def _create_plot_pr(self, estimate=False):
"""
Create and prepare invertor instance from
a plottable data set.
:param path: path of the file to read in
"""
# Sanity check
if self.current_plottable is None:
msg = "Please load a valid data set before proceeding."
wx.PostEvent(self.parent, StatusEvent(status=msg))
return None
# Get the data from the chosen data set and perform inversion
pr = Invertor()
pr.d_max = self.max_length
pr.alpha = self.alpha
pr.q_min = self.q_min
pr.q_max = self.q_max
pr.x = self.current_plottable.x
pr.y = self.current_plottable.y
pr.has_bck = self.has_bck
pr.slit_height = self.slit_height
pr.slit_width = self.slit_width
# Keep track of the plot window title to ensure that
# we can overlay the plots
pr.info["plot_group_id"] = self.current_plottable.group_id
# Fill in errors if none were provided
err = self.current_plottable.dy
all_zeros = True
if err == None:
err = numpy.zeros(len(pr.y))
else:
for i in range(len(err)):
if err[i] > 0:
all_zeros = False
if all_zeros:
scale = None
min_err = 0.0
for i in range(len(pr.y)):
# Scale the error so that we can fit over several decades of Q
if scale == None:
scale = 0.05 * math.sqrt(pr.y[i])
min_err = 0.01 * pr.y[i]
err[i] = scale * math.sqrt(math.fabs(pr.y[i])) + min_err
message = "The loaded file had no error bars, "
message += "statistical errors are assumed."
wx.PostEvent(self.parent, StatusEvent(status=message))
pr.err = err
return pr
def _create_plot_pr(self, estimate=False):
"""
Create and prepare invertor instance from
a plottable data set.
:param path: path of the file to read in
"""
# Sanity check
if self.current_plottable is None:
msg = "Please load a valid data set before proceeding."
wx.PostEvent(self.parent, StatusEvent(status=msg))
return None
# Get the data from the chosen data set and perform inversion
pr = Invertor()
pr.d_max = self.max_length
pr.alpha = self.alpha
pr.q_min = self.q_min
pr.q_max = self.q_max
pr.x = self.current_plottable.x
pr.y = self.current_plottable.y
pr.has_bck = self.has_bck
pr.slit_height = self.slit_height
pr.slit_width = self.slit_width
# Keep track of the plot window title to ensure that
# we can overlay the plots
pr.info["plot_group_id"] = self.current_plottable.group_id
# Fill in errors if none were provided
err = self.current_plottable.dy
all_zeros = True
if err == None:
err = numpy.zeros(len(pr.y))
else:
for i in range(len(err)):
if err[i] > 0:
all_zeros = False
if all_zeros:
scale = None
min_err = 0.0
for i in range(len(pr.y)):
# Scale the error so that we can fit over several decades of Q
if scale == None:
scale = 0.05 * math.sqrt(pr.y[i])
min_err = 0.01 * pr.y[i]
err[i] = scale * math.sqrt(math.fabs(pr.y[i])) + min_err
message = "The loaded file had no error bars, "
message += "statistical errors are assumed."
wx.PostEvent(self.parent, StatusEvent(status=message))
pr.err = err
return pr
def _fit_pr(self, evt):
"""
"""
# Generate P(r) for sphere
radius = 60.0
d_max = 2 * radius
r = pylab.arange(0.01, d_max, d_max / 51.0)
M = len(r)
y = numpy.zeros(M)
pr_err = numpy.zeros(M)
total = 0.0
for j in range(M):
value = self.pr_theory(r[j], radius)
total += value
y[j] = value
pr_err[j] = math.sqrt(y[j])
y = y / total * d_max / len(r)
# Perform fit
pr = Invertor()
pr.d_max = d_max
pr.alpha = 0
pr.x = r
pr.y = y
pr.err = pr_err
out, cov = pr.pr_fit()
for i in range(len(out)):
print "%g +- %g" % (out[i], math.sqrt(cov[i][i]))
# Show input P(r)
title = "Pr"
new_plot = Data1D(pr.x, pr.y, dy=pr.err)
new_plot.name = "P_{obs}(r)"
new_plot.xaxis("\\rm{r}", 'A')
new_plot.yaxis("\\rm{P(r)} ", "cm^{-3}")
new_plot.group_id = "P_{obs}(r)"
new_plot.id = "P_{obs}(r)"
new_plot.title = title
self.parent.update_theory(data_id=self.data_id, theory=new_plot)
wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title=title))
# Show P(r) fit
self.show_pr(out, pr)
# Show I(q) fit
q = pylab.arange(0.001, 0.1, 0.01 / 51.0)
self.show_iq(out, pr, q)
def _fit_pr(self, evt):
"""
"""
# Generate P(r) for sphere
radius = 60.0
d_max = 2 * radius
r = pylab.arange(0.01, d_max, d_max / 51.0)
M = len(r)
y = numpy.zeros(M)
pr_err = numpy.zeros(M)
total = 0.0
for j in range(M):
value = self.pr_theory(r[j], radius)
total += value
y[j] = value
pr_err[j] = math.sqrt(y[j])
y = y / total * d_max / len(r)
# Perform fit
pr = Invertor()
pr.d_max = d_max
pr.alpha = 0
pr.x = r
pr.y = y
pr.err = pr_err
out, cov = pr.pr_fit()
for i in range(len(out)):
print "%g +- %g" % (out[i], math.sqrt(cov[i][i]))
# Show input P(r)
title = "Pr"
new_plot = Data1D(pr.x, pr.y, dy=pr.err)
new_plot.name = "P_{obs}(r)"
new_plot.xaxis("\\rm{r}", 'A')
new_plot.yaxis("\\rm{P(r)} ", "cm^{-3}")
new_plot.group_id = "P_{obs}(r)"
new_plot.id = "P_{obs}(r)"
new_plot.title = title
self.parent.update_theory(data_id=self.data_id, theory=new_plot)
wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title=title))
# Show P(r) fit
self.show_pr(out, pr)
# Show I(q) fit
q = pylab.arange(0.001, 0.1, 0.01 / 51.0)
self.show_iq(out, pr, q)
if len(toks) > 2:
err = float(toks[2])
else:
if scale == None:
scale = 0.05 * math.sqrt(test_y)
#scale = 0.05/math.sqrt(y)
min_err = 0.01 * y
err = scale * math.sqrt(test_y) + min_err
#err = 0
data_x = numpy.append(data_x, test_x)
data_y = numpy.append(data_y, test_y)
data_err = numpy.append(data_err, err)
except:
logging.error(sys.exc_value)
return data_x, data_y, data_err
if __name__ == "__main__":
invert = Invertor()
x, y, erro = load("test/Cyl_A_D102.txt")
invert.d_max = 102.0
invert.nfunc = 10
invert.x = x
invert.y = y
invert.err = erro
# Testing estimator
est = NTermEstimator(invert)
print est.num_terms()
def _create_file_pr(self, data):
"""
Create and prepare invertor instance from
a file data set.
