def load(self):
data = icpformat.read(self.path)
self.detector.wavelength \
= data.check_wavelength(self.default.wavelength,
NG7Icp._wavelength_override)
if 'qz' in data:
# NG7 automatically increases count times as Qz increases
monitor, prefactor = data.monitor, data.prefactor
Mon1, Exp = data.Mon1, data.Exp
Qz = data.column.qz
automonitor = prefactor * (monitor + Mon1 * abs(Qz)**Exp)
if data.count_type == 'NEUT':
self.monitor.counts = automonitor
elif data.count_type == 'TIME':
self.monitor.count_time = automonitor
else:
raise ValueError, "Expected count type 'NEUT' or 'TIME' in " + self.path
if 'monitor' in data:
self.monitor.counts = data.column.monitor
if 'time' in data:
self.monitor.count_time = data.column.time * 60
self.monitor.base = 'counts' if self.monitor.counts is not None else 'time'
if 's1' in data: self.slit1.x = data.column.s1
if 's2' in data: self.slit2.x = data.column.s2
if 's3' in data: self.slit3.x = data.column.s3
if 's4' in data: self.slit4.x = data.column.s4
if 'qz' in data:
Qx = data.column.qx if 'qx' in data else 0
Qz = data.column.qz
A, B = qxqz.QxQzL_to_AB(Qx, Qz, self.detector.wavelength)
self.sample.angle_x, self.detector.angle_x = A, B
# Set initial Qz
self.resetQ()
# TODO: if the dataset is large, set up a weak reference instead
self.detector.counts = data.counts
def load(self):
data = icpformat.read(self.path)
self.detector.wavelength \
= data.check_wavelength(self.default.wavelength,
NG7Icp._wavelength_override)
if 'qz' in data:
# NG7 automatically increases count times as Qz increases
monitor, prefactor = data.monitor,data.prefactor
Mon1, Exp = data.Mon1,data.Exp
Qz = data.column.qz
automonitor = prefactor*(monitor + Mon1 * abs(Qz)**Exp)
if data.count_type == 'NEUT':
self.monitor.counts = automonitor
elif data.count_type == 'TIME':
self.monitor.count_time = automonitor
else:
raise ValueError, "Expected count type 'NEUT' or 'TIME' in "+self.path
if 'monitor' in data:
self.monitor.counts = data.column.monitor
if 'time' in data:
self.monitor.count_time = data.column.time*60
self.monitor.base = 'counts' if self.monitor.counts is not None else 'time'
if 's1' in data: self.slit1.x = data.column.s1
if 's2' in data: self.slit2.x = data.column.s2
if 's3' in data: self.slit3.x = data.column.s3
if 's4' in data: self.slit4.x = data.column.s4
if 'qz' in data:
Qx = data.column.qx if 'qx' in data else 0
Qz = data.column.qz
A,B = qxqz.QxQzL_to_AB(Qx,Qz,self.detector.wavelength)
self.sample.angle_x,self.detector.angle_x = A,B
# Set initial Qz
self.resetQ()
# TODO: if the dataset is large, set up a weak reference instead
self.detector.counts = data.counts
def load(self):
# Load the icp data
data = icpformat.read(self.path)
if data.counts.ndim == 1:
self.detector.dims = (1, 1)
elif data.counts.ndim == 2:
self.detector.dims = (data.counts.shape[1], 1)
elif data.counts.ndim == 3:
self.detector.dims = (data.counts.shape[1], data.counts.shape[2])
else:
raise RuntimeError("Data has too many dimensions in " + self.path)
# Slits are either stored in the file or available from the
# motor information. For non-reflectometry scans they may
# not be available.
if 'a1' in data: self.slit1.x = data.column.a1
if 'a2' in data: self.slit2.x = data.column.a2
if 'a5' in data: self.slit3.x = data.column.a5
if 'a6' in data: self.slit4.x = data.column.a6
# Angles are either stored in the file or can be calculated
# from the motor details. For non-reflectometry scans they
# may not be available.
if 'a3' in data: self.sample.angle_x = data.column.a3
if 'a4' in data: self.detector.angle_x = data.column.a4
# Polarization was extracted from the comment line
self.polarization = data.polarization
# Monitor counts may be recorded or may be inferred from header
if 'monitor' in data:
# Prefer the monitor column if it exists
self.monitor.counts = data.column.monitor
elif data.count_type == 'NEUT':
# if count by neutron, the 'monitor' field stores counts
self.monitor.counts \
= data.monitor*data.prefactor*numpy.ones(data.points,'i')
else:
# Need monitor rate for normalization; the application
# will have to provide the means of setting the rate
# and computing the counts based on that rate.
pass
# Counting time may be recorded or may be inferred from header
if data.count_type == 'TIME':
