def __init__(self):
PluginBase.__init__(self, name="Pr Inversion")
## Simulation window manager
self.simview = None
## State data
self.alpha = self.DEFAULT_ALPHA
self.nfunc = self.DEFAULT_NFUNC
self.max_length = self.DEFAULT_DMAX
self.q_min = None
self.q_max = None
self.has_bck = False
self.slit_height = 0
self.slit_width = 0
## Remember last plottable processed
self.last_data = ""
self._current_file_data = None
## Time elapsed for last computation [sec]
# Start with a good default
self.elapsed = 0.022
self.iq_data_shown = False
## Current invertor
self.invertor = None
self.pr = None
self.data_id = IQ_DATA_LABEL
# Copy of the last result in case we need to display it.
self._last_pr = None
self._last_out = None
self._last_cov = None
## Calculation thread
self.calc_thread = None
## Estimation thread
self.estimation_thread = None
## Result panel
self.control_panel = None
## Currently views plottable
self.current_plottable = None
## Number of P(r) points to display on the output plot
self._pr_npts = 51
self._normalize_output = False
self._scale_output_unity = False
## List of added P(r) plots
self._added_plots = {}
self._default_Iq = {}
self.list_plot_id = []
# Associate the inversion state reader with .prv files
from inversion_state import Reader
# Create a CanSAS/Pr reader
self.state_reader = Reader(self.set_state)
self._extensions = '.prv'
l = Loader()
l.associate_file_reader('.prv', self.state_reader)
#l.associate_file_reader(".svs", self.state_reader)
# Log startup
logging.info("Pr(r) plug-in started")
def __init__(self):
PluginBase.__init__(self, name="Pr Inversion")
## Simulation window manager
self.simview = None
## State data
self.alpha = self.DEFAULT_ALPHA
self.nfunc = self.DEFAULT_NFUNC
self.max_length = self.DEFAULT_DMAX
self.q_min = None
self.q_max = None
self.has_bck = False
self.slit_height = 0
self.slit_width = 0
## Remember last plottable processed
self.last_data = ""
self._current_file_data = None
## Time elapsed for last computation [sec]
# Start with a good default
self.elapsed = 0.022
self.iq_data_shown = False
## Current invertor
self.invertor = None
self.pr = None
self.data_id = IQ_DATA_LABEL
# Copy of the last result in case we need to display it.
self._last_pr = None
self._last_out = None
self._last_cov = None
## Calculation thread
self.calc_thread = None
## Estimation thread
self.estimation_thread = None
## Result panel
self.control_panel = None
## Currently views plottable
self.current_plottable = None
## Number of P(r) points to display on the output plot
self._pr_npts = 51
self._normalize_output = False
self._scale_output_unity = False
## List of added P(r) plots
self._added_plots = {}
self._default_Iq = {}
self.list_plot_id = []
# Associate the inversion state reader with .prv files
from inversion_state import Reader
# Create a CanSAS/Pr reader
self.state_reader = Reader(self.set_state)
self._extensions = '.prv'
l = Loader()
l.associate_file_reader('.prv', self.state_reader)
#l.associate_file_reader(".svs", self.state_reader)
# Log startup
logging.info("Pr(r) plug-in started")
class Plugin(PluginBase):
"""
P(r) inversion perspective
"""
DEFAULT_ALPHA = 0.0001
DEFAULT_NFUNC = 10
DEFAULT_DMAX = 140.0
def __init__(self):
PluginBase.__init__(self, name="Pr Inversion")
## Simulation window manager
self.simview = None
## State data
self.alpha = self.DEFAULT_ALPHA
self.nfunc = self.DEFAULT_NFUNC
self.max_length = self.DEFAULT_DMAX
self.q_min = None
self.q_max = None
self.est_bck = False
self.bck_val = 0
self.slit_height = 0
self.slit_width = 0
## Remember last plottable processed
self.last_data = ""
self._current_file_data = None
## Time elapsed for last computation [sec]
# Start with a good default
self.elapsed = 0.022
self.iq_data_shown = False
## Current invertor
self.invertor = None
self.pr = None
self.data_id = IQ_DATA_LABEL
# Copy of the last result in case we need to display it.
self._last_pr = None
self._last_out = None
self._last_cov = None
## Calculation thread
self.calc_thread = None
## Estimation thread
self.estimation_thread = None
## Result panel
self.control_panel = None
## Currently views plottable
self.current_plottable = None
## Number of P(r) points to display on the output plot
self._pr_npts = 51
self._normalize_output = False
self._scale_output_unity = False
## List of added P(r) plots
self._added_plots = {}
self._default_Iq = {}
self.list_plot_id = []
# Associate the inversion state reader with .prv files
from inversion_state import Reader
# Create a CanSAS/Pr reader
self.state_reader = Reader(self.set_state)
self._extensions = '.prv'
l = Loader()
l.associate_file_reader('.prv', self.state_reader)
#l.associate_file_reader(".svs", self.state_reader)
# Log startup
logger.info("Pr(r) plug-in started")
def delete_data(self, data_id):
"""
delete the data association with prview
"""
self.control_panel.clear_panel()
def get_data(self):
"""
Returns the current data
"""
return self.current_plottable
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 help(self, evt):
"""
Show a general help dialog.
