def plot_theory(self, data=None, name=None):
"""
Receive a data set and post a NewPlotEvent to parent.
:param data: extrapolated data to be plotted
:param name: Data's name to use for the legend
"""
# import copy
if data is None:
id = str(self.__data.id) + name
group_id = self.__data.group_id
wx.PostEvent(
self.parent,
NewPlotEvent(id=id, group_id=group_id, action='Remove'))
return
new_plot = Data1D(x=[], y=[], dy=None)
new_plot.symbol = GUIFRAME_ID.CURVE_SYMBOL_NUM
scale = self.invariant_panel.get_scale()
background = self.invariant_panel.get_background()
if scale != 0:
# Put back the sacle and bkg for plotting
data.y = (data.y + background) / scale
new_plot = Data1D(x=data.x, y=data.y, dy=None)
new_plot.symbol = GUIFRAME_ID.CURVE_SYMBOL_NUM
else:
msg = "Scale can not be zero."
raise ValueError(msg)
if len(new_plot.x) == 0:
return
new_plot.name = name
new_plot.xaxis(self.__data._xaxis, self.__data._xunit)
new_plot.yaxis(self.__data._yaxis, self.__data._yunit)
new_plot.group_id = self.__data.group_id
new_plot.id = str(self.__data.id) + name
new_plot.title = self.__data.title
# Save theory_data in a state
if data is not None:
name_head = name.split('-')
if name_head[0] == 'Low':
self.invariant_panel.state.theory_lowQ = copy.deepcopy(
new_plot)
elif name_head[0] == 'High':
self.invariant_panel.state.theory_highQ = copy.deepcopy(
new_plot)
self.parent.update_theory(data_id=self.__data.id, theory=new_plot)
wx.PostEvent(self.parent,
NewPlotEvent(plot=new_plot, title=self.__data.title))
def show_shpere(self, x, radius=70.0, x_range=70.0):
"""
"""
# Show P(r)
y_true = np.zeros(len(x))
sum_true = 0.0
for i in range(len(x)):
y_true[i] = self.pr_theory(x[i], radius)
sum_true += y_true[i]
y_true = y_true / sum_true * x_range / len(x)
# Show the theory P(r)
new_plot = Data1D(x, y_true)
new_plot.symbol = GUIFRAME_ID.CURVE_SYMBOL_NUM
new_plot.name = "P_{true}(r)"
new_plot.xaxis("\\rm{r}", 'A')
new_plot.yaxis("\\rm{P(r)} ", "cm^{-3}")
new_plot.id = "P_{true}(r)"
new_plot.group_id = "P_{true}(r)"
self.parent.update_theory(data_id=self.data_id, theory=new_plot)
#Put this call in plottables/guitools
wx.PostEvent(self.parent,
NewPlotEvent(plot=new_plot, title="Sphere P(r)"))
def _on_scale_unity(self, evt):
"""
Scale the maximum P(r) value on each displayed plot to 1.
:param evt: Menu event
"""
self._scale_output_unity = True
self._normalize_output = False
self.show_pr(self._last_out, self._last_pr, self._last_cov)
# Now scale the added plots too
for plot in self._added_plots:
_max = 0
for y in self._added_plots[plot].y:
if y > _max:
_max = y
y = self._added_plots[plot].y / _max
new_plot = Data1D(self._added_plots[plot].x, y)
new_plot.symbol = GUIFRAME_ID.CURVE_SYMBOL_NUM
new_plot.name = self._added_plots[plot].name
new_plot.xaxis("\\rm{r}", 'A')
new_plot.yaxis("\\rm{P(r)} ", "cm^{-3}")
self.parent.update_theory(data_id=self.data_id, theory=new_plot)
wx.PostEvent(
self.parent,
NewPlotEvent(plot=new_plot,
update=True,
title=self._added_plots[plot].name))
def set_data(self, data_list=None):
"""
receive a list of data to compute pr
"""
if data_list is None:
data_list = []
if len(data_list) >= 1:
if len(data_list) == 1:
data = data_list[0]
else:
data_1d_list = []
data_2d_list = []
error_msg = ""
# separate data into data1d and data2d list
for data in data_list:
if data is not None:
if issubclass(data.__class__, Data1D):
data_1d_list.append(data)
