def visualize(self, obj, **kwargs):
model = PKPDAllometricScale()
model.load(obj.fnScale.get())
x = np.log10(np.asarray(model.X))
xlabel = "%s [%s]" % (model.predictor, model.predictorUnits)
for varName, varUnits in model.scaled_vars:
plotter = EmPlotter()
y = np.log10(np.asarray(model.Y[varName]))
ylabel = "%s [%s]" % (varName, varUnits)
ax = plotter.createSubPlot(varName, xlabel, ylabel)
ax.plot(x, y, '.', label='Species')
ax.plot(x, np.log10(model.models[varName][0])+x*model.models[varName][1],'r', label='R2=%f'%model.qualifiers[varName][0])
leg = ax.legend(loc='upper right')
if leg:
leg.draggable()
plotter.show()
for varName, varUnits in model.averaged_vars:
plotter = EmPlotter()
y = np.asarray(model.Y[varName])
ylabel = "%s [%s]" % (varName, varUnits)
ax = plotter.createSubPlot("Scatter Plot", xlabel, ylabel)
ax.plot(x, y, '.', label='Species')
ax.plot(x, model.models[varName][0]*np.ones(x.shape),'r',label='Std=%f'%model.qualifiers[varName][0])
leg = ax.legend(loc='upper right')
if leg:
leg.draggable()
plotter.show()
def visualize(self, obj, **kwargs):
prot = obj
fnProfiles = prot._getPath("profiles.txt")
fh = open(fnProfiles,"r")
state = 0
legends = ['Iliver', 'Iinlet', 'Isys']
for line in fh:
if state==0:
tokens = line.split("::")
title = tokens[1].strip()
I3=[]
state=1
elif state==1:
tokens=line.strip().split()
if len(tokens)==0:
plotter = EmPlotter()
ax = plotter.createSubPlot("Simulation", "t [h]", "[I] [mg/mL]")
t = np.asarray(I3[0],dtype=np.float64)/60
for n in range(1,len(I3)):
y = np.asarray(I3[n],dtype=np.float64)
ax.plot(t, y, label=legends[n-1])
ax.legend()
plotter.show()
state=0
else:
if len(I3)==0:
for n in range(len(tokens)):
I3.append([])
for n in range(len(tokens)):
I3[n].append(tokens[n])
fh.close()
def createCtfPlot(ctfSet, ctfId):
from pyworkflow.utils.path import removeExt
ctfModel = ctfSet[ctfId]
psdFn = ctfModel.getPsdFile()
fn = removeExt(psdFn) + "_avrot.txt"
gridsize = [1, 1]
xplotter = EmPlotter(x=gridsize[0], y=gridsize[1], windowTitle='CTF Fitting')
plot_title = "CTF Fitting"
a = xplotter.createSubPlot(plot_title, 'pixels^-1', 'CTF', yformat=False)
legendName = []
for i in range(1, 5):
_plotCurve(a, i, fn)
if i == 1:
legendName.append('rotational avg. No Astg')
elif i == 2:
legendName.append('rotational avg.')
elif i == 3:
legendName.append('CTF Fit')
elif i == 4:
legendName.append('Cross Correlation')
elif i == 5:
legendName.append('2sigma cross correlation of noise')
xplotter.showLegend(legendName)
a.grid(True)
xplotter.show()
def paint(self, labels):
for label in labels:
if (label == 'standard_deviation'):
self.lines[label], = \
self.ax2.plot([], [], '-o',
label='Standard deviation',
color='r')
if (label == 'ratio1'):
self.lines[label], = \
self.ax.plot([], [], '-o',
label='97.5/2.5 percentile',
color='b')
if (label == 'ratio2'):
self.lines[label], = \
self.ax.plot([], [], '-*',
label='max/97.5 percentile',
color='b')
anim = animation.FuncAnimation(self.fig,
self.animate,
interval=self.monitor.samplingInterval *
1000) #miliseconds
self.fig.canvas.mpl_connect('scroll_event', self.onscroll)
self.fig.canvas.mpl_connect('key_press_event', self.press)
EmPlotter.show(self)
def paint(self,labels):
for label in labels:
self.lines[label],=self.ax.plot([], [], '-',label=label)
anim = animation.FuncAnimation(self.fig, self.animate,
interval=self.monitor.samplingInterval * 1000)#miliseconds
self.fig.canvas.mpl_connect('scroll_event', self.onscroll)
self.fig.canvas.mpl_connect('key_press_event', self.press)
EmPlotter.show(self)
def press(self,event):
sys.stdout.flush()
if event.key == 'S':
self.stop = True
self.ax.set_title('Plot is Stopped. Press C to continue plotting')
elif event.key == 'C':
self.ax.set_title(self._getTitle())
self.stop = False
self.animate()
EmPlotter.show(self)
def paint(self, labels):
for label in labels:
self.lines[label], = self.ax.plot([], [], '-', label=label)
anim = animation.FuncAnimation(
self.fig, self.animate,
interval=self.monitor.samplingInterval * 1000) # miliseconds
self.fig.canvas.mpl_connect('scroll_event', self.onscroll)
self.fig.canvas.mpl_connect('key_press_event', self.press)
EmPlotter.show(self)
def press(self,event):
sys.stdout.flush()
if event.key == 'S':
self.stop = True
self.ax.set_title('Plot is Stopped. Press C to continue plotting')
elif event.key == 'C':
self.ax.set_title(self._getTitle())
self.stop = False
self.animate()
EmPlotter.show(self)
def _visualizeHistogram(self, e=None):
views = []
numberOfBins = self.visualizeHistogram.get()
numberOfBins = min(numberOfBins, self.protocol.outputCTF.getSize())
plotter = EmPlotter()
plotter.createSubPlot("Resolution Discrepancies histogram",
"Resolution (A)", "# of Comparisons")
resolution = [ctf.getResolution() for ctf in self.protocol.outputCTF]
plotter.plotHist(resolution, nbins=numberOfBins)