:TODO: replace the text with a nice image
"""
from inversion_panel import HelpDialog
dialog = HelpDialog(None, -1)
if dialog.ShowModal() == wx.ID_OK:
dialog.Destroy()
else:
dialog.Destroy()
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 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 get_npts(self):
"""
Returns the number of points in the I(q) data
"""
try:
return len(self.pr.x)
except:
return 0
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))
def _on_pr_npts(self, evt):
"""
Redisplay P(r) with a different number of points
"""
from inversion_panel import PrDistDialog
dialog = PrDistDialog(None, -1)
dialog.set_content(self._pr_npts)
if dialog.ShowModal() == wx.ID_OK:
self._pr_npts = dialog.get_content()
dialog.Destroy()
self.show_pr(self._last_out, self._last_pr, self._last_cov)
else:
dialog.Destroy()
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 load(self, data):
"""
Load data. This will eventually be replaced
by our standard DataLoader class.
"""
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)
# Use data loader to load file
dataread = data
# Notify the user if we could not read the file
if dataread is None:
raise RuntimeError, "Invalid data"
x = None
y = None
err = None
if dataread.__class__.__name__ == 'Data1D':
x = dataread.x
y = dataread.y
err = dataread.dy
else:
if isinstance(dataread, list) and len(dataread) > 0:
x = dataread[0].x
y = dataread[0].y
err = dataread[0].dy
msg = "PrView only allows a single data set at a time. "
msg += "Only the first data set was loaded."
wx.PostEvent(self.parent, StatusEvent(status=msg))
else:
if dataread is None:
return x, y, err
raise RuntimeError, "This tool can only read 1D data"
self._current_file_data.x = x
self._current_file_data.y = y
self._current_file_data.err = err
return x, y, err
def load_columns(self, path="sphere_60_q0_2.txt"):
"""
Load 2- or 3- column ascii
"""
# Read the data from the data file
data_x = np.zeros(0)
data_y = np.zeros(0)
data_err = np.zeros(0)
scale = None
min_err = 0.0
if path is not None:
input_f = open(path, 'r')
buff = input_f.read()
lines = buff.split('\n')
for line in lines:
try:
toks = line.split()
x = float(toks[0])
y = float(toks[1])
if len(toks) > 2:
err = float(toks[2])
else:
if scale is None:
scale = 0.05 * math.sqrt(y)
#scale = 0.05/math.sqrt(y)
min_err = 0.01 * y
err = scale * math.sqrt(y) + min_err
#err = 0
data_x = np.append(data_x, x)
data_y = np.append(data_y, y)
data_err = np.append(data_err, err)
except:
logger.error(sys.exc_value)
if scale is not None:
message = "The loaded file had no error bars, statistical errors are assumed."
wx.PostEvent(self.parent, StatusEvent(status=message))
else:
wx.PostEvent(self.parent, StatusEvent(status=''))
return data_x, data_y, data_err
def load_abs(self, path):
"""
Load an IGOR .ABS reduced file
:param path: file path
:return: x, y, err vectors
"""
# Read the data from the data file
data_x = np.zeros(0)
data_y = np.zeros(0)
data_err = np.zeros(0)
scale = None
min_err = 0.0
data_started = False
if path is not None:
input_f = open(path, 'r')
buff = input_f.read()
lines = buff.split('\n')
for line in lines:
if data_started:
try:
toks = line.split()
x = float(toks[0])
y = float(toks[1])
if len(toks) > 2:
err = float(toks[2])
else:
if scale is None:
scale = 0.05 * math.sqrt(y)
#scale = 0.05/math.sqrt(y)
min_err = 0.01 * y
err = scale * math.sqrt(y) + min_err
#err = 0
data_x = np.append(data_x, x)
data_y = np.append(data_y, y)
data_err = np.append(data_err, err)
except:
logger.error(sys.exc_value)
elif line.find("The 6 columns") >= 0:
data_started = True
if scale is not None:
message = "The loaded file had no error bars, statistical errors are assumed."
wx.PostEvent(self.parent, StatusEvent(status=message))
else:
wx.PostEvent(self.parent, StatusEvent(status=''))
return data_x, data_y, data_err
def pr_theory(self, r, R):
"""
Return P(r) of a sphere for a given R
For test purposes
"""
if r <= 2 * R:
return 12.0 * ((0.5 * r / R)**2) * (
(1.0 - 0.5 * r / R)**2) * (2.0 + 0.5 * r / R)
else:
return 0.0
def get_context_menu(self, plotpanel=None):
"""
Get the context menu items available for P(r)
:param graph: the Graph object to which we attach the context menu
:return: a list of menu items with call-back function
"""
graph = plotpanel.graph
# Look whether this Graph contains P(r) data
if graph.selected_plottable not in plotpanel.plots:
return []
item = plotpanel.plots[graph.selected_plottable]
if item.id == PR_FIT_LABEL:
#add_data_hint = "Load a data file and display it on this plot"
#["Add P(r) data",add_data_hint , self._on_add_data],
change_n_hint = "Change the number of"
change_n_hint += " points on the P(r) output"
change_n_label = "Change number of P(r) points"
m_list = [[change_n_label, change_n_hint, self._on_pr_npts]]
if self._scale_output_unity or self._normalize_output:
hint = "Let the output P(r) keep the scale of the data"
m_list.append(
["Disable P(r) scaling", hint, self._on_disable_scaling])
if not self._scale_output_unity:
m_list.append([
"Scale P_max(r) to unity",
"Scale P(r) so that its maximum is 1", self._on_scale_unity
])
if not self._normalize_output:
m_list.append([
"Normalize P(r) to unity",
"Normalize the integral of P(r) to 1", self._on_normalize
])
return m_list
elif item.id in [
PR_LOADED_LABEL, IQ_DATA_LABEL, IQ_FIT_LABEL, IQ_SMEARED_LABEL
]:
return []
elif item.id == graph.selected_plottable:
if issubclass(item.__class__, Data1D):
return [[
"Compute P(r)", "Compute P(r) from distribution",
self._on_context_inversion
]]
return []
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))
