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#pylint: disable=no-init,invalid-name
from __future__ import (absolute_import, division, print_function)
import sys
from mantid.api import *
from mantid.kernel import *
import mantid.simpleapi
import math
import copy
import logging
import numpy as np
from scipy.optimize import curve_fit
class MRInspectData(PythonAlgorithm):
def category(self):
return "Reflectometry\\SNS"
def name(self):
return "MRInspectData"
def summary(self):
return "This algorithm inspects Magnetism Reflectometer data and populates meta-data."
def PyInit(self):
self.declareProperty(WorkspaceProperty("Workspace", "", Direction.Input),
"Input workspace")
# Peak finding options
self.declareProperty("UseROI", True,
doc="If true, use the meta-data ROI rather than finding the ranges")
self.declareProperty("UpdatePeakRange", False,
doc="If true, a fit will be performed and the peak ranges will be updated")
self.declareProperty("UseROIBck", False,
doc="If true, use the 2nd ROI in the meta-data for the background")
self.declareProperty("UseTightBck", False,
doc="If true, use the area on each side of the peak to compute the background")
self.declareProperty("BckWidth", 3,
doc="If UseTightBck is true, width of the background on each side of the peak")
self.declareProperty("HuberXCut", 0.0,
doc="Provide a Huber X value above which a run will be considered a direct beam")
self.declareProperty("ForcePeakROI", False,
doc="If true, use the PeakROI property as the ROI")
self.declareProperty(IntArrayProperty("PeakROI", [0, 0],
IntArrayLengthValidator(2), direction=Direction.Input),
"Pixel range defining the reflectivity peak")
self.declareProperty("ForceLowResPeakROI", False,
doc="If true, use the LowResPeakROI property as the ROI")
self.declareProperty(IntArrayProperty("LowResPeakROI", [0, 0],
IntArrayLengthValidator(2), direction=Direction.Input),
"Pixel range defining the low-resolution peak")
self.declareProperty("ForceBckROI", False,
doc="If true, use the BckROI property as the ROI")
self.declareProperty(IntArrayProperty("BckROI", [0, 0],
IntArrayLengthValidator(2), direction=Direction.Input),
"Pixel range defining the background")
def PyExec(self):
nxs_data = self.getProperty("Workspace").value
nxs_data_name = self.getPropertyValue("Workspace")
data_info = DataInfo(nxs_data, cross_section=nxs_data_name,
use_roi=self.getProperty("UseROI").value,
update_peak_range=self.getProperty("UpdatePeakRange").value,
use_roi_bck=self.getProperty("UseROIBck").value,
use_tight_bck=self.getProperty("UseTightBck").value,
bck_offset=self.getProperty("BckWidth").value,
huber_x_cut=self.getProperty("HuberXCut").value,
force_peak_roi=self.getProperty("ForcePeakROI").value,
peak_roi=self.getProperty("PeakROI").value,
force_low_res_roi=self.getProperty("ForceLowResPeakROI").value,
low_res_roi=self.getProperty("LowResPeakROI").value,
force_bck_roi=self.getProperty("ForceBckROI").value,
bck_roi=self.getProperty("BckROI").value)
# Store information in logs
mantid.simpleapi.AddSampleLog(Workspace=nxs_data, LogName='calculated_scatt_angle',
LogText=str(data_info.calculated_scattering_angle),
LogType='Number', LogUnit='degree')
mantid.simpleapi.AddSampleLog(Workspace=nxs_data, LogName='cross_section',
LogText=nxs_data_name)
mantid.simpleapi.AddSampleLog(Workspace=nxs_data, LogName='use_roi_actual',
LogText=str(data_info.use_roi_actual))
mantid.simpleapi.AddSampleLog(Workspace=nxs_data, LogName='is_direct_beam',
LogText=str(data_info.is_direct_beam))
mantid.simpleapi.AddSampleLog(Workspace=nxs_data, LogName='tof_range_min',
LogText=str(data_info.tof_range[0]),
LogType='Number', LogUnit='usec')
mantid.simpleapi.AddSampleLog(Workspace=nxs_data, LogName='tof_range_max',
LogText=str(data_info.tof_range[1]),
LogType='Number', LogUnit='usec')
mantid.simpleapi.AddSampleLog(Workspace=nxs_data, LogName='peak_min',
LogText=str(data_info.peak_range[0]),
LogType='Number', LogUnit='pixel')
mantid.simpleapi.AddSampleLog(Workspace=nxs_data, LogName='peak_max',
LogText=str(data_info.peak_range[1]),
LogType='Number', LogUnit='pixel')
mantid.simpleapi.AddSampleLog(Workspace=nxs_data, LogName='background_min',
LogText=str(data_info.background[0]),
LogType='Number', LogUnit='pixel')
mantid.simpleapi.AddSampleLog(Workspace=nxs_data, LogName='background_max',
LogText=str(data_info.background[1]),
LogType='Number', LogUnit='pixel')
mantid.simpleapi.AddSampleLog(Workspace=nxs_data, LogName='low_res_min',
LogText=str(data_info.low_res_range[0]),
LogType='Number', LogUnit='pixel')
mantid.simpleapi.AddSampleLog(Workspace=nxs_data, LogName='low_res_max',
LogText=str(data_info.low_res_range[1]),
LogType='Number', LogUnit='pixel')
def _as_ints(a): return [int(a[0]), int(a[1])]
class DataInfo(object):
"""
Class to hold the relevant information from a run (scattering or direct beam).