# Prefer the target value to the time column when counting by
# time because the time column is recorded in minutes rather
# than seconds and is not precise enough.
# if count by time, the 'monitor' field stores seconds
self.monitor.count_time \
= data.monitor*data.prefactor*numpy.ones(data.points,'f')
elif 'time' in data:
self.monitor.count_time = data.column.time * 60
else:
# Need monitor rate for normalization; the application
# will have to provide the means of setting the rate
# and computing the time based on that rate.
pass
if 'temp' in data:
# record the temperature in sample environment
self.sample.environment.temperature = data.column['temp']
if 'h-field' in data:
# record the temperature in sample environment
self.sample.environment.magnetic_field = data.column['h-field']
# Set initial Qz
self.resetQ()
# TODO: if counts are huge we may want to make this lazy
self.detector.counts = data.counts
def loadcounts(self):
# Load the counts from the data file
data = icpformat.read(self.path)
return data.counts
def load(self):
# Load the icp data
data = icpformat.read(self.path)
if data.counts.ndim == 1:
self.detector.dims = (1,1)
elif data.counts.ndim == 2:
self.detector.dims = (data.counts.shape[1],1)
elif data.counts.ndim == 3:
self.detector.dims = (data.counts.shape[1],data.counts.shape[2])
else:
raise RuntimeError("Data has too many dimensions in "+self.path)
# Slits are either stored in the file or available from the
# motor information. For non-reflectometry scans they may
# not be available.
if 'a1' in data: self.slit1.x = data.column.a1
if 'a2' in data: self.slit2.x = data.column.a2
if 'a5' in data: self.slit3.x = data.column.a5
if 'a6' in data: self.slit4.x = data.column.a6
# Angles are either stored in the file or can be calculated
# from the motor details. For non-reflectometry scans they
# may not be available.
if 'a3' in data: self.sample.angle_x = data.column.a3
if 'a4' in data: self.detector.angle_x = data.column.a4
# Polarization was extracted from the comment line
self.polarization = data.polarization
# Monitor counts may be recorded or may be inferred from header
if 'monitor' in data:
# Prefer the monitor column if it exists
self.monitor.counts = data.column.monitor
elif data.count_type == 'NEUT':
# if count by neutron, the 'monitor' field stores counts
self.monitor.counts \
= data.monitor*data.prefactor*numpy.ones(data.points,'i')
else:
# Need monitor rate for normalization; the application
# will have to provide the means of setting the rate
# and computing the counts based on that rate.
pass
# Counting time may be recorded or may be inferred from header
if data.count_type == 'TIME':
# Prefer the target value to the time column when counting by
# time because the time column is recorded in minutes rather
# than seconds and is not precise enough.
# if count by time, the 'monitor' field stores seconds
self.monitor.count_time \
= data.monitor*data.prefactor*numpy.ones(data.points,'f')
elif 'time' in data:
self.monitor.count_time = data.column.time*60
else:
# Need monitor rate for normalization; the application
# will have to provide the means of setting the rate
# and computing the time based on that rate.
pass
if 'temp' in data:
# record the temperature in sample environment
self.sample.environment.temperature = data.column['temp']
if 'h-field' in data:
# record the temperature in sample environment
self.sample.environment.magnetic_field = data.column['h-field']
# Set initial Qz
self.resetQ()
# TODO: if counts are huge we may want to make this lazy
self.detector.counts = data.counts
def loadcounts(self):
# Load the counts from the data file
data = icpformat.read(self.path)
return data.counts
def loadcounts(self):
data = icpformat.read(self.path)
return data.counts
import math
import icpformat as i
import simplejson
f = i.read("/Users/Matthew/Desktop/Empty/EMPTY001.bt5")
plottable_data = [{
'type':'1d',
'title':'NG7 Data',
'options':{
'axes':{'xaxis': {'label':'A4'}, 'yaxis': {'label':'Counts'}},
'series':[],
'legend':{'show': False, 'placement': 'se'},
'cursor':{'show': True, 'tooltipLocation':'se', 'tooltipOffset': 0},
},
'metadata':{},
'clear_existing':True,
'data':[[]]
}]
temp1 = f.filename
temp2 = temp1.replace('.bt5','_mod.json')
f_out = open(temp2, 'w')
plottable_data[0]["title"] = f.comment
plottable_data[0]["options"]["axes"]["xaxis"]["label"]="a2"
plottable_data[0]["options"]["axes"]["yaxis"]["label"]="Counts x 10^6 / Monitor"
i = 0
while (i < f.points):
a2 = f.column["a2"][i]
def loadcounts(self):
data = icpformat.read(self.path)
return data.counts
