def setup_canvas(remote):
"""
Setup the canvas, for use by the other functions that popluate it with
plots.
"""
global f, canvas, degreeChar, num_plots
degreeChar = u'\N{DEGREE SIGN}'
num_plots = 38
plot_height = 200
f = Figure(dpi=100)
f.hold(False)
if remote == True:
canvas = FigureCanvasGTK(f) # a gtk.DrawingArea
else:
canvas = FigureCanvasGTKAgg(f) # a gtk.DrawingArea
# the size request sets the size of the area for the plots. Second number is the height
canvas.set_size_request(800, num_plots * plot_height)
app.viewport_matplotlib.add(canvas)
canvas.show()
class HurricaneUI:
def __init__(self):
gladefile = "HurricaneUI.glade"
builder = gtk.Builder()
builder.add_from_file(gladefile)
self.window = builder.get_object("mainWindow")
builder.connect_signals(self)
self.figure = Figure(figsize=(10, 10), dpi=75)
self.axis = self.figure.add_subplot(111)
self.lat = 50
self.lon = -100
self.globe = globDisp.GlobeMap(self.axis, self.lat, self.lon)
self.canvas = FigureCanvasGTK(self.figure)
self.canvas.show()
self.canvas.set_size_request(500, 500)
self.globeview = builder.get_object("map")
self.globeview.pack_start(self.canvas, True, True)
self.navToolbar = NavigationToolbar(self.canvas, self.globeview)
self.navToolbar.lastDir = '/var/tmp'
self.globeview.pack_start(self.navToolbar)
self.navToolbar.show()
self.gridcombo = builder.get_object("gridsize")
cell = gtk.CellRendererText()
self.gridcombo.pack_start(cell, True)
self.gridcombo.add_attribute(cell, 'text', 0)
#self.gridcombo.set_active(2)
# read menu configuration
self.gridopt = builder.get_object("gridopt").get_active()
self.chkDetected = builder.get_object("detectedopt")
self.detectedopt = self.chkDetected.get_active()
self.chkHurricane = builder.get_object("hurricaneopt")
self.hurricaneopt = self.chkHurricane.get_active()
model = builder.get_object("liststore1")
index = self.gridcombo.get_active()
self.gridsize = model[index][0]
radio = [
r for r in builder.get_object("classifieropt1").get_group()
if r.get_active()
][0]
self.sClassifier = radio.get_label()
self.start = builder.get_object("startdate")
self.end = builder.get_object("enddate")
self.chkUndersample = builder.get_object("undersample")
self.chkGenKey = builder.get_object("genKey")
# disable unimplemented classifier selection
builder.get_object("classifieropt2").set_sensitive(False)
builder.get_object("classifieropt3").set_sensitive(False)
builder.get_object("classifieropt4").set_sensitive(False)
self.btnStore = builder.get_object("store")
self.datapath = 'GFSdat'
self.trackpath = 'tracks'
builder.get_object("btnDatapath").set_current_folder(self.datapath)
builder.get_object("btnTrackpath").set_current_folder(self.trackpath)
self.btnDetect = builder.get_object("detect")
# current operation status
self.stormlocs = None
self.detected = None
self.clssfr = None
# for test drawing functions
if os.path.exists('demo.detected'):
with open('demo.detected', 'r') as f:
self.detected = pickle.load(f)
self.stormlocs = pickle.load(f)
self.chkHurricane.set_label(
str(self.stormlocs.shape[0]) + " Hurricanes")
self.chkDetected.set_label(
str(self.detected.shape[0]) + " Detected")
self.setDisabledBtns()
# draw Globe
self.drawGlobe()
def setDisabledBtns(self):
self.chkDetected.set_sensitive(self.detected != None)
self.chkHurricane.set_sensitive(self.stormlocs != None)
self.btnStore.set_sensitive(self.clssfr != None)
self.btnDetect.set_sensitive(self.clssfr != None)
def drawGlobe(self):
self.globe.drawGlobe(self.gridsize, self.gridopt)
if self.hurricaneopt: self.globe.drawHurricanes(self.stormlocs)
if self.detectedopt: self.globe.fillGrids(self.detected)
def main(self):
self.window.show_all()
gtk.main()
def redraw(self):
self.axis.cla()
self.drawGlobe()
self.canvas.draw_idle()
def gtk_main_quit(self, widget):
gtk.main_quit()
###############################################################################
#
# utility functions (dialogs)
#
def getFilenameToRead(self, stitle, save=False, filter='all'):
chooser = gtk.FileChooserDialog(
title=stitle,
parent=self.window,
action=gtk.FILE_CHOOSER_ACTION_OPEN
if not save else gtk.FILE_CHOOSER_ACTION_SAVE,
buttons=(gtk.STOCK_CANCEL, gtk.RESPONSE_CANCEL,