# Need the update flag in the NewPlotEvent to protect against
# the plot no longer being there...
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 _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 start_thread(self):
"""
Start a calculation thread
"""
from pr_thread import CalcPr
# If a thread is already started, stop it
if self.calc_thread is not None and self.calc_thread.isrunning():
self.calc_thread.stop()
## stop just raises the flag -- the thread is supposed to
## then kill itself. In August 2014 it was shown that this is
## incorrectly handled by fitting.py and a fix implemented.
## It is not clear whether it is properly used here, but the
## "fix" of waiting for the previous thread to end breaks the
## pr perspective completely as it causes an infinite loop.
## Thus it is likely the threading is bing properly handled.
## While the "fix" is no longer implemented the comment is
## left here till somebody ascertains that in fact the threads
## are being properly handled.
##
## -PDB January 25, 2015
pr = self.pr.clone()
self.calc_thread = CalcPr(pr,
self.nfunc,
error_func=self._thread_error,
completefn=self._completed,
updatefn=None)
self.calc_thread.queue()
self.calc_thread.ready(2.5)
def _thread_error(self, error):
"""
Call-back method for calculation errors
"""
wx.PostEvent(self.parent, StatusEvent(status=error))
def _estimate_completed(self, alpha, message, elapsed):
"""
Parameter estimation completed,
display the results to the user
:param alpha: estimated best alpha
:param elapsed: computation time
"""
# Save useful info
self.elapsed = elapsed
self.control_panel.alpha_estimate = alpha
if message is not None:
wx.PostEvent(self.parent, StatusEvent(status=str(message)))
self.perform_estimateNT()
def _estimateNT_completed(self, nterms, alpha, message, elapsed):
"""
Parameter estimation completed,
display the results to the user
:param alpha: estimated best alpha
:param nterms: estimated number of terms
:param elapsed: computation time
"""
# Save useful info
self.elapsed = elapsed
self.control_panel.nterms_estimate = nterms
self.control_panel.alpha_estimate = alpha
if message is not None:
wx.PostEvent(self.parent, StatusEvent(status=str(message)))
def _completed(self, out, cov, pr, elapsed):
"""
wxCallAfter Method called with the results when the inversion
is done
:param out: output coefficient for the base functions
:param cov: covariance matrix
:param pr: Invertor instance
:param elapsed: time spent computing
"""
# Ensure hat you have all inputs are ready at the time call happens:
# Without CallAfter, it will freeze with wx >= 2.9.
wx.CallAfter(self._completed_call, out, cov, pr, elapsed)
def _completed_call(self, out, cov, pr, elapsed):
"""
Method called with the results when the inversion
is done
:param out: output coefficient for the base functions
:param cov: covariance matrix
:param pr: Invertor instance
:param elapsed: time spent computing
"""
# Save useful info
self.elapsed = elapsed
# Keep a copy of the last result
self._last_pr = pr.clone()
self._last_out = out
self._last_cov = cov
# Save Pr invertor
self.pr = pr
cov = np.ascontiguousarray(cov)
# Show result on control panel
self.control_panel.chi2 = pr.chi2
self.control_panel.elapsed = elapsed
self.control_panel.oscillation = pr.oscillations(out)
self.control_panel.positive = pr.get_positive(out)
self.control_panel.pos_err = pr.get_pos_err(out, cov)
self.control_panel.rg = pr.rg(out)
self.control_panel.iq0 = pr.iq0(out)
self.control_panel.bck = pr.background
self.control_panel.bck_input.SetValue("{:.2g}".format(pr.background))
# Show I(q) fit
self.show_iq(out, self.pr)
# Show P(r) fit
self.show_pr(out, self.pr, cov)
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 save_data(self, filepath, prstate=None):
"""
Save data in provided state object.
:TODO: move the state code away from inversion_panel and move it here.
Then remove the "prstate" input and make this method private.
:param filepath: path of file to write to
:param prstate: P(r) inversion state
"""
#TODO: do we need this or can we use DataLoader.loader.save directly?