else:
error_msg += " %s type %s \n" % (str(data.name),
str(data.__class__.__name__))
data_2d_list.append(data)
if len(data_2d_list) > 0:
msg = "PrView does not support the following data types:\n"
msg += error_msg
if len(data_1d_list) == 0:
wx.PostEvent(self.parent, StatusEvent(status=msg, info='error'))
return
msg = "Prview does not allow multiple data!\n"
msg += "Please select one.\n"
if len(data_list) > 1:
from pr_widgets import DataDialog
dlg = DataDialog(data_list=data_1d_list, text=msg)
if dlg.ShowModal() == wx.ID_OK:
data = dlg.get_data()
else:
data = None
dlg.Destroy()
if data is None:
msg += "PrView receives no data. \n"
wx.PostEvent(self.parent, StatusEvent(status=msg, info='error'))
return
if issubclass(data.__class__, Data1D):
try:
wx.PostEvent(self.parent,
NewPlotEvent(action='remove',
group_id=GROUP_ID_IQ_DATA,
id=self.data_id))
self.data_id = data.id
self.control_panel._change_file(evt=None, data=data)
except:
msg = "Prview Set_data: " + str(sys.exc_value)
wx.PostEvent(self.parent, StatusEvent(status=msg, info="error"))
else:
msg = "Pr cannot be computed for data of "
msg += "type %s" % (data_list[0].__class__.__name__)
wx.PostEvent(self.parent, StatusEvent(status=msg, info='error'))
else:
msg = "Pr contain no data"
wx.PostEvent(self.parent, StatusEvent(status=msg, info='warning'))
def _on_normalize(self, evt):
"""
Normalize the area under the P(r) curve to 1.
This operation is done for all displayed plots.
:param evt: Menu event
"""
self._normalize_output = True
self._scale_output_unity = False
self.show_pr(self._last_out, self._last_pr, self._last_cov)
# Now scale the added plots too
for plot in self._added_plots:
total = np.sum(self._added_plots[plot].y)
npts = len(self._added_plots[plot].x)
total *= self._added_plots[plot].x[npts - 1] / npts
y = self._added_plots[plot].y / total
new_plot = Data1D(self._added_plots[plot].x, y)
new_plot.symbol = GUIFRAME_ID.CURVE_SYMBOL_NUM
new_plot.group_id = self._added_plots[plot].group_id
new_plot.id = self._added_plots[plot].id
new_plot.title = self._added_plots[plot].title
new_plot.name = self._added_plots[plot].name
new_plot.xaxis("\\rm{r}", 'A')
new_plot.yaxis("\\rm{P(r)} ", "cm^{-3}")
self.parent.update_theory(data_id=self.data_id, theory=new_plot)
wx.PostEvent(
self.parent,
NewPlotEvent(plot=new_plot,
update=True,
title=self._added_plots[plot].name))
def onCircular(self, event, ismask=False):
"""
perform circular averaging on Data2D
:param event: wx.menu event
"""
# Find the best number of bins
npt = math.sqrt(len(self.data2D.data[np.isfinite(self.data2D.data)]))
npt = math.floor(npt)
from sas.sascalc.dataloader.manipulations import CircularAverage
# compute the maximum radius of data2D
self.qmax = max(math.fabs(self.data2D.xmax),
math.fabs(self.data2D.xmin))
self.ymax = max(math.fabs(self.data2D.ymax),
math.fabs(self.data2D.ymin))
self.radius = math.sqrt(
math.pow(self.qmax, 2) + math.pow(self.ymax, 2))
# Compute beam width
bin_width = (self.qmax + self.qmax) / npt
# Create data1D circular average of data2D
Circle = CircularAverage(r_min=0,
r_max=self.radius,
bin_width=bin_width)
circ = Circle(self.data2D, ismask=ismask)
from sas.sasgui.guiframe.dataFitting import Data1D
if hasattr(circ, "dxl"):
dxl = circ.dxl
else:
dxl = None
if hasattr(circ, "dxw"):
dxw = circ.dxw
else:
dxw = None
new_plot = Data1D(x=circ.x, y=circ.y, dy=circ.dy, dx=circ.dx)
