/
peak_editor.py
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/
peak_editor.py
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from traits.api import Str, List, HasTraits, Instance
from traitsui.api import View,UItem, TableEditor, Action, Handler
from traitsui.table_column import ObjectColumn,NumericColumn
from traitsui.extras.checkbox_column import CheckboxColumn
from traitsui.menu import OKButton, CancelButton
from fixes import fix_background_color
fix_background_color()
from xye import XYEDataset
from peak_fitting import fit_peaks_background, createPeakRows, PeakRowUI,autosearch_peaks,updatePeakRows
import re
from chaco.tools.api import LineSegmentTool
from ui_helpers import get_txt_filename
import numpy as np
def createDatasetPeaks(dataset):
peaklist=createPeakRows(dataset.select_peaks_params)
dspeaks=DatasetPeaks(peaks=peaklist,name=dataset.name,dataset=dataset)
return dspeaks
class PeakColumn(ObjectColumn):
pass
class DatasetPeaks(HasTraits):
peaks = List(PeakRowUI)
name=Str
dataset=Instance(XYEDataset)
#background={}
peak_editor=TableEditor(
auto_size=True,
selection_mode='cells',
sortable=False,
editable=True,
auto_add=False,
deletable=True,
configurable=False,
edit_on_first_click=False,
cell_bg_color='white',
label_bg_color=(232,232,232),
selection_bg_color=(232,232,232),
columns=[
PeakColumn(name='peak_number', cell_color='white', editable=False, width=0.9),
CheckboxColumn(name='fit', label="Refine?"),
NumericColumn(name='position', label="Position", cell_color='white', editable=True),
NumericColumn(name='intensity',label='Intensity',cell_color='white', editable=True),
NumericColumn(name='sigma',label='Sigma',cell_color='white', editable=True),
NumericColumn(name='gamma',label='Gamma',cell_color='white', editable=True),
NumericColumn(name='fwhm',label='FWHM',cell_color='white', editable=False)
],
show_toolbar=True
)
traits_view=View(
UItem('name'),
UItem('peaks', editor=peak_editor),
)
def to_dict(self):
newdict={}
for pr in self.peaks:
newdict.update(pr.row_to_dict())
return newdict
def update_peak_list(dspeaks,params):
thisdict= dspeaks.to_dict()
removeregexList=[r'int',r'pos',r'sig',r'gam']
removeList=[key for regex in removeregexList for key in params.keys() if re.search(regex,key)]
for r in removeList:
params.pop(r)
params.update(thisdict)
def update_peak_list_by_list(peaklist,params):
removeregexList=[r'int',r'pos',r'sig',r'gam']
removeList=[key for regex in removeregexList for key in params.keys() if re.search(regex,key)]
for r in removeList:
params.pop(r)
thisdict={}
for peak in peaklist:
thisdict.update(peak.row_to_dict())
params.update(thisdict)
class PeakFitEditorHandler(Handler):
varyList=[r'int',r'sig',r'gam']
# def update_peak_list(self,dspeaks,params):
# thisdict= dspeaks.to_dict()
# removeregexList=[r'int',r'pos',r'sig',r'gam']
# removeList=[key for regex in removeregexList for key in params.keys() if re.search(regex,key)]
# for r in removeList:
# params.pop(r)
# params.update(thisdict)
def close(self,info,is_ok):
if is_ok:
update_peak_list(info.object.selected,info.object.selected.dataset.select_peaks_params)
return True
def do_refine(self,info):
update_peak_list(info.object.selected,info.object.selected.dataset.select_peaks_params)
dataset=info.object.selected.dataset
new_select_peaks=[]
for pr in info.object.selected.peaks:
if pr.fit is True:
new_select_peaks.append(pr)
background,peak_profile,new_params=fit_peaks_background(new_select_peaks,self.varyList,dataset,None,dataset.select_peaks_params)
if not hasattr(dataset,'background'):
background_fit=dataset.copy()
background_fit.metadata['ui'].name = dataset.name+' fit (background)'
background_fit.metadata['ui'].color=None
dataset.background=background_fit
dataset.background.data[:,1]=background
dataset.select_peaks_params.update(new_params)
# update the peaks list
newPeaks=updatePeakRows(new_params,info.object.selected.peaks)
#newPeaks=createPeakRows(new_params)
info.object.selected.peaks=newPeaks
info.object.peak_profile.data[:,1]=peak_profile
def do_save(self,info):
filename=info.object.selected.dataset.name.split()[0]+"_peak_data.txt"
filename=str(get_txt_filename(filename))
with file(filename, 'w') as outfile:
outfile.write("Peak Fit Parameters\n")#filename.write()
outfile.write("Dataset name: "+info.object.selected.dataset.name+"\n")
outfile.write("2Theta\tIntensity\tSigma\tGamma\tFWHM\n")
for peak in info.object.selected.peaks:
outfile.write("%4.5f\t%4.5f\t%4.5f\t%4.5f\t%4.5f\n" % (peak.position, peak.intensity, peak.sigma,peak.gamma,peak.fwhm))
class PeakFittingEditor(HasTraits):