# Add output data and coefficients to state
prstate.coefficients = self._last_out
prstate.covariance = self._last_cov
# Write the output to file
# First, check that the data is of the right type
if issubclass(self.current_plottable.__class__,
sas.sascalc.dataloader.data_info.Data1D):
self.state_reader.write(filepath, self.current_plottable, prstate)
else:
msg = "pr.save_data: the data being saved is not a"
msg += " sas.data_info.Data1D object"
raise RuntimeError, msg
def setup_plot_inversion(self,
alpha,
nfunc,
d_max,
q_min=None,
q_max=None,
est_bck=False,
bck_val=0,
height=0,
width=0):
"""
Set up inversion from plotted data
"""
self.alpha = alpha
self.nfunc = nfunc
self.max_length = d_max
self.q_min = q_min
self.q_max = q_max
self.est_bck = est_bck
self.bck_val = bck_val
self.slit_height = height
self.slit_width = width
try:
pr = self._create_plot_pr()
if pr is not None:
self.pr = pr
self.perform_inversion()
except:
wx.PostEvent(self.parent, StatusEvent(status=sys.exc_value))
def estimate_plot_inversion(self,
alpha,
nfunc,
d_max,
q_min=None,
q_max=None,
est_bck=False,
bck_val=0,
height=0,
width=0):
"""
Estimate parameters from plotted data
"""
self.alpha = alpha
self.nfunc = nfunc
self.max_length = d_max
self.q_min = q_min
self.q_max = q_max
self.est_bck = est_bck
self.bck_val = bck_val
self.slit_height = height
self.slit_width = width
try:
pr = self._create_plot_pr()
if pr is not None:
self.pr = pr
self.perform_estimate()
except:
wx.PostEvent(self.parent, StatusEvent(status=sys.exc_value))
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.est_bck = self.est_bck
pr.slit_height = self.slit_height
pr.slit_width = self.slit_width
pr.background = self.bck_val
# 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 is None:
err = np.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 is 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 setup_file_inversion(self,
alpha,
nfunc,
d_max,
data,
path=None,
q_min=None,
q_max=None,
bck=False,
height=0,
width=0):
"""
Set up inversion
"""
self.alpha = alpha
self.nfunc = nfunc
self.max_length = d_max
self.q_min = q_min
self.q_max = q_max
self.est_bck = bck
self.slit_height = height
self.slit_width = width
try:
pr = self._create_file_pr(data)
if pr is not None:
self.pr = pr
self.perform_inversion()
except:
wx.PostEvent(self.parent, StatusEvent(status=sys.exc_value))
def estimate_file_inversion(self,
alpha,
nfunc,
d_max,
data,
path=None,
q_min=None,
q_max=None,
bck=False,
height=0,
width=0):
"""
Estimate parameters for inversion
"""
self.alpha = alpha
self.nfunc = nfunc
self.max_length = d_max
self.q_min = q_min
self.q_max = q_max
self.est_bck = bck
self.slit_height = height
self.slit_width = width
try:
pr = self._create_file_pr(data)
if pr is not None:
self.pr = pr
self.perform_estimate()
except:
wx.PostEvent(self.parent, StatusEvent(status=sys.exc_value))
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 is None or np.all(err) == 0:
err = np.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 is 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.est_bck = self.est_bck
pr.slit_height = self.slit_height
pr.slit_width = self.slit_width
return pr
except:
load_error(sys.exc_value)
return None
def perform_estimate(self):
"""
Perform parameter estimation
"""
from pr_thread import EstimatePr
# If a thread is already started, stop it
if self.estimation_thread is not None and \
self.estimation_thread.isrunning():
self.estimation_thread.stop()
## stop just raises the flag -- the thread is supposed to
## then kill itself. In August 2014 it was shown that this is
## incorrectly handled by fitting.py and a fix implemented.
## It is not clear whether it is properly used here, but the
## "fix" of waiting for the previous thread to end breaks the
## pr perspective completely as it causes an infinite loop.
## Thus it is likely the threading is bing properly handled.
## While the "fix" is no longer implemented the comment is
## left here till somebody ascertains that in fact the threads
## are being properly handled.
##
## -PDB January 25, 2015
pr = self.pr.clone()
self.estimation_thread = EstimatePr(
pr,
self.nfunc,
error_func=self._thread_error,
completefn=self._estimate_completed,
updatefn=None)
self.estimation_thread.queue()
self.estimation_thread.ready(2.5)
def perform_estimateNT(self):
"""
Perform parameter estimation
"""
from pr_thread import EstimateNT
# If a thread is already started, stop it
if self.estimation_thread is not None and self.estimation_thread.isrunning(
):
self.estimation_thread.stop()
## stop just raises the flag -- the thread is supposed to
## then kill itself. In August 2014 it was shown that this is
## incorrectly handled by fitting.py and a fix implemented.
## It is not clear whether it is properly used here, but the
## "fix" of waiting for the previous thread to end breaks the
## pr perspective completely as it causes an infinite loop.
## Thus it is likely the threading is bing properly handled.
## While the "fix" is no longer implemented the comment is
## left here till somebody ascertains that in fact the threads
## are being properly handled.
##
## -PDB January 25, 2015
pr = self.pr.clone()
# Skip the slit settings for the estimation
# It slows down the application and it doesn't change the estimates
pr.slit_height = 0.0
pr.slit_width = 0.0
self.estimation_thread = EstimateNT(
pr,
self.nfunc,
error_func=self._thread_error,
completefn=self._estimateNT_completed,
updatefn=None)
self.estimation_thread.queue()
self.estimation_thread.ready(2.5)
def perform_inversion(self):
"""
Perform inversion
"""
self.start_thread()
def _on_context_inversion(self, event):
"""
Call-back method for plot context menu
"""
panel = event.GetEventObject()
Plugin.on_perspective(self, event=event)
# If we have more than one displayed plot, make the user choose
if len(panel.plots) >= 1 and \
panel.graph.selected_plottable in panel.plots:
dataset = panel.plots[panel.graph.selected_plottable].name
else:
logger.info("Prview Error: No data is available")