new_plot.dxl = dxl
new_plot.dxw = dxw
new_plot.name = "Circ avg " + self.data2D.name
new_plot.source = self.data2D.source
# new_plot.info = self.data2D.info
new_plot.interactive = True
new_plot.detector = self.data2D.detector
# If the data file does not tell us what the axes are, just assume...
new_plot.xaxis("\\rm{Q}", "A^{-1}")
if hasattr(self.data2D, "scale") and \
self.data2D.scale == 'linear':
new_plot.ytransform = 'y'
new_plot.yaxis("\\rm{Residuals} ", "normalized")
else:
new_plot.yaxis("\\rm{Intensity} ", "cm^{-1}")
new_plot.group_id = "2daverage" + self.data2D.name
new_plot.id = "Circ avg " + self.data2D.name
new_plot.is_data = True
self.parent.update_theory(data_id=self.data2D.id, theory=new_plot)
wx.PostEvent(self.parent,
NewPlotEvent(plot=new_plot, title=new_plot.name))
def _post_data(self, nbins=None):
"""
compute sector averaging of data2D into data1D
:param nbins: the number of point to plot for the average 1D data
"""
## get the data2D to average
data = self.base.data2D
# If we have no data, just return
if data == None:
return
## Averaging
from sas.sascalc.dataloader.manipulations import SectorQ
radius = self.qmax
phimin = -self.left_line.phi + self.main_line.theta
phimax = self.left_line.phi + self.main_line.theta
if nbins == None:
nbins = 20
sect = SectorQ(r_min=0.0,
r_max=radius,
phi_min=phimin + math.pi,
phi_max=phimax + math.pi,
nbins=nbins)
sector = sect(self.base.data2D)
##Create 1D data resulting from average
if hasattr(sector, "dxl"):
dxl = sector.dxl
else:
dxl = None
if hasattr(sector, "dxw"):
dxw = sector.dxw
else:
dxw = None
new_plot = Data1D(x=sector.x, y=sector.y, dy=sector.dy, dx=sector.dx)
new_plot.dxl = dxl
new_plot.dxw = dxw
new_plot.name = "SectorQ" + "(" + self.base.data2D.name + ")"
new_plot.source = self.base.data2D.source
#new_plot.info=self.base.data2D.info
new_plot.interactive = True
new_plot.detector = self.base.data2D.detector
## If the data file does not tell us what the axes are, just assume...
new_plot.xaxis("\\rm{Q}", "A^{-1}")
new_plot.yaxis("\\rm{Intensity}", "cm^{-1}")
if hasattr(data, "scale") and data.scale == 'linear' and \
self.base.data2D.name.count("Residuals") > 0:
new_plot.ytransform = 'y'
new_plot.yaxis("\\rm{Residuals} ", "/")
new_plot.group_id = "2daverage" + self.base.data2D.name
new_plot.id = "SectorQ" + self.base.data2D.name
new_plot.is_data = True
self.base.parent.update_theory(data_id=data.id, theory=new_plot)
wx.PostEvent(
self.base.parent,
NewPlotEvent(plot=new_plot,
title="SectorQ" + self.base.data2D.name))
def set_data(self, data_list=None):
"""
Load the data that's been selected
:param data_list: The data to load in
"""
if data_list is None:
data_list = []
if len(data_list) >= 1:
msg = ""
if len(data_list) == 1:
data = data_list[0]
else:
data_1d_list = []
data_2d_list = []
err_msg = ""
for data in data_list:
if data is not None:
if issubclass(data.__class__, Data1D):
data_1d_list.append(data)
else:
err_msg += "{} type {} \n".format(str(data.name),
str(data.__class__))
data_2d_list.append(data)
if len(data_2d_list) > 0:
msg = "Corfunc doesn't support the following data types:\n"
msg += err_msg
if len(data_1d_list) == 0:
msg += "No data recieved"
wx.PostEvent(self.parent, StatusEvent(status=msg,
info='error'))
return
elif len(data_list) > 1:
msg = "Corfunc does not allow multiple data\n"
msg += "Please select one.\n"
dlg = DataDialog(data_list=data_1d_list, text=msg)
if dlg.ShowModal() == wx.ID_OK:
data = dlg.get_data()
else:
data = None
dlg.Destroy()
if data is None:
msg += "Corfunc recieved no data\n"
wx.PostEvent(self.parent, StatusEvent(status=msg,
info='error'))
return
if issubclass(data.__class__, Data1D):
try:
wx.PostEvent(self.parent, NewPlotEvent(action='remove',
group_id=GROUP_ID_IQ_DATA,
id=self.data_id))
self.data_id = data.id
self.corfunc_panel.set_data(data)
except:
msg = "Corfunc set_data: " + str(sys.exc_value)
wx.PostEvent(self.parent, StatusEvent(status=msg,
info='error'))
def set_state(self, state=None, datainfo=None):
"""
Call-back method for the inversion state reader.