gtk.STOCK_OPEN if not save else gtk.STOCK_SAVE,
gtk.RESPONSE_OK))
chooser.set_default_response(gtk.RESPONSE_OK)
if filter == 'mat' or filter == 'mdat':
filter = gtk.FileFilter()
filter.set_name("Matrix files")
filter.add_pattern("*.mat")
chooser.add_filter(filter)
if filter == 'svm':
filter = gtk.FileFilter()
filter.set_name("SVM")
filter.add_pattern("*.svm")
chooser.add_filter(filter)
if filter == 'dat' or filter == 'mdat':
filter = gtk.FileFilter()
filter.set_name("Data")
filter.add_pattern("*.dat")
chooser.add_filter(filter)
filter = gtk.FileFilter()
filter.set_name("All files")
filter.add_pattern("*")
chooser.add_filter(filter)
chooser.set_current_folder(os.getcwd())
response = chooser.run()
if response == gtk.RESPONSE_OK:
filen = chooser.get_filename()
else:
filen = None
chooser.destroy()
return filen
def showMessage(self, msg):
md = gtk.MessageDialog(self.window, gtk.DIALOG_DESTROY_WITH_PARENT,
gtk.MESSAGE_INFO, gtk.BUTTONS_CLOSE, msg)
md.run()
md.destroy()
###############################################################################
#
# read data files and convert into a training matrix input
#
def on_btnTrackpath_current_folder_changed(self, widget):
self.trackpath = widget.get_current_folder()
def on_btnDatapath_current_folder_changed(self, widget):
self.datapath = widget.get_current_folder()
def on_createMat_clicked(self, widget):
filen = self.getFilenameToRead("Save converted matrix for training",
True,
filter='mat')
if filen is not None:
start = self.start.get_text()
end = self.end.get_text()
bundersmpl = self.chkUndersample.get_active()
bgenkey = self.chkGenKey.get_active()
### FIX ME: currently gridsize for classification is fixed 1 (no clustering of grids - 2x2)
if os.path.exists(filen):
os.unlink(
filen
) # createMat append existing file, so delete it if exist
gdtool.createMat(self.datapath,
self.trackpath,
start,
end,
store=filen,
undersample=bundersmpl,
genkeyf=bgenkey)
self.showMessage("Matrix has been stored to " + filen)
###############################################################################
#
# train the selected classifier
#
def on_train_clicked(self, widget):
# FOR NOW, only SVM is supported
if self.sClassifier == "SVM":
filen = self.getFilenameToRead("Open training data", filter='mat')
if filen is not None:
data = ml.VectorDataSet(filen, labelsColumn=0)
self.clssfr = ml.SVM()
self.clssfr.train(data)
# train finished. need to update button status
self.setDisabledBtns()
self.showMessage("Training SVM is done.")
else:
self.showMessage("The classifier is not supported yet!")
def on_classifieropt_toggled(self, widget, data=None):
self.sClassifier = widget.get_label()
###############################################################################
#
# Classify on test data
#
def on_detect_clicked(self, widget):
if self.clssfr is not None:
filen = self.getFilenameToRead("Open hurricane data",
filter='mdat')
if filen is not None:
fname = os.path.basename(filen)
key, ext = os.path.splitext(fname)
if ext == '.dat':
key = key[1:] # take 'g' out
#testData = gdtool.createMat(self.datapath, self.trackpath, key, key)
#result = self.clssfr.test(ml.VectorDataSet(testData,labelsColumn=0))
tmpfn = 'f__tmpDetected__'
if os.path.exists(tmpfn): os.unlink(tmpfn)
# for DEMO, undersampled the normal data -- without undersampling there are too many candidates
gdtool.createMat(self.datapath,
self.trackpath,
key,
key,
store=tmpfn,
undersample=True,
genkeyf=True)
bneedDel = True
else:
tmpfn = fname
bneedDel = False
result = self.clssfr.test(
ml.VectorDataSet(tmpfn, labelsColumn=0))
gdkeyfilen = ''.join([tmpfn, '.keys'])
with open(gdkeyfilen, 'r') as f:
gridkeys = pickle.load(f)
self.stormlocs = pickle.load(f)
predicted = result.getPredictedLabels()
predicted = np.array(map(float, predicted))
self.detected = np.array(gridkeys)[predicted == 1]
if bneedDel:
os.unlink(tmpfn)
os.unlink(gdkeyfilen)
snstroms = str(self.stormlocs.shape[0])
sndetected = str(self.detected.shape[0])
self.chkHurricane.set_label(snstroms + " Hurricanes")
self.chkDetected.set_label(sndetected + " Detected")
self.showMessage(''.join([
sndetected, "/", snstroms,
" grids are predicted to have hurricane."