return
# Store a reference to the current plottable
# If we have a suggested value, use it.
try:
estimate = float(self.control_panel.alpha_estimate)
self.control_panel.alpha = estimate
except:
self.control_panel.alpha = self.alpha
logger.info("Prview :Alpha Not estimate yet")
try:
estimate = int(self.control_panel.nterms_estimate)
self.control_panel.nfunc = estimate
except:
self.control_panel.nfunc = self.nfunc
logger.info("Prview : ntemrs Not estimate yet")
self.current_plottable = panel.plots[panel.graph.selected_plottable]
self.set_data([self.current_plottable])
self.control_panel.plotname = dataset
#self.control_panel.nfunc = self.nfunc
self.control_panel.d_max = self.max_length
#self.parent.set_perspective(self.perspective)
self.control_panel._on_invert(None)
def get_panels(self, parent):
"""
Create and return a list of panel objects
"""
from inversion_panel import InversionControl
self.parent = parent
self.frame = MDIFrame(self.parent, None, 'None', (100, 200))
self.control_panel = InversionControl(self.frame,
-1,
style=wx.RAISED_BORDER)
self.frame.set_panel(self.control_panel)
self._frame_set_helper()
self.control_panel.set_manager(self)
self.control_panel.nfunc = self.nfunc
self.control_panel.d_max = self.max_length
self.control_panel.alpha = self.alpha
self.perspective = []
self.perspective.append(self.control_panel.window_name)
return [self.control_panel]
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 post_init(self):
"""
Post initialization call back to close the loose ends
[Somehow openGL needs this call]
"""
pass
class Plugin(PluginBase):
"""
P(r) inversion perspective
"""
DEFAULT_ALPHA = 0.0001
DEFAULT_NFUNC = 10
DEFAULT_DMAX = 140.0
def __init__(self):
PluginBase.__init__(self, name="Pr Inversion")
## Simulation window manager
self.simview = None
## State data
self.alpha = self.DEFAULT_ALPHA
self.nfunc = self.DEFAULT_NFUNC
self.max_length = self.DEFAULT_DMAX
self.q_min = None
self.q_max = None
self.has_bck = False
self.slit_height = 0
self.slit_width = 0
## Remember last plottable processed
self.last_data = ""
self._current_file_data = None
## Time elapsed for last computation [sec]
# Start with a good default
self.elapsed = 0.022
self.iq_data_shown = False
## Current invertor
self.invertor = None
self.pr = None
self.data_id = IQ_DATA_LABEL
# Copy of the last result in case we need to display it.
self._last_pr = None
self._last_out = None
self._last_cov = None
## Calculation thread
self.calc_thread = None
## Estimation thread
self.estimation_thread = None
## Result panel
self.control_panel = None
## Currently views plottable
self.current_plottable = None
## Number of P(r) points to display on the output plot
self._pr_npts = 51
self._normalize_output = False
self._scale_output_unity = False
## List of added P(r) plots
self._added_plots = {}
self._default_Iq = {}
self.list_plot_id = []
# Associate the inversion state reader with .prv files
from inversion_state import Reader
# Create a CanSAS/Pr reader
self.state_reader = Reader(self.set_state)
self._extensions = '.prv'
l = Loader()
l.associate_file_reader('.prv', self.state_reader)
#l.associate_file_reader(".svs", self.state_reader)
# Log startup
logging.info("Pr(r) plug-in started")
def delete_data(self, data_id):
"""
delete the data association with prview
"""
self.control_panel.clear_panel()
def get_data(self):
"""
Returns the current data
"""
return self.current_plottable
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:
logging.error("prview.set_state: %s" % sys.exc_value)
def help(self, evt):
"""
Show a general help dialog.
:TODO: replace the text with a nice image
"""
from inversion_panel import HelpDialog
dialog = HelpDialog(None, -1)
if dialog.ShowModal() == wx.ID_OK:
dialog.Destroy()
else:
dialog.Destroy()
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 show_shpere(self, x, radius=70.0, x_range=70.0):
"""
"""
# Show P(r)
y_true = numpy.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 get_npts(self):
"""
Returns the number of points in the I(q) data
"""
try:
return len(self.pr.x)
except:
return 0
def show_iq(self, out, pr, q=None):
"""
Display computed I(q)
"""
qtemp = pr.x
if not q == 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 not pr.q_min == None:
minq = pr.q_min
if not pr.q_max == None:
maxq = pr.q_max
x = pylab.arange(minq, maxq, maxq / 301.0)
y = numpy.zeros(len(x))
err = numpy.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 pr.info.has_key("plot_group_id"):
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 = numpy.zeros(len(x))
err = numpy.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 pr.info.has_key("plot_group_id"):
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))
def _on_pr_npts(self, evt):
"""
Redisplay P(r) with a different number of points
"""
from inversion_panel import PrDistDialog
dialog = PrDistDialog(None, -1)
dialog.set_content(self._pr_npts)
if dialog.ShowModal() == wx.ID_OK:
self._pr_npts = dialog.get_content()
dialog.Destroy()
self.show_pr(self._last_out, self._last_pr, self._last_cov)
else:
dialog.Destroy()
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 = numpy.zeros(len(x))
dy = numpy.zeros(len(x))
y_true = numpy.zeros(len(x))
total = 0.0
pmax = 0.0
cov2 = numpy.ascontiguousarray(cov)
for i in range(len(x)):
if cov2 == 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 == True:
y = y / total
dy = dy / total
elif self._scale_output_unity == True:
y = y / pmax
dy = dy / pmax
if cov2 == 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 load(self, data):
"""
Load data. This will eventually be replaced
by our standard DataLoader class.