This method is called when a .prv file is loaded.
:param state: InversionState object
:param datainfo: Data1D object [optional]
"""
try:
if datainfo.__class__.__name__ == 'list':
if len(datainfo) >= 1:
data = datainfo[0]
else:
data = None
else:
data = datainfo
if data is None:
msg = "Pr.set_state: datainfo parameter cannot "
msg += "be None in standalone mode"
raise RuntimeError, msg
# Ensuring that plots are coordinated correctly
t = time.localtime(data.meta_data['prstate'].timestamp)
time_str = time.strftime("%b %d %H:%M", t)
# Check that no time stamp is already appended
max_char = data.meta_data['prstate'].file.find("[")
if max_char < 0:
max_char = len(data.meta_data['prstate'].file)
datainfo.meta_data['prstate'].file = \
data.meta_data['prstate'].file[0:max_char]\
+ ' [' + time_str + ']'
data.filename = data.meta_data['prstate'].file
# TODO:
#remove this call when state save all information about the gui data
# such as ID , Group_ID, etc...
#make self.current_plottable = datainfo directly
self.current_plottable = self.parent.create_gui_data(data, None)
self.current_plottable.group_id = data.meta_data['prstate'].file
# Make sure the user sees the P(r) panel after loading
#self.parent.set_perspective(self.perspective)
self.on_perspective(event=None)
# Load the P(r) results
#state = self.state_reader.get_state()
data_dict = {self.current_plottable.id: self.current_plottable}
self.parent.add_data(data_list=data_dict)
wx.PostEvent(
self.parent,
NewPlotEvent(plot=self.current_plottable,
title=self.current_plottable.title))
self.control_panel.set_state(state)
except:
logger.error("prview.set_state: %s" % sys.exc_value)
def show_data(self, data, label, reset=False, active_ctrl=None):
"""
Show data read from a file
:param data: The data to plot (Data1D)
:param label: What to label the plot. Also used as the plot ID
:param reset: If True, all other plottables will be cleared
"""
new_plot = Data1D(data.x, copy.deepcopy(data.y), dy=data.dy)
group_id = ""
if label == IQ_DATA_LABEL or label == IQ_EXTRAPOLATED_DATA_LABEL:
new_plot.xaxis("\\rm{Q}", 'A^{-1}')
new_plot.yaxis("\\rm{Intensity} ", "cm^{-1}")
new_plot.y -= self.corfunc_panel.background
# Show data on a log(Q)/I scale
new_plot.ytransform = 'y'
group_id = GROUP_ID_IQ_DATA
if label == IQ_EXTRAPOLATED_DATA_LABEL:
# Show the extrapolation as a curve instead of points
new_plot.symbol = GUIFRAME_ID.CURVE_SYMBOL_NUM
elif label == TRANSFORM_LABEL1 or label == TRANSFORM_LABEL3:
new_plot.xaxis("{x}", 'A')
new_plot.yaxis("{\\Gamma}", '')
# Show transform on a linear scale
new_plot.xtransform = 'x'
new_plot.ytransform = 'y'
group_id = GROUP_ID_TRANSFORM
# Show the transformation as a curve instead of points
new_plot.symbol = GUIFRAME_ID.CURVE_SYMBOL_NUM
elif label == IDF_LABEL:
new_plot.xaxis("{x}", 'A')
new_plot.yaxis("{g_1}", '')
# Linear scale
new_plot.xtransform = 'x'
new_plot.ytransform = 'y'
group_id = GROUP_ID_IDF
# Show IDF as a curve instead of points
new_plot.symbol = GUIFRAME_ID.CURVE_SYMBOL_NUM
new_plot.id = label
new_plot.name = label
new_plot.group_id = group_id
new_plot.interactive = True
new_plot.title = group_id.replace('$', '').replace('\\', '')
wx.PostEvent(self.parent,
NewPlotEvent(plot=new_plot, title=new_plot.title,
reset=reset))
if label == IQ_DATA_LABEL or label == IQ_EXTRAPOLATED_DATA_LABEL:
wx.CallAfter(self.corfunc_panel.plot_qrange, active=active_ctrl,
leftdown=True)
if label == IQ_EXTRAPOLATED_DATA_LABEL:
# Zoom in to the region we're interested in
xlim = (min(self.corfunc_panel._extrapolated_data.x), self.corfunc_panel.qmax[1]*1.2)
wx.CallAfter(wx.PostEvent, self.parent, PlotLimitEvent(id=IQ_DATA_LABEL, group_id=GROUP_ID_IQ_DATA, xlim=xlim))
def post_data(self, new_sector):
""" post data averaging in Q"""
if self.inner_circle.get_radius() < self.outer_circle.get_radius():
rmin = self.inner_circle.get_radius()