# while we store data for all the datasets that are loaded we only operate on "selected"
# Maybe this should change so that there is only the peak list and the dataset, it would simplify things greatly.
raw_dataset=Instance(XYEDataset)
selected=Instance(DatasetPeaks)
peak_profile=Instance(XYEDataset)
index = None
refine = Action(name = "Refine",
action = "do_refine")
save= Action(name = "Save peak list",
action="do_save")
traits_view = View(
UItem('selected@'),
# Item('selected@', editor=ListEditor(use_notebook=True, deletable=False, page_name='.name',selected='selected'), show_label=False),
#VGroup(
#Label('Modify all selected items:'),
# VGroup(
# Item('name'),
# Item('peaks'),
# springy=True
# ),
# ),
resizable=True, width=0.5, height=0.5, kind='livemodal',
title='Edit peaks to fit',
handler=PeakFitEditorHandler(),
buttons=[refine, save, OKButton,CancelButton]
)
def __init__(self, *args, **kwargs):
super(PeakFittingEditor, self).__init__(*args, **kwargs)
self.selected=createDatasetPeaks(self.raw_dataset)
#self.dataset_peaks=createDatasetPeaks(self.raw_dataset)
# self.selected=self.dataset_peaks
self.peak_profile=self.selected.dataset.copy()
self.peak_profile.name=self.selected.dataset.name+'(fitted profile)'
self.peak_profile.metadata['ui'].name=self.selected.dataset.metadata['ui'].name+' (fitted peak profile)'
self.peak_profile.metadata['ui'].color=None
# For now we will do only the selected dataset, the one that is the top tab that is visible see how long it takes, then loop around the other
# datasets if need be
# remove peaks from fit_params if they are not ticked to refine
# do the least squares refinement on the data with those params
# update the display of the table. Clicking ok, "saves" the params for that dataset
#
class PeakSelectorTool(LineSegmentTool):
"""
This class extends the LineSegmentTool, we will use it for selecting peaks in the user window, not working yet
"""
peak_list=List(PeakRowUI)
dataset=Instance(XYEDataset)
params={}
callback=None
def find_nearest(self,array,value):
idx = (np.abs(array-value)).argmin()
return array[idx]
def searchOnePeak(self,position,peakNumber):
window=0.5
peakrows=[]
while (True):
x=self.find_nearest(self.dataset.data[:,0],position)
xmin= x-window
xmax=x+window
peakrows=autosearch_peaks(self.dataset,(xmin,xmax),self.params)
if peakrows is None or len(peakrows)>1:
#reduce limits
window=window/10
if window<1e-6:
break
peakrows=[]
else: break
peakrows[0].peak_number=peakNumber
return peakrows[0]
def __init__(self,peak_list,dataset,callback,*args, **kwargs):
super(PeakSelectorTool, self).__init__(*args, **kwargs)
self.peak_list=peak_list
self.line.close_path=False
self.line.line_width=0
self.line.vertex_size=5.0
self.dataset=dataset
self.callback=callback
self.params=dataset.select_peaks_params
newpoints=[(peak.position,peak.intensity) for peak in peak_list]
#self.selected_peaks_y=[peak.intensity for peak in peak_list]
#self.points[:,0]=self.selected_peaks_x
#self.points[:,1]=self.selected_peaks_y
self.points.extend(newpoints)
self.component.request_redraw()
def _finalize_selection(self):
peaks=[]
iPeak=0
for point in self.points:
peaks.append(self.searchOnePeak(point[0],iPeak))
iPeak+=1
update_peak_list_by_list(peaks,self.dataset.select_peaks_params)
self.peak_list=peaks
self.callback(self.dataset)