:param path: path of the file to read in
"""
# Reset the status bar so that we don't get mixed up
# with old messages.
#TODO: refactor this into a proper status handling
wx.PostEvent(self.parent, StatusEvent(status=''))
try:
class FileData(object):
x = None
y = None
err = None
path = None
def __init__(self, path):
self.path = path
self._current_file_data = FileData(data.path)
self._current_file_data.x = data.x
self._current_file_data.y = data.y
self._current_file_data.err = data.dy
x, y, err = data.x, data.y, data.dy
except:
load_error(sys.exc_value)
return None
# If the file contains no data, just return
if x is None or len(x) == 0:
load_error("The loaded file contains no data")
return None
# If we have not errors, add statistical errors
if y is not None:
if err == None or numpy.all(err) == 0:
err = numpy.zeros(len(y))
scale = None
min_err = 0.0
for i in range(len(y)):
# Scale the error so that we can fit over several decades of Q
if scale == None:
scale = 0.05 * math.sqrt(y[i])
min_err = 0.01 * y[i]
err[i] = scale * math.sqrt(math.fabs(y[i])) + min_err
message = "The loaded file had no error bars, "
message += "statistical errors are assumed."
wx.PostEvent(self.parent, StatusEvent(status=message))
try:
# Get the data from the chosen data set and perform inversion
pr = Invertor()
pr.d_max = self.max_length
pr.alpha = self.alpha
pr.q_min = self.q_min
pr.q_max = self.q_max
pr.x = x
pr.y = y
pr.err = err
pr.has_bck = self.has_bck
pr.slit_height = self.slit_height
pr.slit_width = self.slit_width
return pr
except:
load_error(sys.exc_value)
return None
def _create_file_pr(self, data):
"""
Create and prepare invertor instance from
a file data set.
:param path: path of the file to read in
"""
# Reset the status bar so that we don't get mixed up
# with old messages.
#TODO: refactor this into a proper status handling
wx.PostEvent(self.parent, StatusEvent(status=''))
try:
class FileData(object):
x = None
y = None
err = None
path = None
def __init__(self, path):
self.path = path
self._current_file_data = FileData(data.path)
self._current_file_data.x = data.x
self._current_file_data.y = data.y
self._current_file_data.err = data.dy
x, y, err = data.x, data.y, data.dy
except:
load_error(sys.exc_value)
return None
# If the file contains no data, just return
if x is None or len(x) == 0:
load_error("The loaded file contains no data")
return None
# If we have not errors, add statistical errors
if y is not None:
if err == None or numpy.all(err) == 0:
err = numpy.zeros(len(y))
scale = None
min_err = 0.0
for i in range(len(y)):
# Scale the error so that we can fit over several decades of Q
if scale == None:
scale = 0.05 * math.sqrt(y[i])
min_err = 0.01 * y[i]
err[i] = scale * math.sqrt(math.fabs(y[i])) + min_err
message = "The loaded file had no error bars, "
message += "statistical errors are assumed."
wx.PostEvent(self.parent, StatusEvent(status=message))
try:
# Get the data from the chosen data set and perform inversion
pr = Invertor()
pr.d_max = self.max_length
pr.alpha = self.alpha
pr.q_min = self.q_min
pr.q_max = self.q_max
pr.x = x
pr.y = y
pr.err = err
pr.has_bck = self.has_bck
pr.slit_height = self.slit_height
pr.slit_width = self.slit_width
return pr
except:
load_error(sys.exc_value)
return None
if len(toks) > 2:
err = float(toks[2])
else:
if scale == None:
scale = 0.05 * math.sqrt(test_y)
#scale = 0.05/math.sqrt(y)
min_err = 0.01 * y
err = scale * math.sqrt(test_y) + min_err
#err = 0
data_x = numpy.append(data_x, test_x)
data_y = numpy.append(data_y, test_y)
data_err = numpy.append(data_err, err)
except:
logging.error(sys.exc_value)
return data_x, data_y, data_err
if __name__ == "__main__":
invert = Invertor()
x, y, erro = load("test/Cyl_A_D102.txt")
invert.d_max = 102.0
invert.nfunc = 10
invert.x = x
invert.y = y
invert.err = erro
# Testing estimator
est = NTermEstimator(invert)
print est.num_terms()