rmax = self.outer_circle.get_radius()
else:
rmin = self.outer_circle.get_radius()
rmax = self.inner_circle.get_radius()
if self.right_edge.get_angle() < self.left_edge.get_angle():
phimin = self.right_edge.get_angle()
phimax = self.left_edge.get_angle()
else:
phimin = self.left_edge.get_angle()
phimax = self.right_edge.get_angle()
# print "phimin, phimax, rmin ,rmax",math.degrees(phimin),
# math.degrees(phimax), rmin ,rmax
# from sas.sascalc.dataloader.manipulations import SectorQ
sect = new_sector(r_min=rmin,
r_max=rmax,
phi_min=phimin,
phi_max=phimax)
sector = sect(self.base.data2D)
from sas.sasgui.guiframe.dataFitting import Data1D
if hasattr(sector, "dxl"):
dxl = sector.dxl
else:
dxl = None
if hasattr(sector, "dxw"):
dxw = sector.dxw
else:
dxw = None
new_plot = Data1D(x=sector.x,
y=sector.y,
dy=sector.dy,
dxl=dxl,
dxw=dxw)
new_plot.name = str(new_sector.__name__) + \
"(" + self.base.data2D.name + ")"
new_plot.source = self.base.data2D.source
new_plot.interactive = True
# print "loader output.detector",output.source
new_plot.detector = self.base.data2D.detector
# If the data file does not tell us what the axes are, just assume...
new_plot.xaxis("\\rm{Q}", 'rad')
new_plot.yaxis("\\rm{Intensity} ", "cm^{-1}")
new_plot.group_id = str(new_sector.__name__) + self.base.data2D.name
self.base.parent.update_theory(data_id=self.base.data2D.id, \
theory=new_plot)
wx.PostEvent(
self.base.parent,
NewPlotEvent(plot=new_plot, title=str(new_sector.__name__)))
def show_pr(self, out, pr, cov=None):
"""
"""
# Show P(r)
x = pylab.arange(0.0, pr.d_max, pr.d_max / self._pr_npts)
y = np.zeros(len(x))
dy = np.zeros(len(x))
y_true = np.zeros(len(x))
total = 0.0
pmax = 0.0
cov2 = np.ascontiguousarray(cov)
for i in range(len(x)):
if cov2 is None:
value = pr.pr(out, x[i])
else:
(value, dy[i]) = pr.pr_err(out, cov2, x[i])
total += value * pr.d_max / len(x)
# keep track of the maximum P(r) value
if value > pmax:
pmax = value
y[i] = value
if self._normalize_output:
y = y / total
dy = dy / total
elif self._scale_output_unity:
y = y / pmax
dy = dy / pmax
if cov2 is None:
new_plot = Data1D(x, y)
new_plot.symbol = GUIFRAME_ID.CURVE_SYMBOL_NUM
else:
new_plot = Data1D(x, y, dy=dy)
new_plot.name = PR_FIT_LABEL
new_plot.xaxis("\\rm{r}", 'A')
new_plot.yaxis("\\rm{P(r)} ", "cm^{-3}")
new_plot.title = "P(r) fit"
new_plot.id = PR_FIT_LABEL
# Make sure that the plot is linear
new_plot.xtransform = "x"
new_plot.ytransform = "y"
new_plot.group_id = GROUP_ID_PR_FIT
self.parent.update_theory(data_id=self.data_id, theory=new_plot)
wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot,
title="P(r) fit"))
return x, pr.d_max
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 = np.zeros(M)
pr_err = np.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 plot_data(self, scale, background):
"""
replot the current data if the user enters a new scale or background
"""
new_plot = scale * self.__data - background
new_plot.name = self.__data.name
new_plot.group_id = self.__data.group_id
new_plot.id = self.__data.id
new_plot.title = self.__data.title
# Save data in a state: but seems to never happen
if new_plot != None:
self.invariant_panel.state.data = copy.deepcopy(new_plot)
wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot,
title=new_plot.title))
def _on_disable_scaling(self, evt):
"""
Disable P(r) scaling
:param evt: Menu event
"""
self._normalize_output = False
self._scale_output_unity = False
self.show_pr(self._last_out, self._last_pr, self._last_cov)
# Now replot the original added data
for plot in self._added_plots:
self._added_plots[plot].y = np.copy(self._default_Iq[plot])
wx.PostEvent(self.parent,
NewPlotEvent(plot=self._added_plots[plot],
title=self._added_plots[plot].name,
update=True))
def set_state(self, state=None, datainfo=None):
"""
Call-back method for the state reader.