# ROB: why do I need this show?
plotter.show()
return views.append(plotter)
def _plot(varLabelX, varLabelY, x, y, yp):
plotter = EmPlotter()
ax = plotter.createSubPlot("Plot", varLabelX, varLabelY)
xValues = _values(x, useLog=self.useTimeLog())
ax.plot(xValues, _values(y), 'x', label="Observations")
idx = np.argsort(xValues)
ypValues = _values(yp)
ax.plot(np.asarray(xValues)[idx], np.asarray(ypValues)[idx], 'g', label="Fit")
leg = ax.legend()
if leg:
leg.draggable()
plotter.show()
def onscroll(self, event):
if event.button == 'up':
self.win += self.step
else:
self.win -= self.step
if self.win < self.step:
self.win = self.step
if self.oldWin != self.win:
self.ax.set_title(self._getTitle())
self.oldWin= self.win
self.animate()
EmPlotter.show(self)
def onscroll(self, event):
if event.button == 'up':
self.win += self.step
else:
self.win -= self.step
if self.win < self.step:
self.win = self.step
if self.oldWin != self.win:
self.ax.set_title(self._getTitle())
self.oldWin = self.win
self.animate()
EmPlotter.show(self)
def createCtfPlot(ctfSet, ctfId):
ctfModel = ctfSet[ctfId]
psdFn = ctfModel.getPsdFile()
fn = removeExt(psdFn) + "_avrot.txt"
gridsize = [1, 1]
xplotter = EmPlotter(x=gridsize[0], y=gridsize[1], windowTitle='CTF Fitting')
plot_title = "CTF Fitting"
a = xplotter.createSubPlot(plot_title, 'pixels^-1', 'CTF', yformat=False)
legendName = ['rotational avg. No Astg',
'rotational avg.',
'CTF Fit',
'Cross Correlation',
'2sigma cross correlation of noise']
for i in range(1, 6):
_plotCurve(a, i, fn)
xplotter.showLegend(legendName)
a.grid(True)
xplotter.show()
def createCtfPlot(ctfSet, ctfId):
ctfModel = ctfSet[ctfId]
psdFn = ctfModel.getPsdFile()
fn = pwutils.removeExt(psdFn) + "_EPA.txt"
gridsize = [1, 1]
xplotter = EmPlotter(x=gridsize[0], y=gridsize[1],
windowTitle='CTF Fitting')
plot_title = "CTF Fitting"
a = xplotter.createSubPlot(plot_title, 'Resolution (Angstroms)', 'CTF',
yformat=False)
a.invert_xaxis()
for i in range(1, 5):
_plotCurve(a, i, fn)
xplotter.showLegend(['simulated CTF',
'equiphase avg.',
'equiphase avg. - bg',
'cross correlation'])
a.grid(True)
xplotter.show()
def createCtfPlot(ctfSet, ctfId):
ctfModel = ctfSet[ctfId]
psdFn = ctfModel.getPsdFile()
fn = removeExt(psdFn) + "_avrot.txt"
gridsize = [1, 1]
xplotter = EmPlotter(x=gridsize[0],
y=gridsize[1],
windowTitle='CTF Fitting')
plot_title = "CTF Fitting"
a = xplotter.createSubPlot(plot_title, 'pixels^-1', 'CTF', yformat=False)
legendName = [
'rotational avg. No Astg', 'rotational avg.', 'CTF Fit',
'Cross Correlation', '2sigma cross correlation of noise'
]
for i in range(1, 6):
_plotCurve(a, i, fn)
xplotter.showLegend(legendName)
a.grid(True)
xplotter.show()
def _onPlotSummaryClick(self, e=None):
sampleKeys = self.samplesTree.selection()
n = len(sampleKeys)
if n == 1:
self.showInfo("Please select several samples to plot.")
else:
if n > 1:
samples = [self.experiment.samples[k] for k in sampleKeys]
else:
samples = self.experiment.samples.values()
dataDict = {} # key will be time values
for s in samples:
xValues, yValues = self.getPlotValues(s)
for x, y in izip(xValues, yValues):
if x in dataDict:
dataDict[x].append(y)
else:
dataDict[x] = [y]
sortedTime = sorted(dataDict.keys())
# We will store five values (min, 25%, 50%, 75%, max)
# for each of the time entries computed
percentileList = [0, 25, 50, 75, 100]
Y = np.zeros((len(sortedTime), 5))
for i, t in enumerate(sortedTime):
Y[i,:] = np.percentile(dataDict[t], percentileList)
plotter = EmPlotter()
ax = plotter.createSubPlot("Summary Plot", self.getTimeLabel(),
self.getMeasureLabel())
ax.plot(sortedTime, Y[:, 0], 'r--', label="Minimum")
ax.plot(sortedTime, Y[:, 1], 'b--', label="25%")
ax.plot(sortedTime, Y[:, 2], 'g', label="50% (Median)")
ax.plot(sortedTime, Y[:, 3], 'b--', label="75%")
ax.plot(sortedTime, Y[:, 4], 'r--', label="Maximum")
ax.legend()
plotter.show()
def createCtfPlot(ctfSet, ctfId):
ctfModel = ctfSet[ctfId]
psdFn = ctfModel.getPsdFile()
fn = pwutils.removeExt(psdFn) + "_EPA.txt"
gridsize = [1, 1]
xplotter = EmPlotter(x=gridsize[0],
y=gridsize[1],
windowTitle='CTF Fitting')
plot_title = "CTF Fitting"
a = xplotter.createSubPlot(plot_title,
'Resolution (Angstroms)',
'CTF',
yformat=False)
a.invert_xaxis()
for i in range(1, 5):
_plotCurve(a, i, fn)
xplotter.showLegend([
'simulated CTF', 'equiphase avg.', 'equiphase avg. - bg',
'cross correlation'
])
a.grid(True)
xplotter.show()
def paint(self, labels):
for label in labels:
if (label == 'standard_deviation'):
self.lines[label], = \
self.ax2.plot([], [], '-o',
label='Standard deviation',
color='r')
if (label == 'ratio1'):
self.lines[label], = \
self.ax.plot([], [], '-o',
label='97.5/2.5 percentile',
color='b')
if (label == 'ratio2'):
self.lines[label], = \
self.ax.plot([], [], '-*',
label='max/97.5 percentile',
color='b')
anim = animation.FuncAnimation(self.fig, self.animate,
interval=self.monitor.samplingInterval*1000)#miliseconds
self.fig.canvas.mpl_connect('scroll_event', self.onscroll)
self.fig.canvas.mpl_connect('key_press_event', self.press)
EmPlotter.show(self)
def visualize(self, obj, **kwargs):
model = PKPDAllometricScale()
model.load(obj.fnScale.get())
x = np.log10(np.asarray(model.X))
xlabel = "%s [%s]" % (model.predictor, model.predictorUnits)
for varName, varUnits in model.scaled_vars:
plotter = EmPlotter()
y = np.log10(np.asarray(model.Y[varName]))
ylabel = "%s [%s]" % (varName, varUnits)
ax = plotter.createSubPlot(varName, xlabel, ylabel)
ax.plot(x, y, '.', label='Species')
ax.plot(x,
np.log10(model.models[varName][0]) +
x * model.models[varName][1],
'r',
label='R2=%f' % model.qualifiers[varName][0])
leg = ax.legend(loc='upper right')
if leg:
leg.draggable()
plotter.show()
for varName, varUnits in model.averaged_vars:
plotter = EmPlotter()
y = np.asarray(model.Y[varName])
ylabel = "%s [%s]" % (varName, varUnits)
ax = plotter.createSubPlot("Scatter Plot", xlabel, ylabel)
ax.plot(x, y, '.', label='Species')
ax.plot(x,
model.models[varName][0] * np.ones(x.shape),
'r',
label='Std=%f' % model.qualifiers[varName][0])
leg = ax.legend(loc='upper right')
if leg:
leg.draggable()
plotter.show()
def _onPlotClick(self, e=None):
selection = self.tree.selection()
n = len(selection)
if n < 1 or n > 2:
self.showError("Select one or two variables to plot.")