"""
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)
# Use data loader to load file
dataread = data
# Notify the user if we could not read the file
if dataread is None:
raise RuntimeError, "Invalid data"
x = None
y = None
err = None
if dataread.__class__.__name__ == 'Data1D':
x = dataread.x
y = dataread.y
err = dataread.dy
else:
if isinstance(dataread, list) and len(dataread) > 0:
x = dataread[0].x
y = dataread[0].y
err = dataread[0].dy
msg = "PrView only allows a single data set at a time. "
msg += "Only the first data set was loaded."
wx.PostEvent(self.parent, StatusEvent(status=msg))
else:
if dataread is None:
return x, y, err
raise RuntimeError, "This tool can only read 1D data"
self._current_file_data.x = x
self._current_file_data.y = y
self._current_file_data.err = err
return x, y, err
def load_columns(self, path="sphere_60_q0_2.txt"):
"""
Load 2- or 3- column ascii
"""
# Read the data from the data file
data_x = numpy.zeros(0)
data_y = numpy.zeros(0)
data_err = numpy.zeros(0)
scale = None
min_err = 0.0
if not path == None:
input_f = open(path, 'r')
buff = input_f.read()
lines = buff.split('\n')
for line in lines:
try:
toks = line.split()
x = float(toks[0])
y = float(toks[1])
if len(toks) > 2:
err = float(toks[2])
else:
if scale == None:
scale = 0.05 * math.sqrt(y)
#scale = 0.05/math.sqrt(y)
min_err = 0.01 * y
err = scale * math.sqrt(y) + min_err
#err = 0
data_x = numpy.append(data_x, x)
data_y = numpy.append(data_y, y)
data_err = numpy.append(data_err, err)
except:
logging.error(sys.exc_value)
if not scale == None:
message = "The loaded file had no error bars, statistical errors are assumed."
wx.PostEvent(self.parent, StatusEvent(status=message))
else:
wx.PostEvent(self.parent, StatusEvent(status=''))
return data_x, data_y, data_err
def load_abs(self, path):
"""
Load an IGOR .ABS reduced file
:param path: file path
:return: x, y, err vectors
"""
# Read the data from the data file
data_x = numpy.zeros(0)
data_y = numpy.zeros(0)
data_err = numpy.zeros(0)
scale = None
min_err = 0.0
data_started = False
if not path == None:
input_f = open(path, 'r')
buff = input_f.read()
lines = buff.split('\n')
for line in lines:
if data_started == True:
try:
toks = line.split()
x = float(toks[0])
y = float(toks[1])
if len(toks) > 2:
err = float(toks[2])
else:
if scale == None:
scale = 0.05 * math.sqrt(y)
#scale = 0.05/math.sqrt(y)
min_err = 0.01 * y
err = scale * math.sqrt(y) + min_err
#err = 0
data_x = numpy.append(data_x, x)
data_y = numpy.append(data_y, y)
data_err = numpy.append(data_err, err)
except:
logging.error(sys.exc_value)
elif line.find("The 6 columns") >= 0:
data_started = True
if not scale == None:
message = "The loaded file had no error bars, statistical errors are assumed."
wx.PostEvent(self.parent, StatusEvent(status=message))
else:
wx.PostEvent(self.parent, StatusEvent(status=''))
return data_x, data_y, data_err
def pr_theory(self, r, R):
"""
Return P(r) of a sphere for a given R
For test purposes
"""
if r <= 2 * R:
return 12.0 * ((0.5 * r / R) ** 2) * ((1.0 - 0.5 * r / R) ** 2) * (2.0 + 0.5 * r / R)
else:
return 0.0
def get_context_menu(self, plotpanel=None):
"""
Get the context menu items available for P(r)
:param graph: the Graph object to which we attach the context menu
:return: a list of menu items with call-back function
"""
graph = plotpanel.graph
# Look whether this Graph contains P(r) data
if graph.selected_plottable not in plotpanel.plots:
return []
item = plotpanel.plots[graph.selected_plottable]
if item.id == PR_FIT_LABEL:
#add_data_hint = "Load a data file and display it on this plot"
#["Add P(r) data",add_data_hint , self._on_add_data],
change_n_hint = "Change the number of"
change_n_hint += " points on the P(r) output"
change_n_label = "Change number of P(r) points"
m_list = [[change_n_label, change_n_hint, self._on_pr_npts]]
if self._scale_output_unity or self._normalize_output:
hint = "Let the output P(r) keep the scale of the data"
m_list.append(["Disable P(r) scaling", hint,
self._on_disable_scaling])
if not self._scale_output_unity:
m_list.append(["Scale P_max(r) to unity",
"Scale P(r) so that its maximum is 1",
self._on_scale_unity])
if not self._normalize_output:
m_list.append(["Normalize P(r) to unity",
"Normalize the integral of P(r) to 1",
self._on_normalize])
return m_list
elif item.id in [PR_LOADED_LABEL, IQ_DATA_LABEL, IQ_FIT_LABEL, IQ_SMEARED_LABEL]:
return []
elif item.id == graph.selected_plottable:
if issubclass(item.__class__, Data1D):
return [["Compute P(r)",
"Compute P(r) from distribution",
self._on_context_inversion]]
return []
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 = numpy.copy(self._default_Iq[plot])
wx.PostEvent(self.parent,
NewPlotEvent(plot=self._added_plots[plot],
title=self._added_plots[plot].name,
update=True))