]))
if False:
with open('demo.detected', 'w') as f:
pickle.dump(self.detected, f)
pickle.dump(self.stormlocs, f)
# test data tested. update buttons
self.setDisabledBtns()
self.redraw()
else:
self.showMessage("There is no trained classifier!")
###############################################################################
#
# load and store trained classifier
#
def on_load_clicked(self, widget):
filen = self.getFilenameToRead("Load Classifier", filter='svm')
if filen is not None:
#db = shelve.open(filen)
#if db.has_key('clssfr'):
# self.clssfr = db['clssfr']
#else:
# self.showMessage("Cannot find a classifier!")
#db.close()
#with open(filen, 'wb') as f:
# self.clssfr = pickle.load(f)
datfn = self.getFilenameToRead("Open Training Data", filter='mat')
if datfn is not None:
data = ml.VectorDataSet(datfn, labelsColumn=0)
self.clssfr = loadSVM(filen,
data) ## Why do I need to feed data ???
#self.clssfr = loadSVM(filen,None) ## edited PyML for this
# classifier has been loaded. need to update button status
self.setDisabledBtns()
self.showMessage("The classifier has been loaded!")
def on_store_clicked(self, widget):
if self.clssfr is not None:
filen = self.getFilenameToRead("Store Classifier",
True,
filter='svm')
if filen is not None:
#with open(filen, 'wb') as f:
# pickle.dump(self.clssfr,f)
#db = shelve.open(filen)
#db['clssfr'] = self.clssfr
#db.close()
self.clssfr.save(filen)
self.showMessage("The classifier has been saved!")
else:
self.showMessage("There is no trained classifier!")
###############################################################################
#
# Display Globe
#
def on_right_clicked(self, widget):
self.lon += 10
# rotate Globe
def on_left_clicked(self, widget):
self.lon -= 10
# rotate Globe
def gridsize_changed_cb(self, widget):
model = widget.get_model()
index = widget.get_active()
if index > -1:
self.gridsize = model[index][0]
self.redraw()
def on_gridopt_toggled(self, widget):
self.gridopt = not self.gridopt
self.redraw()
def on_Hurricane_toggled(self, widget):
self.hurricaneopt = not self.hurricaneopt
self.redraw()
def on_detected_toggled(self, widget):
self.detectedopt = not self.detectedopt
self.redraw()
class HurricaneUI:
def __init__(self):
gladefile = "HurricaneUI.glade"
builder = gtk.Builder()
builder.add_from_file(gladefile)
self.window = builder.get_object("mainWindow")
builder.connect_signals(self)
self.figure = Figure(figsize=(10,10), dpi=75)
self.axis = self.figure.add_subplot(111)
self.lat = 50
self.lon = -100
self.globe= globDisp.GlobeMap(self.axis, self.lat, self.lon)
self.canvas = FigureCanvasGTK(self.figure)
self.canvas.show()
self.canvas.set_size_request(500,500)
self.globeview = builder.get_object("map")
self.globeview.pack_start(self.canvas, True, True)
self.navToolbar = NavigationToolbar(self.canvas, self.globeview)
self.navToolbar.lastDir = '/var/tmp'
self.globeview.pack_start(self.navToolbar)
self.navToolbar.show()
self.gridcombo = builder.get_object("gridsize")
cell=gtk.CellRendererText()
self.gridcombo.pack_start(cell,True)
self.gridcombo.add_attribute(cell, 'text', 0)
#self.gridcombo.set_active(2)
# read menu configuration
self.gridopt = builder.get_object("gridopt").get_active()
self.chkDetected = builder.get_object("detectedopt")
self.detectedopt = self.chkDetected.get_active()
self.chkHurricane = builder.get_object("hurricaneopt")
self.hurricaneopt = self.chkHurricane.get_active()
model = builder.get_object("liststore1")
index = self.gridcombo.get_active()
self.gridsize = model[index][0]
radio = [ r for r in builder.get_object("classifieropt1").get_group() if r.get_active() ][0]
self.sClassifier = radio.get_label()
self.start = builder.get_object("startdate")
self.end = builder.get_object("enddate")
self.chkUndersample = builder.get_object("undersample")
self.chkGenKey = builder.get_object("genKey")
# disable unimplemented classifier selection
builder.get_object("classifieropt2").set_sensitive(False)
builder.get_object("classifieropt3").set_sensitive(False)
builder.get_object("classifieropt4").set_sensitive(False)
self.btnStore = builder.get_object("store")
self.datapath = 'GFSdat'