else:
plotter = EmPlotter()
varX = self._variables[selection[0]]
xValues = self.observations[:, varX.index]
def _label(var):
return "%s [%s]" % (var.varName, var.unitStr)
if n == 1:
plotter.createSubPlot("Histogram", _label(varX), "Count")
plotter.plotHist(xValues, 50)
else: # n == 2
varY = self._variables[selection[1]]
yValues = self.observations[:, varY.index]
ax = plotter.createSubPlot("Scatter Plot",
_label(varX), _label(varY))
ax.plot(xValues, yValues, '.')
plotter.show()
def _onPlotClick(self, e=None):
selection = self.tree.selection()
n = len(selection)
if n < 1 or n > 2:
self.showError("Select one or two variables to plot.")
else:
plotter = EmPlotter()
varX = self._variables[selection[0]]
xValues = self.observations[:, varX.index]
def _label(var):
return "%s [%s]" % (var.varName, var.unitStr)
if n == 1:
plotter.createSubPlot("Histogram", _label(varX), "Count")
plotter.plotHist(xValues, 50)
else: # n == 2
varY = self._variables[selection[1]]
yValues = self.observations[:, varY.index]
ax = plotter.createSubPlot("Scatter Plot", _label(varX),
_label(varY))
ax.plot(xValues, yValues, '.')
plotter.show()
class PKPDResponsiveDialog(dialog.Dialog):
def __init__(self, parent, title, **kwargs):
""" From kwargs:
message: message tooltip to show when browsing.
selected: the item that should be selected.
validateSelectionCallback: a callback function to validate selected items.
"""
self.values = []
self.plotter = None
self.targetProtocol = kwargs['targetProtocol']
self.experiment = self.targetProtocol.experiment
self.varNameX = kwargs['varNameX']
self.varNameY = kwargs['varNameY']
self.provider = SamplesTreeProvider(self.experiment)
self.model = self.loadModel()
self.validateSelectionCallback = kwargs.get(
'validateSelectionCallback', None)
self.setLabels()
dialog.Dialog.__init__(self,
parent,
title,
buttons=[('Select', dialog.RESULT_YES),
('Cancel', dialog.RESULT_CANCEL)])
def setLabels(self):
pass
def loadModel(self):
model = self.targetProtocol.createModel()
model.setExperiment(self.experiment)
# if hasattr(self.protODE, "deltaT"):
# model.deltaT = self.protODE.deltaT.get()
model.setXVar(self.varNameX)
model.setYVar(self.varNameY)
return model
def body(self, bodyFrame):
bodyFrame.config(bg='white')
gui.configureWeigths(bodyFrame)
self._createSamplesFrame(bodyFrame)
self._createSlidersFrame(bodyFrame)
self._createLogsFrame(bodyFrame)
def _createSamplesFrame(self, content):
frame = tk.Frame(content, bg='white')
#frame = tk.LabelFrame(content, text='General')
lfSamples = tk.LabelFrame(frame, text="Samples", bg='white')
gui.configureWeigths(frame)
lfSamples.grid(row=0, column=0, sticky='news', padx=5, pady=5)
self.samplesTree = self._addBoundTree(lfSamples, self.provider, 10)
self.samplesTree.itemClick = self._onSampleChanged
frame.grid(row=0, column=0, sticky='news', padx=5, pady=5)
def _createSlidersFrame(self, content):
frame = tk.Frame(content, bg='white')
lfBounds = tk.LabelFrame(frame, text="Parameter Bounds", bg='white')
gui.configureWeigths(frame)
i = 0
self.sliders = {}
paramUnits = self.targetProtocol.parameterUnits
for paramName, bounds in self.targetProtocol.getParameterBounds(
).iteritems():
bounds = bounds or (0, 1)
slider = MinMaxSlider(lfBounds,
"%s [%s]" %
(paramName, strUnit(paramUnits[i])),
bounds[0],
bounds[1],
callback=self._onVarChanged)
slider.grid(row=i, column=0, padx=5, pady=5)
self.sliders[paramName] = slider
i += 1
lfBounds.grid(row=0, column=0, sticky='news', padx=5, pady=5)
frame.grid(row=0, column=1, sticky='news', padx=5, pady=5)
def _createLogsFrame(self, content):
frame = tk.Frame(content)
def addVar(text, col, varName):
varFrame = tk.Frame(frame)
varFrame.grid(row=0, column=col, sticky='new')
label = tk.Label(varFrame, text=text) #, font=self.fontBold)
label.grid(row=0, column=0, padx=5, pady=2, sticky='nw')
combo = tk.Label(varFrame, text=varName, width=10)
combo.grid(row=0, column=1, sticky='nw', padx=5, pady=5)
radioVar = tk.IntVar()
radio = tk.Checkbutton(varFrame, text='Log10', variable=radioVar)
radio.grid(row=0, column=2, sticky='nw', padx=5, pady=5)
return combo, radio, radioVar
self.timeWidget = addVar('Time variable', 0, self.varNameX)
self.timeWidget[2].trace('w', self._onLogChanged)
self.measureWidget = addVar('Measure variable', 1, self.varNameY)
measureVar = self.measureWidget[2]
measureVar.set(True)
measureVar.trace('w', self._onLogChanged)
frame.grid(row=1,
column=0,
columnspan=2,
sticky='news',
padx=5,
pady=5)
def _addBoundTree(self, parent, provider, height):
bt = BoundTree(parent, provider, height=height)
bt.grid(row=0, column=0, sticky='news', padx=5, pady=5)
gui.configureWeigths(parent)
return bt
def apply(self):
self.values = []
def _onVarChanged(self, *args):
sampleKeys = self.samplesTree.selection()
if sampleKeys:
self.computeFit()
self.plotResults()
else:
dialog.showInfo("Warning", "Please select some sample(s) to plot.",
self)
def computeFit(self):
currentParams = []
for paramName in self.targetProtocol.getParameterNames():
currentParams.append(self.sliders[paramName].getValue())
self.targetProtocol.setParameters(currentParams)
self.ypValues = self.targetProtocol.forwardModel(
currentParams, self.xpValues)
def getBoundsList(self):
boundList = []
for paramName in self.targetProtocol.getParameterNames():
boundList.append(self.sliders[paramName].getMinMax())
return boundList
def useTimeLog(self):
return self.timeWidget[2].get()
def useMeasureLog(self):
return self.measureWidget[2].get()
def getUnits(self, varName):
return self.experiment.variables[varName].getUnitsString()
def getLabel(self, varName, useLog):
varLabel = '%s [%s]' % (varName, self.getUnits(varName))
if useLog:
varLabel = "log10(%s)" % varLabel
return varLabel
def getTimeLabel(self):
return self.getLabel(self.varNameX, self.useTimeLog())
def getMeasureLabel(self):
