed per second")
self.ax.set_yscale("log", basey=2)
t = [1, 2, 5, 10, 20, 50, 100, 200, 400, 500]
self.ax.set_yticks([float(i) for i in t])
self.ax.set_yticklabels([str(i) for i in t])
self.ax.set_xlim(0.5, 17)
self.ax.set_ylim(0.5, 500)
self.ax.set_title(self.get_cpu() + " / " + self.get_gpu())
update_fig(self.fig)
def calibration(img, calibrant_file=None, wavelength=None,
calib_collection_uid=None, save_file_name=None,
detector=None, gaussian=None):
""" run calibration process on a image with geometry correction
software
current backend is ``pyFAI``.
Parameters
----------
img : ndarray
image to be calibrated
calibrant_file : str, optional
calibrant file being used, default is 'Ni.D' under
xpdUser/userAnalysis/
wavelength : flot, optional
current of x-ray wavelength, in angstrom. Default value is
read out from existing xpdacq.Beamtime object
calibration_collection_uid : str, optional
uid of calibration collection. default is generated from run
calibration
save_file_name : str, optional
file name for yaml that carries resultant calibration parameters
detector : pyfai.detector.Detector, optional.
instance of detector which defines pixel size in x- and
y-direction. Default is set to Perkin Elmer detector
gaussian : int, optional
gaussian width between rings, Default is 100.
"""
# default params
interactive = True
dist = 0.1
_check_obj(_REQUIRED_OBJ_LIST)
ips = get_ipython()
bto = ips.ns_table['user_global']['bt']
xrun = ips.ns_table['user_global']['xrun']
calibrant = Calibrant()
# d-spacing
if calibrant_file is not None:
calibrant.load_file(calibrant_file)
calibrant_name = os.path.split(calibrant_file)[1]
calibrant_name = os.path.splitext(calibrant_name)[0]
else:
calibrant.load_file(os.path.join(glbl.usrAnalysis_dir, 'Ni.D'))
calibrant_name = 'Ni'
# wavelength
if wavelength is None:
_wavelength = bto['bt_wavelength']
else:
_wavelength = wavelength
calibrant.wavelength = _wavelength * 10 ** (-10)
# detector
if detector is None:
detector = Perkin()
# calibration
timestr = _timestampstr(time.time())
basename = '_'.join(['pyFAI_calib', calibrant_name, timestr])
w_name = os.path.join(glbl.config_base, basename) # poni name
c = Calibration(wavelength=calibrant.wavelength,
detector=detector,
calibrant=calibrant,
gaussianWidth=gaussian)
c.gui = interactive
c.basename = w_name
c.pointfile = w_name + ".npt"
c.ai = AzimuthalIntegrator(dist=dist, detector=detector,
wavelength=calibrant.wavelength)
c.peakPicker = PeakPicker(img, reconst=True, mask=detector.mask,
pointfile=c.pointfile, calibrant=calibrant,
wavelength=calibrant.wavelength)
# method=method)
if gaussian is not None:
c.peakPicker.massif.setValleySize(gaussian)
else:
c.peakPicker.massif.initValleySize()
if interactive:
c.peakPicker.gui(log=True, maximize=True, pick=True)
update_fig(c.peakPicker.fig)
c.gui_peakPicker()
c.ai.setPyFAI(**c.geoRef.getPyFAI())
c.ai.wavelength = c.geoRef.wavelength
return c.ai
def run_calibration(exposure=60, calibrant_file=None, wavelength=None,
detector=None, gaussian=None):
""" function to run entire calibration process.
Entire process includes: collect calibration image, trigger pyFAI
calibration process, store calibration parameters as a yaml file
under xpdUser/config_base/ and inject uid of calibration image to
following scans, until this function is run again.