# Need the update flag in the NewPlotEvent to protect against
# the plot no longer being there...
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 = numpy.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 _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 start_thread(self):
"""
Start a calculation thread
"""
from pr_thread import CalcPr
# If a thread is already started, stop it
if self.calc_thread != None and self.calc_thread.isrunning():
self.calc_thread.stop()
## stop just raises the flag -- the thread is supposed to
## then kill itself. In August 2014 it was shown that this is
## incorrectly handled by fitting.py and a fix implemented.
## It is not clear whether it is properly used here, but the
## "fix" of waiting for the previous thread to end breaks the
## pr perspective completely as it causes an infinite loop.
## Thus it is likely the threading is bing properly handled.
## While the "fix" is no longer implemented the comment is
## left here till somebody ascertains that in fact the threads
## are being properly handled.
##
## -PDB January 25, 2015
pr = self.pr.clone()
self.calc_thread = CalcPr(pr, self.nfunc,
error_func=self._thread_error,
completefn=self._completed, updatefn=None)
self.calc_thread.queue()
self.calc_thread.ready(2.5)
def _thread_error(self, error):
"""
Call-back method for calculation errors
"""
wx.PostEvent(self.parent, StatusEvent(status=error))
def _estimate_completed(self, alpha, message, elapsed):
"""
Parameter estimation completed,
display the results to the user
:param alpha: estimated best alpha
:param elapsed: computation time
"""
# Save useful info
self.elapsed = elapsed
self.control_panel.alpha_estimate = alpha
if not message == None:
wx.PostEvent(self.parent, StatusEvent(status=str(message)))
self.perform_estimateNT()
def _estimateNT_completed(self, nterms, alpha, message, elapsed):
"""
Parameter estimation completed,
display the results to the user
:param alpha: estimated best alpha
:param nterms: estimated number of terms
:param elapsed: computation time
"""
# Save useful info
self.elapsed = elapsed
self.control_panel.nterms_estimate = nterms
self.control_panel.alpha_estimate = alpha
if not message == None:
wx.PostEvent(self.parent, StatusEvent(status=str(message)))
def _completed(self, out, cov, pr, elapsed):
"""
wxCallAfter Method called with the results when the inversion
is done
:param out: output coefficient for the base functions
:param cov: covariance matrix
:param pr: Invertor instance
:param elapsed: time spent computing
"""
# Ensure hat you have all inputs are ready at the time call happens:
# Without CallAfter, it will freeze with wx >= 2.9.
wx.CallAfter(self._completed_call, out, cov, pr, elapsed)
def _completed_call(self, out, cov, pr, elapsed):
"""
Method called with the results when the inversion
is done
:param out: output coefficient for the base functions
:param cov: covariance matrix
:param pr: Invertor instance
:param elapsed: time spent computing
"""
# Save useful info
self.elapsed = elapsed
# Keep a copy of the last result
self._last_pr = pr.clone()
self._last_out = out
self._last_cov = cov
# Save Pr invertor
self.pr = pr
cov = numpy.ascontiguousarray(cov)
# Show result on control panel
self.control_panel.chi2 = pr.chi2
self.control_panel.elapsed = elapsed
self.control_panel.oscillation = pr.oscillations(out)
self.control_panel.positive = pr.get_positive(out)
self.control_panel.pos_err = pr.get_pos_err(out, cov)
self.control_panel.rg = pr.rg(out)
self.control_panel.iq0 = pr.iq0(out)
self.control_panel.bck = pr.background
# Show I(q) fit
self.show_iq(out, self.pr)
# Show P(r) fit
self.show_pr(out, self.pr, cov)
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 == 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 save_data(self, filepath, prstate=None):
"""
Save data in provided state object.
:TODO: move the state code away from inversion_panel and move it here.
Then remove the "prstate" input and make this method private.
:param filepath: path of file to write to
:param prstate: P(r) inversion state
"""
#TODO: do we need this or can we use DataLoader.loader.save directly?
# Add output data and coefficients to state
prstate.coefficients = self._last_out
prstate.covariance = self._last_cov
# Write the output to file
# First, check that the data is of the right type
if issubclass(self.current_plottable.__class__,
sas.dataloader.data_info.Data1D):
self.state_reader.write(filepath, self.current_plottable, prstate)
else:
msg = "pr.save_data: the data being saved is not a"
msg += " sas.data_info.Data1D object"
raise RuntimeError, msg
def setup_plot_inversion(self, alpha, nfunc, d_max, q_min=None, q_max=None,
bck=False, height=0, width=0):
"""
Set up inversion from plotted data
"""
self.alpha = alpha
self.nfunc = nfunc
self.max_length = d_max
self.q_min = q_min
self.q_max = q_max
self.has_bck = bck
self.slit_height = height
self.slit_width = width
try:
pr = self._create_plot_pr()
if not pr == None:
self.pr = pr
self.perform_inversion()
except:
wx.PostEvent(self.parent, StatusEvent(status=sys.exc_value))
def estimate_plot_inversion(self, alpha, nfunc, d_max,
q_min=None, q_max=None,
bck=False, height=0, width=0):
"""
Estimate parameters from plotted data
"""
self.alpha = alpha
self.nfunc = nfunc
self.max_length = d_max
self.q_min = q_min
self.q_max = q_max
self.has_bck = bck
self.slit_height = height
self.slit_width = width
try:
pr = self._create_plot_pr()
if not pr == None:
self.pr = pr
self.perform_estimate()
except:
wx.PostEvent(self.parent, StatusEvent(status=sys.exc_value))
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 setup_file_inversion(self, alpha, nfunc, d_max, data,
path=None, q_min=None, q_max=None,
bck=False, height=0, width=0):
"""
Set up inversion
"""
self.alpha = alpha
self.nfunc = nfunc
self.max_length = d_max
self.q_min = q_min
self.q_max = q_max
self.has_bck = bck
self.slit_height = height
self.slit_width = width
try:
pr = self._create_file_pr(data)
if not pr == None:
self.pr = pr
self.perform_inversion()
except:
wx.PostEvent(self.parent, StatusEvent(status=sys.exc_value))
def estimate_file_inversion(self, alpha, nfunc, d_max, data,
path=None, q_min=None, q_max=None,
bck=False, height=0, width=0):
"""
Estimate parameters for inversion
"""
self.alpha = alpha
self.nfunc = nfunc
self.max_length = d_max
self.q_min = q_min
self.q_max = q_max
self.has_bck = bck
self.slit_height = height
self.slit_width = width
try:
pr = self._create_file_pr(data)
if not pr is None:
self.pr = pr
self.perform_estimate()
except:
wx.PostEvent(self.parent, StatusEvent(status=sys.exc_value))
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 perform_estimate(self):
"""
Perform parameter estimation
"""
from pr_thread import EstimatePr
# If a thread is already started, stop it
if self.estimation_thread != None and \
self.estimation_thread.isrunning():
self.estimation_thread.stop()