return self.getLabel(self.varNameY, self.useMeasureLog())
def computePlotValues(self, xValues, yValues):
useMeasureLog = self.useMeasureLog()
useTimeLog = self.useTimeLog()
if not (useMeasureLog or useTimeLog):
newXValues = xValues
newYValues = yValues
else:
# If log will be used either for time or measure var
# we need to filter elements larger than 0
newXValues = []
newYValues = []
def _value(v, useLog):
if useLog:
return math.log10(v) if v > 0 else None
return v
for x, y in izip(xValues, yValues):
x = _value(x, useTimeLog)
y = _value(y, useMeasureLog)
if x is not None and y is not None:
newXValues.append(x)
newYValues.append(y)
return newXValues, newYValues
def _updateModel(self):
""" This function should be called whenever the sample changes """
pass
def _onLogChanged(self, *args):
# We will treat a log change as a sample change to plot
self._onSampleChanged()
def _onSampleChanged(self, e=None):
sampleKeys = self.samplesTree.selection()
if sampleKeys:
# When the sample is changed we need to re-compute (with log or not)
# the x, y values
self.sample = self.experiment.samples[sampleKeys[0]]
self.xValues, self.yValues = self.sample.getXYValues(
self.varNameX, self.varNameY)
self.newXValues, self.newYValues = self.computePlotValues(
self.xValues[0], self.yValues[0])
self._updateModel()
self.computeFit()
self.plotResults()
else:
dialog.showInfo("Warning", "Please select some sample(s) to plot.",
self)
def plotResults(self):
if self.plotter is None or self.plotter.isClosed():
self.plotter = EmPlotter()
doShow = True
else:
doShow = False
ax = self.plotter.getLastSubPlot()
self.plotter.clear()
ax = self.plotter.createSubPlot("Sample: %s" % self.sample.sampleName,
self.getTimeLabel(),
self.getMeasureLabel())
self.newXPValues, self.newYPValues = self.computePlotValues(
self.xpValues[0], self.ypValues[0])
ax.plot(self.newXValues, self.newYValues, 'x', label="Observations")
ax.plot(self.newXPValues, self.newYPValues, label="Fit")
ax.legend()
if doShow:
self.plotter.show()
else:
self.plotter.draw()
def destroy(self):
"""Destroy the window"""
if not (self.plotter is None or self.plotter.isClosed()):
self.plotter.close()
dialog.Dialog.destroy(self)
class PKPDResponsiveDialog(dialog.Dialog):
def __init__(self, parent, title, **kwargs):
""" From kwargs:
message: message tooltip to show when browsing.
selected: the item that should be selected.
validateSelectionCallback: a callback function to validate selected items.
"""
self.values = []
self.plotter = None
self.targetProtocol = kwargs['targetProtocol']
self.experiment = self.targetProtocol.experiment
self.varNameX = kwargs['varNameX']
self.varNameY = kwargs['varNameY']
self.provider = SamplesTreeProvider(self.experiment)
self.model = self.loadModel()
self.validateSelectionCallback = kwargs.get('validateSelectionCallback', None)
self.setLabels()
dialog.Dialog.__init__(self, parent, title,
buttons=[('Select', dialog.RESULT_YES),
('Cancel', dialog.RESULT_CANCEL)])
def setLabels(self):
pass
def loadModel(self):
model = self.targetProtocol.createModel()
model.setExperiment(self.experiment)
# if hasattr(self.protODE, "deltaT"):
# model.deltaT = self.protODE.deltaT.get()
model.setXVar(self.varNameX)
model.setYVar(self.varNameY)
return model
def body(self, bodyFrame):
bodyFrame.config(bg='white')
gui.configureWeigths(bodyFrame)
self._createSamplesFrame(bodyFrame)
self._createSlidersFrame(bodyFrame)
self._createLogsFrame(bodyFrame)
def _createSamplesFrame(self, content):
frame = tk.Frame(content, bg='white')
#frame = tk.LabelFrame(content, text='General')
lfSamples = tk.LabelFrame(frame, text="Samples", bg='white')
gui.configureWeigths(frame)
lfSamples.grid(row=0, column=0, sticky='news', padx=5, pady=5)
self.samplesTree = self._addBoundTree(lfSamples,
self.provider, 10)
self.samplesTree.itemClick = self._onSampleChanged
frame.grid(row=0, column=0, sticky='news', padx=5, pady=5)
def _createSlidersFrame(self, content):
frame = tk.Frame(content, bg='white')
lfBounds = tk.LabelFrame(frame, text="Parameter Bounds", bg='white')
gui.configureWeigths(frame)
i = 0
self.sliders = {}
paramUnits = self.targetProtocol.parameterUnits
for paramName, bounds in self.targetProtocol.getParameterBounds().iteritems():
bounds = bounds or (0, 1)
slider = MinMaxSlider(lfBounds, "%s [%s]"%(paramName,strUnit(paramUnits[i])),
bounds[0], bounds[1],
callback=self._onVarChanged)
slider.grid(row=i, column=0, padx=5, pady=5)
self.sliders[paramName] = slider
i += 1
lfBounds.grid(row=0, column=0, sticky='news', padx=5, pady=5)
frame.grid(row=0, column=1, sticky='news', padx=5, pady=5)
def _createLogsFrame(self, content):
frame = tk.Frame(content)
def addVar(text, col, varName):
varFrame = tk.Frame(frame)
varFrame.grid(row=0, column=col, sticky='new')
label = tk.Label(varFrame, text=text)#, font=self.fontBold)
label.grid(row=0, column=0, padx=5, pady=2, sticky='nw')
combo = tk.Label(varFrame, text=varName, width=10)
combo.grid(row=0, column=1, sticky='nw', padx=5, pady=5)
radioVar = tk.IntVar()
radio = tk.Checkbutton(varFrame, text='Log10', variable=radioVar)
radio.grid(row=0, column=2, sticky='nw', padx=5, pady=5)
return combo, radio, radioVar
self.timeWidget = addVar('Time variable', 0, self.varNameX)
self.timeWidget[2].trace('w', self._onLogChanged)
self.measureWidget = addVar('Measure variable', 1, self.varNameY)
measureVar = self.measureWidget[2]
measureVar.set(True)
measureVar.trace('w', self._onLogChanged)
frame.grid(row=1, column=0, columnspan=2, sticky='news', padx=5, pady=5)
def _addBoundTree(self, parent, provider, height):
bt = BoundTree(parent, provider, height=height)
bt.grid(row=0, column=0, sticky='news', padx=5, pady=5)
gui.configureWeigths(parent)
return bt
def apply(self):
self.values = []
def _onVarChanged(self, *args):
sampleKeys = self.samplesTree.selection()
if sampleKeys:
self.computeFit()