This method is called when a .inv/.svs file is loaded.
:param state: State object
"""
self.temp_state = None
try:
if datainfo.__class__.__name__ == 'list':
data = datainfo[0]
else:
data = datainfo
if data is None:
msg = "invariant.set_state: datainfo parameter cannot"
msg += " be None in standalone mode"
raise RuntimeError(msg)
# Make sure the user sees the invariant panel after loading
# self.parent.set_perspective(self.perspective)
self.on_perspective(event=None)
name = data.meta_data['invstate'].file
data.meta_data['invstate'].file = name
data.name = name
data.filename = name
data = self.parent.create_gui_data(data, None)
self.__data = data
wx.PostEvent(
self.parent,
NewPlotEvent(plot=self.__data,
reset=True,
title=self.__data.title))
data_dict = {self.__data.id: self.__data}
self.parent.add_data(data_list=data_dict)
# set state
self.invariant_panel.is_state_data = True
# Load the invariant states
self.temp_state = state
# Requires to have self.__data and self.temp_state first.
self.on_set_state_helper(None)
except Exception as exc:
logger.error("invariant.set_state: %s" % exc)
def show_data(self, path=None, data=None, reset=False):
"""
Show data read from a file
:param path: file path
:param reset: if True all other plottables will be cleared
"""
#if path is not None:
if data is not None:
try:
pr = self._create_file_pr(data)
except:
status = "Problem reading data: %s" % sys.exc_value
wx.PostEvent(self.parent, StatusEvent(status=status))
raise RuntimeError, status
# If the file contains nothing, just return
if pr is None:
raise RuntimeError, "Loaded data is invalid"
self.pr = pr
# Make a plot of I(q) data
if self.pr.err is None:
new_plot = Data1D(self.pr.x, self.pr.y)
new_plot.symbol = GUIFRAME_ID.CURVE_SYMBOL_NUM
else:
new_plot = Data1D(self.pr.x, self.pr.y, dy=self.pr.err)
new_plot.name = IQ_DATA_LABEL
new_plot.xaxis("\\rm{Q}", 'A^{-1}')
new_plot.yaxis("\\rm{Intensity} ", "cm^{-1}")
new_plot.interactive = True
new_plot.group_id = GROUP_ID_IQ_DATA
new_plot.id = self.data_id
new_plot.title = "I(q)"
wx.PostEvent(self.parent,
NewPlotEvent(plot=new_plot, title="I(q)", reset=reset))
self.current_plottable = new_plot
# Get Q range
self.control_panel.q_min = min(self.pr.x)
self.control_panel.q_max = max(self.pr.x)
def _post_data(self, nbins=None):
"""
Uses annulus parameters to plot averaged data into 1D data.
:param nbins: the number of points to plot
"""
# Data to average
data = self.base.data2D
# If we have no data, just return
if data == None:
return
from sas.sascalc.dataloader.manipulations import Ring
rmin = min(math.fabs(self.inner_circle.get_radius()),
math.fabs(self.outer_circle.get_radius()))
rmax = max(math.fabs(self.inner_circle.get_radius()),
math.fabs(self.outer_circle.get_radius()))
# if the user does not specify the numbers of points to plot
# the default number will be nbins= 36
if nbins == None:
self.nbins = 36
else:
self.nbins = nbins
# # create the data1D Q average of data2D
sect = Ring(r_min=rmin, r_max=rmax, nbins=self.nbins)
sector = sect(self.base.data2D)
if hasattr(sector, "dxl"):
dxl = sector.dxl
else:
dxl = None
if hasattr(sector, "dxw"):
dxw = sector.dxw
else:
dxw = None
new_plot = Data1D(x=(sector.x - math.pi) * 180 / math.pi,
y=sector.y,
dy=sector.dy)
new_plot.dxl = dxl
new_plot.dxw = dxw
new_plot.name = "AnnulusPhi" + "(" + self.base.data2D.name + ")"