"""
n_x_pixel = 304
n_y_pixel = 256
peak_range_offset = 0
tolerance = 0.02
pixel_width = 0.0007
n_events_cutoff = 10000
huber_x_cut = 4.95
def __init__(self, ws, cross_section='', use_roi=True, update_peak_range=False, use_roi_bck=False,
use_tight_bck=False, bck_offset=3, huber_x_cut=4.95,
force_peak_roi=False, peak_roi=[0,0],
force_low_res_roi=False, low_res_roi=[0,0],
force_bck_roi=False, bck_roi=[0,0]):
self.cross_section = cross_section
self.run_number = ws.getRunNumber()
self.is_direct_beam = False
self.data_type = 1
self.peak_position = 0
self.peak_range = [0,0]
self.low_res_range = [0,0]
self.background = [0,0]
self.huber_x_cut = huber_x_cut
# ROI information
self.roi_peak = [0,0]
self.roi_low_res = [0,0]
self.roi_background = [0,0]
# Options to override the ROI
self.force_peak_roi = force_peak_roi
self.forced_peak_roi = _as_ints(peak_roi)
self.force_low_res_roi = force_low_res_roi
self.forced_low_res_roi = _as_ints(low_res_roi)
self.force_bck_roi = force_bck_roi
self.forced_bck_roi = _as_ints(bck_roi)
# Peak found before fitting for the central position
self.found_peak = [0,0]
self.found_low_res = [0,0]
# Processing options
# Use the ROI rather than finding the ranges
self.use_roi = use_roi
self.use_roi_actual = False
# Use the 2nd ROI as the background, if available
self.use_roi_bck = use_roi_bck
# Use background as a region on each side of the peak
self.use_tight_bck = use_tight_bck
# Width of the background on each side of the peak
self.bck_offset = bck_offset
# Update the specular peak range after finding the peak
# within the ROI
self.update_peak_range = update_peak_range
self.tof_range = self.get_tof_range(ws)
self.determine_data_type(ws)
def log(self):
"""
Log useful diagnostics
"""
logging.info("| Run: %s [direct beam: %s]" % (self.run_number, self.is_direct_beam))
logging.info("| Peak position: %s" % self.peak_position)
logging.info("| Reflectivity peak: %s" % str(self.peak_range))
logging.info("| Low-resolution pixel range: %s" % str(self.low_res_range))
def get_tof_range(self, ws):
"""
Determine TOF range from the data
"""
run_object = ws.getRun()
sample_detector_distance = run_object['SampleDetDis'].getStatistics().mean / 1000.0
source_sample_distance = run_object['ModeratorSamDis'].getStatistics().mean / 1000.0
source_detector_distance = source_sample_distance + sample_detector_distance
h = 6.626e-34 # m^2 kg s^-1
m = 1.675e-27 # kg
wl = run_object.getProperty('LambdaRequest').value[0]
chopper_speed = run_object.getProperty('SpeedRequest1').value[0]
wl_offset = 0
cst = source_detector_distance / h * m
tof_min = cst * (wl + wl_offset * 60.0 / chopper_speed - 1.4 * 60.0 / chopper_speed) * 1e-4
tof_max = cst * (wl + wl_offset * 60.0 / chopper_speed + 1.4 * 60.0 / chopper_speed) * 1e-4
self.tof_range = [tof_min, tof_max]
return [tof_min, tof_max]
def process_roi(self, ws):
"""
Process the ROI information and determine the peak
range, the low-resolution range, and the background range.