self.trackpath = 'tracks'
builder.get_object("btnDatapath").set_current_folder(self.datapath)
builder.get_object("btnTrackpath").set_current_folder(self.trackpath)
self.btnDetect = builder.get_object("detect")
# current operation status
self.stormlocs = None
self.detected = None
self.clssfr = None
# for test drawing functions
if os.path.exists('demo.detected'):
with open('demo.detected','r') as f:
self.detected = pickle.load(f)
self.stormlocs = pickle.load(f)
self.chkHurricane.set_label(str(self.stormlocs.shape[0])+" Hurricanes")
self.chkDetected.set_label(str(self.detected.shape[0])+" Detected")
self.setDisabledBtns()
# draw Globe
self.drawGlobe()
def setDisabledBtns(self):
self.chkDetected.set_sensitive(self.detected!=None)
self.chkHurricane.set_sensitive(self.stormlocs!=None)
self.btnStore.set_sensitive(self.clssfr!=None)
self.btnDetect.set_sensitive(self.clssfr!=None)
def drawGlobe(self):
self.globe.drawGlobe(self.gridsize, self.gridopt)
if self.hurricaneopt : self.globe.drawHurricanes(self.stormlocs)
if self.detectedopt : self.globe.fillGrids(self.detected)
def main(self):
self.window.show_all()
gtk.main()
def redraw(self):
self.axis.cla()
self.drawGlobe()
self.canvas.draw_idle()
def gtk_main_quit(self,widget):
gtk.main_quit()
###############################################################################
#
# utility functions (dialogs)
#
def getFilenameToRead(self, stitle, save=False, filter='all'):
chooser = gtk.FileChooserDialog(title=stitle, parent=self.window,
action=gtk.FILE_CHOOSER_ACTION_OPEN if not save else gtk.FILE_CHOOSER_ACTION_SAVE,
buttons=(gtk.STOCK_CANCEL,gtk.RESPONSE_CANCEL,gtk.STOCK_OPEN if not save else gtk.STOCK_SAVE,gtk.RESPONSE_OK))
chooser.set_default_response(gtk.RESPONSE_OK)
if filter=='mat' or filter=='mdat':
filter = gtk.FileFilter()
filter.set_name("Matrix files")
filter.add_pattern("*.mat")
chooser.add_filter(filter)
if filter=='svm':
filter = gtk.FileFilter()
filter.set_name("SVM")
filter.add_pattern("*.svm")
chooser.add_filter(filter)
if filter=='dat' or filter=='mdat':
filter = gtk.FileFilter()
filter.set_name("Data")
filter.add_pattern("*.dat")
chooser.add_filter(filter)
filter = gtk.FileFilter()
filter.set_name("All files")
filter.add_pattern("*")
chooser.add_filter(filter)
chooser.set_current_folder(os.getcwd())
response = chooser.run()
if response == gtk.RESPONSE_OK:
filen = chooser.get_filename()
else: filen = None
chooser.destroy()
return filen
def showMessage(self,msg):
md = gtk.MessageDialog(self.window, gtk.DIALOG_DESTROY_WITH_PARENT, gtk.MESSAGE_INFO,
gtk.BUTTONS_CLOSE, msg)
md.run()
md.destroy()
###############################################################################
#
# read data files and convert into a training matrix input
#
def on_btnTrackpath_current_folder_changed(self,widget):
self.trackpath = widget.get_current_folder()
def on_btnDatapath_current_folder_changed(self,widget):
self.datapath = widget.get_current_folder()
def on_createMat_clicked(self,widget):
filen = self.getFilenameToRead("Save converted matrix for training", True, filter='mat')
if filen is not None:
start = self.start.get_text()
end = self.end.get_text()
bundersmpl = self.chkUndersample.get_active()
bgenkey = self.chkGenKey.get_active()
### FIX ME: currently gridsize for classification is fixed 1 (no clustering of grids - 2x2)
if os.path.exists(filen): os.unlink(filen) # createMat append existing file, so delete it if exist
gdtool.createMat(self.datapath, self.trackpath, start, end, store=filen,
undersample=bundersmpl, genkeyf=bgenkey)
self.showMessage("Matrix has been stored to "+filen)
###############################################################################
#
# train the selected classifier
#
def on_train_clicked(self, widget):
# FOR NOW, only SVM is supported
if self.sClassifier == "SVM":
filen = self.getFilenameToRead("Open training data",filter='mat')
if filen is not None:
data = ml.VectorDataSet(filen,labelsColumn=0)
self.clssfr = ml.SVM()
self.clssfr.train(data)
# train finished. need to update button status
self.setDisabledBtns()
self.showMessage("Training SVM is done.")