new_plot.source = self.base.data2D.source
# new_plot.info=self.base.data2D.info
new_plot.interactive = True
new_plot.detector = self.base.data2D.detector
# If the data file does not tell us what the axes are, just assume...
new_plot.xaxis("\\rm{\phi}", 'degrees')
new_plot.yaxis("\\rm{Intensity} ", "cm^{-1}")
if hasattr(data, "scale") and data.scale == 'linear' and \
self.base.data2D.name.count("Residuals") > 0:
new_plot.ytransform = 'y'
new_plot.yaxis("\\rm{Residuals} ", "/")
new_plot.group_id = "AnnulusPhi" + self.base.data2D.name
new_plot.id = "AnnulusPhi" + self.base.data2D.name
new_plot.is_data = True
new_plot.xtransform = "x"
new_plot.ytransform = "y"
self.base.parent.update_theory(data_id=data.id, theory=new_plot)
wx.PostEvent(self.base.parent,
NewPlotEvent(plot=new_plot, title="AnnulusPhi"))
def clear_data(self):
wx.PostEvent(self.parent,
NewPlotEvent(action='delete', group_id=GROUP_ID_TRANSFORM))
wx.PostEvent(self.parent,
NewPlotEvent(action='clear', group_id=GROUP_ID_IQ_DATA))
def post_data(self, new_slab=None, nbins=None, direction=None):
"""
post data averaging in Qx or Qy given new_slab type
:param new_slab: slicer that determine with direction to average
:param nbins: the number of points plotted when averaging
:param direction: the direction of averaging
"""
if self.direction is None:
self.direction = direction
x_min = -1 * math.fabs(self.vertical_lines.x)
x_max = math.fabs(self.vertical_lines.x)
y_min = -1 * math.fabs(self.horizontal_lines.y)
y_max = math.fabs(self.horizontal_lines.y)
if nbins is not None:
self.nbins = nbins
if self.averager is None:
if new_slab is None:
msg = "post data:cannot average , averager is empty"
raise ValueError(msg)
self.averager = new_slab
if self.direction == "X":
if self.fold:
x_low = 0
else:
x_low = math.fabs(x_min)
bin_width = (x_max + x_low) / self.nbins
elif self.direction == "Y":
if self.fold:
y_low = 0
else:
y_low = math.fabs(y_min)
bin_width = (y_max + y_low) / self.nbins
else:
msg = "post data:no Box Average direction was supplied"
raise ValueError(msg)
# # Average data2D given Qx or Qy
box = self.averager(x_min=x_min, x_max=x_max, y_min=y_min, y_max=y_max,
bin_width=bin_width)
box.fold = self.fold
boxavg = box(self.base.data2D)
# 3 Create Data1D to plot
if hasattr(boxavg, "dxl"):
dxl = boxavg.dxl
else:
dxl = None
if hasattr(boxavg, "dxw"):
dxw = boxavg.dxw
else:
dxw = None
new_plot = Data1D(x=boxavg.x, y=boxavg.y, dy=boxavg.dy)
new_plot.dxl = dxl
new_plot.dxw = dxw
new_plot.name = str(self.averager.__name__) + \
"(" + self.base.data2D.name + ")"