self.plotResults()
else:
dialog.showInfo("Warning","Please select some sample(s) to plot.",self)
def computeFit(self):
currentParams = []
for paramName in self.targetProtocol.getParameterNames():
currentParams.append(self.sliders[paramName].getValue())
self.targetProtocol.setParameters(currentParams)
self.ypValues = self.targetProtocol.forwardModel(currentParams, self.xpValues)
def getBoundsList(self):
boundList = []
for paramName in self.targetProtocol.getParameterNames():
boundList.append(self.sliders[paramName].getMinMax())
return boundList
def useTimeLog(self):
return self.timeWidget[2].get()
def useMeasureLog(self):
return self.measureWidget[2].get()
def getUnits(self, varName):
return self.experiment.variables[varName].getUnitsString()
def getLabel(self, varName, useLog):
varLabel = '%s [%s]' % (varName, self.getUnits(varName))
if useLog:
varLabel = "log10(%s)" % varLabel
return varLabel
def getTimeLabel(self):
return self.getLabel(self.varNameX, self.useTimeLog())
def getMeasureLabel(self):
return self.getLabel(self.varNameY, self.useMeasureLog())
def computePlotValues(self, xValues, yValues):
useMeasureLog = self.useMeasureLog()
useTimeLog = self.useTimeLog()
if not (useMeasureLog or useTimeLog):
newXValues = xValues
newYValues = yValues
else:
# If log will be used either for time or measure var
# we need to filter elements larger than 0
newXValues = []
newYValues = []
def _value(v, useLog):
if useLog:
return math.log10(v) if v > 0 else None
return v
for x, y in izip(xValues, yValues):
x = _value(x, useTimeLog)
y = _value(y, useMeasureLog)
if x is not None and y is not None:
newXValues.append(x)
newYValues.append(y)
return newXValues, newYValues
def _updateModel(self):
""" This function should be called whenever the sample changes """
pass
def _onLogChanged(self, *args):
# We will treat a log change as a sample change to plot
self._onSampleChanged()
def _onSampleChanged(self, e=None):
sampleKeys = self.samplesTree.selection()
if sampleKeys:
# When the sample is changed we need to re-compute (with log or not)
# the x, y values
self.sample = self.experiment.samples[sampleKeys[0]]
self.xValues, self.yValues = self.sample.getXYValues(self.varNameX,
self.varNameY)
self.newXValues, self.newYValues = self.computePlotValues(self.xValues[0],
self.yValues[0])
self._updateModel()
self.computeFit()
self.plotResults()
else:
dialog.showInfo("Warning","Please select some sample(s) to plot.",self)
def plotResults(self):
if self.plotter is None or self.plotter.isClosed():
self.plotter = EmPlotter()
doShow = True
else:
doShow = False
ax = self.plotter.getLastSubPlot()
self.plotter.clear()
ax = self.plotter.createSubPlot("Sample: %s" % self.sample.sampleName,
self.getTimeLabel(),
self.getMeasureLabel())
self.newXPValues, self.newYPValues = self.computePlotValues(self.xpValues[0],
self.ypValues[0])
ax.plot(self.newXValues, self.newYValues, 'x', label="Observations")
ax.plot(self.newXPValues, self.newYPValues, label="Fit")
ax.legend()
if doShow:
self.plotter.show()
else:
self.plotter.draw()
def destroy(self):
"""Destroy the window"""
if not (self.plotter is None or self.plotter.isClosed()):
self.plotter.close()
dialog.Dialog.destroy(self)
def press(self, event):
def numericKey(key):
self.colorChanged = True
number = int(key)
index = 3+number*3
if (index + 3) > self.lenPlots:
return
if self.color['gpuMem_%d' % number] != 'w':
self.oldColor['gpuMem_%d' % number] = \
self.color['gpuMem_%d' % number]
self.oldColor['gpuUse_%d' % number] = \
self.color['gpuUse_%d' % number]
self.oldColor['gpuTem_%d' % number] = \
self.color['gpuTem_%d' % number]
self.color['gpuMem_%d' % number] = "w"
self.color['gpuUse_%d' % number] = "w"
self.color['gpuTem_%d' % number] = "w"
else:
self.color['gpuMem_%d' % number] = \
self.oldColor['gpuMem_%d' % number]
self.color['gpuUse_%d' % number] = \
self.oldColor['gpuUse_%d' % number]
self.color['gpuTem_%d' % number] = \
self.oldColor['gpuTem_%d' % number]
def cpuKey(key):
self.colorChanged = True
if self.color['cpu'] != 'w':
self.oldColor['cpu'] = self.color['cpu']
self.oldColor['mem'] = self.color['mem']
self.oldColor['swap'] = self.color['swap']
self.color['cpu'] = "w"
self.color['mem'] = "w"
self.color['swap'] = "w"
else:
self.color['cpu'] = self.oldColor['cpu']
self.color['swap'] = self.oldColor['swap']
self.color['mem'] = self.oldColor['mem']
def netKey(key):
self.colorChanged = True
if self.color['%s_send' % self.nifName] != 'w':
self.oldColor['%s_send' % self.nifName] = \
self.color['%s_send' % self.nifName]
self.oldColor['%s_recv' % self.nifName] = \
self.color['%s_recv' % self.nifName]
self.color['%s_send' % self.nifName] = "w"
self.color['%s_recv' % self.nifName] = "w"
else:
self.color['%s_send' % self.nifName] = \
self.oldColor['%s_send' % self.nifName]
self.color['%s_recv' % self.nifName] = \
self.oldColor['%s_recv' % self.nifName]
def diskKey(key):
self.colorChanged = True
if self.color['disk_read'] != 'w':
self.oldColor['disk_read'] = self.color['disk_read']
self.oldColor['disk_write'] = self.color['disk_write']
self.color['disk_read'] = "w"
self.color['disk_write'] = "w"
else:
self.color['disk_read'] = self.oldColor['disk_read']
self.color['disk_write'] = self.oldColor['disk_write']
sys.stdout.flush()
if event.key == 'S':
self.stop = True
self.ax.set_title('Plot has been Stopped. '
'Press C to continue plotting')
elif event.key == 'C':
self.ax.set_title(self._getTitle())
self.stop = False
self.animate()
elif event.key.isdigit():
numericKey(event.key)
self.animate()
elif event.key == 'c':
cpuKey(event.key)
self.animate()
elif event.key == 'n':
netKey(event.key)
self.animate()
elif event.key == 'd':
diskKey(event.key)
self.animate()
EmPlotter.show(self)
class ExperimentWindow(gui.Window):
""" This class creates a Window that will display some Point's
contained in a Data object.