"""
roi_peak = [0,0]
roi_low_res = [0,0]
roi_background = [0,0]
# Read ROI 1
roi1_valid = True
if 'ROI1StartX' in ws.getRun():
roi1_x0 = ws.getRun()['ROI1StartX'].getStatistics().mean
roi1_y0 = ws.getRun()['ROI1StartY'].getStatistics().mean
roi1_x1 = ws.getRun()['ROI1EndX'].getStatistics().mean
roi1_y1 = ws.getRun()['ROI1EndY'].getStatistics().mean
if roi1_x1 > roi1_x0:
peak1 = [int(roi1_x0), int(roi1_x1)]
else:
peak1 = [int(roi1_x1), int(roi1_x0)]
if roi1_y1 > roi1_y0:
low_res1 = [int(roi1_y0), int(roi1_y1)]
else:
low_res1 = [int(roi1_y1), int(roi1_y0)]
if peak1 == [0,0] and low_res1 == [0,0]:
roi1_valid = False
# Read ROI 2
roi2_valid = True
roi2_x0 = ws.getRun()['ROI2StartX'].getStatistics().mean
roi2_y0 = ws.getRun()['ROI2StartY'].getStatistics().mean
roi2_x1 = ws.getRun()['ROI2EndX'].getStatistics().mean
roi2_y1 = ws.getRun()['ROI2EndY'].getStatistics().mean
if roi2_x1 > roi2_x0:
peak2 = [int(roi2_x0), int(roi2_x1)]
else:
peak2 = [int(roi2_x1), int(roi2_x0)]
if roi2_y1 > roi2_y0:
low_res2 = [int(roi2_y0), int(roi2_y1)]
else:
low_res2 = [int(roi2_y1), int(roi2_y0)]
if peak2 == [0,0] and low_res2 == [0,0]:
roi2_valid = False
else:
roi1_valid = False
roi2_valid = False
# Pick the ROI that describes the reflectivity peak
if roi1_valid and not roi2_valid:
roi_peak = peak1
roi_low_res = low_res1
roi_background = [0,0]
elif roi2_valid and not roi1_valid:
roi_peak = peak2
roi_low_res = low_res2
roi_background = [0,0]
elif roi1_valid and roi2_valid:
# If ROI 2 is within ROI 1, treat it as the peak,
# otherwise, use ROI 1
if peak1[0] >= peak2[0] and peak1[1] <= peak2[1]:
roi_peak = peak1
roi_low_res = low_res1
roi_background = peak2
elif peak2[0] >= peak1[0] and peak2[1] <= peak1[1]:
roi_peak = peak2
roi_low_res = low_res2
roi_background = peak1
else:
roi_peak = peak1
roi_low_res = low_res1
roi_background = [0,0]
# After all this, update the ROI according to reduction options
self.roi_peak = roi_peak
self.roi_low_res = roi_low_res
self.roi_background = roi_background
if self.force_peak_roi:
logging.error("Forcing peak ROI: %s", self.forced_peak_roi)
self.roi_peak = self.forced_peak_roi
if self.force_low_res_roi:
logging.error("Forcing low-res ROI: %s", self.forced_low_res_roi)
self.roi_low_res = self.forced_low_res_roi
if self.force_bck_roi:
logging.error("Forcing background ROI: %s", self.forced_bck_roi)
self.roi_background = self.forced_bck_roi
def determine_peak_range(self, ws, specular=True, max_pixel=250):
ws_summed = mantid.simpleapi.RefRoi(InputWorkspace=ws, IntegrateY=specular,
NXPixel=self.n_x_pixel, NYPixel=self.n_y_pixel,
ConvertToQ=False,
OutputWorkspace="ws_summed")
integrated = mantid.simpleapi.Integration(ws_summed)
integrated = mantid.simpleapi.Transpose(integrated)
x_values = integrated.readX(0)
y_values = integrated.readY(0)
e_values = integrated.readE(0)
ws_short = mantid.simpleapi.CreateWorkspace(DataX=x_values[self.peak_range_offset:max_pixel],
DataY=y_values[self.peak_range_offset:max_pixel],
DataE=e_values[self.peak_range_offset:max_pixel])
try:
specular_peak, low_res, _ = mantid.simpleapi.LRPeakSelection(InputWorkspace=ws_short)
except:
logging.error("Peak finding error [specular=%s]: %s" % (specular, sys.exc_value))
return integrated, [0,0], [0,0]
if specular:
peak = [specular_peak[0]+self.peak_range_offset, specular_peak[1]+self.peak_range_offset]
else:
# The low-resolution range finder tends to be a bit tight.
# Broaden it by a third.
#TODO: Fix the range finder algorithm
broadening = (low_res[1]-low_res[0])/3.0
peak = [low_res[0]+self.peak_range_offset-broadening,
low_res[1]+self.peak_range_offset+broadening]
mantid.simpleapi.DeleteWorkspace(ws_short)
mantid.simpleapi.DeleteWorkspace(ws_summed)
return integrated, peak, [low_res[0]+self.peak_range_offset, low_res[1]+self.peak_range_offset]
@classmethod
def fit_peak(cls, signal_x, signal_y, peak):
def gauss(x, *p):
A, mu, sigma, bck = p
if A < 0 or sigma < 5:
return -np.inf
return A*np.exp(-(x-mu)**2/(2.*sigma**2)) + bck
p0 = [np.max(signal_y), (peak[1]+peak[0])/2.0, (peak[1]-peak[0])/2.0, 0.0]
err_y = np.sqrt(np.fabs(signal_y))
# Using bounds would be great but only available with scipy>=0.17. bounds=(0, np.inf),
coeff, _ = curve_fit(gauss, signal_x, signal_y, sigma=err_y, p0=p0)
peak_position = coeff[1]
peak_width = math.fabs(3.0*coeff[2])
return peak_position, peak_width
@classmethod
def scattering_angle(cls, ws, peak_position=None):
"""
Determine the scattering angle
"""
dangle = ws.getRun().getProperty("DANGLE").getStatistics().mean
dangle0 = ws.getRun().getProperty("DANGLE0").getStatistics().mean
direct_beam_pix = ws.getRun().getProperty("DIRPIX").getStatistics().mean
det_distance = ws.getRun().getProperty("SampleDetDis").getStatistics().mean / 1000.0
peak_pos = peak_position if peak_position is not None else direct_beam_pix
theta_d = (dangle - dangle0) / 2.0
theta_d += ((direct_beam_pix - peak_pos) * cls.pixel_width) * 180.0 / math.pi / (2.0 * det_distance)
return theta_d
def check_direct_beam(self, ws, peak_position=None):
"""
Determine whether this data is a direct beam
"""
huber_x = ws.getRun().getProperty("HuberX").getStatistics().mean
#dangle = ws.getRun().getProperty("DANGLE").getStatistics().mean
sangle = ws.getRun().getProperty("SANGLE").getStatistics().mean
self.theta_d = self.scattering_angle(ws, peak_position)
return not ((self.theta_d > self.tolerance or sangle > self.tolerance) and huber_x < self.huber_x_cut)
def determine_data_type(self, ws):
"""
Inspect the data and determine peak locations
and data type.