new_plot.source = self.base.data2D.source
new_plot.interactive = True
new_plot.detector = self.base.data2D.detector
# # If the data file does not tell us what the axes are, just assume...
new_plot.xaxis("\\rm{Q}", "A^{-1}")
new_plot.yaxis("\\rm{Intensity} ", "cm^{-1}")
data = self.base.data2D
if hasattr(data, "scale") and data.scale == 'linear' and \
self.base.data2D.name.count("Residuals") > 0:
new_plot.ytransform = 'y'
new_plot.yaxis("\\rm{Residuals} ", "/")
new_plot.group_id = "2daverage" + self.base.data2D.name
new_plot.id = (self.averager.__name__) + self.base.data2D.name
new_plot.is_data = True
self.base.parent.update_theory(data_id=self.base.data2D.id, \
theory=new_plot)
wx.PostEvent(self.base.parent,
NewPlotEvent(plot=new_plot, title=str(self.averager.__name__)))
def set_data(self, data_list=None):
"""
receive a list of data and compute invariant
"""
msg = ""
data = None
if data_list is None:
data_list = []
if len(data_list) >= 1:
if len(data_list) == 1:
data = data_list[0]
else:
data_1d_list = []
data_2d_list = []
error_msg = ""
# separate data into data1d and data2d list
for data in data_list:
if data is not None:
if issubclass(data.__class__, Data1D):
data_1d_list.append(data)
else:
error_msg += " %s type %s \n" % (str(data.name),
str(data.__class__.__name__))
data_2d_list.append(data)
if len(data_2d_list) > 0:
msg = "Invariant does not support the following data types:\n"
msg += error_msg
if len(data_1d_list) == 0:
wx.PostEvent(self.parent, StatusEvent(status=msg, info='error'))
return
msg += "Invariant panel does not allow multiple data!\n"
msg += "Please select one.\n"
if len(data_list) > 1:
from invariant_widgets import DataDialog
dlg = DataDialog(data_list=data_1d_list, text=msg)
if dlg.ShowModal() == wx.ID_OK:
data = dlg.get_data()
else:
data = None
dlg.Destroy()
if data is None:
msg += "invariant receives no data. \n"
wx.PostEvent(self.parent, StatusEvent(status=msg, info='error'))
return
if not issubclass(data.__class__, Data1D):
msg += "invariant cannot be computed for data of "
msg += "type %s\n" % (data.__class__.__name__)
wx.PostEvent(self.parent, StatusEvent(status=msg, info='error'))
return
else:
wx.PostEvent(self.parent, NewPlotEvent(plot=data, title=data.title))
try:
self.compute_helper(data)
except:
msg = "Invariant Set_data: " + str(sys.exc_value)
wx.PostEvent(self.parent, StatusEvent(status=msg, info="error"))
else:
msg = "invariant cannot be computed for data of "
msg += "type %s" % (data.__class__.__name__)
wx.PostEvent(self.parent, StatusEvent(status=msg, info='error'))
def show_iq(self, out, pr, q=None):
"""
Display computed I(q)
"""
qtemp = pr.x
if q is not None:
qtemp = q
# Make a plot
maxq = -1
for q_i in qtemp:
if q_i > maxq:
maxq = q_i
minq = 0.001
# Check for user min/max
if pr.q_min is not None:
minq = pr.q_min
if pr.q_max is not None:
maxq = pr.q_max
x = pylab.arange(minq, maxq, maxq / 301.0)
y = np.zeros(len(x))
err = np.zeros(len(x))
for i in range(len(x)):
value = pr.iq(out, x[i])
y[i] = value
try:
err[i] = math.sqrt(math.fabs(value))
except:
err[i] = 1.0
print("Error getting error", value, x[i])
new_plot = Data1D(x, y)
new_plot.symbol = GUIFRAME_ID.CURVE_SYMBOL_NUM
new_plot.name = IQ_FIT_LABEL
new_plot.xaxis("\\rm{Q}", 'A^{-1}')
new_plot.yaxis("\\rm{Intensity} ", "cm^{-1}")
title = "I(q)"
new_plot.title = title
# If we have a group ID, use it
if 'plot_group_id' in pr.info:
new_plot.group_id = pr.info["plot_group_id"]
new_plot.id = IQ_FIT_LABEL
self.parent.update_theory(data_id=self.data_id, theory=new_plot)
wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title=title))
# If we have used slit smearing, plot the smeared I(q) too
if pr.slit_width > 0 or pr.slit_height > 0:
x = pylab.arange(minq, maxq, maxq / 301.0)
y = np.zeros(len(x))
err = np.zeros(len(x))
for i in range(len(x)):
value = pr.iq_smeared(out, x[i])
y[i] = value
try:
err[i] = math.sqrt(math.fabs(value))
except:
err[i] = 1.0
print("Error getting error", value, x[i])
new_plot = Data1D(x, y)
new_plot.symbol = GUIFRAME_ID.CURVE_SYMBOL_NUM
new_plot.name = IQ_SMEARED_LABEL
new_plot.xaxis("\\rm{Q}", 'A^{-1}')
new_plot.yaxis("\\rm{Intensity} ", "cm^{-1}")
# If we have a group ID, use it
if 'plot_group_id' in pr.info:
new_plot.group_id = pr.info["plot_group_id"]
new_plot.id = IQ_SMEARED_LABEL
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))