It will allow to launch 1D, 2D and 3D plots by selecting any
combination of the x1, x2...xn from the Point dimension.
Points can be selected by either Click and Drag in the Scatter plot or..
by creating an Expression.
Finally, there is a button 'Create Cluster' that will call a callback
fuction to take care of it.
"""
def __init__(self, **kwargs):
gui.Window.__init__(self, minsize=(420, 200), **kwargs)
self.experiment = kwargs.get('experiment')
self.fitting = kwargs.get('fitting', None)
self.callback = kwargs.get('callback', None)
content = tk.Frame(self.root)
self._createContent(content)
content.grid(row=0, column=0, sticky='news')
content.columnconfigure(0, weight=1)
content.rowconfigure(0, weight=1)
self.plotter = None
def _createContent(self, content):
# Create and fill the frame containing the Experiment
# info, variables and doses
p = tk.PanedWindow(content, orient=tk.VERTICAL, bg='white')
p.grid(row=0, column=0, sticky='news', padx=5, pady=5)
self._createTopFrame(p)
# Create the middle frame containing the Samples Box
self._createSamplesFrame(p)
# Create the last frame with the buttons
self._createButtonsFrame(content)
#self._updateSelectionLabel()
def _createTopFrame(self, content):
frame = tk.Frame(content)
frame.columnconfigure(0, weight=1)
frame.rowconfigure(0, weight=1)
tab = ttk.Notebook(frame)
tab.grid(row=0, column=0, sticky='news', padx=5, pady=5)
def addTab(label):
lf = tk.Frame(tab)
tab.add(lf, text=label)
return lf
lfGeneral = addTab('General')
self._addLabel(lfGeneral, 'Title', 0, 0)
self._titleVar = tk.StringVar()
self._titleVar.set(self.experiment.general['title'])
titleEntry = tk.Entry(lfGeneral, width=26, textvariable=self._titleVar,
bg='white')
titleEntry.grid(row=0, column=1, sticky='nw', padx=5, pady=(5, 0))
self._addLabel(lfGeneral, 'Comment', 1, 0)
commentText = gui.text.Text(lfGeneral, width=30, height=3, bg='white')
commentText.setText(self.experiment.general['comment'])
commentText.grid(row=1, column=1, sticky='nw', padx=5, pady=(5, 0))
self._commentText = commentText
lfVars = addTab('Variables')
self.varsTree = self._addBoundTree(lfVars, VariablesTreeProvider, 5)
lfVias = addTab('Vias')
self.viasTree = self._addBoundTree(lfVias, ViasTreeProvider, 5)
lfDoses = addTab('Doses')
self.dosesTree = self._addBoundTree(lfDoses, DosesTreeProvider, 5)
lfGroups = addTab('Groups')
self.groupsTree = self._addBoundTree(lfGroups, GroupsTreeProvider, 5)
# Fitting tab
if self.fitting:
tabFitting = addTab('Fitting')
t = TaggedText(tabFitting, width=80, height=10, bg='white')
t.grid(row=0, column=0, sticky='nw', padx=10, pady=10)
t.setText('Select one of the samples to see more information.')
t.setReadOnly(True)
self.fittingText = t
# Buttons for fitting
buttonsFrame = self._createFittingButtonsFrame(tabFitting)
buttonsFrame.grid(row=0, column=1, sticky='news', padx=5, pady=5)
#frame.grid(row=0, column=0, sticky='news', padx=5, pady=(10, 5))
content.add(frame, sticky='news', padx=5, pady=5)
def _createSamplesFrame(self, content):
frame = tk.Frame(content)
#frame = tk.LabelFrame(content, text='General')
lfSamples = tk.LabelFrame(frame, text='Samples')
gui.configureWeigths(frame)
lfSamples.grid(row=0, column=0, sticky='news', padx=5, pady=5)
self.samplesTree = self._addBoundTree(lfSamples, SamplesTreeProvider,
10, fitting=self.fitting)
self.samplesTree.itemDoubleClick = self._onSampleDoubleClick
self.samplesTree.itemClick = self._onSampleClick
plotFrame = tk.Frame(lfSamples)
plotFrame.grid(row=1, column=0, sticky='ws', padx=5, pady=5)
# Add a combobox with the variable for time
timeVars = [v.varName for v in self.experiment.variables.values()
if v.role == v.ROLE_TIME]
timeVars += [v.varName for v in self.experiment.variables.values()
if v.role == v.ROLE_MEASUREMENT]
measureVars = [v.varName for v in self.experiment.variables.values()
if v.role == v.ROLE_MEASUREMENT]
def addVar(text, col, choices):
varFrame = tk.Frame(plotFrame)
varFrame.grid(row=0, column=col, sticky='new')
label = tk.Label(varFrame, text=text, font=self.fontBold)
label.grid(row=0, column=0, padx=5, pady=2, sticky='nw')
combo = ComboBox(varFrame, choices, width=10)
combo.grid(row=0, column=1, sticky='nw', padx=5, pady=5)
radioVar = tk.IntVar()
radio = tk.Checkbutton(varFrame, text='Log10', variable=radioVar)
radio.grid(row=0, column=2, sticky='nw', padx=5, pady=5)
return combo, radio, radioVar
self.timeWidget = addVar('Time variable', 0, timeVars)
self.measureWidget = addVar('Measure variable', 1, measureVars)
self.measureWidget[2].set(True)
self.plotButton = Button(plotFrame, ' Plot ', font=self.fontBold,
command=self._onPlotClick,
tooltip='Select one or more samples to plot '
'their measures of the selected '
'variables (optionally in log).')
self.plotButton.grid(row=0, column=2, sticky='ne', padx=5)
self.plotSummaryButton = Button(plotFrame, ' Summary Plot ',
font=self.fontBold,
command=self._onPlotSummaryClick,
tooltip='Select several samples to plot'
' their statistics'
' (min, max, avg and std).')
self.plotSummaryButton.grid(row=1, column=2, sticky='ne',
padx=5, pady=5)
#frame.grid(row=1, column=0, sticky='news', padx=5, pady=5)
content.add(frame, sticky='news', padx=5, pady=5)
def _createButtonsFrame(self, content):
frame = tk.Frame(content)
gui.configureWeigths(frame)
buttonsFrame = tk.Frame(frame)
buttonsFrame.grid(row=0, column=0, sticky='ne')
closeButton = Button(buttonsFrame, 'Close', command=self.close,
imagePath='fa-times.png')
closeButton.grid(row=0, column=0, sticky='ne', padx=5)
self.newButton = HotButton(buttonsFrame, ' New Experiment ',
command=self._onCreateClick,
tooltip='Create a new experiment with the '
'selected samples. You can also edit'
'title and comment.')