## stop just raises the flag -- the thread is supposed to
## then kill itself. In August 2014 it was shown that this is
## incorrectly handled by fitting.py and a fix implemented.
## It is not clear whether it is properly used here, but the
## "fix" of waiting for the previous thread to end breaks the
## pr perspective completely as it causes an infinite loop.
## Thus it is likely the threading is bing properly handled.
## While the "fix" is no longer implemented the comment is
## left here till somebody ascertains that in fact the threads
## are being properly handled.
##
## -PDB January 25, 2015
pr = self.pr.clone()
self.estimation_thread = EstimatePr(pr, self.nfunc,
error_func=self._thread_error,
completefn=self._estimate_completed,
updatefn=None)
self.estimation_thread.queue()
self.estimation_thread.ready(2.5)
def perform_estimateNT(self):
"""
Perform parameter estimation
"""
from pr_thread import EstimateNT
# If a thread is already started, stop it
if self.estimation_thread != None and self.estimation_thread.isrunning():
self.estimation_thread.stop()
## stop just raises the flag -- the thread is supposed to
## then kill itself. In August 2014 it was shown that this is
## incorrectly handled by fitting.py and a fix implemented.
## It is not clear whether it is properly used here, but the
## "fix" of waiting for the previous thread to end breaks the
## pr perspective completely as it causes an infinite loop.
## Thus it is likely the threading is bing properly handled.
## While the "fix" is no longer implemented the comment is
## left here till somebody ascertains that in fact the threads
## are being properly handled.
##
## -PDB January 25, 2015
pr = self.pr.clone()
# Skip the slit settings for the estimation
# It slows down the application and it doesn't change the estimates
pr.slit_height = 0.0
pr.slit_width = 0.0
self.estimation_thread = EstimateNT(pr, self.nfunc,
error_func=self._thread_error,
completefn=self._estimateNT_completed,
updatefn=None)
self.estimation_thread.queue()
self.estimation_thread.ready(2.5)
def perform_inversion(self):
"""
Perform inversion
"""
self.start_thread()
def _on_context_inversion(self, event):
"""
Call-back method for plot context menu
"""
panel = event.GetEventObject()
Plugin.on_perspective(self, event=event)
# If we have more than one displayed plot, make the user choose
if len(panel.plots) >= 1 and \
panel.graph.selected_plottable in panel.plots:
dataset = panel.plots[panel.graph.selected_plottable].name
else:
logging.info("Prview Error: No data is available")
return
# Store a reference to the current plottable
# If we have a suggested value, use it.
try:
estimate = float(self.control_panel.alpha_estimate)
self.control_panel.alpha = estimate
except:
self.control_panel.alpha = self.alpha
logging.info("Prview :Alpha Not estimate yet")
try:
estimate = int(self.control_panel.nterms_estimate)
self.control_panel.nfunc = estimate
except:
self.control_panel.nfunc = self.nfunc
logging.info("Prview : ntemrs Not estimate yet")
self.current_plottable = panel.plots[panel.graph.selected_plottable]
self.set_data([self.current_plottable])
self.control_panel.plotname = dataset
#self.control_panel.nfunc = self.nfunc
self.control_panel.d_max = self.max_length
#self.parent.set_perspective(self.perspective)
self.control_panel._on_invert(None)
def get_panels(self, parent):
"""
Create and return a list of panel objects
"""
from inversion_panel import InversionControl
self.parent = parent
self.frame = MDIFrame(self.parent, None, 'None', (100, 200))
self.control_panel = InversionControl(self.frame, -1,
style=wx.RAISED_BORDER)
self.frame.set_panel(self.control_panel)
self._frame_set_helper()
self.control_panel.set_manager(self)
self.control_panel.nfunc = self.nfunc
self.control_panel.d_max = self.max_length
self.control_panel.alpha = self.alpha
self.perspective = []
self.perspective.append(self.control_panel.window_name)
return [self.control_panel]
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 post_init(self):
"""
Post initialization call back to close the loose ends
[Somehow openGL needs this call]
"""
pass