Parameters
----------
exposure : int, optional
total exposure time in sec. Default is 60s
calibrant_name : str, optional
name of calibrant used, different calibrants correspond to
different d-spacing profiles. Default is 'Ni'. User can assign
different calibrant, given d-spacing file path presents
wavelength : flot, optional
current of x-ray wavelength, in angstrom. Default value is
read out from existing xpdacq.Beamtime object
detector : pyfai.detector.Detector, optional.
instance of detector which defines pxiel size in x- and
y-direction. Default is set to Perkin Elmer detector
gaussian : int, optional
gaussian width between rings, Default is 100.
"""
# default params
interactive = True
dist = 0.1
_check_obj(_REQUIRED_OBJ_LIST)
ips = get_ipython()
bto = ips.ns_table['user_global']['bt']
prun = ips.ns_table['user_global']['prun']
# print('*** current beamtime info = {} ***'.format(bto.md))
calibrant = Calibrant()
# d-spacing
if calibrant_file is not None:
calibrant.load_file(calibrant_file)
calibrant_name = os.path.split(calibrant_file)[1]
calibrant_name = os.path.splitext(calibrant_name)[0]
else:
calibrant.load_file(os.path.join(glbl.usrAnalysis_dir, 'Ni24.D'))
calibrant_name = 'Ni'
# wavelength
if wavelength is None:
_wavelength = bto['bt_wavelength']
else:
_wavelength = wavelength
calibrant.wavelength = _wavelength * 10 ** (-10)
# detector
if detector is None:
detector = Perkin()
# scan
# simplified version of Sample object
calib_collection_uid = str(uuid.uuid4())
calibration_dict = {'sample_name':calibrant_name,
'sample_composition':{calibrant_name :1},
'is_calibration': True,
'calibration_collection_uid': calib_collection_uid}
prun_uid = prun(calibration_dict, ScanPlan(bto, ct, exposure))
light_header = glbl.db[prun_uid[-1]] # last one is always light
dark_uid = light_header.start['sc_dk_field_uid']
dark_header = glbl.db[dark_uid]
# unknown signature of get_images
dark_img = np.asarray(
get_images(dark_header, glbl.det_image_field)).squeeze()
# dark_img = np.asarray(glbl.get_images(dark_header, glbl.det_image_field)).squeeze()
for ev in glbl.get_events(light_header, fill=True):
img = ev['data'][glbl.det_image_field]
img -= dark_img
# calibration
timestr = _timestampstr(time.time())
basename = '_'.join(['pyFAI_calib', calibrant_name, timestr])
w_name = os.path.join(glbl.config_base, basename) # poni name
c = Calibration(wavelength=calibrant.wavelength,
detector=detector,
calibrant=calibrant,
gaussianWidth=gaussian)
c.gui = interactive
c.basename = w_name
c.pointfile = w_name + ".npt"
c.ai = AzimuthalIntegrator(dist=dist, detector=detector,
wavelength=calibrant.wavelength)
c.peakPicker = PeakPicker(img, reconst=True, mask=detector.mask,
pointfile=c.pointfile, calibrant=calibrant,
wavelength=calibrant.wavelength)
# method=method)
if gaussian is not None:
c.peakPicker.massif.setValleySize(gaussian)
else:
c.peakPicker.massif.initValleySize()
if interactive:
c.peakPicker.gui(log=True, maximize=True, pick=True)
update_fig(c.peakPicker.fig)
c.gui_peakPicker()
c.ai.setPyFAI(**c.geoRef.getPyFAI())
c.ai.wavelength = c.geoRef.wavelength
# update until next time
glbl.calib_config_dict = c.ai.getPyFAI()
Fit2D_dict = c.ai.getFit2D()
glbl.calib_config_dict.update(Fit2D_dict)
glbl.calib_config_dict.update({'file_name':basename})
glbl.calib_config_dict.update({'time':timestr})
# FIXME: need a solution for selecting desired calibration image
# based on calibration_collection_uid later
glbl.calib_config_dict.update({'calibration_collection_uid':
calib_collection_uid})
# write yaml
yaml_name = glbl.calib_config_name
with open(os.path.join(glbl.config_base, yaml_name), 'w') as f:
yaml.dump(glbl.calib_config_dict, f)
return c.ai