self.newButton.grid(row=0, column=1, sticky='ne', padx=5)
frame.grid(row=1, column=0, sticky='news', padx=5, pady=5)
def _createFittingButtonsFrame(self, content):
buttonsFrame = tk.Frame(content)
buttonsFrame.grid(row=0, column=0, sticky='ne')
plotValuesButton = Button(buttonsFrame, 'Plot Fit',
command=self._onPlotFitClick)
plotValuesButton.grid(row=0, column=0, sticky='sew', padx=5, pady=5)
openValuesButton = Button(buttonsFrame, 'Open Fit',
command=self._onOpenFitClick)
openValuesButton.grid(row=1, column=0, sticky='sew', padx=5, pady=5)
return buttonsFrame
def _addLabel(self, parent, text, r, c):
label = tk.Label(parent, text=text, font=self.fontBold)
label.grid(row=r, column=c, padx=5, pady=5, sticky='ne')
return label
def _addBoundTree(self, parent, ProviderClass, height, **kwargs):
bt = BoundTree(parent, ProviderClass(self.experiment, **kwargs),
height=height)
bt.grid(row=0, column=0, sticky='news', padx=5, pady=5)
gui.configureWeigths(parent)
return bt
def getUnits(self, varName):
return self.experiment.variables[varName].getUnitsString()
def getLabel(self, varName, useLog):
varLabel = '%s [%s]' % (varName, self.getUnits(varName))
if useLog:
varLabel = "log10(%s)" % varLabel
return varLabel
def getTimeVarName(self):
return self.timeWidget[0].getText()
def useTimeLog(self):
return self.timeWidget[2].get()
def getTimeLabel(self):
return self.getLabel(self.getTimeVarName(), self.useTimeLog())
def getMeasureVarName(self):
return self.measureWidget[0].getText()
def useMeasureLog(self):
return self.measureWidget[2].get()
def getMeasureLabel(self):
return self.getLabel(self.getMeasureVarName(), self.useMeasureLog())
def getPlotValues(self, sample):
xValues, yValues = sample.getXYValues(self.getTimeVarName(),
self.getMeasureVarName())
xValues=xValues[0] # From [array(...)] to array(...)
yValues=yValues[0]
useMeasureLog = self.useMeasureLog()
useTimeLog = self.useTimeLog()
if not (useMeasureLog or useTimeLog):
return xValues, yValues
# If log will be used either for time or measure var
# we need to filter elements larger than 0
newXValues = []
newYValues = []
def _value(v, useLog):
if useLog:
return math.log10(v) if v > 0 else None
return v
for x, y in izip(xValues, yValues):
x = _value(x, useTimeLog)
y = _value(y, useMeasureLog)
if x is not None and y is not None:
newXValues.append(x)
newYValues.append(y)
return newXValues, newYValues
def _onPlotClick(self, e=None):
sampleKeys = self.samplesTree.selection()
if sampleKeys:
if self.plotter is None or self.plotter.isClosed():
self.plotter = EmPlotter()
doShow = True
ax = self.plotter.createSubPlot("Plot", self.getTimeLabel(),
self.getMeasureLabel())
self.plotDict = {}
else:
doShow = False
ax = self.plotter.getLastSubPlot()
samples = [self.experiment.samples[k] for k in sampleKeys]
for s in samples:
if not s.sampleName in self.plotDict:
x, y = self.getPlotValues(s)
ax.plot(x, y, label=s.sampleName)
self.plotDict[s.sampleName] = True
leg = ax.legend()
if leg:
leg.draggable()
if doShow:
self.plotter.show()
else:
self.plotter.draw()
else:
self.showInfo("Please select some sample(s) to plot.")
def _onPlotSummaryClick(self, e=None):
sampleKeys = self.samplesTree.selection()
n = len(sampleKeys)
if n == 1:
self.showInfo("Please select several samples to plot.")
else:
if n > 1:
samples = [self.experiment.samples[k] for k in sampleKeys]
else:
samples = self.experiment.samples.values()
dataDict = {} # key will be time values
for s in samples:
xValues, yValues = self.getPlotValues(s)
for x, y in izip(xValues, yValues):
if x in dataDict:
dataDict[x].append(y)
else:
dataDict[x] = [y]
sortedTime = sorted(dataDict.keys())
# We will store five values (min, 25%, 50%, 75%, max)
# for each of the time entries computed
percentileList = [0, 25, 50, 75, 100]
Y = np.zeros((len(sortedTime), 5))
for i, t in enumerate(sortedTime):
Y[i,:] = np.percentile(dataDict[t], percentileList)
plotter = EmPlotter()
ax = plotter.createSubPlot("Summary Plot", self.getTimeLabel(),
self.getMeasureLabel())
ax.plot(sortedTime, Y[:, 0], 'r--', label="Minimum")
ax.plot(sortedTime, Y[:, 1], 'b--', label="25%")
ax.plot(sortedTime, Y[:, 2], 'g', label="50% (Median)")
ax.plot(sortedTime, Y[:, 3], 'b--', label="75%")
ax.plot(sortedTime, Y[:, 4], 'r--', label="Maximum")
ax.legend()
plotter.show()
def _onCreateClick(self, e=None):
sampleKeys = self.samplesTree.selection()
if sampleKeys and self.callback:
self.callback()
else:
self.showInfo("Please select some sample(s) to create a new experiment.")
def _onSampleClick(self, obj):
# In fitting mode we need to display some information for
# the given sample fit
if self.fitting:
selection = self.samplesTree.selection()
if selection:
sampleKey = selection[0]
sampleFit = self.fitting.getSampleFit(sampleKey)
textMsg = sampleFit.getBasicInfo()
else:
textMsg = 'Select one of the samples to see more information.'
self.fittingText.setReadOnly(False)
self.fittingText.setText(textMsg)
self.fittingText.setReadOnly(True)
if not (self.plotter is None or self.plotter.isClosed()):
self._onPlotClick()
def _onSampleDoubleClick(self, obj):
sampleKeys = self.samplesTree.selection()
if sampleKeys:
MeasureWindow(masterWindow=self,
sample=self.experiment.samples[sampleKeys[0]]).show()
def _onPlotFitClick(self, e=None):
sampleKeys = self.samplesTree.selection()
if sampleKeys:
def _value(v, useLog):
if useLog:
return math.log10(v) if v > 0 else None
return v
def _values(values, useLog=self.useMeasureLog()):
return [_value(float(x), useLog) for x in values]
def _plot(varLabelX, varLabelY, x, y, yp):
plotter = EmPlotter()
ax = plotter.createSubPlot("Plot", varLabelX, varLabelY)
xValues = _values(x, useLog=self.useTimeLog())
ax.plot(xValues, _values(y), 'x', label="Observations")
idx = np.argsort(xValues)
ypValues = _values(yp)
ax.plot(np.asarray(xValues)[idx], np.asarray(ypValues)[idx], 'g', label="Fit")
leg = ax.legend()
if leg:
leg.draggable()
plotter.show()
# Get first selected element
fit = self.fitting.getSampleFit(sampleKeys[0])
if fit==None:
return
varLabelX = self.getLabel(self.fitting.predictor.varName,
self.useTimeLog())
if type(self.fitting.predicted)==list:
i=0
for v in self.fitting.predicted:
varLabelY = self.getLabel(v.varName, self.useMeasureLog())
_plot(varLabelX, varLabelY, fit.x[i], fit.y[i], fit.yp[i])
i+=1
else:
varLabelY = self.getLabel(self.fitting.predicted.varName,
self.useMeasureLog())
_plot(varLabelX, varLabelY, fit.x[0], fit.y[0], fit.yp[0])
else:
self.showInfo("Please select some sample(s) to plot.")