else :
self.showMessage("The classifier is not supported yet!")
def on_classifieropt_toggled(self,widget, data=None):
self.sClassifier = widget.get_label()
###############################################################################
#
# Classify on test data
#
def on_detect_clicked(self, widget):
if self.clssfr is not None:
filen = self.getFilenameToRead("Open hurricane data", filter='mdat')
if filen is not None:
fname = os.path.basename(filen)
key, ext = os.path.splitext(fname)
if ext == '.dat':
key = key[1:] # take 'g' out
#testData = gdtool.createMat(self.datapath, self.trackpath, key, key)
#result = self.clssfr.test(ml.VectorDataSet(testData,labelsColumn=0))
tmpfn = 'f__tmpDetected__'
if os.path.exists(tmpfn): os.unlink(tmpfn)
# for DEMO, undersampled the normal data -- without undersampling there are too many candidates
gdtool.createMat(self.datapath, self.trackpath, key, key, store=tmpfn, undersample=True, genkeyf=True)
bneedDel = True
else:
tmpfn = fname
bneedDel = False
result = self.clssfr.test(ml.VectorDataSet(tmpfn,labelsColumn=0))
gdkeyfilen = ''.join([tmpfn,'.keys'])
with open(gdkeyfilen, 'r') as f:
gridkeys = pickle.load(f)
self.stormlocs = pickle.load(f)
predicted = result.getPredictedLabels()
predicted = np.array(map(float,predicted))
self.detected = np.array(gridkeys)[predicted==1]
if bneedDel:
os.unlink(tmpfn)
os.unlink(gdkeyfilen)
snstroms = str(self.stormlocs.shape[0])
sndetected = str(self.detected.shape[0])
self.chkHurricane.set_label(snstroms+" Hurricanes")
self.chkDetected.set_label(sndetected+" Detected")
self.showMessage(''.join([sndetected,"/",snstroms," grids are predicted to have hurricane."]))
if False:
with open('demo.detected','w') as f:
pickle.dump(self.detected,f)
pickle.dump(self.stormlocs,f)
# test data tested. update buttons
self.setDisabledBtns()
self.redraw()
else:
self.showMessage("There is no trained classifier!")
###############################################################################
#
# load and store trained classifier
#
def on_load_clicked(self, widget):
filen = self.getFilenameToRead("Load Classifier",filter='svm')
if filen is not None:
#db = shelve.open(filen)
#if db.has_key('clssfr'):
# self.clssfr = db['clssfr']
#else:
# self.showMessage("Cannot find a classifier!")
#db.close()
#with open(filen, 'wb') as f:
# self.clssfr = pickle.load(f)
datfn = self.getFilenameToRead("Open Training Data",filter='mat')
if datfn is not None:
data = ml.VectorDataSet(datfn,labelsColumn=0)
self.clssfr = loadSVM(filen,data) ## Why do I need to feed data ???
#self.clssfr = loadSVM(filen,None) ## edited PyML for this
# classifier has been loaded. need to update button status
self.setDisabledBtns()
self.showMessage("The classifier has been loaded!")
def on_store_clicked(self, widget):
if self.clssfr is not None:
filen = self.getFilenameToRead("Store Classifier", True, filter='svm')
if filen is not None:
#with open(filen, 'wb') as f:
# pickle.dump(self.clssfr,f)
#db = shelve.open(filen)
#db['clssfr'] = self.clssfr
#db.close()
self.clssfr.save(filen)
self.showMessage("The classifier has been saved!")
else:
self.showMessage("There is no trained classifier!")
###############################################################################
#
# Display Globe
#
def on_right_clicked(self,widget):
self.lon += 10
# rotate Globe
def on_left_clicked(self,widget):
self.lon -= 10
# rotate Globe
def gridsize_changed_cb(self, widget):
model = widget.get_model()
index = widget.get_active()
if index > -1:
self.gridsize = model[index][0]
self.redraw()
def on_gridopt_toggled(self, widget):
self.gridopt = not self.gridopt
self.redraw()
def on_Hurricane_toggled(self, widget):
self.hurricaneopt = not self.hurricaneopt
self.redraw()
def on_detected_toggled(self, widget):
self.detectedopt = not self.detectedopt
self.redraw()