"""
# Skip empty data entries
if ws.getNumberEvents() < self.n_events_cutoff:
self.data_type = -1
logging.info("No data for %s %s" % (self.run_number, self.cross_section))
return
# Find reflectivity peak and low resolution ranges
# Those will be our defaults
integrated, peak, broad_range = self.determine_peak_range(ws, specular=True)
self.found_peak = copy.copy(peak)
logging.info("Run %s [%s]: Peak found %s" % (self.run_number, self.cross_section, peak))
signal_y = integrated.readY(0)
mantid.simpleapi.DeleteWorkspace(integrated)
signal_x = range(len(signal_y))
_, low_res, _ = self.determine_peak_range(ws, specular=False)
logging.info("Run %s [%s]: Low-res found %s" % (self.run_number, self.cross_section, str(low_res)))
self.found_low_res = low_res
bck_range = None
# Process the ROI information
self.process_roi(ws)
# Keep track of whether we actually used the ROI
self.use_roi_actual = False
if self.use_roi and not self.roi_peak == [0,0]:
peak = copy.copy(self.roi_peak)
if not self.roi_low_res == [0,0]:
low_res = copy.copy(self.roi_low_res)
if not self.roi_background == [0,0]:
bck_range = copy.copy(self.roi_background)
logging.info("Using ROI peak range: [%s %s]", peak[0], peak[1])
self.use_roi_actual = True
# Determine reflectivity peak position (center)
signal_y_crop = signal_y[peak[0]:peak[1]+1]
signal_x_crop = signal_x[peak[0]:peak[1]+1]
# Calculate a reasonable peak position
#peak_mean = np.average(signal_x_crop, weights=signal_y_crop)
peak_position = (peak[1]+peak[0])/2.0
peak_width = (peak[1]-peak[0])/2.0
try:
# Try to find the peak position within the peak range we found
peak_position, peak_width = self.fit_peak(signal_x_crop, signal_y_crop, peak)
# If we are more than two sigmas away from the middle of the range,
# there's clearly a problem.
if np.abs(peak_position - (peak[1]+peak[0])/2.0) > np.abs(peak[1]-peak[0]):
logging.error("Found peak position outside of given range [x=%s], switching to full detector" % peak_position)
peak_position = (peak[1]+peak[0])/2.0
peak_width = (peak[1]-peak[0])/2.0
raise RuntimeError("Bad peak position")
except:
# If we can't find a peak, try fitting over the full detector.
# If we do find a peak, then update the ranges rather than using
# what we currently have (which is probably given by the ROI).
logging.warning("Run %s [%s]: Could not fit a peak in the supplied peak range" % (self.run_number, self.cross_section))
logging.error(sys.exc_value)
try:
# Define a good default that is wide enough for the fit to work
default_width = (self.found_peak[1]-self.found_peak[0])/2.0
default_width = max(default_width, 10.0)
default_center = (self.found_peak[1]+self.found_peak[0])/2.0
default_peak = [default_center-default_width, default_center+default_width]
logging.info("Run %s [%s]: Broad data region %s" % (self.run_number, self.cross_section, broad_range))
x_min = broad_range[0]+10
x_max = broad_range[1]-10
peak_position, peak_width = self.fit_peak(signal_x[x_min:x_max], signal_y[x_min:x_max], default_peak)
peak = [math.floor(peak_position-peak_width), math.floor(peak_position+peak_width)]
#low_res = [5, self.n_x_pixel-5]
low_res = self.found_low_res
self.use_roi_actual = False
logging.warning("Run %s [%s]: Peak not in supplied range! Found peak: %s low: %s" % (self.run_number, self.cross_section, peak, low_res))
logging.warning("Run %s [%s]: Peak position: %s Peak width: %s" % (self.run_number, self.cross_section, peak_position, peak_width))
except:
logging.error(sys.exc_value)
logging.error("Run %s [%s]: Could not use Gaussian fit to determine peak position over whole detector" % (self.run_number, self.cross_section))
# Update the specular peak range if needed
if self.update_peak_range:
peak[0] = math.floor(peak_position-peak_width)
peak[1] = math.ceil(peak_position+peak_width)
logging.info("Updating peak range to: [%s %s]", peak[0], peak[1])
self.use_roi_actual = False
# Store the information we found
self.peak_position = peak_position
self.peak_range = [int(peak[0]), int(peak[1])]
self.low_res_range = [int(low_res[0]), int(low_res[1])]
if not self.use_roi_bck or bck_range is None:
if self.use_tight_bck:
self.background = [self.peak_range[0]-self.bck_offset, self.peak_range[1]+self.bck_offset]
else:
self.background = [4, self.peak_range[0]-30]
else:
self.background = [int(bck_range[0]), int(bck_range[1])]
# Computed scattering angle
self.calculated_scattering_angle = self.scattering_angle(ws, peak_position)
# Determine whether we have a direct beam
self.is_direct_beam = self.check_direct_beam(ws, peak_position)
# Convenient data type
self.data_type = 0 if self.is_direct_beam else 1
# Write to logs
self.log()
# Register
AlgorithmFactory.subscribe(MRInspectData)