def _onOpenFitClick(self, e=None):
sampleKeys = self.samplesTree.selection()
if sampleKeys:
# Get first selected element
fit = self.fitting.getSampleFit(sampleKeys[0])
FitWindow(masterWindow=self,
fitting=self.fitting, sampleFit=fit).show()
else:
self.showInfo("Please select some sample(s) to plot.")
def _onClosing(self):
if self.plotter:
self.plotter.close()
gui.Window._onClosing(self)
def press(self, event):
def numericKey(key):
self.colorChanged = True
number = int(key)
index = 3 + number * 3
if (index + 3) > self.lenPlots:
return
if self.color['gpuMem_%d' % number] != 'w':
self.oldColor['gpuMem_%d' % number] = \
self.color['gpuMem_%d' % number]
self.oldColor['gpuUse_%d' % number] = \
self.color['gpuUse_%d' % number]
self.oldColor['gpuTem_%d' % number] = \
self.color['gpuTem_%d' % number]
self.color['gpuMem_%d' % number] = "w"
self.color['gpuUse_%d' % number] = "w"
self.color['gpuTem_%d' % number] = "w"
else:
self.color['gpuMem_%d' % number] = \
self.oldColor['gpuMem_%d' % number]
self.color['gpuUse_%d' % number] = \
self.oldColor['gpuUse_%d' % number]
self.color['gpuTem_%d' % number] = \
self.oldColor['gpuTem_%d' % number]
def cpuKey(key):
self.colorChanged = True
if self.color['cpu'] != 'w':
self.oldColor['cpu'] = self.color['cpu']
self.oldColor['mem'] = self.color['mem']
self.oldColor['swap'] = self.color['swap']
self.color['cpu'] = "w"
self.color['mem'] = "w"
self.color['swap'] = "w"
else:
self.color['cpu'] = self.oldColor['cpu']
self.color['swap'] = self.oldColor['swap']
self.color['mem'] = self.oldColor['mem']
def netKey(key):
self.colorChanged = True
if self.color['%s_send' % self.nifName] != 'w':
self.oldColor['%s_send' % self.nifName] = \
self.color['%s_send' % self.nifName]
self.oldColor['%s_recv' % self.nifName] = \
self.color['%s_recv' % self.nifName]
self.color['%s_send' % self.nifName] = "w"
self.color['%s_recv' % self.nifName] = "w"
else:
self.color['%s_send' % self.nifName] = \
self.oldColor['%s_send' % self.nifName]
self.color['%s_recv' % self.nifName] = \
self.oldColor['%s_recv' % self.nifName]
def diskKey(key):
self.colorChanged = True
if self.color['disk_read'] != 'w':
self.oldColor['disk_read'] = self.color['disk_read']
self.oldColor['disk_write'] = self.color['disk_write']
self.color['disk_read'] = "w"
self.color['disk_write'] = "w"
else:
self.color['disk_read'] = self.oldColor['disk_read']
self.color['disk_write'] = self.oldColor['disk_write']
sys.stdout.flush()
if event.key == 'S':
self.stop = True
self.ax.set_title('Plot has been Stopped. '
'Press C to continue plotting')
elif event.key == 'C':
self.ax.set_title(self._getTitle())
self.stop = False
self.animate()
elif event.key.isdigit():
numericKey(event.key)
self.animate()
elif event.key == 'c':
cpuKey(event.key)
self.animate()
elif event.key == 'n':
netKey(event.key)
self.animate()
elif event.key == 'd':
diskKey(event.key)
self.animate()
EmPlotter.show(self)
def visualize(self, obj, **kwargs):
prot = obj
fnProfiles = prot._getPath("profiles.txt")
fh = open(fnProfiles,"r")
Rtype = []
Rlegends = []
R = []
state = 0
for line in fh:
if state==0:
tokens = line.split("::")
Rtype.append(tokens[0])
Rlegends.append(tokens[1].strip())
Ri=[]
state=1
elif state==1:
tokens=line.strip().split()
if len(tokens)==0:
R.append(Ri)
state=0
else:
if len(Ri)==0:
for n in range(len(tokens)):
Ri.append([])
for n in range(len(tokens)):
Ri[n].append(tokens[n])
fh.close()
plotter = None
previousType = ""
for legend, Ri, Rtypei in izip(Rlegends, R, Rtype):
if plotter is None or Rtypei!=previousType:
if previousType!="":
self.addLimits(plotter,previousType,minX,maxX)
plotter = EmPlotter()
doShow = True
if Rtypei=="ReversibleLiver" or Rtypei=="TimeDependentLiver" or Rtypei=="InductionLiver" or Rtypei=="StaticLiver" or Rtypei=="TransporterLiver":
Ilabel="[Ih] [uM]"
elif Rtypei=="ReversibleGut" or Rtypei=="TimeDependentGut" or Rtypei=="InductionGut" or Rtypei=="StaticGut" or Rtypei=="TransporterGut":
Ilabel="[Ig] [uM]"
elif Rtypei=="TransporterRenal":
Ilabel="[Cmax] [uM]"
ax = plotter.createSubPlot("Plot", Ilabel, "R")
previousType = Rtypei
minX = None
maxX = None
else:
doShow = False
ax = plotter.getLastSubPlot()
x = np.asarray(Ri[0],dtype=np.float64)
if len(Ri)==2:
y=np.asarray(Ri[1],dtype=np.float64)
else:
y=np.asarray(Ri[2],dtype=np.float64)
ax.plot(x, y, label=legend)
minXi = np.min(x)
maxXi = np.max(x)
if minX==None:
minX=minXi
maxX=maxXi
minX=min(minXi,minX)
maxX=min(maxXi,maxX)
leg = ax.legend()
if leg:
leg.draggable()
if doShow:
plotter.show()
else:
plotter.draw()
self.addLimits(plotter,previousType,minX,maxX)
